Title page 1

Contents 4

About the Editors 8

Contributors 9

Foreword 11

Acknowledgments 13

1. Economic Benefits of Agricultural Mechanization Services: An Analysis of Maize Farmers 14

1.1. Introduction 14

1.2. Conceptual Framework and Estimation Strategies 17

1.2.1. Conceptual Framework 17

1.2.2. Estimation Strategies 18

1.3. Data and Descriptive Statistics 20

1.3.1. Data 20

1.3.2. Variables 21

1.3.3. Descriptive Statistics 22

1.4. Empirical Results 25

1.4.1. Determinants of Mechanization Service Adoption 27

1.4.2. Impacts of Mechanization Service Adoption 27

1.4.3. Impacts of the Mechanization Service Intensity on Economic Welfare 28

1.5. Conclusions and Policy Implications 31

References 33

Appendix 40

2. Reducing Chemical Fertilizer Application through Mechanized Fertilization: Insights from Maize Farmers 41

2.1. Introduction 41

2.2. Conceptual Framework 44

2.3. Empirical Method 46

2.4. Data, Variables, and Descriptive Statistics 48

2.4.1. Data 48

2.4.2. Key Variable Measurements 49

2.4.3. Descriptive Statistics 50

2.5. Empirical Results 52

2.5.1. Determinants of Mechanized Fertilization Adoption 52

2.5.2. Impact of Mechanized Fertilization Adoption on Chemical Fertilizer Application 54

2.5.3. Further Analyses 55

2.6. Conclusion and Policy Implications 57

References 59

Appendix 66

3. Produce More or Purchase More? Agricultural Machinery Use Intensity and Dietary Outcomes 67

3.1. Introduction 67

3.2. Analytical Framework 69

3.3. Data Source, Variable Definitions, and Descriptive Statistics 71

3.3.1. Data Source 71

3.3.2. Variable Definitions 71

3.3.3. Descriptive Statistics 74

3.4. Empirical Strategies 78

3.4.1. Self-Selection Bias Issue of Agricultural Machinery Use Intensity 78

3.4.2. Instrument Variable-Based Approaches 78

3.4.3. Instrumental Variable Selection 79

3.5. Empirical Results and Discussions 80

3.5.1. Determinants of Agricultural Machinery Use Intensity 81

3.5.2. Impact of Agricultural Machinery Use Intensity and Control Variables on Rice Purchasing Decisions 82

3.5.3. Impact of Agricultural Machinery Use Intensity and Control Variables on Dietary Diversity 83

3.5.4. Impact of Agricultural Machinery Use Intensity on Market Purchasing Decisions of Other Food Items 83

3.5.5. Impacts of Different Levels of Agricultural Machinery Use Intensity 85

3.6. Conclusion and Implications 86

References 88

4. Bridging the Gap: Assessing the Equity Implications of Agricultural Mechanization in India 93

4.1. Introduction 93

4.2. Methods 97

4.2.1. Construction of the Vulnerability Indicator 97

4.2.2. Data sources 99

4.3. Descriptive statistics 100

4.4. Welfare Effects of Agricultural Mechanization 104

4.4.1. Direct effects 104

4.5. Discussion 111

4.6. Conclusion and Policy Implications 112

References 114

Appendix 120

5. Changing Climate, Mechanization, and Gender Wages in Indian Agriculture 123

5.1. Introduction 123

5.2. Relevant Literature 125

5.2.1. Farm Mechanization and Wage Rate 125

5.2.2. Climate Change and Wage Rates 126

5.2.3. Theoretical Underpinning 127

5.3. Modelling and Data 129

5.4. Results 132

5.4.1. Model Fit and Diagnostic Tests 132

5.4.2. Estimation Outcomes and Discussion 134

5.5. Conclusion and Policy Implications 139

References 142

Appendix 146

6. Mechanization and Gendered Labor Market Impacts: Evidence from Longitudinal Data in Maharashtra, India 149

6.1. Introduction 149

6.2. Study Sample, Mechanization, Wages, and Labor Use 152

6.2.1. Sample Description 154

6.2.2. Mechanization 155

6.2.3. Wage Rates across Villages 156

6.2.4. Labor Use 157

6.3. Empirical Framework 159

6.4. Results and Discussion 161

6.5. Conclusion 168

References 170

7. Examining the Extent of Agriculture Mechanization Adoption and Internet Use by Gender and the Impacts on the Paddy Yield of Small-scale Farmers in Indonesia 174

7.1. Introduction 174

7.2. Methodology 177

7.2.1. Data and variable selection 177

7.2.2. Empirical Methods 178

7.3. Results and Discussion 184

7.3.1. Gender Differentials in Agriculture Mechanization and Internet Use 184

7.3.2. Impacts of Mechanization Adoption and Internet Use on Yield 187

7.3.3. Impacts of Mechanization Adoption and Internet Use by Gender 189

7.3.4. Gender Paddy Yield Gap Among Small-Scale Farmers 194

7.3.5. Contribution of Mechanization Adoption and Internet Use to the Gender Yield Gap 196

7.4. Conclusion 200

References 202

Table 1.1. Variable Definitions and Summary Statistics 23

Table 1.2. Mean Difference in the Selected Variable Between Mechanization Service Adopters and Non-Adopters 25

