rafmacalaba/datause-encoder-data huggingface dataset:
33 for male wage workers with secondary schooling, they earn approximately 20 percent more than those with primary education. Finally, a male worker with higher education earns about 37 percent more than what he would have earned if he had only been to secondary school. Overall, a similar trend is observed for women, although the orders of magnitude differ and the impact of secondary schooling appears to be weak. Female workers with primary education earn approximately 15 percent more than those having received no schooling. But those with secondary education only earn 3 percent more than those having attended to primary school. The impact of higher education on earnings is similar to that observed for men: female wage earners with higher education earn 34 percent more than those with secondary education.

**Figure 15. Wage Earners - Kernel Density in the Formal and Informal Sectors**<sup>**19**</sup> **(in thousands of MRO)**

<!-- Start of picture text --> 4.312 21 50 100<br>Monthly Wages<br>Formal Sector<br>Informal Sector<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text --> Source: EPCV 2004 _The 2004 minimum wage, which stood at 4,312 MRO, was too low to be relevant._ Figure 15 displays the kernel density distribution of monthly wages for wage earners. It distinguishes the formal sector, where minimum wage regulations are normally applicable, from the informal sector, where they are not. It should be noted here that 70 percent of all wage workers are employed in the informal sector (Table 5). As illustrated by Figure 15, wages in the informal sector are notably lower than that in the formal sector. Table 22 also shows that the coefficients on the informal variable is significant and negative in the female and male wage regressions, thus providing evidence that wages in the informal sector are lower than those in the formal sector. According to the wage distribution for formal sector employees exhibited in Figure 15, few people report earnings close to the 2004 minimum wage. Indeed, less than 1 percent of all formal wage earners were paid below 4,312 MRO in 2004. Even in 19 A wage earner is considered as working in the informal sector if she does not benefit from social security.
et al., 2009), though in general results indicate that many firms rely on both sources. Our analysis is most similar to others for China that examine how political connections and business and personal characteristics affect the sources of financing used by owners of small private firms (Tsai, 2002; Zhang, 2008). Zhang (2008), for instance, uses survey evidence to examine reliance on formal versus informal financing in Chengdu, one of China’s most important inland cities. He finds that proxies for reputation and relationships play crucial roles in explaining firms’ usage of _formal_ finance, though those factors are typically associated with usage of informal finance. Because our survey is more detailed, we are better able to test whether proxies for social networks and other household characteristics related to reputation and relationships affect usage of both formal and informal finance. Another difference between that study and ours is that Zhang (2008) focuses on small private firms whereas the household is our unit of observation, though we admit that household and small business finance are often deeply intertwined. However, our key advantage is that we rely on nationally representative data for China enabling us to compare financial usage patterns across regions and to examine usage by different types of households within the same types of environments (e.g., rural versus urban).

Our investigation yields a number of insights about how household finance works in China, many of them similar to those in other developing countries, but others that are distinctly Chinese. As in other contexts, informal finance proves to be of critical importance for our understanding of household finance, especially in rural areas. For example, we find that a larger social network (as measured by the number of siblings of the household head and his/her spouse) is positively associated with access to informal finance. Informal social networks can facilitate trade where legal enforcement of property rights is weak or uncertain (Grief, 1997; McMillan, 1997). For example, social networks based on family ties have been shown to support extension of credit in Thailand (Townsend, 1995) and Vietnam (McMillan and Woodruff, 1999). Given the nascent state of formal credit markets in many of the locales that we study, informal credit 3
I.3), led by declines in sugar, coffee and cocoa. In contrast, in 1977-78 agricultural exports accounted for about 70% of the value of total exports, with coffee and cocoa accounting for about half of agricultural exports (World Bank, 1981).

