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HUMAN CAPITAL AND AGRICULTURAL GROWTH IN BANGLADESH

/

UTTAM KUMAR DEB

/r

SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL, UNIVERSITY OF THE PHILIPPINES LOS BANOS IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY (Agricultural Economics)

SEPTEMBER 1995


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The dissertation attached hereto, entitled "HUMAN CAPITAL AND AGRICULTURAL GROWTH IN BANGLADESH" prepared and submitted by UTIAM KUMAR DEB in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Agricultural Economics) is hereby accepted.

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COR'AZON T. ARAC5'0N Member, Advisory Comittee

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CRISTINA C. DAVID Member, Advisory Comittee

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/o- lo Date S1gned

Date S1gned

ROBERT R. TEH, JR. Member, Advisory Comittee

MAHABUB HOSSAIN Chairman, Advisory Comittee

.

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Date S1gned

?A, I'?'S

Date S1gned

Accepted as partial fulfillment of the requirements for the Degree of Doctor of Philosophy (Agricultural Economics).

~ar/

NELLY G. Al.AVIAR Chairman Department of Agricultural Economics

l'o-11-CfS Date S1gned

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Dean, Graduate School University of the Philippines at Los Banos

Date Signed


BIOGRAPHICAL SKETCH The author is the eldest son of the late Sadhan Chandra Deb and Mrs. Jyotsna Rani Deb.

He was born in Comilla,

Bangladesh, on January 1, 1964.

He studied in Mohammedpur

Primary School and Barkota Primary School at the elementary level. He passed the Secondary School Certificate (SSC) in 1979 and Higher Secondary Certificate (HSC) Examination in 1981 conducted by the Comilla Board from Barkota High School and Hasanpur S.N. College, respectively.

He obtained the degree

of Bachelor of Science in Agricultural Economics (Honours) in 1985 and the Master of Science in Agricultural Economics in 1986 from the Bangladesh Agricultural University. He garnered First Class rating in all public examinations and placed in the First Class 2nd position in the Bachelor's level and First Class 1st position in

the Masteral level.

In 1988, he worked in a Research Project on Analytical Bibliography on Rural Development in Bangladesh (ABORD) as Research Associate for about one year.

On August 6, 1989, he

joined at the Bangladesh Rice Research Institute (BRRI) as a scientific officer, the post he holds to date.

He completed,

with distinction, a three-month training course on the theory and practices of rice production at BRRI in December 1990.

He

has also completed the Summer Program in Economics at the University of the Philippines Los Banos (UPLB) in 1991 under iii


a

USAID-funded

and

Winrock

International-administered

fellowship. In

1991,

Winrock International

selected him for

an

Overseas Development Administration (ODA) funded fellowship to pursue a Ph.D degree in Agricultural Economics at UPLB.

He

started his graduate studies at UPLB in June 1991 under the ODA-funded fellowship administered by the British Council. In 1993,

the

International

Rice

Research

Institute

(IRRI)

provided him a thesis research fellowship, complementary to the ODA fellowship,

to conduct his doctoral dissertation

research at IRRI. He is an active member of the Bangladesh Agricultural Economists' Association ( BAEA) , the Rural Social Sciences Network (RSSN), and the Krishibid Institution, Bangladesh.

~

UTIAM KUMAR DEB

iv


ACKNOWLEDGEMENT

I wish to acknowledge the immeasurable grace and profound kindness of the Almighty which enabled me to make my dream a reality.

I

individuals

also express my great indebtedness to various and

institutions

for

their

help,

support,

encouragement, and suggestions during my graduate study. My deepest appreciation and sincere gratitude are extended to the following persons and institutions: Dr.

Mahabub

Hossain,

my

adviser,

and

Head,

Social

Sciences Division, the International Rice Research Institute (IRRI), for his day-to-day constructive criticism, unfailing interest, wholehearted assistance, encouragement,

continuous

invaluable suggestions,

stimulation,

dynamic

and

enthusiastic guidance, and all around help in organizing this thesis. Dr. Robert R. Teh, Jr., member of my advisory committee, for his intellectually stimulating suggestions, keen interest, and constructive comments. Dr. Corazon T. Aragon and Dr. Cristina C. David, members of my advisory committee, for their useful and constructive comments and expert advice. To all my professors at UPLB and UP Diliman, whose teaching enabled me to. complete my Ph. D program. My intellectual debt knows no bound to my research guru,

v


Dr. Madan Mohan Dey of ICLARM,

who is a constant source of

criticism and inspiration throughout my academic career. Because of him, I have realized what I would get if I had an elder brother. Professor Robert E. Evenson, Economic Growth Center, Yale University, for finding time to offer his valuable suggestions and intellectually stimulating discussions. Drs. Prabhu Lal Pingali, Sushil Pandey,

Agricultural

Economists at IRRI, for their suggestions and encouragement. The Bangladesh Rice Research Institute, Gazipur, and its former Director General Dr. A.J .M. Ajijul

Islam, and the

Government of Bangladesh for providing me a study leave that enabled me to complete the graduate program. To the United States Agency for International Development (USAID) for financial assistance during the Summer Program in Economics;

Overseas

Development

Administration,

UK,

for

financial assistance throughout the Ph D program ; and the International facilities.

Rice

Research

Institute

for

the

research

To Winrock International for awarding me the ODA

Fellowship. To the British Council, Manila, and its staff especially Ms Chela B. Male for administering the fellowship. Special thanks are due the British Council, Dhaka, for their help during the field research period at Bangladesh. Dr. Bruce Currey, and Professor A.M. Muazzam Husain, former Program Leaders, Human Resource Development Program, vi


Winrock International, Dhaka for their help, encouragement, and moral support. Mrs. Mahatabunnesa Currey and Master Sharif Currey for their encouragement and moral support. Dr. Mamunur Rashid, Director General of BRRI for his encouragement and moral support. Dr. M. Z. Haque, Director (Administration) and Dr. Md. Nasiruddin, Director (Research) of BRRI for their encouragement and help. Dr.

B.A.A.

Mustafi,

Head,

Agricultural

Economics

Division, BRRI for his encouragement, help, and moral support. I

am thankful

carefully

reading

to Akash Bhai the

whole

(Mr.

M.M.

manuscript

Akash)

and

for

providing

suggestions and encouragement. To the Ganges-Kobodak Irrigation Project (GKIP) Authority and the then Project Director Mr. Noajesh Ali for permitting me to utilize the information gathered by the GKIP. To Messrs. P.K. Roy, Momtaj Ali Shah Fakir, Sha Ahmed Ali, Siddiqur Rahman, Abdul Karim, Rejaul Karim, Nurul Haque, Nur Huda Miah, A.T.M. Kuddus and many other officials of the GKIP for their cooperation and help during data collection. I gratefully acknowledge the assistance of Mrityunjoy, Jahangir, Manir, Pipul, Tareq, Hamid, Tapan, Pintu, Milon, Monir,

Khasru,

Bachchu,

Kabir,

Manju,

Sharif,

Santosh,

Mahabbat, Azad, Asad, Mukul, Krishna, Lutfor, Iqbal, Murshed, Zinnah, and Maznu during my field research in Bangladesh. I am also thankful to the Nova Computers, Kushtia; Messrs. Phul

vii


Miah, Abdul Malek and Abdul Hye of BRRI for their help in data entry. Drs.

S. I.

Bhuiyan,

Swap an Datta,

Shamsul Alam, N.M. Miah, S.A. Miah,

Hiralal Chaudhuri,

Quayum Parvez, Messrs.

M.A. Wadud, M. Shahjahan Miyan, Abdul Latif Sarker, Rafiqul Islam, Pradip Dey, M.A. Jabber, Gopal Krishna Bose, Debi Narayan Rudra Paul, Najmul Ehsan Fatmi, Manoranjan Mondal, Mrs. Mukul Chaudhuri, Mrs. Parveen Hossain, Tita Lidya for their help, moral support and affectionate encouragement to complete

this

work.

