rafmacalaba/datause-encoder-data huggingface dataset:
combined with a culture of credit transfer agreement between institutions is expected to improve equity. However, relatively little research has been conducted into cross-country differences in inequality of tertiary education. It is therefore difficult to quantify the importance of each factor.

<u>Gender Parity Index</u> (GPI) measures the equality of access across the two genders (Figure 13). The horizontal axis shows the distance from gender parity (parity as zero) and vertical axis shows the countries on the basis of distance from parity. In terms of scoring the gender parity index, one must not compare based on the highest or lowest GPI scores, but rather based on the distance from the parity score. A positive parity reflects a higher share of females in tertiary education as compared to the share of males in tertiary education.

**Figure 13. Gender Parity Index**

<!-- Start of picture text --> Sweden<br>United States<br>Canada<br>Italy<br>Peru<br>Ireland<br>France<br>Brazil<br>Australia<br>Finland<br>UK<br>Average<br>Austria<br>Belgium<br>High Income<br>Colombia<br>G ermany<br>Netherlands<br>Mexico<br>LAC Average<br>-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6<br>Gender Parity (zero as parity)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries The LAC countries are shown to provide relatively equal access to both genders. Gender parity scores are all positive except for Peru among the LAC countries and Germany among the high-income countries. The two countries have males outnumbering females in tertiary education. Interestingly, the LAC average is close to parity (0.02) compared to high-income countries (0.16). Most high-income countries have gender balances in the range between 1.18 and 1.35, implying that females make up between about 55 and 60 percent of the student body. The finding confirms previous findings for tertiary education in Latin America, IESALC (2005).

<u>Overall Accessibility.</u> Just as the previous section showed that different measures of affordability may lead to different conclusions about which countries are affordable, this 25
Predicting Individual Wellbeing Through Test Scores: Evidence from a National Assessment in Mexico<sup>_∗_</sup> Rafael de Hoyos<sup>_†_</sup> Ricardo Estrada<sup>_‡_</sup> María José Vargas<sup>§</sup>

# **1 Introduction**

There is increasing recognition that education brings about individual and society-wide benefits if students acquire a set of relevant skills, including literacy and numeracy, during their formative years. Literacy and numeracy are seen as the foundation for the acquisition of other skills and a direct determinant of labor market outcomes, health conditions, and democratic participation, among other benefits ( World B ank, 2 018; CAF, 2016). However, there is less agreement about whether and how we can measure these foundational skills and track their progress in an education system in a reliable way.

Standardized tests are one way to directly measure foundational skills, and, in fact, many countries have in recent years adopted standardized testing in large-scale student assessments (Ganimian and Koretz, 2013). The increased availability of test scores in developing countries has helped build public awareness of the importance of numeracy

> _∗_ Acknowledgements: the authors would not have been able to write this paper without the support of the Mexican Secretariat of Public Education (SEP), we are particularly grateful to Ana María Aceves and Proceso Silva for granting us access to the micro data. The paper benefited from comments made by Jérémie Gignoux, Juan Vargas, Paula Villaseñor, Lucila Berniell, and participants at seminars at CAF and the Jobs and Development Conference at Universidad Javeriana. We are grateful to Elizabeth Monroy for putting together the datasets used in this paper. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.

> _†_ The World Bank, 1818 H St. NW, Washington DC 20433, rdehoyos@worldbank.org

> _‡_ CAF-Development Bank of Latin America, Avenida Madero 900, Piso 15, Torre Catalinas Plaza, Ciudad de Buenos Aires, Argentina, restrada@caf.com

> §The World Bank, 1818 H St. NW, Washington DC 20433, mvargasmancera@worldbank.org
|E - Water supply, sewerage, and waste|16|17|1|17|1|
|---|---|---|---|---|---|
|F - Construction|195|198|3|203|9|
|G - Wholesale and retail trade|402|431|29|430|28|
|H - Transportation and storage|86|94|9|89|4|
|I - Accommodation and food services|202|204|2|215|14|
|J - Information and communication|51|51|0|53|2|
|L - Real estate activities|60|61|1|64|4|
|M - Professional, scientific, and technical|108|127|19|112|4|
|R - Arts, entertainment, and recreation|47|48|1|51|5|
|Other|416|446|30|437|21|
|**Total**|**2,032**|**2,213**|**181**|**2,160**|**128**|

Source: Authors based on AECM survey; European Central Bank.

