rafmacalaba/data-use-mentions-extended huggingface dataset:
When the husband belongs to the surveyed household, I can identify mixed refugee-native couple as instances in which the wife is refugee and the husband is born in Greece. Theoretically, although native-born, the husband could still be the child of refugee parents, which would hardly correspond to a mixed couple. This is almost never the case in practice as husbands are typically older than their wife, and hence are born before refugees arrived in Greece for the first time.

**Second generation** I use the European Social Survey (ESS) that provides information on parents’ birthplace for a sample of individuals living in Greece in the 2000s. I identify second-generation refugees as individuals born in Greece with at least one parent born in Turkey. I focus on the cohorts born between 1923 and 1950 as they are the most likely to be the children of first-generation refugees. Table A.2 in the appendix shows that the proportion of ESS respondents with parents born in Turkey is the highest for people born in the 1920s, the 1930s, and the 1940s. It dramatically drops for people born after 1950. In contrast, the share of individuals with parents born outside of Turkey remains stable across birth cohorts, with a peak in the 1990s. This provides empirical support that I am able to identify second-generation refugees. The ESS does not only provide data on the socio-economic outcomes of second-generation refugees (such as education or occupation)

> 19Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 7.3 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D020.V7.3

12
impacts under climate change and sea level rise scenarios (Kebede and Nicholls, 2010). These results are driven by the lack of a coordinated storm water drainage system, inadequate housing development and blocking of water streams by careless dumping of solid waste (Sakijege, Lupala and Sheuya, 2012). Results from the current report indicate that far more people are affected by floods than the models predict.

At the household level, the poor are disproportionately affected by flooding, and often do not have the means to recover. This study is an attempt to quantify the impacts for households living in Dar es Salaam both inside and outside known flood risk areas. Vulnerable populations, such as children, elderly and

> 2 UN-Habitat 2014, using Global Urban Indicators, 2009 data. 3 PreventionWeb, accessed 19 February 2019.

2
Taken together, these studies indicate that changes in the gender gap may be generated by a third variable, such as health status, that may have a direct and independent effect on economic growth. Therefore, the estimated coefficient of gender inequality on economic growth is biased because it captures both its own effect and the effect of health status, with which it is correlated.

The cross-country literature shows that gender inequality is correlated with several other determinants of economic growth, including fertility, health, savings, trade liberalization, and “good institutions.”<sup>3</sup> In contrast with the reverse-causality problem discussed in section 3.1, in principle, the bias due to omitted variables can be eliminated by including all relevant variables in the analysis. In practice, however, this approach is unfeasible because standard cross-country data sets do not contain information on most of the relevant omitted variables. Moreover, because each country is an observation, adding several controls quickly exhausts the available degrees of freedom. Finally, to estimate the effect of gender inequality and its correlates separately, one must observe a sufficient amount of independent variation in the two variables. This method is not available for the most important omitted variables, namely, those that are strongly correlated with gender inequality.

Short of creating exogenous shocks to determinants of gender inequality or analyzing exogenous variation created by natural experiments, such as the spike in female labor force participation during WWII used by Fernandez et al. (2004), the estimated coefficient of gender inequality in cross-country regressions inevitably captures the effect of other determinants of economic growth. Therefore, its use as a policy parameter is limited because reducing inequality does not lead to the desired increase in economic growth if the other determinants are not changed at the same time.

# **_3.3. Mechanisms and External Validity_**

A crucial but often implicit assumption made by most cross-country studies is that the relationship between gender inequality and economic growth is the same across countries and across time periods. Thus, the
better position to extract favors from politicians. On the other two restructuring measures, excess labor and efficiency, privatization leads to a worsening. Only with both deregulation and stabilization is there an improvement in all three measures.

# IV. **Data Description**

We have firn-level data (balance sheet and profit and loss statements) for 1992-95 obtained from private firms (Czech Republic and Hungary) or statistical offices (Bulgaria, Poland, Romania, Slovak Republic, and Slovenia). Typically, the data cover manufacturing firms which were registered as state-owned enterprises (SOEs) in 1991. The data are annual observations at the plant level and cover the majority of plants in manufacturing industries. If some plants are owned by the same parent company, this relationship is accounted for in the data. The data, although not always a complete manufacturing census, are representative of the manufacturing sector in each country. Two types of selection bias are present - informal enterprises are excluded and small formal firms are underrepresented. Thus the sample primarily covers medium and large enterprises in the formal sector.

