Aug 8, 1989 - 191 Guns, howitzers, mortars, related equipment, or parts, 30 mm ...... private truck and for-hire truck; truck and air; truck and water; truck and ...
SPATIAL INTERACTION MODELING OF INTERREGIONAL COMMODITY FLOWS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By Huseyin Murat Celik, B.C.P., M.U.P. *****
The Ohio State University 2001
Dissertation Committee: Professor Jean-Michel Guldmann, Adviser Professor Burkhard von Rabenau Professor Phillip Viton
Approved by _________________________________ Adviser Department of City and Regional Planning
ABSTRACT
In contrast to passenger flows, there has been relatively little empirical research on interregional commodity flows, primarily because of limited available data. However, a better understanding of the determinants of interregional commodity flows may be critical for both transportation infrastructure planning and regional development policies. This research attempts
to expand past spatial interaction models of commodity flows by
incorporating new variables into the model, using a flexible Box-Cox functional form, and applying the analysis to all manufacturing commodities . The recently released 1993 and 1997 U.S. Commodity Flows Surveys provide the empirical basis for estimating state-tostate flow models for 16 manufactured commodity groups over the 48 continental U.S. states. Overall, the results show that the selected variables and functional form are very successful in explaining flow variations. First, having a common physical border increases trade between states for all commodity groups. As other destinations are physically closer to a specific destination, the amount of commodity shipped to that destination decreases because of demand competition effects. Similar effects are uncovered around the origin. Having a state involved in foreign exports and/or imports through a custom district may affect in- or out-shipments of commodities. Transportation costs, as proxied by distance, have a negative effect on out-shipments for all commodities. Population and personal percapita income at the origins have significant negative effects, suggesting that increasing local
demand
at
the
origin
decreases
the
amount
of
commodities
available
for
interregional shipment. The usual origin supply variables, sectoral employment and sectoral value-added, are significant and positive for most commodity groups. Wholesale employment at both the origins and destinations are important facilitators of the flows of many commodities. As a proxy for destinations
intermediate demand, manufacturing employment at
has significant and positive effects on the flows of wood, chemicals, mineral
products, and primary metals. The average size of
industrial establishments
at the
origins has a positive effect in a few cases, implying economies of scale, but mostly 2
negative effects,
pointing to diversification effects. Finally, the population and personal
income per-capita at the destinations, taken as proxies for final demand at destinations, have generally positive and significant effects on flows. In all cases, the Box-Cox specification obtained through maximum likelihood estimation is statistically superior to the log-linear form.
3
To my father, Fikri Celik, who taught me hardworking and honor
4
ACKNOWLEDGMENTS I would like to extend my cordial gratitude to my adviser, Dr. Jean-Michel Guldmann, for not only using his scholarship to guide me through this research, but also for being a patient counselor, a sincere friend, and a perfect draft proofreader, although he often strongly objected to this task. He has been a “father figure”, and, without his personal support, this dissertation would never have been completed. His smile and friendship will be highly missed. I also would like to thank my Ph.D. Committee Members, Dr. Philip Viton and Dr. Burkhard von Rabenau. Their timely advice and criticism contributed to the completion of this dissertation. I would like to thank the Faculty of City and Regional Planning in the Knowlton School of Architecture for supporting my research by awarding me the 2001 Dr. Jerrold Voss Scholarship. The comments of and useful discussions with my friend Erdogan Ozturk were always very helpful. Thank you Erdogan. My friends, Dr. Nuray Unlu and
Sally Eickholt,
were always with me in difficult times and their support was deeply appreciated. I also wish to thank Moriana Siri, whose warm friendship made Columbus home for me. Finally, I would like to thank the Turkish National Educational Ministry and the People of Turkey for financing my graduate education in the U.S.
5
VITA EDUCATION Sep.97- present
Ph. D. Student in City and Regional Planning. The Ohio State University, Knowlton School of Architecture, Graduate Program in City and Regional Planning, Columbus-Ohio. Master of City ( Transportation ) Planning. The University of Kansas, School of Architecture and Urban Design, Graduate Program in Urban Planning, Lawrence- Kansas. Non-Degree Advanced Level English and Orientation Course. Louisiana State University, English Language and Orientation Program, Baton Rouge- Louisiana. Master of City Planning. Mimar Sinan University, Faculty of Architecture, City and Regional Planning Department, Istanbul Turkey. (Discontinued to work with City of Mersin as a planner and advisor to the Mayor ) Bachelor of City Planning. Mimar Sinan University, Faculty of Architecture, City and Regional Planning Department, Istanbul Turkey.
Jan. 95 - Dec. 96 Oct. 94 - Dec. 96 Mar. 89 - Dec. 89
Sep. 83 - June 87
EMPLOYMENT Jan 00- present
Teaching and Research Assistant The Ohio State University, Columbus, OH. • • •
Aug.96- May. 97
Tasks: Teaching the course CRP 310 “Introduction to City and Regional Planning” (taught 3 quarters). Data preparation, manipulation, analysis, and reporting for a telecommunication research project. Assisting in the course CRP 780 “Workshop in City and Regional Planning Techniques” Research and Teaching Assistant The University of Kansas, Lawrence, KS.
•
Tasks: Data preparation, manipulation, and reporting for Central Place Theory Study for both Hokkaido Island, and Washington, Oregon, Idaho Region. 6
•
Jun. 96 - Aug. 96
Assisting Professor Dimitrios Dendrinos to teach the course “Advanced Seminar in Transportation” . Intern Transportation Planner. City of Excelsior Springs, Missouri.
• • May. 91 - Apr. 94
Tasks: Preparation of Excelsior Springs Sketch Transportation Plan. Graphic design of the maps used in Excelsior Springs Master Plan Study. City and Regional Planning Specialist. Prime Ministry of Republic of Turkey, Southeastern Anatolia Project, Regional Development Administration, Regional Directorate, Sanliurfa.
•
• •
•
•
Jan. 90 - May. 91
Tasks: Execution of the authority for urban development and infrastructure projects in the GAP (Southeastern Anatolian Project, a large-scale regional development enterprise including the construction of 22 hydro-power and irrigation dams on the Euphrates and Tigris Rivers) region covering 8 provinces and 140 county municipalities. Coordination of the projects run by various Government Agencies in the region. Supervision of a consultant consortium responsible for the preparation of a regional transportation study, urban infrastructure projects (including 25 urban master and implementation plans, water supply, sewer system, and waste water and solid waste treatment projects for 45 cities in the region), sub-regional development plans, and sectoral development studies. Executive Secretary of the committee responsible for the coordination and supervision of evacuation and resettlement activities of Halfeti and Hasankeyf Communities, which will be overwhelmed by dam constructions. Design of a Master Land-Use Plan, and preparation of zoning ordinances for a 200,000 people community, Karakopru Suburb, in the Sanliurfa Metropolitan Area. City Planner and Advisor to the Mayor. City of Mersin, Turkey.
• • •
Tasks: Participation in master plan amendment works, slum reclamation planning, landscape designs, and capital improvement program preparations. Monitoring planning and implementation activities as the representative of the mayor. Coordination of urban development and infrastructure project investments according to Cukurova Urban Growth Project financed by the World Bank.
7
Aug. 89 - Dec. 89
City Planner. Iller 71 City Planning Office, Istanbul. •
Tasks: Preparation and design of Istanbul Kucukbakkalkoy and Yeni Sahra Slum Reclamation Plans, and Istanbul Kumburgaz Urban Development Plan.
Apr. 88 - Jul. 89
Enlisted Artillery Officer. Turkish Army.
Nov. 87 - Mar. 88
City Planner and Urban Designer. Iller 71 City Planning Office, Istanbul. •
Tasks: Design and implementation of housing projects, preparation of a master plan and implementation plans, and landscape projects for an area covering 200 hectares.
FIELDS OF STUDY Major Field:
City and Regional Planning
Minor Fields: Quantitative Modeling Economic Planning Regional Planning Transportation Planning
8
TABLE OF CONTENTS Abstract………………. ……………………………………………………………………….
Page 2
Dedication…………… ………………………………………………………………………
4
Acknowledgments…. ………………………………………………………………………
5
Vita…………………….. ………………………………………………………………………
6
List of Tables……….. ………………………………………………………………………
11
Chapters: 1.
Introduction…………. ………………………………………………………………………
14
2.
Literature Review…..………………………………………………………………………
18
3.
4.
5.
2.1
Commodity Flow Models……. …………………………………………………… 18 2.1.1. Interregional Input-Output Models……………………………… 18 2.1.2. Spatial Interaction Models of Commodity Flows..………….. 20
2.2 2.3 2.4
Overview of Spatial Interaction Models………………………………………. International Trade Models… …………………………………………………… Summary………………………… ……………………………………………………
28 31 32
Modeling Methodology….…………………………………………………………………
34
3.1 3.2
Theoretical Background….. ……………………………………………………… The Empirical Commodity Flow Model..…………………………………….. 3.2.1 Variables……………………. …………………………………………. a Origin Variables…………… …………………………………………. b Destination Variables……. …………………………………………. c Geographical Variables…. …………………………………………. 3.2.2 Functional Form…………… ………………………………………….
34 38 39 39 40 40 42
Data Sources and Processing……………………………………………………………
44
4.1 4.2
44 50
Dependent Variable…………………… …………………………………………. Independent Variables……………….. ………………………………………….
Results…..……………………………………………………………………………………
5.1 Overview of U.S. Interregional Commodity Flows………………………………….. 9
54 54
5.2 Individual Commodity Equation Results……………… ……………………………… 5.2.1 Food and Kindred Products………… ……………………………… 5.2.2 Lumber and Wood Products……….. ……………………………… 5.2.3 Furniture and Fixture Products……. ……………………………… 5.2.4 Pulp, Paper and Allied Products….. ……………………………… 5.2.5 Chemical and Allied Products……… ……………………………… 5.2.6 Petroleum and Coal Products……… ……………………………… 5.2.7 Rubber & Misc. Plastic Products….. ……………………………… 5.2.8 Clay, Concrete, Glass & Stone Products…………………………… 5.2.9 Primary Metal Products……………… ……………………………… 5.2.10 Fabricated Metal Products………….. ……………………………… 5.2.11 Machinery Products (Non-Electrical)…. …………………………. 5.2.12 Electrical Machinery Products…….. ……………………………… 5.2.13 Transportation Equipment………….. ……………………………… 5.2.14 Precision Instruments……………….. ……………………………… 5.2.15 Miscellaneous Manufacturing Products…………………………… 5.2.16 Apparel Textile Leather Products…. ……………………………… 5.2.17 Synthesis………………………………… ……………………………… 5.3 Elasticity Analysis…………………………………………… ……………………………… 5.3.1 Competing Destination………………. ……………………………… 5.3.2 Intervening Opportunities…………… ……………………………… 5.3.3 Distance………………………………….. ……………………………… 5.3.4 Origin Personal Income per Capita…….. ……………………….. 5.3.5 Origin Population……………………… …………………………….. 5.3.6 Origin Employment……………………. ……………………………… 5.3.7 Origin Value-Added……………………. ……………………………… 5.3.8 Origin Wholesale Employment…….. ……………………………… 5.3.9 Origin Average Plant Size…………… ……………………………… 5.3.10 Destination Manufacturing Employment…… ………………….. 5.3.11 Destination Wholesale Employment…………. ………………….. 5.3.12 Destination Personal Income Per Capita…… ………………….. 5.3.13 Destination Population………………. ……………………………… 5.3.14 Summary………………………………… ………………………………
62 62 63 65 67 68 70 71 72 74 76 77 79 80 82 84 85 86 95 95 96 97 98 99 100 101 102 103 104 105 106 107 108
6.
Conclusions…………. ……………………………………………………………………….
109
Bibliography………. ………………………………………………………………………………
114
Appendixes : A B C D E
Standard Transportation Commodity Classification (STCC)….. Standard Classification Of Transported Goods (SCTG)………… Descriptive Statistics…………………………………………………….. List Of Us. Custom Districts…………………………………………… Descriptions Of Databases……………………………………………… E.1 1993 and 1997 Commodity Flow Surveys………………… E.1.1 1993 Commodity Flow Survey…………………… E.1.2 1997 Commodity Flow Survey…………………… E.2 County Business Patterns……………………………………… E.3 Census of Manufactures…………………………………………
10
……………….. ……………….. ……………….. ……………….. ……………….. ……………….. ……………….. ……………….. ……………….. ………………..
117 122 124 170 171 171 171 175 176 179
LIST OF TABLES Table
Page 2
2.1
Reed’s Supply Model Variables and Their R ……………………………………….
21
2.2
Huxley’s Forms of Gravity Model……………………………………………………….
25
4.1
Magnitudes of Missing Observations in the 1993 CFS……………………………
46
4.2
Magnitudes of Missing Observations in the 1997 CFS……………………………
47
4.3
Matching of the SCTG and STCC Groups……………………………………………
49
4.4
Commodity Groups Codes and Definitions…..……………………………………..
50
4.5
Descriptive Statistics for 1993-All Commodities Combined…………………..
52
4.6
Descriptive Statistics for 1997-All Commodities Combined……...……………
53
5.1
Total Value of Shipments and Their Shares Across Commodity Groups…….
55
5.2
1993 States’ In- and Out-shipments and Their Shares…………………………..
56
5.3
1997 States’ In- and Out-shipments and Their Shares…………………………..
58
5.4
1993 Custom States Imports and Export Shares…………………………………
60
5.5
1997 Custom States Imports and Export Shares………………………………….
61
5.6
Food and Kindred Products Regression Parameters……………………………..
63
5.7
Lumber and Wood Products Regression Parameters……………………………
64
5.8
Furniture and Fixture Products Regression Parameters…………………………
66
5.9
Pulp, Paper and Allied Products Regression Parameters……………………….
67
5.10
Chemical and Allied Products Regression Parameters…………………………..
69
5.11
Petroleum or Coal Products Regression Parameters……………………………..
70
5.12
Rubber and Misc. Rubber Products Regression Parameters…………………..
72
5.13
Clay, Concrete, and Stone Products Regression Parameters..…………………
73
5.14
Primary Metal Products Regression Parameters…………………………………..
75
5.15
Fabricated Metal Products Regression Parameters……………………………….
77
5.16
(Non-Electrical) Machinery Products Regression Parameters………………….
78
5.17
Electrical Machinery Products Regression Parameters………………………….
80
5.18
Transportation Equipment Regression Parameters……………………………….
81
11
5.19
Precision Instruments Regression Parameters…………………………………….
83
5.20
Miscellaneous Products Regression Parameters…………………………………..
84
5.21
Apparel, Textile, Leather Products Regression Parameters…………………….
86
5.22
The 1993 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (20-32)……………………………………………………
5.23
The 1993 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (33-75)……………………………………………………
5.24
92
The 1997 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (20-32)……………………………………………………
5.25
91
93
The 1997 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (33-75)……………………………………………………
94
5.26
Statistics for CD Elasticities……………………………………………………………..
96
5.27
Statistics for IO Elasticities………………………………………………………………
97
5.28
Statistics for DIST Elasticities…………………………………………………………..
98
5.29
Statistics for OPIPC Elasticities…………………………………………………………
99
5.30
Statistics for OPOP Elasticities…………………………………………………………
100
5.31
Statistics for OEMP Elasticities…………………………………………………………
101
5.32
Statistics for OVLAD Elasticities………………………………………………………..
102
5.33
Statistics for OWSEM Elasticities………………………………………………………
103
5.34
Statistics for OAPS Elasticities………………………………………………………….
104
5.35
Statistics for DMNEM Elasticities………………………………………………………
105
5.36
Statistics for DWSEM Elasticities………………………………………………………
106
5.37
Statistics for DPIPC Elasticities………………………………………………………..
107
5.38
Statistics for DPOP Elasticities…………………………………………………………
108
6.1
Performance of Custom District Variables in 1993……………………………….
111
C.1
Descriptive Statistics for 1993-Commodity 20…………………………………….
124
C.2
Descriptive Statistics for 1993-Commodity 24…………………………………….
125
C.3
Descriptive Statistics for 1993-Commodity 25…………………………………….
125
C.4
Descriptive Statistics for 1993-Commodity 26…………………………………….
126
C.5
Descriptive Statistics for 1993-Commodity 28…………………………………….
126
C.6
Descriptive Statistics for 1993-Commodity 29…………………………………….
127
C.7
Descriptive Statistics for 1993-Commodity 30…………………………………….
127
C.8
Descriptive Statistics for 1993-Commodity 32…………………………………….
128
C.9
Descriptive Statistics for 1993-Commodity 33…………………………………….
128
12
C.10
Descriptive Statistics for 1993-Commodity 34…………………………………….
129
C.11
Descriptive Statistics for 1993-Commodity 35…………………………………….
129
C.12
Descriptive Statistics for 1993-Commodity 36…………………………………….
130
C.13
Descriptive Statistics for 1993-Commodity 37…………………………………….
130
C.14
Descriptive Statistics for 1993-Commodity 38…………………………………….
131
C.15
Descriptive Statistics for 1993-Commodity 39…………………………………….
131
C.16
Descriptive Statistics for 1993-Commodity 75…………………………………….
132
C.17
Descriptive Statistics for 1997-Commodity 20…………………………………….
132
C.18
Descriptive Statistics for 1997-Commodity 24…………………………………….
133
C.19
Descriptive Statistics for 1997-Commodity 25……….……………………………
133
C.20
Descriptive Statistics for 1997-Commodity 26…………………………………….
134
C.21
Descriptive Statistics for 1997-Commodity 28…………………………………….
134
C.22
Descriptive Statistics for 1997-Commodity 29…………………………………….
135
C.23
Descriptive Statistics for 1997-Commodity 30……….……………………………
135
C.24
Descriptive Statistics for 1997-Commodity 32……….……………………………
136
C.25
Descriptive Statistics for 1997-Commodity 33……….……………………………
136
C.26
Descriptive Statistics for 1997-Commodity 34……….……………………………
137
C.27
Descriptive Statistics for 1997-Commodity 35……….……………………………
137
C.28
Descriptive Statistics for 1997-Commodity 36……….……………………………
138
C.29
Descriptive Statistics for 1997-Commodity 37……….……………………………
138
C.30
Descriptive Statistics for 1997-Commodity 38……….……………………………
139
C.31
Descriptive Statistics for 1997-Commodity 39……….……………………………
139
C.32
Descriptive Statistics for 1997-Commodity 75……….……………………………
140
C.33
1993 Flow Values and Percentages across States and Commodities……….
141
C.34
Three-Digit Breakdown of 1993 Commodity Flows………………..………………
165
E.1
Variables included in the CFS…………………………….…..…………..……………
174
E.2
Tables in the Commodity Flow Survey…………………..……………………………
175
E.3
Variables in the U.S. data files………………………………………………………….
177
E.4
Variables in States Data File……………………………….……………………………
177
E.5
Variables in County Data Files……………………………..……………………………
178
E.6
Geographic Area Series……………………………………..……………………………
180
E.7
Final General Summary Database Relevant Files …..……………………………
180
13
CHAPTER 1 INTRODUCTION While intercity and intracity passenger flows have been the subject of extensive research
in
the
field
of
urban
and
regional
planning,
geography,
economics,
and
engineering, commodity flows, or freight transportation, have not been analyzed to the same extent. One of the main reasons for this lack of research has been
the relative
unavailability of suitable data, even in developed countries. Although inter-industry
flow
data are available in input-output tables at the national level, this type of data configuration does not allow for interregional origin-destination analyses. Prior to 1993, the most recent commodity flows survey performed in the US was for the year 1977, with data difficult to access and not in electronic format. There has also been a dearth of such data in other countries, as demonstrated by the very limited number of related empirical studies described further on (e.g. India; Great Britain; Alberta, Canada). However, the Bureau of Transportation Statistics, a joint unit of the US department of Transportation and the US Bureau of the Census, has recently released the results of the 1993 and 1997 Commodity Flows Surveys (CFS), making them widely available in electronic form on CDROM. The structure of these data is very suitable for empirical origin-destination analyses of commodity flows, and makes it feasible to develop and test new empirical models aimed at explaining the variations of these flows. Why is it important to better understand the structure of interregional commodity flows?
Fist,
the
ability
to
forecast
such
flows
may be critical for transportation
infrastructure planning, whether highways, railroad tracks, or river/port facilities. Second, a better understanding of interregional and inter-industry dependencies may be important for designing regional development policies aimed at reducing regional disparities through judicious location of commodity-producing activities. Third, commodity flow analyses may be useful to assess the trading area of specific activities at specific locations, and thus may guide location decisions by entrepreneurs. 14
Spatial flows of commodities have been empirically investigated with three types of models: interregional input-output models (Leontief and Strout, 1963; Isard, 1951; Moses, 1955), linear programming (LP) models (Chisholm and O’Sullivan, 1973), and spatial interaction (gravity) models (Ashtakala and Murthy, 1988; Black, 1971 & 1972; Chisholm and O’Sullivan, 1973; Huxley, 1979; Reed, 1967). The main problem with input-output models is that they are data hungry and, since they assume that technology is fixed over time, they are not very dependable for long-term forecasting. LP models are only applicable to highly homogenous commodities, as they do not allow for cross-hauling. Spatial interaction models have had a very wide range of applications, since
they are
easier to implement, but generally lack theoretical foundations. An alternative to the above empirical approaches might be the development and numerical implementation of a spatial price equilibrium (SPE) model, as proposed initially by
Samuelson
(1952),
and
reformulated
as
a
quadratic
programming
problem
by
Takayama and Judge (1964). Samuelson shows that as long as there is a price differential between regions, commodity flows will take place from low-price regions to high-price regions until the price differentials are equal to the transportation costs between the regions. However, implementing such models requires enough observations to empirically estimate local demand and supply functions for any commodity considered. Such data requirements all but preclude the implementation of this approach. Further, the SPE model assumes perfectly homogenous commodities, as cross-hauling is impossible. This requirement is in conflict with the aggregate nature of available data, where cross-hauling patterns are prevalent. In light of the above considerations and of the availability of recent CFS data, the focus of this research is on the development and estimation of spatial interaction (SI) models of interregional commodity flows. While SI models have long lacked sound theoretical foundations, Brocker (1989) recently rehabilitated them by demonstrating that some forms of SI models can be viewed as reduced forms of Samuelson’s SPE model. Thus,
using
spatial
interaction
models
to
analyze
commodity
flows
has
become
theoretically justifiable, beyond mere empirical convenience. Using Brocker’s theoretical framework, this dissertation attempts to expand the empirical research on interregional commodity flows. It specifies a spatial interaction model that incorporates (1) variables similar to those used in studies,
(2)
variables
used
past commodity flow
in international trade models, and (3) a set of
completely
new variables. The selection of the variables is consistent with Brocker’s framework and with inter-industry transactions considerations. For instance, the origins and destinations 15
are characterized by proxy variables representing
final and intermediate demands as
mass variables, and adjacency and custom district dummy, distance, destination 1999)
competing
(Fotheringham, 1983a, 1983b), and intervening opportunities (Guldmann,
variables are also considered. From a specification viewpoint, instead of the
multiplicative
functional
form
used
in
the
past,
a
flexible
Box-Cox
transformation
specification is used in this research. The specified model is used to analyze and compare interregional commodity flows in 1993 and 1997. The geographical coverage is the 48 US continental states, and the industry coverage is 16 two-digit manufacturing sector product groups. Using the econometric estimates, an elasticity analysis of the commodity flows with respect to each independent variable is conducted. Some of the research questions to be answered by this research can be stated as follows: •
Does adjacency increase commodity flows between states?
•
Does having a custom district significantly affect a state in- or out-shipments ? Or, in other words, is
foreign trade an important determinant of interstate commodity
flows? •
What is the effect of distance on interregional trade flows, and how does this effect vary across commodities?
•
What kind of effects do the spatial configurations of the origins and destinations have on commodity flows? Are these effects “competitive” or “agglomerative”?
•
What are the effects of the final demand sectors at the origins and at the destinations on commodity flows?
•
Are
wholesale activities at the origins and destinations facilitators of interstate
commodity trade? •
What is the importance of the employment and value-added of the commodityproducing sector for commodity outshipments?
•
Are commodity outshipments impacted by scale or diversification effects at the establishment level?
•
What is the importance of intermediate demand at the destination in determining commodity flows ?
•
What are the sensitivities of commodity flows to all these variables ? How do these sensitivities change over the years ? 16
The remainder of the dissertation is organized as follows. Chapter 2 is devoted to a thorough literature review. Different types of commodity flow models are reviewed, including input-output,
optimization, and
spatial interaction models,
both empirical and
theoretical. Since the study uses spatial interaction modeling, the broader literature
and
some specific applications of these models are also reviewed. Chapter 3 discusses the theoretical
background
and
methodological
approach
of
the
study,
including
the
specification of the model and the selection rationale and expectation for the explanatory variables. Chapter 4 describes the data used in the study, including approaches to deal with missing observations. Chapter 5 presents and discusses the econometric estimates and elasticity analyses. Conclusions are summarized in Chapter 6.
17
CHAPTER 2 LITERATURE REVIEW Three main groups of articles related to (1) commodity flow models, (2) spatial interaction models, and (3) international trade models, are reviewed in this chapter. The articles related to commodity flows are centrally related to this research, and include input-output models, optimization models, and spatial interaction commodity models. The second group of articles is related to the general structure of spatial interaction models, particularly models accounting for the effect of the spatial structure. The third groups of article focus on international trade models, in particular
applications of spatial interaction
modeling to international trade, which can be viewed as a specific type of interregional commodity flows. 2. 1. Commodity Flows Models 2. 1. 1. Interregional Input-Output Models Input-output analysis, developed by Wassily Leontief in the late 1930’s, is a general framework to analyze interdependency among industries in an economy. Since then, many extensions of the input-output model have been developed to analyze interregional
and
inter-industrial
relationships
within
a
multi-region
and
multi-industry
economy (Miernick, 1963; Richardson 1972). Three main interregional input-output frameworks have been proposed. The first is presented in the article
“Interregional and Regional Input-Output Analysis: a Model of
Space Economy” by Isard (1951). This model is also known as the “pure interregional input-output model”, wherein each of the commodities in each region is treated as a distinct commodity. In other words, alcoholic beverages in Kansas, for example, are treated as different from alcoholic beverages in Louisiana. With this kind of specification, not only is the spatial interdependency of an economy analyzed, but so is also its industrial interdependency. In a sense, it is a simultaneous inter-spatial and interindustrial input-output model. The main disadvantage of this model is that it is 18
ambitious
in terms of
data
requirements. In order
to be able
to calculate
the
technical input
coefficients, commodity flow data from each region to all other regions for each specific industry are necessary. Another study, by Leontief and Strout (1963), entitled “Multiregional Input-Output Analysis”, does not intend “to provide a systematic theoretical description of the many factors and relationship that ultimately determine the pattern of a multiregional economic system; it is designed, rather, as a rough and ready working tool capable of making effective use of the limited amount of factual information” (Leontief & Strout, 1963). In contrast to the pure interregional input-output model, where each commodity in each different region is treated as a distinct commodity, in this framework “it is as if the producers of a specific commodity or service located in one particular region had merged their output in a single regional supply pool, and the users of that commodity or service located in a given region had ordered and received it thorough a regional demand pool. All inter-regional movement of a particular commodity or service within a multiregional economy can thus be visualized as shipment from regional supply to regional demand pools of that good” (Leontief & Strout, 1963).
A gravity-like formula represents the
distribution of the commodities from these pools:
X i . gh =
X i . go X i .oh X i .oo
Qi . gh ,
(2.1 )
where X i.gh = total shipment of good i from the supply pool in region g to its demand pool in region h, X i.oh = demand pool of good i in region h, X i.go = the supply pool of good i in region g, and Q i.gh = empirical constant. Four different estimating methods are suggested for determining the empirical constants. These are called the ‘Exact Solution’, the ‘Simple Solution’, the “Least Squares” and the ‘Point Estimate’ procedures. Parameters are estimated using 1954 US data for four commodity groups, and 13 regions. The model is used for forecasting flows in 1950, 1952, and 1958, and produces high error rates (Leontief & Strout, 1963), which make the model unreliable for long-term regional forecasts, since it is not possible to tell which method of estimation of the interregional coefficients is superior to the others in terms of commodity groups, types of movement, and forecast years. 19
Moses (1955) proposes The Column Coefficient Input-Output Model in his article “The Stability of Interregional Trading Patterns and Input-Output Analysis”.
Like the
Leontief & Strout model, this model is only interested in explaining the interregional flows of
commodities.
Inter-industrial
flows
among
regions,
except
for
intra-region
inter-
industrial flows, are not analyzed in this type of model. The mirror image of the previous model is the Row Coefficient Model. The difference between the models is that trade coefficients are estimated by using row sums, which
represent the total production of a
commodity, instead of using column sums, which represent
the total consumption of this
commodity. The main deficiency of the row coefficient input-output model, like the point estimate procedure of the multiregional input-output model (the Leontief & Strout model), is that it can produce inconsistent, negative flows, which are economically meaningless (Bon, 1984). Regardless of the framework, the main weakness of all input-output models is the assumption of fixed production and/or trade coefficients. Relative supply prices and the state of technology are not stable over time. Thus, any change in these factors alters the trade and/or production pattern of the economy. This fact brings limitations to the longterm predictive power of the input-output model. Besides these shortcomings, it is worth mentioning two of Hua’s critiques concerning the input-output model. According to Hua (1990), ”The matrix inverse, however, is the main attraction of an input-output model, because its elements measure detailed marginal effects of any change in final demand. In either case, the model user has little flexibility in using different kinds of information, in intervening and modifying the structure of the model, or in dividing the whole operation into smaller components. Second, in an input-output inter-regional trade model, only the final demand for goods in each region can be the driving force of inter-regional trade flows. This limitation is unsatisfactory from a theoretical point of view if the reason for using the model is to study market interactions or regional growth, when factors on the supply side need to be analyzed and formally linked. There is little room in the existing input-output inter-regional trade models for accommodating such a linkage, a serious shortcoming that seems to have been underemphasized in the input-output inter-regional trade literature” (Hua, 1990).
2. 1. 2. Spatial Interaction Models of Commodity Flows One of the first empirical studies in the commodity flow literature is
“Areal
Interaction in India: Commodity Flows of the Bengal Bihar Industrial Area” by Reed (1967).
The purpose of the study is to analyze the interaction of the Bengal Bihar area 20
with the rest of India. The study area is a sub-region of the Calcutta economic region and is located in the northeastern part of India. The data are collected at the 24 rail stations of the region during three months in 1962 (April, July, and October), and consist of origins, destinations, and tonnages of goods shipped-out or received by rail, for 47 distinct commodity groups. Two separate models, one for outflows from the Bengal-Bihar region (or demand), and one for inflows to this region (or supply), are used. As a first step, the outflows of all and each individual commodity groups to destination k, F1k, are regressed across the urban population, popk,
at
destination k, considered as the only demand
variable, and distance dk. The model is
F1 k = a ( pop k ) b1 (d k ) b 2 ,
(2.2 )
where a, b1, and b2 are parameters. The R2 is .53 for total outflow, and varies between .38 and .50 for individual commodity groups. The variables of the inflow model are the distance and urban population at the origin k for the total of all commodities, and the employment (or production) at k and distance for specific commodity groups. The inflow model is :
Fk 1 = a( employment / production k ) b1 ( d k ) b 2 ,
(2.3 )
The supply models, their mass variables, and their R2’s are given in Table 2.1. Table 2. 1. Reed’s Supply Model Variables and Their R 2
Dependent Variable All Commodities
Independent Mass Variable Urban Population
R2 .49
Iron and Steel
Steel Production
.40
Electrical Machinery
Electrical Machinery Employment
.52
Other Machinery
Other Machinery Employment
.48
Chemicals
Chemical Employment
.34
Cement
Cement Employment
.26
Limestone
Limestone Production
.37
Metallic Ores
Metallic Ore Production
.41
Non-Metallic Minerals
Non-Metallic Mineral Production
.34
Gram
Gram Production
.21
21
Reed suggests that governmental regulation of commodity pricing, transportation subsidies, competition among transportation industries, licensing practices, branch plant ownership, and other specific inter-firm linkages, could be sources of variation in commodity flows, and these factors are not easy to model. Further, he suggests that “the effects of intervening and otherwise competing supplies and demands may be possible sources of variation not effectively summarized by the distance measure used in this study…measures of relative location based upon the potential model with its distance variable recognize neither the uneven distributions of demand nor the directionality of movement found within an economic system” (Reed, 1967: 171-172). In this context two additional variables are defined to capture competition and intervening opportunities effects. The contribution of demand at a given point k to the total demand potential at i is formulated as
( Pk / d ik ) / ∑ ( Pj / d ij ) , and may be viewed as measuring the competitive j
position of k vis a vis all other points j for supplies available at i.
Alternatively, the
contribution of supplies at a given point k to the total supply potential at j is formulated as
( S k / d jk ) / ∑ ( S i / d ji ) , and may measure the competitive position of supplies at k vis a i
vis all other supply points i. Furthermore, two additional variables are defined to capture redistribution effects and concentration effects. It is stated that “along with the demand effect created by conditions within k itself, one might expect that an additional demand effect would be created by k’s ability to redistribute commodities received from elsewhere. Thus a better account of demand at k should result from the inclusion of both a measure of demand generated at k and a measure of k’s access to outside markets (redistribution effect) such as a market potential at k. Similarly, besides the supply produced locally at i, one might expect an additional supply effect based on i’s ability to concentrate within tiself supplies produced elsewhere. A better accounting of supply at i would then include a measure of supply produced at i and a measure of i’s access to other supplies (concentration effect) such as supply potential.” (Reed, 1967:181). The defined as
∑P
n
redistribution
d kn , and the concentration effect as
n
∑S
n
effect
is
mathematically
d kn .
n
Based on the above definitions, extended forms are proposed for both outflows and inflows. The extended full model is:
22
b1 i
b2 k b3 ik
S .P d
Fik = a .
Si Pk S n b6 Pn b7 d ik b4 d ik b5 . ( ) .( ) . (∑ ) . (∑ ) Sn Pn n d in n d kn ∑n d ∑n d kn in
(2.4 )
Since there is only one region, the outflow model becomes:
F1k = a .
b2 k b3 ik
P d
S1 d 1k Pn b7 . ( )b4 . ( ∑ ) Sn n d kn ∑n d kn
(2.5 )
and the inflow model becomes:
Pj Fk1 = a .
S d
b1 k b3 k1
.(
d k1 b5 S n b6 ) . (∑ ) Pn n d kn ∑n d kn
(2.6 )
Variables of supply and demand potentials, and variables of concentration and redistribution
effects
are
computed
using
various
supply
and
demand
measures.
Equations (2.5) and (2.6) are estimated using stepwise regression. Redistribution and concentration effects are not statistically significant.
Overall, Reed concludes that “the
expanded potential model did explain some additional variations in study area flows, strongly indicating that this model might prove useful in analyzing flows between all parts of an economic system.” (Reed, 1967:195) Another empirical commodity flow study is by Chisholm and O’Sullivan (1973). The title of the study is “Freight Flow and Spatial Aspects of the British Economy”.
The
authors, using U.K. Ministry of Transport 1962 and 1964 commodity flow data, try to analyze commodity flows in Britain over 78 zones and 13 commodity groups.
The main
difference between this study and Reed’s is that this one is divided into two steps. In the first step, the authors attempt to explain zonal freight generation at an aggregate level. In the second step,
two models are used, the Gravity Model (GM) and The Linear
Programming (LP) model. Because of its computational simplicity, the single-constrained GM is retained. The R2’s obtained by comparing actual and estimated flows using the gravity model for 13 commodity groups for road trips vary between .24 for steel and .62 for food. These results are not found satisfactory, and it is concluded that “the relatively low values of the R2 for the commodity classes indicate that the gravity model is not very suitable for examining flows disaggregated by type of goods” (Chisholm & O’Sullivan, 23
1973: 76). On the other hand, the R2’s for the LP solutions for road trips are higher, especially for homogenous commodity groups. The higher the
homogeneity, the higher
the R2. At the same time, the LP gives good R2 values for the flows between 24 major cities. The employed gravity model is
Tij = kOi D j
−α
d ij− β ,
(2.7)
where Tij represents the flow between origin i and destination j, Oi
and Dj the origin and
destination mass variables, and dij the distance between i and j. The mass variables used in the study are population and employment. The primal formulation of the LP is:
Min.C = ∑ ∑ Tij d ij i
s.t:
(2.8 )
j
∑T
ij
= Dj,
ij
= Oi ,
i
∑T j
Tij ≥ 0. The dual formulation is:
MaxV = ∑ V j D j − ∑ U i Oi j
(2.9 )
i
V j and Ui are the shadow prices associated to supplies at origins and requirements at destinations — reflecting locational advantages with respect to transport costs. The shadow prices obtained in solving the LP are thus indicative of geographical comparative cost advantages for production and consumption” (Chisholm & Sullivan, 1973:76). In paper entitled “Indirect Estimation of Interregional Trading Patterns for InputOutput Analysis: Empirical Results for the Gravity Model and Rail Freight Shipments”, Huxley (1979) tries to find the best estimator for the inter-regional flows
component of
the input-output table for 11 regions in Queensland (Australia). The data for this inputoutput study are gathered through a comprehensive survey. The goal is to find a more economical method for predicting future commodity flows among regions and for updating input-output tables. In a first step, the elements of all the main components of the inputoutput table, such as the interregional trade flows matrices, the intra-regional trade flows matrices, the final demand, the primary input, and the gross regional output vectors are regressed across regional employment. The results are, except for interregional trade 24
flows, satisfactory. The R2 is equal to .55 for interregional trade flows and above .99 for the other quantities. In a second step, Huxley attempts to specify a functional form for the gravity model to project interregional flows. The models are specified as linear and logarithmic. In these functional forms, the flows are directly regressed on the mass variables. Then, some interaction forms of these mass variables are defined, and included in the models, using a stepwise regression procedure. The specified models, and the interaction variables are presented in Table 2. 2. Table 2. 2. Huxley’s Forms of Gravity Model
Linear Forms Yij = a + b Xi
Logarithmic Forms Yij = aXib
Interaction Variables X 1 = Yij
Interaction Variables X 8 = Xi / Xj
Yij = a + bXj
Yij =aXjb
X 2 = Xi
X 9= Xj / Xi
Yij = a + bD
Yij =aD
b
X 3 = Xj
X 10 = Xi /D
Yij = a + b1X i + b2X j
X4 = D
X 11 = Xj /D
X 5 = Xi X j
X 12 = Xi X j /D
Yij = a + b1X j + b3D
Yij =aXib1X jb2 Yij =aXib1Db3 Yij =aXjb2Db3
X 6 = Xi D
X 13 = Xi X j D
Yij =a+b 1X i+b 2X j+b 3D
Yij =aXib1X jb2Db3
X 7 = Xj D
X 14 = (X i + Xj)/D
Yij = a + b1X i + b3D
X i and X j are variables that represent masses, and D is the highway distance.
The
following variables are used as measures of masses; Employment
:
Total regional employment at the origins and destinations
Population
:
Total population at the origins and destinations
GRP
:
Gross regional products at the origins and destinations
GRO
:
Gross regional output (includes inter-industry transactions)
PIIFDJ
:
Primary inputs of the region and final demands of the destinations
INTERMED
:
Intermediate sales of the origin and intermediate purchases of the destination
NTEMPL
:
Non-tertiary
regional
employment
at
the
origin
and
destination NTGRO
:
INTRAN
:
Non-tertiary gross regional outputs Intermediate sales of the origin minus intra-regional sales and intermediate purchases of the destinations minus intraregional purchases
EMPDENS
:
Total employees per square mile.
The fourth equation of the Linear Forms, and the seventh equation of the 25
Logarithmic Forms produce the best fit, with the employment as the measure of mass, with R2 of .56 and .57, respectively. However, the best fit with the employment interaction variables from the stepwise regression is reached with R2=0.78. The significant variables in the equation are X 2, X5, X6, X7, X9, X11, and X 13 (see Table 2.2).