**Our analysis has several limitations.** With respect to the transition risk assessment, our transition risk score is based on direct GHG emission in the respective sectors. This data does not encompass effects along the supply chain, such as cost pass-through (see Fabra and Reguant 2013). With respect to our physical risk assessment, the main drawback of our analysis is that not all CGSs in our survey were able to provide exposure data at the NUTS 2 level. This could lead to an overall bias in our spatial assessment, given that 90 percent of the exposures in our NUTS 2 sample are obtained from CGSs in Germany, Poland, Portugal, Spain and the United Kingdom. The latter alone accounts for 63 percent of total NUTS2 exposures in our sample. Results however do not change significantly when we exclude the United Kingdom from the analysis. See annex 5. Finally, our stress test uses values for credit risk parameters that are applied across the sample but may differ between regions and CGSs depending among others on their business model and risk appetite.

**Despite its limitations, this paper points to important policy implications for CGSs.** SMEs are instrumental to the ambitious decarbonization policies of European countries, yet they need to access bank finance to finance their green projects. European CGSs can play an important role in supporting SME investment in climate change mitigation and adaptation. This will require mainstreaming climate action within CGSs. To facilitate this process, the World Bank, in partnership with the industry and stakeholders, including AECM, recently developed the “Guidelines for Integrating Climate Change Mitigation and Adaptation into Public CGSs for SMEs” (World Bank 2022), a set of good practices to help CGSs integrate climate change considerations in their strategies and operations. Supported by their shareholders, CGSs could develop an internal roadmap to help organize the work. Central to these efforts, as the findings of this paper suggest, could be the explicit integration of climate-related financial risks in their respective risk management frameworks, if they have not done so already.

**Future research could include case studies on sectors that are highly relevant for CGSs and investigate the role that CGSs can play to support an energy transition.** Our results point to the importance of specific sectors that are highly relevant for CGSs, including agriculture and transportation. Future studies could provide deep dives on transition and physical risks in those

16
the survey was as all ―senior staff,‖ defined as those at grades ―GG‖ and above, which account for about one-quarter of all staff. They are all likely to be the TTLs of one or more operations. The survey was only sent to GG+ staff excluding DEC.<sup>9</sup> The survey was implemented using a confidential, web-based, survey tool. On top of basic information on Vice-Presidential Unit (VPU), ―sector‖ (essentially synonymous with what is called a ―network‖) and years of Bank service, the survey asked a series of questions on familiarity with WB research, sources of knowledge about that research, and the value of WB research.

There were 2,900 recipients of the survey instrument and 555 responses. There was only a modest variation in response rates across grades, namely 18%, 21% and 15% for grades GG, GH and GI+ respectively (as compared to 19% overall).<sup>10</sup> More worrying is that the response rate varied markedly across VPUs, as can be seen in Table 1. The lowest response rate was for the Middle East and North Africa (MNA) at 12%, while the highest was for Poverty Reduction and Economic Management (PRM) at 50%. To the extent that such differences in response rates are correlated with perceptions of research there will be a bias for drawing inferences about the means for the population of the Bank’s senior staff, although correlations and regressions are likely to be more robust.

# **3. Knowledge about research among the Bank’s operational staff**

Learning about research findings is not costless. To be well-informed, practitioners must spend precious time searching out and studying research products. The costs for doing so will vary across staff, as will the expected benefits.