This

brief

acknowledgement

cannot

adequately reflect the deepest gratitude I feel for them. My friends Rita Das Roy, Shaon Hossain, Subhra Biswas, and Lila Bhabi (Mrs. Lila Rashid) not only encouraged but also forced me to complete my thesis work rather than have chitchat with them. Without their "pushing" it may not have been finished. Samir, Amir, Shimon, Shipon, Tapes, Rasek, Tushi, Tapoti, Mumu, and all my little friends who entertained me in their own way to put me in a mood to study. Mr.

S.K.

Poudel,

a friend who

· definition of real friendship,

first

taught me the

for his warm and inspiring

cooperation and company throughout the study period. Ms. Annie Bola, Mrs. Marivec Campus and her family, Mrs. Luis and her family, Messrs. Kamal Poudyal, Zenaidah, Dewa Swastika,

Moslemuddin Miah,

Jatish Chandra Biswas,

Nandi, Dr. Victor Nikijulu, Dr. Jiban Krishna Biswas,

viii

Semen and all


Bangladeshi family residing in Los Banos for encouragement. Mrs. Maritess Tiongco, Ms. Alice Laborte, Mrs. Ludy, Ms. Elanie R. Cabrera, Mrs. Esther B. Marciano for their cordial help in computer analysis. Mrs. Fe B. Gascon, Ms. Deling Palacpac, Olla,

Joel,

Ms.

Joyce Luis, Ms. Hedda Rada, Anna, Tina, Edgar, for providing me an enjoyable working environment at IRRI. Ms. Reyes,

Jocelyn

Ms. Doris Malabanan, for typing, Ms. Joshie Narsiso

for her help in illustration, and Mrs. Mirla Domingo, for administrative assistance and encouragement.

I am thankful to

Ms. Tess Rola for editorial assistance. To my Didi (Mrs. Sharmila Das), Bhabi (Mrs. Fatmi),

Shikha Boudi,

Mina Boudi,

Usha Bhabiji,

Shamima Bhuktan

Bhabiji, and Tripti Boudi for their care, love, and affection. Finally, to my dear mother Mrs. Jyotsna Rani Deb and father the late Dr. Sadhan Chandra Deb, for their hardship, their unselfish sacrifice in supporting my education, and for raising me up the best way they can. I also want to thank all my brothers, sisters, and relatives for their encouragement and moral support.

I am highly indebted to my respected

teachers -- Shri Anukul Ch. Saha and the late Meghnad Sasmal -

and their family whose teaching,

love,

and inspiration

enabled me to complete higher studies. And to the many others whose names I failed to mention here, I thank you all. ix


With love and respect, this is dedicated to my

grandfather, the late Abani Mohan Deb, father, the late Dr. Sadhan Chandra Deb and mother Jyotsna Rani Deb

whose spontaneous sacrifices, inspirations, blessings, and investment enhanced my human capital to b.ring this into light.

X


TABLE OF CONTENTS

CHAPTER I

II

INTRODUCTION

1

1.1

Statement of the problem

1

1.2

Objectives of the study

6

1.3

Scope of the study

7

1.4

Organization of the dissertation

8

REVIEW OF LITERATURE

10

2.1

Human Capital and Technological Change

10

2.2

Human Capital and Agricultural Growth

16

2.3

Human Capital and Farm Productivity

23

2.3.1 Non-Frontier Approaches

26

2.3.2 Frontier Approaches

28

2.3.2.1

Deterministic frontiers

36

2.3.2.2

Stochastic frontiers

39

2.3.2.3

Panel data models

45

III RESEARCH METHODOLOGY

51

3.1

Conceptual Framework of the Study

51

3.2

Analytical Procedure

57

3.2.1 3.2.2 3.2.3

Human Capital and Regional Technology Adoption

58

Contribution of Human Capital to Aggregate Crop Output

60

Human Capital and Modern Rice Technology Adoption

62

xi


CHAPTER 3.2.4 3.2.5 3.2.6 3.2.7 3.3

IV

V

Contribution of Human Capital to Rice Output

65

Human Capital and Technical Efficiency in Rice Production

66

Determinants of Technical Efficiency in Rice Production

67

Human Capital and Non-farm Income

68

The Data and Specification of Variables

69

3.3.1

Macro-Level Data

69

3.3.2

Farm-Level Data

73

HUMAN CAPITAL, TECHNOLOGY AND PRODUCTION ORGANIZATION IN BANGLADESH

79

4.1

Structure of the Bangladesh Economy

79

4.2

Public Investment on Agriculture

81

4.3

Progress of the Agricultural Sector

91

4.4

The Status of Human Capital

97

4.5

Agricultural Labor Force

100

4.6

Draft Animal

100

HUMAN CAPITAL, TECHNOLOGY ADOPTION AND AGGREGATE CROP OUTPUT

103

5.1

Productivity Differences Among Regions

103

5.2

Human Capital and Technology Adoption

108

5.3

Human Capital and Aggregate Crop Output

119

xii


CHAPTER VI

HUMAN CAPITAL, TECHNOLOGY ADOPTION AND TECHNICAL EFFICIENCY IN RICE PRODUCTION IN THE STUDY AREA 6.1

125

Characteristics of the Study Area and Sample Farms

125

Agrarian Structure, Human Capital, and Technology Adoption

132

Human Capital and Modern Rice Technology Adoption

142

Contribution of Human Capital to Rice Output

149

6.5

Human Capital and Technical Efficiency in Rice Production

152

6.6

Human Capital and Non-farm Income

158

6.7

Concluding Remarks

159

6.2 6.3 6.4

VII SUMMARY AND CONCLUSIONS

163

7.1

Summary

163

7.2

Policy Implications

178

7.3

Limitations of the Study

179

LITERATURE CITED

181

APPENDIX

195

xiii


LIST OF TABLES TABLE 2.1

Summary of empirical studies on the adoption of modern technology in Bangladesh.

15

Summary of non-frontier studies that estimated the effects of human capital on technical efficiency.

29

Summary of frontier studies on technical efficiency and role of education on technical efficiency.

48

Growth and composition of gross domestic product, 1949/50 to 1991/92.

80

Trend in agricultural GDP and contribution of different sub-sectors, 1971/72 to 1991/92.

82

4.3

Value of subsidy on fertilizer.

84

4.4

Rate of economic subsidy on fertilizer.

85

4.5

Allocation of research funds to rice and the crop sector in Bangladesh.

88

4.6

Yearly allocation of agricultural extension and research in Bangladesh.

89

4.7

Proportion of irrigated area, modern variety area and growth rate (%) in aggregate crop output in Bangladesh, by region, 1961-1992.

92

Growth rate in area production and yield of major crops in Bangladesh, 1972/73 to 1992/93.

93

Average years of education of economically active rural male population.

99

2.2

2.3

4.1 4.2

4.8

4.9 4.10

Agricultural labor force (000) in Bangladesh, by region, 1961-1984.

xiv

101


TABLE 4.ll 5.1

5.2

5.3

5.4 6.1 6.2

6.3 6.4

6.5 6.6

6.7

6.8

Yearly average draft animal (000) in Bangladesh, by region, 1961-92.

102

Determinants of the proportion of irrigated area in different regions of Bangladesh, 1961-92.

110

Determinants of the adoption of modern rice technology in different regions of Bangladesh, 1961-92.

ll3

Determinants of per hectare fertilizer use (tk) in different regions of Bangladesh, 1961-92.

ll7

Estimates of the aggregate crop production function, 1961-92.

120

Changes in the pattern of distribution of holdings in Bangladesh, 1960-83.

133

Changes in the distribution of owned land area among different farm size categories in Bangladesh, 1983/84-1988.

