rafmacalaba/data-use-mentions-extended huggingface dataset:
fundamentally different from those to be encountered in the developed world, but they are much more difficult to address, notably because of the absence of an efficient judiciary system.

There are several possible ways which can be considered to improve this situation:

- Firstly, the strengthening of relevant established institutions to give them the necessary independence, legal and financial resources to accomplish correctly the functions they are supposed to fulfill. For instance, a number of countries have competition laws, but there are no mechanisms and appropriate organizations to ensure their enforcement.

- Secondly, the establishment of efficient counter powers, made notably of users of the public services, including entrepreneurs, to get those services better functioning, less sensitive to corruption, etc.  Such initiatives have notably been implemented in the education sector to ensure an appropriate use of public funding<sup>20</sup> and could usefully be extended to other sectors, such as public procurement to ensure more transparency in funding allocations and selections of providers.

- Thirdly, the implementation of institutional audits, focusing on innovationrelated obstacles.  Such audits could be built, or grafted, on those broader surveys implemented by the World Bank to evaluate the business environment<sup>21</sup> or governance conditions<sup>22</sup> .   Such audits – to be implemented by high level, independent bodies - should not be conceived as one shot events, but should be designed as processes, through which recommendations made by the relevant bodies would be followed-up and monitored in their applications and effects.  Note that these audits could also be implemented by international instances, modeled on the peer review mechanisms put in place in Africa within the NEPAD for governance and democracy issues<sup>23</sup> .

# **6. Challenging global trends and policy responses**

Globalization and technological change represent both opportunity and challenge for developing countries, and so the question of how developing countries can harness the globalization process to turn potentially adverse spillovers into windows of opportunity, how they
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This country report provides an overview of the key AICD findings for the Republic of Congo so that the country’s infrastructure situation may be benchmarked against that of African peers—particularly other resource-rich countries.

Due to the cross country nature of the data collection, a time lag is inevitable. The AICD covers the years 2004–07. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short term fluctuations. In addition, the report standardizes the indicators and analysis to ensure consistency and allow for comparisons across countries. For these reasons, some of the indicators may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

The infrastructure sector contributed half a percentage point to the Republic of Congo’s annual per capita growth over the past decade—considerably lower than the regional average of 0.99 percentage points. Growth in the Republic of Congo was generated primarily by increased access to mobile telephony, while the inadequacy of the power supply held back the economy.

Infrastructure’s historically small contribution to growth suggests that the Republic of Congo can achieve significant gains from improvements in these sectors. Simulations suggest that if the country’s infrastructure platform were improved to the level of the African leader—Mauritius—annual per capita growth rates could increase by 3.7 percentage points. This compares with an average of 2.92 percentage points for the continent as a whole (figure 1).

Most of the potential contribution to growth would come from improvements in the power sector, particularly through increases in generation capacity. Evidence from a 2009 World Bank/IFC enterprise survey<sup>1</sup> suggests that power is by far the greatest infrastructure constraint for Congolese firms. Surveyed firms report losing up to 16 percent of sales due to frequent power outages—almost triple the average of 6
**Figure 4.** Average height of youth between 20 and 24 years of age in Mexico in 2000, 2006 and 2012, by gender and income decile

<!-- Start of picture text --> Males Females<br>180 180<br>175 175<br>170 170<br>165 165<br>160 160<br>155 155<br>150 150<br>145 145<br>140 140<br>I II III IV V VI VII VIII IX X I II III IV V VI VII VIII IX X<br>Decile Decile<br>2000 2006 2012 2000 2006 2012<br>Centimeters (cm) Centimeters (cm)<br><!-- End of picture text --> Source: estimations of the authors based on the data from the National Health Survey 2000 (ENSA 2000), National Health and Nutrition Survey 2006 (ENSANUT 2006) and the National Health and Nutrition Survey 2012 (ENSANUT, 2012).

# _A. Intergenerational mobility_

Studies on intergenerational mobility have thus far ignored health, despite its central importance in well-being (Halliday, Mazumder and Wong, 2018). We hypothesize that this is mainly due to the difficulty of obtaining data on a longitudinal panel that includes health indicators. This study incorporates health as a measure of intergenerational mobility and contributes to addressing this gap in the intergenerational mobility literature.

The mobility literature that intersects with health has noted that health conditions are important determinants of income differentials and that inequality in income tends to be correlated between generations, in part due to the intergenerational associations between education and health (Ahlburg, 1998).

