rafmacalaba/data-use-mentions-extended huggingface dataset:
weights. Constrained WorldPop further constrains the distribution of the population to grid cells that contain built-up areas (Stevens et al., 2015).

In this study, our primary choice of input population layer is WorldPop, while GHSPOP serves as an alternative data source. Although GHSPOP was used by the European Commission to develop the DOU method, WorldPop, particularly constrained WorldPop, appears to better reflect the true spatial distributions of populations. This is because a detailed map of all building footprints was used for the development of constrained WorldPop layers for Sub-Saharan Africa.<sup>16</sup> When selecting the resolution for the datasets, we need to consider two opposing factors: computational speed and precision of the constructed urban classifications. We have chosen a 1km resolution for GHSPOP, as it is associated with the development of the DOU method. For WorldPop, we have opted for a resolution of 250 meters, which is four times more precise while still being computationally feasible.<sup>17</sup> The results of our analyses based on GHSPOP are reported in Appendix B.

## **3.2 Household budget surveys**

In this study, we utilized the HBS data collected around 2015, which aligns with the years of WorldPop and GHSPOP. A key data requirement for this study is the information about the location identifiers of individual households, enumeration areas (EAs), or other geographic units within the HBS. Our analysis primarily focuses on HBS data with the required information from 16 SSA countries, including Angola, Burkina Faso, Chad, Côte d’Ivoire, Ethiopia, Gabon, Ghana, Guinea, Guinea-Bissau, Lesotho, Mauritania, Malawi, Niger, Senegal, Tanzania, and Uganda (Table 2). Additionally, we report results based on a few extra countries outside Sub-Saharan

> 15GHSPOP data is available on the following European Commission website: https://ghsl.jrc.ec.europa.eu/ghs_ pop2019.php. WorldPop data is available here: https:/
laere and Mairesse (2013) predicted that competition among employers will result in a single market wage, whereby a small cut in wage by an employer will result in immediate resignation of all workers.

On the other hand, factors such as the lack of perfect information on alternative job opportunities, or search and moving costs can give firms significant market power over their workers. Such market conditions can readily give rise to the situation where a firm can become a monopsony, which we discuss below.

A monopsonist firm has a labor supply curve _Lit_ ( _wit_ ), that is increasing with wage level _wit_ . Short-run profit maximization, taking the labor supply curve as given, can be expressed as where _Rit_ = _PitQit_ represents total revenues.<sup>11</sup> Maximization with respect to materials yields expression (5) with the the superscript _v_ replaced by _m_ . Maximization with respect to labor yields the first-order condition where _Rit_<sup>_L_representsthemarginalrevenueoflabor,</sup><sup>_γit_=</sup> 1+(( _ε_<sup>_L_</sup> _<u>wε</u>_<sup>_L_</sup> _w_<sup><u>)</u></sup><sup>_<u>it</u>_)</sup><sup>_it_measuresthedegreeof</sup> monopsony power, and ( _ε_<sup>_L_</sup> _w_<sup>)</sup><sup>_it∈R_+isthewageelasticityoflaborsupply.</sup> From the first-order condition in equation (7), the degree of monopsony power is the key variable needed to empirically evaluate whether a firm holds market power in the labor market. To derive the degree of monopsony power empirically, notice that equation (7) can be expressed in terms of elasticity of output with respect to labor as from which it follows that the degree of monopsony power can be measured directly from the production data
RO LU HU PT DE ES CZ EL BE EE SI IE SK HR FR IT MT CY<br>Severely affected Less severely affected<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Note: See Figure A2 in the Appendix for results by income groups. Severely refers to losses in market income of more than 11 percent (Scenario A vs. D).

Source: Own calculations based on EUROMOD I4.0+.

