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