There is evidence of a slowdown in overall economic growth in Dominican Republic starting in 2001 (Central Bank of Dominican Republic). This decline is due to a combination of internal policy changes such as higher taxes and tighter monetary conditions, decreases in agricultural production due to a drought and decreases in international prices for major agricultural exports, increases in energy imports, along with lower overall demand due to the global recession. Tourism revenue fell dramatically since September 11, 2001. In the first trimester of 2002, economic growth was negative in agriculture (-7.8%), tourism (-11.2%) and duty free zones (- 7.5%), pointing to a steep slump in these sectors.

**<u>Table I.1:  Dominican Republic Gross Domestic Product  1996-2000</u>**

||In mil|lions of|RD$,1|970 price<br>|s<br>|Annua<br>|lGrowt<br>|h Rates <br>|(%)<br>|
|---|---|---|---|---|---|---|---|---|---|
||1996|1997|1998|1999*|2000*|1997/<br>1996|1998/<br>1997|1999/<br>1998|2000/<br>1999|
|TOTAL<br>ofwhich:|4907|5314|5701|6156|6633|8.3|7.3|8.0|7.8|
|Agriculture|630|650|657|702|737|3.3|1.1|6.7|5.0|
|-Crops|314|325|321|336|344|3.6|-1.5|4.9|2.4|
|- Livestock|292|301|305|332|361|3.1|1.5|8.8|8.6|
|- Forestry,Fishery|24|24|32|33|31|1.0|31.0|4.5|-4.5|
|Minerals|129|133|112|110|120|3.1|-15.9|-1.5|9.2|
|Manufacturing|858|922|974|1037|1130|7.5|5.7|6.4|9.0|
|-Sugar|48|51|40|30|35|7.8|-22.5|-24.5|16.7|
|-Other|640|683|732|809|881|6.6|7.3|10.5|8.9|
|- FreeZones**|170|188|202|197|213|10.6|7.6|-2.5|8.0|
|Other ***|3290|3609|3958|4307|4646|9.7|9.7|8.8|7.9|

Notes: * Data for 2000, 2001, 2002 are preliminary.

** Value-added only.

*** Other includes: construction, wholesale and retail trade, hotels, bars and restaurants, transport, communications, utilities, financial services, real estate, other services. Source: Central Bank of Dominican Republic Bulletin Trimestral Jan-March (2001).

**<u>Table I.2: Dominican Republic Shares of Gross Domestic Product 1996-2000</u>**

||Percen<br>|t(%) S<br>|hares<br>|||
|---|---|---|---|---|---|
||1996|1997|1998|1999*|2000*|
|TOTAL,of which:|100|100|100|100|100|
|Agriculture|12.8|12.2|11.5|11.4|11.1|
|- Crops|6.4|6.1|5.6|5.5|5.2|
|- Livestock|5.95|5.7|5.4|5.4|5.4|
|- Forestry,Fishery|0.5|0.5|0.6|0.5|0.5|
|Minerals|2.6|2.5|2.0|1.8|1.8|
|Manufacturing|17.5|17.3|17.1|16.8|17.0|
|- Sugar Refining|1.0|1.0|0.7|0.5|0.5|
|- Other|13.0|12.8|12.8|13.1|13.3|
|- Free Zones**|3.5|3.5|3.6|3.2|3.2|
|Other|67.1|67.9|69.4|70.0|70.0|

Notes: * Data for 2000 are preliminary.

** Value-added only (e.g., wages and salaries for workers in free zone industries).

*** Other include (2000 shares): construction (13%), wholesale and retail trade (13%), hotels, bars and restaurants (6%), transport (7%), communications (5.5%), utilities (2%), financial services (4%), real estate (5%), other services (7%). Source: Central Bank of Dominican Republic (2002).

9
segments, such as latent gazelles and survivalist middle-size enterprises, making the establishment of common, effective programs across groups difficult. Whereas all such enterprises require assistance through interventions, the latent gazelles are more likely than survivalists to need significantly varied types of assistance.

