rafmacalaba/data-use-mentions-extended huggingface dataset:
Lessons From the Enterprise Survey. London: European Bank for Reconstruction and Development.

European Commission. 2021. The 2021 Ageing Report: Economic and Budgetary Projections for the EU Member States (2019-2070). Brussels: European Commission.

European Commission. 2022. “Report from the Commission to The European Parliament and the Council on the Implementation of the Recovery and Resilience Facility.” European Commission, Brussels.

El-Ganainy, A., E. Ernst, R. Merola, R. Rogerson, and M. Schindler. 2021. “Inclusivity in the Labor Market.” IMF Working Paper 21/141, International Monetary Fund, Washington, DC.

Eris, M. N., and B. Ulasan. 2013. “Trade Openness and Economic Growth: Bayesian Model Averaging Estimate of Cross-country Growth Regressions.” Economic Modelling 33 (July): 867-83.

Estrades, P. C., M. Maliszewska, I. Osorio-Rodarte, S. E. Pereira, and M. Filipa. 2022. “Estimating the Economic and Distributional Impacts of the Regional Comprehensive Economic Partnership.” Policy Research Working Paper 9939, World Bank, Washington, DC.

Eurostat (database). European Commission. Accessed on May 31, 2022. https://ec.europa.eu/eurostat/data/database.

Faal, E. 2005. “GDP Growth, Potential Output, and Output Gaps in Mexico.” IMF Working Paper 05/93, International Monetary Fund, Washington, DC.

Fay, M., H. Lee, M. Mastruzzi, S. Han, and C. Moonkyoung. 2019. “Hitting the Trillion Mark: A Look at How Much Countries Are Spending on Infrastructure.” Policy Research Working Paper 8730, World Bank, Washington, DC.

Ferreyra, M. M., C. Avitabile, J. B. Álvarez, F. H. Paz, and S. Urzúa. 2017. At a Crossroads: Higher Education in Latin America and the Caribbean. Washington
parental education (34 percent), and the lowest in the case of average parental schooling (24 percent), with an average bias of 29 _._ 7 percent.<sup>20</sup> A 30 percent bias on an average vindicates the unease among the researchers and editors of journals that the available household surveys in developing countries may not be particularly helpful in understanding the magnitude of intergenerational persistence in economic status.

We now turn to the IGC estimates for Bangladesh reported in columns 4 (full sample) and 5 (coresident sample) of Table 1. The estimated IGCs for three different indicators of parental education are reported in the top panel and the implied biases are reported at the bottom. The evidence is strikingly different; the estimate of IGC from the coresident sample is much closer to that from the full sample, and this is true for all three different indicators of parental education (top panel). The average bias in the IGC estimates is 8 _._ 7 percent which is less than one-third of the average bias in the IGRC estimates (29 _._ 7 percent). The _highest_ magnitude of bias is 11 percent in the case of IGC which is less than half of the _lowest_ bias found in the IGRC estimates (24 percent).

# **Evidence from India**

Table 2 reports estimates of IGRC and IGC from India data for three different indicators of parental education (father’s schooling, mother’s schooling, and average schooling of mother and father). The difference between the IGRC estimates from the coresident and full samples in the case of India are smaller in magnitude compared to the estimates from Bangladesh (compare top panel of Table 1 to that of Table 2). The average bias is about 17 _._ 6 percent. While the extent of bias is not as dramatic as in the Bangladesh data, the evidence still indicates that the coresident sample selection causes substantial downward bias in the IGRC estimates. The relatively lower selection bias in the
diversity at the time of the follow-up survey (WFP, 2008).<sup>23</sup> _Health practices (Intermediary outcome domain #2)_ . The BCC intervention promoted preventive health behaviors to protect children against disease, as well as encouraged health service utilization at the first sign of illness. We build an index of preventive health behavior capturing whether children have (i) received all their vaccines, (ii) received vitamin A, (iii) received iron supplementation, (iv) received deworming, (v) slept under a treated mosquito net, as well as (vi) an index capturing the frequency at which the main caregiver washes hands. We then analyze impacts on children’s illness, as reported by the

> 22 Note that we standardize indicators with respect to the full control group. When estimations are based on part of the sample only (as outlined in Section 4.2), the mean in the reference group displayed in the result tables is not necessarily exactly 0. 23 The food consumption score captures dietary diversity based on a set of questions asking about the consumption frequency of different food groups during the past seven days. This indicator is the primary dietary diversity measure used by humanitarian actors working on food insecurity in the Sahel.

