rafmacalaba/data-use-mentions-extended huggingface dataset:
*|
|---|---|---|---|---|---|---|
|China|2009|2,206|6,200|43|17.7|85|
|Japan|1960|5,493|6,976|35|20.0|9.7|
|Korea,Rep.|1982|3,709|6,123|25|14.6|2.3|

Sources: World Bank, World Development Indicators database; International Labour Organization, LABORSTA; China, National Bureau of Statistics (2010).

In the absence of detailed data on manufacturing employment in all African countries, one can only conjecture about the size of the potential gains for the region. Still, even back-of-theenvelope calculations suggest that the benefits would be enormous. In 2009 alone, China exported $107 billion of apparel to the world, compared with Sub-Saharan Africa‘s total apparel exports of $2 billion (2 percent of Chinese apparel exports). Let‘s assume that as a result of rising wages, 1 percent of China‘s production of apparel is shifted to lower-wage African countries. All things equal, that alone would boost African production and exports of apparel by 47 percent. A 5 percent shift of Chinese export-related investments in the industry could translate into $5.4 billion in additional exports—a 233 percent increase.

Even rough employment estimates suggest the potential gains in manufacturing jobs. Africa‘s population (north and south of the Sahara) is 1 billion, slightly less than India‘s 1.15 billion. In 2009 manufacturing value added was 16 percent of GDP in India, 13 percent in Sub-Saharan African countries, and 16 percent in North African countries such as Egypt, Morocco, and Tunisia.<sup>24</sup> India‘s employment in manufacturing was 8.7 million in 2009. So it is reasonable to assume that
The technology was used following the 2012 Ebola outbreak to help implement quarantines and other protective measures (SDSN TReNDS 2018b).

By construction, machine-generated data also have the potential to bring about quick and relevant data. One example relates to water pumps, of which one-third in rural Africa has been found to be inoperable. In Kenya, sensors on water handpumps provide real-time data on their functionality, which helped reduce the average time to repair a broken pump from 27 to 3 days and the median time from 6 days to 1 day (Figure 2) (SDSN TReNDS 2018a and Oxford/RFL 2014).

**Figure 2: Real-time sensor data on broken handpumps greatly reduced repair times in Kenya**

<!-- Start of picture text --> Median<br>1<br>repair<br>6<br>time<br>Mean<br>2.6<br>repair<br>27<br>time<br>0 5 10 15 20 25 30<br>Days<br>With mobile-enabled data Baseline<br><!-- End of picture text --> _Source_ : Oxford/RFL 2014 Satellite data can likewise offer a timely response to emerging threats. In Sri Lanka, where more than two million hectares of natural forest are home to over 30 endangered mammals, satellite data have been used to safely and quickly respond to deforestation of protected areas (Jamilla and Ruiz, 2020). The Department of Forest Conservation used to rely mostly on routine patrolling, which can be a strain on resources and be a dangerous task due to the terrain and animals that may be encountered by patrollers. Propelled by COVID-19, which restricted the use of patrolling, the department now relies on a mobile app provided by the Global Forest Watch. The app relays real-time satellite imagery to alert users of possible deforestation and provide evidence when deforestation has occurred. In one case, a popular local TV channel reported that encroachments had taken place without being addressed by
regions in the North Caucasus are lagging; none are in the Far East.

Our main findings were that (i) regional export performance has been uneven, and (ii) there is a close positive association between per capita income in a region and its ability to attract FDI, and (iii) the types of FDI a region attracts tend to be consistent with the comparative advantage of that region in goods markets, and may drive transformation of that comparative advantage in the future.

The data in this analysis come from a variety of sources, which have been triangulated and harmonized to form a fuller picture of Russia’s trade performance both within the world and as it varies across regions. Not all of these sources are readily available. The data for regional trade by sector, which is at the heart of the analysis, was made available by special arrangement with GTIS, a private provider. The Ruslana data, for firm-level FDI, originates from a different private provider (Bureau van Dijk) and requires a subscription to access. The analysis also makes use of official data on the economies of the Russian federal subjects from Rosstat, FDI data from the Russian central bank, and detailed trade data at more aggregate levels from the United Nations’ COMTRADE system via the WITS portal. The result yields a picture of both Russia’s external economic linkages and the manifestation of those linkages at the regional level, which could not have been obtained through a single source.

