rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

As countries continue to invest in their education systems to develop skilled workforces, an inherent question arises: _does_ it pay off economically to be more skilled? Are certain types of skills associated with higher wages? Most of the empirical work in labor economics has been motivated by human capital theory, which posits that human capital—that is, an individual’s stock of skills—is a key determinant of individual and aggregate economic success (Becker, 1964; Schultz, 1999). Much attention has been paid to measuring this human capital and examining how it is rewarded in the labor market.

To do so, most literature on the payoffs of individual skills has used individuals’ level of _schooling_ —typically years or level of education or training received—as the main variable (Card, 1999; Psacharopoulos, 1985). Such research has consistently found that individual returns to schooling are positive and that returns tend to be higher in low- and middle-income countries than in higher-income countries (Montenegro and Patrinos, 2014).

However, schooling is not the only variable that can be used to in calculating the economic payoff of individual skills. More recent research on skills payoffs has broadened beyond schooling to incorporate _skills proficiency_ . Measures of cognitive and noncognitive skills, including those relevant in today’s technology-driven environment, provide information on what individuals can do and what their level of performance can be in a way that goes well beyond measures of schooling. Most of these recent studies have focused on the effects of either _cognitive skills_ or _noncognitive skills_ on wages, and a smaller set of studies has sought to understand the differential contribution of these skills to labor market outcomes (Heckman, Stixrud, and Urzua, 2006; Heineck and 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
20 where _pi_ and _qi_ are the values of the _i_ -th indicator for country p and q, respectively. For a robustness check, the calculations also employ the Mahalanobis distance, which is a generalized version of the Euclidean distance that corrects for correlations among the variables.

Another crucial issue in this analysis is the number of clusters. Generally, the higher is the number of clusters, the higher the ̳precision‘ of the analysis, i.e. the higher the similarity of the points within the cluster (and dissimilarity to points outside of the cluster). But with a very high number of clusters, the informational value of the analysis becomes small, and it becomes more difficult to synthesize and communicate the results in a meaningful way. For these reasons, and to examine the robustness of the results with respects to the number of clusters, results for 3, 4, and 5 clusters are examined.

Once the number is set, the clustering analysis is a relatively straightforward (but computationally demanding) exercise in finding an allocation of the world‘s countries into 3 (or 4 or 5) sets so as to minimize the sum of distances for all pairs of countries within the same set: min , (3), where _k_ is total number of clusters and Sc denotes an individual cluster.

# **5. Selected Findings**

Appendix II illustrates the recent country-by-country data in the Global Financial Development Database (2008–10) for the individual characteristics of financial systems. It shows individual country data in 8 columns: 4 columns for financial institutions and 4 for financial markets.<sup>11</sup> Overall comparisons by levels of development and by region (Table 3<sup>12</sup> and Figure 3<sup>13</sup> ) confirm that while developing economy financial systems tend to be much less deep and also somewhat less efficient and providing less access, their stability has been comparable to developed country financial systems.

> 11 The Appendix II table shows not only the individual dimensions in the 4x2 matrix, but also the overall financial development indicator, which quartile do countries belong to in terms of the distribution of the respective variables. This is added for illustrative purposes and need to be interpreted with the appropriate caveats, mentioned in the appendix.

> 12 Table 3 compares groups of countries by level of income across regions, or between a country and a peer group or  ̳benchmark‘. Clustering countries by income level provides a good way of carrying out cross-country comparisons. Clustering analysis was carried out but the groupings of similar countries were very close to groupings in terms of income levels.

> 13 Figure 3 uses parallel coordinates plots, with each variable plotted on a separate vertical scale and values for observations shown by connected line segments.
labor or capital taxes paid by the corresponding income flow. The database—including . detailed decompositions by type of tax—is available online at https://globaltaxation.world From this database, we are able to make two main contributions. The first is to establish a set of facts on the evolution of factor income taxation. Taking a global perspective, we find that average effective labor and capital tax rates have converged globally since the 1960s, due to a 10 percentage-point increase in labor taxation and 5 percentage-point decrease in capital taxation. This decline in capital taxation is driven by a collapse in the taxation of corporate profits, from close to 30% in the 1960s to less than 20% in the late 2010s. The rise in labor taxation owes primarily to the expansion of payroll taxes.