17
essential to build the technical capacity of analysts in managing and analyzing data, ensuring the sustainability of the initiative.

The use of CDR data raises privacy concerns and requires a strong institutional framework to regulate access and ensure confidentiality. Researchers and governments have worked closely with regulatory authorities and MNOs to leverage CDR data in measuring changes in mobility patterns (Oliver et al., 2020). However, most of these efforts are concentrated in countries with established institutional frameworks, which also reflect recent efforts to integrate CDR data and other big data into the national statistical system.

# **B. Use-case: Tracking mobility in The Gambia**

This paper showcases the use of CDR data to track changes in mobility across The Gambia between March and May 2020, when COVID-19 led to an exodus of the capital city region. This project was undertaken in collaboration with the national regulator Public Utilities Regulatory Authority (PURA) and The Gambia Bureau of Statistics (GBoS) to establish a durable CDR data pipeline in The Gambia. This partnership allows for government ownership and sustainability, investing in both the necessary systems and technical capacity.

Analysis of CDR data suggests that economic lockdown measures reduced human mobility and pushed people to leave the capital city region and return to rural areas. We validate the use of CDR data against the known population distribution from the population census and WorldPop data. Our contribution demonstrates how a system-building approach can make timely, disaggregated analysis based on CDR data available for quick decision making.

This use-case demonstrates how to build an end-to-end data pipeline for CDR data. This pipeline draws raw data produced by the mobile phone operators, encrypts and aggregates it on the regulators premise, before making it available to researchers for analysis. Once automated, it can facilitate the production of rapid, high-resolution insights on population mobility patterns and their economic implications.

**2** | P a g e
disaggregated the overall OD matrices by transport mode using complementary data (e.g., in the case of Buenos Aires, public transport smartcard data from SUBE); identified the trip purposes based on rules of thumb related to where the trips start, end, and their duration as well as complementary land use data; and lastly, expanded the anonymous observed sample to represent the total population of the region.

The assessment of the evolution of mobility indicators by income group was based on the spatial association of the identified travelers’ home locations and the socioeconomic indicators available for the specific neighborhood blocs. In Bogota, this is based on the socioeconomic “strata” designation at the bloc level estimated by the National Administrative Department of Statistics (DANE), while in AMBA the indicator of percentage of households with unsatisfied basic needs is used, available from the National Institute of Statistics and Census of Argentina (INDEC) at the census radius level for most of the study area.

The principles followed for the construction of the indicator framework and the selection of indicators are adapted from international recommendations (e.g., Litman 2021; Olofsson and Brundell Freij 2017). These recommendations emphasize that indicators should be relevant to understanding a specific dimension of the transport phenomenon, easy to understand, reliable and measurable. While the proposed indicator framework is a powerful tool for decision making in mobility planning and management, it does not pretend to cover all the analysis needs of the transport system, as there are additional aspects to transport demand that need to be monitored (e.g., road safety or user satisfaction with the transport system). Thus, for the purposes of longerrun planning, the indicator framework complements the transport demand analysis capabilities provided by other, broader transport indicator frameworks.

# **4. The economic, transport, and regulatory context of the pandemic**

# _4.1. The economic and transport sector context_

Public transport in Bogotá, the capital of Colombia of about 8 million people, is organized around the
Anger, 2010; Ramos et al., 2013; Nikoloski and Ajwad, 2014). Research has also investigated the role of cognitive skills such as _computer use_ in predicting wages (Handel, 2007; Sakellariou and Patrinos, 2003). Most of the research on the payoff of various skills in the labor market has been based in the United States and other OECD countries, with the exception of a handful of recent studies based in low- and middle-income economies (Acosta et al., forthcoming; Ajwad et al., 2014; Bassi et al., 2012; Ramos et al., 2013; Nikoloski and Ajwad, 2014). This overrepresentation of high-income countries has been largely driven by the specific requirements for and availability of data needed to conduct such studies.

