interlinked transactions in cash cropping economies: the determinants of farmer participation and...
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Interlinked Transactions in Cash Cropping Economies: The Determinants of Farmer Participation and
Benefits in Rural Mozambique
Rui M.S. Benfica
Maputo, Mozambique
September, 2006
BACKGROUND Predominance/persistence of Contract farming in cash cropping in
Mozambique, due to:
Cash constraints, poor access to inputs and credit Intensive management and specific production techniques
Difficult to support under spot marketing or plantation arrangements
Processors needing raw materials to achieve scale and recover investments:
Provide inputs on credit and extension assistance Buy all the output from contract farmers at pre-determined prices
(Monopsonic rights under Concession Agreements with the GOM)
Over 100,000 tobacco farmers engage in these contracts nationwide; over 50,000 in the study area
MOTIVATION/CONTRIBUTION
Contract farming institutional arrangements studied at length
However: Lack of Empirical assessment with household Level data Failure to appropriately account for selectivity bias Use of limited data sets and poor specifications
In addition to accounting for possible selection bias, THIS PAPER: Recognizes heterogeneity among participants themselves Investigates threshold effects of education and land holdings to identify
the types of farmers that benefit Gives important indications regarding the effects of contract farming on
differentiation
OBJECTIVES
1. To understand the determinants of farmer participation /selection in tobacco growing schemes
2. To estimate the determinants of performance (profits) with the tobacco crop among participants
3. To assess the effects of participation on agricultural and total household incomes, and to explore what kind of participants are most likely to gain
HOUSEHOLD LEVEL SURVEYS Monopsony concession Areas for two Firms:
Mozambique Leaf Tobacco DIMON – Mozambique
Sample size: 159 farmers
117 tobacco contract growers 42 non-growers
Households were visited twice:
March 2004: Recall on pre-harvesting events September 2004: Harvesting and post-harvesting events
Issues covered: Household production, marketing, factor ownership and allocation, assets, off-farm income sources, cutting and planting of trees, etc …… Ultimately designed to build a SAM for CGE analysis
ECONOMETRIC MODELS Sample Selection Models: Account for
unobservable factors that may affect both the likelihood of participation and performance
Control for selection bias in outcome regressions
1st Stage: Probit Equation for Participation
Pr(ci=1|zi) = Φ(γzi), where c – Participation dummy z – Exogenous determinants vector
γ – Coefficient estimates for z
Vector z includes: education thresholds (Eki), land thresholds (Aji), assets, demographics, technology, diversification, and location or agro-ecological/infra-structural fixed-effects (xi).
Econometric Models2nd Stage: Selection Adjusted OLS Regressions (1) Determinants of Cash Crop Profits
yi = + + βxi + ρλi(γzi) + ui , if ci=1
yi - Net profits from tobaccoAji - Owned land area quartilesEki - Education attainment level dummiesxi - Other demographic, assets, technology and locational factorsλi - Inverse Mills ratio
From the 1st Stage Probit, the IMR (λ) Inverse mills ratio (selection hazard) is obtained for each observation i as λi = ø(γzi)/Φ(γzi), where ø(γzi) and Φ(γzi) are the normal density and
distribution functions.
A, E and x are sub-sets of the set Z from the first stage. Elements in Z not included here are “exclusion restrictions”.
Equation returns estimates of the determinants of cash crop profits (α,δ, and β) and the sample selection bias coefficient (ρ).