Our most striking findings involve the evolution of capital taxation. We uncover an asymmetric evolution of capital taxation across countries of different development levels. In high-income countries, effective capital tax rates collapsed, from close to 40% in the post-World War II decades to about 30% in 2018. For instance, in the United States, the average effective capital income tax rate fell from more than 40% in the 1960s to 25% in 2018. By contrast, in developing countries effective capital tax rates have been on a rising trend since the 1990s, albeit starting from a low level. Effective capital tax rates rose from about 10% in the 1990s to 20% in 2018, with the increase happening primarily in large economies. Between 1995 and 2018, for example, the effective capital tax rate rose from 10% to 30% in China, 18% to 28% in Brazil, 7% to 11% in India, and 5% to 10% in Mexico. This increase is one factor explaining the rise in the overall tax-to-GDP ratio of developing countries, along with the increase of indirect taxes and a slow but steady rise in labor taxation.

This rise of capital taxation in low- and middle-income countries had not been noted in the literature before, due to the lack of data on the evolution of tax structures in developing countries. The finding appears to be robust. It holds when we exclude China and oil-rich countries; when we restrict the analysis to a balanced sample of countries; and under different weighting schemes. It holds with alternative approaches to computing capital and labor income in non-corporate businesses, where factor shares are not directly observable. It is also robust to alternative ways of assigning certain taxes to capital versus labor.

Why did effective tax rates on capital rise in developing countries while they fell in high-income countries in the era of hyper-globalization? Our second main contribution 2
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TAB}LES, PAGES

|Table 1: Testing for Unit Roots: Dickey-Fuller (DF) Augmented<br>Dickey-Fuller (ADF) and Cointegrating Regression<br>Durbin-Watson Tests (CRDW) .......................|33|
|---|---|
|Table 2: Cointegrating Vectors for Investment (Levels Regressions) ...|.....<br>36|
|Table 3: Dynamic Equations for Investment (Difference Regressions) ...|....<br>40|
|Table 4: Unrestricted Dynamic Equations .........................|44|

|FIGRE<br>PAGE|
|---|
|Figure 1: Private investment Ratio .............................<br>18|
|Figure 2: Relative Price of Investment ...........................<br>24|
|Figure 3: Markup Behavior .<br>.................................<br>25|
|Figure 4: Composition of Government Investment ......<br>.............<br>28|
|Figure 5: Estimated Private Investment--From Engle-Granger Procedure . ...<br>42|
|Figure 6: Estimated Private Investment--From Unrest.icted Dynamic Equation 47|

# PAGES
consumer loans, are faster in processing loans and are more likely to accept loan applications through non-traditional channels.

These correlations are simply that – correlations. They do not imply causality. They show, however, that our indicators capture an important dimension of financial sector development: the limited outreach of the banking system implied by higher barriers. They suggest that barriers to banking go hand in hand with less physical access to banking offices and lower use of deposit and credit services by households and firms.

Finally, higher barriers are associated with higher financing obstacles as reported by firms. We use responses to firm-level survey questions on “Is access to financing (e.g. collateral) a problem to the operation and growth of your enterprise?” and “Is cost of financing (e.g. interest rates) a problem to the operation and growth of your enterprise?” from the Investment Climate Assessment (ICA) surveys conducted by the World Bank across 36 (access) and 37 (cost) countries. Responses to these questions are coded between zero (no obstacle) to four (very severe obstacle), with higher values thus indicating more severe financing constraints.<sup>11</sup> We take the average across all firms in a country. We find that firms report higher financing obstacles in countries where banks impose higher minimum amounts to open checking and savings accounts and charge higher fees to maintain these accounts, where banks do not accept loan applications through non-traditional channels and take longer to process SME loan applications. Finally, firms report higher financing obstacles in countries where banks demand a larger number of documents to open bank accounts. It is interesting to note that firms’ financing obstacles are more significantly correlated with barriers related to deposit services than with barriers related to payment or loan services. This suggests that firms rely to a large extent not only on credit services, but on a whole array of financial services from financial institutions.