Recently, the importance of analyzing intergenerational mobility in health to understand the persistence in socioeconomic differentials has been emphasized. This area of study seeks to identify the specific role that transmission of health status plays as a determinant of social mobility (Akbulut-Yuksel and Kugler, 2016). Some analyses have also documented the transmission potential of risk behaviors for health,
multinationals use mostly Tanzanian labor, their expansion is likely to increase the demand for Tanzanian labor in these sectors.20 We obtained estimates of the share of expatriate labor or specialized technology not available to Tanzanian firms that is used by multinational service providers in Tanzania from the survey mentioned above. We found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or

> 20 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement.

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<!-- Start of picture text --> F igure 1<br>A djusted W eekly H ours W orked<br>50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Source: O w n estim ates based on EN EU survey 1988 1994 1999<br>Primary Teacher Pub. Sch.Primary Teacher Priv. Sch.L-Sec Teacher Pub. Sch.L-Sec Teacher Priv. Sch.U-Sec Teacher Pub. Sch.U-Sec Teacher Priv. Sch.Univ. Teacher Pub. Sch.Univ. Teacher Priv. Sch.The Mix-Skilled GroupThe Agricultural GroupThe Low-Skilled Group<br><!-- End of picture text --> Based on ENIGH, the main and secondary occupations listed on Table 4 show hours-worked, adjusted hours-worked and the total number of hours-worked by region. Figure 2 shows that hours-worked vary across school levels, sectors and regions. Public primary school teachers work an average of 32 hours per week, while those in private schools work on an average of 34 hours. Public and private primary school teachers work significantly less than their counterparts. This pattern changes in lower and upper secondary school level, since public school teachers work more hours on average. On the other hand, _university teachers in public schools_ and _the mixed-skilled group_ work an average of 47 hours per week, while people employed in _the agricultural group_ or _the low skilled group_ work on an average of 43 hours per week. There is not a significant difference in the weekly hours-worked between public school teachers in urban areas and those teachers in the rural areas. In sum, total hours-worked for _the Professionals with a B.A in Education not teaching, the mixed-skilled group_ , _the agricultural group_ and _the low-skilled group_ is
then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are reported in Table 4.

# **Share of Market Captured by Multinational Service Providers**

It was necessary to calculate the market share of multinational firms in the services sectors by region of the model. Take the banking sector as an example. We need to know the share of the market captured by Tanzanian, EU, African and Rest of the World firms, where the countries in the regions are defined in table 1. This entailed acquiring a list of all banks operating in Tanzania along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to be consulted to allocate ownership shares in several cases. The results, by region and sector, are presented in table 6. Documentation of the results, with listing by firm, sector and region, and the data sources are presented in appendix B.

# **Share of Expatriate Labor Employed by Multinational Service Providers**

The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use Tanzanian labor less intensively than their Tanzanian competitors, if multinationals use mostly Tanzanian labor, their expansion is likely to increase the demand for Tanzanian labor in these sectors.20 We obtained estimates of the share of expatriate labor or specialized technology not available to Tanzanian firms that is used by multinational service providers in Tanzania from the
For comparison, FSB (2019) finds that the Basel III effects on SME financing in G20 countries are, on average, weak and short-lived. However, FSB (2019) examines the _relative_ effect—that is, whether the SMEs in countries with more binding capital positions are more affected that the SMEs in countries with less binding capital positions. In contrast, we investigate the absolute effect of Basel III, by comparing impacts across countries that have implemented the Basel III package versus countries that have not implemented it. In addition, we consider the varying gestation lags in the Basel III effect—that is, the difference between the year of Basel III implementation and the year of the Enterprise Survey data, when the effect is estimated—while the FSB (2019) study does not consider possible gestation lags, perhaps because these would be less varying in their case.

During the Basel III implementation period, firms with initially more employees and a larger share of foreign ownership perceived their constraints in accessing finance as easing across all the countries in our sample. Our result that greater firm size eases access to finance accords with the relevant literature (Beck, Levine, and Loayza 2000; Cetorelli and Strahan 2006; Beck and Demirgüc-Kunt 2006; Arraiz, Meléndez, and Stucchi 2014; Beck et al. 2006; Ferrando and Griesshaber 2011). In contrast, firms with initially larger (and perhaps overheated) sales experienced subsequent worsening of access to finance constraints. There is also some evidence that older firms may have benefited from better access to finance.

