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He Said/She Said: Testing Respondent Effects in Household Income Reporting
Initial registration date
December 11, 2019
June 17, 2020 2:49 AM EDT
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RWI - Leibniz Institute for Economic Research
Other Primary Investigator(s)
University of Connecticut
Additional Trial Information
Obtaining reliable data on household and respondent income and understanding the quality of this data is of great importance to researchers and policy makers. Several papers have found evidence that household income is often misreported. However, the causes and consequences of intra-household reporting discrepancies remain poorly understood. Commonly, the most knowledgeable household member, often the household head, is interviewed in household surveys. Surveying only one household member could potentially lead to incomplete or erroneous information on the households’ aggregate income for several reasons. Household members could be oblivious of all income-generating activities of their spouses, for example because spouses intentionally hide income from one another. Incomplete pooling of information within the household could also result in difficulties to accurately report on other’s income, even if a household member is aware of all sources of income. These concerns can be overrun by assessing multiple household members separately. However, this comes at a logistical and monetary cost. This survey experiment aims to shed light on the discrepancies in intra-household reporting on aggregate income.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
The primary outcome interest is aggregate household income
Primary Outcomes (explanation)
We calculate aggregate income by summing up the reported income earned in the past 4 weeks from a list of economic activities, for all household members.
Secondary Outcomes (end points)
As secondary outcomes, we will also look at income per household member, and income per economic activity as outcome variables.
Secondary Outcomes (explanation)
This study consists of a survey experiment where we randomize who in the household responds to income questions. The experiment is designed as a stratified clustered-RCT where villages are allocated to one of three treatment arms. Randomization will be stratified at the subcounty level.
Experimental Design Details
Stratified clustered randomization done by a computer
The unit of randomization is the village.
Was the treatment clustered?
Sample size: planned number of clusters
200 clusters in Uganda
165 clusters in Paraguay
Sample size: planned number of observations
3000 households in Uganda
2000 households in Paraguay
Sample size (or number of clusters) by treatment arms
Uganda: 100 clusters T1, 50 clusters T2 and 50 clusters T3
Paraguay: 110 clusters T1, 55 clusters T2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
INSTITUTIONAL REVIEW BOARDS (IRBs)
INNOVATIONS FOR POVERTY ACTION IRB
IRB Approval Date
IRB Approval Number