Highway impacts on regional employment - Wiley Online Library

Journal of Advanced l?ansportation, Vol. 21, Spring 1987.

Highway Impacts on Regional Employment Yorgos J . Stephanedes David Eagle Economic development is increasingly used by state DOT'S as a criterion in arriving at highway funding decisions. However, there exists little evidence in the literature justifying the use of such a criterion, especially since existing techniques only determine correlations between highways and development. In this paper a time-series methodology is developed to differentiate the effects of highways on development from the effects of development on highways. This methodology includes structural plot analysis and causality tests and is based on pooled time-series and cross-sectional data on highway construction expenditures and county employment. The results indicate that increases in highway expenditures do not, in general, lead to statewide increases in employment other than temporary increases in the year of construction. However, in counties that are economic centers of the state, highway expenditures do have a positive long-term effect, i.e. employment increases above the normal trend of the economy. Such gains are apparently counterbalanced by employment losses in counties adjacent to the economic centers.

Introduction Expected economic effects of highways influence highway funding decisions either directly through stated objectives or indirectly through the political arena. For example, DOTS in 36 states explicitly consider regional economic development in their highway program selection [ 11. This paper seeks to determine whether highway projects have a definite and foreseeable effect on economic development, increasing jobs above the normal trend of the economy. If highway projects lack such an effect, then, in certain states, some funds may be inefficiently allocated. On the other Dr. Stephanedes is an associate professor in the Department of Civil Engineering and a faculty affiliate in the State and Regional Research Center, University of Minnesota, Minneapolis, Minnesota. Mr. Eagle is associated with the Departments of Economics and Civil Engineering, University of Minnesota, Minneapolis, Minnesota. An earlier version of this paper was presented at the 66th Annual Meeting of the Transportation Research Board, Washington, D.C., January 1987.

Yorgos J. Stephanedes and David Eagle

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hand, if highway projects significantly affect economic development, and if the aim is to stimulate the economy, more use of highway funds for economic development purposes may be justified. Although transportation historically has had undeniable effects on economic development by “opening up the frontier,” some studies indicate that now that our highway system is mature, additional highway improvements in transportation have little, if any, effect on economic development. Unfortunately, the existing empirical evidence uses only cross-sectional or input-output analysis and is mixed and inconclusive. The purpose of this work is to address the causality issue more directly by using time-series statistical techniques instead of cross-sectional techniques. Our analysis uses annual data on highway expenditures and employment for all 87 Minnesota counties from 1964 to 1982. While we pool cross-sectional data with time-series data, we analyze the time-series aspects of the data. By first paneling the data, we remove the cross-sectional element for each county. Causality tests (Granger-Sims type; [2,3]) are used to test whether highway expenditures affect employment levels. Structural time-series analysis supplements these causality tests by quantifying the dynamics associated with these relationships. The evidence indicates that causality from highway expenditures to employment is weak. However, in counties that are economic centers of the state (see Regional Centers defined below), higher highway expenditures lead to a significant increase in employment, above the normal trend of the economy.

Background Highways may be able to affect economic development by influencing individual residence and work place location, and enterprise location. In particular, the majority of such effects include changes in: (a) Residential Location. In response to changes in the transportation infrastructure, an individual may change residence to take advantage of the new transportation facilities. In urban areas this effect has been well studied and its existence verified [4]. (b) Work Place Location. A new transportation facility may enable people to work far from where they reside [ 5 ] .

Highway Impacts on Regional Employment

69

(c) Enterprise Location resultingfrom changes in labor supply. Stephanedes and Eagle [6] argue that if new transportation facilities allow people to participate in the labor market of an area to which they previously lacked accessibility, then that area’s labor supply may increase. The increased labor supply may attract new industry to the area. (d) Enterprise Location resultingfrom decreased transportation costs. An improvement in transportation often will decrease the transportation costs of companies in the area served by the transportation facility. These decreased costs may attract new firms to the area [7]. This paper tests the validity of points (c) and (d)-whether changes in the transportation network will affect enterprise location or expansion. In particular, we measure enterprise location or expansion in terms of employment levels in the counties where highway changes take place. Several investigators have studied the effects of interstate highways on population and employment growth [8- 131. In general, little correlation exists between highways and economic development [8,9]. When a significant correlation is found, the employment effects are minimal [lo], are highway-service-related, and are mainly observed near metropolitan areas [ll-131. Regional economic forecasting and policy analysis using large-scale regional models (such as [ 14]), often based on the input-output method [15191, have indicated that some economic effects do result from changes in transportation. The implications of these models, however, often depend critically on the users’ assumptions. For instance, an important variable in input-output models is market share, the amount of the total final demand that is produced locally. In some models, such as SlMLAB [lX], the user determines market share. Other models, such as the Amherst Model [ 191, estimate market share using an equation of other variables of the model. The estimation is based on cross-sectional analysis and, therefore, merely represents correlation, not a causal relationship. Most of the studies we have discussed use cross-sectional techniques. However, time-series techniques address the issue of causality more directly than do cross-sectional techniques. In particular, time-series techniques can test whether changes in one series (such as highway expenditures) statistically precede changes in another series (such as employment levels). Initial findings on the causal links between highway investment and eco-