134

Changes in the pattern of land tenure in Bangladesh, 1960-87.

136

Household characteristics and status of human capital among the sample farms, 1985/86 and 1990/91.

137

Trend in irrigated area (ha) in the study area, 1985/86 to 1990/91.

139

Changes in the proportion of irrigated area, adoption of modern varieties and fertilizer use among different categories of sample farms, 1985/86 and 1990/91.

140

Determinants of the adoption of modern rice technology among sample farms, 1985/86 and 1990/91.

143

Determinants of fertilizer use (NPK/ha) among the sample farms, 1985/86 and 1990/91.

148

XV


TABLE 6.9 6.10 6.11

6.12

Estimates of the rice production function, 1985/86 and 1990/91.

151

Technical efficiency estimates of sample farms, 1985/86 and 1990/91.

154

Determinants of technical efficiency (based on material inputs only) in rice production, 1985/86 and 1990/91.

157

Determinants of non-farm income, 1985/86 and 1990/91.

160

xvi


LIST OF FIGURES FIGURE 2.1

Stochastic frontier production function.

41

3.1

Schematic representation of the relationship between farmer education, technical efficiency, technology adoption, agricultural and economic growth.

53

Trends in area, production and yield of paddy in Bangladesh, 1961-92.

95

Trends in area irrigated under different methods, 1969-92.

96

Trends in chemical fertilizer use in Bangladesh, 1963-92.

98

4.1

4.2

4.3 5.1

5.2 5.3

5.4

Trends in aggregate crop output (billion constant taka of 1984/85), proportion of irrigated area (PIRGA), and cropping intensity (CPI-%) in Bangladesh, 1961-92.

104

Trends in partial factor productivity of land and labor in Bangladesh, 1961-92.

106

Trends in labor productivity (per worker aggregate crop output) in different regions of Bangladesh, 1961-92.

107

Trends in land productivity (aggregate crop output per hectare of cropped area) in different regions of Bangladesh, 1961-92.

109

xvii


LIST OF APPENDIX APPENDIX 1

Relationship between study regions and present districts.

xviii

196


ABSTRACT

DEB, UTTAM KUMAR, University of the Philippines Los Banos, September, 1995. Human Capital and Agricultural Growth in Bangladesh.

Major Professor: Dr. Mahabub Hossain

The main objective of the study was to analyze the impact of human capital on agricultural growth in Bangladesh. The study asks: What role does human capital play in promoting agricultural growth? The study conducted both micro and macro level analysis to answer the question. At the macro level, it explores the relationship between human capital, modern technology adoption, and aggregate crop output. At the micro (farm) level, it estimates the impact of human capital on modern rice technology adoption, rice output, and technical efficiency in rice production. Farmers' level of education is used as a proxy for human capital in this study. The macro level analysis is based on regional level data for the past three decades (1%1-92) and micro level analysis is based on two-period household level data (1985/86 and 1990/91) collected by the Bangladesh Water Development Board for selected areas under the GangesKobodak (GK) Irrigation Project.

Sample size for the 1985/86 and 1990/91 are 411 and 825,

respectively. Total sample size is 1,236. To determine the contribution of human capital on the adoption of modern technology at the regional level, we run OLS as well as tobit model. Human capital is measured by the average years of schooling of the rural adult (above 10 years) male population of different regions of Bangladesh. Three types of technology variables --irrigation, modern variety seed (MV), and chemical fertilizer-- were considered for the region level analysis. We found a significant positive effect of human capital on irrigation, MY, and chemical fertilizer technology adoption. This indicates

xix


that the higher the level of human capital, the higher the level of adoption of these components of modern technologies. To assess the impact of human capital on regional level aggregate crop output for the period 1961-92, we used a Cobb· Douglas production function. Following Romer (1990), we have incorporated human capital as one of the inputs for production. Technology Index is also taken as additional input for production besides land, labor and capital. OLS as well as maximum likelihood estimates of the production function show that human capital has a significant negative effect on aggregate crop output while technological index has a highly significant positive effect on aggregate crop output. This indicates that keeping other input use level the same, if human capital of a region increases, the aggregate crop output decreases. On the other hand, if the technology level of a region increases, the aggregate crop output level increases. In Bangladesh, education curriculum is not agriculture-oriented. Agricultural work is backbreaking and arduous, so workers try to escape it if they have better options. Education helps to move from agriculture to non-agricultural works. The present education system prepares educated

people to perform non-agricultural activities in a better way than the agricultural activities. Nonagricultural activities have also higher return. In other words, the existing education system does not encourage the educated people to manage agriculture in an efficient way. This real world limitation pushes the regions with higher level of average education to earn more from non-agricultural activity rather than from agriculture through efficient management. Therefore, regions with higher levels of human capital adopt the technology but cannot reap the fullest potential benefit to be obtained from the technology. Due to non·availability of data, we were not able to support this argument with numerical data at the regional level. However, we analyzed this issue at the farm level. At the farm level, years of schooling of the farm operator and average years of schooling of all adult male members of the farm family were used to develop the index of human capital. To assess the contribution of the farm operators' education on MV rice adoption, we

XX


performed tobit analysis on a seasonal basis -- dry (Aus) season and wet (Aman) season - as well as total MV adoption throughout the year. We found that farm operators' education has significant positive effect on MV adoption in both seasons and on total MV adoption. Statistical estimates show that farm operators' education plays a more important role on MV adoption under a risky and complex environment than under favorable environment. To quantify the impact of farm operators' education on per hectare fertilizer use in rice, we conducted regression analysis and no significant effect of farm operators' education was found. MV adoption level was found to be the major determinant of fertilizer use level. Fertilizer use level in rice actually depends upon the variety cultivated. Since MVs are more fertilizer responsive than the local var!eties, so farms with higher level of MV cultivation generally use higher amounts of fertilizer. Therefore, in our analysis, fertilizer use in rice production is determined by MV adoption and education has no extra role on fertilizer use level in rice. To estimate the contribution of farm operators' education on rice production, beyond technology adoption, we used Cobb-Douglas production function analysis incorporating farm operators' years of schooling as an input. We found that farm operators' education has no significant effect on rice output. On the other hand, technology index had a highly significant positive effect on rice output. This indicates that the higher the level of MV adoption, the higher the level of rice output. It also indicates that farm operators' education increase rice output indirectly through technology adoption, but not as a seperate input. To quantify the impact of farm operators' education on technical efficiency in rice production, we used two approaches -- direct approach and indirect approach. We used stochastic frontier method to estimate the farm-specific technical efficiency in rice production. In the direct approach, we have incorporated farm operators' education as a variable while in the indirect approach, at first, we have estimated the farm specific technical efficiencies based on traditional inputs of land,labor and capital, and then regressed the estimated technical efficiencies on a number

xxi


of explanatory variables which include farm operators' education. Both the direct and indirect method of efficiency analysis show that farm operators' education has no significant effect on technical efficienc)' in rice production. This finding is consistent with the findings of the macro-level analysis reported earlier. Present cd uca tion structure in Bangladesh is not agriculture oriented. Therefore, educated farmers have no more special skill or interest in efficiently managing technology or other resources used for rice production than his non-educated counterpart To examine the determinants of non-farm income of the agricultural households, we conducted a multiple regression analysis. Our estimates show that the higher the level of average education of the family members, the higher the level of non-farm income of the farm family. This emphasizes our earlier argument that present education system merely empowers the rural people to move from agriculture to non-agricultural activities. It does not motivate or equip them to increase their efficiency in farming. Based on the macro and micro level findings, the study concludes that past growth in aggregate crop output as well as in rice production have been achieved mainly through the expansion of MVs. Human capital, among other factors, had a significant positive role on the adoption of irrigation, MV and chemical fertilizer technology and thus contributed to the agricultural growth only indirectly. Our present education system docs not encourage performance of agricultural activities efficiently, so the fullest benefit from the adopted technology and resources is not reaped. Therefore, to attain the full benefit from future adoption of modern technology, we should not only invest more on farmers' education but also reorient the content of that education toward efficient farming.

xxii


CHAPTER I INTRODUCTION 1.1 The

Statement of the Problem

Bangladesh

agricultural sector.

economy

depends

largely

on

the

Until now, 40 percent of the gross

domestic product (GDP) of Bangladesh comes from agriculture which employs 60 percent of the labor force and contributes about

60 percent

country.

of

Bangladesh

the is

total

export

expected

to

earnings face

an

challenge over the next two decades in trying food self-sufficiency individuals period,

and

and to

groups

ensure

in the country.

the population will increase

from

enormous

to

food

of the

sustain for

all

During

this

about

128

million in 1995 to 193 million in 2015 (UN 1993) and the average l. 4

farm size will fall from about 2.2

acres

(Clay et al.

acres to about

1989) . On the other hand,

the

possibility of raising production and productivity through traditional

sources such as expansion of cultivated area,

increase in cropping intensity, and expansion of irrigation facilities has either been exhausted or the scope has been reduced to a large extent.

