- A larger proportion of low and lower middle-income economies implemented ALMP reforms during this period, with 8 percent and 17 percent, respectively, compared to 47 percent in highincome countries and 27 percent in upper middle-income countries.

- Across all income groups, Covid-19 related UB reforms primarily targeted increasing UB amounts, followed by expanding coverage and extending payment durations.

- Labor regulation adjustments were the most prevalent ALMP reform in all income groups. In upper-middle and high-income economies, wage subsidy programs and job search/training initiatives were the second and third most common reforms, respectively. Conversely, public works, while the least common overall, emerged as the second most popular reform in low and lower middle-income economies.

The econometric analysis reveals distinct patterns across income groups:

- In low- and lower-middle-income countries, ALMP show a stronger correlation with labor market outcomes compared to upper middle and high-income countries. Specifically, they exhibit positive associations with productivity growth and employment rate in the agriculture sector, and a negative association with the employment rate in the services sector.

- In upper middle-income countries, both ALMP and UB are positively linked to productivity growth and negatively linked to the aggregate employment rate. UB are also positively associated with the industrial employment rate but does not show any direct association with the employment rates in agriculture or services sectors.

- High-income countries exhibit fewer statistically significant findings. Among these, UB are positively related to agricultural employment rates, but negatively associated with industrial employment rate and productivity growth.

- Across all income groups, ALMP consistently display a negative relationship with the selfemployment rate.

While this analysis does not determine the causality between UB and ALMP indicators and labor market outcomes, its key findings offer valuable insights for policymakers and researchers. The significance of these findings stems from the extensive data coverage across both developing and developed countries and carefully designed regression model, which incorporates numerous control variables informed by the literature and the interactions of income categories with UB and ALMP indicators. Additionally, the regression model accounts for fixed effects across income groups and regions, and eliminates outliers.

As emphasized in the literature, although both UB and ALMP are shown to have many benefits, they are costly programs with many unintended distortionary effects in the economy. Therefore, it is crucial to employ a meticulous design informed by robust empirical evidence, along with pilot programs, to assess their impact before considering widespread implementation.

Some limitations of the analyses should also be noted. Although the data used cover the largest number of countries among existing databases, they are only from 2019, limiting the maximum sample size to 191. In addition, most indicators of UB and ALMP are binary with limited information, and the regression analyses do not fully address the endogeneity issues.

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