rafmacalaba/data-use-mentions-extended huggingface dataset:
Turning to personality characteristics, we find that of the big-5 traits only two – extraversion and openness – are positively correlated with employability assessments. A one standard deviation increase in extraversion reduces the likelihood of being rated _less employable_ by 1.8pp and increases the likelihood of being rated highly employable by 1.6pp. Similarly, a one standard deviation increase in openness reduces the likelihood of being rated _less employable_ by 2.4pp and increases the likelihood of being rated highly employable by 2.2pp. It is important to highlight that personality traits were measured using an independent survey, and not by the counselors themselves.

# **6. Conclusion and Policy Implications**

Outsourcing employment intermediation and matching to the private sector offers greater flexibility and can play an important role in improving employment outcomes for vulnerable groups. This paper studied the effects of an intervention where jobseekers were randomly assigned to receive less intensive (profiling and counseling) or more intensive (profiling, counseling, and matching) employment services. The overall results from the evaluation point to significant positive effects of the matching treatment. Twenty-three percent of jobseekers in the control group who received less-intensive services found employment, and access to matching services in addition to counseling resulted in 5.8 percentage point (25.2 percent) higher likelihood of formal employment. These results were strong for harder to employ jobseekers including youth and jobseekers with low skills, were driven by specific matching, where jobseekers were directly connected with employers and benefitted from an introduction to the employer. This suggests that matching can play an important role in removing certain information asymmetries and related constraints for employers and jobseekers, and improving employment outcomes for vulnerable groups.

This paper also analyzed the incentives and behaviors of the private employment service provider. Assessment of the jobseekers’ employability by the counselor was a key determinant of services provided to them. Consistent with cream skimming behavior, those assessed to have low employment prospects were significantly less likely to be provided matching services. Interestingly, despite the lower likelihood of being allocated to
-|---|
|Port Said|25|41.82|8.28|0|
|Ismailia|10.4|11.95|7.8|1.59|
|6 October|10.2|9.91|11.04|8.51|
|Red Sea|6.1|14.29|0|0|
|Suez|11.6|22.88|3.13|0|

Source: Authors’ calculations using Labor Force Survey Table 21 - Unemployment Rate for Governorates with Highest Net migration for Work Inflows

|Unemployment rate|Overall|Non-migrants|Migrants|Migrated to work|
|---|---|---|---|---|
|Cairo|13.4|14.9|5.79|3.6|
|6<sup>th</sup>of October|10.2|9.9|11|8.5|
|Alexandria|11.6|12.5|4.6|0|
|Ismalia|10.4|11.9|7.8|1.5|
|Qalubia|7.8|8.8|3.3|0|

Source: Authors’ calculations using Labor Force Survey To further explore migrants’ employability we ran a probit model for employment (‘1’ if the person is employed and ‘0’ otherwise). The explanatory variables are educational attainment 22
the worst and then taking the average.

# **_Size of the government_**

The size of government is approximated by the output supplied by State-operatedEnterprises and government investment as a percentage of GDP. The hard data come from the Economic Freedom of the World 2003 annual report.

# **_Corruption Perception Index_**

The Corruption Perception Index is constructed by Transparency International. The data refers to 2002 and ranks the countries according to the degree in which corruption is perceived to exist among public officials and politicians. It draws on 15 different polls and surveys from nine independent institutions.

# **_The tax burden_**

Tax revenue as percentage of GDP. It comes from the World Development Indicators of the World Bank.

> 16 The following set of countries is excluded from the Economic Freedom Index: Kyrgyz Republic, Serbia and Montenegro, Bosnia, Macedonia, Kazakhstan, Uzbekistan, Moldova, Belarus, Azerbaijan, Georgia and Armenia

27
# **Reference**

Acemoglu, D., S, Johnson and J. A. Robinson (2005) “Institutions as a Fundamental Cause of Long-Run Growth.” Handbook of Economic Growth 1A: 386-472.

Alfaro, L., and F. Kanczuk (2019) “Undisclosed Debt Sustainability.” _NBER Working Paper_ 26347.