Looking at the prevalence of the severely affected population across the pre-COVID-19 income distribution (Figure A2 in the Appendix), we can distinguish four groups of countries. In Belgium, Germany, Estonia, Greece, Ireland, Croatia, the Slovak Republic, Malta and Poland, the share of severely affected population increases with income. In contrast, the share of the severely affected decreases with income in Austria, Denmark, Spain and Luxembourg. In some countries, the share of the severely affected is broadly similar across income quintiles. This is the case of Bulgaria, Czechia, Cyprus, Finland, France, the Netherlands and Sweden. A third group is made of countries where the severely affected population is concentrated in the middle of the distribution: Latvia, 10
rise toward advanced-economy levels (Benhabib and Spiegel 1994, 2005; Coe, Helpman, and Hoffmaister 1997; Kato 2016). As a result, the coefficient on secondary school completion rates is only significant for countries with completion rates below the top third.

The results are broadly robust to a number of alternative specifications (tables 5 and 6). Two different methodologies are used to estimate trend TFP growth (a linear-quadratic trend and 3, 5, and 7-year moving averages) instead of the HP-filtered trend. The 3- and 7-year rolling averages of investment growth are used. In most specifications, the coefficient estimates remain significant and retain their signs; however, the working-age

> 18 This approach is similar to Abiad, Leigh, and Mody (2007) and Bijsterbosch and Kolasa (2010). Abiad, Leigh and Mody (2007) estimate five-year non-overlapping averages of TFP growth as a function of per capita GDP, schooling, population growth, trade openness and a nonlinear function of current account deficits and FDI for a sample of 22 European countries for 1975-2004. Bijsterbosch and Kolasa (2010) estimate five-year non-overlapping averages of labor productivity growth as a function of relative productivity levels (which here is proxied with relative per capita GDP), the share of high-skilled workers in employment, and investment in percent of value added for sectoral data for eight European countries for 1996-2005.

33
dividual income components. At the aggregate level, the survey asked households: “What is the total monthly income of your household at the moment?” In addition to the single question on monthly household income, the survey also collected individual income components, including the WWS stipend, through different modules. We define Income-1 as the monthly income as reported by the household and Income-2 as monthly income derived through summation of different income components including WWS stipend. Although the survey collected information on the WWS stipend, it did not collect information on the duration for the WWS stipend payments.<sup>17</sup> Nevertheless, WWS participants received LVL 100 per month and there is no evidence suggesting under or over payments, we added the LVL 100 (LVL 100 for T1; 0 for household in C1) to household monthly income to get total monthly household income including WWS stipend.<sup>18</sup> There are marginal differences in average incomes based on the two income definitions (reported in Table 1). As program impacts differ based on income choice, we report impacts using both income measures. However, our preferred income measure is income-2, because it is obtained by aggregating different income components, and households are more likely to omit the WWS stipend in reporting the total aggregate monthly income.

# **3 Methodology**

## **3.1 Targeting Performance**

To assess WWS program targeting performance, we rank beneficiary households in the welfare distribution of the entire population. However, by design, the WWS Household Survey used in this paper represents _only_ the registered unemployed population; the ranking of

> 17It is also not feasible to find out the duration of payment from the time spent in WWS program, as the duration of payment will not exactly match the time spent in the program.

> 18Almost 99 percent of assigned individuals in group T1 reported that payments were correct. As discussed in the data section, only one person (the assigned person) in each household is participating
questionnaire on 29 business practices and later delivers a dense 6-page report with recommendations for changes.

We randomly assigned firms to a control group that received the 6-page report and to four treatment groups. The treatment groups received the 6-page report plus one of four versions of an information sheet that provides information about business practices in a more succinct and compelling format. Adding to the content of the 6-page report, the basic version of the information sheet benchmarks business practices relative to other firms in the same sector. Further, it lists five simple actions the firm can take to adopt good business practices and invites the firm to register for a free training session. The other three versions of the information sheet additionally include: (i) a motivating sentence, stating that firms who adopt fewer business practices are more likely to close based on SEBRAE data, (ii) pre-enrollment in the free training session, or (iii) both features (i) and (ii).