The annual compound growth rate

in agricultural GDP for the period 1972-73 to 1991-92 was 2.2 percent while the total GDP for the same period grew at


2

3.8 percent (BBS 1993).

The total food

production (rice

and wheat) has increased to 20.3 million tons

in 1991/92

from 9.6 million tons in 1960/61 (BBS 199, Tolley

et al.

1982) but the yield of MVs declined in

In the

absence of

the 1980s.

further public investments in irrigation and

flood control and with a very little unexploited capacity in the

adoption

of

improved

varieties

in

the

irrigated

ecosystem, is there still scope for

raising and sustaining

agricultural growth in Bangladesh?

To address this issue,

we

need

to

analyze

growth

components

of

agricultural

production so that appropriate policies can be formulated on the

basis

of

sound

understanding

of

the

operation

of

economic forces. Human capital refers to the productive capacities of human beings as income- producing agents in the economy.

It

is the stock of skills and productive knowledge embodied in people.

The yield or return on human capital lies in

enhancing

a

person's

skill

and

earning

power

and

in

increasing the efficiency of economic decision making both within and without the market economy 1987).

Human

research

and

developing,

capital

held

by

extension workers

adopting,

(Eatwell et al.,

farmers, plays

farm workers,

a vital

role

in

and diffusing improved technology,

which in turn, increases production and thereby farm income.


3 Evenson (1988)

showed the relationship between types of

human capital skills

and

the

associated products

in a

hierarchical fashion. He mentions that the central product of the agricultural research system is the agricultural invention typified by a new variety.

The extension system

communicates technical and price information to farmers to enhance

their

decisions.

technical

choice

and

farm

management

The farmers select the technology and make farm

management decisions 1 • Physical capital accumulation, by itself, may not be sufficient to produce growth since all production, including investment in physical capital, is subject to diminishing returns in the capital input alone.

Consequently, knowledge

acts as a catalyst to growth either as an input or through technological change or both.

Endogenous growth models

(Romer 1986, 1990; Lucas 1988) view growth as the endogenous outcome of economic forces at work within a decentralized market

system

rather

than

the

product

of

exogenous

technological change over which the market has no control. Romer (1990) stresses that four factors of production must be taken into

account:

capital,

unskilled labor,

human

capital (measured by years of education, for instance), and Farmer~ are also engaged in screening of varieties suitable for their land and adapting crop husbandry practices which Evenson (1988) has not mentioned. 1


5 move from agriculture to non-agriculture sector which will result in the

structural transformation of the economy

{Mellor, 1966) and, in turn, economic growth and prosperity. In this

case,

human

capital may

not work to

agricultural productivity and growth.

increase

Thus, human capital

may contribute to growth independently or in three ways: (a) farmers' education may act as an input to the production function, {b) it may facilitate technological progress, and {c) it may contribute to the mobility of the labor force from low productive

farm activities

to

relatively high

productive non-farm activities. Technological change at the farm level depends to a large extent on the adoption of new technology

and

farmers'

education

may

have

a

strong

influence on this process. The adoption of technology may also depend on the

availability of suitable technology

{supply side) and the extent of information disseminated through the extension services. It is well recognized that development results from human wit,

wisdom,

and

perseverance.

examined the role of human resources of

agricultural

growth.

and price

few studies

as a catalytic agent

Previous

agricultural growth in Bangladesh

But

studies

on

the

focused on technological

changes to explain the causes of

change in

output, area, cropping pattern, or yield {Asaduzzaman 1993).


6 There is no available information on the contribution of human capital to agricultural growth of Bangladesh.

We do

not know whether Bangladesh can achieve substantial growth in agriculture through the more effective use of human capital in production. It will be useful to empirically test the relationship between the human capital formation and agricultural growth, based on micro and macro level data, context of Bangladesh.

especially in the

It is also important to estimate the

contribution of human capital to adoption of new technology and to technical efficiency of production, which finally brings about agricultural growth. 1.2

Objectives of the Study

The broad objective of the study is to analyze the role of

human

capital

Bangladesh. 1.

in

enhancing

agricultural

growth

in

The specific objectives of the study are:

To estimate the rate of growth in aggregate crop output in different regions of Bangladesh.

2.

To determine the impact of human capital on the adoption of new technology.

3.

To estimate the contribution of human capital to agricultural output.


7 4,

To

determine

the

impact

of

human

capital

on

technical efficiency in production. 5.

To recommend some policy options for sustaining agricultural growth in Bangladesh. 1.3

Scope of the StuAy

Human capital is an abstract concept. educational,

health,

and nutrition status.

It encompasses As in other

previous studies (Lau et al. 1993), education was used as a proxy for human capital in the study. there

any

technology

relationship adoption?

between What

The study asks: Is

farmers'

was

the

role

education of

and

farmers'

education on agricultural growth? Are there any differences in technical efficiency in production among farms? How does farmers' education effect technical efficiency in production of different farms? To answer these questions, the study attempts to integrate both micro and

macro level analysis.

It explores the relationship between technology adoption, agricultural growth, and farmers'

education at the macro

level. It determines the impact of human capital on MV rice technology

adoption,

technical

efficiency

in

rice

production,

and non-farm income using longitudinal farm

level data. At

the

macro

level,

we

analyze

the

situation

in


8

different regions of Bangladesh for the last three decades. This is a period of substantial agricultural growth.

The

rates of growth of both total and per capita agricultural GDP

increased

substantially

after

1960.

analysis is confined to 16 regions

The

regional

(greater districts),

namely, Dhaka, Mymensingh, Faridpur, Chittagong, Noakhali, Comilla, Sylhet, Rajshahi, Dinajpur, Rangpur, Bogra, Pabna, Khulna, Barisal, Jessore, and Kushtia.

The relationship

between these regions and the new districts is presented in Appendix

Table

1. 1.

unavailability of

It

is

done

because

disaggregated data for

other

of

the

regions

during the whole period of analysis.

Macro-level analysis

is conducted from 1961-62 to 1991-92.

However, we exclude

1971-72 to 1972-73 from our analysis because of abnormal production

caused

by

the

liberation

war

and

post-

independence rehabilitation activities. The farm level analysis is confined to studying the behavior of farmers in the Ganges-Kobodak Irrigation Project area under the greater Kushtia and Jessore districts for the period 1985-86 and 1990-91. 1.4

Organization of the Dissertation

Chapter 2 reviews the literature related to the study. Chapter 3 discusses the conceptual and theoretical framework


9

of

the

study.

In

addition,

estimation techniques,

the

data sources,

statistical

specification,

estimation of variables are mentioned in this Chapter

4

discusses

the

macro-level

model,

changes

and

chapter, in

human

capital, technology, and production organization for the period

1947/48-1991/92.

technology regional

adoption and

level is

The

role of human capital in

in agricultural

presented

growth at

in Chapter 5.

the

Chapter 6

describes the characteristics of sample farms, and their human resources,

technology,

and production organization

among sample farms during 1985-86 and 1990-91.