The survey asked a number of questions about familiarity and use of World Bank research. The key question was as follows: _“How familiar would you say you are with WB research products/services on a scale from 1 to 10 where 1 means not familiar at all and 10 means extremely familiar?”_<sup>11</sup> Figure 1 gives two density functions for the responses, one for the

> 9 Although six responses came from DEC, presumably reflecting changes in VPU assignments.

> 10 These are based on the grade distribution for Operational Units at September 2011 (67.7%, 27.3% and

> 5.0% in grades GG, GH and GI+ respectively).

> 11 The other options allowed were: ―Not applicable‖ and ―Prefer not to answer.‖ All calculations in this paper filter out these responses as missing values.

5
Policy Research Working Paper 10007

# **Abstract**

Robust poverty reduction in Uganda was disrupted by episodes of shocks during recent years. This paper estimates vulnerability to poverty in Uganda and explores the sources and main correlates of vulnerability using the most recent Uganda National Household Survey 2019/20. The analysis reveals that about 50 percent of population in Uganda is vulnerable to poverty. Vulnerability rates are much higher than poverty in rural areas. Urban vulnerability is predominantly risk induced (high volatility of consumption) and mostly associated with idiosyncratic rather than covariate shocks. Rural vulnerability is equally split between risk-induced and poverty-induced vulnerability (permanently low consumption). Although in absolute terms vulnerability due to covariate shocks is still lower than vulnerability due to idiosyncratic shocks, in relative terms covariate shocks are more important in rural areas. Education is found to be one of the key variables related to lower vulnerability to poverty.

This paper is a product of the Poverty and Equity 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 aatamanov@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
are read the following: “Antonio and Lilibeth are married. Antonio makes all the financial decisions for the family and does not seek Lilibeth’s opinion because he makes all the decisions for the household.”<sup>6</sup> We create a dummy variable which is equal to 1 if a respondent identifies with a specific decision-making style and evaluate the impact of CCTs on increasing the likelihood of identification with a specific decisionmaking style, relative to all other styles.

Finally, we explore the impact on time-use. We check whether exposure to CCTs has affected the fraction of time spent on chores, paid work and unpaid work. The impacts for men and women may be in different directions.

# _Changes in exposure to gender-based violence_

Third, benefitting from CCTs in adolescence may have long-lasting impacts on extreme form of disempowerment, or exposure to GBV through change in behavior/relationship of parents. Buller et al. (2019) demonstrate in a review of literature on the relationship between cash transfers and GBV that CCTs are generally associated with reduction of GBV. Exposure to violence in childhood (either direct exposure or by witnessing violence perpetrated or suffered by parents) is an important predictor of exposure to and/or perpetration of GBV in adulthood (Garcia-Moreno et al. 2006). Thus, the 4P program could reduce incidence of GBV among our sample through lowering GBV their mothers were exposed to a decade ago.

We collect data on intimate partner violence (IPV), domestic violence perpetrated by other household members,<sup>7</sup> and violence outside home.<sup>8,9</sup> For each type of violence, we construct indicators of physical, emotional, sexual and economic violence, with questions largely borrowed from Demographic and Health Surveys (DHS). For IPV, we collect data on prevalence and incidence during the 12 months preceding the survey. For domestic violence and violence outside home, we only administer questions about life-time prevalence.

> 6 The full list of vignettes is included in the Appendix.

> 7 Our questionnaire included the following options for household members: (i) Mother/Step-mother/Mother-inlaw; (ii) Father/step-father/father-in-law; (iii) sister/brother/sister-in-law/brother-in-law; (iv) Other relative (please specify).

> 8Our questionnaire included the following options for non-household members: (i) Employer/someone at workplace; (ii) Community leader; (iii) Health worker; (iv) Teacher; (v) Police/soldier.

> 9 This study’s human subjects’ protection protocols received research ethics review approval in accordance with the requirements of the US Code of Federal Regulations for the Protection of Human Subjects (45 CFR 46 & 45 CFR 46.110) from HML IRB.

