Returns to Contract Farming in Mozambique: a “Jobs-Linked Externality” approach

Ian Walker and Jose RomeroFuture of Work in Agriculture Conference, March 19-20, 2019 Washington, D.C.

Overview of contract farming arrangements in SSA

Much of SSA agriculture and farmers in ‘low level equilibrium’. Even when land is abundant, as in Mozambique

Farmers: Difficulties in securing financing for seasonal labor and quality inputs and accessing growing markets for higher quality, more processed agricultural outputs requiring a consistent quality and reliability of supply.

Firms: Difficulties in access to land, costly supervision, mitigating production risks, securing consistent supply of raw material meeting quality standards, using full processing capacity

Small holder farmers’ incentives: access to production inputs, TA, and assured purchaser / market

Aggregator firm incentives:supply of raw material, de facto access to land, and relief from the need to supervise field labor doing routine daily tasks

Farmers• Sell crop at agreed

price• Deductions for costs of

the inputs and services

Aggregator firms• Inputs• technical assistance• land preparation

(sometimes)

Conventional ModelOverview of contract farming arrangements in SSA

Research objective: background

Welfare of farmers has been focus of the agricultural economics literature (Bellemare and Bloem 2018)Overall literature finds mixed results and two challenges are highlighted: Internal validity of findings stemming from selection

biases (growers joining and firm selecting them) External validity due to arrangements and crops being

highly heterogeneous

Research objectiveCentral premises: (a) Income gains for poor farmers are a legitimate object of public support. (b) Support to commercial aggregation is an increasingly popular approach to solving this problem (e.g. Productive Alliances model in LAC)But little work has been done to understand: The distribution of gains between the commercial firms and

farmersWhether the expansion of the commercial systems be privately

profitable without public supportRequires analyzing firm and household behavior simultaneously

Our approachStudy Contract Farming Arrangement (CFA) expansions for several crop and regions in MozambiqueGather three rounds of data (BL, ML, EL) from: Private aggregator firms: data on variable costs of processing,

overhead, expenditures on inputs and support to growers, revenues from sales of final good, processing capacity being used, number of growers contracted, purchases of grower output, many other variables

Household data: standard (comprehensive) agricultural household surveys with some modifications to capture CFA specific information (e.g. where growers got inputs, who they sold to)

Base study on economic analysis and evaluation of CFA effects on grower incomes.

Central research questions

1. What are the private financial returns to investment in expanding the CFA?2. What are the full social returns on the expansion of CFAs? 3. What is the distribution of benefits of the CFA expansion between growers and the aggregator firms?4. What is the long term financial sustainability of the Contract Farming Arrangement (CFA)? 5. How do the benefits to the growers compare with the amount of public subsidy apparently needed to make the expansion viable ? (IRR > market cost of capital).

Summary of Mozambique Agricultural Aggregator Pilot (MAAP)

In-depth study of existing aggregators’ program expansions

Program operates and monitored over two years

Aggregators agree to provide information, including identifying participating growers for household surveys

Description of MAA firm by firm

Physical inputs TA Land

preparationInternational

markets

Domestic urban markets

Credits from Bank

Sesame seedSesame seed 150 ~200 X X X X X

Sugar caneMolasses, Sugar 1,200 1,000 to 1,500 X X X X

MaizeGrit, Meal, Bran 1,900 3,000 to 3,500 X X X

CottonLint, Cotton seeds 345 20,000 + X X X X X

Chicken Chicken 10 ~200 X X X X

Baby corn Baby corn 180 ~1,000 X X X X

Mark-up on inputs

Significant processing

/ capital heavy

MAAP growers

(planned)Crop

Final (processed)

products

Number of growers,

annually (prior MAAP)

Provides: Access to:

CBA and jobs-linked externalities (JLEs)CBA methods widely used to assess investment on development projects (Belli et al 2002). Two main measures: Financial rate of return (FRR) is based on the direct financial benefits and costs

associated with investment. Economic rate of return (ERR) focuses on economy wide effects and resource

use. Based on the benefits and costs priced at their opportunity costs / shadow prices. Various ways of calculating ERR.

We adapt the approach developed by (Jenkins, Kuo, and Harberger 2011). Basic idea is that from there are benefits resulting from firm investment and production decisions that do not enter their objective function (maximising profits). Labor externalities (LEs) from better jobs: net increase in worker incomes after subtracting economic opportunity costs of labor. LEs can be big in economies characterized by structural dualism. For smallholders, they can be realized through product and capital markets - not just labor markets.Social Externalities (SEs) of jobs: when employment has positive social effects (see Abdel et al. 2018 for links with terrorism, Davis and Heller (2017) for links with crime). So we adjust the LE framework by adding a social mutliplier to the LE, and use this to calculate SRR.

Empirical strategy I: PSM with non-participant villages We use comparison villages with similar characteristics to MAAP participants and then

use propensity score matching (PSM) to model the probability that households would have been treated, if they were in the treated villages.

3 samples are collected: (1) treatment group, (2) comparison households in same village, (3) comparison households in non-participant village

Probit regression estimated for probability of treatment using only the treatment group and comparators in “same village” samples

Results are used to created scores for all samples PSM procedure is used to create a comparison sample for each treatment group in

the non-participant village.

The treatment effect is then estimated with ∆Yic representing the counterfactual for household i from the participant village.