Table 1.3. Impact of Mechanization Service Adoption on Maize Yield, Household Income, and Income Diversification: Conditional Mixed... 26

Table 1.4. Impact of Mechanization Service Intensity on Maize Yield, Household Income, and Income Diversification: Conditional Mixed... 29

Table 2.1. Variable Definitions and Descriptive Statistics 51

Table 2.2. Mean Differences in the Variables For Mechanized Fertilization Adopters and Non-Adopters 52

Table 2.3. Determinants of Mechanized Fertilization Adoption and Its Impact on Chemical Fertilizer Application: Conditional Mixed Process... 53

Table 2.4. Determinants of Mechanized Fertilization Ratio and Its Impact on Chemical Fertilizer Application: Conditional Mixed Process... 56

Table 2.5. Impacts of Mechanized Fertilization Adoption on Chemical Fertilizer Productivity: The Second Stage of the Conditional Mixed Process... 57

Table 3.1. Food Groups Used to Calculate Dietary Diversity 72

Table 3.2. Descriptive Statistics of Agricultural Machinery Use by Production Stages 73

Table 3.3. Variable Definition and Descriptive Statistics 75

Table 3.4. Mean Differences of the Selected Variables Between Groups with Different Agricultural Machinery Use Intensity 77

Table 3.5. Impact of Agricultural Machinery Use Intensity on Rice Purchasing Decision and Dietary Diversity: IV-Probit and IV-Poisson... 81

Table 3.6. Impact of Agricultural Machinery Use Intensity on Market Purchasing Decisions of Food Items: Second Stage of IV-Probit... 84

Table 3.7. Impact of Different Agricultural Machinery Use Intensity on Rice Purchasing Decision and Dietary Diversity: IV-Probit and... 85

Table 4.1. Categorization Criteria for Variables Chosen to Build Vulnerability Indicator 98

Table 4.2. Categorization Criteria for Vulnerability Indicator 98

Table 4.3. Vulnerability Composition Across Operational Landholder Categories 102

Table 4.4. Output Market Access Across Vulnerability Classes 102

Table 4.5. Marketable Surplus Across Vulnerability Classes and Social Groups 103

Table 4.6. Paddy Yield Across Vulnerability Classes and Social Groups 105

Table 4.7. Treatment Effect Estimate of Mechanization on Crop Yield 106

Table 4.8. Treatment Effect Estimate of Mechanization on Paddy Yield 106

Table 4.9. Treatment Effect Estimate of Mechanization on Farm and Market Income 107

Table 4.10. Off-farm Employment and Income Across Vulnerability Classes and Social Groups 108

Table 4.11. Labor Employment Across All Farm Operations for Paddy (hours/ha) 109

Table 4.12. Labor Employment Across All Farm Operations for Paddy (workers/ha) 109

Table 5.1. Defining Components of Three Types of Farm Mechanization 128

Table 5.2. Summary Statistics of Variables of Interest 131

Table 5.3. Diagnostic Tests for Model Selection 133

Table 5.4. Variance Inflation Factors of Study Variables 133

Table 5.5. Panel Regression Results for Determinants of Gender Wages 135

Table 6.1. Summary Statistics 154

Table 6.2. Intensity of Mechanization (hours) 156

Table 6.3. Labor Use across Various Types of Labor (hours) 158

Table 6.4. Machine Use Impacts on Labor Use (All machines, all operations) 162

Table 6.5. Tractor Use Impacts on Labor Use (Land Preparation) 163

Table 6.6. Electric motor Use Impacts on Labor Use (Irrigation) 164

Table 6.7. Sprayer Use Impacts on Labor Use (Weeding and Plant Protection) 165

Table 6.8. Thresher Use Impacts on Labor Use (Threshing) 166

Table 6.9. Combined Harvester Use Impacts on Labor Use (Harvesting) 167

Table 7.1. Definition of Variables Used in the Empirical Models 183

Table 7.2. Descriptive Statistics and Mean Differences 186

Table 7.3. OLS and RIF-OLS (Unconditional Quantile Regression at 10th, 50th, and 90th Percentiles) Regressions of the Impact of Mechanization... 188

Table 7.4. OLS and RIF-OLS (Unconditional Quantile Regression at 10th, 50th, and 90th percentiles) Regressions of the Impact of Mechanization... 191

Table 7.5. OLS and RIF-OLS (Unconditional Quantile Regression at 10th, 50th, and 90th percentiles) Regressions of the Gender Yield Gap 193

Table 7.6. Blinder-Oaxaca Decomposition of Gender Paddy Yield Gap Among Small-Scale Farmers Under Different Scenarios of Weight 197

Table A1. Falsification Test of Instrument Variable 40

Table A2. Falsification Test 66

Table 4.1A. Operational Landholding Composition and Size by Vulnerability Category 120

Table 4.2A. Labor Employment Across All Operations for Paddy Cultivation (by caste) 121

Table 4.3A. Labor Employment for Paddy Cultivation for Specific Operations 122

Table 5.A1. Review Matrix Visiting Climate Change-Farm Machination-Gender Wage Nexus 146