rafmacalaba/data-use-mentions-extended huggingface dataset:
, Criscuolo, & Timmis, 2020).<sup>6</sup> Furthermore, the ORBIS raw database suffers from several issues, including duplicates, omission, and measurement error. Several cleaning steps had to be implemented before the raw data in ORBIS could be processed and analyzed, as detailed in the following sections of the methodology. In addition, to complement and improve upon ORBIS’s coverage of firm-level indicators, the Global BOS database leverages alternative data sources (EMIS, Factiva, Pitchbook, among others), World Bank proprietary data sets, country-level reports, and project information. For most countries, firm-level data collection has been also performed which allowed not only to produce a comprehensive data set of SOEs,<sup>7</sup> but also to complete the data initially available for each SOE in terms of employment, revenues, and other financial and firm level characteristics. World Bank country teams also reviewed and validated the country-level data to ensure completeness and accuracy of the information. The country-specific databases were also subject to technical exchanges with government counterparts.

The BOS database identifies an unprecedented number of firms with state participation across countries and economic activities, as well as providing novel insights on financial performance, economic performance, and governance of SOEs. A deep-dive analysis of 36 countries within the BOS database shows that 69% of SOEs operate in competitive activities (low efficiency-rationale for state participation), 16% are in partially contestable industries (moderate efficiency-rationale), and 15% are natural monopolies (strong efficiency-rationale). Furthermore, this analysis suggests that performance-based productivity of SOEs (revenues per worker) is negatively correlated with government control variables, such as government shareholding percentage and direct vs. indirect government ownership.

The remainder of this paper is organized as follows. Section 2 introduces the rationale for the analysis and describes the conceptual framework that defines and provides the structure of the Global BOS database. Section 3 presents the main data sources used. Section 4 describes the methodology to identify SOEs and create the Global
# 4. Supply shortfalls in secondary education

In 2001, Tanzania eliminated school fees at the primary level, which increased primary enrollment rates from 58.8 percent in 2000 to 80.7 percent by 2002<sup>2</sup> (Government of Tanzania 2011, MoEST 2005). There is evidence that the significant increases in enrollment, particularly among girls and children from households in the lowest wealth quintile, can be attributed to the fee-free primary school policy (Hoogeveen and Rossi 2013). Uganda went through a similar spike in 1997, when it abolished primary school fees (Figure 3).

Figure 3: Gross Primary School Enrollment Rates, 1990-2017 <!-- Start of picture text --> 140%<br>120%<br>100%<br>80%<br>60% Implementation of free primary<br>education in Tanzania<br>40%<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Tanzania Uganda Kenya Sub-Saharan Africa<br><!-- End of picture text --> Source: World Development Indicators (WDI), 1990-2017 However, the increase in the lower secondary enrollment rate has not been as steep. The secondary gross enrollment rate increased from 7 to 22 percent from 2000 to 2007 (SEDP 2010) while the primary GER during the same time went from 67 to 111 percent (WDI). At only 23 percent, low rates of secondary enrollment persist today (MoEST 2016). A key reason for these low rates has been the high cost of secondary schooling.

> 2 The graph below shows that the increase in primary school enrollment in Tanzania began in 2000. This increase in 2000-2001 could have been in anticipation of the policy eliminating primary school fees.

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Building on these findings, this study explores the drivers of vocational training choices in two high-paying sectors: energy and information and communications technology (EICT). Both sectors were identified as male-dominated and high-paying sectors using a nationally representative dataset, the ENV 2015 (Institut National de la Statistique, 2015). Our empirical strategy uses linear regressions with cohort and city fixed effects to examine the correlates of training choices. The analysis is based on a sample of 1128 women and 1400 men who applied for job training in urban areas of Cˆote d’Ivoire (Abidjan and Grand-Bassam). We use data collected as part of the PRO-Jeunes program (or _PRO-Youth_ in English). PRO-Jeunes was designed to provide (self-)employment support services that are particularly flexible to meet a wide range of beneficiaries’ needs. It targets youth aged 15 to 30 in rural and urban areas. During the application process, each candidate could choose between two main tracks: an employment track, which provided support for finding employment, or a self-employment track, which offered either a generalist path or a vocational training path. The generalist path was designed for candidates who were already working or engaged in an incomegenerating activity within a specific sector, or who had a particular sector in mind. For those opting for the vocational training path provided as part of the program, candidates could choose to enhance their skills in high-paying sectors such as energy or information and communications technology (ICT), or in retail, specifically in the sales of Lipton products.