The other mass
variables results are not reported. However, Huxley concludes that the gravity model is not a good predictive device for the indirect estimation of inter-industry trade flows among regions. As this study uses rail data, this conclusion confirms the findings of Chisholm and O’Sullivan. Another result of this study is that distance is not a significant variable to explain freight flows. This result is also reached by Reed (1967). study, a series of additional regressions are run.
In a further step of the
“In the first series, dollar flows were
regressed against tonnage flows coming from the opposite direction, based on the idea of the circular flow of income: goods flowing from region i to j should result in dollars flowing from j to i…a second set of regressions was also run, consisting of dollar flows against tonnage flows in the same direction” (Huxley, 1979:34). However, none of the results were significant. In two subsequent papers, Black (1971, 1972) analyzes the properties and determinants of the distance exponent in the gravity model, using the 1967 U.S. commodity flow survey for 24 major shipper groups. The first article, entitled “Utility of the Gravity Model and Estimates of its Parameters in Commodity Flow Studies”, defines the model as
T = k ij
S ik D kj Fijk
∑D
k j
Fijk
,
(2.10)
j
where, Tijk = Tons of commodity k produced in region i and shipped to region j; Sik = Total shipments of commodity k from region i; Djk = Total demand in region j for commodity k; and Fijk = A friction factor which is equal to 1/d ijb, where dij is the straight line distance between regions i and j, and b is an empirically-derived exponent which may vary across commodity groups. Three hypotheses are formulated concerning the exponent of the friction factor: (1)
The greater the regional specialization for a particular commodity, the lower the exponent of distance.
(2)
The greater the interregional flow of a particular commodity, the greater the 26
exponent of distance. (3)
The greater the per-unit value of a commodity, the lower the exponent of distance for that commodity. The correlation coefficient is 0.55 under hypothesis (1), 0.73 under hypothesis (2),
and 0.51 under hypothesis (3). In the second paper (Black, 1972),
entitled “Inter-Regional Commodity Flows:
Some Experiment with the Gravity Model”, the research questions are whether it is possible to determine the variables related to the variations in the distance exponent, whether it is possible to estimate the distance exponent using these variables, and whether the size of the study area has an effect on the distance exponent. Using the same data set and the same model as specified earlier, Black (1972) empirically obtains exponents values that are regressed across two variables: LMk = the local market for the kth commodity, or the share of the total flow n i the main diagonal of the kth commodity flow matrix; and CPk= the concentration of production for commodity k, or the share of total
flow shipped
by the largest shipping region. The R2 value for this regression is .93, and Black concludes that “(1) the greater the proportion of total shipments from the largest producer (or shipper), the lower the exponent, and (2) the greater the proportion of total flow which is local, the higher the exponent.” Ashtakala & Murthy (1988) use a production-constrained gravity model to forecast commodity flows in Alberta. They hypothesize that the flow between an origin and a destination is a function of production at the origin, consumption at the destination, and distance between them. The model is formulated as
Tij = Pi
C j d ij(λ )
∑C j
j
d ij( λ )
,
(2.11)
where Pi represents production at i, Cj the consumption at j, and dij is distance between i and j. Furthermore, dij (
d
(λ) ij
=
d ijλ − 1 λ
ë)
is the Box-Cox transformation, with
,
(2.12)
27
The only variable in the above model is ë, which is estimated through a grid iterative technique for each commodity group, minimizing the deviation between observed and computed flows. The maximum values of R2 are between 0.71 and 0.88 for six commodity groups. 2. 2. Overview of Spatial Interaction Modeling Spatial interaction models can be used for predicting behavior over space, and for design/planning purposes. According to Fotheringham and O’Kelly (1989), any movement or any communication over space as a result of a decision-making process establishes some sort of spatial interaction. The level of spatial interaction is a function of the level of economic development, and the level of economic development is a function of economic specialization. However, in the long run, these three phenomena are endogenous, and determinants of each other. There are four basic types of spatial interaction models: unconstrained, production constrained, attraction constrained, and doubly constrained.
Unconstrained models
provide information about origin and destination characteristics, production-constrained models address destination characteristics, and attraction-constrained models focus on origin characteristics. Doubly constrained (or origin and destination constrained) models are generally used for predictive purposes. All of the interaction models have four basic elements: a flow matrix, a friction or cost matrix, a matrix of origin propulsiveness measures, and a matrix of destination attractiveness variables
(Fotheringham and O’Kelly,
1989). The cost or friction function employed in spatial interaction models has either an exponential form or a power form. Fotheringham and O’Kelly make four suggestions concerning the use of the cost function. (1) The exponential function is scale sensitive, while the power function is relatively scale independent. Thus, a model with an exponential cost function calibrated for one location should not be used for another location. (2) If a multiplicative cost increase is expected for the cost matrix, an exponential cost function should be employed, and a power function should be used when an additive increase is expected. (3) The power function tends to overestimate low costs movements. (4) The exponential
function
produces
more
consistent
results
when
the
trip
makers
are
homogenous, while the inverse power function is better for a heterogeneous group of trip makers. In two subsequent articles, Fotheringham (1983a, 1983b) discusses a new dimension of spatial interaction modeling. According to Fotheringham, spatial interaction 28
models are often misspecified because the spatial structure is not properly represented in these models (1983a). The distance decay parameter is generally expected to represent spatial relationships, even though some studies indicate that distance is not a significant variable in some spatial interactions (Reed, 1967; Huxley, 1979). The basic issue is that two very different spatial configurations with identical origin/destination distances may produce identical flow patterns. To overcome the problem of representing the spatial configuration in the model, Fotheringham suggests the inclusion of an additional variable that represents the accessibility of the destination to all other destinations available to the origin, as perceived by the residents of the origin (Fotheringham, 1983a). Specifically, an origin-specific production-constrained competing destination model is given by
I ij = Z i Oi m j Aijδ i d ijβ i
(2.13)
where, Zi =
1 n
∑m
j
(2.14) δi ij
A d
βi ij
j
w
Aij =
∑m d k
αi ik
, (k i , k j)
(2.15)
k
Iij is the interaction volume between i and j; mj represents the attractiveness of destination j; dij is the distance between i
and j; Oi represents the known total outflow
from i; A ij is the competing destination variable; and Zi is a balancing factor that ensures that n
∑I j
n
^ ij =
∑I
(2.16)
ij
j
Although this was not noticed by Fotheringham, the competing destination variable Aij, was first mentioned by Reed in 1967 as being demand and supply potentials. While these variables are not exactly the same as those defined by Fotheringham, the idea of the inclusion of a variable that reflects the relative locations of
the destinations was
first mentioned by Reed in his study of commodity flows in India. In a second paper, Fotheringham (1983b) mentions two possible relationships among destinations: competition and agglomeration effects.
Under competition effects,
the interaction with a destination is lower if the destination is
part of a group of
competing destinations. The interaction is higher when the destination is part of a complementary destinations group, in which agglomeration effects take place. If either of these effects are present, the standard gravity model is misspecified. Interestingly, the 29
existence of different types of relationships over space is first mentioned by Edward Ullman (1967) in his “American Commodity Flow” study. Agglomeration effects are then termed “complementary”, and competition effects “intervening opportunities”. Ishikawa (1987), using Japan’s 1960 and 1980 migration, and 1980 university enrollment data, investigates the validity of the competing destination model. Two different
models,
(1)
the
production
constrained
model,
and
(2)
the
production
constrained competing destination model, are formulated. It is concluded that the production-constrained competing destination model provides a noticeable improvement in the R2. Fotheringham (1995) tests whether there is a directional variation in the distance decay parameter, using U.S.
migration data for 48 origins, and concludes that
there is no such directional variation. Finally, Guldmann (1999) tests Fotheringham’s competing
destination
model
with
telecommunication
data.
Following
Fotheringham
(1983a), he introduces into his model a competing destination (CD) factor that measures the accessibility of destination j to all (or a subset of) the other destinations. In addition, Guldmann also considers a similar intervening opportunities variable (IO), which basically represents the spatial configuration of origins. The model specified is:
ln MS ij = ln K 0 + α ln MS ji + β ln Dij + γ ln Pij + δ ln MSOi + ζ ln MSD j + ϑln Aij (2.17) ln MS ji = ln K 0 + α ln MS ij + β ln Dij + γ ln Pji + δ ln MSO j + ζ ln MSDi + ϑln A ji (2.18) where MSij is the flow variable, Dij the distance, Pij the price, MSO i and MSDj the total originating and terminating flows, respectively, and intervening opportunities
Aij the competing destinations or
variable. The system is estimated using the three-stage least-
squares procedure for simultaneous equations estimation, because of the reverse flow effect. The model is first estimated without any spatial structure variable, and later with four different specifications for such variables. The competing destination model provides the strongest improvement over the base model. 2. 3. International Trade Models Frankel and Wei (1998) employ an econometric model to gauge the effects that regional trade arrangements have on world trading patterns, since regional blocks may reduce world welfare if trade-diversion effects dominates trade-creation effects. The data set covers sixty-three countries (3,906 exporter-importer pairs). The specified model is:
30
log( Export ij ) = α + β1 log( GNPi ) + β2 log( GNPj ) + β3 log( GNPi / Pop i ) + β4 log( GNPj / Pop j ) + β5 log( Dist ij ) + β6 log( OverallDis t i ) + β7 log( OverallDis t j ) + β8 log( ADJACENCY ) + β9 log( LANGUAGE ) +
(2.19)
λ1 ( EC _ I ij ) + λ2 ( MERCOSUR _ I ij ) + λ3 ( ASEAN _ I ij ) Most of the variables in Equation (2.19) are self-explanatory. “Overall Distance” measures how far a country is from all other countries. “Adjacency” si a dummy variable for country pairs sharing a common border. “Language” is a dummy variable for countries have linguistic or colonial ties. “EC”, “MERCOSUR”, and “ASEAN” are also dummy variables for trade blocks, indicating the European Community, the Customs Union of the Southern Cone Countries in South America, and the Association of Southeast Asian Nations, respectively.
The results indicate that larger economies trade more but not
proportionately to their GNP. Bilateral distance also has a significant effect. Contiguity and having language commonality also facilitate trade. In terms of trade blocks, the results are mixed. Eichengreen and Irwin (1998) are also interested in whether regional trade blocks have
important effects on trade pattern, and test whether past trade has a significant
impact on current trade. The basic model is
Tij = β0 + β1 ln( Yi Y j ) + β2 ln( Pi Pj ) + β3 ln( DIST ij ) + β4 (CONTij ) (2.20) where Tij is the value of bilateral trade between countries i and j; YiY j is the product of the two countries’ national incomes (the so-called gravity variable); PiPj is the product of the two countries’ per capita incomes; DIST is the straight-line distance; and CONT is a dummy variable indicating whether the two countries are contiguous. After estimating the basic
model,
lagged
trade,
financial
block
and,
finally,
formal
colonial
relationship
variables are added to the basic model. It is concluded that these variables have an important effect In another study, Frankel & Romer (1999) empirically investigate the impact of international trade on standards of living. In the first part of the study, the geographic characteristics of countries are used to construct instruments for international trade, since geographic characteristics might have important effects on income and trade.
Then, these
constructed instruments are used to investigate the impact of trade on income. The estimated bilateral trade equation is
31
ln( Tij / GDPi ) = α0 + α1 ln Dij + α2 ln N i + α3 ln Ai + α4 ln N j + α5 ln A j + α6 ( Li + L j ) + α7 Bij + α8 Bij ln Dij + α9 Bij ln N i +
(2.21)
α10 Bij ln Aij + α11 βij ln N i + α12 Bij ln A j + α13 Bij ln( Li + L j ) where N is population, A is area, L a dummy variables for landlocked countries, and B a dummy variable for common border between two countries.
After
(2.21),
instrument
the
income
equation
is
estimated
using
the
estimating
Equation
constructed.
The
dependent variable, Y i, is the income per person. The model specification is
ln Y = a + bTi + c1 ln N i + c 2 ln Ai ,
(2.22)
where Ti is the trade share (Tij /GDP i), and Ni and A i are the population and the area of country i.
Frankel & Romer (1999) conclude that “trade raises income. The relationship
between the geographic component of trade and income suggests that a rise of one percentage point in the ratio of trade to GDP increases income per person by at least onehalf percent. Trade appears to raise income by spurring the accumulation of physical and human capital and by increasing output for given levels of capital…The point estimates suggest that increasing a country’s size and area by one percent raises income by onetenth of a percent or more.”
2. 4. Summary Although spatial interaction models have been extensively used in such areas as migration, commuting, shopping, and telecommunication, their application to empirical commodity flow modeling has remained very limited, most likely because of limited data availability. The few studies completed so far focus on best-fitting very simple models with little theoretical foundation, instead of searching for
better explanatory models with a
diversified number of variables. For instance, Black (1971 & 1972), Chisholm and O’Sullivan (1973), and Ashtakala and Murthy (1988),
employ a basic gravity model (with two mass and one friction
variables), and their focus is on estimating the exponent of the distance variable. Some try different proxies for the mass variables (Huxley, 1979) while others use total zonal inflows and outflows as mass variables. In addition to these simple models, Reed (1967) adds two more
variables:
supply/demand
potential,
and
redistribution/concentration
effects
variables. Except for Reed, no one has attempted to account for the effects of the spatial configuration of the nodes, despite the risk of model misspecification. Although the mathematical formulation of Reed’s spatial configuration variables is similar to the ones specified in Fotheringham (1983a-b), Guldmann (1999) , and Ishikawa (1987), these 32
variables were not intended to measure competing destinations effects but rather redistributive/concentration effects. The above discussion points to the need to consider expanded spatial interaction models of interregional commodity flows, both in terms of the set of explanatory variables and of the functional form used to relate flows to their determinants. The selection of the variables should be guided by a theoretical framework, and the choice of functional form should be guided by flexibility and best fit with the observed data. The research presented in the following chapter is consistent with these goals.
33
CHAPTER 3 MODELING METHODOLOGY The purpose of this study is to expand empirical research on interregional commodity flows. A theoretical framework is first developed, based upon the Spatial Price Equilibrium model developed by Samuelson (1952), and its reformulation by Brocker (1989). This framework provides guidance for the selection of the explanatory variables. Some of theses variables have been
used in previous commodity flow studies, including
population, sectoral employment, and distance. Other variables have been used in other applications of spatial interaction modeling, and are adapted, for the first time, to interregional
commodity
flow
modeling,
including
competing
destinations,
intervening
opportunities, per capita personal income, and adjacency dummy variables. A third group represents
variables that have never been used in
spatial interaction modeling of
commodity flows, including wholesale employment, sectoral value-added,
average plant
size, and custom district dummies. Finally instead of using the standard multiplicative functional form, a flexible Box-Cox transformation is used to select the best functional form.
3. 1. Theoretical Background The Spatial Price Equilibrium (SPE) Model developed by Samuelson (1952) provides
a
consistent
theoretical
framework
for
trade
in
a
multi-regional spatial
configuration, where commodity flows take place from high-price regions to low-price regions until equilibrium is reached, with price differentials between regions equal to transportation costs. This basic principle is valid for all commodities among all regions as long as the regions are economically connected. Samuelson (1952:290)
defines a net social pay-off (NSP) function. For a two-
region case, the defined NSP is E12
− E12
0
0
NSP = − ∫ s1 ( x ) dx −
∫s
2
( x) dx − t12 ( E12 ) 34
(3.1 )
where s1(x) and s2(x) are the excess supply functions of the two regions (differences between local
demand and supply functions).
E 12 represents the flow between the two
regions, and t12 is the transportation cost between the regions. A multi-regional NSP function is then formulated as (Samuelson, 1952:292): n
NSP = ∑ S i ( Ei ) − ∑∑ t ij ( Eij )
(3.2 )
i< j
1
where S i is the excess supply function of region i, E i is the total output of region i, and tij is the
the transportation cost between regions i and j. Given the excess supply functions and transportation
costs,
Samuelson
shows
that
solving
the
optimization
problem,
consisting in maximizing the NSP, produces (1) the equilibrium prices in each region, and (2) the equilibrium volumes and directions of trade between regions.
At the NSP
maximum, the following condition holds:
− Tij ≤ s i ( Ei ) − s j ( E j ) ≤ Tij ,
(3.3 )
(for all i, j= 1,…, n)
which states that flows among regions will take place as long as price differentials between regions are greater than transportation costs between regions. Despite its sound theoretical framework, this formulation presents three important practical problems: “first, the problem of specifying functional forms, which are a good compromise between the requirements of theoretical consistency, flexibility, parsimony, computational facility and factual conformity; second, the problem of estimating the model parameters with available data; and third, the problem of designing efficient algorithms for a numerical approximation of the equilibria” (Brocker, 1989:8). The first and second problems are related to the specification of the excess supply functions while the third problem is related to the solution of the maximization problem, for which Samuelson did not offer any specific solution algorithm or methodology. To overcome the latter, Takayama & Judge (1964) suggest a quadratic programming approach, assuming linear regional demand and supply functions. The estimation of regional functions with only one price variable would necessitate at least two observations for all regions and for the
commodity
at
stake.
The
goodness-of-fit
for
such
functions
would
be
highly
questionable. As the number of variables increases, the necessary data observations also increase. In a multi-regional and multi-commodity framework, it would be very difficult to obtain so many observations for an empirical study. However, beyond this empirical problem, there are other problems worth mentioning. (1) The basic assumption of the 35
model is that the commodity is perfectly homogenous and spatially uniform. However, commodities in real life are not
homogenous and uniform. The available commodity flow
data almost always do not have this fine level of disaggregation. (2) The model functions under the assumption of perfect spatial information, which fails in real life. (3) The previous factors cause cross-hauling of commodities in practice, which is theoretically impossible according to the SPE model. Brocker (1989) attempts to connect theory and empirical research in trade modeling, and shows that all forms of the gravity model (constrained, unconstrained, and elasticity constrained) are reduced forms of spatial price equilibria of interregional trade. To substantiate this claim, Brocker uses a modified version of the SPE model. In the standard SPE model, buyers satisfy demand at the cheapest supply points in terms of c.i.f prices. However, in Brocker’s model, buyers may choose certain suppliers depending on some characteristics other than c.i.f. prices. At each supply point i, there are firms supplying the commodities, and at each demand point j, there are firms and households demanding
certain quantities
(y1j, y2j, …, yij,… yIj)
from the supply points (i=1->I).
The supply firms are faced with f.o.b. prices, and the buying firms and households with c.i.f. prices. This model consists of four equations. A real-valued supply function is defined as follows:
S i = σi ( p i , s i ),
(3.4 )
where Si is the supply quantity at supply point i, pi is the f.o.b. price at i, and si is a vector of other variables, such as prices of other commodities. ái is monotone, non-decreasing in pi. For each demand point j, there is a demand correspondence, a point-to-set mapping which assigns the vector of O-D flows terminating at j, yj = (y1j ,…,yij ,…,yIj), to the c.i.f. price vector, qj =(q1j ,…,qij ,…,qIj), so that
y j ε δ j (q j , w, d j ),
(3.5 )
where w is a vector of parameters that measure the supply characteristics influencing purchase choices, dj
is a vector measuring demand characteristics, including income,
prices of other commodities, etc. The third equation defines c.i.f. prices:
q ij = p i + c ij ,
(3.6 )
36
where cij is the transportation cost between i and j. And the fourth equation states the equilibrium conditions:
∑y
ij
= S i ∀ i.
(3.7 )
j
A spatial price equilibrium is characterized by prices and quantities satisfying (3.4) –(3.7), which
represent the explicit (or structural) form of the trade model, containing both prices
and quantities as endogenous variables. If prices are eliminated, we obtain
the reduced
form of the model, where equilibrium flows are directly assigned to the vector of exogenous variables, (s, w, d, c) = (s1,…, sI, w 1,…, w I, d1,…dJ, c11,…, cIJ). The reduced form is denoted by æ =( æ matrix Y = *
(y*1,…,
y*J),
11
,…, æ
ij
,…, æ IJ), so that, for any equilibrium flow
we have
Y* = æ (s, w, d, c).
(3.8)
Of course, there is no closed mathematical formulation of æ. One way to think about this function is to solve the equilibrium problem for a wide range of combinations of values for the input parameters (s, w, d, c), for instance over a grid. The resulting flow values Yij* could then be regressed over the input parameters, providing an approximation of the function æ. However, this would remain a purely numerical exercise, unless we are able (1) to select the proper values of (s, w, d, c) characterizing a real interregional setting (e.g., the US and its states), and (2) to assess the goodness-of-fit of the approach. While actual flow data may be available, the proper selection of the input parameters is an extremely difficult task because of data unavailability. An alternative approach is to view (3.8) as a general guide for the selection of simpler, and empirically estimable functional forms. Brocker (1989) shows that the generalized gravity form æij (s, w, d, c) = aij (s, w, d, c) f(cij ) bi (s, w, d, c)
(3.9)
is consistent with (3.8). Equation (3.9) suggests that the origin and destination factors, ai and bj , may be functions of whole vectors (s, w, d, c), and not only of the components of
37
these vectors that are associated with i or j, exclusively. In the standard gravity model, we would have ai = ai (si, w i),
(3.10)
bj = bj (dj) ),
(3.11)
that is, the mass factor at the origin is only a function of the supply variables at the origin, and the mass factor at the destination is only a function of the demand variable at the destination. Equation (3.9) clearly suggests that supply and demand variables associated to other locations k ( i, j) may be included in ai and bj . The following section presents the adaptation of model (3.9) to an empirically estimable model. 3. 2. The Empirical Commodity Flow Model The goal of this research is to empirically identify the determinants of the flows of 16 commodity groups between origin and destination states across the continental US for two separate time periods, 1993 and 1997, and to compare the results. Commodity flows between any two points are expressed as a function of three vectors of variables: a vector that characterizes the supply, a vector that characterizes the demand, and a vector that characterizes the friction between the origin and destination points. The origins serve as supply points, and therefore the variables chosen to represents the origin should be proxies for supply conditions as well as demand conditions at the origin. Likewise, the destinations serve as demand points, and destination variables should mainly be proxies for commodity demands, both intermediate and final. Standard friction variables include distance and adjacency. However, demand and supply conditions at locations other than the origin and destination must also be accounted for. They depend upon the overall spatial configuration (structure) of all origin and destination points. Competing destinations and intervening opportunities
variables may then be used
to capture these effects. In the following sections, the specific variables that make up the above vectors are described, the rationale for their selection is presented, and the choice of functional form for Equation (3.9) and the estimation procedure are discussed.
3. 2. 1. Variables a) Origin Variables The
variables
characterizing
the
origins
(states)
are
employment
in
the
(commodity group) sector (oemp), value-added in the (commodity group) sector (ovlad), 38
wholesale employment (owsem), personal income per-capita (opipc), total population (opop), and average plant size (oaps). Sectoral employment (oemp) and sectoral value-added (ovlad) are used as proxy variables for sectoral production at the origin, and represent supply characteristics. Their expected signs are positive, indicating that as sectoral employment and/or sectoral valueadded increase(s), the outflow of the commodity increases for any given destination. Wholesale employment (owsem) is used to measure the effect of redistribution activities on commodity out-shipment at a given origin. In other words, out-shipment of this commodity may not only be a function of local production activities but also function of redistribution activities taking place at that origin. As
a
wholesale employment
increases, the out-shipment of this commodity is expected to increase. Wholesale activities may also facilitate consumption of the commodity by the final demand sector at the origin. Thus, the expected sign of the coefficient of owsem is positive. Total population (opop) and personal income per-capita (opipc) are two proxy variables for demand conditions at the origin. Although the origins are supposed to be associated with supply conditions for commodity out-shipment, local final demand at the origin may have significant effects. Their expected signs are negative, implying that, as local final demand increases, the out-shipment of the commodity decreases due to increased local consumption. Of course, the extend of this effect would depend on supply elasticity. The average plant size (oaps) is estimated by dividing total sectoral employment by the total number of establishments in that sector. It is intended to capture scale or diversification effects in the industry. Theoretically, as the plant scale of an industrial sector increases, total production and thus total out-shipment in that industry are supposed to increase due to increased production efficiency. However, the two-digit level aggregation may not reflect this effect properly. In other words, the total amount of out shipments by small firms may outrun the out-shipment of the larger firms, because many smaller firms may be characterized by more product diversity, more attractive to export markets than a few larger firms. For this reason, this variable may either (1) have a positive sign, indicating that scale effects control
out-shipments or
that the out-shipment
market is dominated by a few large firms , or (2) have a negative sign, implying that the diversification effect dominates the industry or the market is shared by many small-scale diversified companies. [For a theoretical discussion of these effects, see Krugman (1980)].
39
b) Destination Variables The destinations are approximated by four main variables characterizing the demand: manufacturing employment (dmnem), wholesale employment (dwsem), personal income per-capita (dpipc), and total population (dpop). Manufacturing
employment
(dmnem)
is
the
proxy
for
intermediate
demand;
personal income per-capita (dpipc) and total population (dpop) are to measure final demand conditions;
and wholesale employment (dwsem) is a proxy to measure
redistributions effects at the destination. All of their expected signs are positive, implying that any increase in either of these variables will increase commodity flows to the destinations. c) Geographical Variables A total of six variables characterizing the geographical structure of the interaction space are included in this study: distance (dist), competing destination variable (cd), intervening opportunities variable (io), adjacency dummy (adjncy), origin custom district dummy (ocddmy), and destination custom district dummy (dcddmy). Distance is the most conventional friction variable used in all spatial interaction models. It takes different forms, like highway distance, great circle distance, etc. In this study, the average distances of all hauled commodities are used. The expected sign for the distance variable is always negative, indicating that the interaction between the origin and the destinations decreases as the distance between them increases. Two
specific
variables
configuration of states vis a vis
are
employed
to
capture
the
effect
of
the
spatial
each other: competing destination (cd), and intervening
opportunities (io) variables. These variables may be viewed as integrating into the model the demand/supply effects at locations other than the origin (i) and destination (j). With reference to Eqs. (3.8) and (3.9), these variables represent at least a portion of the vectors (s, w, d).The competing destination variable measures the accessibility of a specific destination to all other destinations. It is estimated using a destination total employment and the distance between two destinations. Mathematically it is expressed as follows:
CDij = ∑ k TEk / d kj
(3.12)
k (i,j)
There is no presumption about the sign of this variable. A negative sign indicates that there is competition among destinations, and as other destinations k get closer to destination j, the amount of the commodity terminating at destination j decreases. The opposite case, a positive coefficient sign,
implies agglomeration effects: flows increase as
other destinations get closer to destination j, and thus make it more attractive to flows. 40
The intervening opportunities (io) variable is defined by a formula similar to (3.12). The distance used in Eq. (3.12) is taken as dki instead of dkj . According to the intervening opportunities concept, flows to a destination decrease when the opportunities between the origin and the destination increase. Just like clusters at destinations, the io variable may be used to describe the spatial configuration of the clusters around origins. According to this idea, a positive sign indicates that when other origins are getting closer, thus implying an economic concentration around the origin, the flow to destination increases. This would suggest possible agglomeration effects at the supply level. However, a negative sign would suggest that the destinations in the origin clusters may act as competing destinations, thus reducing the flow to the destination. An alternative interpretation of a negative sign could be linked to agglomeration diseconomies. The higher the cluster, the larger the negative effects (e.g., congestion), hence the lesser the demand and the flow to destination. In addition, three dummy variables are used. First, the adjacency dummy (adjncy) is intended to measure whether having a common physical border has an effect on commodity flows between states. The expectation for the sign of this parameter is positive, indicating that being adjacent increases trade flows between neighboring states, because of better business information, regional cultural commonalities, etc. Imports and exports are included in the 1993 and 1997 CFS, from and up to the custom districts where the commodity enters or leaves the US. For this reason, two dummy variables, the origin custom district dummy (ocddmy) and the destination custom district dummy (dcddmy), are intended to measure the effect of foreign trade at either origin or destination, on commodity flows. The magnitudes of these variables
depend on
the
significance of the foreign trade share in commodity flows. Those two variables may have either a negative or a positive sign. A positive ocddmy indicates that this sector may have a significant foreign import of the commodity, while a negative sign implies a significant foreign export of the commodity. A positive dcddmy, on the other hand, implies that the sector may have a significant foreign export, while a negative sign would point to imports. The list of the US Custom Districts and the states that include them are presented in Appendix D. States with custom districts are coastal (Ocean or Great Lakes) states and the states along the borders with Canada and Mexico (Montana, North Dakota, and Arizona).
41
3. 2. 2. Functional Form The commodity flow between two points can be written with
the variables
specified above, and may be expressed in the framework of Equation (3.9).
Fij = a i ( ovlad , oemp , opop , opipc, oaps , owsem ,io , ocddmy ) f ij ( dist , adjcny ) b j ( dwsem , dmnem , dpop , dpipc , dcddmy , cd ) where ai is the supply point factor, bj the demand point factor, and fij the
(3.13)
interaction
factor. Equation (3.13) could be, in line with past empirical research, represented by a multiplicative functional form, which would become linear when using the logarithms of the dependent and independent variables. However, other functional forms may be acceptable, and there are no strong theoretical reasons to prefer one functional form to another. In this situation, it is reasonable to allow for the endogenous selection of the functional form. The Box-Cox transformation, wherein the variable X is transformed into the variable X (
ë)
according to
X ( λ ) = ( X λ − 1) λ ,
(3.14)
is ideally suited to this purpose (Box and Cox, 1964). Two different transformation parameters are considered: one for all the independent variables (ë) and one for the dependent variable (è). Dummy variables, however, are not transformed. The Box-Cox model can be expressed as
X nλλ − 1 Y θθ − 1 X 2λλ − 1 = a 0 + a1 X 1 + a2 + .... + an +ε θ λ λ
(3.15)
where ª is assumed to be a normally distributed error term, with E(ª)=0 and E( ª ª )= á ’
I. The Box-Cox transformation (3.14) is continuous at ë =0, because X (
ë)
2
tends toward lnX
when ë ->0. Thus, the linear and log-linear functional forms are simply specific points (ë=1 and ë=0) on a continuum of forms allowing for different degrees of independence and interaction among the variables. The fundamental criterion for comparing the infinite number of a priori possible models is how well they are able to explain the data, with the best model maximizing the 42
likelihood of the original observations under the normality assumption for the error term ª. The optimal values of the parameters (a0, a1,…, an, ë, è) in Equation (3.15) maximize the log-likelihood function
LK = −
N N N [ln( 2π ) + 1 ] − ln[ σ 2 ( θ , λ )] + ( θ − 1 )∑ ln Yi , 2 2 i= 1
where N is the sample size, á (ë, è) is the estimated error variance, and Y i 2
(3.16)
the i-th
observation of the original dependent variable. The log likelihood function is a non-linear function, which is optimized using non-linear programming techniques (Green, 1997). Once the optimal functional form (ë , è ) has been determined, it is possible to test *
*
whether an alternate form (ë, è) is significantly different from the optimal one. If the two forms are equivalent, then the statistics 2[LK(ë , è*) - LK(ë, è)] is approximately distributed *
as a X2 with two degrees of freedom. The null hypothesis of functional form equivalence is accepted if LK(ë , è*) - LK(ë, è) < ½ X 2 (á) *
where á is the selected level of significance.
43
(3.17)
CHAPTER 4 DATA SOURCES AND PROCESSING Four main databases are used in this study: the 1993 and 1997 Commodity Flow Surveys (CFS); the 1993 and 1997 County Business Patterns; the 1992 and 1997 Censuses of Manufactures (Bureau of the Census); and the Annual State Personal Income (Bureau of Economic Analysis). The CFS provides the data for the dependent (commodity flow) and distance variables. The other databases provide the data for the independent variables. Except for the Annual State Personal Income database (selective downloading from the Internet for only the required data items),
detailed descriptions of the databases
are presented in Appendix E. 4. 1. Dependent Variable The dependent variables, flow93 and flow97, are drawn from the 1993 and the 1997 Commodity Flow Surveys. These variables measure the value of commodity outshipments in millions of US dollars. Table
9 in the 1993 CFS, and Table 14 in the 1997
CFS, include the values of these out-shipments from each origin state to every other state for each commodity group at the two-digit level. These two
tables are the main
commodity flow data source in this study. The main reason for choosing the two-digit level of commodity aggregation is that data suitable for O-D analysis in
both the 1993 and 1997 CFSs are not provided at any
more disaggregated levels. O-D flow data are, of course, available at the 1 digit level (i.e. total manufacturing). Using such data would involve less missing data (see below), but would preclude understanding the variations in the factors determining flows across sectors. From a geographical viewpoint, the 1993 CFS data are provided for all states and NTARs (National Transportation Analysis Region), while the 1997 CFS data are provided for all states and major metropolitan areas. The NTARs make up a complete coverage of the US, and each NTAR is made of a set of whole counties. Therefore, there is no areal 44
match between NTARs and metropolitan areas. Since the study is intended to provide comparison between 1993 and 1997, the smallest common spatial aggregation unit is the state. Both Table 9 and Table 14 include missing observations, because of data disclosure and data sampling problems. After analyzing the total magnitude of the missing observations problem in both databases, we conclude that the flow corresponding to these missing observations is not significantly high in either year, and therefore missing observations are eliminated from the database. In the 1993 CFS, there is a total of 83,232 flow observations, with 22,476 of them missing (27 %). In the 1997 CFS, 39,449 (34 %)
of
the 114,036
observations are
missing. However, these percentages do not properly assess the extent of the problem. Table 5, in both years, include the total out-shipments for each origin state and each commodity group, without missing data. Summing up the data in Table 5 across states provides the “unsuppressed” total out-shipments for each commodity group. A similar summation over Table 9 provides the corresponding “suppressed” sums, i.e., without accounting for missing data. The relative difference between these two sums provides a measure of the “flow loss” due to missing data. Table
4.1
presents
the
missing
data
assessment
for
1993.
The
two-digit
commodity codes in this table pertain to the Standard Transportation Commodity Classification (STCC), and code definitions are presented in Appendix A. This classification system is completely compatible with the two-digit Standard Industrial Classification (SIC) system. For all commodities combined, missing flows represent 12 % of the total flow. Across commodities, the highest percent is 36 %, for leather products (later included in group 75 together with groups 22 and 23), and the lowest is 3 %, for food and kindred products. The 1997 missing data analysis is presented in Table 4.2. The commodity codes in this table pertain to the Standard Classification of Transported Goods (SCTG), and commodity code definitions are presented in Appendix B. For all commodities combined, missing flows represents 15 % of the total flow. Across commodities, the highest percentage is 24 %, for pharmaceutical products, and for fertilizers and fertilizers materials, and the lowest is 10 %, for plastic and rubber products.
45
Table 4. 1. Magnitudes of Missing Observations in the 1993 CFS
Code
STCC Definitions
Table 9 Suppres sed Sums ($Million)
Table 5 Unsuppresse d Sums Difference ($Million) ($Million)
Share
20 Food and Kindred Products
856608
835131
21477
0.03
22 Textile Mill Products
100971
89679
11292
0.11
23 Finished Textile Products
291186
235206
55980
0.19
24 Lumber or Wood Products
125004
117579
7425
0.06
25 Furniture of Fixture 26 Pulp, Paper, Allied Products
69278
60182
9096
0.13
194992
186529
8463
0.04
28 Chemicals or Allied Products
529383
486948
42435
0.08
29 Petroleum or Coal Products
346044
311236
34808
0.10
30 Rubber of Plastics Products
168377
149832
18545
0.11
44984
28746
16238
0.36
91340
77613
13727
0.15
33 Primary Metal Products
228603
206442
22161
0.10
34 Fabricated Metal Products
237313
224969
12344
0.05
35 Machinery Products
440852
361511
79341
0.18
36 Electrical Machinery Products 37 Transportation Equipment
411573
350453
61120
0.15
627279
503392
123887
0.20
38 Precision Instruments
199400
145418
53982
0.27
39 Miscellaneous Shipment
142532
125180
17352
0.12
5105719
4496046
609673
0.12
31 Leather Products 32 Clay,Concrete,Glass Products
Total
46
Table 4. 2. Magnitudes of Missing Observations in the 1997 CFS
SCTG Descriptions Code 04 Feed, Cereal, Egg Products
Table 14
Suppressed sums ($Million)
Table 5
Unsuppressed Sums ($Million)
Difference ($Million)
Share
64219
54224
9995
0.16
05 Meat, Fish, Seafood Product
181582
151836
29746
0.16
06 Milled grain products
109497
88571
20926
0.19
07 Prepared foodstuffs
345876
318548
27328
0.08
08 Alcoholic beverages
87222
76337
10885
0.12
19 Petroleum & coal products
74666
60902
13764
0.18
20 Basic chemical
158302
125934
32368
0.20
21 Pharmaceutical products
210793
159458
51335
0.24
22 Fertilizer & materials 23 Chemical products n.e.c.
23017
17434
5583
0.24
209063
167688
41375
0.20
24 Plastics and rubber 25 Logs and other wood prdts
278530
257619
20911
0.08
13054
11327
1727
0.13
26 Wood products 27 Pulp & paper products
120907
114890
6017
0.05
106370
92411
13959
0.13
28 Paper & paperboard artcls 30 Textiles, leather products
97936
86422
11514
0.12
371281
304311
66970
0.18
31 Nonmetallic products
108949
98474
10475
0.10
32 Primary base metal
285254
252141
33113
0.12
33 Articles of base metal
226217
203392
22825
0.10
34 Machinery products
416305
363755
52550
0.13
35 Electrical equipment
854449
737083
117366
0.14
36 Vehicles
568928
485248
83680
0.15
37 Transportation equipment
127666
43098
84568
0.66
38 Precision instruments
143441
120625
22816
0.16
97232
81033
16199
0.17
420882
375150
45732
0.11
5701638
4847911
853727
0.15
39 Furniture products 40 Miscellaneous products Total
47
In order to provide for comparability between the two years, the 1997 commodity classification
system,
SCTG,
needs
to
be
converted
into
classification system, STCC. After analyzing and comparing both
the
1993
commodity
commodity classification
systems at the 3 and 4 digit levels, the conversion is done according to the matching presented in Table 4.3. Since SCTG is a more detailed system than STCC, generally one STCC group includes more than one SCTG group. The only exception is the SCTG group 30, which includes STCC groups 22, 23 and 31. This group is recoded as textile mill, apparel
and
group 75:
leather products. When regrouping SCTG-defined flows in 1997
(e.g. SCTG groups 20, 21, 22, and 23 are summed up, for any O-D pair, to obtain STCC group
28 for this O-D pair), if one SCTG flow is missing for an O-D pair, then this O-D pair
is discarded. This process, however inevitable, further reduces the size of the 1997 database. The analysis is finally done for the 16 commodity groups presented in Table 4. 4 . The geographical coverage of the study is the 48 continental states of the US. Shipments originating from businesses located in Puerto Rico and other territories, shipments traversing the U.S., and shipments from a foreign location to a U.S. location are not included in the CFS.
Imported products shipments are included after they leave the
importer’s domestic location for another location.
Export shipments are also included
until they reach the port of exit from the U.S. Shipments through a foreign country, with both the origin and destination in the U.S., are included. However, in the calculation of the mileages for these types of shipments, the foreign segment is not included. Zero flows have been recorded for several O-D pairs in both years. In the case of a linear model, such observations would not cause any computational problem. However, since this
study uses Box-Cox transformation estimations, zero-valued flows create
computational problems. For this reason, and because the data are
survey-derived rather
census-derived, flows with zero values are replaced by very low values (0.0001 million US dollars). Descriptive statistics for the dependent flow variables across all commodities are presented in Table 4. 5 and Table 4. 6 for 1993 and 1997, respectively. statistics by commodity group are presented in Appendix C.
