rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9356

# **Abstract**

This paper uses administrative, survey, and online vacancy data to analyze the short-term labor market impacts of the COVID-19 lockdown in Greece. The analysis finds that flows into unemployment have not increased; instead, separations were lower than would have been expected given trends in recent years. At the same time, employment was about 12 percent lower at the end of June than it would have been without the pandemic. The interrupted time-series and difference-in-differences estimates indicate that this was due to a dramatic slowdown in hiring during months when job creation typically peaks in normal years, mostly in tourism. Although the reasons for these patterns are not formally tested, the analysis suggests that the measures introduced to mitigate the effects of the crisis in Greece played an important role. These measures prohibited layoffs in industries affected by the crisis and tied the major form of income support to the maintenance of employment relationships.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mtestaverde@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|-0.09|0.00|2126|
|Openness|3.11|3.11|-0.01|0.75|2126|
|Emotional Stability|2.88|2.87|-0.01|0.72|2126|
|Agreeableness|2.94|3.06|0.12|0.00|2126|
|Big fve index (mean of z-scores)|-0.04|0.08|0.12|0.01|2126|
|Grit|2.72|2.68|-0.04|0.07|2126|
|Decision making|3.26|3.13|-0.13|0.00|2126|
|Hostile Bias|3.05|2.91|-0.14|0.00|2126|
|Other Non-Cognitive Index (Grit,Decision,Hostile)|0.12|-0.21|-0.34|0.00|2126|
|Non-Cognitive index (Big Five + Others)|0.02|-0.04|-0.07|0.13|2126|
|_Panel C: Public Sector Motivation_||||||
|Attraction to Policy Making|3.51|3.51|0.00|0.83|2126|
|Commitment to Public Interest|4.08|4.07|-0.01|0.55|2126|
|Social Justice|4.09|4.08|-0.00|0.80|2126|
|Civic Duty|4.05|4.10|0.05|0.00|2126|
|Compassion|3.75|3.67|-0.07|0.00|2126|
|Self-Sacrifce|4.05|4.14|0.08|0.00
Advanced|-2.4|-9.8||-2.4|-9.8||

> * GDP levels are measured in constant 2009 U.S. dollars.

20 (9.0 percent in the high-growth scenario), and to an average of 6.7 percent annual growth between 2020 and 2025 (7.0 percent in the high-growth scenario).

Consistent with the past decade, the expansion of financial globalization, as measured by the rate of growth of cross-border deals, is expected to exceed that of real economic growth: Growth in cross-border deals is likely to outpace expected emerging-market GDP annual growth rates of 4.9 percent over 2010–20, and 4.1 percent over 2020–25. This expected growth in cross-border deals echoes a global trend of financial growth generally exceeding growth in real economic variables.

Preliminary evidence from M&A activity for 2010 and 2011 is supportive of this reasonably rapid recovery from the crisis. Indeed, as evident in Figure 1, deal activity by emerging market firms in 2010 has already exceeded pre-crisis peaks, and M&A activity in 2011 appears well on track for the full recovery, in 2013, implied by our projections. This is also supported by country-level evidence. Outbound cross-border M&A deals concluded by Chinese firms, for example, reached 744 deals in 2010 and 909 deals in 2011, levels comparable to (and in excess of) the pre-crisis peak of 760 deals, attained in 2007.