> 11 There is a growing literature that shows the importance of financing obstacles for firm growth and financing patterns (Beck, Demirguc-Kunt and Maksimovic, 2005; Ayyagari, Demirguc-Kunt and Maksimovic, 2006).

17
and population to exactly match the spatial distribution of the economic activity and population worldwide for the year 2000.

**Table 1. Parameter values and trade costs**

|**Parameter**|**Value**|**Source / Comment**|
|---|---|---|
|**Internally calibrated**|||
|Discount factor|0.965||
|Relation between amenities andpopulation(λ)|0.32||
|Relation betweenpopulation distribution andgrowth(γ1)|0.319||
|Relation betweenpopulation distribution andgrowth(γ2)<br>|0.993||
|Parameter calibrated to match the elasticity of trade flows with<br>respect to distance of -0.93 (Υ)|0.393|The<br>elasticity<br>of<br>-0.93<br>calibrated by Head and Mayer<br>(2014)|
|Trade costs (ζ(s,r))||Estimated matrix for 17,000 x<br>17,000 location pairs|
|**Externally calibrated**<br>|||
|CES elasticity of substitution among differentiated varieties of<br>a represented consumptiongood(ρ)|4|Bernard et al. (2003)|
|Elasticity of migration flows with respect to income (Ω)|0.5|Monte et al. (2018)|
|Relation between subjective well-being and utility in the model<br>(ψ)|1.8|Deaton and Stone (2013)|
|Static elasticityofproductivityto density (α)|0.06|Carlino et al. (2007)|
|Trade elasticity (θ)|6.5|Eaton & Kortum (2002);<br>Simonovska & Waugh (2014)|
|Nonland share of production|0.8|Greenwood et al. (1997);<br>Desmet & Rappaport (2017)|
|Parameter (ξ)|0.125|Desmet & Rossi-Hansberg<br>(2015)|
|Modes of travel parameters: Rail|0.1434|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Rail|0.4302|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Major Road|0.5636|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Other Road|1.1272|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Road|1.9726|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Water/No Water|0.0779|<sup>Allen and Arkolakis (2014)</sup>|

The main source for these data is the G-Econ data set (Nordhaus et al., 2006), which provides gridded GDP and population data covering the entire globe. Desmet et al. (2018) use the Gallup World Poll 10
# **Overview**

Sugar is still an important industry for countries of the Caribbean region,<sup>1</sup> although its economic importance has diminished over time as tourism, construction, and services have increased. Sugar still accounts for more than 20 percent of the merchandise exports of Belize, Guyana, and St. Kitts & Nevis, and roughly 10 percent of employment in these countries. It occupies an average of 31 percent of the cropland in the region and more than 60 percent of the cropland in Barbados, St. Kitts & Nevis, and Trinidad & Tobago (Table 1). It provides jobs to the rural poor who often lack the skills or training to find employment in other sectors. Its importance extends beyond its direct economic contribution, as it also provides attractive vistas and sight-seeing opportunities for tourists. It is an important part of the history and culture of the region, and it is important to the environment because it protects the soil from erosion during heavy rains and occasional hurricanes. This important industry faces major challenges and an uncertain future in the Caribbean.