Banisar, D. (2006) “Freedom of Information around the World 2006: A Global Survey of Access to Government Information Laws.” Privacy International. http://www.freedominfo.org/ Beck, T., G. Clarke, A. Groff, P. Keefer and Patrick Walsh (2001) "New tools in comparative political economy: The Database of Political Institutions." _World Bank Economic Review_ 15(1): 165-176.

Bellver, A., and D. Kaufmann (2005) “Transparenting Transparency.” MPRA Paper 8188, University Library of Munich, Germany.

Blinder, A., C. Goodhart, P. Hildebrand, D. Lipton, and C. Wyploszet (2001) “How the Central Banks talk?” Geneva Report on the World Economy 3, ICMB.

Berliner, D. (2014) “The Political Origins of Transparency.” _The Journal of Politics_ 76(2): 47991.

Calderon, C., and A. Zeufack (2018) “Debt Trends and Risks in Sub-Saharan Africa.” Washington, DC: The World Bank, Manuscript, November.

Calvo and Mendoza (1996) “Petty Crime and Cruel Punishment: Lessons from the Mexican Debacle.” The American Economic Review 86(2): 170-75.

Chinn, M. D. and H. Ito (2006) "What Matters for Financial Development? Capital Controls, Institutions, and Interactions" _Journal of Development Economics_ 81(1): 163-192.

Chinn, M. D. and H. Ito (2008) "A New Measure of Financial Openness." _Journal of Comparative Policy Analysis_ 10(3)
of Environmental and Economic Accounting (SEEA), as satellite accounts to the System of National Accounts (SNA), and the SEEA includes the measure of depletion used in this paper. The three countries of interest in this paper are not yet producing such measures, as still struggling with the production of traditional national accounts. In 2010, Ghana revised upwards its GDP estimate for the year 2006 by 60 percent. Liberia’s official national accounts are considered by most observers, including the government, as severely underestimated and new accounts should be validated after the conduction of a new household living standards survey. Sierra Leone is currently re-basing its national accounts, and preliminary estimates suggest that official GDP numbers are underestimated by about a third.

- 3 -
steps,? or the jump measure J,, which equals the absolute value of the average step change between 1999 and 2001, to be calculated for each state s and the transition matrix as a whole. Such measures treat upward and downward changes symmetrically, even though higher steps are preferred to lower steps. By contrast, the upward mobility measure U, gives the value of the average step at 2001, again calculated for each row and the matrix as a whole.

8 Tn aseries of papers, Ravallion and Lokshin (Ravallion and Lokshin (2001), Ravallion and Lokshin (2002), and Lokshin and Ravallion (2002)) have analysed identical ladder questions using Russian household survey data. ® is no larger than the maximum number of states minus one. is linked to the Shorrocks axiom for transition matrices (Shorrocks (1978)): shifting probability mass from the diagonals to off-diagonals increases mobility.
205,914|147,498||8,335|24,102|

Note: Observations with missing values and for pregnant women have been dropped. Women between 15 and 49 years of age and children between 0 and 5 years of age are included. The LSMS surveys used are Burkina Faso’s 2014 Multisector Survey; the 2013-14 Ethiopian Rural Socioeconomic Survey; Ghana’s 2009 Socioeconomic Panel Survey; Malawi’s 2013-14 Third Integrated Household Survey; Nigeria’s 2012-13 General Household Survey; Tanzania’s 2012-13 National Panel 38
1997 and 2000, captured information from the entirety of the households residing in an experimental sample of 506 rural localities (320 treatment and 186 control) of high or very high marginality, located in seven states of the country: Guerrero, Hidalgo, Michoacán, Puebla, Queretaro, San Luis Potosí and Veracruz. However, the experimental design was lost starting in 2000 when the control localities were incorporated to the program. This has presented a methodological challenge for the impact evaluation, which has had to use quasi-experimental methodology.<sup>18</sup> The last round, collected in 2017, prioritized information that would enable the long-term evaluation of PROSPERA, essentially to answer questions about its effects on children enrolled in the program between 1997 and 2000. For this purpose, 15,457 youths that in 2017 were

> 16 The panel database comprises of variables from all rounds of ENCEL. See World Bank (2018b).