This chapter

also analyzes the contribution of human capi.tal to modern rice

technology

production, data.

adoption,

technical

efficiency in rice

and to non-farm income, based on farm level

Conclusions and policy implications of the findings

are outlined in the last chapter.


CHAPTER II REVIEW OF LITERATURE This chapter reviews literature related to the study. is

divided

into

three

sections:

human

capital

It and

technological change, human capital and agricultural growth, and human capital and farm productivity. 2.1

Human Capital and Technological Change

Schumpter

(1911)

technological change,

first

elaborated

the

concept

of

though it was mentioned earlier in

Smith's (1777) discussion of division of labor as the outcome of induced technological or organizational change. (1911)

viewed

technical

progress

as

a

Schumpter

process

of

transformation arising from within the capitalistic system as an integral part of the competitive process where entrepreneur and entrepreneurial profits play a key role in providing economic weight to the process (Metcalfe,

1987).

Earlier

references on the subject focused mainly on the concept, measurement, and interpretation of total factor productivity change. Solow ( 195 7) defined technological progress as the upward shift

in

the

aggregate

production

function

technological change as exogenous to the system.

and

viewed


11

The concept of neutrality of technological change was first provided by Hicks (1932) and he was also the first to distinguish between neutral and biased technological change. According to him, given a homothetic production function, a neutral technological change does not change the marginal rate of substitution between a pair of factors and does not affect optimal factor proportion.

In a two-input case, a biased

technological change uses (saves) one factor relative to the other factor. A measure of biased technological change in terms of factor shares in the total factor was developed by Binswanger (1974)

using

the

duality

theory

of

production.

A

technological change is treated as x factor saving (using), if it reduces (increases) the cost share of factor x in the proportion of an output. Blackerby, Lovell, and Thursby ( 1976) think that the concept of Hicks Neutral (HN) technological change is unable to differentiate a range of similar situations.

They provided

the concept of Extended Hicks Neutral (EHN)

technological

change.

Hicks neutrality concept is valid only when the

underlying technology is homothetic, technological

change

has

linear

i.e., the firms with

expansion

path

and

the

marginal rate of technical substitution (MRTS) is measured at optimal factor proportions.

If the underlying technology is


12 non-homothetic, Hicks neutrality does not hold. They defined EHN as the one in which the underlying function is strongly separable in time-- i.e., when MRTS between two points is not affected by time.

This implies that no restriction of the

homotheticity on technology and EHN would be the same as HN if the bias is zero.

In cases when technology is homothetic, HN

would imply EHN but not the opposite. Antle and Capalbo (1988) defined technological change as the

change in production process that

comes out of the

application of scientific knowledge. From the above discussions, concept

of

technological

change

it seems that the basic has

remained

unchanged

although a wide range of meanings and interpretations has been given by different authors. According to Antle and Capalbo ( 1988), at the microlevel, three types of technological change can occur.

The

first is disembodied technological change and it occurs if changes in technology brought a parallel shift in production function (shift in the intercept) and higher output per unit of input is attained.

The second type is described as

embodied technological change representing a shift in the slope

of

the

technological

production change

is

function. related

to

intercept and slope at the same time.

The the

third change

type in

of

both

At the macro-level or


13 industry level, technological change is represented by the upward movement of total factor productivity measured by the residual, i.e., the output growth which is not accounted by growth of inputs. The technological change at the farm level occurs when the farmer adopts the new technology.

Pears (1980) mentions

that information about sources of new inputs, knowledge about how

they

can

be

optimally

used,

and

marketing

of

the

additional output is important in determining the differential rate of adoption.

Since education of the household members

provides farm family the opportunity to be more informed and decode the information, then it is expected that the rate of adoption will depend to a large extent on the level of education or knowledge stock of the farmer. A number of previous studies have examined the effect of education on the willingness of farmers to adopt innovations. Roy, Waisanen, and Rogers (1969) made an early and important study in this area.

Villaume (1977) provides a valuable

review of this extensive literature as well as an assessment of the direct and indirect effect of literacy on the adoption of innovations in Brazil and India.

Gerhart

(1975)

and

Rosenzweig (1978) use limited dependent regression techniques to identify factors influencing the probability of farmers adopting new varieties.

Gerhart found that a farmer's level


14 of education was positively associated with the probability of adoption of hybrid maize varieties in Kenya.

Rosenzweig

developed a model in which the time cost associated with acquiring and

using

information was

influencing the decision to innovate.

an important

factor

He applied the model to

cross-section data for a sample of farmers in the Punjab, finding that education was positively associated with the probability of a farmer's adoption of new varieties.

Jamison

and Lau (1982) analyzed the determinants of Thai farmers' use of chemical inputs.

They found that education and extension

play a vital role on the decision of the adoption of a new technology. A schematic review of the studies on adoption of new technology in Bangladesh is presented in Table 2.1.

Out of

the 14 studies reviewed in Table 2.1, only four studies (Ahmed 1981; Hossain 1989, Alauddin 1988, and Hossain et al. 1994) used econometric analytical technique and all of these four econometric studies found significant positive contribution of farmers' education on adoption of MVs.

However, these studies

did not focus on the intensity of the level of adoption and its relation to farmers' education.

Therefore, it would be

useful to analyze technology adoption and its determinants.


15 Table 2.1

Summary of empirical studies on the adoption of modern technology in Bangladesh agricu~ure.

SOURCE

YEAR OF THE STUDY

Rochin (1973) Asaduzzmand (1980) Ahmed (1981) Rahman (1981) Wahhab (1979) Mustafi (1981) de Lasson (1982) Hossain (1989) Kashem (1987) Islam (1985) Bhuiyan (1989) Alauddin and Tisdell(1988) Krain (1986) Hossain et al. (1994)

1970-71 1973-74 1975-76 1975-76 1977-78 1978-79 1980-81 1981-82 1983-84 1984-85 1984-85 1985-86 1985-86 1987-88

SEASONS COVERED

Am an Aman, boro Aman, boro Aman, boro Aman, boro Aus, aman, boro Am an Aus, aman, boro Am an Am an Aman, boro Aman, boro Aus, aman, boro Aus, aman, boro

SAMPLE SIZE UQazilas households (No.) (No.)

8 2 3 2 1 1 3 5 1 31 1 2 2 64

200 275 459 239 292 168 173 640 205 3444 101 116 163 1245


16 2.2

Human Capital and Agricultural Growth

Aggregate crop output or output growth of any commodity comes from increased input use or improvement in efficiency or technological change.

In micro economics literature, these

three effects are known as scale expansion effect, efficiency effect,

and

technology

Capalbo, 1988). growth

are

effect,

respectively

(Antle

and

Empirical studies on the sources of economic

undertaken

theoretical framework.

under

the

neo-classical

growth

The growth accounting approach has

been a major working tool of neoclassical growth economics. Some of these are Abramovitz (1956), Solow (1957), Kutznets (1971, 1973), Kendrick (1961, 1973), and Denison (1962, 1967, 1979, 1985).

Though the major concern of these studies is

overall economic growth, some also focused on the agriculture sector's

growth.

technological

Solow

change

as

type

growth

exogenous.

studies They

considered

accounted

contribution of labor and physical capital to growth. treated

residual

as

contribution

of

human

capital

the They and

technology to growth. Empirical studies on agricultural growth basically tried to focus on the scale expansion effect and analyzed the forces responsible for expansion in input use level and decomposed the sources of agricultural growth into area expansion, price policy effect, and technology effect. Decomposition of growth


17 trends was an interesting agricultural growth.

development in the analysis of

Although

some attempts were made

earlier to explain agricultural growth in

terms of area and

yield components, the first systematic study came from Minhas and Vaidyanathan ( 1965) who analyzed the growth output in India from 1951-59 to 1958-61. yield components, their analysis

of crop

Aside from area and

includes a component on

cropping pattern and a residual component showing interaction between cropping pattern and yield.