To measure the effect of receiving the information sheet on take-up of business services, we used administrative data from SEBRAE-PR on the services they provided to firms in our sample during the 18 months following the intervention. About 28 percent of firms who received an information sheet used any SEBRAE service during the first few months after the intervention, compared to 21 percent of firms in the control group. However, this statistically significant difference disappears over time, as the control group caught up to the treatment group: 18 months after the intervention about 49 percent of firms in both the treatment and control group had used a SEBRAE-PR service. The short-run effect on take-up of SEBRAE-PR services is largest in the treatment group that received the most intense information treatment (including both the motivating sentence and pre-enrollment in a free training session).

The effect on take-up is mainly driven by relatively light touch services, meaning that firms either went to a SEBRAE-PR service center and received materials on best business practices, or accessed material through SEBRAE’s website. Receiving the information sheet increased participation in group training
# 8. Discrimination beyond the screening point: Evidence from survey data

These experimental results are informative about gender discrimination in the job application process. However, discrimination in the labor market is not restricted to the hiring stage; a large share of discrimination occurs during employment, whereby the labor of men and women is valued differently by the market. To further examine gender differential beyond the point of hiring, this study examined the monthly wage differentials between men and women using the 2014 Tunisian Labor Market Panel Survey (TLMPS).<sup>14</sup> Table 10 presents the average differences in monthly wages between men and women at different disaggregation levels. It suggests a systematic tendency to value the labor of men over that of women and pay men higher wages than women. Indeed, the median wage for men is higher than the median wage for women in both urban and rural areas. Moreover, the raw gender gap in median wages persists at all age levels and for all education levels. In terms of sector of employment, the median wage for women is slightly higher than that of men in government jobs, a result consistent with the findings that the public sector tends to have favorable conditions for women’s employment. However, the median wage for men is higher in the private sector and in semipublic enterprises.

**Table 10 Raw wage differentials by area, education level, age, and sector of employment**

|Raw wage differential(m|onthlyin Tunisian din|ars)|
|---|---|---|
|Median, men|Median, women|P-value continuity<br>(median equality)|
|_Overall_<br>444|325|0.00|
|_Area_|||
|Urban<br>500|359|0.00|
|Rural<br>400|300|0.00|
|_Education level_|||
|Illiterate<br>315|260|0.00|
|Read
from nationally representative survey data of about 150,000 people in 148 economies for 2011 and 128,000 people in 123 economies for 2021 as part of the Gallup World Poll.<sup>22</sup> However not all questions are available for all countries. For both years of the Global Findex database, the target population is the entire civilian, noninstitutionalized population age 15 and older. The variable we use indicates whether a respondent has either a financial institution account or a mobile money account, given the strong relationship in the literature on access to mobile money and ability to use informal networks to manage the impact of large climate shocks. We use data from the Global Findex 2021 to overlay with the 2019 poverty data, while the equivalent indicator from the Global Findex 2011 is used to overlay with the 2010 poverty estimates.

The statistics are reported for adults living in the richest 60% and poorest 40% of households for each country.<sup>23</sup> The same method is used to overlay these with poverty (and thereby education), as is used with social protection. We do not have any information on the correlation between access to financial services and access to social protection that we can use to inform the overlap between these two dimensions of welfare at the individual level.

# Coverage

Table 4 presents an overview of data availability across the globe for all dimensions. Overall, out of the total of 218 economies, our analysis is based on a sample of 168 economies, representing 97 percent of

> 21 We use a wsample program written by Corral, P (2023) to perform the random assignment to households.

> 22Refer to Demirgüç-Kunt et al. (2022) for more information on the Global Findex Database 2021 and Demirgüç-Kunt and Klapper (2012) for more information on the Global Findex Database 2011.

> 23 The Findex database is available at <u>https://www.worldbank.org/en/publication/globalfindex. We currently use the global</
questionnaire. International experts contacted by CAPMAS (Kalton) confirmed the feasibility of such an approach. CAPMAS conducted the field work, revisiting all addresses, completing the survey and matching the new sample to the 2004/05 data. The Household Income, Expenditure and Consumption Panel Survey (HIECPS) 2005-2008 follows the same households over time and allows an unprecedented comparability in the analysis of living standards.