48
Descriptive
Table 4. 3. Matching of the SCTG and STCC groups SCTG
DESCRIPTION
STCC
DESCRIPTION
1 LIVE ANIMALS AND FISH
1 FARM PRODUCTS
2 CEREAL GRAINS
1 FARM PRODUCTS
3 AGRICULTURAL PRODUCTS, EXCEPT LIVE ANIMALS, 4
PRODUCTS OF ANIMAL ORIGIN
1 FARM PRODUCTS 20 FOOD OR KINDRED PRODUCTS
5 MEAT, FISH, SEAFOOD, AND PREPARATIONS
20 FOOD OR KINDRED PRODUCTS
6 MILLED GRAIN AND BAKERY PRODUCTS
20 FOOD OR KINDRED PRODUCTS
7 PREPARED FOODSTUF, NEC AND FATS AND OILS
20 FOOD OR KINDRED PRODUCTS
8 ALCHOLIC BEVERAGES
20 FOOD OR KINDRED PRODUCTS
9 TOBACCO PRODUCTS
21 TOBACCO PRODUCTS
10 MONUMENTAL OR BUILDING STONE
32 CLAY, CONCRETE GLASS, OR STONE PRODUCTS
11 NATURAL SANDS
14 NONMETALLIC MINERALS
12 GRAVEL AND CRUSHED STONE
14 NONMETALLIC MINERALS
13 NONMETALLIC MINERALS NEC
14 NONMETALLIC MINERALS
14 METALLIC ORES
10 METALLIC ORES
15 COAL
11 COAL
17 GASOLINE AND AVIATION PRODUCTS
13 CRUDE PETROLEUM, NATURAL GAS, OR GASOLINE
18 FUEL OILS
13 CRUDE PETROLEUM, NATURAL GAS, OR GASOLINE
19 PRODUCTS OF PETROLEUM REFINIRING
29 PETROLEUM OR COAL PRODUCTS
20 BASIC CHEMICALS
28 CHEMICALS OR ALLIED PRODUCTS
21 PHARMACEUTICAL PRODUCTS
28 CHEMICALS OR ALLIED PRODUCTS
22 FERTILIZERS AND FERTILIZER MATERIALS
28 CHEMICALS OR ALLIED PRODUCTS
23 CHEMICAL PRODUCTS AND PREPARATIONS
28 CHEMICALS OR ALLIED PRODUCTS
24 PLASTICS AND RUBBER
30 RUBBER OF MISC. PLASTIC PRODUCTS
25 LOGS AND OTHER WOOD IN THE ROUGH
24 LUMBER OR WOOD PRODUCTS, EXCLUDING FURNITURE
26 WOOD PRODUCTS
24 LUMBER OR WOOD PRODUCTS, EXCLUDING FURNITURE
27 PULP, NEWSPRINT, PAPER, AND PAPERBOARD
26 PULP, PAPER, OR ALLIED PRODUCTS
28 PAPER AND PAPERBOARD ARTICLES
26 PULP, PAPER, OR ALLIED PRODUCTS
29 PRINTED PRODUCTS
27 PRINTED MATTER
30 TEXTILES, LEATHER, AND ARTICLES
75 TEXTILE MILL PRODUCTS, APPAREL , AND LEATHER PRODUCTS
31 NONMETALLIC MINERAL PRODUCTS
32 CLAY, CONCRETE GLASS, OR STONE PRODUCTS
32 BASE METAL IN PRIMARY FINISHED BASIC FORM
33 PRIMARY METAL PRODUCTS
33 ARTICLES OF BASE METAL
34 FABRICATED METAL PRODUCTS
34 MACHINERY
35 MACHINERY, EXCLUDING ELECTRICAL
35 ELECTRONIC AND OTHER ELECTRICAL EQUIPMENT
36 ELECTRICAL MACHINERY, EQUIPMENT, OR SUPPLY
36 VEHICLES
37 TRANSPORT EQUIPMENT
37 TRANSPORTATION EQUIPMENT, NEC
37 TRANSPORT EQUIPMENT
38 PRECISION INSTRUMENTS AND APPARATUS
38 INSTRUMENT, PHOTOGRAPHIC, OPTICAL GOODS
39 FURNITURE, MATTRESSES AND MATRESS SUPPORTS
25 FURNITURE OF FIXTURE
40 MISCELLANEOUS MANUFACTURED PFODUCTS
39 MISCELLANEOUS MANUFACTURED PFODUCTS
49
Table 4. 4. Commodity Groups Codes and Definitions
Codes
Definitions
20
Food and Kindred Products
24 25
Lumber or Wood Products Furniture of Fixture
26
Pulp, Paper, or Allied Products
28 29
Chemicals or Allied Products Petroleum or Coal Products
30
Rubber of Plastics Products
32 33
Clay, Concrete, Glass or Stone Products Primary Metal Products
34
Fabricated Metal Products
35 36
Machinery, excluding electrical, Products Electrical Machinery Products
37
Transportation Equipment
38 39
Precision Instruments Miscellaneous Freight Shipment
75
Textile, Apparel and Leather Products
4. 2. Independent Variables The adjacency dummy variable, adjncy,
is defined as equal to 1 if the origin and
destination states have a common border, and 0 otherwise. The custom district variables, ocddmy and dcddmy, are defined as equal to 1 if the state contains at least one custom district, and 0 otherwise. The list of the US custom districts is presented in Appendix D. All
the employment variables are drawn from the County Business Patterns (CBP)
database, and include (1) origin sectoral employment, oemp; (2) origin
wholesale
employment,
and
owsem;
(3)
destination
manufacturing
employment,
dmnem;
(4)
destination wholesale employment, dwsem. Some sectoral employment data are missing in the CBP because of data disclosure problems, and are replaced in the following manner. While
there are missing observations at the two-digit level, one-digit level state
employment data are all available. Summing up two-digit employment for each state up to the one-digit level, and
subtracting this sum from the exact one-digit employment, we
obtain the total value of all missing observations. For each one-digit sector, the total missing value is apportioned over the “missing” two-digit sectors using CBP data for earlier or later years. An illustrative example is presented in Appendix E. The origin average establishment size variable, oaps, is estimated by dividing the origin sectoral employment by the number of establishments in that sector. The numbers of establishments are drawn from the CBP (no missing observations). 50
The value-added variable, ovlad, is drawn from the 1992 and 1997 Censuses of Manufactures (Bureau of the Census). While it was initially planned to draw the valueadded variable for 1993 from the 1993 Annual Survey of Manufactures, this survey sometimes does not include
value-added and other data for some sectors,
although
theses sectors may have significant employment in the state. For this reason, the 1992 Census of Manufactures value-added values are used for the 1993 analysis. The errors introduced by this approximation are likely to be negligible. The methodology to replace missing value-added data involves computing per-employee value-added
for the US as
whole for each two-digit sector, and then multiply state sectoral employment by this US ratio in case of missing observations. The state personal income per-capita variables, opipc and dpipc, and the state population variables, opop and dpop, are drawn from the Annual State Personal Income database from
of
the
the Bureau of Economic Analysis (BEA). These data have been downloaded BEA
Website:
www.bea.doc.gov/bea/regional/spi
There
are
no
missing
observations for these data. The distance variable, dist, is directly derived from the 1993 CFS as average hauled distance, and drawn directly from the 1997 CFS. Table 9 in the 1993 CFS has both tonnage and ton-miles values for each commodity group. Dividing ton-miles
by ton values,
the average hauled distance for each commodity group between each O-D pair is estimated. Because individual commodity groups have missing observations, the average hauled distances for all commodities combined are uniformly used for each commodity group between each O-D pair. In the 1997 CFS, however, these average hauled distances are directly provided in Table 14. The competing destination variable, cd, and the intervening opportunities variable, io, are estimated using distance and total employment, as explained in Chapter 3. Total employment
is
drawn
from
the
CBP,
with
no
missing
observations
The
distance
computation has been explained previously. Table 4.5 and Table 4.6 present descriptive statistics for all the variables across all commodities. Statistics by commodity groups are presented in Appendix C.
51
Table 4. 5. Descriptive Statistics for 1993-All Commodities Combined
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
26023
92
280
0
7800
2385928
cd93
26023
201995
178751
16323
771064
5256512406
io93
26023
197308
179558
16323
771064
5134543484
dist93 (miles)
26016
1241
757
40
3519
32285248
opipc93 ($)
26023
20660
2841
15468
29602
537622216
opop93
26023 5473805 5785073
oemp93
26023
22100
32709
1
284042
575108111
owsem93
26023
143180
153831
7807
783658
3725983058
ovlad92 ($M)
26023
1730
2889
0
21698
45031541
oaps93
26023
71
94
2
1715
1754252
dmnem93
26023
417532
390355
11285
1898885
10865447978
dwsem93
26023
146218
153495
7807
783658
3805026717
dpipc93 ($)
26023
20736
2881
15468
29602
539601756
dpop93
26023 5585155 5766049
460000 30380000 142444834000
460000 30380000 145342500000
flow93: commodity flow, cd93: competing destination, io93: intervening opportunities, dist93: distance, opop93: origin state percapita income, opop93: origin state population, oemp93: origin state sectoral employment, owsem93: origin state wholesale employment, ovlad92: origin state sectoral value-added, oaps93: origin state sectoral average plant size, dmnem93: destination state manufacturing employment, dwsem93; destination state wholesale employment, dpipc93: destination state per-capita income, dpop93: destination state population
52
Table 4. 6. Descriptive Statistics for 1997-All Commodities Combined
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
17406
119
370
0
13664
2066083
cd97
17406
233802
201514
16357
867414
4069552686
io97
17406
227328
201688
16357
867414
3956863410
dist97 (miles)
17402
1194
735
47
3220
20786551
opipc97 ($)
17406
24782
3456
18885
35596
431362191
opop97
17406 5979589 6204436
oemp97
17406
24123
37131
1
293723
419890235
owsem97
17406
161246
168225
8624
856950
2806641762
ovlad97 ($M)
17406
2087
4262
0
65716
36330530
oaps97
17406
70
82
0
653
1194204
dmnem97
17406
451250
415059
13219
2019053
7854453186
dwsem97
17406
167169
170306
8624
856950
2909735446
dpipc97 ($)
17406
24832
3506
18885
35596
432226748
dpop97
17406 6187611 6279457
480000 32268000 104080723000
480000 32268000 107701561000
flow97: co mmodity flow, cd97: competing destination, io97: intervening opportunities, dist97: distance, opop97: origin state per-capita income, opop97: origin state population, oemp97: origin state sectoral employment, owsem93: origin state wholesale employment, ovlad97: origin state sectoral value-added, oaps97: origin state sectoral average plant size, dmnem97: destination state manufacturing employment, dwsem97; destination state wholesale employment, dpipc97: destination state per-capita income, dpop97: destination state population
53
CHAPTER 5 RESULTS After a brief overview of commodity flows and the general interregional trade structure over the continental US, the estimated sectoral equations, their significant variables, and possible explanations for the
findings are presented. These results are
synthesized to outline general patterns. Finally, a detailed elasticity analysis over the major model variables is conducted and its results analyzed.
5. 1. Overview of US Interregional Commodity Flows The total value of
the 16 commodity groups traded in the U.S. within and across
state-lines is $ 5,160 billions in 1993, and $ 5,780 billions in 1997. The largest share characterizes food and kindred products, with around 15 %. The second largest share pertains to transportation equipment, share is only 8 %
in 1993,
with around 12 %. While the electrical machinery
it increases to 15 % in 1997. The third largest
group is
chemical products, 10.5 %. As can be seen in Table 5.1, the other significant product groups are non-electrical machinery (8 %), and textile, leather and apparel (7.5 %). The highest per-ton value product group is precision instruments in both years, with $ 2,566 in 1993, and $ 5,812 in 1997. The cheapest or bulkiest product group is clay, concrete, glass and stone products, around $12. Other high-value product groups include electrical
and non-electrical machineries, transportation equipment, and textile,
leather, and apparel products. Lumber and wood products, coal and petroleum products, and primary metal products belong to the low value/bulky products groups. It is expected that low-value commodities are characterized by short hauls, and high-value ones by long hauls, reflecting the share of transportation costs in total production costs.
54
Table 5. 1 Total Value of Shipments and Their Shares Across Commodity Groups
Group Name
totsp93 ($ Million)
totsp97 Value (%) 93 $/ton ($ Million)
Value (%) 97 $/ton
20 Food & Kindred
852009
16.5
100
801416
13.9
90
24 Lumber & Wood
125982
2.4
19
141341
2.4
19
69289
1.3
427
97209
1.7
488
26 Paper & Pulp
194512
3.8
90
204899
3.5
96
28 Chemical
528455
10.2
103
624525
10.8
104
29 Coal & Petroleum
358617
6.9
19
74991
1.3
16
30 Rubber
172753
3.3
330
278393
4.8
214
32 Clay, Concrete
90862
1.8
11
112638
1.9
13
33 Primary Metals
228428
4.4
86
285577
4.9
88
34 Fabricated Metals 35 Machinery
237001
4.6
279
226694
3.9
226
441339
8.6
1295
416859
7.2
835
36 Electrical Mach.
411391
8.0
1363
857705
14.8
2162
37 Transportation
640169
12.4
748
705239
12.2
676
38 Precision Inst.
199355
3.9
2566
155537
2.7
5812
39 Miscellaneous
164645
3.2
757
420076
7.3
530
75 Textile & Leather
446096
8.6
1050
377028
6.5
827
Cmt.
25 Furniture
Total
5160903 100.0
5780127 100.0
Cmt: commodity group; totsp93: total outshipments in 1993; totsp97: total outshipments in 1997.
Tables 5.2. and 5.3. present total (all commodities) in- and out-shipments by state ranked by decreasing amount, in 1993 and 1997, respectively. Detailed data by commodity for 1993 are available in Appendix C, Table C.33. In- and out-shipments, and their percentages, display similar magnitudes, not only in the same year, but also across the two years. Thus, it appears that states have balanced and stable import and export patterns. With approximately 12 % in shipments share, California displays a spatial concentration
in
manufacturing
production
and
consumption,
possibly
at
both
intermediate and final levels. The second largest spatial concentration is in
the
Texas,
around 8 % of the U.S. interstate trade. The same 12 states, namely, California, Texas, Illinois, Ohio, New Jersey, Michigan, New York, Pennsylvania, North Carolina, Florida, and
Indiana,
Georgia,
export/import approximately 50 % to 60 % of all shipments in the
US, in both years. 55
Table 5. 2. 1993 States’ In- and Out -shipments and Their Shares
1993 Out -shipment State Code
State Name
6 California
Value ($ Million)
1993 In-shipment State Code
(%)
595577 11.3
State Name
6 California
Value ($ Million)
(%)
542548 11.7
48 Texas
410332
7.8
48 Texas
387569
8.4
17 Illinois
305139
5.8
39 Ohio
260159
5.6
39 Ohio
299750
5.7
36 New York
256718
5.5
34 N. Jersey
240792
4.6
17 Illinois
251640
5.4
26 Michigan
240462
4.6
26 Michigan
240836
5.2
36 New York
238580
4.5
12 Florida
215651
4.6
42 Penn
225637
4.3
42 Pennsylv.
202020
4.4
37 N. Carolina
202509
3.8
34 N. Jersey
162045
3.5
13 Georgia
197280
3.7
13 Georgia
159862
3.4
18 Indiana
160010
3.0
37 N. Carolina
148397
3.2
47 Tennessee
158612
3.0
18 Indiana
126337
2.7
12 Florida
153149
2.9
47 Tennessee
120394
2.6
55 Wisconsin
133409
2.5
51 Virginia
106781
2.3
29 Missouri
117522
2.2
55 Wisconsin
105800
2.3
53 Washington
114175
2.2
53 Washington
102327
2.2
25 Massach.
101357
1.9
29 Missouri
101349
2.2
51 Virginia
100539
1.9
25 Massach.
89781
1.9
21 Kentucky
99302
1.9
27 Minnesota
83261
1.8
27 Minnesota
94878
1.8
22 Louisiana
75984
1.6
24 Maryland
86728
1.6
24 Maryland
75928
1.6
22 Louisiana
85518
1.6
21 Kentucky
72423
1.6
1 Alabama
80772
1.5
45 S. Carolina
70252
1.5
45 S. Carolina
77908
1.5
1 Alabama
68271
1.5
56
Table 5. 2. 1993 States’ In- and Out -shipments and Their Shares (continued)
1993 Out -shipment State Code
State Name
19 Iowa
Value ($ Million)
1993 In-shipment State (%) Code
67069 1.3
State Name
Value ($ Million) (%)
4 Arizona
57324 1.2
9 Connect.
64928 1.2
19 Iowa
53257 1.1
4 Arizona
62014 1.2
41 Oregon
52152 1.1
41 Oregon
59505 1.1
8 Colorado
47756 1.0
20 Kansas
57604 1.1
9 Connect.
44283 1.0
5 Arkansas
53473 1.0
8 Colorado
52447 1.0
5 Arkansas
42164 0.9
28 Mississippi
48311 0.9
40 Oklahoma
41377 0.9
40 Oklahoma
44958 0.9
28 Mississippi
41169 0.9
31 Nebraska
36421 0.7
31 Nebraska
24446 0.5
49 Utah
31571 0.6
49 Utah
24339 0.5
54 W. Virginia
27363 0.5
32 Nevada
20532 0.4
23 Maine
18829 0.4
54 W. Virginia
19500 0.4
44 Rhode Isl.
17202 0.3
35 N. Mexico
15472 0.3
32 Nevada
15620 0.3
23 Maine
12336 0.3
33 New Hemp
15296 0.3
16 Idaho
11917 0.3
10 Delaware
15274 0.3
33 New Hamp
11173 0.2
16 Idaho
14539 0.3
10 Delaware
11095 0.2
35 N. Mexico
10060 0.2
30 Montana
8828 0.2
46 S. Dakota
7932 0.2
46 S. Dakota
7808 0.2
50 Vermont
7881 0.1
44 Rhode Is
7384 0.2
30 Montana
7692 0.1
38 N. Dakota
6383 0.1
38 N. Dakota
7333 0.1
50 Vermont
4747 0.1
56 Wyoming
5134 0.1
56 Wyoming
4155 0.1
57
20 Kansas
44149 1.0
Table 5. 3. 1997 States’ In- and Out -shipments and Their Shares
1997 Out -shipment State Code
State Name
6 California
Value ($ Million)
1997 In-shipment State (%) Code
675478 11.6
State Name
6 California
Value ($ Mil)
(%)
549805 13.7
48 Texas
481045
8.2
48 Texas
429945 10.7
39 Ohio
344921
5.9
26 Michigan
224397
5.6
17 Illinois
298171
5.1
39 Ohio
222371
5.5
26 Michigan
281782
4.8
36 New York
221116
5.5
36 New York
247692
4.2
17 Illinois
221048
5.5
37 N. Carolina
243340
4.2
12 Florida
191592
4.8
34 N. Jersey
242999
4.2
42 Pennsylv.
166358
4.1
42 Pennsylv.
239888
4.1
37 N. Carol
157843
3.9
13 Georgia
191284
3.3
13 Georgia
144867
3.6
18 Indiana
185496
3.2
18 Indiana
112917
2.8
12 Florida
169549
2.9
34 N. Jersey
112168
2.8
55 Wisconsin
157900
2.7
47 Tennessee
93316
2.3
47 Tennessee
141083
2.4
55 Wisconsin
88210
2.2
29 Missouri
128417
2.2
53 Washington
82947
2.1
53 Washington
127438
2.2
51 Virginia
77508
1.9
25 Massach.
127008
2.2
25 Massach.
76635
1.9
27 Minnesota
126244
2.2
27 Minnesota
75244
1.9
21 Kentucky
109555
1.9
29 Missouri
74919
1.9
51 Virginia
102937
1.8
45 S. Carolina
60600
1.5
45 S. Carolina
92466
1.6
21 Kentucky
54430
1.4
19 Iowa
89235
1.5
24 Maryland
53914
1.3
1 Alabama
88212
1.5
1 Alabama
53395
1.3
22 Louisiana
83677
1.4
22 Louisiana
50688
1.3
58
Table 5. 3. 1997 States’ In- and Out -shipments and Their Shares (continued)
1997 Out -shipment State Code
State Name
1997 In-shipment
Value State ($ Million) (%) Code
State Name
8 Colorado
Value ($ Million) (%)
41 Oregon
82450 1.4
4 Arizona
75476 1.3
41 Oregon
42398 1.1
9 Connect.
74770 1.3
4 Arizona
41547 1.0
8 Colorado
65838 1.1
24 Maryland
64063 1.1
9 Connect.
34726 0.9
5 Arkansas
60486 1.0
40 Oklahoma
29648 0.7
20 Kansas
60251 1.0
20 Kansas
28490 0.7
28 Mississippi
47941 0.8
5 Arkansas
24806 0.6
40 Oklahoma
47874 0.8
28 Mississippi
23813 0.6
31 Nebraska
46256 0.8
49 Utah
20688 0.5
49 Utah
35541 0.6
31 Nebraska
16428 0.4
33 New Hamp
28907 0.5
32 Nevada
12252 0.3
54 W. Virginia
28257 0.5
54 W. Virginia
10012 0.2
16 Idaho
21773 0.4
35 N. Mexico
8128 0.2
23 Maine
19314 0.3
16 Idaho
7834 0.2
32 Nevada
17971 0.3
33 New Hamp
7778 0.2
46 S. Dakota
14593 0.2
23 Maine
5966 0.1
44 Rhode Isl.
13331 0.2
38 N. Dakota
4943 0.1
10 Delaware
13249 0.2
44 Rhode Isl.
4500 0.1
35 N. Mexico
13076 0.2
30 Montana
4131 0.1
50 Vermont
11873 0.2
10 Delaware
3640 0.1
19 Iowa
42559 1.1
40446 1.0
38 N. Dakota
8028 0.1
50 Vermont
3507 0.1
30 Montana
7794 0.1
46 S. Dakota
3343 0.1
56 Wyoming
5131 0.1
56 Wyoming
1547 0.0
As mentioned earlier, foreign trade flows are
included in the CFSs in flows to and
from US custom states. Tables 5.4 and 5.5, present, for 1993 and 1997, respectively, shares
of
custom district foreign imports to custom district states’
(as foreign imports is included in these out-shipments), and
CFS out-shipments
the share of custom district
foreign exports out of all states out-shipments (potentially all states have some foreign
59
trade flows in their out-shipments). Furthermore, the last column of these tables presents the ratio of total out-shipments of custom district states to total out-shipments of all states. This ratio varies between 80 to 90 % , depending on the commodity group and in both years. All of the states that have custom districts are located either on the Table 5. 4. 1993 Custom States Imports and Export Shares
Cmd.
Group Name
All States Totsp93 Cdex93 (%)
Custom States Totsp93 Cdim93
20 Food & Kindred
852009 20789 2.4 692770
24 Lumber & Wood
125982
25 Furniture
69289
Cd. St./ All St. (%) (%)
21874
3.2
81
5282 4.2 107428
8656
8.1
85
3627 5.2
7940 13.1
88
60638
26 Paper & Pulp
194512
8886 4.6 169767
10388
6.1
87
28 Chemical
528455 35229 6.7 453635
25428
5.6
86
7248 2.0 322124
52423 16.3
90
172753 17137 9.9 144335
14651 10.2
84
78176
18893 24.2
86
228428 12962 5.7 196260
22994 11.7
86
29 Coal & Petroleum 358617 30 Rubber 32 Clay, Concrete 33 Primary Metals
90862 16116 17.7
34 Fabricated Metals 237001
2.6
87
441339 85813 19.4 382119
85303 22.3
87
411391 59883 14.6 359963
74481 20.7
87
37 Transportation
640169 74032 11.6 549739
88885 16.2
86
38 Precision Inst.
199355 21995 11.0 181591
20117 11.1
91
39 Miscellaneous
164645
12940
9.3
85
75 Textile & Leather
446096 15709 3.5 376529
56240 14.9
84
35 Machinery 36 Electrical Mach.
3700 1.6 205261
3800 2.3 139141
5300
Cmt: commodity group; totsp93: total outshipments in 1993; cdex93: custom district export in 1993, cdim93: custom district import in 1993
coasts or along the U.S.-Canada / U.S.-Mexico border (only Montana, North Dakota, and Arizona). These ratios reveal another important geographic fact: a major proportion of U.S. manufacturing is concentrated on coastal areas because of accessibility to water transportation. In terms of foreign export in 1993, non-electrical machinery products have the highest share (19.4 %). The other high-export sectors are clay, concrete, glass and stone products
(17.7
%), electrical machinery (14.6 %),
transportation equipment and
precision instruments (around 11 %). The product groups with high shares of foreign imports include clay, concrete, glass and stone products (24.2
%) ,
non-electrical
machinery (22.3 %), electrical machinery (20.7 %), transportation equipment (16.2 %), 60
coal and petroleum products (16 %), textile, leather and apparel (15 %), furniture and fixture products (13 %,), and precision equipments (11.1 %).
Table 5. 5. 1997 Custom States Imports and Export Shares
Cmd.
Group Name
All States Totsp97 Cdex97 (%)
Custom Cd.St./ States All St Totsp97 Cdim97 (%) (%)
20 Food & Kindred
801416
30000
3.7 632991
32155 5.10
79
24 Lumber & Wood
141341
5841
4.1 118752
13018 11.0
84
5.4
13868 16.5
86
25 Furniture
97209
5255
26 Paper & Pulp
204899
13743
6.7 178801
14256
8.0
87
28 Chemicals
624525
54526
8.7 534774
41369 7.70
.86
72188 110
88
29 Coal & Petroleum
74991
9038 12.1
84213
65988
30 Rubber
278393
28221 10.1 240577
22183
9.2
86
32 Clay, Concrete
112638
16883 15.0
95404
27137 28.4
85
33 Primary Metals
285577
21114 7.40 245685
37726 15.4
86
34 Fabricated Metals
226694
5773 2.50 196057
8548 4.40
86
35 Machinery
416859 136533 32.8 351348 140225 39.9
84
36 Electrical Mach.
857705 106310 12.4 785650 113969 14.5
92
37 Transportation
705239 100214 14.2 574515 120738 21.0
82
38 Precision Inst.
155537
34114 21.9 143729
28949 20.1
92
39 Miscellaneous
420076
5323 1.30 363945
19352 5.30
87
75 Textile & Leather
377028
24609 6.50 340521
77551 22.8
90
Cmt: commodity group; totsp97: total outshipments in 1997; cdex97: custom district export in 1997; cdim97: custom district import in 1997
In 1997, non-electrical products still have the highest export share (32.8 %). The other high-export sectors are precision instruments (21.9 %), transportation equipments (14.2), electrical machinery (12.4 %), and clay, concrete, glass and stone products (15 %). The highest import share in 1997 applies to petroleum products (110 %). This of course, reflects the fact that the US imports more that 50 % of its total petroleum consumption. Non-electrical machinery has the second highest import share (40 %), while textile and leather products (22.8 %), transportation equipments (21.0 %),
precision
instruments (20.1 %), electrical machinery (14.5 %), and clay, concrete, glass and stone products (15 %) are the other high-import product groups in 1997.
61
5. 2. Individual Commodity Equation Results 5. 2. 1. Food and Kindred Products The regression results for commodity group 20, food and kindred products, are presented in Table 5.6. Except for opipc, which is insignificant,
and dcddmy, which is
significant at the 10 % level only, all the other variables are significant at the 5 % level in the 1993 equation. The Box-Cox model performs significantly better than the log-linear model in 1993. In the 1997 equation, however, only occdmy and dist are significant. One possible explanation for this poor result, as discussed earlier, is that the conversion from the SCTG (Standard Classification of Transported Goods) system into the STCC (Standard Transportation
Commodity
Classification)
system
forced
the
elimination
of
many
observations, producing a final sample of only 320 observations (versus 1663 in 1993) In the 1993 equation, the sign of the adjncy coefficient is positive and significant. It indicates that sharing a common border increases food and kindred product flows between neighboring states.
Ocddmy is negative and dcddmy is positive, suggesting that
this commodity group may involve a significant amount of foreign export.
Although the US
foreign export share is only 3.7 % for this commodity, food products represent the largest US foreign commodity shipment, hence even a small export share may be significant. Cd and io are both negative and significant, indicating that destinations clusters at both ends compete in their demands for the commodity.
Dist is negative and highly significant as
expected: the flow decreases as distance (or transport cost) increases. A negative opop indicates that, as the origin population increases, the outflows of food products decrease because of increased local final consumption. Oaps is negative and significant, implying diversification effects. The two variables, oemp and ovlad, representing supply conditions, are positive as expected: the commodity outflow increases as the productive potential of the origin increases.
The redistribution variables, owsem and dwsem, are both positive
and significant: the wholesale sector appears to be an important facilitator in interregional food and kindred products trade. The destination final demand variables, dpipc and dpop, and the destination intermediate demand variable, dmnem, are positive and significant: commodity flows increase as
the manufacturing employment, personal income per capita,
and total population increase at the destination. The 3-digit breakdown of food and kindred products includes meat, poultry and fish, diary products, preserved fruit and vegetables, sugar, bakery products, beverages etc (see Table C.34 in Appendix C). These products are not only supplied for household consumption but also as other industries’ inputs. This explains that the final demand variables at the origin, opop, and at the destination, dpop and dpipc, and the intermediate 62
demand variable, dmnem, are significant and with the expected sign. Furthermore, since this is a product group that is supplied to both the final and intermediate demand sectors, wholesale companies are likely to be involved in this procurement. Table 5. 6. Food and Kindred Products Regression Parameters
20 constant adjncy ocddmy dcddmy
1993
T
-16.312 *
-2.89
-77.759 **
-1.70
1.624 *
6.92
0.106
0.07
-0.273 *
-2.18
0.213 **
1997
-1.992 *
T
-2.48
1.85
0.172
0.27
cd
-0.852 *
-4.58
-1.112
-0.92
io
-0.670 *
-4.32
-1.349
-1.06
dist
-2.199 *
-9.38
-3.379 **
-1.94
opipc
-0.086
-0.34
2.432
1.00
opop
-0.569 *
-3.39
-0.709
-0.76
oemp
0.872 *
4.64
0.838
0.88
owsem
0.896 *
4.11
1.840
1.11
ovlad
1.054 *
5.63
0.662
1.44
oaps
-0.876 *
-4.78
-0.430
-0.54
dmnem
0.194 *
2.77
0.488
0.94
dwsem
1.024 *
4.94
1.089
1.10
dpipc
1.045 *
3.64
2.271
1.00
dpop
0.474 *
2.78
0.440
0.50
lambda
0.078 *
5.08
0.088
1.29
theta
0.194 *
51.78
-0.133 *
MLHD
-6284.19
704.05
LLHD
-7411.55
663.50
X2 N
2254.72 *
27.59
1663
* Significant at 5 % level **
81.10 *
-8.13
27.59
320
Significant at 10 % level
5. 2. 2. Lumber and Wood Products Table 5.7 presents the regression results for commodity group 24, lumber and wood products. Adjncy is positive and significant in both years. Among the custom district variables, only
occdmy is significant at the
10 % level in 1993, pointing a weak export
effect. The US total foreign trade data show that the share and magnitude of import in this 63
product group are higher than for export. This may explain why foreign trade effect are not statistically noticeable. Both the co and io variables are negative and significant in
both
years, implying competition effects at both ends. Distance is negative and significant in Table 5. 7. Lumber and Wood Products Regression Parameters
24
1993
T
1997
T
-65.812 *
-5.87
-86.252 *
-4.25
1.817 *
7.82
2.118 *
5.49
-0.204 **
-1.70
0.132
0.80
0.65
-0.038
-0.23
constant adjncy ocddmy dcddmy
0.074
cd
-1.843 *
-3.62
-1.813 *
-2.40
io
-1.276 *
-3.25
-1.507 *
-2.53
dist
-2.218 *
-6.62
-2.355 *
-4.71
opipc
2.217 *
3.16
4.528 *
2.71
opop
0.087
0.29
0.156
0.29
oemp
-0.228
-0.85
1.643 *
3.68
owsem
0.423
1.49
ovlad
2.369 *
6.04
oaps
0.333
1.39
dmnem
1.016 *
3.67
0.813 *
2.21
dwsem
0.759 *
2.56
1.266 *
2.18
dpipc
3.398 *
4.08
2.993 *
2.55
dpop
1.123 *
2.26
1.002
1.35
lambda
0.016
0.75
0.007
0.25
theta
0.170 *
-0.144
0.121 *
-2220.28
-611.76
LLHD
-2682.28
-722.38
2
924.00 *
N * Significant at 5
as
1.188 *
MLHD X
both years,
33.37
-0.127
1599 % level
expected.
significant. The magnitude
27.59
In
**
221.25 *
-0.25 2.93 -0.37
15.94
27.59
894
Significant at 10 % level
contrast
of opipc,
to in
expectations,
opipc
is positive and
this case, may represent total productive
capacity at the origin rather than being a proxy for local final demand. For example, as can be seen in Appendix C, one of the main producers of this commodity, Oregon, outships nearly 10 % of all US shipments, and this product accounts for 20 % of Oregon total shipments. This share is also around 20 % for both Idaho and Montana. In other 64
words, this product group may be an income generator for some states. The sectoral employment variable oemp is positive and significant only in
1997. However, ovlad is
significant in each year as expected. Wholesale activity at the origin is not a significant redistributive factor in this case. The destination mass variables, namely dmnem, dwsem, and dpipc, are significant with the expected signs in each year. Dpop is not significant in 1997, but is so in 1993. Since lumber and wood products are generally consumed as intermediate products, and not directly by the final consumption sectors, this result may seem counter intuitive. However, one of the main demanders of this product in the US is naturally the construction sector, and this sector is close to the final demand sector. For this reason, the final demand sector may be an indirect determinant of the consumption of wood and lumber products via the construction sector. The Box-Cox model is statistically superior to the log-linear model for this product group.
5. 2. 3. Furniture and Fixture Products The regression parameters of commodity group 25,
furniture and fixture products,
are presented in Table 5.8. Adjncy is positive and significant in each year. Dcddmy is positive and significant at the 10 % level in 1993, pointing to a foreign export effect at destination. Cd and io
are negative and significant each year (io is significant at the 10
% level in 1997), indicating competition effects at both ends. Distance is negative and significant each year, as expected. Opop is negative and significant in 1997, but opipc is never significant, suggesting that there is little final demand effect at the origin. Oemp is significant each year, with the expected sign, implying that flow increases with origin sectoral employment. The other sectoral production variable, ovlad,
is only significant in
1997, with the expected positive sign. Owsem and dwsem are both positive and significant each year, suggesting that wholesale employment at both the origin and destination is a facilitator of flow. Oaps is positive and significant each year, implying that furniture outflows are encouraged by scale economies;
the destination final demand variables,
dpipc and dpop, are highly significant, with the expected signs, suggesting that the furniture industry mainly supplies
final demand. This is confirmed by the weak and sign-
inconsistent effect of the destination manufacturing variable dmnem.
65
Table 5. 8. Furniture and Fixture Products Regression Parameters
25 constant adjncy ocddmy dcddmy
1993
T
1997
-53.690 *
-5.70
-32.251 *
-4.41
0.502 *
2.24
0.840 *
4.08
-0.098 0.198 **
T
-0.88
-0.064
-0.56
1.80
0.154
1.35
cd
-1.133 *
-3.69
-0.494 *
-2.99
io
-0.578 *
-2.77
-0.256 **
-1.84
dist
-1.644 *
-6.99
-1.033 *
-6.46
opipc
0.095
0.27
-0.192
-0.74
opop
0.066
0.29
-0.486 *
-2.68
oemp
1.377 *
5.52
0.695 *
3.81
owsem
0.675 *
2.80
1.004 *
3.75
-0.50
0.335 *
3.25
2.38
0.518 *
2.35
-0.91
-0.167 *
-1.97
ovlad oaps dmnem
-0.060 0.451 * -0.088
dwsem
0.705 *
2.96
0.533 *
2.72
dpipc
2.424 *
4.03
1.236 *
3.24
dpop
1.250 *
2.81
0.688 *
2.50
lambda
0.040 *
1.99
0.066 *
3.16
theta
0.118 *
21.45
0.163 *
30.70
MLHD
-369.13
-1906.76
LLHD
-565.93
-2313.69
X2
393.60 *
N
1573
*
Significant at 5 % level
**
27.59
813.85 *
27.59
1148
Significant at 10 % level
The 3-digit breakdown of furniture products confirms that this group provides mainly for the final demand sector (see Table C.34 in Appendix C). This is supported by our findings in the case of the final demand variables at destination. One possible explanation for not having significant origin final demand variables is that the furniture industry in the US is spatially concentrated and displays economies of scale (as shown by the variable oaps). As can be seen in Appendix C, 15 states provide approximately 71 % of all US shipments. Since final demand dominates in this group, wholesalers may also facilitate this final demand.
66
The
Box-Cox models are statistically superior to the log-linear models in both
years.
5. 2. 4. Pulp, Paper and Allied Products Table 5.9 presents the regression results for commodity group 26: pulp, paper or allied products.
is positive and significant at the 5 % level in 1993, but not
Adjncy
significant in 1997. Among the custom district dummy variables, only ocddmy is positive and significant in 1993, implying an import trade effect in 1993. However, this effect is weak in 1997, as can be confirmed by the custom districts foreign trade data. Cd and io Table 5.9. Pulp, Paper and Allied Products Regression Parameters
1993
T
-15.176 *
-2.91
-7.481
-0.92
adjncy
0.511 *
2.20
0.515
1.61
ocddmy
0.271 *
1.97
0.285
1.28
26 constant
T
dcddmy
-0.014
-0.12
0.156
0.83
cd
-0.601 *
-3.55
-0.399 *
-2.00
io
-0.504 *
-3.35
-0.228 **
-1.80
dist
-1.617 *
-7.18
-1.329 *
-4.40
opipc
-0.373
-1.64
-0.381
-1.08
-0.086
-0.54
opop
0.223 **
1.77
oemp
0.518 *
3.05
0.699 *
2.70
owsem
0.149
1.16
0.205
0.99
ovlad
0.485 *
3.80
0.278 *
2.02
-1.19
-0.756 *
-3.11
oaps
-0.159
dmnem
0.367 *
3.67
0.231 *
2.02
dwsem
0.667 *
3.72
0.417 *
2.09
dpipc
0.838 *
3.12
0.584 **
1.71
dpop
0.167
1.47
0.180
1.12
lambda
0.094 *
4.67
0.115 *
3.41
theta
0.181 *
37.47
0.126 *
15.83
MLHD
-3387.18
-1005.18
LLHD
-3989.37
-1122.33
X
2
1204.37 *
N *
1997
Significant at 5 % level
27.59
1560 **
234.30 * 722
Significant at 10 % level
67
27.59
are both negative and significant each year. Competition effects dominate at destinations clustered around both ends. Distance is significant, with the expected sign in both years. 30 % of this product group shipments is paper, and the rest is
paper products used in
manufacturing industries, such as paperboards, boxes etc. For this reason, the final demand variables at both the origin and destination are not very significant. The sectoral production variables, oemp and ovlad, are positive and significant, as expected, year.
each
Oaps is only significant in 1997. However, the negative sign each year may indicate
strong diversification effects. The wholesale employment is significant, with the expected sign, only at the destination in each year. Since wholesale employment at the origin is never significant, we may be conclude that out-shipments are provided by producers, and not by wholesalers. The demand induced by wholesale trade at the destination is significant for intra-state redistribution. Also, the intermediate demand at the destinations is significant each year, as dmnem is positive and significant. It is also possible to say that wholesalers in this product group provide mainly the intermediate demand sectors. The Box-Cox model is statistically superior to the log-linear model for both years. 5. 2. 5. Chemical and Allied Products The regression results for commodity group 28, chemical and allied products, are presented in Table 5.10. Because of the sample reduction problem caused by the conversion of SCTG into STCC, as discussed earlier, the Box-Cox equation of 1997 is not significantly better than the log-linear model, and none of the variables in the equation is significant. For this reason, only the 1993 equation is discussed below. A positive and significant adjncy
suggests that a common physical border
increases the chemical products trade between states.