The rest of this paper proceeds as follows. Section 2 discusses Russia’s export performance and ability to attract FDI in recent years, analyzing outcomes in detail through indicators that provide insight into regional and sectoral differences, comparisons with other BRICS, and references to future prospects. Section 3 goes into greater depth in terms of the regional disparities and categorizes Russian federal areas as leading, middle-range, or lagging status. This categorization is used as the basis for quantitative and qualitative analysis of export performance and FDI inflows. Specifically, Russian outward orientation is categorized across
the Lerner index are not consistent with results using concentration measures as proxies for competition. They conclude that concentration is not a good measure of competition.

As described above, existing studies on the link between competition and financial access either analyze cross-sectional data or are only able to look at multi-year data for a single country. In contrast, our paper combines multi-year firm-level data with panel country-level data on bank competition. One advantage of our dataset is that it contains repeated cross-sections of firms for the countries in our sample. This allows us to control for unobserved differences between countries, using country fixed effects in our estimations. Such unobserved differences may be correlated with both access to finance and the extent of competition. Thus, our methodology isolates within country variation in competition and access. This is an improvement over previous cross-country studies, such as Beck, Demirguc-Kunt, and Maksimovic (2004). Furthermore, in contrast to US and cross-country studies that measure competition with concentration indicators, we also present results using the Lerner index, a direct measure of bank pricing behavior. Several studies have argued theoretically and empirically that pricing behavior measures such as the Lerner index are superior to concentration measures as indicators of competition.<sup>8</sup> Concentration is a measure of market structure, while competition is a measure of market conduct. There can be competition in concentrated markets, if there is a credible threat of entry and exit (i.e., if markets are contestable). A contribution of our paper is the ability to distinguish the impact of concentration and competition in a multi-country setting. Also, the fact that we offer cross-country evidence using the Lerner index allows for more general results relative to those that focus on individual countries.

We find that low competition, as proxied by high levels of the Lerner index, is associated with diminished access to finance by firms, while concentration has a less robust relationship with access. We use different weighting schemes to account for differences in the number of firms across countries and the variance
of traditional questions on corruption, along with a set of randomized response questions to evaluate these problems.

Our analysis suggests that there is a large underreporting of corruption in firmlevel surveys. One mechanism driving these erroneous results is through the systematic pattern of nonresponse rates by firms. We also show evidence that firms providing nonresponses are also the types of firms exhibiting patterns of answers consistent with false responses.

Unfortunately, our paper shows that RRT clearly is not a panacea for solving problems for biased responses. Even with the anonymity provided with this technique, we observe systematic patterns that suggest at least 10% of the sample is providing false responses to survey questions. Yet this technique does allow researchers to ex post evaluate systematic problems with nonresponse and potential false responses in survey research. Perhaps most importantly, it allows us to examine the types of firms (or managers) that provide false responses to politically sensitive questions.

25
# **Table 1: Data Coverage and Summary Statistics of Distance-Default.**

This table shows the summary statistics for the distance-to-default measure for countries for which we have at least one observation. Our original data from both BankScope and Datastream covers 2,294 publicly traded banks across 86 countries. Of these banks, 1,942 of them satisfy our requirement that 1) they have at least 36 weekly non-zero volume and non-missing returns over the previous 12 months, 2) they have non-missing liabilities and market capitalization, in order to compute distance-to-default.

|**Country**|**Number of Banks**<br>**with BANKSCOPE**<br>**and DATASTREAM**<br>**Coverage **|**Number of Banks**<br>**with Distance-to-**<br>**Default Measure**|**Mean**|**Standard**<br>**Deviation**|**p5**|**p50**|**p95**|
|---|---|---|---|---|---|---|---|
|**ARGENTINA**|8|8|4.09|1.81|1.87|3.80|7.81|
|**AUSTRIA**|14|13|9.33|3.22|2.77|10.83|12.00|
|**AUSTRALIA**|20|19|6.47|1.95|3.05|6.56|9.73|
|**BELGIUM**|6|6|6.99|2.98|1.84|6.78|12.00|
|**BAHRAIN**|16|11|5.30|2.
11

# **5. NEW ECONOMETRIC EVIDENCE**

This section discusses our econometric estimates of the impact of two of the main telecom reform policies (i.e. the presence of private capital and the establishment of an independent regulator) on access, affordability, quality and labor productivity based on the experience during the period 1990-2003. We also test the extent to which reform policies affect differently developed and developing countries.

Focusing on these two reform policies would however tend to ignore the fact that reforms do not take place in a vacuum. Indeed, there are wide differences in governance capacity and commitment across countries, whether developed or developing, which may influence the effects of reforms on performance. The extent to which private capital may be attracted to a country may be driven by more commercial considerations such as the investment risk of the country—and this is the case even if competition and support to private investors are formally part of the laws of the country. Similarly, the scope of action of an independent regulator may be quite different in a country with a high degree of corruption than in a country with a low degree of corruption.