**_The Sample:_** The data used in this analysis as panel (for 2005 and 2008) are based on a one-month subsample of the household from the full HIECPS 2004/05 interviewed in February 2005 and 2008 (Figure 1).  The sample of HIECS 2004/05 was based on the 1996 Population Census's updated sample frames of 1,200 area sampling units (PSUs) distributed between urban and rural areas of all governorates. The area sample consists of a number of neighbouring census blocks containing 1,500 households. The

between the first and the second rolls (the die is fair), there is a correlation between the initial outcome and the change in outcome.

6
several censuses. In addition, censuses do not identify the legal status of immigrants and do not track return migration. Finally, throughout decades, there have been changes in methodology and coverage. Thus, the fact that the question asking for the year of immigration was reformulated between the last two censuses complicates the task of accurately isolating immigrants by cohort. The 1980 and 1990 questionnaires require that individuals report the year they came to stay, while the 2000 one asks for the year they came to live in the US, implying that for those having entered the country a multiple number of times, prior experience may not be observed. In addition, some individuals may declare the year of immigration as the year when they received their permanent residence, although they may have already spent time in the US as students, on work visas or as undocumented migrants.<sup>17</sup> Since censuses are filled out in the spring, we assume the information gathered by Census 2000 about immigrants arriving in 2000 cannot fully mirror the degree of comprehensiveness available for that year. Therefore, we choose to exclude the 2000 arrivals from the 2000 dataset. The same issue also arises with respect to Censuses 1980 and 1990. For the reasons detailed above, we consider interval 1975-1980 from Census 1980 to be equivalent to interval 1975-1979 as reported in Census 1990.

# **_3.2. EMPIRICAL RESULTS_**

The coefficients based on the estimation of equation (1) with Education Index 1 as a dependent variable are provided in Table 4. Annex table A1 provides the results for the other two specifications. Assuming migrants arriving in the first and the second half of a decade from a given country are homogeneous with respect to ability, the coefficient for years since migration ( _β3_ ) provides the part of the assimilation effect that is common to all migrants regardless of country of origin.<sup>18</sup> That effect is negative and significant in each specification, indicating that, other things being constant, the occupational placement
percent of the world population. The GIDD model projects household survey data using three sets of ex-ante macroeconomic information: (a) changes in demographic composition which consist of projection of population by age and by educational attainment; (b) movement of labor between agriculture and non-agriculture; and (c) economic growth. Figure 1 illustrates how the GIDD model incorporates these three changes to adjust the data in the household surveys in the base year to some year in the future.

6
*|0.047|0.503***|0.474***|0.432***|0.564***|
||(0.089)|(0.140)|(0.103)|(0.090)|(0.113)|(0.129)|
|S&P Returns*FG Flow|7.192***|8.136***|3.580***|4.596***|5.193***|2.620**|
||(1.020)|(1.366)|(0.788)|(0.636)|(0.765)|(1.155)|
|FG Flow|0.031|0.192***|0.046|0.053***|0.119***|0.017|
||(0.026)|(0.049)|(0.041)|(0.016)|(0.025)|(0.034)|
|Observations|1,428|567|861|1,428|567|861|
|||**Bond**|||||
|US HY Returns|0.464***|0.234***|0.620***|0.448***|0.400***|0.499***|
||(0.0430)|(0.0446)|(0.166)|(0.0507)|(0.0546)|(0.103)|
|US HY Returns*FG Flow|4.999***|8.295***|1.719|3.857***
sample had announced policy rate cuts, and only a few had either taken measures to assist borrowers or announced prudential measures (e.g., Italy, Germany, U.K., and South Korea).

In response to the unprecedented, and rapidly evolving situation, policymakers increased the scope and number of measures to support the financial system. After March 18, most government and monetary authorities included liquidity support measures and borrower assistance. For instance, by April 17, 35 countries had taken some measure of direct borrower support, in most cases, dealing with SME financing. The use of countercyclical prudential rules also became ubiquitous; by the end of our sample, 40 countries had introduced prudential actions, typically through a temporary relaxation of certain key regulatory and supervisory requirements.