Foreign trade does not display a
significant effect on chemical product flows, since none of the custom district dummy variables is significant. Cd and io are both negative and significant, implying that competing effects dominate at both end destinations. Distance is significant and negative, as expected. The product composition of this sector includes industrial organic and inorganic chemicals, plastic materials and fibers, paints and enamels, and agricultural chemicals (see Table C.34 in Appendix C). All these product items are consumed by other industries. For this reason, the final demand variables,
opipc, opop, dpipc, and dpop, at both the
origins and destinations, are not significant. Oemp and ovlad are both positive and significant, as expected. Wholesale employment at the origin and destination is positive and significant, facilitating interregional chemical products flows. The negative sign of 68
oaps suggests that exports in the chemical industry are boosted by diversification effects. The intermediate demand effect, proxied by dmnem, are
also positive and significant, as
expected. Wholesale employment, at both the origin and the destination, is positive and significant, pointing a significant redistribution activity. Since the industry is characterized by mainly intermediate demand, wholesalers are probably providing to demand sectors. Table 5. 10. Chemical and Allied Products Regression Parameters
28
1993
constant
-3.078
T
1997
T
-0.71
-85.641
-0.87
adjncy
1.838 *
7.65
0.966
0.96
ocddmy
0.009
0.07
-0.156
-0.28
dcddmy
0.155
1.25
0.148
0.33
cd
-0.233 *
-2.94
-0.168
-0.29
io
-0.200 *
-2.82
-0.400
-0.42
dist
-1.288 *
-8.32
-2.077
-1.12
opipc
0.001
0.01
1.853
0.54
opop
-0.100
-1.22
2.028
0.41
oemp
0.447 *
2.91
0.169
0.25
owsem
0.226 *
1.96
-0.488
-0.37
ovlad
0.601 *
6.44
-0.335
-0.57
oaps
-0.707 *
-3.26
0.680
0.49
dmnem
0.102 *
2.16
-0.139
-0.29
dwsem
0.730 *
4.54
0.825
0.67
dpipc
-0.120
-0.70
0.774
0.41
dpop
0.050
0.65
1.147
0.45
lambda
0.111 *
6.43
0.050
0.38
theta
0.185 *
45.88
-0.012
-0.68
MLHD
-5769.75
89.48
LLHD
-6716.11
87.89
X2
1892.72 *
N
1568
* Significant at
5 % level
**
27.59
3.17 223
Significant at 10 % level
69
27.59
intermediate
5. 2. 6. Petroleum and Coal Products Table 5.11 presents the regression results for commodity group 29, petroleum and coal products. In each year, the flow of this commodity is mainly explained by
origin or supply conditions, since none of the destination
variables is significant.
Adjncy is positive and significant each year. Ocddmy is positive and significant in 1993, indicating a foreign import effect. Considering the US dependency on result seems
reasonable.
petroleum, this
The custom districts data also shows relatively high import
share for both years; however,
not such effect is uncovered
in 1997.
Distance is a
significant variable each year, with the expected negative sign. Cd is significant at the 10 Table 5. 11. Petroleum or Coal Products Regression Parameters
T
1997
T
64.297 *
3.18
278.711 *
3.53
adjncy
8.254 *
5.68
28.834 *
6.61
ocddmy
1.843 *
2.62
0.358
0.21
dcddmy
0.329
0.56
0.316
0.21
29
1993
constant
cd
-0.413 **
-1.80
-1.112
-1.57
io
-0.182
-1.20
0.329
0.87
dist
-2.998 *
-3.91
-6.641 *
-3.25
opipc
-2.188 *
-2.49
-11.057 *
-2.25
opop
-0.450
-1.63
-2.145
-1.52
oemp
1.949 *
3.25
4.535 *
2.78
owsem
0.780 *
1.99
3.132 **
1.79
0.470
0.70
ovlad
-0.122
-0.68
oaps
-2.848 *
-3.93
-3.33
dmnem
0.033
0.39
0.481
1.32
dwsem
0.278
1.26
0.859
1.07
dpipc
-0.104
-0.26
-1.793
-1.23
dpop
0.314
1.47
0.715
1.12
lambda
0.186 *
5.60
0.164 *
4.20
-0.240 *
-24.07
-0.391 *
-28.95
theta MLHD
4892.51
5328.21
LLHD
4502.05
4651.76
X
2
N *
-5.692 *
Significant at 5 % level
780.92 *
27.59
1557 **
1352.91 * 1321
Significant at 10 % level
70
27.59
% level in 1993, indicating a weak competition effect at destinations. Opipc is significant as expected: local consumption of petroleum products negatively affects out-shipments of this commodity. The sign of opop confirms this effect, but this variable is barely significant. A negative and significant oaps indicates that the sector is characterized by diversification effects. The variables that positively affect out-shipments in this commodity group are oemp and owsem. In both years, the Box-Cox models are statistically superior to the log-linear specifications. The results show that only the origin final demand sector variables are significant for this product group. When we consider that this group has a high distance coefficient, and thus is very sensitive to transportation, this result makes sense. One can expect that petroleum and coal products are bulkier and can relatively be more dangerous to haul over longer distances. For this reason, the supply at the origins may not be very sensitive to the demand at the destinations.
5. 2. 7. Rubber & Miscellaneous Plastic Products The regression results for rubber and other plastic products, commodity group 30, are
presented
in
Table
5.12. Adjncy
is significant, with the expected sign. The
insignificance of the custom district dummy variable suggests that there are no effects of foreign trade in this sector, although the share of foreign trade for this group is around 10 % for both imports and exports. The negative cd variable implies competition effects at the node cluster around destinations, but the io variable is not significant. highly significant, with the
expected sign
each year.
Opipc and
Distance is
opop are
both
negative and significant, as expected: outflows decrease with increasing local final consumption. Oemp is not significant in 1993, yet negatively significant in 1997.
On the
other hand, ovlad, the other sectoral production variable, is significant, with the expected sign, each year. Wholesale employments is significant at both the origin and destination, suggesting that redistribution effects are important for this commodity. The intermediate demand variable, dmnem is significant each year, and the final demand variables at the destination, dpipc and dpop, are significant and positive, as expected, in 1993, but not in 1997. The 3-digit breakdown shows that this product group includes rubber tires, plastic footwear, plastic hose, belting
and all other miscellaneous plastic products (see Table
C.34 in Appendix C). These products are used at both the final and intermediate demand 71
levels, which is supported by our statistical findings. Wholesalers of this commodity provide for both demand sectors. In both years, the Box-Cox models are statistically superior to the log-linear specifications. Table 5.12. Rubber and Misc. Rubber Products Regression Parameters
30 constant adjncy
1993
T
1997
T
-7.088 **
-1.78
9.425 *
2.60
1.142 *
5.64
0.882 *
4.47
ocddmy
-0.166
-1.63
0.036
0.32
dcddmy
-0.048
-0.48
0.111
1.00
cd
-0.422 *
-4.05
-0.155 *
-3.12
io
-0.093
-1.46
0.036
1.34
dist
-1.069 *
-8.37
-0.718 *
-6.81
opipc
-0.692 *
-3.53
-0.545 *
-3.59
opop
-0.326 *
-3.31
-0.150 *
-2.85
oemp
0.123
1.34
-0.152 *
-2.69
owsem
0.755 *
4.53
0.405 *
3.95
ovlad
0.679 *
5.55
0.594 *
5.16
oaps
-0.009
-0.09
-0.114
-0.90
dmnem
0.244 *
4.06
0.142 *
3.56
dwsem
0.422 *
3.84
0.276 *
3.71
dpipc
0.445 *
2.51
0.014
0.17
dpop
0.240 *
2.47
-0.006
-0.25
lambda
0.103 *
6.25
0.179 *
8.38
theta
0.193 *
44.43
0.196 *
38.76
MLHD
-3837.35
-5605.73
LLHD
-4684.41
-6526.13
X2 N
1694.11 *
27.59
1604
* Significant at 5 % level **
1840.82 *
27.59
1308
Significant at 10 % level
5. 2. 8. Clay, Concrete, Glass and Stone Products Table 5.13. presents the regression results for commodity group 32: clay, concrete, glass or stone products. The significant and positive adjncy indicates that sharing a physical border increases commodity trade. Ocddmy is negative and significant 72
in 1997, pointing to a significant export effect. The coefficient of this variable is also negative in 1993, but, insignificant. Foreign trade data show that the import in this sector is larger than its export, but this is not captured by these variables. The cd variable is negative and significant
each year, suggesting a competition effect at destinations.
However, the variable io is positive
each year, although not significant in 1997.
This
suggests that intervening opportunities around the origins do not decrease the shipments to farther destinations, unlike other commodity groups. One possible explanation for this Table 5. 13. Clay, Concrete, Glass and Stone Products Regression Parameters
32 constant adjncy
1993
T
T
-34.0399 *
-4.730
-23.159 *
-2.890
1.3662 *
5.757
1.391 *
4.218
-0.457 *
-2.630
-0.029
-0.179
-0.549 *
-2.178
ocddmy
-0.1157
-0.898
dcddmy
0.0778
0.662
cd
-0.7736 *
-3.049
io
0.2632 *
2.021
-1.4072 *
-5.867
dist
1997
0.170 -1.368 *
1.424 -4.275
opipc
0.1425
0.490
0.141
0.433
opop
-0.2930
-1.639
-0.116
-0.762
oemp
0.9571 *
2.943
owsem
0.0691
0.400
ovlad
0.3584 **
1.651
oaps
0.885 * -0.144 0.454 ** -0.858 *
2.485 -0.798 1.958
-0.6571 *
-2.250
dmnem
0.3606 *
2.903
0.188 **
1.809
dwsem
0.3458 *
2.062
0.547 *
2.087
dpipc
1.2599 *
3.060
0.710 **
1.873
dpop
0.6517 *
2.197
0.239
1.267
lambda
0.0684 *
2.795
0.096 *
2.872
theta
0.1306 *
23.301
0.088 *
10.973
MLHD
-756.08
-100.73
LLHD
-1001.00
-165.82
X
2
489.85 *
N * Significant at 5 % level **
27.59
1644
130.17 * 892
Significant at 10 % level
73
-2.251
27.59
situation is that, as for furniture products, this product group also displays a spatial concentration. Ten states produce approximately 70 % of total US shipments. Dist is negative and significant. The origin final demand variables, opipc and opop, are not significant, suggesting that this sector does not provide for the final demand sector at the origin. Oemp and ovlad, representing sectoral productive capacities, are positive and significant each year. This sector may also be characterized by diversification effects, since oaps is negative and significant each year. Dmnem and dwsem are both significant each year, with the expected signs, indicating that intermediate and redistributive demands at the destinations have significant effects on the trade of this commodity. The destination final demand variables, dpipc and dpop, are positive and significant. Two main items in this commodity group are glass and concrete based products. Although these items may not demanded directly by final demand sectors, they are mainly supplied for the construction industries, just like lumber and wood products, and they are demanded indirectly by the final demand sectors via the construction sector. Finally, the Box-Cox models in both years are statistically superior to the log-linear specifications. 5. 2. 9. Primary Metal Products The regression results for primary metal products, commodity group 33, are presented in Table 5.14. The variable adjncy is significant in both years. The foreign trade variables, ocddmy and dcddmy, are negative and significant, although at the 10 % level, indicating foreign trade effects in terms of both imports and exports. The foreign export and import shares are 6 % and 12 %
in 1993, and these effects are captured by the
custom district variables. Also, the nodes clustered around both origins and destinations display competition effects, since the variables cd and io are negative and significant. Dist is significant and negative, implying that flows decrease with distance. Only opipc is significant in 1997, with the expected signs at the 5 % level. Oemp, the sectoral production variable, is highly significant and positive in each year. The other sectoral production variable, ovlad, is negative in 1993, yet significant at the 10 % level. However, ovlad is significant with the expected sign in 1997. Owsem is positive and significant each year,
indicating that wholesale employment
at the origin has a significant
impact on trade flows. Oaps has a negative sign in both years, but is not significant in 1993.
Dmnem is positive and
significant
effects at destinations. Dwsem is also
each
year, pointing to intermediate
demand
positive and significant each year. Only one 74
Table 5.14. Primary Metal Products Regression Parameters
1993
T
-14.839 *
-2.32
0.848 *
ocddmy
33 constant
T
5.045
0.88
3.73
0.420 **
1.67
-0.222 **
-1.78
-0.265 **
-1.80
dcddmy
-0.358 *
-2.97
-0.234 **
-1.72
cd
-0.479 *
-3.18
-0.447 *
-3.14
io
-0.415 *
-3.06
-0.320 *
-3.17
dist
-1.574 *
-7.24
-1.482 *
-7.50
adjncy
opipc
0.039
0.14
-0.656 *
-2.12
opop
0.203
1.28
-0.267 **
-1.66
oemp
1.230 *
4.49
0.668 *
5.46
owsem
0.297 **
1.79
0.909 *
3.51
ovlad
-0.213 **
-1.69
0.233 *
2.70
oaps
-0.107
-0.82
-0.527 *
-3.68
dmnem
0.485 *
3.88
0.539 *
3.87
dwsem
0.290 **
1.88
0.302 **
1.86
dpipc
-0.450
-1.64
-0.339
-1.33
dpop
0.568 *
2.43
0.246
1.64
lambda
0.071 *
3.48
0.088 *
4.55
theta
0.176 *
38.97
0.143 *
28.37
MLHD
-3671.21
-3011.63
LLHD
-4319.39
-3366.60
X2
1296.36 *
N
1511
* Significant at 5 % level **
of the destination
1997
27.59
709.93 *
27.59
1178
Significant at 10 % level
final demand variables, dpop, is significant and positive in 1993. This
product group includes steel works, iron or steel castings, nonferrous metal products, metal based alloy castings, and other miscellaneous primary metal products. Obviously, all of these items are consumed at the intermediate demand level. Although one or two final demand variables display some degree of significance, the final demand proxies do
75
not show a systematic significance. On the other hand, the variable that measures the effect of intermediate demand is highly significant in both years. For this reason, wholesalers in this sector may be selling mainly to the intermediate demand sectors. In both years, the Box-Cox models are statistically superior to the log-linear models. 5. 1. 10. Fabricated Metal Products Table 5.15 presents the regression results for commodity group 34: fabricated metal products. As for all other commodity groups so far, adjncy is a positive and significant variable each year. Among foreign trade variables, only occdmy is negative and significant
in
1993,
implying
an export
effect. However, this product group is the
weakest foreign trade sector in the US. Its export share is about 2 %, and its import share is around 4 %.
Cd is negative and significant each year: competition effects dominate at
the destinations. Io is significant and negative in 1993, but is not significant in 1997. Dist is highly significant each year, with a negative sign. Opipc has a
negative sign
in
both
years, yet is only significant in 1997. On the other hand, opop is both negative and significant each year, as expected. Owsem is positive and significant each year, implying that wholesale employment at the origin facilitates the trade of fabricated metal products. The only significant sectoral production variables are ovlad in 1993 and oemp in 1997. Oaps is significant in 1997, with a negative sign,
implying that this product is
characterized by diversification effects. All destination variables, dmnem, dwsem, dpipc, and dpop are positive and significant each year, as expected. The main products in this group are metal cans, hand tools or general hardware, plumbing fixtures, structural metal products, bolts, nuts, screws, rivets, metal stampings, miscellaneous wire products, and other fabricated metals. (see Table C.34 in Appendix C) As the breakdown shows, this commodity final
demand
sectors
together.
group
supplies
both the intermediate
and
Our findings also support this intuition since both final
and intermediate demand proxy variables are significant. Since the wholesale trade sector is supplying
both demand categories, it is also significant in the flow of fabricated metal
products. The Box-Cox models are statistically superior to the log-linear specifications.
76
Table 5. 15. Fabricated Metal Products Regression Parameters
1993
34 constant
1997
T
-12.803 *
-2.83
1.218 *
6.88
ocddmy
-0.217 *
-2.26
-0.017
-0.14
dcddmy
0.110
1.25
0.120
1.10
cd
-0.537 *
-4.75
-0.709 *
-3.51
io
-0.215 *
-2.92
-0.032
-0.33
dist
-1.256 *
-9.68
-1.427 *
-7.09
opipc
-0.299
-1.49
-1.371 *
-3.49
opop
-0.313 *
-3.01
-0.675 *
-2.70
oemp
-0.040
-0.32
0.805 *
2.80
adjncy
9.103
1.53
0.996 *
5.09
owsem
0.896 *
5.46
1.000 *
3.86
ovlad
0.857 *
5.63
0.259
1.38
oaps
0.247
1.50
-0.980 *
-3.43
dmnem
0.236 *
4.38
0.246 *
2.93
dwsem
0.347 *
3.63
0.506 *
2.83
dpipc
0.368 *
2.30
0.087
0.38
dpop
0.430 *
3.45
0.575 *
2.48
lambda
0.096 *
6.50
0.076 *
3.68
theta
0.211 *
54.33
0.192 *
38.34
MLHD
-5429.08
-4430.00
LLHD
-6718.99
-5239.25
X2
2579.82 *
N *
T
Significant at 5 % level
27.59
1744 **
1618.50 *
27.59
1174
Significant at 10 % level
5. 2. 11. Machinery Products (Non-Electrical) The regression results for commodity group 35, non-electrical machinery, are presented in Table 5.16. Adjncy is positive and significant, implying increasing trade between contiguous states. Dcddmy is significant and positive in both years while ocddmy is negative and significant in 1997, which suggests that this product group is subject to significant foreign export effects, especially in 1997. Non-electrical machinery
has the
highest share of foreign trade in the whole US, both in terms of export and import. Cd and io are negative and significant each year, implying competition effects at both ends. 77
Dist
is again highly significant and negative, as expected. The origin variables, opipc and opop are
negative and
products
significant,
indicating
that
local
consumption
of
machinery
at the origin decreases out-shipments. Ovlad is significant and positive both
years, as expected. Oemp , on the other hand, is positive and significant only in 1993, and at the 10 % level.
The redistribution variables at the origin and destination, owsem and
dwsem, are both significant and positive
each year. The intermediate demand variable at
the destination, dmnem, has also a positive and significant effect on the flows of this commodity group. The only significant final demand variable at the destination is dpop. Table 5. 16. (Non-Electrical) Machinery Products Regression Parameters
35 constant
1993
T
-15.730 *
-3.53
adjncy
1.524 *
ocddmy dcddmy
1997
T
0.736
0.11
7.55
0.943 *
4.07
0.039
0.36
-0.250 *
-1.96
0.172 **
1.67
0.257 *
2.07
cd
-0.423 *
-3.95
-0.593 *
-3.24
io
-0.145 *
-2.51
-0.304 *
-2.15
dist
-0.860 *
-7.84
-1.329 *
-6.96
opipc
-0.261
-1.61
-1.175 *
-3.08
opop
-0.321 *
-3.29
-0.290
-1.60
oemp
0.165 **
1.90
0.213
1.50
owsem
0.877 *
4.99
0.877 *
3.70
ovlad
0.488 *
4.24
1.074 *
5.42
oaps
0.069
0.47
0.173
0.92
dmnem
0.091 *
2.41
0.185 *
2.14
dwsem
0.468 *
4.08
0.527 *
2.46
dpipc
0.262 **
1.73
dpop
0.329 *
2.83
0.695 *
2.47
lambda
0.114 *
6.42
0.066 *
3.19
theta
0.198 *
48.29
0.190 *
37.13
-0.268
MLHD
-6324.75
-5519.14
LLHD
-7520.52
-6356.18
X2
2391.54 *
N * Significant at 5 % level **
27.59
1563
1674.06 * 1271
Significant at 10 % level
78
-0.92
27.59
Sub-products
of
this
group
include
engines,
turbines,
farm
machinery,
construction and mining equipments, metal working machinery, office, computing or accounting machineries, service industry machines and other miscellaneous machineries (see Table C.34 in Appendix C). All of these products are used both demand sectors, and this facts supports our empirical findings that both of the demand proxy variables are significant as expected. Furthermore, the wholesale sector is likely supplying both demand sectors. The Box-Cox models
are statistically superior to the log-linear models in both
years.
5. 2. 12. Electrical Machinery Products Table 5.17 presents the regression results for electrical machinery products, product group 36. Adjncy is positive and significant, as in all other previous commodity groups. Among the custom district variables, only ocddmy is positive and significant in 1997, pointing to an import effect this year. However, this group had a significant amount of foreign imports and exports in 1993 as much as in 1997, but these 1993 flows are not accounted
for by the custom district variables. Cd is negative and significant each year,
implying competition effects at the clusters around destinations. However, io is only significant in 1997, with a negative sign. The final demand variables, opipc and opop, are negative and significant, as expected, except for opipc in 1997. Although oemp is positive and significant, the other sectoral production variable, ovlad, is negative and significant each year, an obviously counterintuitive result. Owsem and dwsem are significant and positive each year, pointing to the effects of redistributive activities in the electrical machinery sector. Oaps is negative and significant, implying diversification effects. The effects of the final demand variables at the destination, dpipc and dpop, each year, but the intermediate demand
are significant
variable at destinations, dmnem, is not, which is
surprising in light of the role of such equipment products in manufacturing. The sub-categories of this product group include household appliances, radio and television
sets,
communication
equipments,
electronic
components
or
accessories,
electrical industrial equipments and other electrical machinery and equipments (see Table C.34 in Appendix C). These products items are generally consumed by the final demand sectors, and, for this reason, only the final demand variables at both the origins and the destinations display significance, in addition to the wholesale employment variable. In this case, wholesalers are supplying the final demand sectors. 79
The Box-Cox models are statistically superior to the log-linear specifications in both years. Table 5. 17. Electrical Machinery Products Regression Parameters
36 constant
1993
T
1997
T
-30.241 *
-4.74
-25.810 *
-4.51
1.301 *
6.49
0.862 *
4.34
ocddmy
-0.178 **
-1.67
0.267 *
2.29
dcddmy
-0.075
-0.73
-0.138
-1.25
cd
-0.675 *
-4.28
-0.256 *
-3.45
io
-0.023
-0.28
-0.198 *
-2.60
dist
-0.957 *
-8.30
-0.649 *
-7.35
opipc
-1.159 *
-2.92
-0.045
-0.25
opop
-0.445 *
-2.65
-0.452 *
-3.40
oemp
1.537 *
6.06
0.929 *
5.46
owsem
0.805 *
4.29
0.831 *
4.51
ovlad
-0.360 *
-3.66
-0.235 *
-4.66
oaps
-1.027 *
-5.63
-0.520 *
-4.40
adjncy
dmnem
0.053
0.88
0.011
0.30
dwsem
0.562 *
3.62
0.391 *
3.48
dpipc
1.517 *
4.57
0.708 *
3.72
dpop
0.931 *
3.53
0.316 *
2.86
lambda
0.070 *
4.33
0.122 *
6.62
theta
0.185 *
45.51
0.156 *
29.48
MLHD
-5967.28
-6746.63
LLHD
-7053.58
-7418.27
X2
2172.61 *
N
1581
* Significant at 5 % level **
27.59
1343.29 *
27.59
1274
Significant at 10 % level
5. 2. 13. Transportation Equipment The regression results for transportation equipment, commodity group 37, are presented in Table 5.18. In contrast to all previous commodity groups, adjncy is not significant. Except for ocddmy in 1993, which implies a foreign export effect, none of the custom district variables are significant. However, this product group is one of the important foreign trade industries in the US, both in 1993 and 1993. This is not accounted for the foreign trade variables. One of the geographical structure variables, cd, is significant and negative, indicating competition effects at the destinations. Io is 80
Table 5. 18. Transportation Equipment Regression Parameters
37
1993
constant adjncy
T
1997
T
-5.709
-1.00
5.265
0.61
0.062
0.21
0.279
0.60
-3.71
-0.107
-0.40
0.45
-0.038
-0.15
-0.511 *
-2.25
ocddmy
-0.597 *
dcddmy
0.071
cd
-0.559 *
-3.90
io
0.294 *
3.63
dist
-1.241 *
-7.54
-1.220 *
-4.49
opipc
-0.791 *
-3.54
-0.194
-0.66
opop
-0.298 *
-3.00
-0.493 *
-2.11
oemp
0.151 *
2.02
-0.128
-0.94
owsem
0.411 *
3.19
0.758 *
3.02
ovlad
0.585 *
5.75
0.971 *
3.39
oaps
-0.804 *
-5.37
-1.148 *
-3.87
dmnem
0.155 *
2.81
0.073
0.97
dwsem
0.180 **
1.74
0.365 **
1.90
dpipc
0.760 *
3.21
0.047
0.17
dpop
0.424 *
2.95
0.324
1.59
lambda
0.131 *
6.91
0.135 *
4.42
theta
0.139 *
30.51
0.110 *
13.65
0.059
MLHD
-4061.13
-862.86
LLHD
-4488.01
-939.80
X2
853.76 *
N
1292
* Significant at 5 % level **
27.59
153.89 *
0.62
27.59
420
Significant at 10 % level
significant and positive only in 1993, indicating that intervening opportunities do not decrease the supply to farther away destinations. When we consider that adjncy is not significant for this product group, this result seems consistent with the fact that transportation equipment supply is not necessarily consumed by close-by destinations. Dist is negative and significant, as expected. Opipc and opop are negative and significant (except for opipc in 1997), pointing to local consumption effects at the origin, that reduce out-shipments. Oemp significant, as expected.
Oaps
in 1993, and ovlad in each year,
are positive and
is negative and significant, implying diversification effects.
Owsem and dwsem are positive and significant each year: redistributive activities appear 81
to be important in the trade of transportation equipment. The intermediate demand variable, dmnem, at the destination is significant only in 1993, while the
final demand
variables at destinations, dpipc and dpop, are both positive and significant each year. Transportation equipment includes motor vehicles and equipments, aircrafts or parts,
ships
or
boats,
railroad
equipments,
motorcycles,
bicycles,
and
other
transportation equipments (see Table C.34 in Appendix C). All these products are equally important for both the final and intermediate demand sectors, and the empirical findings support this intuition. Wholesalers of these products groups are selling to both demands sectors. The Box-Cox
models are statistically superior to the log-linear specifications in
both years. 5. 2. 14. Precision Instruments Table 5.19 presents the regression results for
commodity group 38, instruments:
photographic goods, optical goods, watches, or clocks. Adjncy is a significant variable in the trade of precision instruments: sharing a common border increases the trade between states. Foreign trade variables are not significant, except for dcddmy in 1993, which is positive and may point to an export effect. Although import in this sector is as important as export, import effects are not captured by the foreign trade variables. Cd is negative and significant
each year,
while io is significant and negative only in 1997: both end
destinations are characterized by competition effects. Dist is, as expected, negative and significant. The origin final demand on
the
trade
dpipc and dpop,
of
variables, opipc and
opop, have negative
effect
precision instruments, while the destination final demand variables,
have positive and significant effects in each year. Oemp is positive and
significant, as expected, each year. However, ovlad has a significant and negative coefficient in 1997.
Redistribution effects are significant and positive, as expected, at
both the origins and destinations each year. Oaps is negative and significant, implying diversification effects. The intermediate demand variable at destination, significant.
82
dmnem, is not
Table 5. 19. Precision Instruments Regression Parameters
38 constant
1993
T
1997
T
-18.672 *
-3.00
-38.356 *
-3.56
adjncy
1.515 *
6.63
0.898 *
3.94
ocddmy
0.180
1.51
0.012
0.09
dcddmy
0.228 *
2.01
0.105
0.87
cd
-0.258 *
-2.72
-0.392 *
-1.99
io
0.089
1.20
-0.484 *
-2.12
dist
-0.696 *
-7.44
-0.739 *
-5.71
opipc
-1.010 *
-3.16
-0.509
-0.86
opop
-0.981 *
-3.90
-1.830 *
-3.37
oemp
0.924 *
5.88
2.162 *
5.06
owsem
1.415 *
4.96
2.644 *
4.48
ovlad
0.074
0.83
-0.506 *
-4.53
oaps
-0.855 *
-5.06
-1.210 *
-4.70
dmnem
-0.105 **
-1.67
-0.159
-1.13
dwsem
0.492 *
3.45
0.555 **
1.84
dpipc
0.983 *
3.66
1.455 *
2.73
dpop
0.577 *
3.12
1.507 *
2.75
lambda
0.080 *
5.05
0.024
1.25
theta
0.165 *
37.56
0.153 *
MLHD
-2900.42
-2321.54
LLHD
-3471.28
-2724.45
X2
1141.71 *
N * Significant at 5
27.59
1421 % level
**
29.01
805.83 *
27.59
942
Significant at 10 % level
This product group is made of engineering, laboratory, or scientific instruments, measuring, controlling, or indicating instruments, optical instruments and lenses, surgical, medical, dental instruments and supply, opticians goods, photographic goods, and clocks and watches (see Table C.34 in Appendix C). These products are mainly consumed by either households or service sectors. In either case, they serve
final consumption. This
fact supports the model findings that only the final demand variables are significant in the out-shipment of this product, supported by a significant wholesale sector. The Box-Cox models are statistically superior to the log-linear specifications in both years. 83
5. 2. 15. Miscellaneous Manufacturing Products The
regression
results
for
miscellaneous
manufacturing
products,
commodity
group 39, are presented in Table 5.20. Adjncy is positive and significant, but the custom district variables are not. The US custom district data do not indicate an important foreign trade share for this group of products. Cd is negative and significant each year, indicating competition effects at destinations, while io is significant and negative only in 1993. Dist is again highly significant and negative. Although only significant in 1993, opipc has a positive sign in contrast to expectation. On the other hand, opop is negative but only Table 5. 20. Miscellaneous Products Regression Parameters
1993
T
1997
T
-47.395 *
-6.84
-44.502 *
-4.93
adjncy
1.469 *
7.57
1.229 *
7.27
ocddmy
0.008
0.08
0.098
1.00
dcddmy
0.134
1.40
0.078
0.87
cd
-0.581 *
-3.53
-1.160 *
-3.63
io
-0.162 **
-1.96
0.217
1.39
dist
-0.926 *
-7.03
-1.430 *
-7.28
0.695 *
2.54
0.537
1.23
-1.09
-1.000 *
-2.35
39 constant
opipc opop
-0.135
oemp
0.785 *
4.99
1.090 *
4.33
owsem
0.573 *
3.52
1.713 *
3.89
ovlad
0.261 *
3.28
0.110
1.13
oaps
0.037
0.23
-0.096
-0.42
dmnem
0.052
0.94
0.211 **
1.83
dwsem
0.316 *
2.42
0.987 *
3.23
dpipc
1.055 *
3.48
0.842 *
2.19
dpop
0.817 *
2.87
1.550 *
2.72
lambda
0.072 *
3.51
0.015
0.74
theta
0.191 *
44.87
MLHD
-4166.82
-7173.09
LLHD
-5089.58
-7998.06
X
2
N *
0.173 *
Significant at 5 % level
1845.53 *
27.59
1559 **
1649.93 * 1466
Significant at 10 % level
84
31.55
27.59
significant in 1997. Oemp is positive and significant each year, while ovlad is only significant and positive in 1993. Redistribution activities have a significant and positive effect on the trade of this commodity group, and dwsem variables
as implied by the positive signs of
owsem
each year. The other significant variables are the destination final
demand variables, opipc and opop, each year. The intermediate demand variable, dmnem, is positive and significant at the 10 % level only in 1997. Jewelry, silverware, musical instruments, toys, amusements, sporting or athletic goods, pens, pencils, artists and office supplies, are the main items in this product group, and obviously they are consumed at the final demand level (see Table C.34 in Appendix C). For this reason, intermediate demand proxy variables do not display a high degree of significance. The Box-Cox models are statistically superior to the log-linear specifications in both years. 5. 2. 16. Apparel, Textile, Leather Products The commodity group 75 is a combination of three similar product groups (apparel, textile and leather products) and Table 5.21 presents the corresponding regression results. Adjncy is positive and significant. Although this product group has relatively high imports in both 1993 and 1997, they are not captured by the model variables. Cd and io are both negative and significant each year, implying competition effects at both ends. Dist is again negative and highly significant each year. Although this commodity group basically provides for the final demand sectors, opop is negative, as expected, only in 1993. All the other final demand variables at the origin are positive and significant, in contrast to expectations. Oemp is positive and significant, as expected, but ovlad is insignificant or weakly significant. Redistribution activities have an impact on this trade (except for dwsem in 1993). Since this product is consumed at both demand levels, the intermediate and final demand variables at the destinations are positive and significant. Finally,
the
Box-
Cox
models
are
specifications in both years.
85
statistically
superior
to
the
log-linear
Table 5. 21. Apparel, Textile, Leather Products Regression Parameters
75
1993
constant adjncy
1997
T
-85.186 *
-4.03
-98.340 *
-6.34
1.045 *
2.72
0.550 *
2.54
ocddmy
-0.213
-1.07
-0.010
-0.08
dcddmy
-0.118
-0.62
0.142
1.24
cd
-2.612 *
-2.64
-1.066 *
-3.22
io
-1.122
-1.60
-1.438 *
-3.19
dist
-2.037 *
-5.07
-1.400 *
-6.82
1.04
4.330 *
4.09
opipc
1.591
opop
-3.104 **
-1.94
0.885 *
1.99
oemp
3.789 *
3.38
1.484 *
4.33
owsem
3.460 *
2.94
0.556 **
1.83
ovlad
-0.355 **
-1.65
-0.008
-0.05
oaps
-1.348 *
-3.50
0.153
0.93
dmnem
1.484 *
2.50
0.577 *
2.93
dwsem
0.643
0.97
0.550 **
1.90
dpipc
4.822 *
2.80
1.505 *
2.76
dpop
3.216 *
1.99
1.465 *
2.54
-1.01
0.021
1.01
20.87
0.172 *
lambda
-0.026
theta
*
T
0.125 *
MLHD
-1272.02
-5558.05
LLHD
-1457.25
-6343.57
X2
370.46 *
N
680
Significant at 5 % level
**
27.59
1571.05 *
34.06
27.59
1221
Significant at 10 % level
5. 2. 17. Synthesis In
addition
to
reviewing
each
commodity
instructive to review them in a comparative fashion.
group
equation
separately,
it
is
The coefficients of the variables and
their significance levels across commodity groups are presented in Tables 5.22 and 5.23 for 1993, and in Tables 5.24 and 5.25 for 1997. A common physical border significantly increases commodity exchange between contiguous states. For 15 out of the 16 commodity groups in 1993 the dummy variable Adjncy is significant at the 5 % level and positive. In 1997, for 11 out of the 16 commodity groups, the variable is positive and significant at the 5 % level, and for 3 86
groups it is so at the 10 % level. This result is consistent with the estimates of all empirical, gravity-type models of international trade that include a contiguity dummy variable. While an increasing distance guarantees a declining interaction, this decline is attenuated among contiguous states. The ability to obtain better business information about supplies and/or consumers, as well as possible cultural commonalities, are most likely factors explaining this phenomenon. It is also likely that a business trying to expand its market beyond state boundaries will first focus on neighboring states before expanding beyond, thus ensuring a differential advantage to these states. It is also possible that short-haul transportation between contiguous states may be different, and less expensive than in the case of greater distances. The foreign trade dummy variables, ocddmy and dcddmy, do not display the same level of consistency as adjncy. In 1993, only 5 commodity groups have a significant occdmy at the 5 % level, and 4 groups at the 10 % level. However, in 1997, ocddmy is significant in only 4 groups at the 5 % level, and 1 at the 10 % level. The other foreign trade variable, dcddmy, performs even more poorly: it is significant at the 5 % level for 3 commodity groups, and at the 10 % level for only 2
groups, in 1993. In 1997 only 1
sector is significant at the 5 % level, and 1 sector at the 10 % level. Focusing on ocddmy in 1993, we note that seven out of nine significant coefficients are negative (commodity groups 20, 24, 30, 33, 34, 36, 37), which suggests that foreign exports taking place at the origin node reduce the interregional commodity outflows from these nodes. These results are consistent with the significant foreign export volumes of sectors 36 (non-electrical machinery) and 37 (transportation equipment), and, to a lesser extent, of sector 20 (food), 30 (rubber and plastics), and 33 (primary metals) (see Table 5.4). In contrast, the coefficient is positive for commodity group 26 and 29, which suggests that when the origin node imports pulp and paper, and petroleum/coal products, these foreign imports stimulate interregional flows out of these origin nodes. In the case of petroleum, this result is very much consistent with the importance of foreign imports in the US economy. The competing destination variable, cd, is uniformly negative and significant in all groups in 1993: in 15 groups at the 5 % level, and 1 group at the 10 % level. In 1997, cd is significant at the 5 % level in 13 groups, and in 1 group at the 10 % level. These results suggest that competition effects at destinations are strong determinants of interregional commodity flows. As other destinations are physically closer (clustered) to a specific destination, the flow of commodities reaching this destination decreases. Every other factor remaining constant, this clustering absorbs part of the flow that would have ended at this destination under a less clustered configuration. This result is consistent 87
with similar effects empirically uncovered in the case of other spatial interactions (e.g., migrations, telecommunications). The
intervening
opportunities
consistent effects as cd. It
variable,
io, does not have the same highly
displays mostly negative signs.
In 1993, 10 groups have
negative signs at the 5 % significance level, 1 group has a negative sign at the 10 % level, and 2 groups have a positive and significant io coefficient. In 1997, 7 groups have a negative sign at the 5 % level, and 1 group at the 10 % level. Overall, competition effects at the supply level appear dominant. Destination nodes clustered around the origin serve as alternative destinations for the commodity, and absorb past of the flow that would have ended at the selected destination. The distance variable, dist, is always negative and highly significant for all commodity groups in both years. Distance can be viewed as a proxy for transportation cost, and increasing transportation costs are an obvious deterrent to trade. Also, from an information
viewpoint,
opportunities, and
the
farther
away
the
lesser
the
information
about
business
hence the lesser the interactions. The distance coefficients for sectors
35 through 39 are generally lower (in absolute terms) than those for the other sectors, which indicate that the shipping distances for these goods are greater. This is consistent with the value per weight of these commodities (see Table 5.1), which ranges from $748/ton to $ 5,566/ton in 1993. The latter characterizes precision instruments (group 38), which have the lowest distance coefficient (-0.70). The other groups, which have values per weight ranging from $11/ton to $427/ton, have distance coefficient varying from –1.07 to –2.20. The origin state personal income per capita, opipc, is presumed having a negative sign:
when
the
local
consumption
of
the
commodity
increases,
decreases. This is generally verified for the commodity groups that are
its
out-shipment
involved in final
consumption (e.g., petroleum, rubber, machinery, transportation equipment, precision instruments). In 1993, 6 groups have negative signs at the 5 % significance level, and 2 groups at the 10 % level. In 1997,
6 groups have negative signs at the 5 % level. The
origin state total population variable , opop, is similar to opipc: both opop and opipc are used as surrogate for local final consumption. In 1993, 8 commodity groups have negative and significant signs at the 5 % level, and 2 groups at the 10 % level. In 1997, 7 groups have a negative sign at the 5 % significance level, and 2 groups at the 10 % level. When significant, opop and opipc have generally the same sign, which supports their use as measures of origin final consumption. 88
The origin sectoral employment, oemp, is generally positive and significant, as hypothesized. In 1993, 13 commodity groups are positive at the 5 % significance level. In 1997, 11 groups are positive and significant at the 5 % level, and only 1 group has a negative sign, in contrast to expectation. The other sectoral production variable, ovlad, is also mostly positive. In 1993, 9 commodity groups have positive signs at the 5 % level, and 1 group at the 10 % level. In 1997, however, only 8 groups have positive signs at the 5 % level. In 1993, 3 groups, and in 1997, 2
groups, have negative
signs, a result
contrary to expectations The variable used to measure scale and diversification effects at the origin states, oaps, has generally a negative sign when significant: as plant size decreases (for a fixed total output), out-shipments increase. More plants suggest more diversified products being produced, making them more attractive to export markets. In 1993, 9 commodity groups have negative signs at the 5 % significance level, and
8 groups do so in 1997.
Only commodity group 25 (furniture) has a positive sign in 1993 and 1997. Another important result of the study is that wholesale employment, representing redistributive activities, is
important in facilitating interregional commodity flows. In
1993, 13 groups have a significant positive sign for owsem (12 of which are at the 5 % level), and 14 groups have a positive dwsem (13 of which are significant at the 5 % level). In 1997, 11 groups have a significant positive sign for owsem, (9 of which are at the 5 % level), and 13 groups have a positive dwsem (all of which are at the 5 % level). The destination manufacturing employment, dmnem, is a proxy for the effects of intermediate demand sectors at the destination, and it is presumed to have a positive sign. In 1993, this presumption is verified for 11 commodity groups at the 5 % significant level. In 1997, 9 groups have significant and positive signs (7 at the 5 % level). The variables representing final demand at destinations, dpipc and dpop, are expected to be positive. In 1993, this is verified for 13 commodity groups for both variables. In 1997, however, dpipc is significant and positive for 8 commodity groups only, and dpop is so for 9 groups only. When comparing the overall results for 1993 to 1997, it is clear that the 1993 models perform much better. The likely explanation is that the 1993 analysis uses the STCC system directly, while the 1997 analysis converts quantities defined in the SCTG system into the STCC system, and this conversion requires deleting many incomplete
89
observations, eventually leading to smaller, and possibly less representative, samples. Overall, and in particular in 1993, the selected variables are generally significant in explaining interregional commodity flows in line with expectations. It is also noteworthy that the optimized Box-Cox specification is always statistically superior to the log-linear specification, which has been a mainstay of past empirical work.