70

nomic development were reported from an earlier analysis [20]. However, that analysis was limited in scope as it only considered manufacturing and retail employment changes, and was restricted to a small sample of nonmetropolitan counties. In addition, it did not explicitly demonstrate the variation in the causal links as a function of the regional role of a local economy. Finally, lack of treatment of the data prior to the analysis may have introduced bias into the results. The findings from subsequent analysis that addressed the above limitations, are reported here.

Data The data consist of annual observations of state highway construction expenditures and employment for all 87 Minnesota counties. The expenditure data, provided by the Minnesota Department of Transportation, are broken down by county for the fiscal years 1957-1982. The employment data from the County Business Patterns for the years 1964- 1982 represent the employment in the middle of March each year. Because the majority of each fiscal year's highway budget is spent before March, we view the yearly expenditure data as preceding the employment data. Traditional time-series analysis could accomplish very little with such short series. However, by pooling the cross-sectional with the time-series data, our data elements increase from 19 to 1653 for employment and from 26 to 2262 for highway expenditures. Prior to the analysis, we filter out changes reflecting regional or national trends, inflation, and other effects that are common to each grouping of counties under study. To accomplish this filtering, we define the variables for the statistical analysis as follows: Let F,., be the basic variable (such as expenditures or employment) for county i in year t . Then, -

i,,, =

XI.1

~

c

%.I

IinG

where G is the group of counties we are considering. The new variables compare each county to the total of the counties in the grouping. These groupings are: (a) Statewide: All 87 Minnesota counties.


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(b) Urban: Counties in the Twin Cities metropolitan area and counties containing a city of population 28,000 (such as the city of Mankato) or larger. (c) Next to Urban: Counties bordering the counties in (b). (d) Regional Center counties: In the Twin Cities metropolitan area, these counties include Hennepin and Ramsey counties, which include the cities of Minneapolis and St. Paul. Outside the metropolitan area, counties are included if they are Urban. These counties are the economic centers of the state as they employ two thirds of the state workers and contain approximately half the population. (e) Next To Regional Centers: Counties in (c) plus the Twin Cities metropolitan counties other than Hennepin and Ramsey. (f) Rural: Counties not included in categories (b) and (c). Naturally, other types of groupings are possible, e.g. counties whose economy is agriculturally-based, light manufacturing-based, border counties, etc. Such groupings are now under investigation.

Method We hypothesize that the variables yi.,and x f , , (e.g., employment and highway expenditures) are stationary stochastic processes having the following form for some q and constants a for each county i in the grouping: ji.1

= ai+ a1 j i . , - t +

a2ji.r-2

+ . . . + 0qii.i--y

where p i.l is the error term assumed to be serially uncorrelated. That the a’s and b’s are the same across counties is a crucial assumption of this formulation and implies that the processes behave similarly across counties. The a’s, reflecting differences among the counties, are eliminated from the equation by subtracting out the sample mean of each variable over time. This subtraction panels the data, forming the new variables, Xi,r, where k and m are respectively the beginning and the last years of the data,


72

and we can rewrite equation (1) as: ~ 1 . r

-

=

+ . . . + aqYr.r-q + bIZ,,,-, + b 2 X I , , - 2 + . . . + byZ,,,-q+ p,.,. yt,i-~+ a2Ll.r-2

(2)

To filter out the effects of county size on employment and highway data fluctuations, we first assume that the number of random variables in a county is proportional to its total employment, E,; then p , . , / J E , , where we consider p,,, to be the sum of n independent and identical random variables, is serially uncorrelated with variance independent of the county. We next divide both sides of equation (2) by J E l , where El is the total county employment, yielding:

+ 01 Y1,r-I + a 2 ~ i . r - 2 + . . . + + b I X1,r-l + 62 + . . . + b y ~ i . , - y +

Y1.r = Y

aq~1.r-q

x1,r-z

e1.r

(3)

where y l . r - j l . , / J E , , x,.,= Z,.,IJE,, p , , , / J E , and the constant term accounts for the possible inefficiencies in the estimation. We use two methods to investigate the link between transportation and economic development: (a) Granger-causality tests, and (b) structural time-series plots. Granger Causality

The direct Sims test of whether a variable x Granger-causes a variable y first formulates the null hypothesis that x does not Granger-cause y. Then, x is regressed on past, present, and future values of y : Xi,,

=y

+

U,X,,-l

+

u2xi,,-2+.

