contract farming and gender equity in african landscapes (congenial)

Contract Farming and Gender Equity in

African Landscapes (CONGENIAL)



Martin Prowse, Betty Chinyamumnyamu, Ron Ngwira and Jytte Agergaard

[email protected]

http://www.mycongenial.com/

mailto:[email protected]






Rationale

Contract farming is expanding rapidly in Africa but often

suffers from high rates of default and claims of exploitation

from smallholders



Rationale



from smallholders

How can we reduce default rates?



Rationale

Farmer

Default

Sell inputs

Side-sell produce

Sell others’ produce

Firm

Default

Late supply of inputs

Purchases from spot markets

Changes in price

ACDI-VOCA (2012)

Source: Gow and Swinnen (2000)

Price (P)

Capital and reputation losses (K)

P0

P1

K1A

P-1

K-2B

Side selling

range for Farm A

Spot market

purchase range

for Firm B

P-2



Reducing default through self-enforcing contracts



Self-enforcing contracts

Contracts can be designed to

limit the likelihood of default

through increasing the amount

of capital and reputation

included in the scheme, thus

increasing the self-

enforcement range:

• Both parties invest in specific assets (capital)

• Name-and-shame methods (reputation)

Can including wives in contracts increase the self-enforcement range?



Rationale



from smallholders



Literature from the 1970s and 1980s highlighted how contract

farming can, inter alia, contribute to:

• dependency on the firm and a form of self-exploitation in which

smallholders bear all production risk

• an intra-household distribution of labour/income that is

detrimental to women’s interests

• harmful spillover effects in local markets (e.g. food, input and

output markets)

Rationale



Intra-household bargaining

• Wives tend to see contract farming in a favourable light if it increases

aggregate income, providing them with an incentive to co-operate in

production

• These incentives are weakened when they are not remunerated according

to labour input or when income is diverted away from household priorities

Who typically controls contract farming income?

What purposes might this income be used for?

What might the response of wives be if they are not receiving

a fair share of income?



Research Question

To what extent and how does including wives within a

contract farming scheme improve the benefits to the

firm, farms and families?



Null hypotheses

Husbands: Including wives in the contract will not be welcomed by

husbands?

Firm: Including wives in the contract has no impact on default rates?

Farms: Including wives in the contract has no impact on productivity and

the intra-household division of labour?

Families: Including wives in the contract has no impact on household

well-being?



Methodology

We combined a randomised design - where

clubs/members are randomly assigned the

intervention - with an interview schedule that

included biographical, open and closed questions

Husbands and wives interviewed separately using

the same questionnaire

Time: 18 months from October 2013

Fieldwork cash: US$12,000



Kasungu District, Malawi We worked with AOI to evaluate the inclusion of wives

growing soya within a standard tobacco contract

Clustered randomised design

- assumed standardised effect size (Delta) @ 0.34 for

a 20% change in impact variables

- aimed for 100 clubs (50 vs. 50)

- assumed club size of 6 members

- intra-club correlation coefficient estimated at 0.22 in

Kasungu District for tobacco yields

462 households in total

227 HHs were randomly selected to receive soya

(could select multiples of 12.5kgs up to 50kgs)

235 control households

Scheme Sample size Participants % of participants Total kgs distributed

Chatoloma 39 16 41.0 225

Kasungu Central 1 75 53 70.7 1162.5

Kasungu Central 2 29 17 58.6 212.5

Mangwazu 32 28 87.5 350

Mphomwa 38 32 84.2 400

Wimbe 14 8 57.1 174

Total 227 154 66.53 2524

Huge attrition from treatment sample

Of the 227 selected for treatment, our partner, AOI

informed us that only 154 complied and only 114 planted

Why? Once bitten, twice shy - AOI distributed soya seed

to HHs in 2012/13 and deducted the cost directly from

gross tobacco proceeds

Quality of the soya seed distributed to wives was

very poor





Implications of attrition

Reduced sample of treatment households

reduces likelihood of significant findings

Power of the RCT unlikely to reach 80%

Randomisation procedure





Fieldwork data – Did the randomisation work? ANOVA

Sum of Squares df Mean Square F Sig.