To include corruption and investment risk in the explanation of telecom performance, we rely on two sets of data from the International Country Risk Guide. The first is a corruption index, defined as an assessment of corruption within the political system. It includes financial corruption (like bribes connected with trade, taxes or protection), but it focuses more on actual corruption in the form of excessive patronage, nepotism, job reservations, 'favor-for-favors', secret party funding, and suspiciously close ties between politics and business. The second is an investment risk index, defined as the sum of three subcomponents with equal weights (contract viability or expropriation, profits repatriation, and payment delays). We have transformed the two original indexes so that they now vary continuously between 0 and 1, with zero meaning low value and 1 high value. Table 4 presents basic statistics for these indexes. Developing
# **1 Introduction**

The school-to-work transition is a critical and often challenging period for young people, with youth unemployment rates often far exceeding those of adults. Employers are frequently reluctant to hire recent graduates because the skills acquired at school may not match those demanded by the firm. Surveys of employers from around the world indicate that firms struggle to find workers with appropriate levels of soft skills (Cunningham and Villase ̃nor, 2016). These soft skills are often complementary to technical skills, and have been shown to be predictive of labor market success and productivity (Adhvaryu et al., 2023; Ajayi et al., 2022; Borghans et al., 2008; Campos et al., 2017; Heckman and Kautz, 2012). Despite growing recognition of their importance, there remains relatively limited evidence on how to effectively develop soft-skills (Deming, 2022). Dedicated soft skills training may help bridge the skills gap, allowing prospective employees to acquire the skills that are not developed in school but are highly demanded by employers.

This project seeks to test whether an intensive soft-skills training course for recent tertiary education graduates can effectively improve their soft skills and boost their downstream labor market outcomes. The soft skills training was commissioned by the Rwanda Development Board in response to findings from a survey of Rwandan employers in which they reported that they valued employees with strong communication skills but that these employees were particularly difficult to find.<sup>1</sup> The training was prepared and implemented by professors of the University of Rwanda, the largest education institution in the country, and focused on effective communication and networking, including material on both interpersonal (teamwork, collaboration, trust, empathy, and negotiation) and intrapersonal (self-awareness, personal initiative, and perseverance) skills.<sup>2</sup> The training was delivered through an intensive two-week bootcamp (160 hours in total) in the South of Rwanda.<sup>3</sup> A group of 450 randomly-assigned, eligible
existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.19:** Economic Outcomes (z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Economic outcomes index**(SUR)||0.053|0.036|0.070|5053|
|||(0.032)|(0.037)|(0.040)<sup>_∗_</sup>||
|||[0.138]|[0.262]|[0.119]||
|(1) Physical asset index|0.000|0.020|_−_0.023|0.063|5052|
||(1.000)|(0.048)|(0.053)|(0.059)||
|||[1.000]|[1.000]|[0.404]||
|(2) Agricultural asset index|0.000|0.120|0.174|0.065|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup
gives a third equation of the form _q_ 2 = _Rq_ 3 where _R_ is a constant obtained from our variance formulas. Our system of equations consists of four equations: (i) _L_ = _n_ (0 _._ 2 _q_ 1 + 0 _._ 5 _q_ 2 + 0 _._ 8 _q_ 3), (ii) _q_ 1 = _q_ 3, (iii) _q_ 2 = _Rq_ 3 and (iv). We use the results in Theorem 1 to approximate the variances and calculate the ratio _R_ .

For our MDE calculations, and to illustrate our methods, we consider three scenarios: one with “substantial” heterogeneity (scenario 1), one with “moderate” heterogeneity (scenario 2), and one with “limited” heterogeneity (scenario 3). The first one uses our raw data set that contains all clusters with eight households or more. This raw data contains one cluster with a very large number of units. This is the scenario where cluster heterogeneity is most substantial. The second scenario considers an intermediate case where we drop all clusters with more than 500 units. This second data set still exhibits substantial heterogeneity but eliminates one extreme outlier. Finally, the third scenario considers clusters of size between eight and 50, which is the sample we use in our experiment. While scenarios 1 and 2 are not used in our empirical analysis, we use them to illustrate how ignoring cluster heterogeneity can result in severely underpowered experiments.