This study identifies several key factors influencing individuals’ decisions to pursue training in the EICT sectors. A significant portion of the sample, 72% of men and 51% of women, chose this path. Education and training are pivotal: an additional year of education increases the likelihood of seeking EICT training by 3.2 percentage points for women and 4.4 percentage points for men. This is partly due to the stringent educational entry requirements of vocational training programs. For women, prior training in EICT sectors
children. Ferreira and Schady (2009) propose a conceptual framework in which an increase in infant/child mortality is driven by increased food insecurity and reduced access to (or demand for) essential health services (including preventive services such as vaccination), and review the available literature. Baird et al. (2011) document that indeed, downward deviations in GDP from trend increase child mortality in a sample of 1.7 million births in 59 countries. The food security/nutrition channel does appear to be important - not only is child mortality counter-cyclical, but so is child wasting and other measured nutritional outcomes (Headey and Ruel, 2020).

There is an emerging body of empirical evidence that food insecurity and access to healthcare services have worsened during this pandemic period (e.g. Egger et al., 2021; Shapira et al., 2021b). Osendarp et al. (2021), for example, project between 47,000 to 283,000 additional child deaths in 2020-2022 due to increases in child wasting and declines in nutrition intervention coverage in 118 low- and middle-income countries.

The survival of other vulnerable groups could also be impacted by income shocks. Unfortunately, there is very little existing evidence on economic contractions and mortality in non-child populations. Cutler et al. (2002) show higher elderly mortality during economic crises in Mexico, Khvan et al. (2020) show a similar relation in Russia, and Hone et al. (2019) find a generalized increase in adult mortality in Brazil.<sup>7</sup> We are not aware of a data source that would allow us to estimate the impact of economic downturns on elderly mortality in a systematic way across countries in the same way we estimated it for children. We rely on standardized Demographic and Health Surveys for child health measures as vital statistics in low- and lower-middle-income countries are unreliable.

Allowing for a mortality increase among non-children due to lockdowns would only increase the ratio between the non-COVID-19 deaths caused by the lockdowns
attendance at school and lack of teacher enthusiasm (in itself, a contributory factor to a school environment that is not conducive to student learning).

Teacher training represents another area for improvement. Results from this paper show that despite male teachers’ greater exposure to education during initial training and their higher qualifications compared to female teachers,<sup>10</sup> boys in Saudi schools achieve much poorer outcomes than girls in both mathematics and science. Although holding higher qualifications does not necessarily suggest a higher standard of teaching or improved student performance (Harris and Sass 2011), especially when the focus of the qualification is unknown, these patterns may signal the poor quality of teacher education and training. Further study of these dynamics as they relate to student outcomes would be useful.

In general, efforts to raise educational achievement in Saudi Arabia require taking a broader view beyond the necessary focus on schools and teachers. As noted above, early child development (physical, cognitive, social) and early learning provide foundations for achievement in elementary and intermediate school, and beyond. Ongoing support for learning at home throughout childhood is also crucial, including modeling of positive behaviors (e.g., reading) and involvement in children’s education by their parents.

# 6.1 Suggestions for further research

Future efforts to explain the observed differences between boys’ and girls’ achievement in Saudi Arabia should, if possible, seek to include a broader range of out-of-school factors in the analysis than were possible with the TIMSS 2019 data set. For example, some variables that are not available in TIMSS 2019 or NALO 2018 but that could be usefully considered in a future analysis of gender differences in achievement include students’ reading proficiency; students’ engagement in reading for leisure; and the (gendered) nature of parents’ expectations and aspirations for their child’s education, qualifications, and future career. In particular, considering the importance of literacy as a foundational skill (see also Gregory et al. 2021), the inclusion
# 3.3. Supplier Access

We construct three measures of supplier access. The fi rst one is SA_own<sup>i</sup> p,t<sup>whichcaptures</sup> the availability of inputs used by industry i in province p where it is operating: where Yp,t<sup>kistheoutputofindustrykproducedinprovincepattimet.Itisdividedbythe</sup> total output of industry k produced in China, to get the share of output of each industry k produced in each province. Since industries use more than one intermediate input, these output shares are weighted by aik, which are the coefficients from the China national inputoutput (I/O) table for 1997. Given that this is the most recent I/O table available, we assumed that the technology is constant throughout the sample period. There is variation in the supplier access variables due to entry and exit of fi rms. There are 70 manufacturing I/O codes, which we concord with the industrial data. Thus, while we analyze entry for 515 industries, our proxies for supplier access are de fi ned for 70 I/O codes. In order to make this variable comparable with the proxy for input availability in other provinces (SA_outerp,t<sup>i)</sup> we adjust it for the within province distance, which is de fi ned as DISTpp = ~~�~~ Areaπ <u>p</u> .

