G²LM|LIC

Datasets

The datasets from the programme offer a data repository service for researchers who are interested in labour market issues in low-income countries (LICs). Files with projects/data sources listed below are associated with the GLM|LIC and G2LM|LIC programme.

Check the IZA dataverse: https://dataverse.iza.org/dataverse/G2LM-LIC for latest datasets stemming from G²LM|LIC projects.

  • Returns to Childcare and Capital: Experimental Evidence from Uganda
    Datasets. The study “Returns to Childcare and Capital: Experimental Evidence from Uganda” is designed to investigate how access to childcare and capital impacts the labor supply and business outcomes of women in Uganda, particularly those managing microenterprises. The central aim is to understand whether providing childcare can alleviate the constraints faced by female entrepreneurs due to familial obligations, thus enhancing their business productivity and overall household well-being. The study collected data through multiple survey rounds, primarily conducted face-to-face, with two rounds completed via phone during COVID-19 lockdowns. The 13 survey rounds were designed to gather detailed information from female caregivers (household surveys) and their children (child surveys). The data is organized into separate files for each survey round, ensuring anonymity and consistency in variable naming. By comparing the outcomes across the different groups, the researchers can identify the causal effects of childcare support, business grants, and their combination on women’s economic participation and household welfare. The findings are expected to provide insights into how integrating family support with business support can lead to better economic outcomes for women, particularly in low-income countries.
  • Can Temporary Financial Incentives for Female Industrial Workers Lead to Long-Term Retention and a Better Allocation of Talent?
    Datasets. A G²LM|LIC research project by the University of Oxford investigates whether worker turnover contributes to misallocation of talent in low-income countries, focusing on a female-dominated occupation in Ethiopia’s nascent garment manufacturing industry. The study evaluates the impact of financial incentives for worker retention, hypothesizing that many of the young female workers who quit the position have not spent enough time on the job to learn the true quality of the match, which, for some of them, may be high. The study contributes to the literature on the allocation of talent by testing whether labor turnover is partly driven by suboptimal decisions that destroy good matches, which may particularly affect young female workers from low-income backgrounds. It explores also worker retention in developing countries, where turnover rates are notably higher, and applies behavioral economics to study belief biases in the decision to leave jobs, experimentally identifying and quantifying in the field a bias in belief formation. The study aims to determine whether a later bonus generates higher retention, increased earnings, and greater job satisfaction in the long term, also testing whether turnover destroys potentially good job matches. The findings will have implications for firms and policymakers in high-turnover environments in low-income countries.
  • The Jobs of the World Project (JWP)
    Developed within the G2LM|LIC programme, the Jobs of the World Project is a publicly available resource built to enable researchers to analyse comparable labor market outcomes across countries and time using micro data. At the core of the project is the collation and harmonisation of all available Censuses (IPUMS) and Demographic and Health Surveys (DHS) for low and middle income countries. The two sources combined provides coverage of countries representing about 81 percent of the world’s population, and more than 90 percent of the population in low- and middle-income countries. A key innovation is the construction of comparable wealth measures that allow researchers to analyse labor market outcomes across wealth classes. The project has three outputs:
    • Codes that allow researchers to implement the harmonisation and produce customised data sets from publicly available IPUMS and DHS.
    • The Jobs of the World Database (JWD): The database focuses on a wide range of labor market characteristics including, but not limited to, labor force participation, type of employment (e.g., waged or self-employment), sector of employment (e.g., agriculture, manufacturing, or service), skill level (e.g., managers and professionals, white collar, or blue collar).
    • Web platform to build downloadable maps and charts based on JWD.
  • Paternalistic Discrimination
    Datasets. The study combines field experiments in Bangladesh with a structural labor model to introduce paternalistic discrimination. This discrimination involves the differential treatment of two groups to protect one, even against its will, from harmful or unpleasant situations. The main findings are derived from two field experiments conducted in Bangladesh to measure paternalistic discrimination against women. The observations are based on real hiring and application decisions for a night-shift job, with a focus on the impact of paternalistic discrimination on the labor market. The study framework incorporates other-regarding employers, who value their workers’ welfare.
