rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Variables Sector of Accounting financial Revenues, Financial data, 22 indicators on Share of SOEs in Number of firms, Financial<br>operation, data, SOE ownership employment, Ownership data financial total rail total of labor, performance<br>structure and Return on performance turnover vs and total of and netted fiscal<br>Assets. OECD Public revenues transfers.<br>Age of company, Ownership<br>income (netted fiscal Index in Rail;<br>Em Profit/loss, Revenues, ployment, transfers) (WGI), and productivity Corruption performance of Financial SOEs<br>State participation,<br>Level of<br>government<br>(central,<br>subnational), audit<br>status,<br>Ownership<br>structure<br>(participation and<br>shareholders)<br>Source Orbis; Government sources Orbis; S&P Orbis, external Orbis, Orbis, Amadeus SOE Surveys to SOE Surveys to<br>government’s and company Capital IQ; databases government databases; authorities authorities<br>sources, EMIS websites; EMIS; Surveys to public sources, government (2012, 2015 and<br>intelligence, stock exchanges authorities; external sources; 2017)<br>Factiva, external sources databases external<br>government databases<br>sources (full list in<br>annex)<br><!-- End of picture text --> Source: Authors’ compilation based on listed sources.

Preliminary results underscore the power of the methodological approach developed in this paper to capture an unprecedented number of firms with state participation, in comparison to other existing databases. Figure A.1 shows a comparison between the number of firms captured by the World Bank’s Global BOS database and the OECD database. We found that in almost all cases, the BOS database captures significantly more firms with state participation and in some cases, as many as 30 times more than comparable exercises, as in the case of Poland. Figure A.2 shows a similar finding when comparing the BOS database to the IADB database of SOEs.

_Figure A.1 Comparison of Country-level SOE figures. World Bank’s Global BOS database vs. OECD database_ <!-- Start of picture text --> Comparison OECD Total WB SOE<br><!-- End of picture text --> <!-- Start of picture text --> Comparison OECD Total WB SOE<br>4,500 4051<br>4,000<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500 1226<br>841<br>1,000 593<br>370 360 406 431<br>500 32 28 66 209 71 59 25 184 39 37 39 126<br>Costa Rica Estonia Hungary Latvia Argentina Chile Turkey Slovenia Colombia Poland<br>Number of firms<br><!-- End of picture text --> <!-- Start of picture text --> Costa Rica Estonia Hungary Latvia Argentina Chile Turkey Slovenia Colombia Poland<br><!-- End of picture text --> Source: Authors’ compilation based on Global BOS database and OECD (2017) data 37
ratio of non-performing loans to total loans, the ratio of equity to capital, and the return over assets. _S_ stands for systemic risk; it is a matrix that includes measures of country and exchange rate risks. The former is captured by the spread on Argentine and, separately, Uruguayan sovereign bonds over comparable U.S. bonds, as expressed in Argentina’s Emerging Market Bond Index Plus or EMBI+ and the Uruguay Bond Index or UBI, respectively. Exchange rate risk (or more precisely the currency premium) is measured by the 12-month forward (NDF) exchange rate relative to the spot exchange rate for Argentina. For Uruguay, we use the spread of the average interest rate on peso time deposits (with maturity of more than one month and less than six months, in the top private banks) relative to the rate on similar dollar deposits. _E_ stands for exposure and is a matrix that includes indicators of individual banks’ exposure to systemic risks. More precisely, we use the share of government debt (bonds and loans) over total bank assets as a proxy for exposure to “country” (sovereign default) risk. For exposure to exchange rate risk, we use the ratio of dollar loans over bank capital for Argentina and the ratio of dollars loans over assets for Uruguay.<sup>12</sup> All regressions control for bank specific effects, α _i_ ..

Bank fundamentals and the indicators of bank exposure to systemic risks are lagged for two reasons. First, in both countries, balance sheet data are released to the public by bank regulators with a delay of three to four months; hence, the choice of our lag structure, which captures more precisely the information set available to depositors at any point in time. Second, this lag structure also helps to reduce potential endogeneity problems.

Results for Argentina and Uruguay are reported separately in Tables 1 to 6. Simple inspection of the tables reveals a similar pattern. The first column presents a regression of the

> 12 The traditional way of measuring exchange rate risk is the difference between dollar assets and liabilities. However, here we are more interested in the embedded credit risk that arises from the dollar loans that banks often grant to debtors without dollar incomes. For Uruguay, we examine the ratio of dollar loans to assets, since equity turned negative for some banks during the crisis period.