# **6 Conclusion**

In this paper, we have explored the factors associated with bilateral M&A activity by emerging market firms. Our main finding is that these firms’ decisions to pursue acquisition targets depend critically on whether they are investing in advanced or emerging market targets. Southern acquisitions tend to be located in countries with lower levels of per capita income, which likely reflects a desire to take advantage of lower wage costs in those countries. Acquisitions of 18
|200|Figure A1. D|ecomposition of Chan|ges in US$2.50-a-DayPovertyRate—Al|l Countries||||
|---|---|---|---|---|---|---|---|
|-50<br>0<br>50<br>100<br>150<br>Percent Contribution to Total Change in Poverty||||||||
|-100|9<br>0<br>9<br>5|3<br>0<br>0|9<br>0<br>9<br>8<br>0<br>0|0<br>9|0|9|9|
||Romania 2001–0<br>Moldova 2001–1<br>Thailand 2000–0<br>Ghana 1998–200|Nepal 1996–200<br>Peru 2004–1<br>Bangladesh 2000–1|El Salvador 2000–0<br>Mexico 2000–1<br>Chile 2000–0<br>Costa Rica 2000–0<br>Argentina 2000–1<br>Ecuador 2003–1|Paraguay 1999–201<br>Brazil 2001–0|Colombia 2002–1|Honduras 1999–200|Panama 2001–0|
||Consumption-based|measure of welfare|Income-based measu|re of welfare||||
||Share of working-age|family members<br>Lab|or Income<br>Nonlabor Income<br>Consu|mption-to-incom|e ratio|||

**_Source:_** Authors’ calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru and Thailand.

_Notes:_ “Labor income” refers to the change in employment and earnings
To understand better the meaning of exposure effects consider the following hypothetical scenario. If I were able to randomly assign individuals to new regions _d_ starting at age _m_ for the rest of their childhood, a simple econometric approach to estimate the mean impact of spending year _m_ of childhood and onward in a region where permanent residents have 1 percentage point better outcomes (here defined as _ξm_ ) would be: where _αm_ are age at the time of move fixed effects, _y_ ̄ _db_ is the average outcomes of permanent residents in region _d_ of the same cohort _b_ as the individual _i_ , and _εi_ captures all other determinants of the outcomes studied. The fact that individuals were randomly assigned would ensure that the OLS estimate of _ξm_ in equation 1 is consistent. These coefficients then could be used to compute the exposure effect as _ωm_ = _ξm − ξm−_ 1.

However, the estimation of equation 1 in observational data will almost surely give an inconsistent estimate because there may be unobservables correlated to the outcome of interest. Hence, the empirical approach to obtain an estimate of exposure effects requires a strong assumption, which is discussed after presenting the baseline semi-parametric specification.

I use the research design proposed by Chetty and Hendren (2018a) to estimate the role of commuting zones in shaping intergenerational income mobility in the US, which was adapted to educational mobility by Alesina et al. (2020, 2021). This empirical strategy uses observational data to isolate regional childhood exposure effects from sorting and consists of two steps. First, I estimate the observed outcomes for individuals who are permanent residents, by region and birth decade. Second, I use individuals who move during childhood between regions to estimate how much of the previously estimated differences in intergenerational mobility between the place of origin and destination (for individuals in their same birth decade) is reflected in their chances of finishing primary school and how this changes with the age at the time of the move. If regions affect individual mobility, the impact should be 9
.038***|0.024**|
||(0.015)|(0.015)|(0.015)|(0.010)|(0.009)|
|Year-month FE|Yes|Yes|Yes|Yes|Yes|
|Establishment FE<br>Post-Feb ’22|Yes|Yes|Yes|Yes|Yes|
|_×_Firm Exposure|Yes|Yes|Yes|Yes|Yes|
|Observations|1,982,647|1,948,455|1,954,918|1,796,280|1,982,647|
|Adjusted R-squared|0.608|0.608|0.609|0.613|0.444|

Note: Regressions of the number of green technology-related job postings by an establishment each month on indicators for a month after February 2022 interacted with measures of the establishment’s ex-ante exposure to the energy price shock. Standard errors clustered at the country level in parentheses. Sample is firm establishments (defined by a firm-country-region triplet) with monthly data spanning from Q2 2021 to Q4 2022 (with singleton observations automatically dropped). Dependent variable in Columns (1) to (4) is inverse hyperbolic sine of total number of green technology-related job postings by establishment in each month. Country exposure measure is the fraction of total energy use from imported natural gas in 2019. Firm exposure is energy intensity, calculated as fuel and SHEC consumption of the firm divided by its sales (both in 2020). Only firms with information in CDP included in the regressions. Coefficients on Post-February 2022 × Country exposure × Firm exposure multiplied by 10,000. Robustness check in Column (2) excludes establishments of fossil fuel firms, defined those with NAICS codes categorized as upstream or midstream oil and gas or upstream coal industries in Greenspon and Raimi (2024); Column (3) includes a control for fossil fuel subsidies (% of GDP) from
25 the impact of international remittances on poverty and household consumption and investment. The paper has three key findings, and two of these findings merit comment.