𝐴𝐴𝐴𝐴𝐴𝐴 =1𝑁𝑁𝑡𝑡𝑡𝑡𝑡𝑡

�𝑖𝑖∈𝑡𝑡𝑡𝑡𝑡𝑡

∆𝑌𝑌𝑖𝑖 − ∆𝑌𝑌𝑖𝑖𝑐𝑐

Empirical strategy II: Pipeline approach Have two samples: (1) treatment group (2) existing/prior growers already

receiving services from firm (MAAP or non-MAAP) Treatment group receives support starting at midline, comparison group

received services in all 3 survey periods We will use difference-in-difference regressions that include baseline levels of

covariates:

Post is binary indicator observation from period after the treatment group received support, MAAP is binary indicator that the household is in treatment group, X is a vector of household and village level baseline covariates

Parameter 𝝉𝝉 represents the treatment effect (ATT)

𝑌𝑌i,t = 𝛼𝛼 + 𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿 + 𝛾𝛾𝛾𝛾𝐴𝐴𝐴𝐴𝛿𝛿𝑖𝑖 + 𝜏𝜏(𝛾𝛾𝐴𝐴𝐴𝐴𝛿𝛿𝑖𝑖 � 𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿) + 𝛽𝛽𝑋𝑋𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡

Conclusion Our aim is to understand the viability of using public support to

commercial aggregator firms to transform the jobs of smallholder farmers We hypothesize that BOTH the farm households AND the aggregators

might be stuck in “low level equilibria”. Could a “one shot” injection of public support to the aggregators

leverage the system into a higher equilibrium? If we find that the expansions are financially precarious for the

commercial aggregators, but highly profitable for the farm households, that will “sustain” our hypothesis.

If we find the opposite, there would be a weak case for public support. We expect to find a lot of variance and to be able to generate useful

recommendations on principles and metrics for evaluation of the case for public support for this sort of model

Thank you

Methodology: Economic analysisEstimating Financial Rate or Return (FRR) and Social Rate of Return (SRR) done by setting Net Present Value to zero. The JLEs (jobs linked externalities) are the LEs plus the social multiplier.

0 = �𝑡𝑡=0

𝑇𝑇𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝛿𝛿𝑡𝑡 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐶𝐶𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝑡𝑡

(1 + 𝐹𝐹𝑅𝑅𝑅𝑅)𝑡𝑡

0 = �𝑡𝑡=0

𝑇𝑇𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝛿𝛿𝑡𝑡 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐶𝐶𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝑡𝑡 + 𝐽𝐽𝐽𝐽𝐽𝐽𝛿𝛿𝑡𝑡

(1 + 𝑆𝑆𝑅𝑅𝑅𝑅)𝑡𝑡

Methodology: Economic analysisUnpacking Social Rate of Return (SRR)

0 = �𝑡𝑡=0

𝑇𝑇𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝛿𝛿𝑡𝑡 − 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐶𝐶𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝑡𝑡 + 𝐽𝐽𝐽𝐽𝐽𝐽𝛿𝛿𝑡𝑡

(1 + 𝑆𝑆𝑅𝑅𝑅𝑅)𝑡𝑡

𝐽𝐽𝛿𝛿𝐽𝐽𝛿𝛿 𝐽𝐽𝐹𝐹𝑅𝑅𝐿𝐿𝑅𝑅𝐿𝐿 𝐽𝐽𝐸𝐸𝛿𝛿𝑅𝑅𝐹𝐹𝑅𝑅𝐸𝐸𝐸𝐸𝐹𝐹𝛿𝛿𝐸𝐸 𝐽𝐽𝐽𝐽𝐽𝐽 =𝐽𝐽𝐸𝐸𝐽𝐽𝛿𝛿𝐹𝐹 𝐽𝐽𝐸𝐸𝛿𝛿𝑅𝑅𝐹𝐹𝑅𝑅𝐸𝐸𝐸𝐸𝐹𝐹𝛿𝛿𝐸𝐸 𝐽𝐽𝐽𝐽 + 𝑆𝑆𝛿𝛿𝑆𝑆𝐹𝐹𝐸𝐸𝐸𝐸 𝐽𝐽𝐸𝐸𝛿𝛿𝑅𝑅𝐹𝐹𝑅𝑅𝐸𝐸𝐸𝐸𝐹𝐹𝛿𝛿𝐸𝐸 (𝑆𝑆𝐽𝐽) =

(1 + 𝜇𝜇) 𝐽𝐽𝐽𝐽

𝝁𝝁 is social multiplier (~20%)??

𝐽𝐽𝐽𝐽 = 𝐺𝐺𝐹𝐹𝛿𝛿𝐺𝐺𝑅𝑅𝐹𝐹 𝑁𝑁𝑅𝑅𝛿𝛿 𝐹𝐹𝐹𝐹𝑅𝑅𝐸𝐸𝑅𝑅𝑆𝑆𝐹𝐹𝐸𝐸𝐸𝐸 𝐵𝐵𝑅𝑅𝑅𝑅𝑅𝑅𝐵𝐵𝐹𝐹𝛿𝛿𝛿𝛿 − 𝑂𝑂𝑂𝑂𝑂𝑂.𝐶𝐶𝛿𝛿𝛿𝛿𝛿𝛿𝛿𝛿 = 𝛿𝛿𝐹𝐹𝑅𝑅𝐸𝐸𝛿𝛿𝐹𝐹𝑅𝑅𝑅𝑅𝛿𝛿 𝑅𝑅𝐵𝐵𝐵𝐵𝑅𝑅𝑆𝑆𝛿𝛿

LE based on estimated average treatment effects on households