14
Annex 9: <u>Nigeria - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br> <br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||||<br>|**Nigeria National**<br>||||
|Rice|0.04|0.73|**54.68**|55.28|55.88|55.19|55.65|
|Corn and cornflour|0.01|0.35||54.81|54.88|54.65|54.57|
|Maize and Maize flour|0.01|0.42||54.86|55.03|54.73|54.80|
|Wheat flour and bread|0.01|0.70||54.90|55.12|54.90|55.12|
|cassava|0.03|0.65||55.10|55.42|55.07|55.35|
|All|0.10|0.95||56.20|57.77|55.19|55.65|
|||||**Nigeria Urban**||||
|Rice|0.05|0.72|**43.13**|43.83|44.56|43.81|44.48|
|Corn and cornflour|0.01|0.23||43.26|43.30|43.19|43.20|
|Maize and Maize flour|0.01|0.42||43.38|43.61|43.37|43.54|
|Wheat flour and bread|0.02|0.77||43.38|43.65|43.38|43.65|
|cassava|0.03|0.65||43.62|43.95|43.60|43.91|
|All|0.11|0.93||45.06|47.14|43.81|44.48|
|||||**Nigeria Rural**||||
|Rice|3.73|74.78|**63.80**|64.32|64.81|64.18|64.46|
|Corn and cornflour|0.75|44.90||63.92|64.02|63.69|63.54|
|Maize and Maize flour|0.59|41.93||63.92|64.05|63.71|63.69|
|Wheat flour and bread|1.17|64.73||64.00|64.18|64.00|64.18|
|cassava|1.99|65.11||64.16|64.46|64.12|64.37|
|All|8.22|97.35||65.00|66.16|64.18|64.46|

Source: Authors’ estimation using country household survey data 28
Table 4. Poverty rates of male and female principal applicant households before and after assistance from UNHCR and the WFP, percentage

|**Poverty rate:**|**B**|**efore assistanc**|**e**||**After assistance**||**Gend**|**er gap %**||
|---|---|---|---|---|---|---|---|---|---|
||**Male**<br>**PA**|**Female PA**|**All**|**Male**<br>**PA**|**Female PA**|**All**|**Before**<br>**assistance**|**After Assistance**|**Observations**|
|All|53.1|53.0|53.1|10.6|13.0***|11.2|−0.13|22.83|45,919|
|Marital status||||||||||
|Married with spouse in the HH|62.2|62.6|62.2|12.0|12.7|12.0|0.76|6.06|24,895|
|Married without spouse in the HH|46.0|57.1***|52.9|10.0|13.0***|11.9|24.03|29.75|11,190|
|Widowed|41.2|46.4*|45.9|9.4|12.5|12.2|12.65|33.45|3,135|
|Single or engaged|16.6|41.0***|20.8|4.3|15.8***|6.2|153.31|266.52|5,830|
|Divorced or separated|25.3|39.5***|36.4|6.3|10.9*|9.9|56.23|72.56|869|
|Family type||||||||||
|Couple with children|67.5|67.6|67.5|12.8|13.5|12.9|0.19|5.20|21,779|
|Only‐person|8.5|25.8***|13.9|2.2|6.9***|3.7|204.54|223.14|7,720|
|Single caregiver|45.2|60.9***|59.5|9.1|14.5***|14.0|34.70|58.67|8,172|
|Couple without children|21.6|38.8***|22.7|5.4|11.8***|5.8|79.60|119.78|2,842|
|Unaccompanied children|34.0|53.7***|41.2|16.0|31.5***|21.6|57.75|97.28|296|
|Siblings|36.2|55.8***|42.8|8.0|24.2***|13.4|54.24|203.44|566|
|Extended and other|44.5|29.5***|38.0|9.4|6.1**|8.0|−33.75|−34.47|1,367|
|All Nontraditional|19.0|50.9***|35.6|4.6|12.7***|8.8|167.86|177.52|20,963|

_Source:_ Own calculations based on ProGres and JD‐HV database. Gender gap is calculated as the percentage difference between female and male poverty rates.