  • Urban Density and Labour Markets: Evaluating Slum Redevelopment in Addis Ababa
    Datasets. Many of the world‘s poorest people live and work in dense informal settlements in Africa’s growing megacities. These communities have both positives and negatives. On one side, settlements, often located in central areas, provide workers with access to geographically proximate jobs, dense informational and social networks, and a large demand base for entrepreneurial ventures. Density provides scope for agglomeration economies, and thicker labour markets where matching between workers and employment opportunities is facilitated. At the same time, settlements often occupy prime land in the city center, reducing city centrality and increasing commute times for others. These dense environments are also the home to many negative externalities; the downsides of density. Poor infrastructure and difficulty of access lead to poor health, fire risk and crime. In addition, these areas may be locked into low-productivity informal uses, preventing growth and job-creation that could be happening if these areas were formalized. In the face of these costs and benefits, and in the absence of significant evidence on the impact of different approaches, the appropriate policy response is unclear: should slums be cleared, and new communities built, or should they be left as is, or is the best option somewhere in between? While concerns over health and housing often motivate urban planning policy, the labour market implications are potentially profound and are largely understudied. This project will provide much-need evidence on these questions.
  • Leveraging “Big Data” to Improve Labour Market Outcomes
    Datasets. This study sought to understand the impacts of providing customized and timely labor market information on the search and employment decisions of job seekers. The sample of job seekers was recruited from an online job portal, QuirkJobs.com, via advertisements on the website and on email between February 2021 and January 2022.
  • Entrepreneurship Education and Teacher Training in Rwanda
    Datasets. An experiment across 207 secondary schools shall investigate, how a comprehensive teacher training program affects the delivery of a major entrepreneurship curriculum reform in Rwanda. The reform introduced interactive pedagogy and a focus on business skills in the country’s required upper secondary entrepreneurship course. In addition to the government’s standard training, a random sample of schools received intensive training organized by an NGO for two years. The training consisted of (i) six training sessions during school breaks, ii) exchange visits each term where teachers provided feedback to their peers, and (iii) outreach and support from NGO staff at least twice per year.
  • Assessing the Impact of the Shock on the Most Vulnerable: Sudan Labor Market Panel Survey 2022
    Datasets. The Sudan Labor Market Panel Survey 2022 (SLMPS 2022) is the first wave of a planned longitudinal study of the Sudanese labor market designed to elucidate the way in which human resources are developed and deployed in the Sudanese economy. The SLMPS 2022 is a nationally representative household survey on a panel of about 5,000 households planned to be repeated every six years. The focus of the survey is to understand key relationships between labor market processes and outcomes and other socio-economic processes such as education, training, family formation and fertility, internal and international migration, gender equality and women’s empowerment, enterprise development, housing acquisition, and equality of opportunity and intergenerational mobility.
  • The Impact of Subsidized Access to Nurseries and Employment Services on Mothers’ Labour Market Outcomes and Child Development in Egypt
    Datasets. There are three main explanations as to why female labor force participation, globally, remains low: (1) women’s high opportunity cost of time (2) weak labor demand and (3) restrictive gender norms. The research team will test aspects of all three constraints on FLFP in a low-income context in Egypt. They will specifically answer the following research questions: (i) does lowering the cost of childcare increase the labor supply of economically vulnerable mothers? (ii) are childcare subsidies more effective when combined with employment services and when gender norms are more favorable to female employment? (iii) are there dynamic effects of childcare subsidies on employers’ demand for female workers and on gender norms? (iv) what is the impact of expanding nursery usage on children’s development?
  • Gender Differences in the Effects of the ‘Great Lockdown’ on Time Use
    Datasets. The project collaborated with PY Insights, an internet-browser analytics platform, and Dynata, a global first-party data platform, to field the survey between mid-May and early June 2020. Individuals drawn from Dynata’s marketing pools in India, Kenya and Nigeria were invited to participate in an online survey that ended with a consensual browser data upload using the PY Insights software. Participants with valid data were compensated for their effort. PY Insights’ internet browser extension collects retrospective data stored in each user’s browser account history. This is identical to what a participant would observe if they visited the History section of their internet browser on their personal computer. The records cover up to 90 days of past activity on the browser account, accumulated across all electronic devices (computer, smartphone, tablet). The researchers observe every website visit, including the URL (uniform resource locator, i.e., web address) and timestamp.
  • Migration, Changing Residence Patterns, and Covid-19 in India
    Datasets. Data is based on a phone survey of 1,545 rural Indian households collected in August 2020 in 20 districts across 6 states (Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, and Maharashtra) in Northern India in August 2020. Households participated in a 20–30 min survey with two parts, a household head module and a female respondent module. In the household head module the household head surveyed about the household’s socioeconomic conditions, household head’s income, the male and female heads’ nutrition, and the number of days the respondent wished for more food for themselves or their children. The nutrition questions were taken from the National Family and Health Survey (NFHS) 2015–16, allowing to use the pre-pandemic responses to the survey from the same district to benchmark nutritional outcomes.