> 17 In some cases, where necessary information of ENCASEH 1997 was not available, the information from the closest round was used.

> 18 See INSP (2007) and Yaschine, Hernández and Urquieta (2008), for a discussion on the loss of the experimental design between 1997 and 2007.

13
MIGEPROF and the research team set the treatment sample size at 640 couples based on budget constraints. The research team first conducted a desk-randomization to assign villages to treatment or control groups using Stata. Then in all study villages, public VSLA lotteries were used to rank eligible couples from 1 to 30 (the maximum VSLA size). The first 60% of couples from each VSLA lottery were assigned to treatment, stratified by sector and number of lottery applicants. The implementing NGO then organized the 640 treated couples into 32 training groups based on location, combining members from nearby VSLAs.

## **2.5 Representative Baseline Community Survey**

To assess selection into the program, we randomly sampled and interviewed 686 couples at baseline from a complete household listing of study villages. VSLA couples were similar to randomly selected non-VSLA couples on most characteristics, though slightly younger, more educated, less economically vulnerable, and more progressive on gender issues (see table A2 for a comparison on key characteristics).

# **3 Data and Estimation**

## **3.1 Data Collection**

Data collection included two rounds of panel surveys. We conducted baseline surveys from November 2017 to February 2018, before the intervention (March–August 2018), and endline surveys six to eight months after completion (February–March 2019).<sup>10</sup> Of the 2,042 baseline study couples, 98.3% of women and 97.5% of men were re-interviewed at endline, with no differential attrition across treatment arms.<sup>11</sup> Women’s and men’s surveys covered household demographics, socio-economic characteristics, time use, economic activity, decision-making, well-being, gender attitudes and norms, experience of IPV, and likelihood of perpetrating violence in hypothetical scenarios (men only). Surveys were administered by enumerators of the same sex as the respondent in private settings (empty classrooms, village offices, or churches), with strict privacy protocols requiring
45 missing data do arise from seasonal lack of availability (Gilbert et al. 2017). In related work we collected a survey in all the markets for which we have price data and asked about the usual availability of each item every month. Comparing the missing data in the CPI price data set to the results of this survey from August 2016 – July 2017, we found concordant results 79% of the time where there was a price observation present (absent) when the item was reported to be available (unavailable) (Kaiyatsa et al. 2021). This study also confirmed that the potential impact of discordant missing prices (missing when reportedly available) on the CoNA estimate was likely small in practical terms.

Third, the food composition data are the best available for Malawi, however more of certain nutrients may be available than we estimate but would only affect the results if that item would have been selected into the diet. One way to examine where this might be the case is to model scenarios. Doing so in a related study for the shared diets scenario, we found selenium to be the limiting nutrient (the nutrient causing the diet to be infeasible) (Schneider 2021b). Selenium is the nutrient for which there are more foods with no data than for any other nutrient, however there is also strong evidence that selenium is lacking in Malawian soils and diets, and there is widespread deficiency in the population (Joy, Kumssa, et al. 2015; Joy, Broadley, et al. 2015; Phiri et al. 2019; Schneider 2021a; Schneider 2021b). So there is some evidence that little selenium is present and available to consumers in Malawi, but also note that our results show this does not hinder the individualized diets, so there is sufficient selenium present given the data to identify a solution to meet individual needs. What Schneider (2021b) demonstrates – to support the intuition behind these results – is that there is enough selenium present, but it comes from sources that also add
support in order to avoid costly liquidation.<sup>11</sup> Growing size and concentration of banks poses a critical challenge for ECA because of the economic and political ramifications connected to the failure of these large financial institutions. Although the banks in the ECA developing region are small in absolute terms, they are large in comparison to the local economies. Sberbank in Russia with assets over US$400 billion, for instance, is one of the largest banks in Europe. Since 2007, the top 5 banks by asset size have grown larger in all ECA sub-regions except in Central Europe and the Baltic countries (Figure 16).