1.11 Total members Between Groups 4.454 2 2.227 .496 .609

Within Groups 1386.389 309 4.487

Total

1390.843 311

1.2 Adult equivalent score Between Groups 1.695 2 .847 .246 .782

Within Groups 1056.972 307 3.443

Total

1058.667 309

Total land under production

2014

Between Groups .449 2 .224 .024 .976

Within Groups 2873.900 309 9.301

Total

2874.348 311

4.3 Total expenditure Between Groups 5.222E11 2 2.611E11 .461 .631

Within Groups 1.751E14 309 5.666E11

Total

1.756E14 311

3.4 Total income

Between Groups 3.745E11 2 1.873E11 .209 .812

Within Groups 2.775E14 309 8.979E11

Total 2.778E14 311

N Mean Std. Deviation Std. Error

1.11 Total members Control 173 6.70 2.108 .160

Supposed 66 6.86 2.293 .282

Planted 73 6.51 1.973 .231

Total 312 6.69 2.115 .120

1.2 Adult equivalent score Control 171 5.5498 1.81242 .13860

Supposed 66 5.6973 2.02602 .24939

Planted 73 5.4822 1.79403 .20997

Total 310 5.5653 1.85097 .10513

Total land under production 2014 Control 173 5.7881 2.87265 .21840

Supposed 66 5.8805 3.24817 .39982

Planted 73 5.7866 3.26758 .38244

Total 312 5.8073 3.04011 .17211

4.3 Total expenditure Control 173 798100.91 831163.861 63192.218

Supposed 66 693604.47 595629.634 73316.940

Planted 73 766172.15 679049.079 79476.683

Total 312 768525.38 751430.874 42541.396

3.4 Total income Control 173 760807.95 1089658.252 82845.183

Supposed 66 772151.97 800824.718 98574.709

Planted 73 682774.40 661939.763 77474.189

Total 312 744949.80 945174.348 53509.960



Fieldwork data – Did the randomisation work?

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square .191a 2 .909

Likelihood Ratio .190 2 .909

Linear-by-Linear

Association

.155 1 .694

N of Valid Cases 311

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 5.303a 6 .506

Likelihood Ratio 5.724 6 .455

Linear-by-Linear Association .702 1 .402


Randomisation procedure

Logistic regression (0,1) shows asymmetric treatment

assignment in one zone – Kasungu Central 2

Implication: Control for KU2 when comparing impact

variables across T and C groups (t-tests)

Multinomial logistic regression (0, 1, 2) and Kruskal

Wallis tests showed after attrition we had an asymmetric

frequency of treatment, supposed and control

households in 4 of 6 zones

Implication: Control for spatial confounding factors when

comparing impact variables across T, S, C groups





Fieldwork data – Did the randomisation work?