We exclude all firms which have missing observations and form balanced panels, i.e. all firms show up throughout the 1992-95 period.' The majority of the excluded firms have missing values between the beginning and end of the sample period, which suggests that they were not liquidated. There is no new entry of SOEs in the sample period (entry through split-ups and spinoffs is captured in the data). The information concerning exit of SOEs is not utilized here since we cannot distinguish between apparent exit (due to non-reporting) and true exit (due to liquidation or bankruptcy).

In three countries (Romania, Slovak Republic, Slovenia) we have almost complete coverage, while the data for the other four countries contain about half of the formal manufacturing sector. This is due to two reasons. First, as explained earlier, small firms are not well-covered in the analysis. In the countries where small business was allowed to operate even prior to
Venezuelan households.

Table 3. Household Descriptive Statistics

||Colombian|Venezuelan|Diff.|
|---|---|---|---|
||(1)|(2)|(1)-(2)|
|Household size|4.05|4.33|-0.28***|
||[1.41]|[1.47]|(0.06)|
|# Adults (3,617)|2.38|2.14|0.25***|
||[1.11]|[0.92]|(0.04)|
|# Children (3,169)|1.48|1.81|-0.32***|
||[0.74]|[0.95]|(0.04)|
|Assets (Max=15)|9.86|5.92|3.94***|
||[4.34]|[2.93]|(0.16)|
|Own dwelling [=1]|0.36|0.01|0.35***|
||[0.48]|[0.10]|(0.02)|
|Observations|1,338|918|2,256|

_Notes:_ Columns (1) and (2) describe the mean values for the households with members born in Colombia and Venezuelan, respectively. The last column illustrates a mean difference test between both groups. Standard errors are reported in brackets. Household size is the number of family members. Total assets is the number of assets owned by the households which can take a maximum number of 15, and own dwelling is a dichotomous variable [=1] if the household owns the place where they live.

We also explore the distribution of
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE A number of methodological issues must be considered. First, due to the cross country nature of the data collection, there is inevitably a time lag involved. The period covered by the AICD runs from 2001 to 2006. Most technical data presented is for 2006 (or the most recent year available), while financial data is typically averaged over the available period to smooth out the effect of short term fluctuations. Second, to make comparisons across countries, it was necessary to standardize the indicators and analysis to allow everything to be calculated on a consistent basis. This means that some of the indicators may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

From 2003 to 2007, Tanzania’s economic growth averaged 7.0 percent, compared with 3.7 percent from 1990 to 2002. Infrastructure contributed 1.4 percentage points to annual per capita economic growth over that period. This was primarily due to Tanzania’s ICT revolution, although the power sector also had a positive impact. Simulations suggest that if Tanzania’s portfolio of infrastructure assets could be improved to the level of the African leader, Mauritius, annual per capita growth rates could increase by 3.4 percentage points. The largest share of this impact would come from improvements in the power sector, but telecommunications and roads would have substantial impact as well (figure 1).

Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 34 percent of the productivity handicap faced by Tanzanian firms over the period 2002-2006, with the remainder being due to governance, red tape, and financing constraints (figure 2). Transportation is reportedly the infrastructure constraint that weighs most heavily on Tanzanian firms, with water a close second.

3
<sup>5</sup> of vehicles, calculated based on the purchase and resell prices, and the date of acquisition. The standard LSMS household survey questionnaires of Albania, Malawi and Nepal even involve a question on whether the vehicle was purchased by the household or received as a gift, payment for services, or inheritance.