Firms facing banking systems that were initially less competitive (as captured by a higher Lerner index), more profitable, and more plagued with nonperforming loans experienced growing constraints in accessing finance. The result that the lack of competition undermines access to financing corresponds with that of Beck, Demirgüç-Kunt, and Levine (2010).

Interestingly, capital adequacy levels did not have an effect across all the countries—perhaps because banks in EMDEs were initially well-capitalized relative to banks in the advanced economies.
More advanced forms of text analysis can use machine-learning techniques to improve skills matching and enable workers to seek training in high-demand skills. By creating algorithms to alert suitable candidates to newly posted jobs, online job portals can reduce search costs for both employers and job seekers. Algorithms can also help employers describe in greater detail both the technical skills and non-cognitive skills required for the position, and portals can identify suitable job seekers based on characteristics that go beyond observable qualifications such as academic degrees and years of experience. This type of contentbased matching can also be useful to job seekers, especially those whose interests and qualifications are not restricted to a single occupational category.

Collecting additional information on non-cognitive skills could improve the quality of job seeker information. Employers typically rely on interviews to assess non-cognitive skills, which tend to be abstract and subjective. Encouraging employers to identify the type of non-cognitive skills that they require and then assessing the non-cognitive skills offered by job seekers could reduce employers’ reliance on timeconsuming interview processes. LinkedIn and other job-matching services are already pursuing similar initiatives and have achieved some preliminary success (e.g., Gee, 2015; Horton, 2016).

# **_Limitations and Caveats_**

There are a number of important limitations and caveats to the use of big data in general, and online jobsearch portal data in particular. These include compliance and privacy concerns, sample representativeness and selection biases, and overall data quality. Askitas and Zimmermann (2015) identify privacy issues and data ownership as major challenges involved in big data analytics. While data collected by private firms offer key insights that can inform better public policies, the legal and ethical frameworks for obtaining and using such data are not always clearly established. Moreover, big data analytics often involve micro-level data associated with individual transactions, which can give rise to serious privacy concerns.

The representativeness of the data is also a major issue, as online job-search platforms are subject to selfselection biases and other forms of sampling error. Carnevale et al. (2014) argued that
of TTBs to macroeconomic fluctuations after 1995 represents a significant departure from how the major emerging economies used these trade policy instruments before 1995, suggesting an 5 Bown and Crowley (2013a) examines data from the United States, European Union, Australia, Canada, and South Korea and is most closely related to a prior literature examining antidumping use by the United States and a handful of other high income countries on data from the 1980s and 1990s, including Knetter and Prusa (2003) and Feinberg (1989). One substantial difference is that while the current paper relies on the best available data across countries at the annual frequency, Bown and Crowley (2013a) was able to access data for high-income economies at the quarterly frequency. Another related paper is Crowley (2011), which is the first that we are aware of that highlights the channel of policy-imposing economies using country-specific bilateral import restrictions against trading partners that were experiencing negative growth shocks at home. Bown (2008) presents an approach that considers macroeconomic and industry-level determinants of antidumping for a number of the emerging economies in our sample for the period 1995-2002.

> 6 The evolving literature on import protection taking place during the Great Recession also includes Bussiere, Perez-Barreiro, Straub and Taglioni (2010), Kee, Neagu and Nicita (2013), Gawande, Hoekman, and Cue (2014), and Davis and Pelc (2012), in addition to Bown (2011).

<mark>3</mark>
>60% 60<br>50%<br>40<br>40%<br>30% 20<br>20%<br>10% 0<br>0% India to EU15 within EU15 NMS to EU15<br>2002 2004 2002 2004 2002 2004 -20<br>India to EU15 EU15 to EU15 NMS to EU15<br>-40<br>Construction Computer and business services<br>modern traditional<br><!-- End of picture text --> _Source:_ EuroStat, Direction of Trade Statistics. 2011.

In summary, the stylized facts show that services dominate growth in EU-15 countries and to a lesser extent in NMS and ACC. Service productivity is many times higher in EU-15 than in the other two groups but at least NMS are starting to catch up. Highest growth in ―service tradability‖ is recorded for NMS followed by EU-15 while ACC are falling behind. Both EU15 and NMS have revealed comparative advantage in services, but this is not the case for ACC. Moreover, growth in modern services outweigh growth in traditional services in EU-15 and NMS, while modern services are even shrinking in ACC. Hence, EU-15 and NMS do seem to change the nature of their services while for ACC - even though the contribution of services to growth is strong in ACC - it seems to be less due to an improved quality of services. We next turn to the sophistication of services. After a short description of the methodology, we will present some descriptive statistics for Europe that may help us further understand the patterns in Europe that we have just observed.