This paper uses data from the _Skills towards Employability and Productivity (STEP)_ surveys of urban adults in eight countries, namely Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine, and Vietnam. The paper analyzes the extent to which there is an independent association between earnings and cognitive skills—proxied by measures of reading proficiency and complexity of computer use on the job—and noncognitive skills—using personality and 2
Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Extraversion (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br
- Clarke, P.M., U. Gerdtham, and L.B. Connelly (2003) ‘A note on the decomposition of the health concentration index.’ _Health Economics_ 12, 511–516

- Curtis, S.L., and M. Hossain (1998) ‘West Africa spatial analysis prototype explanatory analysis: The effects of aridity zone on child nutritional status.’ Technical Report, Demographic and Health Survey, Macro International

- de Onis, M., E.A. Frongillo, and M. Bl ̈ossner (2000) ‘Is malnutrition declining? an analysis of changes in levels of child malnutrition since 1980.’ _Bulletin of the World Health Organization_ 78, 1222–1233

- Deaton, A. (2003) ‘Health, inequality, and economic development.’ _Journal of Economic Literature_ XLI, 113–156

- Demombynes, G., J. Elbers, C.and Lanjouw, J. Lanjouw, J. Mistiaen, and B. Ozler<sup> ̈</sup> (2002) ‘Producing an improved geographic profile of poverty: Methodology and evidence from three developing countries.’ WIDER Discussion Paper 2002/39, United Nations University

- Dibley, M.J., J.B. Goldsby, N.W. Staehling, and F.L. Trowbridge (1987a) ‘Development of normalized curves for the international growth reference: historical and technical considerations.’ _American Journal of Clinical Nutrition_ 46(5), 736–48

- Dibley, M.J., N.W. Staehling, P. Nieburg, and F.L. Trowbridge (1987b) ‘Interpretation of Z- score anthropometric indicators derived from the international growth reference.’ _American Journal of Clinical Nutrition_ 46(5), 749–762

- Elbers, C., J.O. Lanjouw, and P. Lanjouw (2000) ‘Welfare in villages and towns: Micro-level estimation of poverty and inequality.’ Timbergen Institute Discussion Paper TI
Policy Research Working Paper 9896

# **Abstract**

This paper examines the factors that are associated with boys’ underachievement in mathematics and science in Saudi Arabia, where students attend gender-segregated schools from grade 1 onward, as well as student achievement in these two subjects in grades 4 and 8 more generally. The paper employs data from two recent large-scale assessments of education: Trends in International Mathematics and Science Study 2019 and Saudi Arabia’s National Assessment of Learning Outcomes 2018. The results suggest that in grade 4, school climate was more strongly associated with boys’ compared with girls’ achievement in both mathematics and science, with boys attending schools of poorer school climate having a considerably lower performance compared with girls attending such schools. The findings also indicate that although greater literacy and numeracy readiness was linked with higher science achievement among boys and girls, grade 4 boys tended to benefit more from this readiness than girls. In addition, the results show that student absenteeism in grade 4 is particularly strongly associated with decreases in mathematics achievement among boys. In grade 8, interactions between student gender and students’ confidence in science, the degree of schools’ emphasis on academic success, and teachers’ age are observed. The paper concludes by discussing some of the implications of these findings for educators and policy makers in Saudi Arabia.