90
Table 5. 22. The 1993 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (20-32)
1993 adjncy ocddmy dcddmy
20
24
25
26
28
29
30
32
1.62 *
1.82 *
0.50 *
0.51 *
1.84 *
8.25 *
1.14 *
1.37 *
-0.27 *
-0.20 **
0.27 *
0.01
1.84 *
-0.17 **
0.16
0.33
-0.05
0.21 **
0.07
-0.10 0.20 **
-0.01
-0.12 0.08
cd
-0.85 *
-1.84 *
-1.13 *
-0.60 *
-0.23 *
-0.41 **
-0.42 *
io
-0.67 *
-1.28 *
-0.58 *
-0.50 *
-0.20 *
-0.18
-0.09
dist
-2.20 *
-2.22 *
-1.64 *
-1.62 *
-1.29 *
-3.00 *
-1.07 *
opipc
-0.09
2.22 *
0.09
0.00
-2.19 *
-0.69 *
0.14
opop
-0.57 *
0.09
0.07
0.22 **
-0.10
-0.45 **
-0.33 *
-0.29
oemp
0.87 *
-0.23
1.38 *
0.52 *
0.45 *
1.95 *
0.12
0.96 *
owsem
0.90 *
0.42
0.68 *
0.15
0.23 *
0.78 *
0.75 *
0.07
ovlad
1.05 *
2.37 *
0.49 *
0.60 *
0.68 *
0.36 **
oaps
-0.88 *
0.33
dmnem
0.19 *
1.02 *
dwsem
1.02 *
0.76 *
dpipc
1.05 *
dpop Lambda Theta *
Significant at 5 % level
-0.06 0.45 *
-0.16
-0.71 *
-0.12 -2.85 *
-0.01
0.26 * -1.41 *
-0.66 *
0.37 *
0.10 *
0.03
0.24 *
0.36 *
0.71 *
0.67 *
0.73 *
0.28
0.42 *
0.35 *
3.40 *
2.42 *
0.84 *
-0.12
-0.10
0.44 *
1.26 *
0.47 *
1.12 *
1.25 *
0.17
0.05
0.31
0.24 *
0.65 *
0.08 *
0.02
0.04 *
0.09 *
0.11 *
0.19 *
0.10 *
0.07 *
0.19 *
0.17 *
0.12 *
0.18 *
0.18 *
-0.24 *
0.19 *
0.13 *
**
-0.09
-0.37
-0.77 *
Significant at 10 % level
91
Table 5. 23. The 1993 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (33-75)
1993
33
34
35
36
0.85 *
1.22 *
1.52 *
1.30 *
ocddmy
-0.22 **
-0.22 *
dcddmy
-0.36 *
cd
-0.48 *
-0.54 *
-0.42 *
-0.67 *
io
-0.41 *
-0.21 *
-0.15 *
-0.02
dist
-1.57 *
-1.26 *
-0.86 *
-0.96 *
-1.24 *
adjncy
-0.18 **
0.17 **
-0.08
0.06 -0.60 * 0.07 -0.56 * 0.29 *
38
39
75
1.51 *
1.47 *
1.05 *
0.18
0.01
-0.21
0.23 *
0.13
-0.12
-0.26 *
-0.58 *
-2.61 *
-0.16 *
-1.12 **
-0.70 *
-0.93 *
-2.04 *
0.69 *
0.09
opipc
0.04
-0.30
-0.26 **
-1.16 *
-0.79 *
-1.01 *
opop
0.20
-0.31 *
-0.32 *
-0.45 *
-0.30 *
-0.98 *
oemp
1.23 *
-0.04
0.16 *
1.54 *
0.15 *
0.92 *
0.78 *
3.79 *
owsem
0.30 **
0.90 *
0.88 *
0.81 *
0.41 *
1.42 *
0.57 *
3.46 *
ovlad
-0.21 **
0.86 *
0.49 *
-0.36 *
0.59 *
0.07
0.26 *
-0.36 **
oaps
-0.11
0.25
0.07
-1.03 *
-0.80 *
-0.85 *
0.04
-1.35 *
0.05
1.48 *
-0.13
1.59 -3.10 *
dmnem
0.49 *
0.24 *
0.09 *
0.05
0.16 *
-0.11 **
dwsem
0.29 *
0.35 *
0.47 *
0.56 *
0.18 **
0.49 *
0.32 *
0.64
0.37 *
0.26 **
1.52 *
0.76 *
0.98 *
1.05 *
4.82 * 3.22 *
dpipc
*
0.11
0.04
37
-0.45
dpop
0.57 *
0.43 *
0.33 *
0.93 *
0.42 *
0.58 *
0.82 *
Lambda
0.07 *
0.10 *
0.11 *
0.07 *
0.13 *
0.08 *
0.07 *
Theta
0.18 *
0.21 *
0.20 *
0.18 *
0.14 *
0.16 *
0.19 *
Significant at 5 % level
**
Significant at 10 % level
92
-0.03 0.13 *
Table 5. 24. The 1997 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (20-32)
1997 adjncy ocddmy dcddmy
20 0.11 -1.99 * 0.17
24
25
2.12 *
0.84 *
26 0.52 **
28
29
30
32
0.97
28.83 *
0.88 *
1.39 *
0.13
-0.06
0.29
-0.16
0.36
0.04
-0.46 *
-0.04
0.15
0.16
0.15
0.32
0.11
-0.03
cd
-1.11
-1.81 *
-0.49 *
-0.40 *
-0.17
io
-1.35
-1.51 *
-0.26 *
-0.23 **
-0.40
dist
-3.38 *
-2.35 *
-1.03 *
-1.33 *
-2.08
-6.64 *
-0.72 *
4.53 *
-0.19
-0.38
1.85
-11.06 *
-0.55 *
0.14
0.16
-0.49 *
-0.09
2.03
-2.15
-0.15 *
-0.12
opipc
2.43
opop
-0.71
oemp
0.84
owsem
1.84
ovlad
0.66
oaps
-0.43
1.64 * -0.13 1.19 * -0.14
0.69 *
0.70 *
1.00 *
-1.11 ** 0.33
-0.15 * 0.04
0.17
4.53 *
-0.15 *
0.21
-0.49
3.13 **
0.41 *
0.33 *
0.28 *
-0.34
0.47
0.59 *
0.52 *
-0.76 *
0.68
-5.69 *
-0.11
-0.55 * 0.17 -1.37 *
0.89 * -0.14 0.45 * -0.86 *
dmnem
0.49
0.81 *
-0.17 *
0.23 *
-0.14
0.48
0.14 *
0.19 **
dwsem
1.09
1.27 *
0.53 *
0.42 *
0.83
0.86
0.28 *
0.55 *
dpipc
2.27
2.99 *
1.24 *
0.58 **
0.77
-1.79
0.01
0.71 *
dpop
0.44
1.00
0.69 *
0.18
1.15
0.72
-0.01
Lambda
0.09
0.01
0.07 *
0.12 *
0.05
0.16 *
0.18 *
0.10 *
0.12 *
0.16 *
0.13 *
-0.01
-0.39 *
0.20 *
0.09 *
Theta *
Significant at 5 % level
-0.13 *
**
Significant at 10 % level
93
0.24
Table 5. 25. The 1997 Model Variable Coefficients and Their Significance Levels Across Commodity Groups (33-75)
1997 adjncy
0.42 **
34
35
36
37
38
39
75
1.00 *
0.94 *
0.86 *
0.28
0.90 *
1.23 *
0.55 *
0.27 *
-0.11
0.01
0.10
-0.01
-0.14
-0.04
0.11
0.08
0.14
-0.51 *
ocddmy
-0.27 **
-0.02
-0.25 *
dcddmy
-0.23 **
0.12
0.26 *
cd
-0.45 *
-0.71 *
-0.59 *
-0.26 *
io
-0.32 *
-0.03
-0.30 *
-0.20 *
dist
-1.48 *
-1.43 *
-1.33 *
-0.65 *
-1.22 *
-0.74 *
opipc
-0.66 *
-1.37 *
-1.18 *
-0.05
-0.19
-0.51
opop
-0.27 **
-0.67 *
-0.29 **
-0.45 *
-0.49 *
-1.83 *
-1.00 *
0.89 *
oemp
0.67 *
0.81 *
0.21
0.93 *
2.16 *
1.09 *
1.48 *
owsem
0.91 *
1.00 *
0.88 *
0.83 *
0.76 *
2.64 *
1.71 *
0.56 **
ovlad
0.23 *
0.26
1.07 *
-0.23 *
0.97 *
-0.51 *
0.11
-0.01
oaps
-0.53 *
-0.98 *
0.17
-0.52 *
-1.15 *
-1.21 *
-0.10
0.15
dmnem
0.54 *
0.25 *
0.19 *
0.01
0.07
dwsem
0.30 *
0.51 *
0.53 *
0.39 *
0.37 *
0.71 *
dpipc
*
33
-0.34
0.09
-0.27
0.06
-0.13
-0.39 * -0.48 *
-0.16
-1.16 * 0.22 -1.43 * 0.54
-1.07 * -1.44 * -1.40 * 4.33 *
0.21 **
0.58 *
0.56 *
0.99 *
0.55 *
0.05
1.46 *
0.84 *
1.50 *
dpop
0.25 **
0.57 *
0.69 *
0.32 *
0.32 **
1.51 *
1.55 *
1.47 *
Lambda
0.09 *
0.08 *
0.07 *
0.12 *
0.14 *
0.02
0.02
0.02
Theta
0.14 *
0.19 *
0.19 *
0.16 *
0.11 *
0.15 *
0.17 *
0.17 *
Significant at 5 % level
**
Significant at 10 % level
94
5. 3. Elasticity Analysis In a log-linear specification of an econometric equation, the estimated parameter of variable Xi represents the elasticity of the dependent variable Y with respect to
Xi.
However, in the case of a Box-Cox equation, the determination of the elasticity of Y with respect to X i requires some calculations. Mathematically, the elasticity is defined as
εXi =
∂Y X i ∂X i Y
(5.1)
As the Box-Cox equation is defined as
X λ −1 X λ −1 Yθ −1 = a 0 + a1 X 1 + a 2 2 + .... + a n n θ λ λ
,
(5.2)
the elasticity becomes
εX i = ai
X iλλ Y θθ
(5.3)
Note that when ë -> 0 and è -> 0, and Equation (5.2) becomes the log-linear specification, the elasticity becomes ai,
as expected. It is possible to estimate these
elasticities at the sample means. However, in this study, this would be complicated by the existence of three dummy variables, for which there are no sample means. In order to assess the range of variation of the elasticities, these were computed for all the sample observations, where the dependent variable (the commodity flow) values are strictly positive. This computation was carried out for both 1993 and 1997.
5. 3. 1. Competing Destinations The elasticities for the variable cd are presented
in Table 5.26. The mean
elasticities of both years are close for each commodity separately (e.g., -1.60 in 1993 and –1.59 in 1997 for commodity 24). In 1993, the mean elasticity varies across commodities from –0.45 (precision instruments) to –1.63 (transportation equipment). Food and kindred products have the highest negative elasticity in 1997 (-5.42), but the lowest elasticity in 1997 is for precision instruments ( -0.32). In general, it appears that the commodity groups with bulky products have higher elasticities: lumber and wood products (–1.60 and –1.59); clay, concrete, glass (–1.48 and –1.50); transportation equipment (–1.63 and – 1.65).
Table 5. 26. Statistics for CD Elasticities
Mean Elasticity CD
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
-1.00
-5.42
-6.07
-33.80
-0.45
-0.76
24 Lumber & Wood
-1.60
-1.59
-10.28
-6.19
-0.58
-0.69
25 Furniture
-1.49
-0.73
-4.97
-2.40
-0.83
-0.41
-1.05
-1.02
-41.52
-6.07
-6.45
-0.66
-0.48
-0.33
-26.78
-0.37
-0.22
-0.26
29 Coal & Petroleum
-0.71
-0.35
-1.07
- 0.53
-0.32
-0.17
30 Rubber
-0.86
-0.66
-9.17
-2.10
-0.42
-0.34
32 Clay, Concrete
-1.48
-1.50
-7.84
-3.13
-0.74
-0.98
33 Primary Metals
-0.67
-0.78
-3.39
-2.71
-0.31
-0.46
34 Fabricated Metals
-0.91
-0.92
-6.75
-3.50
-0.42
-0.49
35 Machinery
-0.86
-0.61
-19.54
-4.22
-0.36
-0.34
36 Electrical Mach.
-0.82
-0.55
-6.65
-1.54
-0.39
-0.29
37 Transportation
-1.63
-1.65
-6.17
-4.99
-0.82
-1.00
-0.45
-0.32
-2.87
-1.16
-0.21
-0.18
-0.86
-0.72
-8.60
-5.29
-0.39
-0.38
-1.35
-0.74
-7.45
-2.15
-0.63
-0.40
26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 2. Intervening Opportunities Table 5. 27 presents the elasticities for the variable io. The mean elasticity in 1993 varies between 0.86 (for transportation equipment) and –1.10 (for lumber and wood products), and, in 1997, between 0.46 (for clay, concrete, and glass products) and –6.48 (for
food products). Although most of the
elasticities are negative, there are some
positive elasticities, such as for transportation equipment (0.86), clay, concrete and stone products (0.50), and precision equipment products (0.10),
(0.16), in 1993, and coal and petroleum
rubber and plastic products (0.16); clay, concrete and glass products
(0.46), transportation equipment (0.19), and miscellaneous product (0.14), in 1997. These positive elasticities result from positive regression coefficients, and may point to agglomeration effects at the supply level.
96
Table 5. 27. Statistics for IO Elasticities
IO
Mean Elasticity Group Name
Min. Elasticity
Max. Elasticity
93
97
93
97
93
97
20 Food & Kindred
-0.78
-6.48
-4.38
-40.19
-0.34
-1.13
24 Lumber & Wood
-1.10
-1.32
-7.21
-5.19
-0.39
-0.57
25 Furniture
-0.77
-0.38
-2.52
-1.15
-0.42
-0.21
-0.89
-0.58
-28.43
-2.78
-0.42
-0.34
-0.41
-0.78
-21.70
-0.89
-0.17
-0.60
29 Coal & Petroleum
-0.29
0.10
-2.63
0.06
-1.28
0.13
30 Rubber
-0.19
0.16
-1.94
0.08
-0.09
0.53
32 Clay, Concrete
0.50
0.46
0.25
0.28
2.65
0.95
33 Primary Metals
-0.58
-0.56
-2.64
-2.01
-0.25
-0.33
34 Fabricated Metals
-0.37
-0.04
-2.63
-0.14
-0.16
-0.02
35 Machinery
-0.29
-0.31
-7.41
-2.08
-0.12
-0.17
36 Electrical Mach.
-0.03
-0.44
-0.23
-1.13
-0.01
-0.22
0.86
0.19
0.44
0.11
3.11
0.43
26 Paper & Pulp 28 Chemical
37 Transportation 38 Precision Inst. 39 Miscellaneous
0.16
-0.40
0.08
-1.46
0.96
-0.22
-0.24
0.14
-2.42
0.07
-0.11
1.00
75 Textile & Leather
-0.58
-1.00
-3.22
-2.87
-0.27
-0.53
5. 3. 3. Distance As can be seen in Table 5.28, the mean elasticities of the variable dist are always negative, varying in 1993 between –1.88 (clay, concrete and glass product) and –0.81 (precision instruments). In 1997, food and kindred products has the highest mean elasticity (–9.26) and
chemical products the second highest
(–3.04).
However, since
these product groups have lost many observations due to the commodity classification conversion, it is possible that these values represent overestimates. Except for these two sectors, the mean elasticity varies between –2.20 (clay, concrete and glass product) and – 0.53 (precision instruments) in 1997. As can be expected, bulky products have the highest elasticities: food products (–1.79 and –9.26); lumber and wood products
(–1.77 and –
1.98); furniture and fixture products (–1.75 and –1.07); pulp, paper, and allied products (– 1.79 and –1.70); transportation equipment (–1.85 and –1.83) in 1993 and 1997 respectively. These high values, of course reflect the impact of transportation costs.
97
Table 5. 28. Statistics for DIST Elasticities
Mean Elasticity DIST
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
-1.73
-9.26
-11.92
-53.83
-0.63
-1.93
24 Lumber & Wood
-1.77
-1.98
-11.71
-7.87
-0.62
-0.85
25 Furniture
-1.75
-1.07
-5.96
-3.61
-0.85
-0.53
-1.79
-1.70
-61.99
-11.39
3.25
-0.82
-1.54
-3.04
-109.46
-3.40
-0.45
-2.66
29 Coal & Petroleum
-1.09
-0.86
-2.63
-1.09
-1.28
-0.60
30 Rubber
-1.31
-1.19
-15.44
-5.54
-0.44
-0.38
32 Clay, Concrete
-1.88
-2.20
-11.72
-5.67
-0.76
-1.17
33 Primary Metals
-1.52
-1.58
-8.23
-6.18
-0.54
-0.75
34 Fabricated Metals
-1.33
-1.24
-11.91
-5.11
-0.44
-0.53
35 Machinery
-1.00
-0.98
-26.88
-7.26
-0.34
-0.46
36 Electrical Mach.
-0.83
-0.74
-6.96
-2.66
-0.33
-0.31
37 Transportation
-1.85
-1.83
-8.17
-6.12
-0.65
-0.95
-0.81
-0.53
-5.40
-2.00
-0.31
-0.28
-0.97
-0.82
-11.59
-6.33
-0.35
-0.40
-1.20
-0.87
-6.29
-2.58
-0.62
-0.43
26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 4. Origin Personal Income per Capita The variable opipc is expected to have a negative sign, yet there are a few cases of positive parameter estimates. As we have no clear explanation for this result, the elasticities have not been computed in these cases. As can be seen in Table 5. 29, in 1993 the mean elasticities vary between –0.09 (food products) and –2.44 (coal and petroleum products), and, in 1997, between –2.50 (coal and petroleum products) and –0.08 (electrical
machinery
products).
Other
high
mean
elasticities
in
1993
are
–1.78
(transportation equipments), –1.50 (precision instruments), -1.24 (electrical machinery products), and –1.16 (rubber and plastic products). In 1997, the high
mean elasticity
commodity groups are rubber and plastic products (–1.63), fabricated metal products (– 1.54), and machinery products (-1.08).
98
Table 5. 29. Statistics for OPIPC Elasticities
Mean Elasticity OPIPC
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
-0.09
***
-0.56
***
-0.56
***
24 Lumber & Wood
***
***
***
***
***
***
25 Furniture
***
-0.25
***
-0.80
***
-0.14
-0.56
-0.74
-20.82
-4.29
1.09
-0.45
***
***
***
***
***
***
29 Coal & Petroleum
-2.44
-2.50
-2.99
-3.12
-1.56
-1.75
30 Rubber
-1.16
-1.63
-14.23
-6.19
-0.51
-0.85
32 Clay, Concrete
***
***
***
***
***
***
33 Primary Metals
26 Paper & Pulp 28 Chemical
***
-0.95
***
-3.54
***
-0.53
34 Fabricated Metals
-0.42
-1.54
-3.62
-5.92
-0.18
-0.79
35 Machinery
-0.42
-1.08
-12.11
-7.86
-0.19
-0.57
36 Electrical Mach.
-1.24
-0.08
-11.50
-0.25
-0.58
-0.04
37 Transportation
-1.78
-0.47
-7.91
-1.33
-0.86
-0.29
-1.50
-0.40
-10.75
-1.48
-0.69
-0.22
***
***
***
***
***
***
***
***
***
***
***
***
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 5. Origin Population Table 5.30 presents elasticities for the variable opop. As for the variable opipc, we have not computed elasticities when the estimated parameter is positive. The mean elasticities, in 1993, vary between –2.24 (precision instruments) and –0.25 (miscellaneous products). In 1997, the highest mean elasticity is for food products (-4.51), and the lowest for machinery products (–0.27). Some other high mean elasticities in 1993 are: –1.47 (textile
and
leather
products),
–1.35
(transportation
equipment),
-1.36
(coal
and
petroleum products), -0.94 (rubber and plastic products, and machinery products), and –0.86 (food products). In 1997, transportation equipment had the second highest mean elasticity (–2.46), followed by precision instruments (-1.62), coal and petroleum products (-1.49), electrical machinery products (–1.47), fabricated metal products (–1.13), and rubber and plastic products (-1.14)..
99
Table 5. 30. Statistics for OPOP Elasticities
Mean Elasticity OPOP
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
-0.86
-4.51
-4.88
-27.52
-0.39
-0.80
24 Lumber & Wood
***
***
***
***
***
***
25 Furniture
***
-0.90
***
-2.73
***
-0.50
26 Paper & Pulp 28 Chemical
***
-0.31
***
-1.61
***
-0.19
-0.30
***
-15.03
***
-0.12
***
29 Coal & Petroleum
-1.36
-1.49
-2.30
-1.65
-0.78
-0.75
30 Rubber
-0.94
-1.14
-9.89
-4.38
-0.43
-0.60
32 Clay, Concrete
-0.70
-0.43
-3.80
-0.89
-0.35
-0.27
33 Primary Metals
***
-0.62
***
-2.25
***
-0.36
34 Fabricated Metals
-0.73
-1.13
-5.32
-3.72
-0.32
-0.61
35 Machinery
-0.95
-0.37
-25.00
-2.53
-0.44
-0.20
36 Electrical Mach.
-0.69
-1.47
-5.76
-4.06
-0.33
-0.79
37 Transportation
-1.35
-2.46
-4.90
-5.86
-0.68
-1.57
-2.24
-1.62
-13.91
-5.95
-1.06
-0.91
-0.25
-0.65
-2.49
-4.87
-0.11
-0.34
-1.47
***
-8.15
***
-0.61
***
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 6. Origin Employment Table 5. 31 presents the elasticities of the variable oemp. This variable is expected to have a positive parameter estimate and thus positive elasticities. The elasticities have not been computed for negative parameters. Textile, apparel and leather products have the highest mean elasticity in 1993 (2.08), which is not surprising since this product is labor intensive. In 1997, the highest elasticity applies to food products (3.47). The lowest mean elasticities
in
both years are for commodity group 35, machinery products (0.27
in 1993 and 0.20 in 1997), which
can be
explained by the capital intensiveness of the
sector. Commodity group 36, electrical machinery, is another high-labor elasticity group (1.65 in 1993 and 1.62 in 1997), followed, in 1993, by furniture and fixture products (group 25) with 1.62,
clay concrete and glass products (group 32) with 1.51,
primary
metal products (group 33) with 1.40, and precision instruments, (group 38) with 1.31. Other high-elasticity sectors in 1997 are group 32 (1.84), (1.62), group 24 (1.41), and group 26 (1.30). 100
group 38 (1.67),
group 36
Table 5. 31. Statistics for OEMP Elasticities
Mean Elasticity OEMP
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
0.86
3.47
0.40
0.55
5.10
20.71
24 Lumber & Wood
***
1.41
***
0.62
***
5.53
1.62
0.85
0.91
0.49
5.05
2.46
0.72
1.30
0.36
0.75
18.35
5.07
0.70
0.30
0.30
0.21
31.80
0.33
29 Coal & Petroleum
1.36
0.68
0.64
0.36
2.62
1.62
30 Rubber
0.19
***
0.10
***
1.61
****
32 Clay, Concrete
1.51
1.84
0.77
1.17
8.16
3.75
33 Primary Metals
1.40
0.92
.069
0.53
6.19
3.19
25 Furniture 26 Paper & Pulp 28 Chemical
34 Fabricated Metals
***
0.91
***
0.47
***
2.97
35 Machinery
0.27
0.20
0.12
0.11
6.18
1.23
36 Electrical Mach.
1.65
1.62
0.80
0.86
11.36
3.81
37 Transportation
0.34
***
0.16
***
1.01
***
1.31
1.67
0.65
0.94
6.37
6.04
0.93
0.65
0.42
0.33
9.45
4.81
2.08
0.99
0.96
0.52
11.82
2.75
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 7. Origin Value-Added The elasticities of the other sectoral production variable, ovlad, are presented in Table 5. 32. Like oemp, this variable is expected to have a positive sign, and thus positive elasticities. They
have not been computed in the cases of negative estimates. Commodity
group 24 (lumber and wood products) has the highest mean elasticity (1.88) in 1993, and commodity group 38 (precision instruments) has the lowest (0.09). The other high mean elasticities correspond to
transportation equipment (0.95), fabricated metal products
(0.91), food products (0.88), and rubber and plastic products (0.81). In 1997, food products have the highest mean elasticity (2.23), and the lowest one is very close to zero for textile and leather products. Transportation equipment (1.80),
rubber and plastic
products (1.06), lumber and wood products (1.00), machinery products (0.83), and clay, concrete and glass products (0.74) are the other high-elasticity product groups.
101
Table 5. 32. Statistics for OVLAD Elast icities
Mean Elasticity OVLAD
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
0.88
2.23
0.40
0.33
5.01
13.70
24 Lumber & Wood
1.88
1.00
0.70
0.44
12.16
3.90
***
0.34
***
0.20
***
0.97
0.53
0.40
0.00
0.24
3.11
1.50
0.75
***
0.33
***
31.57
***
25 Furniture 26 Paper & Pulp 28 Chemical 29 Coal & Petroleum
***
0.06
***
0.03
***
0.10
30 Rubber
0.81
1.06
0.41
0.50
6.47
3.46
32 Clay, Concrete
0.47
0.74
0.24
0.48
2.43
1.53
33 Primary Metals
***
0.26
***
0.15
***
0.89
34 Fabricated Metals
0.91
0.24
0.42
0.13
5.84
0.80
35 Machinery
0.59
0.83
0.26
0.47
11.69
4.92
***
***
***
***
***
***
0.95
1.80
0.47
1.12
2.64
3.12
0.09
***
0.04
***
0.45
***
0.25
0.06
0.11
0.03
2.52
0.46
***
0.00
***
-0.01
***
0.00
36 Electrical Mach. 37 Transportation 38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 8. Origin Wholesale Employment The variable owsem represents redistributive activities and is expected to have positive parameter and elasticity estimates. Elasticities have not been computed for the few cases (sector 24, 28, and 32 in 1997) where the estimate is negative. The elasticities are presented in Table 5.33. The precision products sector has the highest mean elasticity in 1993 (2.41), and pulp, paper and allied products the lowest one (0.25). The other highelasticity
commodity
groups
in
1993
include
textile
and
leather
products
(1.81),
machinery products (1.71), fabricated metal products (1.47), rubber and plastic products (1.49),
transportation equipments (1.15), and food products (1.00). In 1997, food
products have the highest mean elasticity (8.54); however, this value is possibly an overestimate. Beside this value, the transportation equipment sector has a high mean elasticity (2.33), and textile and leather products the lowest one
(0.38). The other high-
elasticity sectors in 1997 are precision instruments (2.15), food products (2.09), electrical machinery products (1.74), rubber and plastic products (1.62), primary metal products (1.54),furniture, fixture products (1.45), and fabricated metal products (1.27). 102
Table 5. 33. Statistics for OWSEM Elasticities
Mean Elasticity OWSEM
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
1.02
8.54
0.46
1.46
5.87
51.75
24 Lumber & Wood
0.36
***
0.13
***
2.38
***
25 Furniture
0.88
1.45
0.48
0.81
2.84
4.38
0.25
0.49
0.12
0.29
8.46
2.44
0.44
****
0.18
***
22.38
***
29 Coal & Petroleum
1.19
0.91
0.67
0.62
2.02
1.33
30 Rubber
1.49
1.62
0.67
0.82
15.58
6.15
32 Clay, Concrete
0.13
***
0.06
***
0.68
***
33 Primary Metals
0.40
1.54
0.17
0.88
1.86
5.48
34 Fabricated Metals
1.47
1.27
0.64
0.70
10.99
4.15
35 Machinery
1.71
0.89
0.81
0.48
43.44
5.98
36 Electrical Mach.
0.96
1.74
0.46
0.94
8.01
4.70
37 Transportation
1.15
2.33
0.58
1.47
4.13
5.40
2.41
2.15
1.16
1.22
15.24
7.85
0.82
1.06
0.39
0.55
8.09
7.89
1.81
0.38
0.85
0.20
9.95
1.11
26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 9. Origin Average Plant Size The estimated elasticities for the variable oaps are presented in Table 5. 34. While there was no initial expectations concerning the sign of this variable, they turn out to be, in general, negative, and thus so are the elasticities In 1993, negative elasticities vary between –1.04 (coal and petroleum products) and –0.01 (rubber and plastic products), and
positive elasticities vary between 0.03 (miscellaneous products) and 0.43 (furniture
and fixtures). Other high negative elasticities
in 1993 are –0.92 (transportation
equipment), -0.86 (textile and leather products), –0.80 (precision instruments), and -0.76 (electrical machinery products). In 1997,
negative elasticities vary between –1.34 (again
transportation equipment) and –0.05 (miscellaneous products), and positive elasticities vary between 0.09 (textile and leather products), and 0.89 (chemical products). Other high negative elasticities in 1997 are –1.03 (food products), -1.02 (clay, concrete and glass
103
Table 5. 34. Statist ics for OAPS Elasticities
Mean Elasticity OAPS
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
-0.57
-1.03
-3.59
-6.22
-0.24
-0.16
24 Lumber & Wood
0.25
-0.12
0.09
-0.47
1.67
-0.05
25 Furniture
0.43
0.42
0.24
0.24
1.43
1.31
-0.14
-0.80
-1.03
-4.12
-0.06
-0.48
-0.62
0.89
-37.48
0.71
-0.25
1.02
29 Coal & Petroleum
-1.04
-0.47
-1.56
-0.70
-0.60
-0.28
30 Rubber
-0.01
-0.11
-0.09
-0.41
0.00
-0.06
32 Clay, Concrete
-0.70
-1.02
-4.02
-2.57
-0.34
-0.64
33 Primary Metals
26 Paper & Pulp 28 Chemical
-0.09
-0.48
-0.43
-1.74
-0.03
-0.27
34 Fabricated Metals
0.19
-0.65
0.08
-2.52
1.60
-0.33
35 Machinery
0.05
0.10
-0.02
-0.06
1.44
0.75
36 Electrical Mach.
-0.76
-0.51
-6.08
-1.40
-0.34
-0.27
37 Transportation
-0.92
-1.34
-3.48
-2.89
-0.43
-0.87
-0.80
-0.81
-5.04
-3.06
-0.36
-0.45
0.03
-0.05
0.01
0.40
0.33
-0.03
-0.86
0.09
-4.59
0.05
-0.43
0.26
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
products), – 0.81 (precision instruments),
-0.80 (paper and allied product), -0.65
(fabricated metal products), and –0.48 (primary metal products). 5. 3. 10. Destination Manufacturing Employment The variable dmnem is a proxy for the effects of intermediate demand at the destination, and the expectation about its sign is positive. The elasticities are presented in Table 5. 35. Elasticities have not been computed in
a few cases (sectors 25 and 38 in
1993, 25, 28 and 38 in 1997) when the estimates are negative. The highest mean elasticity in 1993 is 0.89 (lumber and wood products), and the lowest 0.07 for electrical machinery. Other high-elasticity sectors in 1993 include textile and leather products (0.76), clay, concrete and glass products (0.72), paper and allied product (0.68), and primary metal products (0.71). The highest mean elasticity in 1997 is 0.99 (primary metal products), and the lowest, as in 1993, is 0.02 for electrical machinery. Lumber and wood products (0.71), rubber and plastic products (0.67), paper and allied products (0.63), and clay, concrete, and glass products (0.55) are the other high-elasticity sectors in 1997. 104
Table 5. 35. Statistics for DMNEM Elasticities
Mean Elasticity DMNEM
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
0.24
2.54
0.10
0.34
1.44
16.22
24 Lumber & Wood
0.89
0.71
0.32
0.31
5.63
2.78
***
***
***
***
***
***
0.68
0.63
0.35
0.40
4.05
3.36
0.23
***
0.10
***
13.81
***
29 Coal & Petroleum
0.07
0.17
0.03
0.07
7.56
6.89
30 Rubber
0.53
0.67
0.25
0.31
5.46
2.27
32 Clay, Concrete
0.72
0.55
0.35
0.36
3.87
1.09
33 Primary Metals
0.71
0.99
0.33
0.58
3.57
3.38
34 Fabricated Metals
0.42
0.34
0.19
0.18
3.27
1.26
35 Machinery
0.20
0.15
0.08
0.11
4.02
1.36
36 Electrical Mach.
0.07
0.02
0.03
0.01
0.50
0.07
37 Transportation
0.50
0.26
0.23
0.14
1.82
0.73
25 Furniture 26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous
***
***
***
***
***
***
0.08
0.13
0.04
0.07
0.84
0.96
75 Textile & Leather
0.76
0.41
0.36
0.22
4.12
1.19
5. 3. 11. Destination Wholesale Employment Table 5.36 presents the estimated elasticities for the variable dwsem. This variable is a proxy for the effects of redistributive activities at the destination, and it is presumed to have a positive parameter estimate. This is the case of all sectors in both years. Chemical products have the highest mean elasticities in both years: 1.46 in 1993, 1.59 in 1997 (except food products in 1997 with 5.14, possibly another overestimate). Coal and petroleum products have the lowest mean elasticity in 1993 (0.26), and textile and leather products the lowest in 1997 (0.38). The high-elasticity sectors in 1993 include paper and allied products (1.13), food products (1.16), furniture and fixture products (0.92), and machinery products (0.91). In 1997, the high elasticity sectors include clay, concrete and glass products (1.44), transportation equipment (1.12), lumber and wood products (1.10), rubber and plastic products (1.10), and paper and allied products (1.02).
105
Table 5. 36. Statistics for DWSEM Elasticities
Mean Elasticity DWSEM
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
1.16
5.14
0.47
0.74
6.85
31.93
24 Lumber & Wood
0.65
1.10
0.24
0.48
4.18
4.32
25 Furniture
0.92
0.77
0.50
0.42
3.02
2.45
1.13
1.02
0.59
0.65
7.10
5.76
1.46
1.59
0.62
1.29
88.96
1.78
29 Coal &Petroleum
0.44
0.26
0.19
0.13
7.30
3.71
30 Rubber
0.83
1.10
0.40
0.59
8.77
3.31
32 Clay, Concrete
0.65
1.44
0.32
0.97
3.44
3.16
33 Primary Metals
0.39
0.51
0.18
0.30
1.95
1.71
34 Fabricated Metals
0.57
0.64
0.26
0.35
4.38
2.46
35 Machinery
0.91
0.53
0.40
0.30
19.88
3.69
36 Electrical Mach.
0.67
0.81
0.33
0.46
5.25
2.16
37 Transportation
0.50
1.12
0.26
0.70
1.93
3.22
0.83
0.45
0.41
0.26
5.29
1.64
0.45
0.61
0.21
0.32
4.69
4.46
0.34
0.38
0.16
0.20
1.84
1.10
26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5.3. 12. Destination Personal Income Per Capita The variable dpipc measures the effect of final demand at the destination, and presumed
is
to have positive parameter estimates. Elasticities have not been calculated in
the few cases where the estimates are negative (sectors 28, 29, 33 in 1993, and 29, 33, 35 in 1997). The elasticities are presented in Table 5.37. The highest mean elasticity in 1993 is 2.94, for furniture and fixture products, while the lowest one is 0.43, for machinery products. The other high mean elasticities of 1993 are 2.85 (lumber and food products), 2.63 (textile and leather products), 2.09 (clay, concrete and glass products), 1.71 (transportation equipment), and 1.63 (electrical machinery products). In 1997, however, lumber and wood products have the highest mean elasticity (2.58) (except food products, 8.97, which is possibly another overestimate), while af bricated metal products have the lowest elasticity (0.10). The other high mean elasticities in 1997 correspond to furniture and fixture products (1.60), clay, concrete and glass products (1.59), paper and allied products (1.14),
and textile and leather products (1.01). Comparing the 1993 and
1997 elasticities suggests that the effect of this variable has declined from 1993 to 1997. 106
Table 5. 37. Statistics for DPIPC Elasticities
Mean Elasticity DPIPC
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
1.05
8.97
0.46
-1.56
6.90
54.68
24 Lumber & Wood
2.85
2.58
1.04
1.13
19.01
10.22
25 Furniture
2.94
1.60
1.59
0.88
9.95
5.18
1.21
1.14
0.53
0.70
47.80
6.85
***
1.34
***
1.18
***
1.46
26 Paper & Pulp 28 Chemical 29 Coal & Petroleum
***
***
***
***
***
***
30 Rubber
0.74
0.04
0.33
0.02
9.16
0.16
32 Clay, Concrete
2.09
1.59
1.00
0.99
12.07
4.06
33 Primary Metals
***
***
***
***
***
***
34 Fabricated Metals
0.52
0.10
0.22
0.05
4.46
0.38
35 Machinery
0.43
***
-9.47
***
12.40
***
36 Electrical Mach.
1.63
1.21
0.76
0.65
15.17
3.85
37 Transportation
1.71
0.11
0.85
0.07
7.58
0.33
1.46
1.13
0.67
0.63
10.50
4.28
1.37
0.51
0.59
0.26
15.20
3.86
2.63
1.01
1.28
0.53
13.91
2.95
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 13. Destination Population The other destination final demand variable, dpop, is also presumed to have positive parameter estimates and positive elasticities. This is the case of all sectors in both years. Elasticity statistics are presented in table 5. 38. Overall it appears mean elasticity has
declined from 1993 to 1997, suggesting that the effect of the final
demand at destinations on commodity flows has made in
that the
declined, supporting the observation
the case of the variable dpipc. The highest mean elasticity in 1993 is 1.92
(transportation equipment), and the lowest is 0.15 (chemical products). The other high mean elasticities are 1.89 (furniture and fixture products), 1.56 (clay, concrete and glass product),
1.53
(miscellaneous,
and
textile
and
leather
products),
1.44
(electrical
machinery products), and 1.32 (precision instruments). In 1997, food products have the highest mean elasticity (2.85) and primary metal products the lowest (0.57). Chemical products (2.64), transportation equipment (1.62), precision instruments (1.33), furniture and fixture products (1.27), textile and leather products (1.09), and miscellaneous products (1.01) are the other high-mean elasticities in 1997. 107
Table 5. 38. Statist ics for DPOP Elasticities
Mean Elasticity DPOP
Min. Elasticity
Max. Elasticity
Group Name
93
97
93
97
93
97
20 Food & Kindred
0.72
2.85
0.32
0.42
4.07
17.85
24 Lumber & Wood
1.02
0.90
0.37
0.39
6.73
3.52
25 Furniture
1.89
1.27
1.02
0.70
6.19
4.06
0.40
0.67
0.19
0.43
13.09
3.83
0.15
2.64
0.06
2.18
7.55
2.96
29 Coal & Petroleum
0.94
0.39
0.54
0.20
6.10
5.60
30 Rubber
0.69
***
0.31
***
7.28
***
32 Clay, Concrete
1.56
0.89
0.77
0.60
8.44
1.90
33 Primary Metals
1.00
0.57
0.39
0.33
4.69
1.92
34 Fabricated Metals
1.00
0.96
0.44
0.52
7.33
3.66
35 Machinery
0.98
0.89
0.46
0.49
25.63
6.07
36 Electrical Mach.
1.44
1.02
0.70
0.59
12.05
2.72
37 Transportation
1.92
1.62
0.96
1.04
6.96
4.59
1.32
1.33
0.63
0.76
8.19
4.88
1.53
1.01
0.69
0.53
15.06
7.44
1.53
1.09
0.72
0.58
8.45
3.17
26 Paper & Pulp 28 Chemical
38 Precision Inst. 39 Miscellaneous 75 Textile & Leather
5. 3. 14. Summary The estimated mean elasticities for both years across the commodity groups have been presented in Tables 5.26 through 5.38. Although the mean elasticities of some variables display systematic increases or decreases between 1993 and 1997, for instance owsem,
dpipc, and dpop, the others do not display such a pattern. As a general
observation, it is also possible to say that the variables with insignificant t-statistics values have relatively small elasticities, as can be expected. Although in many cases the magnitudes of the elasticities are similar,
sometimes they show significant variations
between the two years. These variations can be explained either because different samples have been used (in terms of time and size), or because the industry group that displays such variations has had structural changes over the 1993-1997 period.