The availability of intermediate inputs in the rest of China is proxied by which is analogous to the own province supplier access measure. To take account of the additional cost of accessing inputs from other provinces, we weight the output shares produced in each province by the inverse of distance from province p to province l. While this 13
Letters_ 8 (2): 024015. https://doi.org/10.1088/1748‐9326/8/2/024015.

- Samad, Hussain A., and Fan Zhang. 2016. “Benefits of Electrification and the Role of Reliability : Evidence from India.” WPS7889. The World Bank.

- http://documents.worldbank.org/curated/en/980911479147772730/Benefits‐of‐ electrification‐and‐the‐role‐of‐reliability‐evidence‐from‐India.

- Schmidt, Tobias S. 2014. “Low‐Carbon Investment Risks and de‐Risking.” _Nature Climate Change_ 4 (4): 237.

- “The Sustainable Development Goals Report 2018.” 2018. United Nations.

United Nations. 2015. “United Nations Energy Statistics Yearbook.” New York: United Nations.: United Nations.

- The World Bank. 2018. “Tracking SDG7 : The Energy Progress Report 2018.” 126026. The World Bank. http://documents.worldbank.org/curated/en/495461525783464109/Tracking‐SDG7‐ the‐energy‐progress‐report‐2018.

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# _Quality of education_

The quality of education in Malaysia and Thailand is not up to the standard of the highest Asian performers in world rankings. This may lead to competitive challenges if the labor force is not being endowed with the appropriate technical and creative talent.

Both PISA and TIMSS international assessments show that compared to Indonesia and the Philippines, Malaysia and Thailand perform better in both math and reading. But secondary school students are still learning much less than their peers in wealthier countries like Hong Kong SAR, China; Macao; Japan; Korea; and Singapore (Figure 4). A Malaysian or Thai student on average scores between1.2-1.5 standard deviations below a Korean student in mathematics (TIMSS 2007). This underperformance can be a serious issue as nearly half of 15-year-old Thai students fail to achieve the level of functional literacy in PISA assessments. In other words, most of them are unable to locate information which may need to be inferred or perform other tests (World Bank 2012a).

**Figure 4: Learning Outcomes in East Asia over Time**

<!-- Start of picture text --> Mean Reading Score (PISA) Mean Math Score (TIMSS)<br>Korea, Rep.<br>Singapore<br>Korea, Rep.<br>Hong Kong SAR, China Hong Kong SAR, China<br>Singapore Japan<br>Japan<br>Macao SAR, China<br>Malaysia<br>Thailand Thailand<br>Indonesia<br>Indonesia<br>Philippines<br>2000 2003 2006 2009 1995 1999 2003 2007<br>Year Year<br>550 600<br>550<br>500<br>500<br>450<br>450<br>400<br>400<br>350 350<br><!-- End of picture text --> Source: OECD Programme for International Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS
Likewise, individuals may trust those who are more similar to themselves (i.e., the same social, racial, religious or ethnic group), and may trust others they have known longer (Alesina and La Ferrara, 2002; 2005). Hence, we control for religiosity and immigration status. The country and year fixed effects capture any national-level policies or shocks, such as storms, conflict, and economic downturns, and controls for any changes in the overall trend of trust in a country.

# **3. Results**

Table 1 presents the descriptive statistics for trust and food insecurity, as well as mean comparison tests examining differences across economic development rankings.<sup>9</sup> Roughly 53% of individuals trust their national institutions, and roughly 80% trust their friends and family.<sup>10</sup> About 30% of the world’s population experienced food insecurity in 2014-17. The descriptive statistics suggest there are systematic differences between trust and food insecurity depending on economic development. Respondents in low-income countries have higher likelihoods of trust in national institutions, but significantly lower trust in their friends and family. As expected, food insecurity is much more prevalent in low-income countries.

Table 1. Descriptive statistics.

|||Econo<br>|mic development<br>Lower-middle-<br>|rankings (GNI per<br>Upper-middle-<br>|capita)<br>|
|---|---|---|---|---|---|
||World sample|Low-income|income|income|High-income|
|Dependent variables||||||
|Trust in national institutions|0.530|0.566|0.564***|0.439***|0.557***|
||(0.367)|(0.369)|(0.371)|(0.
is to check the reliability of the self‐reported outcome, an additional challenge arises when trying to capture the introduction of “significantly improved” products, processes, organization practices and marketing methods. The definition of “significantly improved” is highly subjective and can vary across individuals in the same country but also across countries.

Subjective data have strong limitations as measures of actual outcomes. In an influential study, Bertrand and Mullainathan (2001) show the likely bias when analyzing subjective data given their likely correlation with context variables. The authors conclude that while subjective variables can be used carefully as explanatory variables or to explain behavioral differences between individuals, models that use subjective data as dependent variables are likely to produce biased estimates.