This paper is a product of the Education 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 melsayed@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
Table 3: Impacts on Socio-Emotional Skills

||(1)|(2)|(3)|
|---|---|---|---|
||Aggregate soft-skills<br>index|Intrapersonal<br>skills index|Interpersonal<br>skills index|
|Offered Training|0.03|0.04|0.03|
||(0.03)|(0.03)|(0.04)|
|Baseline soft-skills index|0.51<sup>_∗∗∗_</sup><br>(0.05)|||
|Baseline intrapersonal index||0.44<sup>_∗∗∗_</sup><br>(0.04)||
|Baseline interpersonal index|||0.50<sup>_∗∗∗_</sup><br>(0.05)|
|Control Mean|-0.02|-0.02|-0.02|
|Observations|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. Intrapersonal skills include emotional awareness, self awareness, emotional regulation, self control, perseverance, personal initiative, and problem solving. Interpersonal skills include listening, empathy, expressiveness, relatedness, influence, negotiation, and collaboration.

Table 4: Impacts on Search

||(1)<br>Searche<br>in Last|(2)<br>d For Job<br>Month|(3)<br>Confdenc<br>Empl|(4)<br>e Approaching<br>oyers (0-3)|(5)<br>CV<br>Digital|(6)<br>in<br>Format|(
|61.2|-2.2|57.1|64.1|-7.0|58.0|63.3|**-5.3***|
|Somalia|62.5|62.6|-0.1|62.4|64.4|**-2.0****|62.5|63.0|-1.3|
|South Sudan|59.9|58.3|1.7|62.5|59.1|**3.4****|61.9|59.0|**2.9****|
|Sudan|58.7|58.2|0.6|58.2|55.2|3.0|58.6|57.1|**1.5***|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). H is the incidence of multidimensional poverty; A is the intensity of multidimensional poverty and MPI=H*A. Asterisks indicate statistical significance at 1% ***, 5% ** and 10% * levels.

The results reveal that in all countries, female-headed IDP/refugee households have a higher incidence of poverty and MPI than female-headed households that are not displaced. We also note the emerging pattern that, for female-headed households in camps (Ethiopia, Sudan) multidimensional poverty appears higher and more intense than for their female-headed counterparts living outside camps. In Ethiopia and Sudan, there is a 28 and 18 percentage point difference in incidence between female-headed households in camps and in the host community, respectively, whereas the differences between the female-headed IDP households and female-headed host community households of Nigeria, Somalia, and South Sudan are 12 percentage
# **Table S1.3: Impact of cyclical fluctuations in total (male plus female) unemployment rates on IPV**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A: Physical Violence|||||
|ln(U)|-0.104|-0.107|0.061|0.063|
||(0.089)|(0.093)|(0.104)|(0.109)|
|Observations|381730|381730|381730|348165|
|B: Sexual Violence|||||
|ln(U)|-0.093*|-0.089*|0.003|0.002|
||(0.051)|(0.048)|(0.049)|(0.050)|
|Observations|275718|275718|275718|268173|
|C: Any Violence|||||
|ln(U)|-0.146|-0.177*|0.066|0.061|
||(0.100)|(0.087)|(0.121)|(0.123)|
|Observations|274002|274002|274002|266517|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

## _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

## _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. The dependent variables, controls and standard error adjustments are as in Table 1. Relative to Table 1, the study now uses
)|20.01|17.48|21.04|
|Field of study fixed effects|14.59|9.47|13.59|
|R-squared|0.265|0.354|0.282|
|Obs.|1,214|1,201|1,206|

_Source_ : Own estimations using data from the WBSCPS and Ecuador individual-level data. For variable definitions, see Section 3 and Appendix 1.

_Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Table A14, estimated with individual- and program-level data for Ecuador. For each regression, the table shows R- squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of students (equal to the number of observations in the underlying regression).

46
Adjusting to Transitory Shocks: Worker Impact, Firm Channels, and (Lack of) Income Support Ana Margarida Fernandes<sup>_∗_</sup> Joana Silva<sup>_†_</sup> Originally published in the Policy Research Working Paper Series on _May 2023._ This version is updated on _March 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL classifications:** F14, F16, J24, J46, L25.

**Keywords:** employment, wages, firms, exports, foreign shocks, global financial crisis.

> _∗_ Ana Margarida Fernandes is a Lead Economist in the Trade and International Integration Unit of the Development Research Group at the World Bank, Washington DC, USA (email: afernandes@worldbank.org).