108
CHAPTER 6 CONCLUSIONS
The overall results of this research can be summarized as follows. (1) Although past spatial interaction models of commodity flows have generally used
gravity-type log-
linear specifications, the optimized Box-Cox specification proves to be superior to the loglinear one for all commodity groups in both years (except in one case in 1997 because of a much smaller sample). (2) While past empirical research has tried to best-fit simple functional forms with very few variables, that provide the highest fit (Reed, 1967; Huxley, 1979; Chisholm & O’Sullivan, 1973), this study, on the other hand, selects a large number of explanatory variables in a more directed process. First, it chooses the variables representing the origin’s productive capacities, the origin’s demand conditions, and destination’s demands characteristics (at both the intermediate and
the
final levels),
consistent with the input-output framework. Next, besides the variables that represent the origin and destination characteristics, distance, contiguity and
variables that represent
nodal spatial configurations are considered. Although Reed (1967) had also tried to include
similar spatial configuration variables, his study focused on only one region. Here
we apply to commodity flows the concept of
“competing destinations” (Fotheringham,
1983a & 1983b) and “intervening opportunities” (Ullman, 1967; Guldmann, 1999) used in other applications of spatial interaction modeling. The empirical results show that, as in the case of international trade flow models (Frankel & Wei, 1998), state adjacency significantly affects interstate trade flows. In other words, neighboring states trade more with one another
than non-neighboring ones, even
after accounting for distance. The dummy variable measuring the effect of adjacency is significant for 15 commodity groups out of 16 in 1993, and for 13 groups in 1997. Beyond possibly lower transportation cost, cultural similarities, and ease in business information gathering between neighboring states are possible reasons for this result. The
findings
about
distance,
as
proxy
for
transportation
cost,
confirm
the
importance of this factor: the distance coefficient is negative and highly significant for all 109
commodity
groups
in
both
1993,
and
1997,
with
bulkier products having larger
coefficients, that is, being hauled over shorter distances, while higher value-per-weight commodities are shipped over longer distances. In contrast, Reed (1967) and Huxley (1979) did not find distance a significant variable in their studies. The competing destination variable has a negative and significant effect for all commodity groups in 1993, and for 14 groups in 1997. This result implies a “competition effect” at the state destinations, that is, the spatial proximity of state destinations around the terminating state causes flows to decrease to this terminating state. The same argument is valid for the spatial
configurations of destinations around the origins as
measured by the intervening opportunities variable. In 1993, 11 out of 16 commodity groups have negative and significant intervening opportunities variables, and 8 do so in 1997, pointing to a
“competition effect” in the clusters around supply nodes. Looking at
these results, it is possible to conclude that manufacturing activities are characterized by competitive destination effects. In geographical term, when other destinations get closer to either the supply or destination state, the amount of manufactured products (almost in all two-digit sub-categories) shipped to this destination state decreases. The custom district variables, ocddmy and dcddmy, are to measure foreign trade effects on commodity flows within the continental US, but are not consistent in capturing these effects. The foreign trade flows, their ratios to US total and custom district states total out-shipments, and the significant coefficients of the dummy variables, are presented in Table 6.1 for 1993. Some of the effects of exports and imports are captured by these variables. For example, the effects of export are captured in the case of food products (group 20), rubber (group 30), primary metal (group 33), electrical machinery (group 36) and transportation equipment (group 37), which are all large exporting sectors. In 1997, these variables are less consistent in terms of capturing the effects of foreign trade, possibly due to sampling problems.
110
Table 6. 1. Performance of Custom District Variables in 1993
Cmd.
Group Name
Custom States Totsp93 Cdim93
Cdex93 (%)
(%) ocddmy
20 Food & Kindred
20789 2.4 692770
21874
24 Lumber & Wood
5282 4.2 107428
8656
25 Furniture
3627 5.2
7940 13.1
26 Paper & Pulp
8886 4.6 169767
10388
6.1 (0.27)*
35229 6.7 453635
25428
5.6
28 Chemicals 29 Coal & Petroleum
60638
7248 2.0 322124
3.2 (-0.27)** (0.21)* 8.1 (-0.20)** -
-
14651 10.2 (-0.17)**
32 Clay, Concrete
16116 17.7
18893 24.2
33 Primary Metals
12962 5.7 196260 3700 1.6 205261
(0.20)**
52423 16.3 (1.84)*
17137 9.9 144335
34 Fabricated Metals
-
-
30 Rubber
78176
dcddmy
-
-
22994 11.7 (-0.22)** (-0.36)* 5300
2.6 (-0.22)*
35 Machinery
85813 19.4 382119
85303 22.3
36 Electrical Mach.
59883 14.6 359963
74481 20.7 (-0.18)**
-
37 Transportation
74032 11.6 549739
88885 16.2 (-0.60)*
-
38 Precision Inst.
21995 11.0 181591
20117 11.1
-
(0.23)
39 Miscellaneous
3800 2.3 139141
12940
9.3
-
-
56240 14.9
-
-
75 Textile & Leather
15709 3.5 376529
-
(0.17)**
* Significant at 5 % level ** Significant at 10 % level
Totsp93: Total Shipments in 1993; Cdex93: Custom District States Exports; Cdim93: Custom District States Imports
The variables measuring the final demand effects at the origins, the origin state per capita income and the origin state population, have negative effects in most cases, as expected. This result confirms the initial expectation that, as consumption opportunities at the origin increase,
less
export shipments
take place. On the other hand, increasing
consumption opportunities at the destinations were expected to positively affect the outflows, and this is also confirmed by many positive and significant parameter estimates for destination state per capita income and destination state population. One important finding is that wholesale activities at both the origins and destinations are important facilitators of commodity flows, by buying commodities from the production sector and reselling them to the intermediate and final demand sectors. Origin state wholesale employment is significant and positive in 13 groups in 1993 and 11 in 1997, while destination state wholesale employment is significant in 14 groups in 1993 and 13 in 1997.
111
Based on their likely demand structure, it is possible to group commodities into three main groups. (1) Those that are mainly inputs to final demand sectors, (2) those that are mainly inputs to intermediate demand sectors, and (3) those
that are inputs to
both demand sectors. (1) Product group 25, furniture and fixture; product group 29, coal and petroleum; product group 36, electrical machinery; product group 38, precision instruments; and products group 39, miscellaneous manufactured products, are primarily inputs to final demand sectors. According to the empirical findings, in none of these groups is the intermediate demand proxy variable, dmnem, very significant, confirming the hypothesis. The origin local demand for product group 25 is not significant, probably because the production of this commodity is spatially concentrated, with economies scale (oaps is positive and significant in only this product group). Product group 29 however, is very sensitive to origin local demand conditions but not to destination local demand, probably because this product is highly sensitive to shipping distance. The other groups, however, are sensitive to both origin and destination final demands. (2) Product group 24, lumber and wood; product group 28, chemical products; product group 32, clay, concrete, glass and stone products, and products group 33, primary metal products are the sectors that are producing primarily for intermediate demand sectors, and the variable dmnem is very significant for
all four groups, whereas
the final demand variables are not significant. Although lumber and wood; clay, concrete, glass and stone; and primary metal products may also be assumed to be important for final demand sectors via the construction sector, the performances of the final demand variables do not support this assumption. (3) Product group 20, food and kindred products; product group 26, pulp and paper products; product group 30, rubber and plastic products; product group 34 fabricated metal products; product group 35, non-electrical machinery; product group 37, transportation equipment; and product group 75, textile, apparel and leather products, on the other hand, are demanded by and supplied to both intermediate and final demand sectors. Variables representing both final and intermediate demand sectors are highly significant in these products groups. The supply variables at the origin, state sectoral employment and state sectoral value added, are expected to positively impact out-shipments. Although there are a few cases where these variables have negative signs, they are positive and significant for most commodities, confirming initial expectations. 112
Finally, scale or diversification effects for origin state sectoral establishments have been tested using the average establishment size variable, oaps. In both years, 8 of the 16 group have significant negative signs, implying that these sectors are characterized by “diversification effects”. Only 1 group, furniture and fixture products, displays a positive and significant coefficient in both years, pointing to economies of scale effect. Overall the results confirm the validity of the approach used to select the explanatory variables and the functional form of the model. The results also point to further research into better explaining the structure of interregional commodity flows. Depending upon
data availability, new explanatory variables could be introduced into the
model, that would better represent (1) final and intermediate demand, (2) wholesale sector effects, (3) supply potential, (4) spatial structure effects, and (5) actual foreign imports and exports. The cd and io variables have been computed with total state employment
data,
but
other
formulations
are
possible,
using,
for
instance,
total
shipments, or total value added, or specific sectoral employments. While this research shows that the Box-Cox specification is superior to the historically-preferred log-linear specifications, further research could focus on alternate expanded specification, such as the quadratic or translog ones. Finally research could be conducted towards a better understanding of the relationship between commodity flows and modes of transportation. The modal data available in the CFS should be useful for such research.
113
BIBLIOGRAPHY Ashtakala, B. and Murthy, A. S. N., 1988, “Optimized Gravity Models for Commodity Transportation”, Journal of Transportation Engineering, Vol. 114, 4: 393-408. Black, W. R., 1971, “The Utility of the Gravity Model and Estimates of its Parameters in Commodity Flow Studies, Proceedings of the Association of American Geographers, V.3, 132-143. Black, W. R., 1972, “lnterregional Commodity Flows: Some Experiments with the Gravity Model”, Journal of Regional Science, Vol. 12, 1: 5-21. Bon, R., 1984, “Comparative Stability Analysis of Multiregional Input-output Models. Column, Row, and Leontief-Strout Gravity Coefficient Models”, The Quarterly Journal of Economics, Vol. 99, 4: 212-235.
Box, G. E. P. and Cox, D. R., 1964, “An Analysis of Transformations”, Journal of the Royal Statistics Society, 26, 3:211-243. Brocker, J., 1989, “Partial Equilibrium Theory of lnterregional Trade and The Gravity Model”, U Papers of the Regional Science Association, V. 66, 7-18. Chisholm, M., and O’Sullivan, P., 1973, Freight Flows and Spatial Aspects of the British Economy , Cambridge University Press, New York and London.
Eichengreen, B., and Irwin, D. A., 1998, “The role of History in Bilateral Trade Flows” ”, in the Regionalization of the World Economy, ed. Frankel, J. A., The University of Chicago Press, Chicago. Frankel, J. A., and Wei, S. J., 1998, “Regionalization of World Trade and Currencies: Economics and Politics”, in The Regionalization of the World Economy, ed. Frankel, J. A., The University of Chicago Press, Chicago. Frankel, J. A., and Romer, D., 1999, “Does Trade Cause Growth?”, The American Economic Review, V. 89, 3: 155-169. 114
Fotheringham, A. S., and O’Kelly M. E., 1989, Spatial Interaction Models: Formulation and Applications, Kluwer Academic Publishers, Dordrecht/Boston/London. Fotheringham, A. S., 1983a, “A New Set of Spatial-Interaction Models: the Theory of Competing Destinations”, Environment and Planning A, V. 15, 15-36. Fotheringham, A. S., 1983b, “Some Theoretical Aspects of Destination Choice and Their Relevance to Production-Constrained Gravity Models”, Environment and Planning A, V. 15, 1121 -1132. Fotheringham, A. S., and Pitts, T. C., 1995, “Directional Variation in Distance Decay”, Environment and Planning A, V. 27, 715-729. Green, W. H., 1997, Econometric Analysis, Third Edition, Prentice Hall, N. Jersey. Guldmann, J M., 1999, “Competing Destinations and Intervening Opportunities Interaction Models of Inter-City Telecommunication Flows”, Paper in Regional Science, Vol. 78, 179194. Hua, C., 1990, “A Flexible and Consistent System for Modeling lnterregional Trade Flows”, Environment and Planning A, Vol. 22, 98-121. Huxley, S. J.,1979, “Indirect Estimation of lnterregional Trading Patterns for Input-Output Analysis: Empirical Results for the Gravity Model and Rail Freight Shipments”, Presented to the 26th North American Meeting of the Regional Science Association, Los Angles. Isard, W., 1951, “lnterregional arid Regional Input-Output Analysis: A Model of a SpaceEconomy, The Review of Economics and Statistics, Vol. 33, 4: 157-169. Ishikawa, Y., 1987, “An Empirical Study of the Competing Destinations Model Using Japanese Interaction Data”, Environment and Planning A, V. 19, 1359-1373. Krugman, P., 1980, “Scale Economies, Product Differentiation, and the Pattern of Trade”, American Economic Review, V.70, 5:950-959. Leontief, W. and Strout, A., 1963, “Multiregional Input-Output Analysis”, Structural Dependence and Economic Development, ed. by Barna Tibor, St Martin Press, New York. Miernick, W. H., 1965, The Elements of Input-Output Analysis, Random House.
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Moses, L. N., 1955, “The Stability of Interregional Trading Patterns and lnput-Output Analysis”, The American Economic Review, December 1955. Reed, W. E., 1967, Areal Interaction in India, Duke University Research Papers, Chicago, Illinois. Richardson, H. W., 1972, Input Output and Regional Economics, Wiley, New York. Samuelson, P. A., 1952, “Spatial Price Equilibrium arid Linear Programming”, American Economic Review, V.42. 67-93. Takayama, T. and Judge, G. G., 1964, “Spatial Equilibrium and Quadratic Programming”, Journal of Farm Economics, V.46:1, p. 67-93. Ullman, E. L., 1967, American Commodity Flow, University of Washington Press, Seattle and London.
116
APPENDIX A STANDARD TRANSPORTATION COMMODITY CLASSIFICATION (STCC)∗∗ Code 00 01 011 012 013 014 015 019 08 084 086 09 091 098 10 101 102 103 104 105 106 107 108 109 11 111 112 13 131 132 14 141 142 144 145 147 ∗
Description ALL COMMODITIES Farm products Field crops Fresh fruits or tree nuts Fresh vegetables Livestock or livestock products Poultry or poultry products Miscellaneous farm products Forest products Barks or gums, crude Miscellaneous forest products Fresh fish or other marine products Fresh fish or other marine products Fish hatcheries, farms or preserves Metallic ores Iron ores Copper ores Lead or zinc ores Gold or silver ores Bauxite ores or other aluminum ores Manganese ores Tungsten ores Chromium ores Miscellaneous metal ores Coal Anthracite Bituminous coal or lignite Crude petroleum, natural gas, or gasoline Crude petroleum or natural gas Natural gasoline Nonmetallic minerals Dimension stone, quarry Crushed or broken stone or riprap Gravel or sand Clay, ceramic, or refractory minerals Chemical and fertilizer minerals
US Department of Commerce, Bureau of the Census, 1997, CFS CD-ROM: CD-THE CFS-93-2, Washington D.C.
117
149 19 191 192 193 194 195 196 199 20 201 202 203 204 205 206 207 208 209 21 211 212 213 214 22 221 222 223 224 225 227 228 229 23 231 233 235 237 238 239 24 241 242 243 244 249 25 251 253 254 259
Miscellaneous nonmetallic minerals Ordnance or accessories Guns, howitzers, mortars, related equipment, or parts, 30 mm Ammunition, over 30 mm Full tracked combat vehicles or parts Military sighting or fire control equipment Small arms, 30 mm or under, or parts Small arms ammunition, 30 mm or under Miscellaneous ordnance, accessories, or parts Food or kindred products Meat, poultry or small game, fresh, chilled, or frozen Dairy products Canned or preserved fruits, vegetables, or seafood Grain mill products Bakery products Sugar, beet, or cane Confectionery or related products Beverages or flavoring extracts Miscellaneous food preparations or kindred products Tobacco products, excluding insecticides Cigarettes Cigars Chewing or smoking tobacco, or snuff Stemmed or re-dried tobacco Textile mill products Cotton broad-woven fabrics Man-made fiber or silk broad-woven fabrics Wool broad-woven fabrics Narrow fabrics, cotton, silk or wool, or man-made fabrics Knit fabrics Floor coverings Thread or yarn Miscellaneous textile goods Apparel or other finished textile products Men’s', youths', or boys' clothing or uniforms Women’s', misses', children’s, or infants clothing Caps, hats or millinery, or hat bodies Fur goods Miscellaneous apparel or accessories Miscellaneous fabricated textile products Lumber or wood products, excluding furniture Primary forest or wood raw materials Sawmill or planning mill products Millwork or prefabricated wood products or plywood or veneer Wooden containers Miscellaneous wood products Furniture or fixtures Household or office furniture Public building or related furniture Lockers, partitions, or shelving Miscellaneous furniture or fixtures 118
26 261 262 263 264 265 266 27 271 272 273 274 276 277 278 279 28 281 282 283 284 285 286 287 289 29 291 295 299 30 301 302 303 304 306 307 31 311 312 313 314 315 316 319 32 321 322 324 325 326 327 328
Pulp, paper, or allied products Pulp or pulp mill products Paper Fiberboard, paperboard, or pulp board Converted paper or paperboard products Containers or boxes, paperboard, fiberboard, or pulp board Building paper or building board Printed matter Newspapers Periodicals Books Miscellaneous printed matter Manifold business forms Greeting cards, seals, labels, or tags Blank books, loose-leaf binders, or devices Service industries for printing trades Chemicals or allied products Industrial inorganic or organic chemicals Plastic materials or synthetic fibers, resins, or rubber Drugs Soap or other detergents, cleaning preparations, cosmetics, perfumes Paints, enamels, lacquers, shellacs, or varnishes Gum or wood chemicals Agricultural chemicals Miscellaneous chemical products Petroleum or coal products Products of petroleum refining Paving or roofing materials Miscellaneous coal or petroleum products Rubber or miscellaneous plastics products Rubber tires or inner tubes Rubber or plastic footwear Reclaimed rubber Rubber or plastic hose or belting Miscellaneous fabricated rubber products Miscellaneous plastic products Leather or leather products Leather Industrial leather belting Boot or shoe cut stock or findings Footwear, leather, or other materials Leather gloves or mittens Luggage or handbags, leather, or other materials Leather goods, etc. Clay, concrete, glass, or stone products Flat glass Glass and glassware, pressed or blown Hydraulic cement Structural clay products Pottery or related products Concrete, gypsum, or plaster products Cut stone or stone products 119
329
Abrasive, asbestos products, or miscellaneous nonmetallic
33 331 332 333 335 336 339 34 341 342 343 344 345 346 348 349 35 351 352 353 354 355 356 357 358 359 36 361 362 363 364 365 366 367 369 37 371 372 373 374 375 376 379 38 381 382 383 384 385 386
Primary metal products Steel works, rolling mill, or other reduction plant products Iron or steel castings Nonferrous metal primary smelted products Nonferrous metal basic shapes Nonferrous metal or nonferrous metal base alloy castings Miscellaneous primary metal products Fabricated metal products Metal cans Cutlery, hand tools, or general hardware Plumbing fixtures or heating apparatus Fabricated structural metal products Bolts, nuts, screws, rivets, washers, or other industrial fasteners Metal stampings Miscellaneous fabricated wire products Miscellaneous fabricated metal products Machinery, excluding electrical Engines or turbines Farm machinery or equipment Construction, mining, or materials handling machinery, or equipment Metal working machinery or equipment Special industry machinery General industrial machinery or equipment Office, computing, or accounting machines Service industry machines Miscellaneous machinery or parts Electrical machinery, equipment, or supplies Electrical transmission or distribution equipment Electrical industrial apparatus Household appliances Electric lighting or wire equipment Radio or television receiving sets Communication equipment Electronic components or accessories Miscellaneous electrical machinery, equipment, or supplies Transportation equipment Motor vehicles or equipment Aircraft or parts Ships or boats Railroad equipment Motorcycles, bicycles, or parts Guided missile or space vehicle parts Miscellaneous transportation equipment Instruments, photographic goods, optical goods, watches, or clocks Engineering, laboratory, or scientific instruments Measuring, controlling, or indicating instruments Optical instruments or lenses Surgical, medical, or dental instruments, or supplies Ophthalmic or opticians goods Photographic equipment or supplies 120
387
Watches, clocks, clockwork operated devices, or parts
39 391 393 394 395 396 399 40 401 402 41 411 412 42 429 48 489 99
Miscellaneous products or manufacturing Jewelry, silverware, or plated ware Musical instruments or parts Toys, amusements, sporting, or athletic goods Pens, pencils, or other office materials, or artists' materials Costume jewelry, buttons, novelties, or notions Miscellaneous manufactured products Waste or scrap materials Ashes Waste or scrap Miscellaneous freight shipment Miscellaneous freight shipments Special commodities not taken in regular freight service Containers, carriers or devices, shipping, returned empty Containers, carriers or devices, shipping, returned empty Waste hazardous materials or waste hazardous substances Hazardous materials Commodity unknown
121
APPENDIX B STANDARD CLASSIFICATION OF TRANSPORTED GOODS (SCTG)∗∗
01-05 Agricultural products and fish 01
Live animals and live fish
02
Cereal grains
03
Agricultural products, except live animals, cereal grains and forage products
04
Animal feed and feed ingredients, cereal, straw, and eggs and other products of animal origin, n.e.c.
05
Meat, fish, seafood, and preparations
06-09 Grains, alcohol, and tobacco products 06
Milled grain products and preparations and bakery products
07
Prepared foodstuffs, n.e.c. and fats and oils
08
Alcoholic beverages
09
Tobacco products
10-14
Stone, nonmetallic minerals, and metallic ores
10
Monumental or building stone
11
Natural sands
12
Gravel and crushed stone
13
Nonmetallic minerals, n.e.c.
14
Metallic ores
15-20 Coal and petroleum products 15
Coal
17
Gasoline and aviation turbine fuel
18
Fuel oils
US Department of Transportation, Bureau of The Transportation Statistics & US Department of Commerce, the Bureau of the Census, 2000, Commodity Flow Survey CD-ROM, CD-EC97-CFS, Washington D.C., ∗
122
19
Products of petroleum refining, n.e.c. and coal products
20
Basic chemical
21-24 Pharmaceutical and chemical products 21
Pharmaceutical products
22
Fertilizer and fertilizer materials
23
Chemical products and preparations, n.e.c.
24
Plastics and rubber
25-30 Wood products and textiles and leather 25
Logs and other wood in the rough
26
W ood products
27
Pulp, newsprint, paper, and paperboard
28
Paper or paperboard articles
29
Printed products
30
Textiles, leather, and articles
31-34 Base metal and machinery 31
Nonmetallic mineral products
32
Base metal in primary or semi-finished forms and in finished basic shapes
33
Articles of base metal
34
Machinery
35-38 Electronics, motorized vehicles, and precision instruments 35
Electronic and other electrical equipment and components, and office equipment
36
Vehicles
37
Transportation equipment, n.e.c.
38
Precision instruments and apparatus
39-43 Furniture and miscellaneous manufactured products 39
Furniture, mattresses and mattress supports, lamps, lighting fittings, and illuminated signs
40
Miscellaneous manufactured products
41
Waste and scrap
43
Mixed freight 123
APPENDIX C DESCRIPTIVE STATISTICS
Table C. 1. Descriptive Statistics for 1993-Commodity 20
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1663
225
462
0
7010
373382
cd93
1663
206288
178933
16323
771064
343056362
io93
1663
201798
179309
16323
771064
335590892
dist93 (miles)
1662
1187
752
40
3500
1972551
opipc93 ($)
1663
20680
2827
15468
29602
34391391
opop93
1663
5589936
5818829
460000
30380000
9296063000
oemp93
1663
38028
34636
1067
184324
63240646
owsem93
1663
146655
154839
7807
783658
243887508
ovlad92 ($M)
1663
3588
3797
87
19585
5966710
oaps93
1663
84
38
23
205
140231
dmnem93
1663
426175
395305
11285
1898885
708728698
dwsem93
1663
150445
156146
7807
783658
250189394
dpipc93 ($)
1663
20748
2846
15468
29602
34503153
dpop93
1663
5736646
5868199
460000
30380000
9540042000
124
Table C. 2. Descriptive Statistics for 1993-Commodity 24
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1599
32
88
0
1908
50573
cd93
1599
201990
179893
16323
771064
322982316
io93
1599
186602
175444
16323
771064
298375944
dist93 (miles)
1599
1257
776
40
3519
2010599
opipc93 ($)
1599
20469
2770
15468
29602
32729391
opop93
1599
5291226
5853632
460000
30380000
8460670000
oemp93
1599
18735
15524
581
59138
29957457
owsem93
1599
137035
154807
7807
783658
219118478
ovlad92 ($M)
1599
740
733
27
3267
1182676
oaps93
1599
22
6
13
45
35589
dmnem93
1599
415107
389168
11285
1898885
663756759
dwsem93
1599
145861
153633
7807
783658
233231221
dpipc93 ($)
1599
20764
2934
15468
29602
33201301
dpop93
1599
5575505
5779705
460000
30380000
8915233000
Table C. 3. Descriptive Statistics for 1993-Commodity 25
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1573
22
51
0
429
35241
cd93
1573
198293
178190
16323
771064
311915433
io93
1573
188969
174030
16323
771064
297248408
dist93 (miles)
1573
1257
752
40
3500
1977876
opipc93 ($)
1573
20503
2849
15468
29602
32251602
opop93
1573
5289980
5685506
460000
30380000
8321139000
oemp93
1573
12100
16474
147
81743
19033484
owsem93
1573
136959
150905
7807
783658
215437079
ovlad92 ($M)
1573
521
673
0
2765
819306
oaps93
1573
44
27
11
118
69382
dmnem93
1573
407945
382204
11285
1898885
641697263
dwsem93
1573
142627
149683
7807
783658
224351561
dpipc93 ($)
1573
20723
2901
15468
29602
32597573
dpop93
1573
5454962
5622184
460000
30380000
8580656000
125
Table C. 4. Descriptive Statistics for 1993-Commodity 26
Variable
N
Mean
St. Dev.
Minimum
Maximum 1563
Sum
flow93 ($M)
1560
68
134
0
105745
cd93
1560
204749
179034
16323
771064 319408369
io93
1560
200209
178931
16323
771064 312326373
dist93 (miles)
1559
1181
735
40
3500
1841585
opipc93 ($)
1560
20604
2855
15468
29602
32142726
opop93
1560
5476608
5586756
460000
oemp93
1560
15546
13802
7
owsem93
1560
143533
149263
7807
ovlad92 ($M)
1560
1343
1227
0
5182
2095757
oaps93
1560
122
75
18
462
185287
dmnem93
1560
424016
393666
11285
1898885 661464705
dwsem93
1560
147988
155483
7807
783658 230861582
dpipc93 ($)
1560
20756
2855
15468
dpop93
1560
5662519
5838848
460000
30380000 8543508000 51953
24252414
783658 223911790
29602
32378924
30380000 8833530000
Table C. 5. Descriptive Statistics for 1993-Commodity 28
Variable
N
Mean
St. Dev.
Minimum
Maximum
flow 93 ($M)
1568
180
387
0
cd93
1568
210865
180195
16323
771064 330636055
io93
1568
207353
179427
16323
771064 325129616
dist93 (miles)
1568
1186
749
45
3519
1859002
opipc93 ($)
1568
20770
2944
15468
29602
32567483
opop93
1568
5768788
5938802
oemp93
1568
25364
27306
199
owsem93
1568
151342
158213
7807
ovlad92 ($M)
1568
3861
4346
13
18381
6054550
oaps93
1568
87
89
10
667
136140
dmnem93
1568
437871
396807
11285
1898885 686581817
dwsem93
1568
152890
157067
7807
783658 239731417
dpipc93 ($)
1568
20851
2923
15468
dpop93
1568
5824610
5898777
126
4208
Sum 281612
460000 30380000 9045459000 108096
39770458
783658 237304911
29602
32693696
460000 30380000 9132989000
Table C. 6. Descriptive Statistics for 1993-Commodity 29
Variable
N
Mean
St. Dev.
Minimum
Maximum 2194
Sum
flow93 ($M)
1557
25
127
0
39110
cd93
1557
178204
168813
16323
771064 277463495
io93
1557
168414
167279
16323
771064 262220643
dist93 (miles)
1557
1378
759
45
3519
2145590
opipc93 ($)
1557
20598
2871
15468
29602
32071490
opop93
1557
4638593
5151814
oemp93
1557
2549
5109
4
owsem93
1557
120886
137338
7807
ovlad92 ($M)
1557
410
828
0
4566
638705
oaps93
1557
51
45
3
233
77467
dmnem93
1557
366182
363566
11285
1898885 570145829
dwsem93
1557
126997
139343
7807
783658 197733742
dpipc93 ($)
1557
20549
2839
15468
dpop93
1557
4870905
5206945
460000 30380000 7222290000 29893
3969019
783658 188220076
29602
31994131
460000 30380000 7583999000
Table C. 7. Descriptive Statistics for 1993-Commodity 30
Variable
N
Mean
St. Dev.
Minimum
Maximum
flow93 ($M)
1604
59
108
0
cd93
1604
207385
180764
16323
771064 332645016
io93
1604
202408
181540
16323
771064 324663035
dist93 (miles)
1603
1251
768
40
3519
2005020
opipc93 ($)
1604
20606
2822
15468
29602
33052611
opop93
1604
5627241
5856473
oemp93
1604
20840
21281
227
owsem93
1604
146925
155960
7807
ovlad92 ($M)
1604
1339
1369
10
5314
2148221
oaps93
1604
60
27
10
120
95472
dmnem93
1604
430793
399722
11285
1898885 690991984
dwsem93
1604
151863
158215
7807
783658 243587673
dpipc93 ($)
1604
20782
2867
15468
dpop93
1604
5782890
5959983
127
969
Sum 94640
460000 30380000 9026095000 91648
33426685
783658 235667872
29602
33333952
460000 30380000 9275756000
Table C. 8. Descriptive Statistics for 1993-Commodity 32
Variable
N
Mean
St. Dev.
Minimum
Maximum 569
Sum
flow93 ($M)
1644
22
49
0
35659
cd93
1644
198902
179700
16323
771064 326994791
io93
1644
196283
181873
16323
771064 322689406
dist93 (miles)
1644
1252
768
40
3519
2058272
opipc93 ($)
1644
20584
2852
15468
29602
33839343
opop93
1644 5457164
5855273
oemp93
1644
12183
12584
691
owsem93
1644
142185
155859
7807
ovlad92 ($M)
1644
757
797
31
3115
1244567
oaps93
1644
31
10
10
53
51006
dmnem93
1644
409853
389239
11285
1898885 673798230
dwsem93
1644
143649
153377
7807
783658 236159080
dpipc93 ($)
1644
20736
2884
15468
dpop93
1644 5499333
5768369
460000 30380000 8971578000 51147
20029656
783658 233751896
29602
34089484
460000 30380000 9040904000
Table C. 9. Descriptive Statistics for 1993-Commodity 33
Variable
N
Mean
St. Dev.