The empirical literature highlights many features of the heterogeneity in the informal sector, including involuntary wage labor, queuing for formal employment, and voluntary selfemployment. These are similar in many ways to the characteristics of the entrepreneurial small firm sector in developed countries (Grimm, Lay, Roubaud and Vaillant 2011). Fields (2004) differentiates between rationed upper-tier activities, which are based on methods of production and types of jobs that are similar to those of formal firms, and a lower tier, which is concentrated on easy entry activities and corresponding to the residual subsistence sector in the dualistic view (Bosch and Maloney 2010). However, Cunningham and Maloney (2001), using data on Mexico, argue that there is strong heterogeneity among small firms that is of the same nature as the heterogeneity in developed countries, whereby small firms that have reached their optimal longrun size coexist with profitable starting firms and start-up firms that will fail.

The key policy implication of the existence of distinct groups of entrepreneurs is that these groups call for distinct policy approaches: a directed business development method for top performers and a more widespread program to help improve employability and reduce poverty among the survivalists (Berner, Gomez, and Knorringa 2012).

Using a sample of informal entrepreneurs in seven West African countries, Grimm, Knorringa, and Lay (2012) in addition to the groups of top performers and survivalists, empirically identify a third segment of informal entrepreneurs, constrained gazelles. These entrepreneurs show characteristics—managerial abilities and motivation—similar to those of growth-oriented entrepreneurs. The authors mostly find a larger share of constrained gazelles in more dynamic economies. They also show that the size of the group of constrained gazelles relative to the group of survivalists is correlated with structural characteristics, such as the urbanization rate, the share of agriculture in the economy, and the size of the public and formal private sectors of the respective countries.

Gindling and Newhouse (2014) investigate heterogeneity among the self-employed, using the international Income Distribution Database (I2D2) on almost 100 developing countries. Unlike Grimm, Knorringa and Lay (2012), this paper uses two different measures of success (a) whether the self-employed worker is an employer and (b) whether the self-employed worker belongs to a family with per capita consumption above the $2 per day poverty line. The paper relies on the methodology developed in Grimm, Knorringa and Lay (2012) to identify self-employed with a high potential to become successful. Results show that one-third of the unsuccessful entrepreneurs have the potential to become successful.

4
# 2. Methodological framework

Stress testing is a useful (off-site) tool to analyze the resilience of banking systems to adverse events and can complement other supervisory information, tools, and actions. A reverse stress test is a targeted exercise that quantifies how much current viability conditions should change for a bank to hit a pre-determined adverse outcome (Basel Committee on Banking Supervision, 2009). Reverse stress tests can be particularly useful in countries where supervisory capacity and enforcement are weak, resulting in a lack of complete and reliable data which prevents the application of more sophisticated analyses, or when the effects of an ongoing crisis, such as COVID, obscure data reliability.

In this paper, we extend the reverse stress test framework by computing a banking system resilience measure from reverse stress test results for individual banks. Our approach hypothesizes an increase in the nonperforming loans (NPLs) ratio for each bank to identify the point at which it “breaks”, the Break Point (BP). Specifically, because an increase in NPLs is accompanied by new provisions for loan losses, the BP is the NPL level at which the new provisions deplete the bank’s buffers above minimum capital requirements. For each bank, we then compute the “Distance from Break Point” (DBP) as the difference between the BP and the actual NPL ratio.

Next, we aggregate this bank-level information at the country level by sorting banks according to their DBP to identify the group of most fragile banks (i.e., the lowest DBP) that collectively represent at least 20 percent of total banking system assets. For banks with the same DBP value, we select the largest banks first. The 20 percent threshold is commonly used to identify a systemic banking crisis (Laeven & Valencia, 2013).<sup>2</sup> We define this set of vulnerable banks as “Banks at Risk” which could consist of a few large institutions or several smaller ones. In a final step, we consolidate the balance sheets of the “Banks at Risk” and calculate their consolidated Break Point (CBP) to arrive at the Consolidated Distance from Break Point (CDBP).

> 2 The threshold could be set at different values, depending on the granular composition of domestic banking sectors. For example, in economies where the banking sector counts hundreds of banks, the threshold could be set at a higher value to reflect the fact that distress may be related to a large number of small banks.