_Note_ : PA = principal applicant; HH = household. Nontraditional family types are all family types apart from couples with children. Differences between male and female PA poverty rates by marital status and family type are statistically significant at *10 percent level, **5 percent level, or ***1 percent level.

18
29 Incentives. The potential for transfer pricing and other financial contortions to shift company profits into branches that enjoy tax concessions is likely to rcduce revenue as well. A better explanation for why investment incentives are so popular may come from industrialist political pressure groups who enjoy influence with legislators and governments.

Econometric evidence from the United States seems to suggest a pronounced role for tax policy in affecting the location of investment. Boskin and Gale (1986), updating and extending work by Hartman (1984), suggest that the own-price elasticity of foreign Investment in the U.S. is approximately 1.0, while the own-price elasticity of U.S. investment abroad is in excess of 1.0. There appears to be some stability in these estimates, despite the substantial fluctuation in direct foreign investment in the United States during the early 1980s.

To this point, the negative impact of taxation on investment has been emphasized. However, comparative data across countries provides a dissenting view. Figure 4 shows average investment/GDP ratios over the period 1970-80 compared to average tax effort (tax revenue/GDP) over the same period.<sup>11</sup> There is a strong positive l<sup>1</sup> The investment and GDP data come from Summers and Heston (1984), and are in real terms. The Summers and Heston series is calculated to provide internationally comparable measures of national accounts. See Lindauer (1987) for a comparison of these data with standard government statistics.

Note that only average tax rates are used here. While marginal tax rates would be more useful, they are not available for developing countries.
Figure 8: Methyl Bromide Event Study Around Signature of RTA <!-- Start of picture text --> −3 −2 −1 0 1 2 3<br>Event Time (RTA Signature)<br>0.5<br>0.0<br>% Deviation from MP<br>−0.5<br>−1.0<br><!-- End of picture text -->

**Notes:** This figure presents event study coefficients for Methyl Bromide emissions (represented as percent deviations from target). Black lines correspond to coefficient estimates and 95% confidence intervals for RTAs without ODS provisions, red lines correspond to coefficient estimates and 95% confidence intervals for RTAs with ODS provisions.

35
productivity and � supply elasticity.

<u>1 Zt</u> !+� Equation (1) thus de...nes the theoretical market price as p<sup>�</sup> t<sup>=</sup> h At i . Also, to quantify the impact of di¤erent shocks, one can calculate the ...rst-order e¤ect for each of them.

where the right-hand side gives us the sum of the e¤ect of changes in all i 2 ffood,feed,ethanol,exportsg demand fundamentals and production fundamental A: USDA databases have time series for prices, all four demand uses (ethanol, feed, food, and exports), production, and yield per acre. The model uses these to estimate elasticities and identify exogenous shocks. For demand price elasticities, it uses instrumental variables, taking yield per acre shocks as a ...rst-stage instrument for prices. Changes in yield per acre are explain mostly by weather factors. Farmers have almost no control over short run productivity levels. Therefore, it is reasonable to assume that they are exogenous to the production process and are appropriate instruments for prices. Supply elasticities are estimated following Roberts & Shlenker (2013). Estimation results can been seen in Table I.A. Results fall within other literature’s ...ndings<sup>1</sup> . Once elasticities are estimated, given demand, supply and price data, one can identify exogenous components by reversing the isoelastic equation and setting the exogenous component equal to the demand or supply level times prices elevated to inverse elasticity.

Given time series for At; Zt<sup>i;andpt;andestimatedvaluesfor!; �,onecandecomposeprice</sup> changes per year by estimating the di¤erent terms of equation (2). As a result, changes in inventories explain the remaining residual. Results per variable can be seen in Figures II to V.

> 1 The Food and Agriculture Policy Institute shows elasticity estimations for corn in di¤erent countries, giving results that are always between 0.1 and 0.5. for both demand and supply. http://www.fapri.iastate.edu/tools/elasticity.aspx

11