The paper contributes to the literature in three ways: first, it is the first study that covers a large number of developing as well as developed countries to analyze the interlinkages between UB and ALMP and labor market outcomes; second, it utilizes rich, novel data from the World Bank covering 191 economies in 2019 and 2020; third, it is the first study assessing the association of both UB and ALMP with employment, unemployment and productivity using macro data and distinguishing among different income groups. There is no previous study analyzing the relationship between ALMP and productivity, and there are only few studies assessing the association of UB with productivity, including Solter (2017) and Acemoglu and Shimer (2000). All other studies focus on the interlinkages between UB or ALMP with employment, unemployment, matching quality, or earnings, and the overwhelming majority of these studies focus on developed economies.

To the best of our knowledge, Pignatti and Van Belle (2018) is the only other study that uses data from both developed and developing economies—covering 36 developed, 64 emerging and 21 developing economies— to analyze the impact of ALMP and UB on unemployment and employment.<sup>3</sup> The authors find that in developing countries ALMP spending decreases unemployment and increases employment, while the spending on passive labor market policies (PLMP), like UB, lacks a significant impact on either. They also point out that ALMP and PLMP in developing countries are substitutes, rather than complements, in promoting employment and reducing unemployment. In contrast, in developed economies, both ALMP and PLMP increase unemployment and decrease employment. However, there is a complementary relationship between them, in that when ALMP spending is sufficiently high, higher PLMP spending reduces unemployment and boosts employment.

Different from the work of Pignatti and Van Belle (2018), this study covers a larger number of developing and developed countries. In addition to employment and unemployment, it also investigates the linkages of UB and ALMP with productivity growth and incorporates an interaction term between income categories of countries and UB and ALMP. Moreover, it assesses the association of different sub indicators of UB and ALMP with labor market outcomes and controls for a wide range of relevant indicators put forward in the literature.

The key findings of the descriptive statistical analyses can be summarized as follows:

- Of the 191 countries, 47.6 percent have UB schemes and 85.3 percent have ALMP.

- Among ALMP, public employment services (PES) are most prevalent (employed in 81.7 percent of countries), followed by national skills funds (52.9 percent) and government incentives offered for training (53.9 percent).

- As income level decreases, the use of ALMP vis-à-vis UB increases substantially.

- On average, employees need to contribute at least 11.6 months to become eligible for UB, and the average maximum UB period is 37.3 weeks.

- Among the six eligibility conditions for UB covered by the data, all of which are also ALMP, periodic reporting on active job search, participation in a training program, and registration with PES are most commonly used. Regarding the six services offered by PES, training programs, access to the

> 3 All other cross-country studies analyzing the interlinkages between UB or ALMP and labor market outcomes focus on developed economies in Europe or OECD, such as Escudero (2018) and Estevão (2003).

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