Therefore, the hypothesis of this analysis is that UB and ALMP are positively associated with employment and productivity and negatively associated with unemployment, assuming that their unintended negative effects are weaker than their positive effects. In addition, given that the countries with higher per capita income have more resources and stronger institutional capacity to design more comprehensive UB and ALMP as well as to implement and monitor them more effectively, impact of UB and ALMP on labor market outcomes might be stronger in these countries. To account for this impact, we incorporated interaction terms between income levels of countries and UB and ALMP indicators in the regression model. In addition, although our analysis does not disentangle the different types of unintended effects of UB and ALMP, since it employs macro level data, these effects are factored in the values of the indicators, allowing us to obtain the overall association of UB and ALMP indicators with labor market outcomes.<sup>15</sup> The crosscountry OLS estimations using data from 191 economies in 2019 are based on the following linear regression equation:<sup>16</sup> where i denotes country i and _Y_ is the dependent variable referring to labor market outcomes (i.e., aggregate unemployment rate; aggregate and sectoral employment rates, and self-employment rate; and growth rate of labor productivity). The variable _X1_ represents UB and ALMP indicators used in the analysis: the availability of UB schemes and UB after one year of contribution; maximum length of UB duration; minimum length of contribution period; the availability of PES and types of PES; and the availability of incentives for training and national skills funds.<sup>17</sup> The _X2_ variable refers to control variables informed by the literature: real GDP per capita, labor force participation, income share of labor, investment rate, openness to international trade, world governance indicator, life expectancy, innovation, employeeemployer cooperation, severance pay, labor tax and social contribution rates, inflation rate, gross government debt as a share of GDP, and budget balance as a share of GDP.<sup>18</sup> The X3 variable includes income groups; X1*X3 refers to the interaction term between income groups and UB and ALMP; and ei denotes robust white noise residuals. The analysis also controls for fixed effects across regions.

Outliers were removed using a standard method.<sup>19</sup> To increase the number of observations, control variables were averaged over 2017–2019 (real GDP per capita, income share of labor, labor force participation, world governance indicators, life expectancy, severance pay, and labor tax and contribution

> 15 Models using micro data or data from certain local geographic areas are not able to capture the secondary effects of UB and ALMP on other sectors and geographic locations, and yield results that are limited to that sector or location.

> 16 Since UB and ALMP data used in this analysis did not change much from mid-2019 to mid-2020, it was not possible to conduct a panel data analysis. Moreover, given that the labor market outcome indicators were only available until 2019, we used the 2019 values of UB and ALMP in the regression analysis instead of their 2020 values. Number of countries in the analysis changes based on the availability of indicators on labor market outcomes and control variables.

> 17 The maximum length of the UB indicator is the average of the maximum length of UB specified in labor laws for employees with one-, five- and ten-year tenures.

> 18 Please see Table A9 in the appendix for the definition, source, and summary statistics of all variables used in the regression model and Table A10 for the sample size and summary statistics of indicators used in the analysis after dropping outliers.

> 19 We used _hadimvo_ command in STATA developed by Billor, Hadi, and Velleman (2000).

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