3
<!-- Start of picture text --> 600<br>500<br>400<br>Mean<br>300 Ratio<br>Regression<br>Linear<br>EGRA LLECE PASEC PISA SACMEQ<br>SUPPLEMENT FIGURE V<br>Comparison of Scores Across Linking Methods by Test<br><!-- End of picture text --> _Notes:_ We compute Harmonized Learning Outcomes scores using multiple methods including regression, linear, mean and ratio linking functions. This figure compares scores using a density plot. We only compute scores using the regression method for LLECE, EGRA and PISA since SACMEQ and PASEC only have a single country used to make score comparisons.

# _C. Potential Limitations_

A potential limitation is the representativeness of the data of the total stock of cognitive skills in a given country. While the tests used are nationally representative, they are conducted at the school. To this end, learning data might be affected by enrollment patterns, and we advise users of the data to analyze learning outcomes alongside enrollment trends. For example, as marginal students enter the schooling system, average test scores might be driven by selection rather than true learning progress. While this is a potential concern, it is mitigated for a few reasons. First, primary enrollment rates are relatively high, reaching 90 percent on average, and above 75 percent even in the furthest behind regions, such as Sub-Saharan Africa. Second, the direction of the bias is likely to yield a conservative upper bound of learning in a given country. If all students enrolled, the average test score would be even lower, since the marginal students would pull the average down. Since most countries at the bottom of the distribution of learning are also those with relatively lower enrollments, it is unlikely this will alter substantive conclusions – the lowest preforming countries will be revealed to be even lower performing. In addition, data at the primary level should be largely unaffected, since at this level students are being taught basic skills, such as reading “the name of the dog is Puppy.” Thus, even if marginal students enter the system, these students should still be expected to attain basic skills by the time they are tested in later primary school grades. Of note, in future work, we aim to include household-based learning data to sign and quantify the 31
Employers and workers would probably agree whether or not the labor market is flexible, but they could disagree on the assessment of labor market flexibility. While employers would probably unambiguously prefer more flexible labor markets, some workers-especially the insiders, i.e., those with secure jobs-may prefer higher degree of employment protection. However, those workers who are unemployed-the outsiders-could prefer to look for jobs in a dynamic labor market, where their chances of finding work are better, and unemployment spells are shorter.

From the view point of macroeconomic performance flexible labor markets tend deliver better outcomes. This point was forcefully made in the influential OECD Jobs Study (1994), and since than has been further supported by additional empirical evidence. For example, Boeri et al. (2000) found that countries with tight employment protection legislation tend to have lower employment rates. Garibaldi and Mauro (1999) provide evidence that flexible employment protection legislation is associated with high job creation. Di Tella and McCullloch (1998) show, based on the assessment of labor market flexibility by employers, that a more flexible labor market leads to lower unemployment rates and to a lower proportion of long-term unemployed in the unemployment pool. Moreover, rigid labor markets produce "jobless recoveries," introduce unemployment persistence, and reduce the country's vulnerability to external shocks. Recent work by Blanchard (2000) and Bertola et al. (2001) provides further theoretical and empirical support to the proposition that flexible labor market institutions reduce unemployment, particularly of long-term nature. This is thanks to greater wage flexibility and higher labor turnover associated with less regulated labor markets. Accordingly, it is real and relative wages, rather than employment, that bear the brunt of adjustment to adverse shocks. In addition, high labor turnover decreases the average time a worker spends in unemployment. Thus, flexible labor-market institutions positively influence the speed at which an economy can return to its equilibrium rate of unemployment following a negative shock. By contrast, it takes a regulated labor market much longer to get back to its equilibrium unemployment rate after a shock.