Minimum
Maximum
flow93 ($M)
1511
84
212
0
cd93
1511
206818
178779
16323
771064 312501511
io93
1511
206977
186843
16323
771064 312742721
dist93 (miles)
1511
1187
751
40
3500
1793938
opipc93 ($)
1511
20727
2842
15468
29602
31318289
opop93
1511
5648388
5807533
oemp93
1511
15986
21028
9
owsem93
1511
147441
154157
7807
ovlad92 ($M)
1511
1211
1683
0
6975
1830549
oaps93
1511
95
54
9
321
144069
dmnem93
1511
430735
392098
11285
1898885 650840534
dwsem93
1511
149660
154148
7807
783658 226135652
dpipc93 ($)
1511
20664
2855
15468
dpop93
1511
5721323
5783419
128
3060
Sum 126686
460000 30380000 8534714000 90671
24154712
783658 222782707
29602
31223261
460000 30380000 8644919000
Table C. 10. Descriptive Statistics for 1993-Commodity 34
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1744
75
150
0
1766
131075
cd93
1744
205250
179546
16323
771064
357955830
io93
1744
208015
181494
16323
771064
362778533
dist93 (miles)
1743
1216
760
40
3519
2119993
opipc93 ($)
1744
20777
2907
15468
29602
36235828
opop93
1744
5748641
5918395
oemp93
1744
32372
34546
451
123315
56456256
owsem93
1744
150846
157328
7807
783658
263076089
ovlad92 ($M)
1744
1974
2163
23
8040
3443345
oaps93
1744
37
13
13
66
64815
dmnem93
1744
424354
393493
11285
1898885
740073640
dwsem93
1744
148475
154435
7807
783658
258940155
dpipc93 ($)
1744
20752
2896
15468
29602
36192113
dpop93
1744
5677553
5807918
460000 30380000
9901652000
460000 30380000 10025630000
Table C. 11. Descriptive Statistics for 1993-Commodity 35
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1563
147
298
0
3721
229194
cd93
1563
206782
177771
16323
771064
323200402
io93
1563
208491
179976
16323
771064
325871221
dist93 (miles)
1563
1194
750
40
3500
1866223
opipc93 ($)
1563
20777
2776
15468
29602
32474343
opop93
1563
5732906
5813970
460000 30380000
8960532000
oemp93
1563
47182
47880
934
198366
73745358
owsem93
1563
150969
154357
7807
783658
235964459
ovlad92 ($M)
1563
3162
3811
35
19854
4942780
oaps93
1563
36
12
10
63
56608
dmnem93
1563
430299
390085
11285
1898885
672557204
dwsem93
1563
149630
153309
7807
783658
233871227
dpipc93 ($)
1563
20750
2879
15468
29602
32431681
dpop93
1563
5709766
5764197
460000 30380000
8924365000
129
Table C. 12. Descriptive Statistics for 1993-Commodity 36
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1581
149
341
0
5606
236180
cd93
1581 206543
178203
16323
771064
326543695
io93
1581 210263
183965
16323
771064
332426366
dist93 (miles)
1580
1221
755
40
3500
1928688
opipc93 ($)
1581
20850
2867
15468
29602
32964388
opop93
1581 5828548
5938516
460000
30380000
9214934000
oemp93
1581
36454
41199
115
219375
57634344
owsem93
1581 153134
157542
7807
783658
242104256
ovlad92 ($M)
1581
2843
3770
0
21356
4494315
oaps93
1581
102
54
11
248
161790
dmnem93
1581 426134
386032
11285
1898885
673717702
dwsem93
1581 148993
152145
7807
783658
235557429
dpipc93 ($)
1581
20826
2960
15468
29602
32925678
dpop93
1581 5686082
5707851
460000
30380000
8989696000
Table C. 13. Descriptive Statistics for 1993-Commodity 37
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1292
252
646
0
7800
325979
cd93
1292 205882
179429
16323
771064
265999044
io93
1292 201563
180279
16323
771064
260419375
dist93 (miles)
1292
1181
748
40
3519
1524304
opipc93 ($)
1292
20599
2748
15468
29602
26613480
opop93
1292 5653083
5948699
460000 30380000
7303783000
oemp93
1292
40071
55798
236
278083
51772342
owsem93
1292 147735
157826
7807
783658
190873599
ovlad92 ($M)
1292
3525
4952
12
21698
4554884
oaps93
1292
134
90
13
537
172903
dmnem93
1292 430360
395014
11285
1898885
556024616
dwsem93
1292 150099
155614
7807
783658
193927709
dpipc93 ($)
1292
20706
2793
15468
29602
26751533
dpop93
1292 5705684
5832969
460000 30380000
7371744000
130
Table C. 14. Descriptive Statistics for 1993-Commodity 38
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1421
61
153
0
1999
87165
cd93
1421 207569
183648
16323
771064
294955471
io93
1421 207493
184816
16323
771064
294847176
dist93 (miles)
1421
1290
766
40
3519
1833786
opipc93 ($)
1421
20856
2816
15468
29602
29636386
opop93
1421 5893861
6302444
460000 30380000
8375177000
oemp93
1421
22302
36003
79
184992
31690826
owsem93
1421 154789
166963
7807
783658
219955276
ovlad92 ($M)
1421
2215
3607
0
16790
3147951
oaps93
1421
67
31
10
164
95365
dmnem93
1421 427322
398113
11285
1898885
607223898
dwsem93
1421 151028
157855
7807
783658
214611106
dpipc93 ($)
1421
20798
2902
15468
29602
29554395
dpop93
1421 5770931
5940739
460000 30380000
8200493000
Table C. 15. Descriptive Statistics for 1993-Commodity 39
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
1559
53
125
0
2062
82468
cd93
1559 205015
179546
16323
771064
319618503
io93
1559 202109
181203
16323
771064
315088202
dist93 (miles)
1559
1252
763
40
3519
1951995
opipc93 ($)
1559
20745
2857
15468
29602
32342206
opop93
1559 5594112
5893783
460000 30380000
8721220000
oemp93
1559
10105
10540
238
47003
15753520
owsem93
1559 146708
156686
7807
783658
228717318
ovlad92 ($M)
1559
536
572
14
2272
836036
oaps93
1559
27
10
9
51
42240
dmnem93
1559 421390
391436
11285
1898885
656946247
dwsem93
1559 149912
155571
7807
783658
233712898
dpipc93 ($)
1559
20772
2860
15468
29602
32383591
dpop93
1559 5717928
5840324
460000 30380000
8914250000
131
Table C. 16. Descriptive Statistics for 1993-Commodity 75
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow93 ($M)
680
209
517
0
5670
141929
cd93
680 209695
189956
16323
771064
142592566
io93
680 184085
185481
16323
771064
125177916
dist93 (miles)
680
1242
746
47
3500
844415
opipc93 ($)
680
20358
2722
15468
29602
13843471
opop93
680 5107374
5829019
460000 30380000
3473014000
oemp93
680
42844
62839
401
284042
29133950
owsem93
680 134274
156050
7807
783658
91306072
ovlad92 ($M)
680
1718
2522
3
10913
1168183
oaps93
680
59
44
11
167
40300
dmnem93
680 428490
410954
11285
1898885
291373351
dwsem93
680 148676
159023
7807
783658
101099960
dpipc93 ($)
680
20728
2866
15468
29602
14095252
dpop93
680 5689369
5998679
460000 30380000
3868771000
Table C. 17. Descriptive Statistics for 1997-Commodity 20
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
319
197
491
0
3559
62973
cd97
319 175732
185426
16357
867414
56058374
io97
319 139307
167658
16357
867414
44438823
dist97 (miles)
319
1489
772
75
3075
475100
opipc97 ($)
319
24176
3400
18885
35596
7712048
opop97
319 3626408
4427888
480000 32268000
1156824000
oemp97
319
22125
27126
1057
182745
7057906
owsem97
319
95434
124643
8624
856950
30443434
ovlad97 ($M)
319
2077
2902
18
16831
662420
oaps97
319
63
42
19
205
20186
dmnem97
319 346108
390158
13219
2019053
110408381
dwsem97
319 121858
150736
8624
856950
38872826
dpipc97 ($)
319
24029
3120
18885
35596
7665103
dpop97
319 4553279
5500129
480000 32268000
1452496000
132
Table C. 18. Descriptive Statistics for 1997-Commodity 24
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
894
30
112
0
1954
26930
cd97
894 213777
193456
16357
867414
191116839
io97
894 180141
175210
16357
867414
161045900
dist97 (miles)
894
1317
744
47
3185
1176435
opipc97 ($)
894
24653
3355
18885
35596
22040223
opop97
894 4899326
5541071
480000 32268000
4379997000
oemp97
894
17765
16861
523
64312
15882009
owsem97
894 132368
151360
8624
856950
118336750
ovlad97 ($M)
894
653
658
29
2667
584129
oaps97
894
51
19
20
97
45765
dmnem97
894 403395
384173
13219
2019053
360634825
dwsem97
894 148936
156323
8624
856950
133148915
dpipc97 ($)
894
24764
3592
18885
35596
22138898
dpop97
894 5513568
5745358
480000 32268000
4929130000
Table C. 19. Descriptive Statistics for 1997-Commodity 25
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1148
38
72
0
605
44001
cd97
1148 241803
204762
16357
867414
277590214
io97
1148 227350
193200
16357
867414
260997648
dist97 (miles)
1147
1152
738
57
3206
1321562
opipc97 ($)
1148
24530
3353
18885
35596
28160909
opop97
1148 5948402
5994118
480000 32268000
6828766000
oemp97
1148
14210
17809
230
77898
16312629
owsem97
1148 158907
162813
8624
856950
182425264
ovlad97 ($M)
1148
880
1023
13
3890
1010503
oaps97
1148
27
18
8
74
30752
dmnem97
1148 470930
431254
13219
2019053
540627894
dwsem97
1148 176321
177894
8624
856950
202416778
dpipc97 ($)
1148
24992
3525
18885
35596
28690600
dpop97
1148 6508839
6579843
480000 32268000
7472147000
133
Table C. 20. Descriptive Statistics for 1997-Commodity 26
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
722
110
191
0
1429
79061
cd97
722 243448
212553
16357
867414
175769516
io97
722 207828
207046
16357
867414
150051803
dist97 (miles)
722
1160
768
70
3185
836010
opipc97 ($)
722
24242
3256
18885
35596
17502775
opop97
722 5453819
5902962
480000 32268000
3937657000
oemp97
722
14823
14445
11
53251
10702220
owsem97
722 144779
159897
8624
856950
104530789
ovlad97 ($M)
722
1532
1480
0
5813
1106231
oaps97
662
132
67
28
436
87528
dmnem97
722 467242
449128
13219
2019053
337348825
dwsem97
722 173284
184837
8624
856950
125111223
dpipc97 ($)
722
24962
3573
18885
35596
18022879
dpop97
722 6425677
6832812
480000 32268000
4639339000
Table C. 21. Descriptive Statistics for 1997-Commodity 28
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
223
227
456
0
3716
50559
cd97
223 177063
180557
16357
867414
39485081
io97
223 197871
228843
16357
867414
44125174
dist97 (miles)
223
1425
726
80
3026
317721
opipc97 ($)
223
24408
3827
18885
35596
5442912
opop97
223 5437453
7078573
480000 32268000
1212552000
oemp97
223
22400
27939
198
97010
4995141
owsem97
223 147921
193518
8624
856950
32986482
ovlad97 ($M)
223
4891
6765
40
27993
1090626
oaps97
223
84
90
10
444
18681
dmnem97
223 362399
416835
13219
2019053
80815042
dwsem97
223 135882
172745
8624
856950
30301608
dpipc97 ($)
223
24027
3311
18885
35596
5358029
dpop97
223 5031242
6282182
480000 32268000
1121967000
134
Table C. 22. Descriptive Statistics for 1997-Commodity 29
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1321
10
63
0
1459
13232
cd97
1321 191524
187584
16357
867414
253003453
io97
1321 170545
172937
16357
867414
225289751
dist97 (miles)
1321
1400
730
70
3220
1848077
opipc97 ($)
1321
24558
3501
18885
35596
32441551
opop97
1321 4517860
5241297
480000 32268000
5968093000
oemp97
1321
2138
4119
1
26119
2824705
owsem97
1321 120798
141984
8624
856950
159573665
ovlad97 ($M)
1321
554
1326
0
8878
731756
oaps97
1321
55
43
6
200
59202
dmnem97
1321 360703
369019
13219
2019053
476488240
dwsem97
1321 133184
147871
8624
856950
175935762
dpipc97 ($)
1321
24527
3540
18885
35596
32400141
dpop97
1321 4949058
5409779
480000 32268000
6537706000
Table C. 23. Descriptive Statistics for 1997-Commodity 30
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1308
124
201
0
1844
161892
cd97
1308 253076
202850
16357
867414
331023024
io97
1308 260107
205136
16357
867414
340219636
dist97 (miles)
1308
1079
692
57
3220
1411669
opipc97 ($)
1308
24944
3436
18885
35596
32626360
opop97
1308 6887646
6629288
480000 32268000
9009041000
oemp97
1308
26624
23697
358
93222
34824722
owsem97
1308 186398
179142
8624
856950
243808493
ovlad97 ($M)
1308
2190
1922
11
6944
2864804
oaps97
1308
61
21
12
107
79391
dmnem97
1308 492830
425211
13219
2019053
644621881
dwsem97
1308 182899
175470
8624
856950
239231939
dpipc97 ($)
1308
25024
3459
18885
35596
32730779
dpop97
1308 6748151
6487591
480000 32268000
8826582000
135
Table C. 24. Descriptive Statistics for 1997-Commodity 32
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
892
20
42
0
395
18064
cd97
892 216163
194302
16357
867414
192817623
io97
892 192083
180344
16357
867414
171337825
dist97 (miles)
892
1247
739
75
3220
1112056
opipc97 ($)
892
24330
3628
18885
35596
21702166
opop97
892 5058957
5728891
480000 32268000
4512590000
oemp97
892
11049
11737
720
50758
9856029
owsem97
892 136391
157603
8624
856950
121660666
ovlad97 ($M)
892
939
1018
32
4124
837848
oaps97
892
32
10
13
53
28149
dmnem97
892 417859
403036
13219
2019053
372730476
dwsem97
892 153307
163464
8624
856950
136749676
dpipc97 ($)
892
24605
3469
18885
35596
21947709
dpop97
892 5714849
6014271
480000 32268000
5097645000
Table C. 25. Descriptive Statistics for 1997-Commodity 33
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1178
127
305
0
5013
149157
cd97
1178 239813
205359
16357
867414
282499337
io97
1178 240029
208532
16357
867414
282754666
dist97 (miles)
1178
1129
732
47
3168
1329405
opipc97 ($)
1178
24665
3382
18885
35596
29055912
opop97
1178 6291378
6379829
480000 32268000
7411243000
oemp97
1178
18761
22623
11
92482
22100561
owsem97
1178 168934
173358
8624
856950
199003688
ovlad97 ($M)
1178
1850
2469
3
10800
2178942
oaps97
1178
141
72
6
329
166464
dmnem97
1178 458715
409735
13219
2019053
540366539
dwsem97
1178 168325
168059
8624
856950
198286768
dpipc97 ($)
1178
24855
3581
18885
35596
29278699
dpop97
1178 6243575
6203342
480000 32268000
7354931000
136
Table C. 26. Descriptive Statistics for 1997-Commodity 34
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1174
95
177
0
1908
111193
cd97
1174 249765
203565
16357
867414
293224511
io97
1174 262020
205214
16357
867414
307611525
dist97 (miles)
1174
1067
702
47
3206
1252460
opipc97 ($)
1174
25087
3578
18885
35596
29452156
opop97
1174 6839641
6506866
480000 32268000
8029739000
oemp97
1174
41257
38965
687
131948
48435694
owsem97
1174 184652
175854
8624
856950
216781358
ovlad97 ($M)
1174
3612
3471
52
13940
4240695
oaps97
1174
25
7
8
39
28940
dmnem97
1174 482187
415941
13219
2019053
566087697
dwsem97
1174 178189
171326
8624
856950
209193798
dpipc97 ($)
1174
24851
3497
18885
35596
29175255
dpop97
1174 6589895
6304310
480000 32268000
7736537000
Table C. 27. Descriptive Statistics for 1997-Commodity 35
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1271
173
310
0
4667
220077
cd97
1271 248732
197583
16357
867414
316137739
io97
1271 247576
203104
16357
867414
314669124
dist97 (miles)
1271
1110
714
57
3220
1411043
opipc97 ($)
1271
24850
3330
18885
35596
31584318
opop97
1271 6408937
6184452
480000 32268000
8145759000
oemp97
1271
56775
55146
1224
237065
72161135
owsem97
1271 173852
167867
8624
856950
220965471
ovlad97 ($M)
1271
3589
3545
40
13216
4561879
oaps97
1271
75
23
27
157
94702
dmnem97
1271 483340
409209
13219
2019053
614325331
dwsem97
1271 177730
167604
8624
856950
225895349
dpipc97 ($)
1271
24984
3454
18885
35596
31754780
dpop97
1271 6541420
6181908
480000 32268000
8314145000
137
Table C. 28. Descriptive Statistics for 1997-Commodity 36
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1274
356
863
0
13664
452990
cd97
1274 255316
204382
16357
867414
325272805
io97
1274 260495
205727
16357
867414
331871255
dist97 (miles)
1274
1144
735
47
3206
1457713
opipc97 ($)
1274
25227
3473
18885
35596
32139473
opop97
1274 6788222
6427557
480000 32268000
8648195000
oemp97
1274
45113
49253
222
263474
57473923
owsem97
1274 184358
173658
8624
856950
234871505
ovlad97 ($M)
1274
1522
1347
0
5306
1938890
oaps97
1234
266
114
43
653
328665
dmnem97
1274 496430
422865
13219
2019053
632451859
dwsem97
1274 184294
175028
8624
856950
234790847
dpipc97 ($)
1274
25028
3470
18885
35596
31886008
dpop97
1274 6797768
6479000
480000 32268000
8660357000
Table C. 29. Descriptive Statistics for 1997-Commodity 37
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
420
407
1088
0
12136
171138
cd97
420 238232
220128
16357
867414
100057553
io97
420 217726
198255
16357
867414
91444831
dist97 (miles)
420
1179
743
57
3186
495172
opipc97 ($)
420
24738
2882
18885
35596
10390033
opop97
420 5943695
6695202
480000 32268000
2496352000
oemp97
420
45670
65457
318
293723
19181441
owsem97
420 159881
180513
8624
856950
67150037
ovlad97 ($M)
420
5609
8819
15
39045
2355952
oaps97
420
134
82
15
324
56077
dmnem97
420 460149
449771
13219
2019053
193262712
dwsem97
420 177012
192995
8624
856950
74344983
dpipc97 ($)
420
24888
3441
18885
35596
10452954
dpop97
420 6536612
7123164
480000 32268000
2745377000
138
Table C. 30. Descriptive Statistics for 1997-Commodity 38
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
942
82
161
0
1649
76825
cd97
942 258334
205935
16357
867414
243350587
io97
942 256306
216966
16357
867414
241440295
dist97 (miles)
942
1205
739
70
3206
1134806
opipc97 ($)
942
25232
3562
18885
35596
23768154
opop97
942 6768212
6987278
480000 32268000
6375656000
oemp97
942
24988
37483
73
179674
23538773
owsem97
942 183774
187193
8624
856950
173115106
ovlad97 ($M)
942
6671
13010
9
65716
6284218
oaps97
942
51
25
5
130
47575
dmnem97
942 499904
431407
13219
2019053
470910013
dwsem97
942 186627
179844
8624
856950
175802240
dpipc97 ($)
942
25043
3508
18885
35596
23590322
dpop97
942 6886877
6644480
480000 32268000
6487438000
Table C. 31. Descriptive Statistics for 1997-Commodity 39
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1466
158
271
0
2373
231334
cd97
1466 243681
201364
16357
867414
357235771
io97
1466 251070
202768
16357
867414
368069284
dist97 (miles)
1466
1135
723
47
3206
1664098
opipc97 ($)
1466
25019
3538
18885
35596
36677892
opop97
1466 6543836
6250388
480000 32268000
9593264000
oemp97
1466
12136
12255
350
62926
17791326
owsem97
1466 177090
168815
8624
856950
259613674
ovlad97 ($M)
1466
1569
1720
18
9446
2300450
oaps97
1466
16
6
7
32
24156.33
dmnem97
1466 472926
421644
13219
2019053
693309914
dwsem97
1466 176408
173967
8624
856950
258614642
dpipc97 ($)
1466
24934
3500
18885
35596
36552749
dpop97
1466 6536293
6436029
480000 32268000
9582205000
139
Table C. 32. Descriptive Statistics for 1997-Commodity 75
Variable
N
Mean
St. Dev.
Minimum
Maximum
Sum
flow97 ($M)
1221
158
324
0
3424
192385
cd97
1221 246561
203193
16357
867414
301051044
io97
1221 268152
205883
16357
867414
327413098
dist97 (miles)
1221
1138
726
47
3220
1389980
opipc97 ($)
1221
25189
3534
18885
35596
30756129
opop97
1221 7080614
6686546
480000 32268000
8645430000
oemp97
1221
46256
57417
428
232810
56478128
owsem97
1221 191510
180779
8624
856950
233833608
ovlad97 ($M)
1221
2245
2816
11
11088
2740702
oaps97
1221
60
38
15
146
73318
dmnem97
1221 478865
421829
13219
2019053
584694204
dwsem97
1221 176578
174340
8624
856950
215601740
dpipc97 ($)
1221
24869
3453
18885
35596
30365450
dpop97
1221 6539216
6442263
480000 32268000
7984383000
140
Table C. 33. 1993 Flow Values and Percentages Across States and Commodities State Obs Code State 1
6 California
totsp93 Cmdt ($ Mil)
stprc93 (%)
cmprc93 (%)
20
89385
10.5
15
2
17 Illinois
20
56826
6.7
18.6
3
48 Texas
20
53908
6.3
13.1
4
36 New York
20
42011
4.9
17.6
5
42 Penn
20
42103
4.9
18.7
6
12 Florida
20
37300
4.4
24.4
7
26 Michigan
20
34748
4.1
14.5
8
39 Ohio
20
33419
3.9
11.1
9
34 N. Jersey
20
32343
3.8
13.4
10
55 Wisconsin
20
31961
3.8
24
11
19 Iowa
20
25355
3
37.8
12
13 Georgia
20
23971
2.8
12.2
13
27 Minnesota
20
24038
2.8
25.3
14
29 Missouri
20
22837
2.7
19.4
15
24 Maryland
20
22205
2.6
25.6
16
37 N. Carolina
20
21710
2.5
10.7
17
47 Tennessee
20
19656
2.3
12.4
18
53 Washington
20
19256
2.3
16.9
19
31 Nebraska
20
18771
2.2
51.5
20
51 Virginia
20
17699
2.1
17.6
21
18 Indiana
20
16958
2
10.6
22
20 Kansas
20
17202
2
29.9
23
5 Arkansas
20
14719
1.7
27.5
24
8 Colorado
20
12524
1.5
23.9
25
1 Alabama
20
12152
1.4
15
26
25 Mass.
20
12259
1.4
12.1
27
21 Kentucky
20
10955
1.3
11
28
22 Louisiana
20
11401
1.3
13.3
29
41 Oregon
20
9459
1.1
15.9
30
4 Arizona
20
8187
1
13.2
31
28 Mississippi
20
7731
0.9
16
32
40 Oklahoma
20
6744
0.8
15
Cmdt: Commodity; totsp93: total shipment in 1993; cmprc93: Commodity percentage in 1993 for the state ; stprc93: State percentage in 1993 for the commodity.
141
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 33 45 S. Carolina
20
6785
0.8
8.7
34
20
5705
0.7
8.8
35 49 Utah
20
4760
0.6
15.1
36 16 Idaho
20
4468
0.5
30.7
37 23 Maine
20
2425
0.3
12.9
38 38 N. Dakota
20
2666
0.3
36.4
39 54 W. Virginia
20
2318
0.3
8.5
40 10 Delaware
20
1928
0.2
12.6
41 30 Montana
20
1713
0.2
22.3
42 32 Nevada
20
1824
0.2
11.7
43 33 New Hemp
20
1308
0.2
8.6
44 35 New Mexico 20
1502
0.2
14.9
45 44 Rhode Is
20
1282
0.2
7.5
46 46 S. Dakota
20
2040
0.2
25.7
47 50 Vermont
20
1184
0.1
15
48 56 Wyoming
20
308
0
6
49 48 Texas
21 29737
27.7
7.2
50 37 N. Carolina
21 18330
17.1
9.1
51
21 17858
16.6
3
9 Connect.
6 California
52 51 Virginia
21
8894
8.3
8.8
53 21 Kentucky
21
7167
6.7
7.2
54 34 N. Jersey
21
2208
2.1
0.9
55 27 Minnesota
21
2184
2
2.3
56 17 Illinois
21
1862
1.7
0.6
57 12 Florida
21
1361
1.3
0.9
58 36 New York
21
1350
1.3
0.6
59 26 Michigan
21
1313
1.2
0.5
60 20 Kansas
21
1186
1.1
2.1
61 29 Missouri
21
1191
1.1
1
62 47 Tennessee
21
1217
1.1
0.8
63 18 Indiana
21
1094
1
0.7
64
21
783
0.7
1
65 25 Mass.
21
722
0.7
0.7
66 39 Ohio
21
793
0.7
0.3
1 Alabama
142
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 67 42 Penn
21
765
0.7
0.3
68
21
659
0.6
1
69 22 Louisiana
21
615
0.6
0.7
70
21
491
0.5
0.8
71 49 Utah
21
556
0.5
1.8
72 55 Wisconsin
21
516
0.5
0.4
73
21
455
0.4
0.9
74 28 Mississippi 21
454
0.4
0.9
75 45 S. Carolina
21
455
0.4
0.6
76 19 Iowa
21
275
0.3
0.4
77 24 Maryland
21
321
0.3
0.4
78 54 W. Virginia
21
360
0.3
1.3
79 13 Georgia
21
248
0.2
0.1
80 32 Nevada
21
228
0.2
1.5
81 40 Oklahoma
21
267
0.2
0.6
82 41 Oregon
21
185
0.2
0.3
83 44 Rhode Is
21
185
0.2
1.1
84
8 Colorado
21
75
0.1
0.1
85 10 Delaware
21
95
0.1
0.6
86 16 Idaho
21
72
0.1
0.5
87 23 Maine
21
120
0.1
0.6
88 30 Montana
21
54
0.1
0.7
89 31 Nebraska
21
110
0.1
0.3
90 33 New Hemp
21
82
0.1
0.5
91 35 New Mexico 21
137
0.1
1.4
9 Connect.
4 Arizona
5 Arkansas
92 46 S. Dakota
21
90
0.1
1.1
93 50 Vermont
21
88
0.1
1.1
94 53 Washington 21
106
0.1
0.1
95 56 Wyoming
21
123
0.1
2.4
96 38 N. Dakota
21
53
0
0.7
97 41 Oregon
24 11832
9.4
19.9
98
24 11294
9
1.9
9402
7.5
8.2
5346
4.2
2.6
6 California
99 53 Washington 24 100 37 N. Carolina
24
143
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 101 13 Georgia
24 4680
3.7
2.4
102 48 Texas
24 4596
3.6
1.1
103 42 Penn
24 4405
3.5
2
104 55 Wisconsin
24 4272
3.4
3.2
105
24 3977
3.2
4.9
106 27 Minnesota 24 3759
3
4
1 Alabama
107 39 Ohio
24 3402
2.7
1.1
108 51 Virginia
24 3361
2.7
3.3
109 18 Indiana
24 3235
2.6
2
110 12 Florida
24 3138
2.5
2
111 28 Mississipi
24 3124
2.5
6.5
112 26 Michigan
24 2974
2.4
1.2
113
24 2895
2.3
5.4
114 16 Idaho
24 2926
2.3
20.1
115 22 Louisiana
24 2817
2.2
3.3
116 17 Illinois
24 2662
2.1
0.9
117 34 N. Jersey
24 2639
2.1
1.1
118 47 Tennessee 24 2683
2.1
1.7
119 45 S. Carolina 24 2446
1.9
3.1
120 25 Mass.
24 2048
1.6
2
121 29 Missouri
24 2020
1.6
1.7
122 36 New York
24 2040
1.6
0.9
123 21 Kentucky
24 1725
1.4
1.7
124 24 Maryland
24 1727
1.4
2
125 23 Maine
24 1586
1.3
8.4
126 30 Montana
24 1542
1.2
20
127
24 1171
0.9
2.2
128 19 Iowa
24 1171
0.9
1.7
129
24 1034
0.8
1.7
130 44 Rhode Is
24
985
0.8
5.7
131
24
774
0.6
1.2
132 33 New Hemp 24
711
0.6
4.6
133 54 W. Virginia 24
814
0.6
3
134 56 Wyoming
654
0.5
12.7
5 Arkansas
8 Colorado
4 Arizona
9 Connect.
24
144
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 135 20 Kansas
24
564
0.4
1
136 35 New Mexico 24
458
0.4
4.6
137 40 Oklahoma
24
542
0.4
1.2
138 49 Utah
24
537
0.4
1.7
139 50 Vermont
24
452
0.4
5.7
140 10 Delaware
24
432
0.3
2.8
141 31 Nebraska
24
392
0.3
1.1
142 46 S. Dakota
24
361
0.3
4.6
143 38 N. Dakota
24
194
0.2
2.6
144 32 Nevada
24
183
0.1
1.2
145
25 7592
11
1.3
146 37 N. Carolina
25 6292
9.1
3.1
147 26 Michigan
25 5238
7.6
2.2
148 48 Texas
25 4305
6.2
1
149 42 Penn
25 3386
4.9
1.5
150 18 Indiana
25 3120
4.5
2
151 51 Virginia
25 2985
4.3
3
152 39 Ohio
25 2936
4.2
1
153 17 Illinois
25 2556
3.7
0.8
154 34 N. Jersey
25 2563
3.7
1.1
155 36 New York
25 2514
3.6
1.1
156 28 Mississipi
25 2404
3.5
5
157 47 Tennessee
25 2406
3.5
1.5
158 12 Florida
25 1877
2.7
1.2
159 13 Georgia
25 1856
2.7
0.9
160 29 Missouri
25 1541
2.2
1.3
161 55 Wisconsin
25 1521
2.2
1.1
162
1 Alabama
25 1330
1.9
1.6
163
4 Arizona
25 1311
1.9
2.1
164 25 Mass.
25 1233
1.8
1.2
165 27 Minnesota
25 1151
1.7
1.2
166
25
948
1.4
1.8
167 53 Washington 25
971
1.4
0.9
168 24 Maryland
909
1.3
1
6 California
5 Arkansas
25
145
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 169 45 S. Carolina
25
749
1.1
1
170 32 Nevada
25
720
1
4.6
171
25
620
0.9
1.2
172 19 Iowa
25
601
0.9
0.9
173 31 Nebraska
25
493
0.7
1.4
174 41 Oregon
25
441
0.6
0.7
175 49 Utah
25
388
0.6
1.2
176
25
332
0.5
0.5
177 21 Kentucky
25
367
0.5
0.4
178 40 Oklahoma
25
265
0.4
0.6
179 20 Kansas
25
226
0.3
0.4
180 10 Delaware
25
126
0.2
0.8
181 22 Louisiana
25
127
0.2
0.1
182 38 N. Dakota
25
112
0.2
1.5
183 44 Rhode Is
25
133
0.2
0.8
184 54 W. Virginia
25
142
0.2
0.5
185 16 Idaho
25
53
0.1
0.4
186 23 Maine
25
93
0.1
0.5
187 30 Montana
25
37
0.1
0.5
188 33 New Hemp
25
81
0.1
0.5
189 35 New Mexico 25
65
0.1
0.6
190 50 Vermont
25
92
0.1
1.2
191 56 Wyoming
25
51
0.1
1
192 46 S. Dakota
25
30
0
0.4
193
6 California
26 16289
8.4
2.7
194 55 Wisconsin
26 11840
6.1
8.9
195 42 Penn
26 11325
5.8
5
196 17 Illinois
26 11083
5.7
3.6
197 39 Ohio
26
9424
4.8
3.1
198 13 Georgia
26
9119
4.7
4.6
199 36 New York
26
8695
4.5
3.6
200 48 Texas
26
7287
3.7
1.8
201 34 N. Jersey
26
7069
3.6
2.9
202 37 N. Carolina
26
7097
3.6
3.5
8 Colorado
9 Connect.
146
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 203 26 Michigan
26 6552
3.4
2.7
204
26 6271
3.2
7.8
205 53 Washington 26 5399
2.8
4.7
206 12 Florida
26 5212
2.7
3.4
207 29 Missouri
26 5282
2.7
4.5
208 25 Mass.
26 5131
2.6
5.1
209 47 Tennessee
26 4977
2.6
3.1
210 51 Virginia
26 4868
2.5
4.8
211 45 S. Carolina
26 4393
2.3
5.6
212 22 Louisiana
26 4301
2.2
5
213 27 Minnesota
26 4279
2.2
4.5
214
26 3869
2
7.2
26 3727
1.9
19.8
216 28 Mississippi 26 3466
1.8
7.2
217 41 Oregon
26 3357
1.7
5.6
218 18 Indiana
26 3194
1.6
2
219 24 Maryland
26 3132
1.6
3.6
220
26 2516
1.3
3.9
221 21 Kentucky
26 2422
1.2
2.4
222 19 Iowa
26 1852
1
2.8
223
26 1539
0.8
2.9
224 20 Kansas
26 1516
0.8
2.6
225 40 Oklahoma
26 1425
0.7
3.2
226 49 Utah
26 1004
0.5
3.2
227
26
859
0.4
1.4
228 16 Idaho
26
739
0.4
5.1
229 33 New Hemp
26
806
0.4
5.3
230 31 Nebraska
26
631
0.3
1.7
231 50 Vermont
26
536
0.3
6.8
232 10 Delaware
26
375
0.2
2.5
233 30 Montana
26
362
0.2
4.7
234 32 Nevada
26
325
0.2
2.1
235 35 New Mexico 26
156
0.1
1.6
236 38 N. Dakota
128
0.1
1.7
1 Alabama
5 Arkansas
215 23 Maine
9 Connect.
8 Colorado
4 Arizona
26
147
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 237 44 Rhode Is
26
250
0.1
1.5
238 46 S. Dakota
26
143
0.1
1.8
239 54 W. Virginia
26
270
0.1
1
240 56 Wyoming
26
20
0
0.4
241 48 Texas
28 59862
11.3
14.6
242 17 Illinois
28 46167
8.7
15.1
243
6 California
28 43637
8.3
7.3
244 34 N. Jersey
28 35773
6.8
14.9
245 39 Ohio
28 29223
5.5
9.7
246 42 Penn
28 26369
5
11.7
247 22 Louisiana
28 22762
4.3
26.6
248 37 N. Carolina 28 22030
4.2
10.9
249 36 New York
28 18936
3.6
7.9
250 13 Georgia
28 18185
3.4
9.2
251 47 Tennessee
28 17486
3.3
11
252 12 Florida
28 14630
2.8
9.6
253 26 Michigan
28 14867
2.8
6.2
254 29 Missouri
28 13660
2.6
11.6
255 18 Indiana
28 11474
2.2
7.2
256 21 Kentucky
28 10289
1.9
10.4
257 51 Virginia
28 10196
1.9
10.1
258 25 Mass.
28
9362
1.8
9.2
259 45 S. Carolina
28
9201
1.7
11.8
260 55 Wisconsin
28
8505
1.6
6.4
261
28
8079
1.5
10
262 19 Iowa
28
6671
1.3
9.9
263 24 Maryland
28
6956
1.3
8
264
28
6322
1.2
9.7
265 27 Minnesota
28
6218
1.2
6.6
266 54 W. Virginia
28
6080
1.2
22.2
267 28 Mississippi 28
4482
0.8
9.3
268 53 Washington 28
4233
0.8
3.7
269 20 Kansas
28
3888
0.7
6.8
270 49 Utah
28
3854
0.7
12.2
1 Alabama
9 Connect.
148
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 271 10 Delaware
28
3409
0.6
22.3
272 40 Oklahoma
28
3319
0.6
7.4
273 41 Oregon
28
3232
0.6
5.4
274
4 Arizona
28
2732
0.5
4.4
275
5 Arkansas
28
2724
0.5
5.1
276 31 Nebraska
28
2412
0.5
6.6
277
28
2240
0.4
4.3
278 16 Idaho
28
1477
0.3
10.2
279 32 Nevada
28
1645
0.3
10.5
280 44 Rhode Is
28
864
0.2
5
281 46 S. Dakota
28
1262
0.2
15.9
282 23 Maine
28
732
0.1
3.9
283 30 Montana
28
411
0.1
5.3
284 33 New Hemp
28
581
0.1
3.8
285 35 New Mexico 28
613
0.1
6.1
286 38 N. Dakota
28
520
0.1
7.1
287 50 Vermont
28
366
0.1
4.6
288 56 Wyoming
28
519
0.1
10.1
289 48 Texas
29 72090
20.1
17.6
290
6 California
29 44750
12.5
7.5
291 22 Louisiana
29 26179
7.3
30.6
292 17 Illinois
29 15566
4.3
5.1
293 39 Ohio
29 15156
4.2
5.1
294 13 Georgia
29 12379
3.5
6.3
295 34 N. Jersey
29 12211
3.4
5.1
296 26 Michigan
29 11686
3.3
4.9
297 42 Penn
29 11674
3.3
5.2
298 12 Florida
29
9515
2.7
6.2
299 18 Indiana
29
9008
2.5
5.6
300 36 New York
29
8210
2.3
3.4
301 37 N. Carolina
29
8069
2.3
4
302 53 Washington 29
7897
2.2
6.9
303 40 Oklahoma
29
6083
1.7
13.5
304 51 Virginia
29
6020
1.7
6
8 Colorado
149
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 305 21 Kentucky
29
5595
1.6
5.6
306
29
5181
1.4
6.4
29
4845
1.4
8.4
308 28 Mississippi 29
4977
1.4
10.3
309 41 Oregon
29
5009
1.4
8.4
310 47 Tennessee
29
4970
1.4
3.1
311 55 Wisconsin
29
4996
1.4
3.7
312 29 Missouri
29
3693
1
3.1
313 24 Maryland
29
3144
0.9
3.6
314 25 Mass.
29
3238
0.9
3.2
315 45 S. Carolina
29
3233
0.9
4.2
316 54 W. Virginia
29
3159
0.9
11.5
317 27 Minnesota
29
3028
0.8
3.2
318 44 Rhode Is
29
2727
0.8
15.9
319 19 Iowa
29
2476
0.7
3.7
320 35 New Mexico 29
2073
0.6
20.6
321 49 Utah
29
2313
0.6
7.3
322
4 Arizona
29
1895
0.5
3.1
323
5 Arkansas
29
1889
0.5
3.5
324
9 Connect.
29
1661
0.5
2.6
325 30 Montana
29
1850
0.5
24.1
326
8 Colorado
29
1371
0.4
2.6
327 10 Delaware
29
1408
0.4
9.2
328 56 Wyoming
29
1335
0.4
26
329 23 Maine
29
968
0.3
5.1
330 32 Nevada
29
953
0.3
6.1
331 33 New Hamp. 29
1209
0.3
7.9
1 Alabama
307 20 Kansas
332 38 N. Dakota
29
977
0.3
13.3
333 31 Nebraska
29
884
0.2
2.4
334 16 Idaho
29
469
0.1
3.2
335 46 S. Dakota
29
468
0.1
5.9
336 50 Vermont
29
130
0
1.7
337
30 14892
8.6
2.5
30 13596
7.9
4.5
6 California
338 39 Ohio
150
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 339 34 N. Jersey
30 10988
6.4
4.6
340 17 Illinois
30 10829
6.3
3.5
341 42 Penn
30
8853
5.1
3.9
342 26 Michigan
30
8483
4.9
3.5
343 48 Texas
30
8168
4.7
2
344 37 N. Carolina 30
7244
4.2
3.6
345 13 Georgia
30
6047
3.5
3.1
346 21 Kentucky
30
5624
3.3
5.7
347 36 New York
30
5509
3.2
2.3
348 47 Tennessee
30
5538
3.2
3.5
349 18 Indiana
30
5109
3
3.2
350 55 Wisconsin
30
5233
3
3.9
351 20 Kansas
30
4707
2.7
8.2
352 27 Minnesota
30
4380
2.5
4.6
353 25 Mass.
30
4163
2.4
4.1
354 12 Florida
30
3560
2.1
2.3
355 51 Virginia
30
3466
2
3.4
356 29 Missouri
30
3285
1.9
2.8
357
1 Alabama
30
3078
1.8
3.8
358
5 Arkansas
30
2966
1.7
5.5
359 45 S. Carolina
30
2772
1.6
3.6
360 24 Maryland
30
2564
1.5
3
361 40 Oklahoma
30
2533
1.5
5.6
362 28 Mississippi 30
2422
1.4
5
363 41 Oregon
30
2043
1.2
3.4
364 53 Washington 30
2010
1.2
1.8
365
30
1829
1.1
2.8
366 19 Iowa
30
1957
1.1
2.9
367 22 Louisiana
30
1200
0.7
1.4
368
8 Colorado
30
1067
0.6
2
369
4 Arizona
30
813
0.5
1.3
370 31 Nebraska
30
862
0.5
2.4
371 49 Utah
30
860
0.5
2.7
372 10 Delaware
30
551
0.3
3.6
9 Connect.
151
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 373 23 Maine
30
507
0.3
2.7
374 33 New Hamp. 30
581
0.3
3.8
375 44 Rhode Is
30
538
0.3
3.1
376 16 Idaho
30
279
0.2
1.9
377 32 Nevada
30
413
0.2
2.6
378 54 W. Virginia
30
407
0.2
1.5
379 30 Montana
30
105
0.1
1.4
380 35 New Mexico 30
188
0.1
1.9
381 38 N. Dakota
30
129
0.1
1.8
382 46 S. Dakota
30
246
0.1
3.1
383 50 Vermont
30
140
0.1
1.8
384 56 Wyoming
30
19
0
0.4
385 48 Texas
32 10521
11.6
2.6
386
32
8831
9.7
1.5
387 39 Ohio
32
6620
7.3
2.2
388 42 Penn
32
5746
6.3
2.5
389 13 Georgia
32
4322
4.8
2.2
390 17 Illinois
32
4016
4.4
1.3
391 36 New York
32
3897
4.3
1.6
392 37 N. Carolina
32
3882
4.3
1.9
393 12 Florida
32
3595
4
2.3
394 34 N. Jersey
32
3195
3.5
1.3
395 26 Michigan
32
3017
3.3
1.3
396 18 Indiana
32
2748
3
1.7
397 47 Tennessee
32
2162
2.4
1.4
398 51 Virginia
32
2097
2.3
2.1
399 29 Missouri
32
2008
2.2
1.7
400 21 Kentucky
32
1631
1.8
1.6
401 24 Maryland
32
1552
1.7
1.8
402 55 Wisconsin
32
1422
1.6
1.1
403 27 Minnesota
32
1406
1.5
1.5
404
32
1284
1.4
1.6
32
1293
1.4
2.9
406 53 Washington 32
1310
1.4
1.1
6 California
1 Alabama
405 40 Oklahoma
152
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 407 19 Iowa
32
1083
1.2
1.6
408 22 Louisiana
32
1091
1.2
1.3
409 25 Mass.
32
1091
1.2
1.1
410
32
975
1.1
1.9
411 20 Kansas
32
986
1.1
1.7
412 45 S. Carolina
32
958
1.1
1.2
413 54 W. Virginia
32
918
1
3.4
414
4 Arizona
32
845
0.9
1.4
415
9 Connect.
32
781
0.9
1.2
32
644
0.7
1.1
417 28 Mississippi 32
576
0.6
1.2
418 49 Utah
32
509
0.6
1.6
419
5 Arkansas
32
457
0.5
0.9
420 31 Nebraska
32
474
0.5
1.3
421 32 Nevada
32
498
0.5
3.2
422 23 Maine
32
255
0.3
1.4
423 33 New Hamp. 32
277
0.3
1.8
424 35 New Mexico 32
305
0.3
3
425 46 S. Dakota
32
273
0.3
3.4
426 50 Vermont
32
276
0.3
3.5
427 56 Wyoming
32
254
0.3
4.9
428 10 Delaware
32
148
0.2
1
429 16 Idaho
32
153
0.2
1.1
430 30 Montana
32
172
0.2
2.2
431 44 Rhode Is
32
203
0.2
1.2
432 38 N. Dakota
32
105
0.1
1.4
433 39 Ohio
33 26226
11.5
8.7
434 18 Indiana
33 17485
7.7
10.9
435 42 Penn
33 17639
7.7
7.8
436 17 Illinois
33 17053
7.5
5.6
437
33 14998
6.6
2.5
438 48 Texas
33 14453
6.3
3.5
439 26 Michigan
33 14241
6.2
5.9
440
33
3.6
10.3
8 Colorado
416 41 Oregon
6 California
1 Alabama
8307
153
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 441 36 New York
33 7186
3.1
3
442 21 Kentucky
33 5861
2.6
5.9
443
33 5182
2.3
8.4
444 47 Tennessee
33 5300
2.3
3.3
445 55 Wisconsin
33 4996
2.2
3.7
446 13 Georgia
33 4472
2
2.3
447 24 Maryland
33 4454
1.9
5.1
448 34 N. Jersey
33 4443
1.9
1.8
449 53 Washington 33 4431
1.9
3.9
450 54 W. Virginia
33 4360
1.9
15.9
451 29 Missouri
33 4129
1.8
3.5
452 12 Florida
33 3519
1.5
2.3
453 37 N. Carolina
33 3504
1.5
1.7
454
5 Arkansas
33 3108
1.4
5.8
455
9 Connect.
33 3062
1.3
4.7
456 19 Iowa
33 2497
1.1
3.7
457 27 Minnesota
33 2576
1.1
2.7
458 41 Oregon
33 2491
1.1
4.2
459 22 Louisiana
33 2293
1
2.7
460 49 Utah
33 2317
1
7.3
461 51 Virginia
33 2204
1
2.2
462 25 Mass.
33 2104
0.9
2.1
463 40 Oklahoma
33 2132
0.9
4.7
464 45 S. Carolina
33 2096
0.9
2.7
465 28 Mississippi 33 1851
0.8
3.8
466
33 1367
0.6
2.6
33
825
0.4
1.4
468 35 New Mexico 33
936
0.4
9.3
469 44 Rhode Is
33
942
0.4
5.5
470 31 Nebraska
33
702
0.3
1.9
471 33 New Hamp. 33
629
0.3
4.1
472 10 Delaware
33
505
0.2
3.3
473 30 Montana
33
388
0.2
5
474 32 Nevada
33
369
0.2
2.4
4 Arizona
8 Colorado
467 20 Kansas
154
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 475 16 Idaho
33
176
0.1
1.2
476 23 Maine
33
254
0.1
1.3
477 46 S. Dakota
33
150
0.1
1.9
478 38 N. Dakota
33
65
0
0.9
479 50 Vermont
33
79
0
1
480 56 Wyoming
33
71
0
1.4
481
6 California
34 21397
9
3.6
482 26 Michigan
34 20425
8.6
8.5
483 17 Illinois
34 19407
8.2
6.4
484 39 Ohio
34 18539
7.8
6.2
485 48 Texas
34 14651
6.2
3.6
486 42 Penn
34 14433
6.1
6.4
487 18 Indiana
34 10363
4.4
6.5
488 36 New York
34
9380
4
3.9
489 37 N. Carolina 34
8007
3.4
4
490 55 Wisconsin
34
7103
3
5.3
491 34 N. Jersey
34
6880
2.9
2.9
492 13 Georgia
34
6476
2.7
3.3
493 29 Missouri
34
6245
2.6
5.3
494 12 Florida
34
5990
2.5
3.9
495 47 Tennessee
34
5957
2.5
3.8
496 27 Minnesota
34
4852
2
5.1
497
34
3929
1.7
6.1
34
4105
1.7
4.1
499 53 Washington 34
3791
1.6
3.3
500 21 Kentucky
34
3223
1.4
3.2
501 45 S. Carolina
34
3213
1.4
4.1
502
34
3053
1.3
3.8
503 19 Iowa
34
3181
1.3
4.7
504 24 Maryland
34
3031
1.3
3.5
505 51 Virginia
34
2989
1.3
3
506 40 Oklahoma
34
2689
1.1
6
507
5 Arkansas
34
2405
1
4.5
508
8 Colorado
34
2160
0.9
4.1
9 Connect.
498 25 Mass.