14
# **Comparing condom use with different types of partners: Evidence from national HIV surveys in Africa**

# **Damien de Walque**

The World Bank Development Research Group **Rachel Kline** The World Bank Development Research Group

> * We thank Adam Wagstaff for useful discussions and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Working papers describe research in progress by the author and are published to elicit comments. and to further debate.
facilitating _current_ migration. The network can provide _ex ante_ information and assistance and _ex post_ support for the migrant upon arrival (e.g., short-term accommodation, job-searching expertise, paperwork). Thus, pre-existent migrant networks effectively reduce the costs of migration (e.g., <u>McKenzie and Rapoport 2010). Secondly, the growth of GDP per capita at the</u> destination is a proxy for the country’s economic performance, and, more importantly, employment conditions that are exogenous to potential migrants in Moldova. An expanding job market is highly attractive for potential migrants and hence a pull factor to this destination (e.g., Antman <u>2011).</u><sup>22</sup> As a whole, our instrument captures the exogenous variation of migrant networks at the community level,which lowers migration costs, and economic conditions at the destination country, which increase the expected returns of migration. Exploiting variation at the community level, our instrument does not allow exogenizing household-level choices regarding migration such as the identity of the migrant or the duration of the stay abroad. We can only successfully predict the probability of at least one household member becoming a migrant and, therefore, use the household’s migration status as the main variable of interest in our analysis. Therefore, our results should be interpreted as the average effects across all migrants and migratory spells. The validity of the instrument depends on the exclusion restriction that _Network-Growth_ must only affect the provision of private educational inputs through migration status. This seems self-evident for the growth of GDP per capita at the destination. It is hard to conceive of a different relationship (i.e., other than migration) through which the changes in per capita growth rates in a set of foreign countries would affect the education investment decisions of a Moldovan household differentially between communities. For the migration network, we assume that past migration rates are predictors of current migration rates only via network effects and, otherwise, have no influence on the household’s education spending. Accordingly, we include the 2004 share of the community’s population who is a migrant to Italy, Romania, Russia, and Ukraine as additional controls in the 2SLS setup to account for proximity to the border and any systematic differences in development that may have arisen because of migration to any of these important destinations between the take-off of migration, in 1999, and 2004, as in

> 22. To better capture the individual gains from migration, rather than the rise in opportunities, we alternatively use the change in GDP per capita. This results in comparable results in magnitude and significance. The KleibergenPaap weak identification statistic is however smaller than when using GDP growth for the IV.

11
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Table 4: School Retention (2017–2018)

||||||Grade|Level||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|11|
|Natives|94%|89%|90%|87%|95%|95%|93%|93%|89%|79%|94%|
|Migrants|53%|57%|60%|58%|63%|65%|61%|59%|64%|54%|60%|
|Forced Migrants|46%|49%|54%|54%|57%|60%|55%|50%|55%|45%|45%|

**Note:** The table shows school retention measured as the proportion of students in 2017 that continued in 2018 for native, migrant, and forced migrant children for each grade.

The number of forced migrants enrolled in higher education around the world increased dramatically from 2017 to 2018 (from 1 percent to 3 percent), in contrast to 37 percent of natives; online alternatives can boost this number even more (UNHCR, 2019). Higher education can really change the lives of forced migrants, giving them a better expected income and increased life standards perspectives, but certificates, languages and cost are some of the biggest barriers for students. In Chile, higher education attendance for those aged 18 to 24 years is significantly lower in migrants compared to natives (11 percent vs. 39 percent); the most frequently reason given for not attending is looking for a job (Servicio Jesuita a Migrantes, 2019).

# **Transition from secondary school to higher education**

The transition between high school and higher education is a crucial step for every child. Decisions made at this stage can significantly impact children’s economic prospects and quality of life, as the returns to higher education are particularly high in Chile. In 2017, those aged between 25 and 64 years old with a tertiary degree earned 141 percent more than their counterparts with only a secondary education; the Organisation for Economic Development and Co-operation average is around 54 percent (OECD, 2020) In Grade 12, students in Chile must make two important decisions that directly affect their likelihood of enrolling in higher education. The first is whether to register for the college selection test, which determines placement as well as access to state benefits for higher education. The second decision is whether to apply for state financial aid in the form of free tuition, scholarships, and/or student loans; this aid is managed through a centralized system. Although a few tertiary institutions (mostly TVET) do not require the scores from the standardized college admission test, all students must complete the Ministry of Education’s socioeconomic form to apply for financial aid.