  • Differential Earnings, Household Division of Labour and Fertility Choices: An Application of the “Doing Gender” Hypothesis in Ghana
    Datasets. The study employed both purposive and snowball sampling techniques for the selection of its participants. Participants were obtained from the Ashanti, Northern, Volta, Greater-Accra and Upper East regions. The sample selection was intended to reflect the five major ethnic groupings in Ghana, based on the country’s 2010 population and housing census which considers Akans, Mole-Dagbani, Ewe, Ga-Dangme and Gurma as the five major ethnic groupings in Ghana. Participants from these ethnic groups were therefore sampled from across the regions where they are more likely to be concentrated. There are noticeable differences in socio-cultural norms and economic practices among women from the northern (i.e. Northern and Upper East regions) and southern (i.e. Greater Accra, Ashanti, Volta regions) parts of the country. Traditional gender roles tend to be more pronounced in the northern, compared to southern regions, and women tend to carry out a larger proportion, if not all, of domestic chores. With respect to economic practices, in northern Ghana, poverty is more pronounced and gender inequality is more pronounced. This is typically demonstrated by higher incidences of early marriage, lower female enrollment rates and lower labor force participation.
  • Characterizing Urban Labor Market Effects of COVID and Speeding Recovery Through a Job Search Platform
    Datasets. The data collection from jobseekers and firms is done as part of the enrollment and operations for the job search platform called “Job Talash”. The Firm survey dataset consists of all the attempts made to employers to enlist vacancies on the platform. The dataset also has the ads listing data which specifies the requirements of firms for vacancies that are listed on the platform, Job Talash. All registered firms on the platform receive a call every 3 months, asking them if they’d like to list a vacancy on the platform. If they decide to list a vacancy, the information about the requirements for the vacancy is collected so that relevant candidates can be matched to those jobs. The Jobseeker dataset is based on the job matches generated for the jobseekers periodically based on their profile that includes, work experience, gender, education level and job interest. These job matches are communicated to the jobseeker via text message and phone call. A screening instrument is used by the field team while making phone calls for giving job updates to the jobseekers and recording their interest in the available positions. Along with the application interest, also information) is collected about whether they have been employed to earn an income in the last 14 or 30 days (randomized recall period for each jobseeker.
  • Relaxing Seasonal Constraints to Improve Labour Productivity
    Datasets. The project investigates a sample of over 3,000 small-scale farmers in Zambia, who were given the opportunity to exchange randomly assigned household items for alternative items of similar value. Analyzing a total of 5,842 trading decisions over a range of items, including cash, this study shows that exchange asymmetries are sizable and remarkably robust across items and experimental procedures. Using cross-sectional, seasonal, and randomized variations in financial resource availability, the authors show that exchange asymmetries decrease in magnitude when subjects are more constrained. Consistent with the interpretation that variation in decision stakes drives the results, the authors also show that trading probabilities increase when the value of the items involved is exogenously increased.
  • Impacts of Microfranchising on Young Women’s Occupational Choices
    Datasets. The project conducted a randomized evaluation of two labor market interventions between 2013 and 2017 targeted to 905 young women aged 18 to 19 in three of Nairobi’s poorest neighborhoods, Baba Dogo, Dandora, and Lunga Lunga. Applicants to the program were stratified by neighborhood and application date and then randomly assigned to one of three treatment arms: a franchise treatment, a cash grant treatment, and a control group. This design allows to estimate the impact of the franchise and grant treatments on those invited to the program, and to compare the impacts of the cash grant treatment — which relaxes the credit constraint but provides no other training or support — to a multifaceted program designed to address many of the obstacles to youth entrepreneurship simultaneously.
  • Taming Counterfeits Markets with Consumer Information
    Datasets. A field experiment was carried out in which randomly selected markets received community-wide trainings to help local farmers identify government-certified seeds. The dataset makes reference to “treated” households, which are households that were sampled from market areas that were selected for treatment, and therefore reside within 1 kilometer from the market center. The researcher examined differential impacts on female-headed versus male-headed households, potential mechanisms through information-sharing networks, and implications of the results for closing the gender gap in productivity.
  • Remittances and Coping with COVID‐19
    Datasets. The study seeks to support the Philippine COVID-19 response by quantifying the role of international migrant remittances in helping households cope with the pandemic’s economic consequences. The research team conducted surveys of an existing study sample of 2,000 Filipino workers in Dubai, United Arab Emirates (UAE) and their origin‐households in the Philippines.