A large number of interventions were announced simultaneously with other financial sector policies. For example, prudential measures in developed countries were often announced on the same day of borrower support program announcements (Table A6 in the Appendix). However, as Tables A6 shows, there were also multiple single policy announcements for each of the policy categories we study. In developed countries, 42% of policies were announced on days with no other financial sector program announcements. This share is higher among developing countries, where 51% of policies were made on single announcement days.

# **4.2. Empirical methodology**

We use an event study technique (Brown & Warner, 1996; Kothari & Warner, 2007) to test the effect of policy announcements on the stock returns of banks. More specifically, we want to assess whether different policy initiatives convey new information about the ability of banks to properly operate during and after the crisis. Measuring bank-level stock returns instead of an aggregate index of bank stocks prices (or interbank credit and liquidity risk premia), allows us to 18
“Fiscal Policy in Good Times and Bad,”Quarterly Journal of Economics, 114, 1399-1436.

- [26] – – —(2005). “Estimating the Effects of Fiscal Policy in OECD Countries,”CEPR Discussion Paper 4842.

- [27] Ramey, V.A. (2011). “Identifying Government Spending Shocks: It’s all in the Timing,”Quarterly Journal of Economics 126, 1-50.

- [28] Ramey, V.A. and S. Zubairy (2014). “Government Spending Multipliers in Good Times and in Bad: Evidence from U.S. Historical Data,”NBER Working Paper No. 20719

- [29] Riera-Crichton, D., C. A. Vegh, and G. J. Vuletin (2014). “Procyclical and Countercyclical Fiscal Multipliers: Evidence from OECD Countries,” Policy Research Working Paper 6993, NBER, Washington, DC.

- [30] Romer, C.D. and D. H. Romer (2010). “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks,”American Economic Review, 100(3), 763-801.

- [31] Sa, F. G., P. Towbin, and T. W. Wieladek (2014). “Capital Inflows, Financial Structure and Housing Booms,”Journal of the European Economic Association, 12, 522-546.

- [32] Sutherland, A. J. (1997). “Fiscal Crises and Aggregate Demand: Can High Public Debt Reverse the Effects of Fiscal Policy?”Journal of Public Economics, 65, 147-162.

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15
participation trends becomes important to fully understand labor market dynamics during the crisis (ILO, 2020b; Abraham, 2020; Hamermesh, 2020).

Several efforts have been made over the last months to address these challenges. Some researchers have complemented administrative and survey data with online vacancy data that record information in real-time and are available with short time lags (Kahn et al, 2020; Campello et al., 2020; Hensvick et al.; 2020). Kong and Prinz (2020) and Goldsmith-Pinkham and Sojourner (2020) use Google search data to predict UI claims in the United States with the objective to reduce the time lag with which this information becomes available. Other authors have leveraged private sector data, specifically payroll data (Cajner et al., 2020), data from a time and scheduling software (Kurman et al., 2020) or data from daily purchases (Coibion et al., 2020). By combining these data with information from traditional data sources or augmenting these data with newly collected COVID-related information, they have been able to provide timely and detailed insights on the labor market impacts of the crisis.

Several other researchers have used newly collected data based on surveys specifically implemented to better understand the impacts of the crisis. In the United States, Bick and Blandin (2020) fielded a survey that follows a similar structure to the CPS but that generates more timely estimates. Brynjolfsson et al. (2020) used Google Consumer Surveys to collect two waves of survey data in April and May 2020. Bartik et al. (2020) focused on firms and collected data from approximately 5,800 businesses using an online survey. Online surveys were also used in the United Kingdom (Gardiner and Slaughter, 2020) and in Belgium (Baert at al. 2020) to collect data from workers, and in Denmark (Bennedsen et al., 2020) to collect data from firms. Finally, some researchers implemented multi-country online surveys. Adams-Prassl et al. (2020) covered Germany, the United