First, using an instrumental variables approach to control for selection and endogeneity, the paper finds that international remittances have a large, statistical effect on reducing poverty in Indonesia. When we compare households receiving international remittances in 2007 with a counterfactual situation in which these households did not receive remittances in 2007, we find that the poverty headcount falls by 26.7 percent and the squared poverty gap declines by 69.9 percent. These results are much larger than those produced by broader, cross-national studies on the relationship between remittances and poverty. For example, Adams and Page (2005) find that a 10 percent increase in international remittances in a country will lead, on average, to a 3.5 percent decline in the poverty headcount and a 2.8 percent decline in the squared poverty gap.

Second, when we compare households receiving remittances in 2007 with a counterfactual situation in which they did not receive remittances in 2007, we find that households receiving remittances increase their marginal expenditures on one key consumption good – food – by 8.5 percent.

Third, when we compare households receiving international remittances in 2007 with a counterfactual situation in which they did not receive remittances in 2007, we find that households receiving remittances reduce their marginal expenditures on one key investment good – housing – by 39.1 percent.

The second and third findings of this paper deserve comment because they are at odds with recent research on the impact of remittances on consumption and investment in other countries. For example, using a large, nationally-representative household data set and a similar instrumental variables approach in Guatemala, Adams and Cuecuecha (forthcoming) find that
20 are also lower in the NRXGDP countries, the difference with MFGSERV countries is not significant. Thus, any significant impact of resource exports on the employment composition in urban areas may take place over more than one decade. Next, the shares of non-tradables (e.g., NTR3) in the urban areas of NRXGDP and DEINDU countries are higher than those in MFGSERV countries. Informality is also higher in the urban areas of NRXGDP countries than in MFGSERV countries, but the difference is not significant.

**Robustness: I2D2.** Another data source for employment information by country is the _International Income Distribution Database_ (I2D2) of the World Bank. The full database consists of about 1,500 individual-level household and labor force surveys for more than 150 countries. Detailed information on sectoral and can employment self-employment be found in about 1,000 survey samples for almost 100 countries. The data was initially compiled by the World Bank’s _World Development Report_ (WDR) unit between 2005 and 2011. The database has expanded since then allowing us to include more countries than IPUMS. We use its December 2017 vintage to replicate the IPUMS-based results with the expanded country sample. The surveys are nationally representative and sufficiently large for our purpose (i.e., they have more than 10,000 observations).<sup>23</sup>

We obtain for each country the _mean_ labor shares of various sectors and types of employment in urban areas in 1990-2015.<sup>24</sup> We then use the same cross-sectional model as for IPUMS (eq. (10)). Since 2005 is the mean population-weighted year in I2D2, the variables of interest are defined relative to 2000 (as in Panel A of Web Appx. Table D.5) or 2010 (as in Panel B of the same table). While we observe a strong correlation (about 0.9) between I2D2 and IPUMS for MFGFIRE, the correlation is weaker (0.55) for urban non-tradables
not influenced by information provided by women.

We then study whether the campaign influenced candidate and party choice. We find that it did. Treated women are significantly more likely to vote for the second most voted for political party. Control women in treated clusters behaved again as if directly treated, confirming once more the importance of social interactions. Polling station data show as well that an increase of 10 percent in the share of treated women led to decrease in the share of female votes for the winning party of 6 percent. These results suggest that the campaign could have influenced the share of votes at the constituency level, had it been implemented at a larger scale.

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which adverse weather conditions influence migration. They combine South African census data with climate data on spatiotemporal weather variability to examine South African bilateral inter-district migration flow patterns and determinants during the periods 1997-2001 and 2007-2011. The results reveal that precipitation scarcity and higher temperatures act as push effects for migration. However, the importance of the effect of climate on migration varies greatly depending on migrant characteristics, including ethnicity. In particular, the flows of black and low-income migrants in South Africa are strongly influenced by climate variables, while white and high-income migrants are weakly or not affected.