# _Travel to social services or facilities_

Many of the LSMS household questionnaires ask a group of questions regarding the accessibility to social services, including travel mode, time, distance, and cost to a long list of social facilities which varies widely by country. Typically, the list of social services includes school, health facilities (for example, hospital, private pharmacy, private doctor), telephone service, bus station, paved and dirt roads, bank branch and local shop. An example of the access to facilities module

> 5 The annual use value of transport durables = Stpt(rt −πt + δ) . Stpt is the current value of durables. rt −πt refers to the real rate of interest. δ is the depreciation rate of durables, which can be estimated using purchase 1 value, current resell value and age of durables using the formula <u>pt</u> T (Deaton and Zaidi, 2002). δ −π = 1 −�pt−T ~~�~~

8
Table 2 Statistical distribution: firms according to country and sector (year 2005).

|**Country/industry**|**Beverages**|**Food**|**Garments**|**Metals and**<br>**machinery**|
|---|---|---|---|---|
|Bulgaria|10|2|5|9|
|Czech Republic|7|3|3|29|
|Estonia|4|1|5|8|
|Hungary|7|28|41|153|
|Latvia|2||4|5|
|Lithuania|4||3|8|
|Moldova|40|4|14|20|
|Poland|15|46|97|178|
|Romania|56|36|75|86|
|Slovak Republic||1|1|12|
|Slovenia|1|1|2|23|
|Total|146|122|250|531|

Source: Authors‘ calculation from the Investment Climate Survey Databank, World Bank.

The previous table 2 comprises the sectors analyzed in this research paper respectively and includes secondary activity (industry) sectors.

# 4.2 Variable Selection

In order to estimate technical efficiency with DEA, there exists considerable disagreement in empirical literature on the definition of outputs (in the context of multiple-outputs firms) of an industrial institution. Nevertheless, according to Coelli et al. (2005), a commonly-used classification of inputs involves five categories: capital (K), labour (L), energy (E), material inputs (M), and purchase services (S). The use of data according to these categories in productivity measurement is sometimes referred to as KLEMS approach<sup>19</sup> .

To ensure the validity of the DEA model specification, an isotonicity test (according to Golany and Roll, 1989
Inpres_ + _θ_ 7 _× Inpres × Eiy_<sup>_p_+</sup><sup>_θ_11</sup><sup>_× Inpres ×_(</sup><sup>_E_</sup> _iy_<sup>_p_)2, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of schooling for</sup><sup>_y_= 0</sup><sup>_,_6</sup><sup>_,_9</sup><sup>_,_12</sup><sup>_,_16.</sup> _EPCy_ are calculated at _y_ father’s years of schooling, where _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. The EPC values are based on coefficients reported in Table 3. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized EPC is the EPC value relative to EPC of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized EPC values are reported in percentage. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

42
subsection. For each question, we create a binary variable to identify the correct response and perform PCA analysis based on polychoric correlations, following the method developed to adapt PCA to ordinal data by Kolenikov and Angeles (2004). We estimate the financial literacy index as the first principal component of the four financial literacy questions. The procedure is described in greater detail in Appendix A for the year 2008 (the analysis for 2009 is available upon request). The corresponding distribution of eigenvalues is presented in Appendix A, Panel B (the first component accounts for 53% of the variation) and the factor loadings/scoring coefficients for the index are detailed in Panel C.<sup>11</sup> The financial literacy index distribution is shown in Panel E.

# 4.4.2 _<u>Who Is Financially Literate in Russia?</u>_

Table 1, columns 2 and 3, show summary statistics for individuals with high and low levels of financial literacy; asterisks indicate significant mean differences. We identify ―high‖ financial literacy as individuals with a financial literacy index greater than the sample median. Univariate tests find that financially literate individuals are more likely to be male, married or cohabiting, younger, and residents of urban Russian regions. They are more likely to have vocational/technical, or some level of higher education, and be employed in skilled or non-manual occupations. Importantly, individuals in the lowest income quartile are more likely to score low in terms of their financial literacy, while those in the highest income quartile are more likely to be highly financially literate. This is consistent with many other surveys on financial literacy in other countries (see Lusardi

> 11 Although we retain the first factor for the purpose of parsimony, the optimal number of factors, identified by Humphrey-Ilgen parallel analysis (Lance, Butts, and Michels, 2006), is in fact two. We compare the number of factors derived from our survey data against factors for random numbers representing the same number of
Sport (MoEYS). SPS teachers benefited from two years of formal training in a MoEYS teacher training center in Phnom Penh. They received a monthly salary of roughly $250 to teach for three hours a day, five days a week. Almost all SPS were attached to a public primary school and had access to classrooms equipped with teaching and play materials, along with better overall infrastructure (including sanitation facilities).<sup>8</sup> In contrast, IPS were typically not attached to a primary school. The local community established an IPS and covered operational costs. This included the IPS teacher stipend, which was at the discretion of the local commune council. Constrained by council budgets, it varied from $30 to $50 per month at the time of

> 5Source: Data from UNESCO Institute for Statistics.