Measuring labor market flexibility is not easy for three reasons. First, there is no single indicator that would capture all dimensions of flexibility. As a result, different measures of flexibility usually produce different and sometimes inconsistent results, 3
# Table 2: **Who are America’s Stars?**

This table reports estimates from the following regression model: _ROIC Starijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _ijt_ + _β_ 2 _× Log_ ( _Age_ ) _it_ + _β_ 3 _× HHIjt_ + _β_ 4 _× Market shareijt_ + _β_ 5 _× Markupsijt_ + _φj_ + _γt_ + _εijt_ ROIC Star is a dummy variable that takes the value 1 if the firm _i_ ’s ROIC is above the 90th percentile of ROIC across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is the number of years the firm is in the database. HHI is Herfindahl Index of market share in each industry in each year. Markups are estimated using operating expenses as a variable input of production. Market Share is the ratio of the firm _i_ ’s sales to total industry _j_ ’s sales in a particular year. Columns (1)-(5) include the full sample; column (6) is manufacturing sub-sample, column (7) is large firm sample (Real value of assets is _≥_ USD 200million) and column (8) is sample of young firms (Age _≤_ 5 years). All regressions are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (<sup>_∗∗∗_</sup> ) _,_ (<sup>_∗∗_</sup> ) _,_ (<sup>_∗_</sup> ) denote statistical significance at 1%, 5%, and 10% levels respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||ROIC Star|ROIC Star|ROIC Star|ROIC Star|ROIC Star|ROIC Star|ROIC Star|ROIC Star|
||Full Sample|Full Sample|Full Sample|Full Sample|Full Sample|Manufac|Large Firms|Young Firms|
|Log(Assets)|0.013***|0.013***|0.004***|0.014***|0.002|0.000|0.001|0.004|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.003)|
|Log(Age)|-0.037***|-0.037***|-0.030***|-0.038***|-0.032***|-0.034***|-0.028***|-0.175***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.005)|(0.005)|(0.036)|
|HHI||-0.022|||-0.011|-0.026|0.056|-0.097|
|||(0.031)|||(0.038)|(0.087)|(0.056)|(0.089)|
|Markups|||0.280***||0.287***|0.348***|0.325***|0.301***|
||||(0.015)||(0.016)|(0.024)|(0.027)|(0.023)|
|Market Share||||-0.025|0.248***|0.413**|0.374***|0.297|
|||||(0.084)|(0.096)|(0.186)|(0.119)|(0.251)|
|N|97241|97241|78668|96237|77875|41100|39539|6703|
|Adj. R-sq|0.059|0.059|0.081|0.058|0.082|0.061|0.120|0.089|
|Fixed Efects|——|——————|———————|———— Industr|y, Year ———|———————|———————|——|
earthquake. Journal of Transportation and Statistics, 1:21-36.

Haimes, Y., and Jiang, P., 2001. Leontief-based model of risk in complex interconnected infrastructures. Journal of Infrastructure Systems, 7, 1-12.

Haimes, Y., Horowitz, B., Lambert, J., Santos, J., Lian, C., and Crowther, K., 2005. Inoperability input-output model for interdependent infrastructure sectors. I: Theory and methodology. Journal of Infrastructure Systems, 11, 67-79.

Hallegatte, S., 2008. An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina”, Risk Analysis 28, 779-799.

Hallegatte, S., M. Ghil, 2008: Natural disasters impacting a Macroeconomic Model with Endogenous Dynamics, Ecological Economics, 68(1-2), pp. 582-592 Hallegatte, S., J.-C. Hourcade, P. Dumas, 2007, Why economic dynamics matter in assessing climate change damages : illustration on extreme events, Ecological Economics, volume 62(2), 330-340 Hallegatte S., Przyluski, V., 2010, The economics of natural disasters: concepts and methods, World Bank Policy Research Working Paper 5507, The World Bank, Washington D.C.

Hallegatte S., N. Ranger, O. Mestre, P. Dumas, J. Corfee-Morlot, C. Herweijer, R. Muir Wood, 2011, Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen, Climatic Change, 104(1), 113-137 Henriet, F., and S. Hallegatte, 2008, Assessing the consequences of natural disasters on production networks: a disaggregated approach, FEEM Working Paper No. 100.2008 Henriet, F., S. Hallegatte, L. Tabourier, 2012. Firm-network characteristics and economic robust44