**Table 1. Sugar in the Caribbean**

|_Country_|_Production_<br>_thousand_<br>_tons_|_Exports_<br>_thousand_<br>_tons_|_Share of_<br>_employment_<br>_percent_|_Share of_<br>_cropland in_<br>_sugar cane_|_Share of_<br>_Merchandise_<br>_exports_|_Gross_<br>_national_<br>_income per_<br>|
|---|---|---|---|---|---|---|
||_avg._<br>_1999-2001_|_avg._<br>_1999-2001_|_avg._<br>_2000-2002_|_percent_<br>_1999-2001_|_percent_<br>_1999-2001_|_capita $s_<br>_2000_|
|Bahamas|6,200|5||35.1||14,960|
|Barbados|54,532|51,187|2.0|63.4|9.5|9,250|
|Belize|115,218|106,677|12.5|43.0|20.1|3,110|
|Dominica|400|38||1.2|||
|Dominican|439,329|199,630||27.3|9.5|2,130|
|Republic|||||||
|Guyana|293,072|271,626|6.0|22.8|24.0|860|
|Haïti|8,333|0||1.9||510|
|Jamaica|205,000|167,996|2.5|32.0|5.4|2,610|
|St. Kitts &|19,026|14,472|7.0|77.1|22.1|6,570|
|Nevis|||||||
|St. Vincent/|1,600|0||4.4||2,720|
|Grenadines|||||||
|Suriname|9,000|0||4.7||1,890|
|Trinidad &|96,068|69,475|8.0|64.0|1.0|4,930|
|Tobago<br>Region|1,241,177|881,107||31.4|3.7||

Sources: Sugar production and exports are in tons of raw sugar from FAOSTAT. The sugar sector’s share of national employment is calculated relative to the labor force using various sources of sugar sector field and factory employment and labor force figures from the World Bank’s Development Indicators. The share of cropland planted to sugar cane is computed from FAOSTAT data. The share of merchandise exports from sugar is from FAOSTAT. The gross national income per capita is from the Development Indicators, 2002, Tables 1.1 and 1.6. Blanks indicate that data was not available.

1 The Caribbean region in this report refer to the countries included in the World Bank’s regional classification which includes the following countries: Antigua and Barbuda, The Bahamas, Barbados, Belize, Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Suriname, Trinidad and Tobago, Saint Vincent and the Grenadines.

1
respectively. The results show correspondingly significant impacts on the number of days businesses are operational, the number of days they can survive on existing cash flow, the number of employees, and business investment.

On confidence and life satisfaction, our results show that youth assigned to grants or both grants and BDS report 29% higher life satisfaction than the control group (0.26 SD), 5% more confidence that their households will maintain living standards (0.12 SD), and 10% more preparedness to navigate the business and job environment (0.21 SD).

Despite these positive impacts on business and individual outcomes, our analysis does not show complete mitigation on any of the outcomes that we measure. For example, while the impacts on business sales and profits are substantial and significant, we still find a more than 50% drop on both dimensions compared to pre-pandemic averages for grant recipients. In addition, we do not detect any treatment effects of BDS alone, although the BDS curriculum was not focused on the pandemic as it was designed well before the crisis emerged as part of the original study. Furthermore, the period between training rollout and the follow-up phone surveys is extremely short for training-induced effects to materialize, so it is not surprising that we do not detect significant impacts.

Collectively, these findings show that grants are instrumental in keeping businesses afloat immediately following an aggregate crisis, while also sustaining entrepreneurial confidence and satisfaction levels. Our results on business operational frequency, investment, and employee retention suggest that grant recipients were not simply substituting business income with grant money, but rather were using the money to keep their businesses afloat during the crisis. While not fully compensating for the pandemic-related declines, these effects are likely to play an important role in recovery after the pandemic as youth who are able to get through the crisis with relatively high levels of confidence and satisfaction as well as operational businesses may ultimately be better suited to ramp up business activity quickly, re-engage with customers, and be more motivated to expand their entrepreneurship activities.

A simple back-of-the-envelope calculation shows that the extra revenue generated among pre-existing businesses fully recoups the cost of business grants in 13.5 months, and this estimate yet does not include the substantial multiplier benefits to employees and trading partners as well as the individual and psychological benefits to the youth themselves. Hence, from a cost-benefit perspective, our results suggest that business grants are a compelling policy solution to mitigating a crisis.