jij
j A
4
2
0 kik
k E
3
2
0
Econometric Models(2) Treatment Effects with Land and Education Thresholds
Yi = γCi + + + + + βxi + ρhi(γzi) + ui
Yi – Crop or total household incomeCi - Participation dummyAji - Owned land area quartilesEki - Education attainment level dummiesxi - Other demographic, assets, technology and locational factors
hi - selection hazard ratio
hi = ø(γzi)/Φ(γzi) if Ci =1 and hi = ø(γzi)/[1-Φ(γzi)] if Ci =0
Land and schooling interacted with participation to test for threshold effects
jij
j A
4
2
0 jij
j ACi4
2
kik
k E
3
2
0 kik
k ECi3
2
MODEL RESULTS (1)
1st Stage: Determinants of ParticipationVariables Coef. P>|z| Comments
Demographics
Female headed household
Age of household head
Labor adult equivalents
Education:1-3 years
Education: > 3 years
- 0.375
- 0.013
- 0.154
- 0.071
0.024
0.40
0.38
0.20
0.84
0.95
- Weak Demographic Effects
- No differences by gender, age, or education of the head
Assets and Technology
Area_Q2
Area_Q3
Area_Q4
0.333
0.027
0.500
0.36
0.95
0.34
- No effect on participations of land ownership
Use of Animal traction
Value of tools
Value of other equipment
1.198
0.023
0.004
0.02*
0.09*
0.22
- Animal traction and household assets drive up participation
Diversification Activities
Has livestock income
Has Self-employment income
Has wage labor income
-1.026
0.257- 0.879
0.06*
0.37
0.00*
- Households with livestock and wage labor less likely to grow tobacco – inverse relationship
N : 159
Wald chi2 (18) : 45.25
Prob > chi2 : 0.000
Pseudo R2 : 0.25Implications: growth in the tobacco sector could reduce differentiation through employment linkages
MODEL RESULTS (2)2nd Stage: Selection Adjusted OLS Regressions
(1) Determinants of Tobacco ProfitsVariables Coef. P>|z| Comments
Demographics
Female headed household
Age of household head
Labor adult equivalents
Education:1-3 years
Education: > 3 years
-405.56
-5.44
106.51
-148.86
17.55
0.05*
0.42
0.21
0.51
0.94
- Female headed households less profitable
- No effects of education on profits;
Assets and Technology
Area_Q2
Area_Q3
Area_Q4
247.07
78.32
780.34
0.18
0.74
0.02*
- Land has an effect at the highest threshold
Use of Animal traction
Value of tools
Value of other equipment
198.83
8.47
3.86
0.63
0.08*
0.13
- Value of assets important
Agro-Ecological/Local
Fixed Effects
(*) (*) - Profits higher in mid/high altitude areas than in drier and lower altitude areas
Lambda (Inverse Mills Ratio) 229.53 0.31 - No evidence of sample selection bias
N : 117
F(16, 100) : 4.12
Prob > F : 0.000
Adj-R2 : 0.46Implications: Economies of scale to be explored in tobacco
MODEL RESULTS (3)(2) Treatment Effects/Thresholds: Crop & HH income
Variables Crop Income Total Income
CommentsCoef. P>|z| Coef. P>|z|
Participation in CF 407.70 0.46 85.87 0.88
Demographics
Female headed
Age of head
Labor adult equivs
-488.01
4.85
25.44
0.04*
0.64
0.80
0.66
15.85
- 3.99
0.99
0.15
0.97
- Off-farm Income reduces gender differentiation
Education Thresholds
Education: 1-3 years
Education >3 years
[Education : 1-3]*CF
[Education >3]*CF
195.32
361.14-482.02 -
637.32
0.45
0.25
0.40
0.28
269.76
718.92-452.16
- 703.27
0.30
0.03*
0.44
0.23
- No effect on crop income regardless of participation- Effect on Total income, BUT…no interaction effects
Land Threshold Effects
Area_Q2
Area_Q3
Area_Q4
Area_Q2*CF
Area_Q3*CF
Area_Q4*CF
527.93
665.13
723.32
-129.33
166.40
1,305.86
0.02*
0.05*
0.07*
0.71
0.76
0.04*
401.17
820.94
691.65
4.26
-18.28
1,575.96
0.12
0.00*
0.06*
0.99
0.97
0.02*
- Higher land areas reflected in both crop and total incomes- Interaction Effects only strong and significant at the fourth quartile for both crop and total income- Even large farmers appear strongly engaged in off-farm activities
Agro-Ecological/Local (*) (*) (*) (*) - No location fixed effects
Lambda (Inv Mills Ratio) 331.11 0.18 68.56 0.78 - No sample selection bias
N: 159 R2: 0.44
Prob>F 0.000
N: 159 R2: 0.43
Prob>F 0.000
The results driven by efficient use of readily available experienced labor in the area
POLICY IMPLICATIONS Lack of returns to education suggest
high scope for improvement in productivity enhancing field practices capable of rewarding more educated farmers;
Growth in tobacco through larger areas and increased productivity, associated with labor hiring – may be inequality reducing Important to promote growth as a poverty/inequality reduction strategy Along with increased off farm opportunities also reduce gender differences
Linkage effects appear important, especially through labor markets Issue need to be looked at on an economy-wide framework (CGE)
Important to keep open migration and trade policy with Malawi
Technological and environmental spillovers need more attention: On the positive side, fertilizer use in food crops by growers and non-growers On the negative, long term consequences of deforestation and soil erosion
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