# **Figure 16: Top 5 Bank Assets as a % of GDP**

<!-- Start of picture text --> 191<br>169<br>114<br>67<br>63 53 56 59 63<br>44 48<br>34<br>26 28 28 29<br>18 20<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017<br>200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>Total assets - Top 5 banks (% GDP) 40<br>20<br>0<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe.

Following guidelines and international standards set by the Financial Stability Board (FSB) and the Basel Committee on Banking
looking at both paid and unpaid work. Women in Burkina Faso earn 82 percent less in monthly wage earnings compared to men, while their business revenues are on average 61 percent lower. Within agriculture, women’s harvest values and crop sales are both approximately 60 percent lower than that of men’s. We further find that between 2014 and 2019, the overall gender gap in wage earnings increased, along with a small increase in the harvest value gender gap—but stayed the same for crop sales, business revenues and overall labor force participation. This means that Burkina Faso has not witnessed progress in lifting this enormous structural barrier to economic growth in the country.

We run a series of decompositions to understand and explain the causes of the current gender gaps, which allow us to identify how much of the gender gap is driven by differences in endowments (differences in the

> 1 Goal 5: https://sdgs.un.org/goals/goal5.

> 2 Human capital is measured through the Human Capital Index, which captures the amount of human capital that a child born today can expect to attain by age 18. The Gender Development Index was accessed at: <u>https://hdr.undp.org/gender-development-index#/indicies/GDI.</u>

2
US<br>dollar public investment on<br>capital spending per capita;<br>_Result: insignificant_|n.a.|Country-level panel data of<br>78 developed and developing<br>countries over the period<br>1970-1994. The countries of<br>the sample are grouped in<br>three categories: countries<br>facing high political<br>constraints, countries facing<br>low political constraints and<br>weak interest group<br>competition, and countries<br>facing low political<br>constraints and strong interest<br>group competition.|

56
itive influence on long‐term economic growth, probably thanks to both the stimulus effect of reconstruc‐ tion and the productivity effect (also labeled “Schumpeterian creative destruction effect” or productivity effect, described earlier in Section 4.b.). Others, like Noy and Nualsri (2007), Noy (2009), Hochrainer (2009), Jaramillo (2009), and Raddatz (2009), suggest exactly the opposite conclusion, i.e. that the overall impact on growth is negative. As suggested by Cavallo and Noy (2010) and Loayza et al. (2009), the dif‐ ference between both conclusions may arise from different impacts from small and large disasters, the latter having a negative impact on growth while the former enhance growth.

There are also examples of local‐scale econometric approaches. For instance, Strobl (2008) investigates the impact of hurricane landfall on county‐level economic growth in the US. This analysis shows that a county that is stuck by at least one hurricane over a year sees its economic growth reduced on average by 0.79 percentage point, and increased by only 0.22 percentage point the following year. On Vietnam, Noy and Vu (2009) investigate the impact of disasters on economic growth at the province level, and found that lethal disasters decrease economic production while costly disasters increase short‐term growth. GDP is not the only relevant indicator of disaster economic consequences, and Rodriguez‐Oreggia et al. (2009) focus instead on poverty and the World Bank’s Human Development Index at the municipality level in Mexico. They show that municipalities affected by disasters see an increase in poverty by 1.5 to 3.6 percentage point. Looking at different economic variables, Hallstrom and Smith (2005) assess the

> 4 The Weather project is a 7FAP of the EU concerned with weather extremes and transport working more precisely on these issues.

17
in response to economic growth that was driven by sectors that generate low employment: mining and commercial oil production.