> 6According to government data (MoEYS, 2017), of the 7,241 preschool facilities in Cambodia in 2016, 55% were SPS, 39% were IPS, and 6% were private preschools. However, these preschools are not evenly distributed across the country, and 38% of the 1,646 communes in Cambodia had no preschool facility.

> 7See Bouguen et al. (2013) for an impact evaluation of each type of preschool developed in the context of the GPE I.

> 8The program designers and research team did not anticipate the presence of SPS in the experimental villages. Villages with a preexisting SPS were not supposed to be eligible for the program.

8
), existing occupations that are expected to experience changes in the tasks they entail (“ _enhanced skills_ ”), and existing occupations that will not change substantially but that will be in higher demand (“ _increased demand_ ”). A methodological contribution of this paper is to develop a mapping of the O*NET classification of green occupations into the 4-digit International Standard Classification of Occupations (ISCO)-08 taxonomy.

Using detailed occupational tabulations (2-digit ISCO-08) from labor force surveys for 120 countries from 2011 to 2020,<sup>5</sup> the paper finds that, on average, 21.4 percent of employment is in green occupations, and

> 1 A broad definition of green policies includes all policies to remove barriers to green, clean, and resilient growth (see Awe, 2012).

> 2 Some exceptions include the 2011 UNEP ILO Green Jobs report. More recently, see the 2018 ILO Greening with Jobs report and <u>2021 K4D FDCO Creating Green Jobs in Developing Countries report. See also Timilsina (2022) for a discussion of related issues.</u> 3 See also Alfonso et al. (2022) for a recent analysis of green jobs in LAC.

> 4 O*NET is a database developed by the U.S. Department of Labor on occupational information for the U.S. workforce (see <u>https://www.onetcenter.org/overview.html).</u>

> 5 Data is available for each year in the period 2012-2019 for a set of 47 countries.

2
replace this linear variable with a binary variable for more- or less-forested. Model (1) measures conflict as total mortality; Model (2) measures conflict as husbands migrated; and Model (3) measures conflict as women divorced, separated, widowed, or alone. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

41
Policy Research Working Paper 10017

# **Abstract**

This paper examines the monetary benefits and costs of the quantity of public schooling (that is, years of schooling completed) in Ghana. The paper also examines the monetary benefits and costs of some aspects of the quality of public schooling, measured by the gains in achievement produced by selected interventions in public schools. The analysis uses estimates of (i) labor-earnings returns to schooling and private spending on public schooling, based on the latest national household sample survey data; (ii) government spending on public schooling, based on administrative information; (iii) impacts on test scores, and costs, of education interventions in public schools, drawn from experimental studies; and (iv) conversions of impacts on test scores produced by education interventions to (future) labor earnings, all for Ghana. The results are a set of benefit-cost ratios in the style of the Copenhagen Consensus.

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 draju2@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
29 _more_ educated urban residents. However, the difference is not significant.<sup>38</sup> Angrist et al. (2019) provide a measure of education quality that is built on globally harmonized test scores, obtained for all areas of a country, for 101 countries (c. 2020).<sup>39</sup> If we use it as a proxy for urban education (human capital) quality, we find significantly lower values in NRXGDP countries ((2)). However, if we use the learning adjusted years of education,<sup>40</sup> we again find no significant differences at the country level ((3)).

Another imperfect proxy for education quality is a country’s return to education. Abstracting from human capital demand factors, a lower education quality should reduce the wage gains from higher educational attainment. We use the I2D2 data (1990-2017) and follow the methodology of Lagakos et al. (2018) and Jedwab et al. (2020a) to estimate the returns to education for urban areas, as well as the returns to experience for urban areas.<sup>41</sup> More precisely, for individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one (for 18-67 year-old “urban” workers only): The dependent variable is the log of monthly earnings ( _lnWit_ ). As in Lagakos et al. (2018), experience is categorized into seven bins ( _expite_ ): [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We include the number of years of education ( _eduit_ ) and country-year-sample fixed effects ( _θt_ ).<sup>