> _†_ Joana Silva is a Senior Research Economist at the World Bank and Associate Professor at Catolica Lisbon, Lisbon, Portugal (email: joana.silva@worldbank.org).

We are grateful to Erhan Artuc, Fernanda Brollo, Isabel Horta Correia, Marcio Cruz, Rafael Dix-Carneiro, Romain Duval, Raquel Fernandez, Penny Goldberg, Alaka Holla, Beata Javorcik, Filippo di Mauro, Naercio Menezes-Filho, Julian Messina, Sara Moreira, Nina Pavcnik, Jose Perez, Martin Rama, Pedro Raposo, Hugo Reis, Raymond Robertson, Norbert Schady, Peter Schott, Andres Rodriguez-Clare, Rui Silva, Carlos Vegh, Guillermo Vuletin, and participants in several seminars for helpful comments. We thank Sebastian Melo, Natalia Camelo, Daniel Quintana, Leandro Veloso, Martim Leit ̃ao, Catarina Gaspar, and Glaucia Ferreira for their exceptional research assistance. Finally, we thank the Ministry of Employment of Brazil for access to the Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) data, the Ministry of Economics of Brazil for access to SECEX data, the National Institute of Statistics of Ecuador (INEC) for access to the Ecuadorian social security records
restricted our sample to two of the five states: Yucatán and Oaxaca. Morover, we only ran the RCT and collected data in a subset of the available localities in these states, choosing a sample of 113 localities<sup>7</sup> with characteristics relevant to the project and evaluation, such as the presence of forests. Nevertheless, this was not a representative sample of Mexican rural localities or of those that ultimately applied to this call for proposals (see application-level analysis).

From the 25,631 localities in the official catalog for these two states, 12,880 had locality-level sociodemographic data available in the census dataset.<sup>8</sup> We applied the filters outlined in Table 2 to the available information as part of the sampling process, also excluding localities without information

> 6 National Laboratory of Public Policy at Centro de Investigación y Docencia Económicas (CIDE). 7 Localities are the smallest census units in Mexico. They vary widely in size and demographic characteristics. In the sample, the smallest localities had an adult population of 16 and the largest, 552, although localities can have many thousands.

> 8 The localities not included in the census have no population or have populations larger than 4,999 inhabitants.

7
the interaction between foreign and domestic firms. The latter would include programs for capacity building which allow domestic firms to become competitive suppliers of multinationals, and the development of entrepreneurial parks for both domestic and foreign firms, among others.

Summarizing, since the presence of foreign subsidiaries in Turkey is having a significant productivity-enhancing effect in domestic firms, there is a case for authorities to actively pursue the attraction of long-term foreign capital. In a context of slowdown in the EU accession and privatization processes, achieving larger FDI inflows in Turkey would need mitigating exchange rate variability, expanding infrastructure (penetration of telecommunications), and strengthening institutions (property rights protection and law and order).

# **5. Conclusion**

This paper has addressed two main research questions: firstly, understanding the extent to which foreign investment is contributing to productivity gains by domestic firms (both the firms receiving the investment and other, related firms) and thus, indirectly, to economic growth; and, secondly, identifying the determinants of foreign direct investment attraction, in order to understand how the Turkish economy, which traditionally underperformed in terms of FDI inflows, could increase its appeal to foreign investors.

In order to tackle the first question we draw from the ORBIS/AMADEUS data set, featuring financial accounting information from detailed harmonized balance sheets and profit and loss accounts for 7,000 companies established in Turkey, over the period 2005-2012. This data set has also the advantage of providing detailed ownership information, including the “links” between companies and their shareholders, together with the country of origin of the investor. Both labor productivity and total factor productivity (as in Woolridge, 2009) are computed. We first find that the main (and only) determinant of foreign direct investment at the micro level is firm and sector productivity. Thus, to mitigate a potential selection bias, we opt for a dynamic regression in differences, following FonsRosen et al. (2014).

We find that firms that receive foreign direct investment in Turkey see an increase in