Table 5 presents our data from 2017–2018 regarding the differences between native, 15
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46 Table 1: Calibration Statistics: Aggregate Analysis

|**A. Control Parameters**|Parameter|High (HIC)|Middle (MIC)|Low (LIC)|
|---|---|---|---|---|
|Population growth rate|_gb_|0_._5%|0_._6%|2_._2%|
|Labor force participation|_lp_|84_._0%|75_._7%|89_._6%|
|Youth unemployment|_uy_|13_._7%|13_._8%|6_._3%|
|Adult unemployment|_u_|4_._9%|3_._8%|2_._5%|
|Retirement age|_R_|65|65|65|
|Schooling|_πs_|See text|See text|See text|
|Mortalityfunction|_δ_ (_·_)|See text|See text|See text|
|**B. Return parameters**|Parameter|High (HIC)|Middle (MIC)|Low (LIC)|
|Returns to education, before college|_r_<br>_~~s~~_|6_._7%|8_._8%|5_._5%|
|Returns to education, college +| ̄_rs_|13_._0%|11_._5%|13_._6%|
|Returns to experience, before college|_r_<br>_~~e~~_|4_._4%|2_._1%|1_._9%|
|Returns to experience, college +| ̄_re_|4_._2%|2_._7%|3_._2%|

_Notes:_ Calibration parameters for the stationary equilibrium of the model economy.

Table 2: Calibration Statistics: Magnitude of the Education and Experience Shocks

|Group|Year|Effective|Reach (_c_)_×_|Lost Educ.|Education|Empl|oyment|Shock %|
|---|---|---|---|---|---|---|---|---|
|||Closure Rate (_s_)|Effectiveness (_e_)|_ν_ =_s×_(1_−ce_)|Shock %|All|Male|Female|
|HIC|1|44.0|40|26.4|21|8.3|7.2|9.5|
|HIC|2|32.5|50|16.3|||||
|MIC|1|63.5|35|41.3|33|9.5|8.8|10.9|
|MIC|2|37.5|35|24.4|||||
|LIC|1|53.0|20|42.4|28|6.7|6.2|7.7|
|LIC|2|18.0|20|14.4|||||

_Notes:_ Calibration parameters for the education and employment shocks in different groups. The effective closure rate is equal to the share of open schools + 0.5 _×_ the share of partially open schools.

Table 3: Overall Returns and Calibration Statistics: Aggregate Analysis

|Group:||Male|||Female||Diff.|Male - Fe|male|
|---|---|---|---|---|---|---|---|---|---|
|**A. Overall Returns**|HIC|MIC|LIC|HIC|MIC|LIC|HIC|MIC|LIC|
|Ret to education|9_._4|7_._9|8_._5|11_._1|9_._0|9_._9|_−_1_._7|_−_1_._1|_−_1_._4|
|Ret to experience|4_._1|1_._9|2_._2|3_._0|1_._5|2_._1|1_._1|0_._4|0_._1|
|**B. Parameters**|HIC|MIC|LIC|HIC|MIC|LIC|HIC|MIC|LIC|
|Pop. growth|0_._5|0_._6|2_._2|0_._5|0_._6|2_._2|0_._0|0_._0|0_._0|
|Labor force part.|91_._6|94_._9|96_._7|74_._8|55_._2|81_._2|16_._8|39_._7|15_._5|
|Youth unemp.|14_._0|15_._9|7_._0|14_._2|18_._4|7_._4|_−_0_._2|_−_2_._5|_−_0_._4|
|Adult unemp.|4_._7|4_._0|2_._9|5_._5|5_._1|3_._5|_−_0_._8|_−_1_._1|_−_0_._6|
|Ret to edu, pre-coll.|6_._4|9_._0|5_._1|7_._1|9_._2|6_._8|_−_0_._7|_−_0_._2|_−_1_._7|
|Ret to edu, coll. +|12_._6|11_._2|13_._7|13_._3|11_._8|13_._5|_−_0_._7|_−_0_._6|0_._2|
|Ret to exp, pre-coll.|4_._8|2_._3|2_._0|3_._8|1_._8|1_._4|1_._0|0_._5|0_._6|
|Ret to exp, coll. +|5_._0|2_._8|3_._2|3_._5|2_._6|3_._2|1_._5|0_._2|0_._0|

_Notes:_ Calibration parameters for the stationary equilibrium of the model economy - male and female workers. Retirement age = We use 65 for all. Schooling and Mortality function: See text.
needs to be evaluated as well. The net welfare effect of using a regressive tax to fund a progressive program is very hard to evaluate. Combining the incidence of raising and of spending revenue is rarely done, mainly because of the lack of data. Table 9 summarizes the findings of two recent studies have attempted to combine what data there was to evaluate the combined incidence. Both studies found that the incidence of taxes was roughly proportional but public expenditures were progressive leaving the overall incidence to be progressive. That is, as a share of household income (or expenditures) the poor benefit more from the combined effect of taxes and public expenditures.