These three scenarios are described in Table 1. In scenario 1, the average cluster size is around 21 households, but the data set contains one very large outlier with 2,754 units. This large cluster makes the standard deviation of cluster sizes very large, indeed larger than the average size, so our theoretical results indicate that the adjustment for heterogeneity will make a substantial difference when calculating power and MDEs. In scenario 2, we remove this outlier from the data and this reduces the standard deviation of cluster size, slightly below but very
the time of loan approval. To address this difficulty, I rely on predicted rather actual disbursements. Predicted disbursements are based on the combination of actual loan commitments with typical disbursement profiles such as those shown in Figure 1, but for more finely-disaggregated groups of loans. Specifically, I begin by assigning loans to a set of creditor/decade/region-specific bins. The creditor bins are based on the major creditors listed in Table 1, as well as the residual categories of other multilateral and bilateral creditors. I then separate loans issued by each of these creditor groups into decades by approval year, and further divide them into six geographical regions in which the borrower is located.<sup>12</sup> This procedure results in 443 creditor/decade/region bins, with a median of 65 loans in each bin. For each loan, I compute the average disbursement profile across all other loans within the same creditor/decade/region bin, i.e. excluding the loan in question. I then apply this average disbursement profile to the original commitment to obtain a series of predicted loan-level disbursements. Finally, I aggregate predicted disbursements across all loans to the country-year level, but excluding loans committed in the same year. By construction, the only borrower-specific information in this measure of aggregate predicted disbursements consists of the original loan commitment decisions made in previous years, which I assume are uncorrelated with contemporaneous macroeconomic shocks.

Figure 2 helps to visualize the steps in the construction of my instrument based on predicted disbursements on previously-approved loans, using data for Kenya. Kenya is a fairly typical country in my sample, in that financing from official creditors accounts for 3% of GDP and 15% of government spending on average over the period 1970-2010. The overall height of the bars in the graph display disbursements on loans from official creditors as a fraction of GDP. These vary considerably from year to year, ranging from lows around 1% of GDP to highs around 6% of GDP. The dark-shaded lower portion of each bar
|`IND`|`294.56`|_`0.23`_|
|---|---|---|
|`ITA`|`438.06`|_`0.34`_|
|`JPN`|`1293.39`|_`1.00`_|
|`KOR`|`541.96`|_`0.42`_|
|`MEX`|`1288.42`|_`0.99`_|
|`MYS`|`274.08`|_`0.21`_|
|`NGA`|`230.40`|_`0.18`_|
|`NLD`|`249.97`|_`0.19`_|
|`RUS`|`501.60`|_`0.39`_|
|`SAU`|`232.88`|_`0.18`_|
|`TTO`|`271.61`|_`0.21`_|
|`VEN`|`1547.39`|_`1.19`_|

``` Outputsavedas:wdr_4_9b.xlsintothecurrentworkingdirectory ``` As in Figure 1, we deliberately reported in the above Stata output only the 20 countries with the highest foreign value-added in the bilateral exports between the United States and China. Actually, by running the previous syntax, `icio` would report also the value-added of the other countries in the EORA MRIO database. Since the complete list is very long, the user may find it useful to exploit the `save()` option. As can be seen, by adding this option, the complete `icio` output has been saved into a file called `wdr_4_9b.xls` within the current working directory.

Lastly, we consider the analysis of value-added in the total imports of a country
covered is 1995-2018 in specification (1) 1995-2017 in specifications (2)-(4) and 1995-2014 in specification (5). Growth indicators are computed as log-differences. The number reported in each cell comes from a specific regression and represents the coefficient of log of EPU index, either contemporaneous or lagged, depending on the specification. All regressions include country and time fixed effects as well as controls for growth in absorption and in relative prices.

A simple calculation based on our main specification in Table 2 indicates that EPU may explain a significant proportion of the recent decline in trade growth. Since the EPU index increased by approximately 50 percent from mid-2018 to mid-2019 relative to the same period in the previous year, increased policy uncertainty may have reduced growth in goods and services import volumes by one percentage point (i.e., 50*0.02). Preliminary estimates from the CPB World Trade Monitor suggest a difference in the growth rates of global merchandise import volumes of –3 percentage points between the same two periods, which implies that economic policy uncertainty could explain about a third of the decline in trade growth.

We undertake a number of tests to assess the robustness of the relationship between economic policy uncertainty and trade growth. Varying the scope of import volume growth to cover goods and services, goods alone (using data from different sources), using dynamic specifications, and/or replacing REER by the ratio of import price deflator to GDP deflator, consistently yield negative and significant coefficients of policy uncertainty (Tables 3 and A1). The use of changes in economic policy uncertainty instead of levels also leads to consistently significant coefficients of interest (Table 4). Trade policy creates a wedge between domestic and international prices which is accounted for by our measures of relative prices. Nevertheless, to rule out a potential omitted variable bias, we run regressions that explicitly control for trade policy in the form of tariffs and find that the coefficient of policy uncertainty is not affected (see Appendix Table A2).

**