5.31 Soya area in acres Between Groups 1.930 2 .965 4.464 .012

Within Groups 66.781 309 .216

Total

68.711 311

5.33 Soya production in kgs Between Groups 267442.916 2 133721.458 6.483 .002

Within Groups 3753733.219 182 20624.908

Total

4021176.135 184

5.33y Soya yield in kgs per

acre

Between Groups 553983.200 2 276991.600 7.223 .001

Within Groups 6902386.330 180 38346.591

Total

7456369.530 182

5.34 Soya % sold Between Groups 37385.977 2 18692.989 13.431 .000

Within Groups 239382.203 172 1391.757

Total

276768.180 174

5.36 Soya price per kg Between Groups 6447.450 2 3223.725 2.934 .057

Within Groups 130770.583 119 1098.912

Total

137218.033 121


5.31 Soya area in acres Control 173 .333 .4610 .0350

Supposed 66 .371 .5156 .0635

Planted 73 .526 .4241 .0496

Total 312 .386 .4700 .0266

5.33 Soya production in kgs Control 87 178.37 143.369 15.371

Supposed 35 198.49 187.864 31.755

Planted 63 105.44 112.599 14.186

Total 185 157.34 147.832 10.869

5.33y Soya yield in kgs per acre Control 86 289.09 190.899 20.585

Supposed 35 331.06 233.636 39.492

Planted 62 189.67 178.741 22.700

Total 183 263.43 202.408 14.962

5.34 Soya % sold Control 83 61.49 38.184 4.191

Supposed 33 66.33 29.579 5.149

Planted 59 32.18 39.790 5.180

Total 175 52.52 39.883 3.015

5.36 Soya price per kg Control 66 118.26 24.984 3.075

Supposed 30 126.50 41.939 7.657

Planted 26 136.54 39.592 7.765

Total 122 124.18 33.675 3.049



Fieldwork data Husbands

• AOI data on repayment / default rates

Wives



Null hypotheses


husbands?





well-being?



Fieldwork data – Wife data

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square .205a 2 .902

Likelihood Ratio .204 2 .903



Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 3.336a 2 .189

Likelihood Ratio 3.414 2 .181



Soya Groundnuts



Fieldwork data – Husband farm data Soya Groundnuts



Null hypotheses


husbands?





well-being?



Fieldwork data – Wife well-being data



Fieldwork data – Husband well-being data



Null hypotheses


husbands?





well-being?



Alliance One repayment data at club level


Recovery Rate Control 44 79.2173 28.50410 4.29715

Supposed 17 78.5888 32.44447 7.86894

Plant 33 75.8894 32.01017 5.57226

Total 94 77.9353 30.19393 3.11427



Null hypotheses


husbands?





well-being?



Fieldwork data – Husband farm data

No significant differences for tobacco acreage, production, yield and MKW

prices

F Sig.

3.280 .041

But households that were

supposed to and did plant soya

had significantly lower $ prices for

tobacco (at 95% level)



Fieldwork data – Husband farm data

No significant differences for maize production nor intra-household

distribution of labour

No significant differences for groundnut acreage, production, yield or price

But S and P households sold a

significantly greater % of

groundnuts (at the 90% level)

How do we interpret this?

Replacing lower soya sales?

F Sig.

2.545 .081



Fieldwork data – Wife farm data No significant differences for tobacco acreage, production, yield and $ prices

But S and P households had

significantly lower MKW prices for

tobacco (at 90% level)

2.839 .061

F Sig.



Fieldwork data – Wife farm data No significant differences for maize or groundnut production, nor husband

and wife labour. But significantly less child labour at 99% level…..


6.14 Maize - what proportion

did children perform?

Between Groups 981.873 2 490.937 3.343 .037

Within Groups 38037.245 259 146.862

Total

39019.118 261

6.24 Tobacco - what

proportion did children

perform?

Between Groups 863.763 2 431.882 3.712 .026

Within Groups 30131.767 259 116.339

Total

30995.531 261

6.34 Soya - what proportion


Between Groups 652.833 2 326.416 1.306 .274

Within Groups 32480.837 130 249.853

Total

33133.669 132

6.44 Mtedza - what proportion


Between Groups 1255.768 2 627.884 3.283 .040

Within Groups 28302.842 148 191.235

Total

29558.609 150


6.14 Maize - what proportion did children

perform?

Control 139 13.26 12.925 1.096

Supposed 65 9.00 11.632 1.443

Planted 58 9.91 10.534 1.383

Total 262 11.46 12.227 .755

6.24 Tobacco - what proportion did children

perform?

Control 139 10.99 11.908 1.010

Supposed 65 7.46 9.606 1.192

Planted 58 7.24 9.040 1.187

Total 262 9.29 10.898 .673

6.34 Soya - what proportion did children

perform?

