Data and code for: Seasonal liquidity, rural labor markets and agricultural production

Principal Investigator(s): View help for Principal Investigator(s) Günther Fink, Swiss Tropical and Public Health Institute; B. Kelsey Jack, University of California-Santa Barbara; Felix Masiye, University of Zambia

Version: View help for Version V1

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ReadMe.pdf	application/pdf	86.2 KB	07/11/2020 07:45:AM

Project Citation:

Fink, Günther, Jack, B. Kelsey, and Masiye, Felix. Data and code for: Seasonal liquidity, rural labor markets and agricultural production. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-10-21. https://doi.org/10.3886/E119649V1

Project Description

Summary:

Rural economies in many developing countries are characterized by a lean season in the months preceding harvest, when farmers have depleted their cash and grain savings from the previous year. To identify the impacts of liquidity during the lean season, we offered subsidized loans in randomly selected villages in rural Zambia. 98 percent of households took up the loan. Loan eligibility led to increases in on-farm labor and agricultural output, driving up wages in local labor markets. Larger effects for poorer households suggest that liquidity constraints contribute to inequality in rural economies.

Funding Sources: Growth and Labor Markets in Low Income Countries; International Growth Centre; Agricultural Technology Adoption Initiative

Scope of Project

Subject Terms: agriculture; consumption smoothing; labor markets

JEL Classification:
      C93 Field Experiments
      J20 Demand and Supply of Labor: General
      O13 Economic Development: Agriculture; Natural Resources; Energy; Environment; Other Primary Products
      Q12 Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

Geographic Coverage: Zambia

Collection Date(s): 11/2013 – 9/2015

Data Type(s): survey data

Methodology

Response Rate: See Table 1 for additional detail

Baseline N=3139 (study sample)
Midline/Harvest N=3028 (96.5% response rate)
Endline N=3005 (95.7% response rate)

Other survey rounds were conducted on sub-samples only.

Sampling:

We randomly sampled 5 villages from 50 of the 53 administrative blocks in the district, using village lists from the Ministry of Agriculture’s farm registry and omitting villages with less than 20 or more than 100 farms and those containing Chipata town. Study enumerators visited the 250 sampled villages to screen for eligibility. Our final sample covers 175 villages.

Within each eligible village, households were sampled from the village rosters collected during the initial screening visits. Only farms of less than 5 hectares were eligible for the program. Eligible households were randomly sorted and the first 22 selected for the baseline survey. This resulted in 53 percent of households on average being selected for the project; across all villages, the share of households enrolled in the study ranged from 15 to 100 percent. A total of 3,701 households were sampled for the baseline and 3,139 were surveyed at baseline (85 percent).

Data Source: Household surveys conducted between 2013 and 2015 with up to 7 observations per household (unbalanced panel)

Collection Mode(s): face-to-face interview

Unit(s) of Observation: Household

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