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ownership, while in countries such as Belarus, Moldova and Ukraine less than 40 percent of the population favored privatization;

- In East Asia, a survey of eight countries (excluding Vietnam, Laos and Cambodia) found that only a third of the population surveyed favored privatization over state ownership (Asia Barometer 2001-02).  While support was quite high in Korea (60 percent), it was low in China (17 percent), Thailand (33 percent), and the Philippines (39 percent).
maps of communes in Mali, and of ‘departments’ in Burkina which are administrative units similar to counties in the United States. We use digital maps from censuses to create commune/department level panel data. The panel data set based on the population censuses includes 371 departments in Burkina and 629 communes in Mali, giving us a relatively rich sample of 1,000 sub-districts for our empirical analysis.<sup>14</sup> The source of built-up and population density data is the Global Human Settlement Layer (GHSL) data set made available by European Joint Research Centre (Pesaresi et al. 2015) and Columbia University, Center for International Earth Science Information Network - CIESIN (2015). With spatially consistent information from four Landsat image collections, these data are available for 1975, 1990, 2000 and 2014. The built-up data are 250 meters by 250 meters, whereas we use 1 km squares for the population grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the percentage of built-up area and population density at the commune/department levels. The GHSL data on built-up density have at least two periods before the war which allow us to check the pre-war trends for both population density and built-up density.

The data on the luminosity of night-lights are drawn from global satellite database. The night-light data are available from 1992 to 2013.<sup>15</sup> We focus on 1992-2011 and exclude the last two years from the sample as data for some of sub-districts are missing in those two years. We use the census borders to construct a panel at commune/department levels. Our focus is on the average night-light luminosity per sq km. An important advantage of the night-light data is that they are available before, during and after the CIV civil war. To guard against possible serial correlation, we follow the suggestion of Bertrand et al.

> 14Total number of departments/communes in Burkina Faso is 374 and Mali 695. Some of the subdistricts lack data on building
and other educational disruptions to losses in average lifetime educational attainment. In general, as child age, the opportunity cost of staying in school increases. This may make it harder for households to justify sending older children back to school after a forced interruption, especially if households are under financial stress. To simulate dropouts, we take the expected shock on income from Global Economic Prospects (GDP per capita growth projections) and estimate the expected effect of this income shock on dropouts using agegroup specific dropout-income elasticities.<sup>21</sup> In 2021, the world economy experienced a strong recovery from the first phase of the pandemic (Global Economic Prospects 2021 and 2022). The GDP per capita is expected to have grown by 4.4 percentage points in 2021, which signals a strong recovery compared to the October 2020 estimate of -0.8 (Macro Poverty Outlook, percentage points). However, growth has been uneven and concentrated in higher-income countries. Thus, while the estimated increase in dropout is lower than in 2021, that improvement is mostly driven by growth in advanced economies.

Income shocks from COVID-19 could increase the out-of-school population by 0.26% for primary school children and 0.23% for secondary school children, or an additional 4.0 million children to drop out from school around the world. Of these children, 3.7 million live in low-income countries, and 1.8 million are between 12 to 17 years of age. Figure 5 shows how dropout relates to changes in income shocks across the different economic projections, during a period in which growth rates have improved under the most recent projections but still underperform pre-pandemic growth.

**Figure 5. Estimates of student dropout by 2020 and 2021 growth projections release**

<!-- Start of picture text --> 12 5<br>10.8 4.4<br>10 4<br>3<br>8<br>6.9 2<br>6 1.
Entities files to identify the shareholders that are public authorities. We do so using the entity category S, which corresponds to governmental agencies, public departments, and local authorities. We exclude sovereign wealth funds and institutional investors from this set, as

> 7Each BvD firm ID has two components: a country code that refers to the country where the firm operates and a number.

> 8Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Finland, France, Germany, Hungary, Italy, Luxembourg, Montenegro, North Macedonia, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Serbia and Ukraine.

> 9The authors develop a methodology and SQL code to recreate the ownership trees for all the firms in the ORBIS database.

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