..

+ a,xl,,-q

+ b0~i.r+ b~Y L - ~+ . . . + bqYi,r--q + Yi.r+l + c2yi.r+2 + . . + C h Y i , r + k + ci./ cI

*

(4)

for some integers q and k , and an F-test is used to test the null hypothesis of no causality, i.e., whether all future coefficients of y are zero: c h = 0 for h = 1,2, . . . , k . If the null hypothesis is rejected, we conclude that x does Granger-cause y . Structural Plots

To estimate a structural specification of employment E, we hypothesize that, for some q and k, and with H representing highway expenditures:


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""L 100

STATEWIDP

100.00.

-

.

100.16 URBAN

100.00

N m M URBAN

100.21

REGIONAL CENTERS 100.00

NEXT To REGIONAL CENTERS

k

100.03 100.00 99.98

mmOn4ENT (%)

RURAL

Figure 1. Effect of a change in highway expenditures on employment.

+ .. . + bZI El,,-, + b22 EL-2 + . . b2q El.!-k +

El., = Y I

+ a20 HI,,+ a21

a2q

HI.,-,

*

fi.!-

(5)

To interpret the structural equation (5), we simulate an exogenous change in highway expenditures. In the resulting structural plot (see Figure 1 for examples), which explains employment, we exogenously increase highway expenditures 10% for one period and then return the expenditures to their original level. The reason the change in highway expenditures for equation ( 5 ) is temporary is that we are interested in the


74

effects of highways after construction, not the effects from the construction of the highway. We argue that all variables on the right-hand side of (5) are predetermined (even Hi,, occurs months before E;,,),and thus the structural equation (5) is identified. However, since highway expenditures is not the only variable that affects employment, we expect a degree of misspecification in (5). To address this issue, we are currently developing vector autoregressions that will complement the structural equations.

Results For the autoregressive structure of the structural plots and the causality tests we use 3 to 5 lags, e.g., q = 5 and k = 5 in equation ( 5 ) , as suggested in [21]. Similarly, we use 3 to 6 leads in the causality tests, i.e. k = 3 or k = 6 in equation (4). A low significance level in the three-lead tests indicates the existence of a short-term effect, whereas a low significance level in the six-lead tests indicates a long-term effect. Based on the results from the causality tests and structural plots, Table I summarizes, for each grouping, the influence of highway expenditures on employment. For the causality tests, we consider a significance level around 1% or less as a very strong indication of causality. On the other hand, significance levels greater than 10% provide very little indication of causality. For the structural plots, a temporary 10% increase in highway expenditures, occurring completely within one year, is indicated in the top graph of Figure 1. The resulting effect on employment is illustrated in the remaining graphs of Figure 1. AN counties

The statewide structural plot indicates that, for the typical Minnesota county, a one-year increase in highway expenditures of 10% ($274,000) leads to an increase of 0.01% (2 jobs) on average over the 10 years following the first change in highway expenditures-this calculation is based on measuring the area under the curve. This implies, as summarized in Table 1, an elasticity of jobs with respect to changes in highway expenditures that equals 0.00 1. The small magnitude of this effect is probably the major reason that the causality tests (see Table 1) find it to be statistically

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Highway Impacts on Regional Employment Table 1. Effect of highways on employment

F-test of Ho: H W Y F E M P L 3 leads F-statistic (sign. level) Statewide

0.43

(>20%)

(>20%)

7.5 (0.001) 52.0 (0.008) - 14.0 (-0.002)

2.5 I (4.82%)** 0.24 (>20%)

3.3 1 (0.60%)*** 0.77 (>20%)

108 (0.0 13) -3.1 (-0.00 I )

2.10 (7.83%)*

(>20%)

(>20%)

Urban

I .26 (> 20%)

Next to Urban Regional Centers Next to Regional Centers Rural

6 leads F-statistic (sign. level)

0.49

0.37

Effect of $ 1 mil. Hwy. Exp. StructLraI Plot+ jobs (elasticity)

(>20%)

I .66 ( I 2.9%)

0.47

0.83

5.3 (0.00 I )

* **

significant at the 100; level significant at the 596 level ***significant at the 1% level The number of jobs is the average annual increase of jobs over the base year’s employment for a typical county in each grouping. The structural plots simulate the direct effects on employment of a one-year impulse in highway expenditures. Thereafter, highway expenditures is exogenously set at its base level.

+

insignificant; specifically, neither the 3-lead nor the 6-lead tests indicate any evidence of highways Granger-causing employment. In the evaluation of the statewide data set, the model assumes that all counties behave similarly. If, in fact, they behave differently, then the above results may be true on average, but not for every type of county. To isolate differences in behavior, we have analyzed different groupings of counties according to their urbanization. County groupings

The group-by-group tests indicate strong causality only for the Regional Centers. In particular, the six-lead significance level of 0.60% strongly indicates a long-term effect of highways on employment.

