Gender & labor allocation in smallholder farms. The case of chili producers in West Java

Transcription

1 Gender & labor allocation in smallholder farms The case of chili producers in West Java

2 Review of Gender Research in Agriculture Women produce 60-80% of food in developing countries (FAO, 2011) Women face discrimination in access to key productive assets, inputs and services (FAO, 2011) Assume that men are the only producers in the household and the sole decision-makers regarding farming activities (ADB, 2013) (Quisumbing, 2014) 2

3 Review of Gender Research in Agriculture Misunderstanding gender s role in Ag o o o Misunderstanding 60-80% of labor force Misunderstanding household constraint in accessing technologies & markets Misunderstanding how farm households make decisions (Quisumbing, 2014) 3

4 Review of Gender Research in Agriculture 601 studies in total 409 country case studies Country and region specific studies Book chapter General studies Total Africa South Asia Southeast Asia Latin America Other 5. Gender asset gap Gender equity and land Nonland agricultural inputs, technology and services Access to financial services Livestock Gender and social capital Nutrition and health Geographical spread 59% 22% 6% 10% 2% Heavily African Context (50/66) Inputs access women constrained Productivity x mixed results (mostly null effect) Source: (Rutsaert et al., IRRI) 4 (Van de Fliert et al., 2001) - Indonesia: Female sweet potato farm π (Rola et al., 2002) - Philippines: Excess female labor: lower OC

5 Review of Gender Research in Agriculture Gender systems are diverse o Community o Country o Region Generalizing results may be an issue o Vast majority of research in SSA o Very little in Asia particularly SE Asia 5

6 Labor For smallholders, labor is critical Family labor Hired labor Inefficient labor allocation constrains development Under-allocation Over-allocation Research on household labor decisions Large body of work on Labor supply & demand technology & income The few studies on labor allocation (Barret et al., 2008; Andrews et al., 2015) African context Focus on allocation of family labor 6

7 Research Questions How do gender roles affect efficient allocation of labor? hired labor male vs female family labor male vs female 7

8 Profit: The Neo-Classical Farm $ π = P y f x P x x FOC Allocative Efficiency * : AE? P y f P x = zγ 0 What about: Transaction costs? Capacity? Utility? P x zγ AE>0 x Under Allocated X AE<0 P y f X Over Allocated 8

9 Interpreting Gamma P zγ AE>0 Conventional Way*: AE = zγ + ε quantile regression time? Problems: -Bad fit -Interpretation? $ γ OLS AE P x AE<0 P y f X X Over Under Under Allocated Over Allocated *Henderson, 2015; Barret et al., 2008

10 Estimation Strategy Stage 1 Estimate the value of marginal product Production Function Function: Cobb-Douglas Technical Efficiency (TE) Equation 1 ln y = β 0 + σ j ln x j β j + v Stage2 Quantile regress on AE Allocative Efficiency for input k Equation 2 AE k = zγ k + ε k AE k P y f k ( ) P xk Z ~ HH chars; Market chars; Gender 10 β f k ( ) = k eln y x k

11 Why Chili? Commercial production Female participation 11

12 Province: West Java Districts: Garut, Tasik, Ciamis 12

13 Province: West Java Districts: Garut, Tasik, Ciamis Sub-Districts: Garut 8, Tasik 3, Ciamis 3 13 Villages: 3 villages each

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16 Chili Data June 2016 N=213 Input Module (Most recently completed cycle) Fertilizers Chemicals Land Fixed inputs Labor Module Male vs Female Hired vs Family Gender Module Perception of responsibility in farming (male vs female) 16

17 LABOR SHARES Female Male TOTAL 59% 41% HARVEST 73% 27% GROWOUT 71% 29% PLANTING 63% 37% LAND PREP 38% 62% 17

18 Gender Perception Module 1 Preparing the land 2 Buying farm equipment 3 Buying inputs 4 Spreading seed 5 Mulching 6 Planting 7 Installing stakes 8 Fertilizing 9 Spraying chemicals 10 Weeding 11 Watering 12 Harvesting 13 Transporting chilli to point sale 14 Sorting and grading 15 Negotiating with buyer 16 Preparing meal For each of the following activities in chili production, please indicate who has the main responsibility between the husband and wife? 1. Husband 3. Both 2. Wife P16 Score Male 0 Female = 1 2 Scores Range set [0 to 10] Perception (M): 2.07 Perception (F): 2.33 if Husband if Both if Wife (ρ =.52) 18

19 VARIABLES (1) (2) (3) (4) Male Female Hired Family Female Hired Male Family Male Perception ** (2.106) (5.551) (1.488) (0.684) Female Perception ** (1.358) (3.581) (0.96) (0.441) Constant 27.18** 104.0** 65.62** 4.450* (5.782) (15.24) (4.085) (1.879) Observations R-squared

20 Estimation Stage 1 Estimate the value of marginal product Plot-Production Function Function: Cobb-Douglas Technical Efficiency (TE) Equation 1 ln y = β 0 + σ j ln x j ln y = β 0 + σ j ln x j β j + v + u β j + ρσ λ + ε Stage2 Quantile regress on AE Allocative Efficiency for input k AE k P y f k ( ) P xk Equation 2 AE k = zγ k + ε k Z ~ HH chars; Market chars; Gender 20 β f k ( ) = k eln y x k

21 Labor Parameter Estimates (1) (2) EQUATION VARIABLES OLS SF SINGLE Labor-hired female (days) (0.022) (0.021) Labor-hired male (days) (0.031) (0.028) Labor-own female (days) (0.083) (0.075) Labor-own male (days) (0.019) (0.017) N Other inputs in model: N, P, K, Mg, Ca, S, Insect/herb/fung-icides, Plot Area, Seeds, Altitude, Animal Traction, Water pump, Chili-Variety, 21 IMR ~ Not significant TE ~ Gender Perception Scores: Insignificant (p~0.2)

22 Kernel Densities - AE P y f P x 22

23 Gamma Z: Gender Perception HH/Farm characteristics HH head & spouse characteristics Age; education HH characteristics # of male adults # of female adults # of dependents Assets Land Mobile phones Motorcycles Water pumps Market characteristics Distance to market from plot Fertilizer price Seed price Over P Under γ OLS AE

24 60 Pseudo R 2 by Quantiles % 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% Quantiles Female - Hired Male - Hired Female - Family Male - Family 24

25 Female - Hired Over Under 25

26 Female - Hired Male - Hired Female - Family Male - Family 26

27 Female - Hired Male - Hired Female - Family Male - Family 27

28 Conclusions Method OLS not the best The important variation happens at the tail of the distribution Relationship to AE is not consistent across distribution Quantiles can help to identify Where constraints start to bind The effect of variables at important parts of the distribution Empirical Generally corroborate gender findings Hired Male, female, and Female family labor Exception: Male Family labor more efficiently allocated by women Future work? Robustness to functional forms, different IMR calculations Improving Gender Score measure 28

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