Control 58 15.43 17.226 2.262

Supposed 38 10.13 15.487 2.512

Planted 37 12.78 13.634 2.241

Total 133 13.18 15.843 1.374

6.44 Mtedza - what proportion did children

perform?

Control 83 12.77 14.964 1.643

Supposed 36 5.97 11.072 1.845

Planted 32 12.66 13.499 2.386

Total 151 11.13 14.038 1.142

But instead of running three separate ANOVAs, we need to run a

MANOVA to check to see if the combination of changes in 2+ impact

variables are a function of the intervention in question (the soya

distribution) as these variables co-vary (in other words, labour is limited)

We can do this by creating a new impact variable (dependent variable)

from the 2+ treatment variables

When doing so, we can also add control variables to account for any

confounding factors from the attrition (so we move from a MANOVA to a

MANCOVA)

Our null hypothesis is that there is no significant difference in the

changes in the amount of labour children apply to both tobacco,

groundnut and maize as a consequence of the treatment



Fieldwork data – Wife farm data



Descriptive Statistics

9.5 Control supposed

planted Mean Std. Deviation N

6.14 Maize - what proportion


Control 13.61 13.711 83

Supposed 5.69 8.956 36

Planted 11.72 11.188 32

Total 11.32 12.566 151

6.24 Tobacco - what proportion


Control 10.48 12.012 83

Supposed 5.14 7.220 36

Planted 7.97 9.908 32

Total 8.68 10.781 151

6.44 Mtedza - what proportion


Control 12.77 14.964 83

Supposed 5.97 11.072 36

Planted 12.66 13.499 32

Total 11.13 14.038 151

Effect Value F Hypothesis df Error df Sig.

The number of

observations is reduced

to 151 due to fewer

households growing

mtedza

But significance remains

at 95%

Control_supposed_planted Pillai's Trace ,086 2,154 6,000 286,000 ,048

Wilks' Lambda ,914 2,169b 6,000 284,000 ,046

Hotelling's Trace ,093 2,184 6,000 282,000 ,045

Roy's Largest Root ,083 3,959c 3,000 143,000 ,010



Findings

Husbands and families: Including wives in the contract is welcomed by

husbands but they feel the soya intervention by AOI reduced the

household’s well-being

Firm: Including wives in the contract has no impact on default rates (but

consider the high attrition and dodgy soya seed from AOI)

Farms: Including wives in the contract led to no impact on crop

productivity but significantly reduced tobacco prices and child labour on

maize, tobacco and groundnuts

What goes around, comes around: if AOI had distributed good soya

seed in good faith their farmers and AOI would have achieved higher

prices!!



Lessons learnt

One needs more than $12,000 to run an RCT with qualitative

components if power calculations are going to hold

Double and triple check random assignment to reduce likelihood of

spatial confounding factors

Do not trust your implementing partner to do anything right – monitor

them, evaluate their performance at every stage



Patsogolo

Which tobacco company is going to take these results forward?

Which donor is going to fund an RCT at scale and with a trustworthy

tobacco company to investigate this further?

Which Malawian economists with expertise in RCTs would like to be part

of this impact evaluation?



CONGENIAL Phase II

Malawi: Double the sample. Allowing wives to choose from soya and

groundnuts

Tanzania: Double the sample. Allowing wives to choose from hybrid maize

and beans



In Tanzania, we are working with S&C

Ginning to evaluate the inclusion a

hybrid maize seed distribution

alongside cotton

Next steps for Tanzania Phase I….

Propensity score matching due to large

size of clubs within the clustered

randomised design

Mara Region, Tanzania



Martin Prowse, Betty Chinyamumnyamu, Ron Ngwira and Jytte Agergaard

[email protected]






contract farming and gender equity in african landscapes (congenial)

Presentations & Public Speaking

contract farming income

contract farming scheme

gender equity

default rates

high rates of default

likelihood of default

claims of exploitation

output markets rationale