Most job creation in recent years has taken place in economic sectors with relatively low labor productivity, and in urban self-employment, rather than wage employment (World Bank 2016). In fact the main source of job creation has been the trade services (wholesale and retail) sectors.<sup>2</sup> In total, Ghana’s economy created nearly 3 million jobs between 2005 and 2012, of which most were in urban areas and with nearly half in the trade sector. The agriculture sector, while shrinking in relative terms, continued to create a significant number of jobs in absolute terms. By contrast, with the exception of construction, the industry sector has created very few jobs. Put differently, the bulk of job expansion has occurred in precisely the two economic sectors with the lowest levels of labor productivity — as measured by value added per worker (World Bank 2016).

The dynamics of firms in Ghana’s private sector — which types are the main employers and which are the characteristics of the fastest growing firms — add evidence to the heterogeneous aspects underlying recent job growth and to whether job growth is narrowly concentrated or more widespread. If firms employ workers and add jobs commiserate with their sales (in terms of volume and growth), then this recent growth

> 1 Based on authors’ calculations, using data from the World Development Indicators.

> 2 Notably employment also increased in other services activities such as financial and business services and public administration.

3
> **(1995)** and Rea **(1996)** claim that shareholders did not redeem shares during crisis

> periods. Rather, they argue that net inflows to emerging markets are usually steady, and crisis-period outflows are small and short-lived (at least during Mexico's crisis). Froot, O'Connell, and Seasholes (2000) present a related picture, but without focusing on institutional investors. They analyze, among other things, aggregated net flows into

> individual emerging markets. Though net inflows decreased during the Mexican and Asian

> crises, they find little evidence of net outflows.

In this section we provide evidence on the stability of mutual **fund** investment and the behavior of mutual funds following speculative attacks. Where possible, we distinguish the behavior of mutual fund managers from the actions of underlying investors. This section examines data sets from various sources, including the Emerging Market Funds Research, Morningstar, the **SEC,** and the BIS.

# IV.a Mutual Fund Flows

> We first examine the evidence of net flows from dedicated emerging market mutual funds to Asia, Latin America, and transition economies (data from Emerging Market Funds Research). Figure 4 shows the average quarterly net flows to these regions from **1995** to **1999.** On balance, mutual fund flows to emerging markets since **1995** have been muted, reaching about $20 billion, with booms in the capital flows being followed **by** pronounced outflows, which were not persistent in the aftermath of the tequila crisis. Outflows from Latin America reached about $4 billion in **1995,** but mutual funds increased their positions in Latin America **by** about $2 billion in the first half of **1996.** The tequila crisis did not have any spillovers in Asia or in transition economies. In fact, flows to Asia ballooned to

**16**
middle class for three Latin American countries. The conceptual basis follows the vulnerability to poverty approach, by which, the middle class is defined the level of income that allows individuals to protect themselves from falling into poverty over time.

**Table 2: Incidence of health shocks; 5-year period: Chile 2001-06, Mexico 2000-05, and Peru 2001-06**<sup>**/a**</sup>

_Percentage of households_

|**Income**<br>**quintiles**|**Chile**|**Mexico**|**Per**<br>**At least one**<br>**member**|**u**<br>**Head**|
|---|---|---|---|---|
|1|11.1|11.5|30.7|7.2|
|2|11.6|12.0|39.8|10.0|
|3|14.2|12.2|44.3|12.5|
|4|15.1|12.1|46.3|15.0|
|5|18.2|11.0|43.6|17.1|
|**Total**|**14.0**|**11.8**|**40.9**|**12.4**|

_Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases. /a The differences in the incidence for Chile and Mexico with respect to Peru is probably due, among other factors, to the way the surveys collects the information (first three columns). In Chile and Mexico the surveys in 2006 and 2005, respectively, asks for the occurrence of health shocks during the previous 5 years for
2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>Variation (quarter to quarter) Variation (quarter to quarter)<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04<br>Variation (quarter to quarter) Variation (quarter to quarter)<br>Variation (quarter to quarter) 2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03 2010/04 Variation (quarter to quarter)<br><!-- End of picture text --> _Source:_ Own calculations using data from ENOE (various years). Figures for the fourth quarter of 2010 are preliminary.

9