# **1. Introduction**

Unemployment benefits (UB) and active labor market policies (ALMP) have gained widespread recognition in both developed and developing economies to improve labor market conditions and address social challenges.<sup>1</sup> Despite their effectiveness in cushioning vulnerable groups from shocks, their role in significantly bolstering overall employment and productivity remains ambiguous. Theoretically, UB and ALMP have the potential to increase both employment and productivity by extending job search time through income support, offering active job search support, and enhancing skills through training programs, among other benefits. However, these policies can also strain government budgets, displace or substitute productive jobs, and result in deadweight loss. Therefore, their collective impact on labor market outcomes hinges on which effects dominate at the macro level (Gautier et al. 2018).

Current research investigating the interlinkages between UB and labor market outcomes generally suggests that longer UB periods lead to prolonged unemployment duration and lower formal employment in both developing and developed economies (e.g., Filiz 2017; Hagedorn et al. 2015). The evidence regarding the impact of UB on job quality and earnings primarily concentrates on developed countries and generally indicates a positive association (Caliendo et al. 2013; Tatsiramos 2009). Only two studies have evaluated the relationship between UB and productivity and both indicate a positive relationship between them (Soltes 2017; Acemoglu and Shimer 2000).

Most studies analyzing ALMP use micro-level data to evaluate the effects of a specific program on participants' employment and earnings. Findings indicate that the effects of ALMP on labor market outcomes vary based on program type and design. For instance, evidence from both developed and developing countries suggests that job search assistance, placement, and training programs generally increase participant’s employment rates. However, these effects tend to be contingent on program design and differ across participant groups (e.g., Levy Yeyati et al. 2019; Autor et al. 2017; Card et al. 2018; Groh et al. 2012). Research provides limited support for the notion that ALMP enhance earnings (Betcherman et al. 2005; Levinsohn and Leibbrandt 2014). Among the rare macro-level studies, Caicedo et al. (2022) indicate an overall positive impact of ALMP on output and welfare, but also highlight their significant distortionary effects.

Drawing on new global data from the World Bank that covers 191 countries in 2019 and 2020, this paper aims to provide an analysis of the patterns of UB and ALMP across different regions and income groups and investigate their association with labor market outcomes.<sup>2</sup> It also utilizes the comprehensive labor market and social assistance policy inventory of the World Bank to present the patterns of UB and ALMP reforms implemented in 223 economies throughout the Covid-19 pandemic. Econometric analysis of the linkages between UB, ALMP and labor market outcomes is conducted using new data from 2019 and the ordinary least squares (OLS) regression model that incorporates interaction terms between income groups and UB and ALMP and includes a wide range of control variables.

> 1 Throughout this paper, the term “unemployment benefits (UB)” is used to refer to “unemployment insurance benefits” and it does not include unemployment assistance.

> 2 Data set is available at https://www.worldbank.org/en/research/employing-workers/data.

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