Focusing on Uganda, Agamile, Ralitza and Golan (2021) assess gender-differentiated reactions of smallholder farmers to droughts, finding that adverse weather conditions are an occasion for women to enter the commercial crop market by exploiting land from subsistence for income-generating crops, while relatively wealthier and better-educated people, especially men, are among those who benefit most from 14
# **Figure 2.3 Distribution of the working age population by education level. 16 LA countries, c.1989-c.2009**

<!-- Start of picture text --> ARG BOL BRA CHL<br>90 95 00 05 10 95 00 05 10 90 95 00 05 10 90 95 00 05 10<br>COL CRI ECU HND<br>95 00 05 10 90 95 00 05 10 02 04 06 08 10 90 95 00 05 10<br>MEX NIC PAN PER<br>90 95 00 05 10 90 95 00 05 90 95 00 05 10 95 00 05 10<br>PRY SLV URY VEN<br>95 00 05 10 90 95 00 05 90 95 00 05 10 90 95 00 05<br>Less than high school High school degree Some tertiary<br>100<br>50<br>0<br>100<br>50<br>0<br>100<br>50<br>0<br>100<br>50<br>0<br><!-- End of picture text --> Source: Own computations based on SEDLAC (CEDLAS and World Bank).

**Figure 2.4 Gini coefficient, poverty based on the 4USD international line and GDP. Average for Latin America, 1992-2009**

<!-- Start of picture text --> 2.5<br>2.0<br>The 1990s Growth<br>1.5<br>1.0<br>0.5<br>0.0 Crisis<br>-0.5<br>-1.0 Stagnation<br>-1.5<br>-2.0<br>-2.5<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>GDP Poverty Inequality<br><!-
**Figure 4:** Trends in Household Monthly Per Capita Expenditures

<!-- Start of picture text --> Monthly Per Capita Expenditures Secondary Educated Worker Non-Farm Worker<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>55 61 66 68 No Educated Educated Farm Non-Farm<br>Non-Farm Non-Farm Non-Farm<br>Self-Employed Casual Worker Salaried Worker<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>No Self-Employed Self-Employed No Casual Casual No Salaried Salaried<br>Manufacturing Services<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>No Manufacturing Manufacturing No Service Service<br>600 600 600<br>Rupees 300 Rupees 300 Rupees 300<br>0 0<br>0<br>600 600 600<br>Rupees 300 Rupees 300 Rupees 300<br>0 0 0<br>600 600<br>Rupees 300 Rupees 300<br>0 0<br><!-- End of picture text --> _Notes:_ The above figures shows trends in monthly per capita consumption for rural households over time. The data is disaggregated by the type of worker present in the household. NSS survey rounds 55, 61, 66 and 68 correspond to the 1999-00, 2004-05, 2009-10 and 2011-12 years.

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Φ , (10) cc ccii ccii AA = rr<sup>cc</sup> where the spillover rate remains a free parameter in this model. rr<sup>cc</sup> In this section, we return to the Ethiopia case as an example to demonstrate how PPI can be used to infer the potential effect of spillovers within a country’s poverty dynamics.

**Estimation Strategy:** PPI offers two distinct methods for analyzing multidimensional poverty. First, the “retrospective” analysis seeks to understand past dynamics by leveraging historical data from an initial survey year, revealing how poverty indicators and policy priorities (budget allocations for an indicator) have evolved over a certain observation period to reach observed values in a final year. This analysis can infer an optimal spillover rate (the spillover rate that best fits the data) and other model parameters (e.g., c and c′ ). Understanding these parameters is essential as spillover rates can vary widely or modestly between dimensions and countries, influencing policy intervention outcomes. αα αα Second, the “prospective” analysis uses historical insights to understand how changes in the spillover rate or the Poverty Space could affect multidimensional poverty dynamics over a future period. This approach guides policymakers on which poverty indicators to prioritize and when, helping to inform effective intervention strategies for future improvements.