1 Alabama
155
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 509 20 Kansas
34
2082
0.9
3.6
510 41 Oregon
34
2020
0.9
3.4
511 22 Louisiana
34
1805
0.8
2.1
512
34
1633
0.7
2.6
513 28 Mississippi 34
1742
0.7
3.6
514 31 Nebraska
34
1737
0.7
4.8
515 49 Utah
34
1358
0.6
4.3
516 32 Nevada
34
1074
0.5
6.9
517 33 New Hamp. 34
1071
0.5
7
518 54 W. Virginia
34
1244
0.5
4.5
519 23 Maine
34
678
0.3
3.6
520 44 Rhode Is
34
618
0.3
3.6
521 46 S. Dakota
34
410
0.2
5.2
522 50 Vermont
34
430
0.2
5.5
523 10 Delaware
34
174
0.1
1.1
524 16 Idaho
34
273
0.1
1.9
525 30 Montana
34
173
0.1
2.2
526 35 New Mexico 34
265
0.1
2.6
527 38 N. Dakota
34
214
0.1
2.9
528 56 Wyoming
34
126
0.1
2.5
529
35 61794
14
10.4
530 48 Texas
35 46901
10.6
11.4
531 17 Illinois
35 33302
7.5
10.9
532 39 Ohio
35 25775
5.8
8.6
533 37 N. Carolina
35 18633
4.2
9.2
534 36 New York
35 17904
4.1
7.5
535 42 Penn
35 17163
3.9
7.6
536 26 Michigan
35 16627
3.8
6.9
537 55 Wisconsin
35 15576
3.5
11.7
538
35 12233
2.8
18.8
539 27 Minnesota
35 11689
2.6
12.3
540 47 Tennessee
35 11232
2.5
7.1
541 12 Florida
35 10729
2.4
7
542 13 Georgia
35 10448
2.4
5.3
4 Arizona
6 California
9 Connect.
156
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 543 18 Indiana
35 9504
2.2
5.9
544 34 N. Jersey
35 9515
2.2
4
545 25 Mass.
35 9087
2.1
9
546
8 Colorado
35 7931
1.8
15.1
547 29 Missouri
35 7511
1.7
6.4
548 19 Iowa
35 7199
1.6
10.7
549 21 Kentucky
35 7249
1.6
7.3
550 45 S. Carolina
35 7010
1.6
9
551 51 Virginia
35 6619
1.5
6.6
552 53 Washington 35 6167
1.4
5.4
553
35 5147
1.2
8.3
554 24 Maryland
35 5347
1.2
6.2
555 40 Oklahoma
35 4787
1.1
10.6
556
35 4094
0.9
5.1
557 41 Oregon
35 4084
0.9
6.9
558 20 Kansas
35 3491
0.8
6.1
559 22 Louisiana
35 2995
0.7
3.5
560 28 Mississippi 35 3067
0.7
6.3
561
4 Arizona
1 Alabama
5 Arkansas
35 2728
0.6
5.1
562 31 Nebraska
35 2499
0.6
6.9
563 16 Idaho
35 2152
0.5
14.8
564 49 Utah
35 2398
0.5
7.6
565 33 New Hamp. 35 1949
0.4
12.7
566 35 New Mexico 35 1125
0.3
11.2
567 38 N. Dakota
35 1119
0.3
15.3
568 54 W. Virginia
35 1425
0.3
5.2
569 23 Maine
35
843
0.2
4.5
570 32 Nevada
35
868
0.2
5.6
571 44 Rhode Is
35 1068
0.2
6.2
572 46 S. Dakota
35 1026
0.2
12.9
573 10 Delaware
35
256
0.1
1.7
574 30 Montana
35
224
0.1
2.9
575 50 Vermont
35
463
0.1
5.9
576 56 Wyoming
35
386
0.1
7.5
157
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 577
6 California
36 81196
19.7
13.6
578 34 N. Jersey
36 27309
6.6
11.3
579 17 Illinois
36 23523
5.7
7.7
580 48 Texas
36 23017
5.6
5.6
581 39 Ohio
36 21863
5.3
7.3
582 36 New York
36 17964
4.4
7.5
583 18 Indiana
36 15914
3.9
9.9
584 25 Mass.
36 13977
3.4
13.8
585 42 Penn
36 14035
3.4
6.2
586 12 Florida
36 13755
3.3
9
587 47 Tennessee
36 12271
3
7.7
588 37 N. Carolina 36 12046
2.9
5.9
589 13 Georgia
36 11533
2.8
5.8
590 55 Wisconsin
36
9052
2.2
6.8
591 29 Missouri
36
8412
2
7.2
592
4 Arizona
36
7507
1.8
12.1
593 51 Virginia
36
7434
1.8
7.4
594 27 Minnesota
36
7192
1.7
7.6
595 24 Maryland
36
6668
1.6
7.7
596 26 Michigan
36
6490
1.6
2.7
597 21 Kentucky
36
6228
1.5
6.3
598
1 Alabama
36
5229
1.3
6.5
599
8 Colorado
36
5190
1.3
9.9
36
5311
1.3
6.8
601 53 Washington 36
4675
1.1
4.1
602
5 Arkansas
36
4003
1
7.5
603
9 Connect.
36
4249
1
6.5
36
4215
1
6.3
605 28 Mississippi 36
3664
0.9
7.6
606 40 Oklahoma
36
3510
0.9
7.8
607 41 Oregon
36
3323
0.8
5.6
608 33 New Hamp. 36
2970
0.7
19.4
609 20 Kansas
36
2430
0.6
4.2
610 50 Vermont
36
2293
0.6
29.1
600 45 S. Carolina
604 19 Iowa
158
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 611 54 W. Virginia
36
2271
0.6
8.3
612 49 Utah
36
2150
0.5
6.8
613 22 Louisiana
36
1615
0.4
1.9
614 31 Nebraska
36
1641
0.4
4.5
615 44 Rhode Is
36
1328
0.3
7.7
616 23 Maine
36
817
0.2
4.3
617 32 Nevada
36
741
0.2
4.7
618 10 Delaware
36
213
0.1
1.4
619 16 Idaho
36
550
0.1
3.8
620 35 New Mexico 36
575
0.1
5.7
621 46 S. Dakota
36
427
0.1
5.4
622 56 Wyoming
36
324
0.1
6.3
623 30 Montana
36
119
0
1.5
624 38 N. Dakota
36
172
0
2.3
625 26 Michigan
37 84208
13.2
35
626
37 67297
10.5
11.3
627 39 Ohio
37 61951
9.7
20.7
628 13 Georgia
37 35161
5.5
17.8
629 18 Indiana
37 34401
5.4
21.5
630 53 Washington 37 34510
5.4
30.2
631 17 Illinois
37 30443
4.8
10
632 29 Missouri
37 23930
3.7
20.4
633 48 Texas
37 22786
3.6
5.6
634 21 Kentucky
37 21764
3.4
21.9
635 34 N. Jersey
37 21302
3.3
8.8
636 36 New York
37 19762
3.1
8.3
637 12 Florida
37 17400
2.7
11.4
638 47 Tennessee
37 17331
2.7
10.9
639 24 Maryland
37 15922
2.5
18.4
640 42 Penn
37 13957
2.2
6.2
641 55 Wisconsin
37 11393
1.8
8.5
642
37
9297
1.5
14.3
643 37 N. Carolina
37
8016
1.3
4
644 20 Kansas
37
7989
1.2
13.9
6 California
9 Connect.
159
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 645 25 Mass.
37
6951
1.1
6.9
646 51 Virginia
37
7313
1.1
7.3
647
4 Arizona
37
6212
1
10
648
1 Alabama
37
5892
0.9
7.3
649 27 Minnesota
37
5739
0.9
6
650 40 Oklahoma
37
6077
0.9
13.5
651 41 Oregon
37
6081
0.9
10.2
652
37
3848
0.6
7.3
653 19 Iowa
37
3528
0.6
5.3
654 22 Louisiana
37
3684
0.6
4.3
655 45 S. Carolina
37
4097
0.6
5.3
656
37
3114
0.5
5.8
657 28 Mississippi 37
3520
0.5
7.3
658 10 Delaware
37
2760
0.4
18.1
659 49 Utah
37
2697
0.4
8.5
660 31 Nebraska
37
1799
0.3
4.9
661 54 W. Virginia
37
1932
0.3
7.1
662 32 Nevada
37
1142
0.2
7.3
663 16 Idaho
37
327
0.1
2.2
664 23 Maine
37
712
0.1
3.8
665 33 New Hamp. 37
933
0.1
6.1
666 35 New Mexico 37
451
0.1
4.5
667 38 N. Dakota
37
465
0.1
6.3
668 44 Rhode Is
37
894
0.1
5.2
669 50 Vermont
37
400
0.1
5.1
670 30 Montana
37
203
0
2.6
671 46 S. Dakota
37
272
0
3.4
672 56 Wyoming
37
306
0
6
673 36 New York
38 44379
22.3
18.6
674
6 California
38 24754
12.4
4.2
675 34 N. Jersey
38 15273
7.7
6.3
676 17 Illinois
38 13299
6.7
4.4
677 25 Mass.
38
8597
4.3
8.5
678 48 Texas
38
8456
4.2
2.1
8 Colorado
5 Arkansas
160
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 679 12 Florida
38 7188
3.6
4.7
680 42 Penn
38 6969
3.5
3.1
681 13 Georgia
38 6716
3.4
3.4
682
8 Colorado
38 5109
2.6
9.7
683
9 Connect.
38 5010
2.5
7.7
684 39 Ohio
38 4680
2.3
1.6
685 27 Minnesota
38 4375
2.2
4.6
686 18 Indiana
38 3925
2
2.5
687 24 Maryland
38 3636
1.8
4.2
688 26 Michigan
38 3376
1.7
1.4
689 37 N. Carolina
38 3442
1.7
1.7
690 47 Tennessee
38 2977
1.5
1.9
691
38 2724
1.4
4.4
692 55 Wisconsin
38 2748
1.4
2.1
693 29 Missouri
38 2639
1.3
2.2
694 51 Virginia
38 2206
1.1
2.2
695 53 Washington 38 1927
1
1.7
4 Arizona
696 41 Oregon
38 1597
0.8
2.7
697 49 Utah
38 1450
0.7
4.6
698 31 Nebraska
38 1284
0.6
3.5
699 45 S. Carolina
38 1282
0.6
1.6
700
38
972
0.5
1.2
701 20 Kansas
38
935
0.5
1.6
702 40 Oklahoma
38 1008
0.5
2.2
703 44 Rhode Is
38
722
0.4
4.2
704
38
624
0.3
1.2
705 19 Iowa
38
694
0.3
1
706 21 Kentucky
38
667
0.3
0.7
707 22 Louisiana
38
564
0.3
0.7
708 33 New Hamp. 38
531
0.3
3.5
709 10 Delaware
38
370
0.2
2.4
710 28 Mississippi 38
433
0.2
0.9
711 35 New Mexico 38
397
0.2
3.9
712 54 W. Virginia
443
0.2
1.6
1 Alabama
5 Arkansas
38
161
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 713 23 Maine
38
115
0.1
0.6
714 32 Nevada
38
180
0.1
1.2
715 38 N. Dakota
38
142
0.1
1.9
716 46 S. Dakota
38
225
0.1
2.8
717 16 Idaho
38
51
0
0.4
718 30 Montana
38
91
0
1.2
719 50 Vermont
38
84
0
1.1
720 56 Wyoming
38
89
0
1.7
721
39 22430
13.6
3.8
722 48 Texas
39 12153
7.4
3
723 36 New York
39 11655
7.1
4.9
724 42 Penn
39 10529
6.4
4.7
725 17 Illinois
39
9238
5.6
3
726 34 N. Jersey
39
8492
5.2
3.5
727 12 Florida
39
6755
4.1
4.4
728 39 Ohio
39
6609
4
2.2
729 27 Minnesota
39
5956
3.6
6.3
730 25 Mass.
39
5378
3.3
5.3
731 13 Georgia
39
5231
3.2
2.7
732 53 Washington 39
4282
2.6
3.8
733 29 Missouri
39
4149
2.5
3.5
734
39
4001
2.4
7.6
735 18 Indiana
39
3649
2.2
2.3
736 47 Tennessee
39
3425
2.1
2.2
737 26 Michigan
39
3178
1.9
1.3
738 55 Wisconsin
39
3141
1.9
2.4
739
5 Arkansas
39
2946
1.8
5.5
740 44 Rhode Is
39
2852
1.7
16.6
741 21 Kentucky
39
2615
1.6
2.6
742 37 N. Carolina 39
2552
1.6
1.3
743
39
2382
1.4
3.7
744 49 Utah
39
2156
1.3
6.8
745 24 Maryland
39
1842
1.1
2.1
746 51 Virginia
39
1771
1.1
1.8
6 California
8 Colorado
9 Connect.
162
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 747 45 S. Carolina
39
1708
1
2.2
748 20 Kansas
39
1560
0.9
2.7
749
4 Arizona
39
1304
0.8
2.1
750 32 Nevada
39
1172
0.7
7.5
751
39
911
0.6
1.1
752 19 Iowa
39
1022
0.6
1.5
753 40 Oklahoma
39
1004
0.6
2.2
754 41 Oregon
39
995
0.6
1.7
755 31 Nebraska
39
748
0.5
2.1
756 22 Louisiana
39
595
0.4
0.7
757 28 Mississipi
39
611
0.4
1.3
758 54 W. Virginia
39
728
0.4
2.7
759 23 Maine
39
441
0.3
2.3
760 33 New Hamp. 39
576
0.3
3.8
761 10 Delaware
39
314
0.2
2.1
762 16 Idaho
39
252
0.2
1.7
763 35 New Mexico 39
329
0.2
3.3
764 46 S. Dakota
39
389
0.2
4.9
765 50 Vermont
39
385
0.2
4.9
766 30 Montana
39
167
0.1
2.2
767 38 N. Dakota
39
55
0
0.8
768 56 Wyoming
39
12
0
0.2
769
75 47183
10.6
7.9
770 37 N. Carolina
75 46309
10.4
22.9
771 34 N. Jersey
75 38589
8.7
16
772 47 Tennessee
75 39024
8.7
24.6
773 13 Georgia
75 36436
8.2
18.5
774 45 S. Carolina
75 22199
5
28.5
775 39 Ohio
75 19538
4.4
6.5
776 36 New York
75 17188
3.9
7.2
777 48 Texas
75 17441
3.9
4.3
778 42 Penn
75 16286
3.7
7.2
779
75 14138
3.2
22.8
75 11911
2.7
11.8
1 Alabama
6 California
4 Arizona
780 25 Mass.
163
Table C.33. 1993 Flow Values and Percentages Across States and Commodities (Continued) 781
1 Alabama
75 10179 2.3
12.6 10.4
782
51 Virginia
75 10417 2.3
783
18 Indiana
75
8829
2
5.5
784
55 Wisconsin
75
9134
2
6.8
785
12 Florida
75
7625 1.7
5
786
17 Illinois
75
7307 1.6
2.4
787
21 Kentucky
75
5920 1.3
6
788
29 Missouri
75
4990 1.1
4.2
789
23 Maine
75
4556
1
24.2
75
4187 0.9
6.4
790
9 Connect.
791
53 Washington
75
3808 0.9
3.3
792
5 Arkansas
75
3623 0.8
6.8
793
28 Mississippi
75
3787 0.8
7.8
794
19 Iowa
75
3292 0.7
4.9
795
20 Kansas
75
3172 0.7
5.5
796
24 Maryland
75
3318 0.7
3.8
797
26 Michigan
75
3039 0.7
1.3
798
32 Nevada
75
3285 0.7
21
799
41 Oregon
75
2712 0.6
4.6
800
10 Delaware
75
2210 0.5
14.5
801
27 Minnesota
75
2056 0.5
2.2
802
49 Utah
75
2264 0.5
7.2
803
44 Rhode Is
75
1611 0.4
9.4
804
8 Colorado
75
1259 0.3
2.4
805
22 Louisiana
75
1474 0.3
1.7
806
40 Oklahoma
75
1280 0.3
2.8
807
31 Nebraska
75
982 0.2
2.7
808
33 New Hamp.
75
1001 0.2
6.5
809
35 New Mexico
75
485 0.1
4.8
810
50 Vermont
75
483 0.1
6.1
811
54 W. Virginia
75
492 0.1
1.8
812
56 Wyoming
75
537 0.1
10.5
813
16 Idaho
75
122
0
0.8
814
30 Montana
75
81
0
1.1
164
Table C. 34. Three-Digit Breakdown of 1993 Commodity Flows
Com.
Value ($Million) Share
20
855597
1.000
201
162693
0.190
202
77194
0.090
203
162142
0.190
204
73322
0.086
205
40840
0.048
206
8944
0.010
207
26411
0.031
208
134932
0.158
209
131874
0.154
22
99532
1.000
221
10537
0.106
222
8806
0.088
223
337
0.003
224
12966
0.130
225
6609
0.066
227
13989
0.141
228
16893
0.170
229
14475
0.145
23
263205
1.000
231
62852
0.239
233
125404
0.476
235
1499
0.006
237
225
0.001
238
29895
0.114
239
25198
0.096
24
126657
1.000
241
15095
0.119
242
39862
0.315
243
47344
0.374
244
1273
0.010
249
16663
0.132
Com: Commodity
165
Table C. 34.Three-Digit Breakdown of 1993 Commodity Flows (Continued)
25
68705
1.000
251
54056
0.787
253
1379
0.020
254
6154
0.090
259
3005
0.044
26
194066
1.000
261
3108
0.016
262
59799
0.308
263
20920
0.108
264
60633
0.312
265
36267
0.187
266
2730
0.014
28
532909
1.000
281
96086
0.180
282
66372
0.125
283
140165
0.263
284
74197
0.139
285
21866
0.041
286
888
0.002
287
25851
0.049
289
40730
0.076
29
346712
1.000
291
325858
0.940
295
8733
0.025
299
5035
0.015
30
172527
1.000
301
20132
0.117
302
2641
0.015
303
0
0.000
304
5505
0.032
306
12096
0.070
307
102484
0.594
31
44092
1.000
311
2032
0.046
166
Table C. 34.Three-Digit Breakdown of 1993 Commodity Flows (Continued)
312
0
0.000
313
174
0.004
314
29557
0.670
315
138
0.003
316
2128
0.048
319
725
0.016
32
86142
1.000
321
6142
0.071
322
17656
0.205
324
6290
0.073
325
6364
0.074
326
6638
0.077
327
21714
0.252
328
1649
0.019
329
13183
0.153
33
227828
1.000
331
112303
0.493
332
16146
0.071
333
10088
0.044
335
48674
0.214
336
8583
0.038
339
9082
0.040
34
236993
1.000
341
11385
0.048
342
35423
0.149
343
15671
0.066
344
54264
0.229
345
17268
0.073
346
25969
0.110
348
7446
0.031
349
57937
0.244
35
406496
1.000
351
9356
0.023
352
27809
0.068
167
Table C. 34.Three-Digit Breakdown of 1993 Commodity Flows (Continued)
353
56330
0.139
354
30730
0.076
355
27446
0.068
356
47089
0.116
357
136199
0.335
358
23203
0.057
359
14557
0.036
36
408148
1.000
361
33156
0.081
362
36345
0.089
363
34350
0.084
364
31580
0.077
365
52119
0.128
366
38089
0.093
367
122815
0.301
369
20647
0.051
37
625681
1.000
371
455111
0.727
372
90883
0.145
373
12369
0.020
374
2780
0.004
375
2210
0.004
376
1091
0.002
379
10228
0.016
38
183310
1.000
381
14576
0.080
382
10281
0.056
383
3322
0.018
384
59331
0.324
385
4142
0.023
386
66392
0.362
387
4075
0.022
168
Table C. 34.Three-Digit Breakdown of 1993 Commodity Flows (Continued)
39
146934
1.000
391
15734
0.107
393
1945
0.013
394
35635
0.243
395
5880
0.040
396
7164
0.049
399
62816
0.428
169
APPENDIX D LIST OF US. CUSTOM DISTRICTS∗∗
District Code
∗
District Name
01
PORTLAND, MAINE
02
ST. ALBANS, VERMONT
04
BOSTON, MASSACHUSETTS
05
PROVIDENCE, RHODE ISLAND
06
BRIDGEPORT, CONNECTICUT
07
OGDENSBURG, NEW YORK
09
BUFFALO, NEW YORK
10
NEW YORK CITY, NEW YORK
11
PHILADELPHIA, PA.
13
BALTIMORE, MARYLAND
14
NORFOLK, VIRGINIANIA
15
WILMINGTON, N. CAROLINA
16
CHARLESTON, S. CAROLINA
17
SAVANNAH, GEORGIA
18
TAMPA, FLORIDA
19
MOBILE, ALABAMA
20
NEW ORLEANS, LOUISIANA
21
PORT ARTHUR, TEXAS
23
LAREDO, TEXAS
24
EL PASO, TEXAS
25
SAN DIEGO, CALIFORNIA
26
NOGALES, ARIZONA
27
LOS ANGELES, CALIFORNIA
28
SAN FRANCISCO, CALIF.
29
COLUMBIA-SNAKE
30
SEATTLE, WASHINGTON
31
ANCHORAGE, ALASKA
32
HONOLULU, HAWAII
Bureau of the Census, 1993, US export and Imports of Merchandise on CD-ROM, Washington D.C.
170
33
GREAT FALLS, MONTANA
34
PEMBINA, NORTH DAKOTA
35
MINNEAPOLIS, MINNESOTA
36
DULUTH MINNESOTA
37
MILWAUKEE, WISCONSIN
38
DETROIT, MICHIGAN
39
CHICAGO, ILLINOIS
41
CLEVELAND, OHIO
45
ST. LOUIS, MISSOURI
49
SAN JUAN, PUERTO RICO
51
VIRGINIAN ISLANDS OF THE US
52
MIAMI, FLORDIA
53
HOUSTON/GALVESTON, TEXAS
54
WASHINGTON, D.C.
55
DALLAS/FORT WORTH, TEXAS
58
SAVANNAH/WILMINGTON
59
NORFOLK/MOBILE/CHARLESTON
60
VESSELS UNDER OWN POWER
70
LOW VALUE SHIPMENTS
80
MAIL SHIPMENTS
89
ELECTRICITY
90
UNDOCUMENTED EXPORTS TO CANADA
171
APPENDIX E DESCRIPTIONS OF DATABASES
E. 1. 1993 and 1997 Commodity Flow Surveys E. 1. 1. 1993 Commodity Flow Survey The 1993 Commodity Flow Survey (CFS) is produced by the U.S. Department of Commerce, Bureau of the Census, as part of the Economic Census, in partnership with the Bureau of Transportation Statistics of the U.S. Department of Transportation, and sponsored by the U.S. Department of Transportation. The CFS provides data on the movement of goods both for each of the 50 States and the District of Columbia, and for each of the 89 U.S. National Transportation Analysis Regions (NTARs). The NTARs may cross state boundaries, but are made of a set of whole counties. Thus, the structure of the data is suitable to conduct both state and NTAR level analyses. Shipments originating from businesses located in Puerto Rico and other territories, shipments traversing the U.S., and shipments from a foreign location to a U.S. location are not included in the CFS. Imported products shipments are included after they leave the importer’s domestic location for another location.
Export shipments are also
included until they reach the port of exit from the U.S. Shipments through a foreign country, with both the origin and destination in the U.S., are included. However, in the calculation of the mileages for these types of shipments, the foreign segment is not included. Movements of commodities in the CFS are generated by establishments in manufacturing, mining, wholesale, selected retail, service, and auxiliary activities. Sectors such as agriculture, government, and some other retail, that may have significant shipments, are not included. Also, shipments from “Oil and Gas Extraction” are not included since most of the establishments in this sector have undeliverable mailing 172
addresses.
However, agricultural products from initial processing centers onward are
included. The CFS includes 11 data tables, and the same table layout is used for all 11 tables. The table layout includes 20 variables. However, in each of the tables, only specific variables are
recorded.
The Origin variable indicates the place where the commodity flow originates, while the Destgeo variable is the destination of the flows. Both variables are recorded with the same codes. When origin and destination are states, the code is the two-digit census code, (50 states, and Washington D. C.). However, for the NTARs the codes are three-digit (89 NTARs). The Commodity distributed
by
variable
establishments.
represents the items that are produced, sold or They
are
classified
according
to
the
Standard
Transportation Commodity Classification (STCC) system. The data presented in the CFS are aggregated to the two and three-digit levels. A three-digit STCC list is given in Appendix A. The Modeseq and Mode variables are defined together. Both of them uniquely define one mode or a modal combination. For example, the Modeseq
value 12 and the
Mode value 02 together define private truck. Four different levels of mode aggregation are defined. The first is the sum of all modes. The second defines only single modes, multiple modes, and other and unknown modes. The third level sums modal totals: parcel, U.S. postal service, or courier total; truck total; air total; rail total; inland water total; deep sea water total; pipeline total; and other and unknown modes total. The fourth level is the most disaggregated level and includes the following: parcel, U.S. postal service, or courier; private truck; for-hire truck; air; rail; inland water; Great Lakes; deep sea water; pipeline; private truck and for-hire truck; truck and air; truck and water; truck and pipeline; rail and water; inland water and Great Lakes; inland water and deep sea; other and unknown modes. The Strat variable defines either a distance stratum or a weight stratum. In each case 10 strata are defined.
This variable is defined only for tables 3, 4, 10 and 11. In
tables 3 and 11, it denotes distance, while it denotes weight in tables 4 and 10. The other variables are the measurement variables: Value is the value of the commodities shipped in $ million. This is the net selling value, f.o.b. plant, exclusive of freight charges and excise taxes; Weight is the weight of the commodities shipped in million (short) tons (one short ton is 2000 pounds); Wgtdis is the weight-distance of the 173
commodities shipped in million ton-miles; and Avgdis defines average miles per shipment. It should be noted that in several tables distance shipped is given by Table E. 1. Variables included in the CFS Variable Name ORIGIN
Type
Len
Dec
Description
C
3
0
Geographic code for state/NTAR of origin
COMMODITY
C
3
0
Commodity classification code
MODESEQ
C
2
0
Mode of transportation sequencing code
MODE
C
2
0
Mode of transportation code
DESTGEO
C
3
0
Geographic code for state/NTAR of destination
STRAT
C
2
0
Weight or distance stratum code
VALUE
N
9
0
Value of commodities shipped (in $ 1,000,000)
VALUEF
C
1
0
Flag for above field
VALUEPCT
N
5
1
% of total value of commodities shipped
VALUEPCTF
C
1
0
Flag for above field
WEIGHT
N
9
0
Weight of commodities shipped (in 1,000,000 tons)
WEIGHTF
C
1
0
Flag for above field
WEIGHTPCT
N
5
1
% of total weight of commodities shipped
WEIGHTPCTF
C
1
0
Flag for above field
WGTDIS
N
9
0
Weight-distance of commodities shipped(in1,000,000ton-miles)
WGTDISF
C
1
0
Flag for above field
WGTDISPCT
N
5
1
% of total weight-distance of commodities shipped
WGTDISPCTF
C
1
0
Flag for above field
AVGDIS
N
9
0
Average miles per shipment
AVGDISF
C
1
0
Flag for above field
intervals (as the strata mentioned above), and these distances are estimated along the Great Circle between origin and destination. However, except for these cases, mileage calculations were made by Oak Ridge National Laboratories. Valuef, Weightf, Wgtdisf, and Avgdisf denote the flags for above-mentioned variables. Two types of flag are used: (D) denotes figures that were withheld to avoid disclosing data for individual companies; and (S) denotes data that do not meet publication standards due to high sampling variability. Valuepct,
Weightpct,
and
Wgtdispct
define
percentage
distribution
of
measurement variables. The universe of percentage distribution changes according to table specifications. For example in Table 1, the percentage distribution is computed according to transportation modes for each variable and for each origin, while Table 2 defines % distributions for the Wgtdis variable only. Finally, the Valuepctf, Weightpctf, and Wgtdispctf variables denote flags for the above-defined percentage variables, and they are defined in the same manner as the other 174
flags.The CFS includes 11 data tables reporting data with different levels of aggregation and data organizations. Since the study directly uses Table 5, and Table 9 in the 1993 CFS, only these tables are explained below. Table E. 2. Tables in the Commodity Flow Survey Table Title Table_01. dbf
Table Content Mode of transportation for all origins
Table_02. dbf
Total modal activity for all origins
Table_03. dbf
Mode of transportation and distance shipped for all origins
Table_04. dbf
Mode of transportation and shipment size for all origins
Table_05. dbf
Commodity for all origins
Table_06. dbf
Commodity and mode of transportation for all origins
Table_07. dbf
All destinations for all origins
Table_08. dbf
All destinations and modes of transportation for all origins
Table_09. dbf
All destinations and commodities for all origins
Table_10. dbf
Commodity and shipment size for all origins
Table_11. dbf
Commodity and distance shipped for all origins
Table 5 is titled “Commodity for All Origins”. In this table, the values, weights, weight-distances and average distances of commodity shipments (at the two-digit STCC level) going out from each origin (both state and NTAR) are reported. The modes, destinations, and percentages of measurement variables are not reported. In this table, it is specifically possible to monitor the value, weight, weight-distance, and average-distance of, say, forestry products going out from Kansas. There are 4,726 records in Table 5: 1,700 states, and 3,026 NTARs. Table 9 is titled
“All Destinations and Commodities for All Origins”. In Table 9,
Table 7 is further disaggregated by two-digit STCC commodity groups. The values, weights, and weight-distances of two-digit commodity groups going out from each origin to every other destination are provided.
From this table, it is possible to read off the value,
weight, and weight-distance of forestry products going out from Kansas to Alabama. In Table 9, there are 356,014 records: 86,700 states, and 269,314 NTARs. E. 1. 2. 1997 Commodity Flow Survey The 1997 CFS is the second and latest survey in its series. The general data structure, variables, industrial coverage, and geographic coverage have not been changed drastically in the 1997 CFS. For this reason, the content of each table is not analyzed and reported here in detail as for the 1993 CFS. However, there are some notable differences in the 1997 CFS. 175
First of all, the sample size of the survey decreased to 100,000 establishments in 1997 from 200,000 in 1993, out of a universe of about 800,000 establishments. In the 1993 CFS, data are reported by states and by NTARs. However, in 1997, in order to obtain a certain degree of coordination with other Census Bureau statistics, the data are reported for three major geographical bases: Census Regions and Divisions, States, and Major Metropolitan Areas and the Remainder of States. Since we are planning to conduct the research at the state level, this change will not affect our study. Although the industry coverage has not been changed in 1997, the commodity classification system has been changed. The 1993 CFS uses the Standard Transportation Commodity
Classification
(STCC)
system
to
report
data,
whereas
the
Standard
Classification of Transported Goods (SCTG) code is used in 1997. The American Association of Railroads developed the STCC for rate assignments by the Interstate Commerce Commission (ICC) in the 1960’s. The SCTG coding system was created by the US and Canadian Governmental Agencies, and it is based on the Harmonized System (HS) of commodity classification in order to address worldwide logistics requirements.
Again,
since this study will be conducted at the two-digit commodity aggregation level for the two periods (1993 and 1997), this change should not create a significant problem. A list of the SCTG codes at the two-digit level is presented in Appendix B. E. 2. County Business Patterns The County Business Patterns (CBP) data are produced each year by The Bureau of the Census.
CBP uses three levels of geographic aggregation: the US, state, and
county. Data for agricultural production, railroads, much of government, and household employment are not included in CBP, as CBP data are derived from Social Security Administration files. CBP provide data on the total # of establishments, and # of establishments by employment size classes, mid-march total employment, total annual payroll, and total first quarter payroll. The universe of the database is all establishments with one or more paid employees.
176
Table E. 3. Variables in the US Data Files Variables
Type
Width
Description
USIND
C
2
US Summary Code
SICCODE(1)
C
4
SIC Code
TOTFLAG
C
1
Data Suppression Flag- Total
TOTTEMPM
N
8
Total Mid-March Employees
TOTPAYQ1
N
9
Total First Quarter Payroll ($1,000)
TOTPAY
N
10
Total Annual Payroll ($1,000)
TOTTEST
N
8
Total # of Establishments
FLAGX-Y*
C
1
DataSuppression Flag: Establishments Having X-Y Emp
EMPX-Y
N
8
Mid-March Employees: X-Y Employee Size Class
EMPWGX-Y
N
8
First Quarter Wages: X-Y Employee Size Class
EMPANX-Y
N
9
Annual Wages: X-Y Employee Size Class
ESTNUX-Y
N
7
Number of Establishments: X-Y Employee Size Class
X and Y denote lower and upper limits of employee size classes, and 9 size classes are defined for the US file. The intervals are as follows: 1-4; 5-9; 10-19; 20-49; 50-99; 100-249; 250-499; 500-999; 1000+.
Data are reported by 2, 3, and 4 digit SIC level for all geographic aggregations. The name of the variables and their definitions for county, state, and the US tables are presented in Tables 5, 6, and 7, respectively. Table E. 4. V ariables In States Data File. Variables FIPSTATE
Type C
Width 2
Description FIPS State Code
SICCODE1
C
4
SIC Code
TFLAG
C
1
Data Suppression Flag
TFLAG
C
1
Data Suppression Flag
TEMPMM
N
8
Total Mid-March Employees
TPAYQ1
N
8
Total First Quarter Payroll ($1,000)
TANPAY
N
9
Total Annual Payroll ($1,000)
TESTAB
N
6
Total Number of Establishments
EMPXQY*
N
6
Mid-March Employees: X-Y Employee Size Class
FQWGXQY
N
7
First Quarter Wages: X-Y Employee Size Class
AWGXQY
N
8
Annual Wages: X-Y Employee Size Class
ESTNOXQY
N
6
Number of Establishments: X-Y Employee Size Class
FLAGX
C
1
DataSuppression Flag: Establishments Having X-Y Emp.
STATE1B
C
2
Census State Code
X and Y denote lower and upper limits of employee size classes, and 9 size classes are defined for the state files. The intervals are as follows: 1-4; 5-9; 10-19; 20-49; 50-99; 100-249; 250-499; 500-999; 1000+.
177
Table E. 5. Variables In County Data Files. Variables FIPSTATE2
Type C
Width 2
Description State Code
FIPSCTY2
C
3
County Code
SICCODE2
C
4
SIC Code
TFLAG
C
1
Data Suppression Flag
TEMPMM
N
12
Total Mid-March Employees
TPAYQ1
N
12
Total First Quarter Payroll ($1,000)
TANPAY
N
12
Total Annual Payroll ($1,000)
TESTAB
N
6
Total Number of Establishments
CTYEMPL1
N
6
# of Establishments/Employment Size Class 1-4
CTYEMPL2
N
6
# of Establishments/Employment Size Class 5-9
CTYEMPL3
N
6
# of Establishments/Employment Size Class 10-19
CTYEMPL4
N
6
# of Establishments/Employment Size Class 20-49
CTYEMPL5
N
6
# of Establishments/Employment Size Class 50-99
CTYEMPL6
N
6
# of Establishments/Employment Size Class 100-249
CTYEMPL7
N
6
# of Establishments/Employment Size Class 250-499
CTYEMPL8
N
6
# of Establishments/Employment Size Class 500-999
CTYEMPL9
N
6
# of Establishments/Employment Size Class 1000+
CTYEMPL10
N
6
# of Establishments/Employment Size Class10001499
CTYEMPL11
N
6
# of Establishments/Employment Size Class 15002499
CTYEMPL12
N
6
# of Establishments/Employment Size Class 25004999
CTYEMPL13
N
6
# of Establishments/Employment Size Class 5000+
STATE2
N
2
Census State Code
COUNTY2
C
3
Census County Code
As mentioned in Chapter 4, some sectoral employment data are missing in the CBP because of data disclosure problems, and are replaced in the following manner. While there are missing observations at the two-digit level, one-digit level state employment data are all available. Summing up two-digit employment for each state up to one digit level, and
subtracting this sum from the exact one-digit employment, we obtain the total value
of all missing observation. For each one-digit sector, the total missing value is apportioned over the “missing” two-digit sectors using CBP data for earlier or later years. For example, say, in 1993, in state A, sector 20 and 25 employments are suppressed. State A’s given manufacturing employment in 1993 is say
2500. When we
sum given sub-sectors employments, we get 2300. Obviously, sum of the sector 20 and 25 employment is 200 in this case. The question is then how to distribute this sum among these sectors. We search previous or next years database where sectors 20 and 25 are not missing and They are 225 and 75 respectively, say in 1992. Their relative percentage is 0.75 and 0.25 in this case, and we distribute our 200 with these percentages. 178
E. 3. Census of Manufactures Manufacturing
is
defined
as
“the
mechanical
or
chemical
transformation
of
substances or materials into new products. The assembly of component parts of products also is considered to be manufacturing if the resulting product is neither a structure nor other fixed improvement. These activities are usually carried on plants, factories, or mills that characteristically use power-driven machines and materials-handling equipment”* The employees.
universe The
is
all
manufacturing
establishments
are
establishments
classified
in
with
one
manufacturing
or
more
Standard
paid
Industrial
Classification Codes 2011 through 3999. Data contents and aggregation levels vary significantly across files. However, in general, the files contain data on the number of establishments, employment, payroll, value of shipments, value added, capital expenditures, and other statistics for the establishments engaged in manufacturing. The census of manufactures is produced every 5 years, for years ending in 2 and 7. Data for the intervening years are collected in the Annual Survey of Manufactures (ASM). The 1992 Census of Manufactures is published in three report series: the Industry Series, the Geographic Area Series, and the Subject Series. The Final Industry Series report data for the US with two exceptions: File MC92F2, “Industry Statistics by State”, and MC92F6B, “Product Statistics by State”.
Auxiliary
establishments are not included in these records. The File MC92F2, “The Industry Statistics by State” has 11,453 records and may be useful for our study. Data are aggregated by 4 digit SICC, and by state. Geographic Area Series includes both operating establishments and auxiliary establishments data records. Explanations of the data files included in the Geographic Area Series, and their attributes, are presented in Table E. 6. The Subject Series include “the Final General Summary”, and “Manufactures’ Shipments
to
Characteristics
Federal of
Government
Manufacturing
and
Agencies” Wholesale
files.
Additionally,
Establishments
that
“The Export”
Selected file
is
produced for the Analytical Report Series. Only some files of the “Final General Summary” database may be relevant to our study since they are presented at the state level. Explanations of these files and their attributes are presented in Table E. 7.
*
MC92GS.TXT, 1992, in 1992 Economic Census CD-ROM/MC92, Bureau of The Census, Washington D.C..
179
Table E. 6. Geographic Area Series Table Name MC92A1
Title
SICC
Geography
# of Rcrds 1,243
Years
Employment Statistics for States and Metro Areas
All industries
States, MSAs, PMSAs
and
MC92A2
General Statistics for States and Metro Areas
All Industries
States, MSAs, PMSA
and
500
92, 87, 82, 77
MC92A3A
Summary Statistics for States
All Industries
States
52
92
MC92A3B
Assets and Related Statistics for States
All Industries
States
52
92
MC92A4
General Statistics for States, Metro Areas, Counties& Places
2, 3, and 4 Digit
States, MSA, PMSA, Cnt&Plc.
86, 986
92
MC92A9
Establishments by Employment Size for States and Counties
2 Digit
US, States, Counties
69,410
92
92, 87, 82, 77
Table E. 7. Final General Summary Database Relevant Files Table Name MC92SF2
Title
SICC
Geography
Industry Statistics
2, 3 digits
States
# Rcrds 5,886
MC92SA1
Historical Employment
All industries
States
208
92, 87, 82,77
MC92SA2
Historical Statistics for Operating and Auxiliary Establishments
All Industries
States
208
92, 87, 82, 77
MC92SA3A
Detailed Statistics
All Industries
States
52
92
MC92SA3B
Assets
All Industries
States
52
92
MC92SA4
Metropolitan Areas
All Industries
States, MSA. PMSA
861
92
MC92SA5
Employment Size
2 digit
States
1144
92
MC92SM4
Auxiliary Establishments
All Industries
States
52
92
MC92L4
Location Manufacturing
4 digit
States, Cnts, Places
357,475
92
of
180
of
Years 92