The possible sources of bias when using subjective data are numerous, such as those that derive from cognitive problems as shown in Bertrand and Mullainathan (2001). A large amount of experimental 6
was driven by the respondents who believed their village had the most conservative norms. There was no IPV reporting difference across the list and direct methods for respondents who believed their village had progressive norms. As with vulnerability, these results demonstrate that the relationship between community gender norms and IPV can be very different if estimated using anonymous list experiment data.

> 29In Nigeria, the lack of IPV reporting difference across the methods by perceived norms may be due to the different gender norms questions that comprise the index in the two countries. The Nigerian norms index included only unincentivized questions about perceptions of other villagers’ attitudes regarding the acceptability of women working or traveling outside the home. In Rwanda, the norms index focused on IPV-specific norms and included both unincentivized questions about perceptions of village IPV prevalence and others’ attitudes, as well as four incentivized norms vignettes based on an adapted version of the Krupka-Weber norms coordination game (Krupka and Weber, 2013). In this game, respondents were paid if they selected the modal answer in their village about whether described behaviors about IPV and women leaving violent husbands were considered socially acceptable or unacceptable in their village. This incentive-compatible measure was intended to overcome social desirability bias.

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# **Appendix. Data Description and Full Results of the Regressions, Including Alternative Specifications**

_Data description_ All data come from the World Bank WDI database, unless stated otherwise.

GNI per capita: Gross national income divided by midyear population, Atlas method (current US$) IDA operational cutoff: Threshold which GNI per capita should be above to be considered a candidate for graduation. _Source:_ IDA Creditworthiness: Institutional Investors Creditworthiness Index, ranging from 0 (least creditworthy) to 100.

Poverty headcount: The proportion of a population that lives below the international poverty line (US$1.90 a day in 2011 PPP). _Source:_ PovcalNet database Institutional development: The ICRG Quality of Government index produced by the PRS Group, with scores ranging from 0 to 1 (highest quality of government) Urban population share: Percentage of population living in urban areas as defined by the country Resource rents/GDP: Total natural resources rents are defined as the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents Worldwide Governance Indicators: ‘Government effectiveness’, ‘regulatory quality,’ and ‘rule of law’ used to proxy ICRG, where data are not available.

**Table 4. Countries with GNI Per Capita over 80 Percent of IDA Operational Cutoff: Summary Statistics**

||**N**|**Mean**|**Min**|**Max**|**Standard deviation**|
|---|---|---|---|---|---|
|GNI per capita|764|1,893|510|8,690|1,354|
|GNI per capita/operational cutoff|764|1.75|0.80|7.59|1.09|
|Years over cutoff|764|5.86|0|26|5.
resulted in only a small increase in the perceived risk. These findings are very much consistent with the small effect of the salience intervention.

# **5 Conclusions and Implications**

In order to assess the impact of COVID-19 on migration intentions, we use a large-scale panel survey of Gambian young males conducted in May 2019 (pre-pandemic) and 18 months later. Consistent with a simple theoretical framework describing the different channels through which the COVID-19 pandemic may have changed migration intentions, we find that migration intentions to both Europe and especially to Senegal decreased. We find that the desire to migrate decreased especially for two groups of individuals: those who were at the margin, i.e. those who were unsure about migrating pre-COVID-19; and poorer individuals for whom COVID-19 may have made 18
be homogeneous across all countries. This complicates cross-country comparison of licensing fees and patent application data. Balance of payment data for license fees can also be misleading because it captures many intra-firm transactions that are not specifically tied to knowledge creation and patent monetization. For example, a country may have a high level of receipts because of a favorable tax environment or because of the prevalence of profit-shifting techniques such as transfer pricing. Finally, the quality of patents is very likely to vary across countries and is significant in determining the amount of IP revenues. All of these points are important limitations to the conclusions that can be drawn from this data. However, it is the best data currently available worldwide and it still offers valuable insights on capital flows associated with various forms of IP and IC. While certainly not sufficient to establish a causal relationship between patent ownership and licensing revenue, it provides a useful illustration of the IP commercialization divide. It is an interesting fact in itself that the only data available on monetary streams associated with IP does not offer clearly disaggregated indicators. This may reflect a lack of awareness regarding the need to grasp the value of IP in terms of its commercial relevance. The majority of existing innovation indexes continue to measure innovation output primarily in terms of patents held rather than according to the economic value of those patents. This is unfortunate because it suggests to policy makers in the developing world that stimulating higher levels of patent filings is sufficient to foster domestic innovation. Patent quality, discussed later, is an equally significant concern.

4
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