Table 9: Evaluating the net effect of taxes and spending

||Mon|golia||**|**|Phili|ppines||
|---|---|---|---|---|---|---|---|
|**|**Qui_nti_l_e}|<br>Taxes<br>||<br>Expendi-<br>tures*|Combined<br>incidence||I|xpendi-<br>I<br>tures*|omb<br>incidence|
|||as a sh|are of per-capita|household expe|nditure|||
|**Poorest**|**.080**|**.238**|**-.160**|Poorest<br>2nd|.208<br>.205|.469<br>.222|-.261<br>-.017|
|**2d**|**090**|**168**|**080**|3rd<br>4h|.201<br>200|.175<br>144|.026<br>056|
|**n**<br>3rd|**.**<br>.070|**.**<br>.100|**-.**<br>-.030|t<br>5th<br>6th|.<br>.198<br>.199|.<br>.122<br>.102|.<br>.076<br>.097|
|4th|.100|.092|.008|4 7th<br>8th i|.201<br>.197|.087<br>.069|.114<br>.128|
|**Richest**|**.100**|**.064**|**.036**|9th<br>Richest|.197<br>.196|.051<br>.001|.146<br>.195|

Notes: ***Expenditures** include Health and Education in Mongolia, Health, Education, and Infrastructure in the Philippines. Sources: Mongolia from World Bank (1996), Philippines from Devarajan anid Hossein (1995) Part of the case frequently made is that expenditures on PHC are more pro-poor than are aggregate health expenditures, which include hospitals and the like, and table 10 presents some recent evidence on this. The table shows the ratio of the share of benefits received by each quintile from two different types of spending. For instance, in Indonesia 18 percent of 45
input rates are relatively low with 15% (4%) of plots being treated with inorganic fertilizer (pesticide). Average maize yields, as measured via crop-cutting, are 1068 kg/ha. Socio-economic indicators, plot manager characteristics, and agricultural variables are also included in Table 1, as they will be relevant for the analysis that follows.

_Table 1_ . Summary Statistics

||Mean||Mean|
|---|---|---|---|
|Household||Plot||
|Household size|6.12|Maize yield (kg/ha)|1068|
|Dependency ratio|1.35|Plot area (ha)|0.15|
|Female household head°|0.21|Distance from plot to HH (km)|0.13|
|Head age (years)|43.6|HH labor days|45.1|
|Head years of education|6.71|Hired labor days|5.89|
|HH received extension services°|0.36|HH purchased parcel°|0.23|
|Agricultural asset count|6.02|HH leased in parcel°|0.16|
|Distance to nearest market (km)<sup>+</sup>|33.5|Plot treated with organic fertilizer°|0.04|
|Number of cultivated plots|2.90|Plot treated with inorganic fertilizer°|0.15|
|Number of maize plots|1.89|Plot treated with pesticide°|0.04|
|Plot Manager||Rainfall:||
|Female manager°|0.39|Flowering season rainfall (2015, mm)|246|
|Manager age (years)|41.9|Long term mean flowering season rainfall (|211|
|Manager years of education|6.18|||
|Manager completed primary education°|0.39|||
|Manager received extension services°|0.31|||
|Primary occupation is agriculture°|0.80|||

Notes: N = 840; ° Binary variable; + Road network distance to nearest FEWSNET market

# **3.2.AFSIS**

Geospatially-derived soil data is more widely available to researchers and policy makers than plotlevel soil sampling linked to household surveys. Yet, geospatial data in this realm is of coarser granularity than what would be observed at the plot-level. In order to understand the implications of relying on geospatial soil data in cases where plot-level sampling is unavailable, we complement the analysis using MAPS-based soil properties with that using soil data drawn from one of the premier, publicly available geospatial soil databases – the Africa Soil Information Service’s AfSoilGrids250m data product.<sup>10</sup> The AfSoilGrids250m product, henceforth referred to simply as _AFSIS_ , utilizes multiple inputs to construct a map of more than 15 key soil properties at 250-meter resolution across the entire African continent. Inputs to the product, including the Africa Soil Profiles database (Leenars et al.,

> 10 AfSoilGrids250m is a product developed by the World Soil Information (ISRIC) in collaboration with the World Agroforestry Centre (ICRAF), The Earth Institute (Columbia University), and the International Centre for Tropical Agriculture (CIAT).