The yield

component

version of this earlier model which was subsequently used by Misra

(1971)

Gujrat,

for

India,

comparative

the

and

by

decomposition of output Sondhi

and

Singh

growth in

(1975)

for

a

analysis of the pre-green revolution and the

green revolution

periods in India.

further decomposed the

yield,

interaction components.

The

these studies are contribution

In this version, Minhas

cropping pattern, and the

factors which are ignored in of knowledge, technological

factors, and price structure. Sagar (1977) included a variable on price structure in the model to analyze the growth of agricultural production in Rajasthan, India, for the period 1956-61 to 1969-74.

He has

decomposed the change in the value of gross agricultural output area,

at prevalent prices into three gross components 1 productivity and price, and their interactions.

The


18 results and

showed that contribution of area, yield increases,

changes in relative price structure were 63.64 percent,

38.45

percent,

respectively.

and 7. 82 percent of total output growth, The interaction between changes in cropping

pattern and yield accounted for 3.5 percent of output growth. The change in cropping pattern gave a negative

contribution

(-2.19 percent) and all negative components added up

to 13

percent of total output growth.

sharp

contrast

with

those

These values were in

earlier

obtained

by

Minhas

and

model

and

Vaidyanathan. Narain

(1977)

decomposed the

also

followed

a

similar

index of productivity into yield, locational

shifts, cropping

pattern components, and their interactions

in examining the growth of productivity in Indian agriculture from 1952-53 to 1960-61

and from 1961-62 to 1972-73.

results showed that almost 70

percent of the increase in

productivity in the first period cropping

pattern

and

was produced by changes in

locational

individual crops and only about increases in per hectare yields.

The

shifts

of

areas

under

30 ·percent by the pure The

situation

reversed in

the second period, with pure increases in yields accounting for more than 60 percent of the

increase in productivity

while cropping pattern changes and locational shifts accounted for less than 40 percent of the increase.


19 Decomposition analysis was again used by Mukhopadhaya et al. (1982) to analyze the growth of agricultural output in West

Bengal, India, during 1960-78.

wheat

The growth of rice and

output was decomposed into four components: change due

to area,

change due to yield, change due to cropping pattern,

and change

due to interaction.

Their analysis revealed that

during 1960-71 (first period), the contribution of yield to increased

output of wheat was about 15 percent, followed by

a negative 1968-78

contribution of yield about 5 percent during (second period).

increased output of

The

contribution of area to

wheat was substantial during the second

period (1968-78) only.

But during the first period (1960-71)

and the overall period (1960-78), substantial contribution to output growth in

wheat came from the interaction term,

signifying a positive

reinforcement mechanism between yield

and cropping pattern.

In

in

rice

production was

clear contrast with wheat, due

caused by irrigation induced

primarily

to

growth

change in area

increases in cropping intensity

and yield. Murshed (1983) examined the production performance of 15 major crops in Bangladesh from 1967-70 to 1976-79.

It

identified the component elements of growth and measured their relative contribution to growth in crop output. components

of

growth

area,

productivity,

Three broad and

their


20 interaction -- were considered. analyzed in terms their

of

interaction

The

area component was

net area, cropping pattern shifts, and

effects.

The

yield

element

of

the

productivity component was further decomposed into 'technology spread'

and

'technology-neutral'

interaction effects.

yields,

and

A wide variation in the

contribution of various components to the

their

relative

output growth of

individual crops was observed. Bouis (1984) used a framework, which was developed by Herdt,

Te and Barker (1978), to examine the past trends and

sources of

growth of rice and corn yields in the Philippines

during 1970-82.

The increase in aggregate yield from one

period to another was

attributed to four sources:

increase in the proportion of

land which is irrigated, (b)

increase in the proportion of land

planted to HYVs,

increase in fertilizer application, and (d) the fertilizer response curves.

revealed that

accounted for half

of the growth in the wet season and three-fourths in the

dry

season yields.

(c)

upward shifts in

The analysis

shift in the fertilizer response functions

growth

(a)

Increased

of the

fertilizer

application accounted for 10 percent of the growth in yields in both seasons.

The remaining 40 percent of the growth in

yield in wet season

and 15 percent of the growth in yield in

dry season were attributed to

the increase in proportion of


21 land planted to HYV. disaggregated by

farm

framework used time series data

region, by season, by irrigated and rainfed

land, and by modern area,

This

output

and traditional varieties for yield, and

fertilizer

prices,

fertilizer

applications and the coefficients for the fertilizer response function. Hossain

(1984)

estimated

the

growth

of

major

crop

production fitting a semi-logarithmic function for the period 1949-84 (excluding the years 1971-72 and 1972-73) and the estimated rate of growth for all crops was found to be 2.23 percent per annum. subperiods: 1970/71

to

He had also done the analysis for three

1949/50 to 1983/84.

1957/58,

1957/58

Following

to

1970/71,

and

Vaidyanathan-Minhas•

decomposition technique, he has shown that during the 1960s, 57 percent growth was due to acreage expansion while in the 1970s, 69 percent was due to increased yield derived from HYV technology. Parthasarathy and Chowdhury (1988) analyzed the sources of growth in cereal production for the period 1975-87.

They

analyzed the overall growth situation in cereal production both at the country and regional levels to test the hypothesis of slowdown in cereal production in the 1980s. To substantiate their claim, they compared the annual growth rate for the periods 1975-87 and 1980-87.


22 The basic shortcoming of these studies is that they failed

to focus on the role of human resources engaged in

agriculture

to

analyze

agricultural

growth,

though

development results from human wit, wisdom, and perseverance. The endogenous growth theory emphasizes this fact.

Romer

(1990) presented a model of endogenous growth. In his model growth is driven by technological change that arises from intentional investment decisions made by profit maximizing agents.

The

model

is

based

on

three

premises:

(i)

technological change-- improvement in instructions formaxing together raw materials --

lies at the heart of economic

growth; (ii) technological change arises in large part because of intentional actions taken by people who respond to market incentives, i.e., market incentives play an essential role in the process whereby new knowledge is translated into goods with practical value; (iii) instructions for working with raw materials are inherently different from other economic goods. Based on these premises, it takes four inputs for production: capital, labor, human capital and an index of technology. The distinguishing feature of the technology as an input is that it is neither a conventional good nor a public good; it is a non-rival partially excludable good. that

the

The main conclusions are

stock of human capital determines

the

rate

of

growth, that too little human capital is devoted to research


23 equilibrium, that integrating into world markets will increase growth rates,

and that having a large population is not

sufficient to generate growth.

Some recent studies, based on

cross-section data from a particular country (Lau et al. 1993) or using support

cross country data set (De Gregorio 1992), also this

new theory.

contribution of economy. analyze

These

studies

human capital to

But no study has agricultural

assessed

the

overall growth of

the

yet been attempted to empirically

growth under

the

endogenous

growth

theoretic framework.

2.3

Human Capital and Farm Productivity

T.W. Schultz in his pioneering study (1954) showed that human capital associated with formal schooling enabled farmers to be more productive.

Since then, numerous studies have been

conducted by Schultz and his followers, and they

established

the fact that human capital held by farmers, farm workers, research

and

extension workers

plays

a

vital

role

in

developing, diffusing, and adopting improved technology which, in turn, increases production and consequently farm income. Chaudhuri (1971), Welch (1970), Griliches (1964), Nelson (1966), and Tang (1963) all emphasized the role of education in production, noting that it enables the farmer to acquire


24

the ability

(1) to decode information; (2) to evaluate costs

and benefits of alternative sources of economically useful information; (3) to establish quick access to newly developed economically

useful

information;

(4)

to

choose

optimum

combinations of crops, new inputs, and agricultural practices in the least number of trials; and (5) to perform agricultural operations more effectively in the economic sense

(Hong,

1975).