In the retrospective analysis, we use national data on poverty indicators from the first survey year (2011) and the last survey year (2019), government expenditure data (per capita in constant USD) from 2011 to 2019, data on the quality of law and monitoring from the World Bank’s World Governance Indicators for 2011 (the quality of law is approximated with the rule of law variable, whereas the quality of monitoring with the control of corruption), and the estimated Poverty Space for 2011. We estimate the model parameters and find the optimal spillover rate by calibrating the model with spillover rates ranging from 0 to 1 (in increments of 0.
importance ranking (decreasing order) from Random Forest and rate of appearance ranking (decreasing order) from LASSO Stability Selection. The concavity of the pseudo R-square curves indicate that there is no substantial gain from additional predictors (beyond our lists of 30 predictors) from each method. The inflection points for adjusted R-square are around 10 to 15 variables, lower than our base specification with 30 variables, which also indicates that we are not losing from the data by capping it at 30 variables.

Interestingly, the maximum value of the R-squared never exceeds 0.19 for men and women, which also suggest that a large part of the variance in the data remains unexplained, even when adding the complete list of 301 variables in the logistic regression. This argument complements the discussion on predictive power of the model in Section 5.1.

# 5.3. Migration interest and actual migration

The positive correlation between interest in migration and actual migration has been shown in the literature (Creighton, 2013; Milasi, 2020; van Dalen & Henkens, 2013). Interest in migration illustrates aspirations that are less impacted by physical mobility barriers than actual migration. The relevance of studying interest in migration is therefore to overcome the selection bias of only observing migrants. For instance, if such predictions are aimed at better targeting potential migrants to provide them with information on safe migration processes, using actual past migration to do so would suffer from important endogeneity biases, blurring the profile of potential migrants. An interesting addition to our analysis would be to observe the extent to which individuals who are interested in migration are able to materialize this aspiration and whether the predictors of interest in migration perform well to predict actual migration.

In order to look further into the relationship between interest in migration and more materialized migration plans, we compute two indices aggregating different variables for each family of outcomes following Kling, Liebman, and Katz (2007): (i) knowledge about migration process index and (ii) preparation to migrate index.<sup>22<
# **C Alternative Estimates**

Figure C.1. Women’s Marriage Age Post the Gujarat Riots of 2002: Borusyak et al. (2021) Estimation <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>.5<br>0<br>-.5<br>Diff-in-diff Coefficients<br>-1<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is women’s age at marriage. The control states include all other states of India. Standard errors were clustered at the state-year level.

35
. Indeed 379 women were interviewed, of which 317 (84 percent) were owners. 134 men were interviewed, 73 of which (54 percent) were owners. The interviews were conducted face-to-face.

# • **Snapshot of the business environment**

In this section we provide an overall snapshot of the business environment of the firms in the sample, highlighting some of the most interesting findings stemming from the MGES survey.<sup>21</sup>

**Women tend to operate smaller, younger, and home-based businesses.** Starting with firm-level characteristics, one of the relationships that stands out is between the size of the firm and the gender of the owner: women are more likely to be part of smaller firms than men. Women are also more likely to have their businesses at home, implying that they usually sleep and work in the same location. This could indicate the limiting influence of cultural and gender-based traditions in the opportunities to establish a business, as well as family obligations that may prevent them from growing their business. These include family discouragement towards women becoming entrepreneurs and mobility restrictions constraining women’s access to markets (places where they can go and cannot go, security issues).<sup>22</sup> Moreover, there is also a relationship between the size of the firm and number of years in operation. While 48 percent of micro firms have been operating for 5 years or less, larger firms have been in the business for a longer time. In fact, 37 percent of small, and 40 percent of medium sized firms have been operating for 11 years or more.

> 20 Myanmar Gender Entrepreneurship Survey Report, Myanmar Survey Research, 2019. 21 This section also relies on the MGES report, Myanmar Survey Research, 2019. 22 ILO and Sasakawa, National Assessment of Women’s Entrepreneurship Development Framework Conditions – Myanmar, Draft executive summary.

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