  • Resilience and Recovery: The Economic Impact of COVID-19 on the Informal Sector in Uganda
    Datasets. This study does examine the resilience of young micro-entrepreneurs in the informal sector and their families in rural Uganda against the COVID-19 shock. More specifically, the study investigates how firms have built up considerable amounts of physical and human capital over the past decade versus those that have not. The survey focuses on economic resilience and how it relates to skilled labor and assets. It also provides information on the impact of COVID-19 on frequently discussed outcomes (e.g. health status, food security, urban-rural migration).
  • Tracking the Value of Time of Informal Sector Workers during and Post-Curfew in Nairobi, Kenya
    Datasets. The broad objective of this project was to provide information that could be used to target aid and welfare or employment support to those who are stuck in an unemployment or underemployment trap due to the COVID-19 pandemic. To meet these objectives, a large sample of respondents was targeted living in Kibera, Kenya. N = 597 respondents were successfully recruited. The sample comprised mostly of women (67.7%), in a marriage or marriage-like relationship (51.4%), with three+ kids living at home (M=2.67, SD=1.50).
  • Meet Your Future: Job Search Effort and Aspirations of Young Jobseekers
    Datasets. The Meet Your Future Project (MYF) is an RCT designed in partnership with BRAC Uganda to investigate the relative importance of several barriers to quality employment that students face when transitioning from the educational sector into labor markets characterized by high levels of informality. The main study is aimed at understanding whether career-services are specifically beneficial for disadvantaged populations, including: (i) economically disadvantaged students sponsored by NGOs, (ii) women and (iii) minorities, such as under-represented ethnic groups and migrants. The experimental setting is that of Vocational Training Institutes (VTIs) in Uganda.
  • Social Insurance and Labour Market Outcomes in Ethiopia
    Datasets. The data were collected during April and May 2016 on a sample of 300 Ethiopian manufacturing firms and 3.000 workers. As sampling frame, the 2015 Census of Manufacturing Firms was used conducted by the Central Statistical Agency (CSA) of Ethiopia that captures all manufacturing firms that use power-driven machinery and employ at least 10 workers. The Census is officially referred to as “Large and Medium Scale Manufacturing and Electricity Survey”.
  • Migration, Changing Residence Patterns, and Covid-19 in India
    Datasets. Data is based on a phone survey of 1,545 rural Indian households collected in August 2020 in 20 districts across 6 states (Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, and Maharashtra) in Northern India in August 2020. Households participated in a 20–30 min survey with two parts, a household head module and a female respondent module. In the household head module the household head surveyed about the household’s socioeconomic conditions, household head’s income, the male and female heads’ nutrition, and the number of days the respondent wished for more food for themselves or their children. The nutrition questions were taken from the National Family and Health Survey (NFHS) 2015–16, allowing to use the pre-pandemic responses to the survey from the same district to benchmark nutritional outcomes.
  • Is Heading Home a Dead End? COVID-Induced Migration and Local Labour Market Opportunities in Rural India
    Datasets. The Dataset contains information on COVID-Induced Migrants in India. In total, 8,265 migrants were surveyed across four survey rounds: April to June 2020; July to August 2020; January to March 2021; and June to July 2021. In the first round, enrollment of approximately 5,000 migrants was targeted. In subsequent rounds, the researchers attempted to re-interview approximately 4,000 of these respondents, drawing randomly from each initial state sample frame. Up to four observations per respondent were collected (an average of 2.37 observations per respondent, with 23.41% of migrants participating in all waves of the panel). If it was impossible to reach respondents from a previous round, participants were replaced with other migrants of the same gender, drawn randomly from the initial state sample frames.
  • Training, Financing, and Matching between Workers and Firms: Evidence from Bangladesh
    Datasets. The intervention was carried out in October 2017. A baseline survey was conducted prior to the intervention in 2017 and two follow-up surveys were carried out in December 2018 and end of 2019. In addition, a special follow-up survey of the workers was carried out in January 2021 following the Covid-19 pandemic outbreak to gauge their wellbeing.
  • The surveys undertaken in 2017-2019 also include workers’ characteristics such as workers’ age, occupation (foreman, skilled worker, or apprentice), monthly salary, daily hours, experience in the workshop, and experience in the occupation. This information is collected in all three rounds, although the data provides only a repeated cross-section of workers and does not allow to link the same worker over time.
  • Seasonal Migration and Agricultural Labour Markets in Nepal
    Datasets. This dataset contains phone survey responses from the Western Terai Panel Survey. The data includes phone surveys from 2,636 rural households in 90 villages in the districts of Kailali and Kanchanpur in the western Terai region of Nepal, collected from August 2019 through October 2020.