Studies on farmer education and farm productivity on economic efficiency.

Following Farrel

( 195 7),

focus

economic

efficiency is disaggregated into technical efficiency and price or allocative efficiency.

Technical efficiency (TE) is

the ability of a farm to achieve maximum possible output with available resources while allocative efficiency refers to the marginal conditions for profit maximization.

The usual test

for allocative efficiency is to compare the MVP of an input to its price. Since technical efficiency is the ability of a farm to achieve the maximum possible

output,

then it

can be

an

indicator of productivity of the farm and the variation in TE can reflect the productivity differences among farms. this

reason,

we

reviewed

For

the methodologies used and the

empirical findings of these studies to look into productivity differences among farms.


25 To estimate the technical efficiency of the sample farms, these

studies

followed

two major approaches

approach and non-frontier approach.

frontier

In the frontier approach,

the production frontier is estimated as the most efficient set of points in the input-output space. Deviation from this frontier is used as a measure of technical inefficiency. Farm specific

inefficiency,

then,

is

explained

characteristics of the farm and the farmer, farmer's

age,

education,

contact,

farm size,

technical

by

the

such as,

knowledge,

the

extension

and tenancy. On the other hand, non-

frontier or direct approach studies included human capital, managerial information, and systems variable directly into the production function as non-conventional inputs -- that is, Y=f(X,M,E) where Y is output, X is matrix of conventional inputs

(both

fixed

and variable),

M is

matrix of non-

conventional inputs, annd E is the matrix of environmental factors.

Conventional

inputs

include

land,

labor,

bullockpower, chemical fertilizer, irrigation etc. while nonconventional

inputs

include

education

level,

extension

contact, technical knowledge score, age of the farm operator, etc. Environmental factors include soil type and rainfall. These environmental factors are not controlled by the farmer and may only be known with a certain probability distribution at the time of making decision on X.


26 2.3.1 Non-Frontier Approaches

Non-frontier method or direct approach studies typically use

cross-sectional

effects

data

from

individual households

of education on productivity is

and

estimated using

production functions in any of the following ways: ln y = a 0 + a 1 lnx 1 + a, ln x, + a 3 ln E + a 4 EXT

(2.1)

ln y = a 0 + a 1 lnx 1 + a, ln x 2 + a 3 E + a 4 EXT

(2.2)

ln y = a0 + a 1 lnx 1 + a, ln x 2 + a 3 D + a 4 EXT

(2.3)

y = a0 + a 1 x 1 + a 2 x 2 + a3 E

(2.4)

y

=

a0 + a 1 x 1 + a 2 x 2 + a3 D

(2.5)

Where y is total output, x 1 s are inputs (e.g., land, labor, fertilizer, etc.), EXT is exposure to extension (EXT=l if exposed,

EXT=O

otherwise),

and

E is

educational

level,

sometimes represented by indicator variable D as in equations (2.3)

and

(2.5)

(D=l if E takes a value in a specified range

and 0, otherwise) or as a continuous variable (Lockheed et al.l980). The estimated education parameter is then used to estimate the

effect

of

education

on

agricultural

productivity.

Strictly speaking, this process provides estimates of how much, on average, output will increase if farmer's level of


27

education is increased by one year (continuous model) or if farmers have certain level of education rather than one (dummy variable model). Jamison and Lau ( 1982) analyzed the role of education using a Cobb-Douglas production function as in equation (2.6)

1

(2.6)

where Y is quantity of output, X is a vector of quantities of variable inputs, Z is a vector of quantities of fixed input and E is a vector of characteristics variables of the farm household, which includes availability

of

location,

agricultural

education,

extension

age,

sex,

services,

and

availability of credit. They assumed that all the physical inputs of production such as capital, labor, land, fertilizer etc. are always included in the production function and the characteristics

variables

such

as

education,

extension

services, etc. effect the whole production by a multiplicative scalar

factor.

The

effect

of

education

and

other

characteristics variable was then estimated using equation (2.7)1

r. '

olnY,

= --

oEI

(2.7)

They measured E1 in terms of years of schooling and then


28 approximated r 1 as the percentage change in output in response to an increase of one year of education at margin. The nonfrontier studies are summarized in Table 2.2. Most of the studies found a positive relationship between human capital (represented by schooling years, training, etc.) and farm productivity. Lockheed, Jamison, and Lau (1980) reviewed 20 studies on farmer education and farm efficiency in the context of developing countries. On the basis of their review, Lockheed et al. (1980) reported that the weighted mean gain in production for four years of education was 7.4 percent. In other words, holding other inputs constant at their mean levels, a farmer with four years of education would produce 7. 4 per cent more compared with a farmer who had no education. Similarly, the studies reviewed also indicate that the effect of education was more likely to be positive in a modernizing environment than in traditional ones.

Ali and Byerlee (1991)

summarized some studies that were not included in the Lockheed et al. ( 1980)

survey.

Their findings

also supported the

conclusions of Lockheed et al. 2.3.2 Frontier Approaches 2 A number of frontier approaches used to estimate the production frontier have been extensively reviewed by Forsund, 2

This section draws heavily from Battese (1992).

Rice

Rice

Cotton

Wheat

1974

1975-77

1970

1975

1972

100

33

40

120

172

Bernsten, 11977 (Central Luzon, Philippines)

Amoloza, 1983 (Philippines)

Shapiro and Muller, 1977

Makary and Rees, 1982 (Egypt)

Salam, 1976 (Punjab, Pakistan)

Lockheed, et al., 1980 (Review on article of 20 countries, crops & years)

Cotton

Rice

1969

CROP

42

Bhati, 1973 (Malaysia)

STUDY AND LOCATION

SURVEY YEAR

In most cases, education had a positive and significant effect productivity. The a modernizing environment and only 1.3%. In a traditional studies covering different settings.

Management variable based on farmer's knowledge increased productivity for the main crop by 15%.

Management index based on education and farmers' experiencewas highly signfficant and increased productivity by 6-26%, depending on region.

Highly signfficant and positive effect of knowledge score was observed on labor productivity.

Three management groups based on age, education, experience, technical knowledge and attitudes, and motivation were identffied. Farmers in the lowest management group achieved lower yields than farmers in highest group.

Score of technical knowledge has highly signfficant effect on yield and leads to up to 1 t/ha difference in yields. Knowledge was related to years of experience using new inputs and extension contact.

Farmers' technical knowledge has highly signfficant and strong effect on farm income and productivity

MAIN FINDING

Summary of non frontier studies that estimated the effects of human capital on technical efficiency.

SAMPLE SIZE

Table 2.2


Wheat

Rice

Rice

Wheat, rice

Rice

Wheat

Potato

1977

11971

1981

1978

1972-78

1963

1983

2,0002

1,438

307

683

174

365

555

Antle, 1984 (India)

Fuller, 1983 (Bangladesh)

Jamison and Moock, 1984 (NepaQ

Chou and Lau, 1987 (Thailand)

Feder et. al., 1987 (Haryana, India)

Cotlear, 1987 (Peru)

Butt, 1984 (Irrigated Pakistan)

Rice

1972

2,459

CROP

Antiporta, 1978

SURVEY YEAR

SAMPLE SIZE

STUDY AND LOCATION

Table 2.2 continued.

....0

Little effect of etther education or extension in a tradttional setting was observed. HowENer, primary schooling increased productivity by up to305% in modernizing agricutture wtth complex technology.

Education increased productivity by 1% per year of schooling, T & Vextension system also increased productivity by 9 percent.

Education increased productivity by 2.2-2.9% per year of schooling.

