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Impact Evaluation of Agricultural Microcredit Product in Myanmar
Initial registration date
May 11, 2020
May 13, 2020 3:45 PM EDT
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University of Sydney
Other Primary Investigator(s)
Myanmar Economic Association
Yangon University of Economics
Additional Trial Information
We study the impacts of an agricultural microcredit product for smallholder farmers on farm investment, farm performance, broader measures of economic performance, and household welfare.
Oo, Khin Pwint, Russell Toth and Tin Tin Wai. 2020. "Impact Evaluation of Agricultural Microcredit Product in Myanmar." AEA RCT Registry. May 13.
The intervention is the provision of a seasonal agricultural microcredit loan.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Our theory of change is that seasonal agricultural microcredit loans should primarily be invested in agricultural activity. Since the loans are provided very close to planting season, they should lead to a genuine increase in access to finance for the winter agricultural season, rather than providing a slightly lower-cost source of financing. Hence loan recipients should have greater capital to invest in agriculture, rather than substituting for other loans.
Given their timing and purpose, we expect loans to be used on variable inputs such as seeds, fertilizer, and labor inputs. Hence we should see impacts on these agricultural investments, both in the monetary value invested in inputs, and the likelihood of accessing key types of inputs. It is also possible that access to loans would lead to a change in cropping patterns, e.g., devoting more land or investment to a more input intensive crop. So we will study the crop mix, but we don't have strong priors here.
In principle, increases in agricultural investment should lead to increases in agricultural productivity and outcomes, in particular, total yield, which can be measured in quantity terms (e.g., kilograms or volume-based measures of yield). Increases in yields should lead to increases in agricultural revenue. While loan recipients might also increase expenditure through use of loan proceeds, on net they should have higher profits than non-recipients.
Primary Outcomes (explanation)
Total agricultural revenue will be measured by adding up the revenue from individual crops. We will look also look at the revenue from the "main" (largest revenue) crop.
Total agricultural expenditure will be measured by adding up the expenditure from individual crops. We will look also look at the expenditure on the "main" (largest revenue) crop.
To calculate agricultural profit, we will subtract agricultural expenditure from agricultural revenue.
In some cases, the endline survey will be conducted before all farmers have harvested their winter season crop. In that case we will calculate their yield as the sum of what they have harvested so far, with what they still expect to harvest. Similarly, we will calculate their revenue as the sum of what they have sold so far, and what they still plan to sell.
Secondary Outcomes (end points)
Secondary outcomes concern off-farm outcomes: non-farm business and earnings activity, household labour market outcomes, and broader outcomes for the household.
With respect to other economic activity, we will focus on investments and returns from non-farm businesses, and labour earnings.
With respect to other household outcomes, we will construct the Probability of Poverty Index (based on the Myanmar PPI), and calculate other monetary measures of household outcomes including expenditure on health and education, transfer receipts, and food security. In particular, we will look at a measure of 4-weeks' consumption, and likelihood of household needing to ration meals.
Secondary Outcomes (explanation)
Access to agricultural microcredit is provided to a randomly-selected treatment group and not to a control group, within a set of farmers who showed interest in the agricultural microcredit product.
Experimental Design Details
Randomization is done based on the last digit of farmers' National Registration Card (NRC) number, which is effectively random. Treatment-control was divided based on even-odd last NRC digits.
Individual borrower (representing an agricultural household).
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
453 borrowers (representing agricultural households).
Sample size (or number of clusters) by treatment arms
There are 356 treatment and 97 control. The distribution of treatment-control varies by cluster (village) as randomization was done at individual rather than cluster level.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
INSTITUTIONAL REVIEW BOARDS (IRBs)
Human Research Ethics Committee, University of Sydney
IRB Approval Date
IRB Approval Number