FOOD CONSTRAINTS, YIELD UNCERTAINTY AND GANYU LABOR: A PILOT STUDY IN ZAMBIA

Principal Investigators:

•  Günther Fink – Harvard School of Public Health

•  Kelsey Jack – Tufts University

•  Felix Masiye – University of Zambia

Presented by:

•  Austin Land – Innovations for Poverty Action

IGC Growth Week - September 2013

Outline

1.  Background

2.  The research question

3.  Pilot study

4.  Plans for scale up

Part 1: Background Small-scale farming in rural Zambia

¤  Farming remains primary source of income in many developing countries - in Zambia, 80% of employment is in agriculture

¤  Farms are generally small (<5 hectares) and not very productive, with average net production values < US$ 500 per household and year (~ $ 0.20 per capita and day)

¤  Zambian agriculture is based on only one harvest per year, which means that the harvest income needs to “last” for long periods

¤  Resources are particularly scarce during planting and weeding seasons

Farm Season/Agricultural Time Line in Zambia

Field Preparation Planting Weeding Harvesting

Income

Credit Constraints & Ganyu Labor

¤  In the absence of formal credit markets, covering short-term consumption (and investment) needs is difficult

¤  While households get some support from extended families and the community, borrowing capacity is limited

¤  The most common form of raising money in the short-run is to engage in piece-work (ganyu) labor for better endowed farms

èThe cost of leaving their own farm is potentially high, and this may explain part of why yields are low

Seasonal Ganyu Labor Supply (Pilot Data)

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Part 2: The Research Question What drives rural labor supply and agricultural productivity?

Broad question:

Why do small-scale farms fail to achieve higher yields?

Specific questions:

1.  How important are short-term credit constraints for farm labor allocation?

2.  Can medium- to long-run farm productivity be increased by easing short-term constraints?

•  If so, is ganyu labor the mechanism?

Part 3: Pilot study Design and preliminary results

Randomized controlled trial with 3 arms 1. Control group

2. Basic “full treatment”

3.  “Partial treatment”: public lottery selection to

•  generate within cluster variation/more statistical power

•  measure (potential) spillovers to other communities

Pilot study design

Pilot Questions

1.  Are farmers willing to take up maize loans?

2.  Are farmers willing/able to repay loans?

3.  Does easing short-term food/credit constraints change consumption patterns and labor allocation?

Maize Loan Details

Maize loan offer: Receive: 25kgs of maize flour in Jan, Feb & March (weeding season) Pay back: 50kgs of grain for each bag borrowed in June (post harvest)

è Interest somewhere between 0 and 30 percent?

Take-up Results

Repayment Results

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All eligible Lottery

Preliminary Results I: Maize Consumption

Dependent var: Times ate nshima last 24 hours (1) (2) (3) (4) Village ITT: lottery 0.0457

(0.0579) Village ITT: full 0.183***

(0.0516) Household ITT 0.129**

(0.0571) Bags received 0.0465**

(0.0175) Maize in February 0.153***

(0.0454)

Control group average 1.74 1.74 1.74 1.74

Observations 426 426 413 413 R-squared 0.108 0.106 0.107 0.112

Notes: Standard errors in parentheses are clustered at the village level.

Preliminary Results II: Hunger

Dependent var: Missed Meals Due to Lacking Food Last Week

(1) (2) (3) (4) Village ITT: lottery -0.0501

(0.0686) Village ITT: full -0.159***

(0.0566) Household ITT -0.0787*

(0.0428) Bags received -0.0282*

(0.0161) Maize in February -0.0748

(0.0482)

Control group mean 0.37 0.37 0.37 0.37

Observations 426 426 413 413 R-squared 0.049 0.041 0.047 0.046

Notes: Standard errors in parentheses are clustered at the village level.

Preliminary Results III: Selling Ganyu

Dependent var: Household Members Engaged in Ganyu last 2 Weeks?

(1) (2) (3) (4) Village ITT: lottery -0.104*

(0.0613) Village ITT: full -0.126*

(0.0676) Household ITT -0.0829*

(0.0479) Bags received -0.0316*

(0.0170) Maize in February -0.0834

(0.0504)

Control group mean 0.47 0.47 0.47 0.47

Observations 426 426 413 413 R-squared 0.056 0.053 0.058 0.057

Notes: Standard errors in parentheses are clustered at the village level.

Preliminary Results IV: Hiring Ganyu

Dependent var: Did Household Members Hire Ganyu last 2 Weeks?

(1) (2) (3) (4) Village ITT: lottery -0.0258

(0.0591) Village ITT: full -0.0159

(0.0670) Household ITT -0.0290

(0.0404) Bags received -0.0122

(0.0146) Maize in February -0.0325

(0.0410)

Control group mean 0.17 0.17 0.17 0.17

Observations 422 422 410 410 R-squared 0.038 0.038 0.040 0.039

Notes: Standard errors in parentheses are clustered at the village level.

Pilot Results (summary)

¤ Overall very high (> 90%) take up of maize loans

¤ Very high (>90%) repayment rate

¤ Maize recipients •  eat more maize on average •  are less likely to miss meals •  are less likely to work on other farms •  do not change (reduce?) ganyu hiring

¤ Effects are smaller in lottery households (household level ITT vs. village level full treatment ITT)

Control Group (p=1/3)

50% Control

50% Program Access Pre-Season

Maize Loan (p=1/3)

50% Loan Program Continues

50% Loan Program Ends

Cash Loan (p=1/3)

50% Loan Program Continues

50% Loan Program Ends

Year 1

Year 2

Part 3: Full Study Design Assessing productivity impacts

Hypotheses for scale up

¤ H1. Access to short-term credit increases on-farm labour •  H1a. Access to short-term credit increases agricultural

productivity. •  H1b. The impact of credit programs increases with plot size,

and decreases with the number of workers and the financial resources available at baseline.

¤ H2. The impact of credit programs on agricultural production is larger when credit programs are announced at the beginning of the farming season.

¤ H4. Maize loans have a larger impact on agricultural productivity than cash loans.

Conclusion

¤ Seasonal food constraints are an important driver of labour supply in rural Zambia •  Contrasts with standard economic theories of labour supply

when households have credit access

¤ Short-term seasonal loans offer a promising solution •  High take up, high repayment, substantial impacts on

household labor

¤ Remaining question: Impacts on agricultural productivity

The pilot study was made possible by:

¤  International Growth Centre, Zambia Country Programme

¤ Agricultural Technology Adoption Initiative (ATAI) at J-PAL/CEGA

¤  Innovations for Poverty Action

¤ Ministry of Agriculture, Zambia