Completion of at least 7 years of schooling increased productivity in wheat by 27-31% and in rice by 135%.

Posttive effect of adutt ltteracy was observed.

Strong posttive effect on productivity of 4% per year of schooling was observed.

Primary education increased productivity by 7% and secondary education by 10. 7%. Strong posttive interaction of education and fertilizer use.

Education increased productivity by less than half a percent. However, farming experience improved productivity up to 6%.

MAIN FINDING


One addttional year of schooling reduces the value of farm production by 0.29%.

One addttional years of schooling increased the value of farm production by 6.1 0%. One addttionalyearof schooling increased farm production by 3.09%. One addttional year of schooling reduced farm production by 3.12%.

Tobacco, coffee, corn, cassava, guayabano, cotton, sesame, rice and livestock -do-

-do-

-do-

1969

1969

1969

1969

74

74

74

75

Hailer, 1972 (Espinal, Colombia)

Hailer, 1972 (Malaga, Colombia)

Hailer, 1972 (Moniquira, Colombia)

Hailer, 1972 (Chinchina, Colombia)

Five or more years of schooling increased the value of farm production. One addttional year of schooling increases the farm productiviTy by 1.49%.

Mixed field crop

1970

63

Pachico and Ashby, 1976 (Guarani, BraziQ

Highly posttive effect of education in commercialized farms observed. One addttional year of schooling increased the value of farm production by 4.605%.

101

Pachico and Ashby, 1976 (Garibaldi, Brazi~

Mixed field

117

Pachico and Ashby , 1976 (Candelaria, Brazi~

Highly posttive effect of education in commercialized farms observed. One addttional year of schooling increased the value of farm production by 2.69%.

MAIN FINDING

1970

CROP Mixed field

SAMPLE SURVEY SIZE YEAR 1970

STUDY AND LOCATION



236

369

Sidhu, 1976 (Punjab, India)

Sidhu, 1976 (Punjab, India)

Technical efficiency was pos~ively related to education. One add~ionalyearof schooling increased the output by 1.49%. Pos~ive but insignfficant effect of education on gross farm One add~ional year of schooling increased productiv~ by 1.41%.

Trad~ional

& Mexican wheat

Mexican wheat

1968-71

""'N

Below 4 years, education has negative effect on maize yield. However, beyond 3 years of education has pos~ive effect and one add~ional year of schooling increased maize yield by 1.73%.

Maize

1971-72

152

Moock, 1973 (Vihiga, Kenya)

There is no strong correlation (0.02) between gross farm sales and education. However, low correlation observed w~h communication behavior and agricuttural adoption variables (0.31).

Rice

1966

971

Dairy farms

1969-70

0 ne add~ional year of schooling increased the gross value added by 1.08%.

effect of education on yield was observed.

Pos~ive

Wheat

1961-64

The marginal product for one year of schooling was 606.40 drachmas. One add~ional year of schooling increased the value of agricuttural production by 6.4 7%.

MAIN FINDING

Wheat, cotton

CROP

1963

SURVEY YEAR

Harker, 1973 (Honshu, Shikoku & Kyushu, Japan)

1841

1038

Chaudri, 1974 (Punjab, Haryana & Uttar Pradesh, India)

Sadan, Macmias, & Bar-lev, 1976 (lsraeQ

430

SAMPLE SIZE

Yotopoulus, 1967 (Epirus, Greece)

STUDY AND LOCATION



Pos~ive effect of education on output observed. However, the coefficient of labor on output was negative. One add~ional year of schooling increased the value of agricunural production by 2.33%.

Posttive effect of schooling in rice production. One addttional year of schooling increased the rice production by 5.11 %. Schooling had positive effect on farm revenue. One addttional year of schooling increases the farm revenue by 1.3%. Education had a threshold effect of a minimum of 6-7 years. Farmers w~h 7 or more years of farm production. Ltterate farms had more output. One additional year of schooling increased rice yield by 2.85%.

Rice & other crops

Rice

Rice, wheat, sugarcane

Rice, wheat

Wheat

1973

1973

1975

1973-74

1968-69

541

403

102

540

138

Jamison & Lau, 1982 (Korea)

Jamison & Lau, 1982 (Kedah and Perils, Malaysia)

Pudasaini, 1976 (Bara, Nepa~

Calkins, 1976 (Nuwakot, Nepa~

Sharma, 1974 (Rupendehi, Nepa~

add~ional

..... .....

effect of education on the output observed one year of schooling increased the value of agricu~ural production by 2.22%.

Pos~ive

Rice & other crops

1973

1363

Jamison & Lau, 1982 (Korea)

No conclusive resuns obtained.

Rice & other crops

1961

895

Hong, 1975 (Korea)

MAIN FINDING Dummy variable tor education used and negative relationships between output and education was found.

CROP Maize, livestock tea

674

Hopcraft, 1974

SIZE YEAR 1969-70

SAMPLE SIZE

STUDY AND LOCATION



Patrick and Kehrberg, 1973 (Vicosa, Brazi~

Maack, 1981 (Vihiga, Kenya)

(Taiwan)

Wu, 1971

(Taiwan)

62

152

316

1971

Positive effect. One additional year of schooling increased gross farm income by 3.87%. Extension contact and four or more years of schooling had signfficant positive effect on maize yield. However, formal education (beyond 3 years) and extension contact appeared to be substitutes in terms of acquisition of knowledge). Positive effect. One additional year of schooling increased the value of farm production by 2.33%.

Maize

Dairy

Positive effect but no threshold effect. One additional year of schooling increased gross farm income by 0.7%.

Positive effect of education on rice production and on net farm earnings. One additional year of schooling increased rice production by 2.74%.



MAIN FINDING

Banana

Rice

1964-65

333

Wu, 1971 1964-66

Rice

1973

220

Halim, 1976 (Philippines)

Halim, 1976

Rice

274

Halim, 1976

1968

CROP

273

SIZE YEAR

Rice

SAMPLE SIZE

1963

STUDY AND LOCATION



91

184

Jamison and Lau, 1982 (Chiang Mai, Thailand)

310

SAMPLE SIZE

Jamison and Lau, 1982 (Chiang Mai, Thailand)

Wu, 1977 (Taiwan)

STUDY AND LOCATION


SIZE YEAR

Less than 6.6years of education has negative effect on crop production, however, more than 6.6 years of education had positive effect on crop production. One additional! year of schooling increased gross garm income by 0.9%. The coefficient of education had an increase between the indicator for primary education (4 years) and more than 4 years. One additional year of schooling increased rice output by 3.15%. The coefficient of education had an increase between the indicator for primary education (4 years) and more than 4 years). One additional year of education increased rice production by 2.43%.

Rice

Rice

MAIN FINDING

Mixed

CROP



36

Lovell and Schmidt (1980), Schmidt (1986) and Battese (1992). In this section, we briefly discuss all these models and provide a schematic review of their empirical applications. Production frontier models can be categorized into three major groups: deterministic frontiers, stochastic frontiers, and panel data models. For convenience of exposition, these models are presented such that the dependent variable is the original output of the production process, denoted by Y, which

is assumed to be expressed in terms of the product of a known function of a vector, x, of the inputs of production and a function

of unobservable

random variables

and

stochastic

errors. 2.3.2.1 Deterministic Frontiers The deterministic frontier model is defined by Y, = f(X,; B) exp(- U,)

(2.8)

i = 1, 2, ... ,N

where Y1 represents the possible production level for the ith sample firm; f(xd B) is a suitable functional form (e.g., Cobb-Douglas or Translog) of the vector, x 1 , of inputs for the ith firm and a vector, B, of unknown parameters; U1 is a nonnegative random variable associated with firm-specific factors

which

contribute

to

the

i[YJ/f(xJ;B) ]. That is, firm j is judged technically

more

efficient

relative

to

the

unfavorable

conditions associated with its productive activity (i.e., VJ