This paper is related to several emerging strands of research on the impact of COVID-19 on socioeconomic outcomes in emerging market economies (e.g. Akim et al., 2021; Bottan et al., 2021; Egger et al., 2021; Janssens et al., 2021; Kansiime et al., 2021; Londoño-Vélez and 4
# **Table 5. Share of Sector Value-Added by Regional Market of Russia**

(in percent)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|Sector<br>Total|
|---|---|---|---|---|---|---|---|---|---|---|---|
|MAR|10.0|2.8|2.9|15.2|8.8|31.3|8.5|3.7|14.7|2.0|100.0|
|AGR|20.0|3.2|1.3|15.4|22.7|28.2|2.8|2.1|1.5|2.7|100.0|
|AIR|7.9|2.8|2.1|11.6|10.0|22.1|32.9|3.6|5.5|1.4|100.0|
|CHM|16.3|0.4|1.8|12.4|10.9|41.6|9.1|2.4|0.1|4.9|100.0|
|CNM|21.3|2.3|1.5|9.6|13.9|25.2|18.2|4.9|1.6|1.4|100.0|
|COL|0.3|11.8|7.3|74.2|3.8|2.7|||||100.0|
|CON|12.6|1.2|1.0|9.0|17.4|25.1|22.9|4.9|4.0|1.9|100.0|
|CRU||1.0|3.7|2.2|8.4|21.7|||62.8|0.2|100.0|
|ELE|11.1|3.2|2.9|14.4|9.5|26.2|17.9|4.4|7.9|2.4|100.0|
|FIN|9.1|3.4|3.0|12.7|9.2|22.3|24.2|4.4|9.8|1.9|100.0|
|FME|23.0|0.6|1.9|10.9|4.8|40.9|2.8|1.4||13.7|100.0|
|FOO|15.2|5.6|2.2|10.6|14.6|17.1|20.6|10.8|0.3|2.8|100.0|
|GAS||0.3|0.7|0.8|2.5|4.8|||90.8|0.0|100.0|
|HEA|8.9|3.7|3.1|12.3|9.4|21.1|26.5|4.6|8.5|1.9|100.0|
|HOU|9.2|3.7|2.8|12.1|10.3|21.2|28.4|4.7|5.7|1.9|100.0|
|MWO|14.7|4.3|1.0|7.4|6.8|38.8|17.3|7.2|1.1|1.4|100.0|
|NFM|1.3|4.1|4.7|57.9|2.5|23.6|3.6|2.2||0.1|100.0|
|OIL|12.1|4.7|1.5|17.3|10.0|38.9|4.4|7.2|3.9||100.0|
|OTH|12.3|3.2|3.6|14.0|7.8|29.3|10.0|5.2|11.7|3.0|100.0|
|OTI|30.8|1.3|1.6|5.7|9.1|20.9|22.5|5.8|0.2|2.1|100.0|
|PIP|7.8|2.7|2.5|10.5|8.9|28.9|6.4|3.6|27.7|1.0|100.0|
|PST|9.3|3.5|2.8|12.2|10.1|22.0|23.9|4.5|9.9|1.9|100.0|
|RLW|11.3|3.3|3.0|14.6|9.9|25.9|16.6|4.5|7.8|3.0|100.0|
|SCI|10.1|3.6|2.8|11.9|8.9|26.0|18.9|4.7|11.3|1.7|100.0|
|TMS|9.1|3.3|2.7|12.1|9.9|22.4|25.3|4.3|8.9|1.8|100.0|
|TPP|9.3|5.9|20.5|17.8|3.5|17.6|9.2|9.8|0.8|5.7|100.0|
|TRD|8.5|3.3|2.8|12.5|9.3|22.3|19.0|4.2|16.1|2.0|100.0|
|TRK|9.3|3.2|2.8|12.5|10.4|22.6|21.1|4.3|11.8|2.0|100.0|
|TRO|9.2|3.1|2.7|11.7|10.5|23.0|21.9|4.2|11.8|1.9|100.0|
|CLI|9.0|3.7|2.8|11.7|10.5|20.4|30.6|4.7|4.6|1.9|100.0|
|Total|329.2|99.4|96.2|453.3|284.4|734.1|465.5|128.4|340.9|68.7|3000.0|

Source: Regions of Russia and authors calculations.

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