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Furthermore, the structural plot for Regional Centers indicates two effects occurring. First, the short-term construction effect lasts 2-3 years. During this period, construction jobs are created, the workers spend some of their earnings in the county, and the construction companies make local purchases causing multiplier effects throughout the county’s economy. The second effect, the longer-term employment change, is the more sustaining effect of highways and the one in which we have been primarily interested. Some causality is also indicated for the Rural counties. However, the structural plot for the Rural counties indicates that the effects of highway expenditures on rural economies are short-term and primarily due to the construction effect. The lack of a long-term effect of highways in rural economies is also indicated by the causality tests. In particular, the causality for three-leads (short-term) has a significance level of 7.83% but the significance level for the six-lead test is greater than 20%, i.e., there is no evidence of long-term causality. While causality is not indicated for the other groupings, the structural plots do indicate that the effects of highways on employment are not always positive. For the Next-to-Urban counties, for instance, Figure I indicates that a 10% increase in highway expenditures over one year is followed by an average 0.02% (3.5 jobs) decline over the next decade. As summarized in Table I , this effect amounts to an elasticity of -0.002. One explanation of this negative employment effect is that improved highways into a regional center allow business activity to move from the Next-to-Urban counties to the Regional Centers [22]. This may also be the reason that employment in Regional Centers significantly increases following an increase in highway expenditures. The finding that improved highways tend to favor regional centers and hurt areas that are adjacent to regional centers should not be surprising. In particular, we note that regional centers are most likely to have the infrastructure that is necessary for absorbing the highway improvements. For instance, the Minnesota counties that are defined as regional centers are central to the Minnesota economy as they employ two thirds of the state workers and contain approximately half of the population. On the other hand, the areas adjacent to regional centers tend to depend on the centers for the infrastructure necessary for development. Generalizing the above employment-related conclusions to make inferences regarding the entire state economy should be attempted with extreme care. For example, while better highways may improve the employment


77

situation in regional centers, the areas adjacent to these centers may also benefit since the income of their residents, who seek employment in the centers, is likely to increase.

Conclusions The time-series analysis presented here indicates that increases in highway expenditures do not, in general, lead to increases in employment levels. Certain observers have mistaken the high correlation between highway expenditures and employment as an indication that highway expenditures do have a substantial effect on economic development. However, our analysis indicates that the correlation stems from two other factors: (a) Higher employment levels attract higher levels of expenditures, and (b) During the year of construction, employment levels do increase. However, this effect is only temporary and disappears when the construction ends. We conclude that, generally, changes in highway expenditures do not cause changes in total employment. However, in Regional Center counties, highway expenditures do have an effect on total employment. In these counties the elasticity of jobs with respect to highway expenditures equals 0.001. Furthermore, results from our ongoing work in this area indicate that, for specific economic sectors, the causality of highway expenditures is significant, even for groupings other than the Regional Centers. These findings imply that the effects of highways on the economy, when they exist, are distributional, not absolute. The distributional nature of these effects is also evident when analyzing different parts of the state. In particular, while certain counties are likely to gain from improved roads, others are likely to lose and the statewide effect may not be significant. To be sure, government policies and objectives are often particularly sensitive to the local nature of such distributional effects. Finally, conclusions regarding the relationship between highways and economic development greatly depend on how development is defined. For instance, while improved roads may hurt the employment in certain counties, the same road improvements may support increases in household income in those counties. Results from our ongoing work in addressing the possible relationships between highways and additional economic indicators will provide a more complete picture of these relationships.

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Acknowledgement This paper is based on work being performed at the University of Minnesota under contract to the Minnesota Department of Transportation.

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18. Maki. W., R. Barrett and R. Brady (1978). Use of simulation in planning, Chapter I I , Rural policy research alternatives. Iowa State University Press, Ames, Iowa, 174- 192. 19. Stevens, B.H., D.J. Ehrlich, J.R. Bower and M.D. Walfel (1982). Regional economic analysis for transportation planning. Final report, NCH R P 8-15A, Amherst. Mass. 20. Stephanedes, Y.J. and D.M. Eagle (1986). Highway expenditures and nonmetropolitan employment. Journal of Advanced Transporrurion 20: I , 43-61. 21. Box, G.E.P. and G.M. Jenkins (1976). Time Series Analysis-Forecasting and Conrrol. Holden-Day, Oakland, California. 22. Stephanedes, Y.J. and D.M. Eagle (1986). Influence of transportation on economic development. Final report, M NDOT-59757M-62179, Minneapolis, Minnesota.