Data and Code for Hidden Income and the Perceived Returns to Migration

Principal Investigator(s): View help for Principal Investigator(s) Travis Baseler, University of Rochester

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Project Citation:

Baseler, Travis. Data and Code for Hidden Income and the Perceived Returns to Migration. Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-09-12. https://doi.org/10.3886/E176081V1

Project Description

Summary: This deposit includes code and data to reproduce the findings of this paper. Paper abstract below.

Abstract: In many developing economies, urban workers earn substantially more than rural workers with the same level of education. Why don't more rural workers migrate to cities? I use two field experiments in Kenya to show that low migration is partly due to underestimation of urban incomes, which is sustained by income hiding by migrants. Parents at the origin underestimate their migrant children's incomes by nearly half, and underestimation is greater when a migrant's remittance obligations are high. Providing information about urban earnings increases migration to the capital city by about 40% over two years.

Funding Sources: Weiss Family Fund; Stanford Institute for Economic Policy Research; Stanford Center on Global Poverty and Development; Stanford School of Humanities and Sciences; Freeman Spogli Institute

Scope of Project

Subject Terms: migration; hidden income; urban-rural income gap

JEL Classification:
      D82 Asymmetric and Private Information; Mechanism Design
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D84 Expectations; Speculations
      J61 Geographic Labor Mobility; Immigrant Workers
      O15 Economic Development: Human Resources; Human Development; Income Distribution; Migration
      R12 Size and Spatial Distributions of Regional Economic Activity
      R23 Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics

Geographic Coverage: Kenya

Time Period(s): 5/25/2016 – 5/10/2019

Collection Date(s): 5/25/2016 – 5/10/2019

Universe: Rural households in western Kenya with at least one person aged 18-35 living at home.

Data Type(s): experimental data; program source code; survey data

Methodology

Response Rate: 497 households participated in the ULM (Urban Labor Market) experiment. 460 of those households participated in a 1-year follow-up survey, and 436 participated in a 2-year follow-up survey. 4,994 households participated in the MR (Migrant Remittances) experiment.

Sampling: Households in the ULM experiment were selected systematically by local survey staff. Households in the MR experiment were selected from an independent sample of rural households in western Kenya, excluding households that currently had a migrant living in Nairobi.

Data Source: Surveys conducted by the author.

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

Weights: None.

Unit(s) of Observation: Family, Household, Individual

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Published Versions

V1 [2023-09-12]

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