The implications of worker, match and firm heterogeneity for unemployment and self-employment in Ethiopia

More than half of the labour force in Addis Ababa is self-employed, in casual or temporary employment, or unemployed (2012 Urban Employment and Unemployment Survey of the Ethiopian CSA). These numbers are similar in other sub-Saharan African economies but much smaller in developed economies. To understand whether policies can affect these outcomes, and if so, how, it is necessary to first understand their causes.

Existing work mostly suggests that low aggregate productivity and dysfunctional labour markets can lead to high self-employment and unemployment. However, this work says very little about which aspects of labour market (mal)functioning are the relevant ones. Policy design requires more precise information.

This project aims to provide just that, by tailoring the analysis more closely to the Ethiopian labour market. My previous work, as well as conversations with other researchers working on Ethiopia and with Ethiopian policymakers, suggests that key features to consider are:

1. Worker heterogeneity (reflecting the different experiences of e.g. different education groups in the Ethiopian labour market);
2. Match heterogeneity and fragility (high ex-ante uncertainty about the quality of a new employment relationship leads to high breakup rates, and potentially too low hiring), and;
3. Casual work.

The project thus aims to

1. Document pertinent evidence on these features from household data and;
2. Quantitatively evaluate their importance for household choices and aggregate outcomes using a model.

The model can then also be used for analysing policies, like e.g. income support, works programmes (like the PSNP), and the regulation of business entry. Some of these policies are already undertaken or considered by the Ethiopian government. Urban unemployment, at all skill levels, is a major concern for the government. Hence, more precise information about labour market frictions and a rich model suitable for policy analysis will be very useful tools for policy design.

For the first objective, I will use data from the Urban Employment and Unemployment Survey (UEUS), which has been conducted by the Ethiopian Central Statistical Agency (CSA) for many years now, combined with data from the 2013 Labour Force Survey. These datasets contain information on cross-sections of individual labour market experiences. Crucial for the intended analysis is information on the duration of unemployment and employment spells and on firm characteristics, combined with demographic information.

For the second objective, I will augment a search and matching model with an occupational choice between job search and self-employment with the features above. Search and matching models have come to dominate macroeconomic analysis of labour markets with frictions for developed economies. The models have been refined and can now capture many facets of labor market activity, but have rarely been used for the analysis of labour markets in developing economies