  • Assisting Job Search in Low-Employment Communities
    Datasets. Two interventions designed to help young urban dwellers search for employment by removing spatial and informational obstacles to job search were evaluated experimentally in Addis Ababa. The first intervention was a transport subsidy lowering the cost of job search. The second intervention was a job application workshop, designed to improve job seekers’ ability to signal their skills to employers. Participants were offered orientation on how to make effective job applications using CVs and cover letters, and on how to approach job interviews. Further, they took a mix of standardized personnel selection tests.
  • Gender Effects of COVID-19: Evidence from the Kenya Life Panel Survey
    Datasets. 20+ years longitudinal survey on health, educational, nutritional, demographic, social, and labor market outcomes among a sample of thousands of Kenyans who were participants in one or more randomized health, skills training, and financial capital interventions during childhood and adolescence. These surveys measured labor market activity, childcare hours, food security, migration, the home learning environment (including parent-child reading), knowledge of COVID-19 and social distancing adherence.
  • Wage Compression in Low Income Labour Markets
    Datasets. Two types of data have been collected during the project: the experiment data as well as the survey data on subjects’ past wage and employment history. The data have been used in the publications: Breza, E., Kaur, S. and Shamdasani, Y., 2017. The morale effects of pay inequality. The Quarterly Journal of Economics, 133(2), pp.611-663.
  • Measuring Labour in Farm Households in Africa
    Datasets. This survey experiment studies the accuracy of farm labour data in household surveys. They tested four alternative survey designs across 854 households from 18 communities in the Mara Region of Tanzania during the main 2014 agricultural season (roughly January to June).
  • The Formal-Informal Labour Nexus and Growth: The Case of Bangladesh
    Datasets. The datasets include a sample of worker histories and valuation for formal work from approximately 2,000 workers in Dhaka and Chittagong. In addition, it combines the labor force data from 2002, 2005 and 2010 LFS with information from the HIES survey rounds in 2000, 2005 and 2010, which also include modules on employment.
  • Building Management Hierarchies for Growth in LICs
    Datasets. The project collects administrative data from the factories and also conducts surveys with supervisors and machine operators in the factories. The survey of selected samples of workers within the factory monitors wages, extra-hours, working conditions, relationships and communication patterns with supervisors, work attitudes, aspirations, career plans, and other individual characteristics.
  • Labour Migration and Structural Change in Rural Labour Markets
    Datasets. This project collates, digitises and uses individual-level and aggregate Census data from the 1940s through 1990s to investigate whether these large migration flows and corresponding cash inflows through deferred pay schemes affected the employment patterns of men and women over the long­ term.
  • Globalization and the Gender Gap
    Datasets. The paper associated with this dataset analyzes theoretically and empirically the impact of comparative advantage in international trade on fertility. It builds a model in which industries differ in the extent to which they use female relative to male labor and countries are characterized by Ricardian comparative advantage in either female labor or male labor-intensive goods.
  • Matched Employee-Employer Panel-Data for Labour Market Analysis in Zimbabwe
    Datasets. This project addresses some of the data constraints that inhibit appropriate research and policy formulation on Zimbabwean manufacturing. The objective of the project was to build a panel of firm data in Zimbabwe that covers various years over the period 1993 to 2015 and to add a matched survey of employees covering the period 2015 to 2016. The basis of the firm panel is the Regional Programme on Enterprise Development (RPED) surveys of manufacturing firms in Zimbabwe conducted in 1993, 1994, and 1995.
  • Gender and Employment in Central Asia
    Datasets. The dataset covers a wide range of topics – from household demographics, assets, income sources, expenditure, migration to individual well-being, employment, social networks, decision-making, and attitudes among many other topics.
  • Fairtrade, Labour Markets and Women’s Economic Empowerment in Ethiopia
    Datasets. The datasets of the baseline, midline and endline surveys contain information on several domains of Ethiopia such as women empowerment, agricultural production, prices, labor markets, shocks, financial and non-financial assets which will have the potential to be used in a variety of different studies on labor agricultural markets and women’s empowerment beyond the scope and purpose of the original research.
  • Reducing Extreme Poverty through Skill Training for Industry Job Placement
    Datasets. The datasets cover the sample for the project’s experiment in the Northern Bangladesh by employing randomized control trial (RCT) technique from a short survey to recruit eligible participants (eligibility requires that the prospective participant is interested in training if offered an opportunity) from a large population on the basis of age, education and poverty status.