8
# **_2.3 Necessary Conditions for Market-Based Housing Finance_**

From a macroeconomic viewpoint, three necessary conditions for the revival of the housing sector are the resumption of economic growth, the lowering of inflation to at least a moderate level, the recovery of real wages and a return to a perception of employment stability for the greatest majority of the population. These conditions were beginning to be met among the "advanced reformers" of Group 1, especially the Czech Republic and some of the "high intermediate reformers" of Group 2, Estonia in particular. Figure 2 shows that these two groups of countries were experiencing a positive rate of GDP growth again by 1984, while the countries of 'low intermediate reformers" of Group 3 and the "slow reformers" of Group 4 were enduring economic contraction.

**FIGURE 2: GDP Growth Path Across the Four Country Groups**

<!-- Start of picture text --> 5<br>-5 ... .X ... . ..<br>-10<br>-15 ~~.Group 1<br>-20 iGroup 2<br>-2--Group 3<br>Group 4<br>1989 1990 1991 1992 1993 1994<br>Source. EBRD: Transition Report<br><!-- End of picture text --> Macroeconomic stabilization and the return to some degree of price stability has been extremely difficult to achieve in most transition economies, partly because of the deep but hidden macroeconomic disequilibria that had accumulated during the 1980s. The Czech Republic is a rare exception. As Figure 3 shows, the scale of inflation has been massive by the standards of market economies across the four country groups. However, each group has followed a significantly different path with Group 4 still experiencing annual average inflation rates well above 1500 percent per year in 1983 and 1984. Even the inflation experience of the best reformers has prevented the emergence of market-based long-term finance needed for housing until 1994. Figure 4 shows the improving performance of the members of Group 1 between 1989 and 1994. Yet, if we zoom further onto the years 1992 to 1994 (Figure 5) we see how difficult it remains even for these leading countries to achieve what Latin American economists used to call a moderate inflation" around 20 percent (Figure 5). Such an inflation rate would be seen 6
Table 1: Balance: Impact Evaluation Sample: Treatment vs Control

|Variable|N|(1)<br>Control<br>Mean/SE|N|(2)<br>Treatment<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|
|Number of Toilets|1197|14_._335<br>(0_._284)|1194|14_._091<br>(0_._262)|0_._244|
|Pupil Classroom Ratio|1188|108_._807<br>(1_._710)|1188|108_._229<br>(1_._683)|0_._578|
|Pupil Teacher Ratio|1196|80_._684<br>(1_._037)|1193|79_._708<br>(1_._122)|0_._976|
|Enrollment Grades 1-4|1197|608_._030<br>(12_._275)|1194|613_._517<br>(13_._909)|_−_5_._487|
|Enrollment Grades 5-8|1197|295_._145<br>(8_._354)|1194|307_._861<br>(9_._957)|_−_12_._716|
|Repetition Rate|1185|0_._259<br>(0_._003)|1181|0_._253<br>(0_._003)|0_._006|
|Dropout Rate|1185|0_._048<br>(0_._002)|1181|0_._044<br>(0_._002)|0_._004|

_Notes_ : Indicators extracted from EMIS 2017. The value displayed for t-tests are the differences in the means across the groups. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 2: Balance: Impact Evaluation Sample: Treatment vs Control

|Variable|N|(1)<br>Control<br>Mean/SE|N|(2)<br>Treatment<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|
|Number of Toilets|184|14_._973<br>(0_._734)|202|13_._267<br>(0_._530)|1_._705_∗_|
|Pupil Classroom Ratio|182|107_._964<br>(4_._526)|201|105_._360<br>(4_._364)|2_._604|
|Pupil Teacher Ratio|184|75_._928<br>(1_._984)|201|77_._714<br>(2_._343)|_−_1_._786|
|Enrollment Grades 1-4|184|618_._212<br>(31_._354)|202|577_._119<br>(27_._025)|41_._093|
|Enrollment Grades 5-8|184|316_._685<br>(21_._046)|202|285_._287<br>(17_._675)|31_._398|
|Repetition Rate|184|0_._258<br>(0_._008)|200|0_._248<br>(0_._008)|0_._011|
|Dropout Rate|184|0_._048<br>(0_._005)|200|0_._049<br>(0_._005)|_−_0_._001|

_Notes_ : Indicators extracted from EMIS 2017. The value displayed for t-tests are the differences in the means across the groups. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

27