rafmacalaba/datause-encoder-data huggingface dataset:
# 2 Methodology

## 2.1 Trends in Non-Financial Corporate Debt in EMDEs

Non-financial corporate sector debt in many EMDEs has risen significantly since the global financial crisis<sup>3</sup> (Figure 1.1). Moreover, given unprecedented accommodative monetary policies in the developed world, EMDE corporates have raised significant volumes of financing from global capital markets, mostly denominated in foreign currencies and typically not fully (naturally) hedged. This has had the effect of changing the composition of corporate debt in EMDEs away from bank credit and towards debt securities, with bond issuance from EMDE corporates more than doubling since 2010 (Figure 1.2).<sup>4</sup> With the sharp decline in commodity prices since 2014 and the downward revisions to growth prospects across EMDEs, both firms’ profitability and debt service capacity has trended down in those countries. Consequently, financial risks from the EMDE non-financial corporate sector have emerged, which have been reflected by the widening of corporate Credit Default Swaps (CDS) spreads across regions, the rise of non-performing loans in the EMDE banking sector, and the significant declining of corporate bond issuance since 2015.

> 3 See World Bank (2016), IMF (2015), IIF (2015), BIS (2015) and Geneva Report (2015).

> 4 Becker and Ivanshina (2014), Cortina, Didier and Schmukler (2016). For details on the evolution and financial threats of corporate indebtedness, see Acharya et al (2015).

4
_We underscore the critical importance of rejecting protectionism and not turning inward in times of financial uncertainty. In this regard, within the next 12 months, we will refrain from raising new barriers to investment or to trade in goods and services, imposing new export restrictions, or implementing World Trade Organization (WTO) inconsistent measures to stimulate exports._

- G20 Declaration, November 15, 2008

# **1. Introduction**

The imposition of the Smoot-Hawley tariffs during the early days of the Great Depression has since established a widespread presumption that import tariffs and other forms of trade protection rise during periods of macroeconomic weakness. During the Great Recession, the fear of new import restrictions led to pre-emptive statements like the G20 Declaration of November 2008.

This paper uses quarterly data for the United States, European Union, Australia, Canada, and South Korea to estimate the impact of macroeconomic shocks on import protection policies over 1988-2010. We find evidence of a robust countercyclical trade policy response in the pre-Great Recession period of 1988:Q1-2008:Q3. For example, a one standard deviation increase in the change in the domestic unemployment rate is associated with a 52 percent increase in the number of imported products over which an economy initiates new temporary trade barrier investigations in the following quarter. Other macroeconomic factors also have important effects on trade policy; a one standard deviation appreciation in the bilateral real exchange rate leads to a 33 percent increase in import protection while a one standard deviation _decrease_ in the growth rate of real GDP in a foreign trading partner results in a 60 percent increase. Finally, when we extend our analysis through 2010:Q4 so as to analyze the Great Recession, one noteworthy change is to the relationship between import protection and a _trading partner’s_ economic growth. During the Great Recession, governments refrained from imposing new temporary trade barriers against trading partners experiencing their own weak or negative economic growth.

Our evidence paints a complex picture of the role that macroeconomic shocks play in determining trade policy for countries bound by the WTO. In particular, our results indicate the empirical relationships between macroeconomic shocks and trade policy are changing over time and across trading partners in ways that ultimately impact the worldwide distribution of import protection beyond that which takes place through tariff liberalization negotiations and trade agreements. To document this phenomenon, our approach is to analyze determinants of _temporary trade barriers_ 1
levels after a v-shape recovery early in the crisis. We include the 30-day mobility average in our estimations to correct for these cross-country differences in the potential severity of the pandemic measures adopted by public authorities at the time of the COV-BPS. Notice the mobility indicator resembles a country fixed effect in our linear regressions because most of the variation occurs across countries, with smaller differences across firms within a country when businesses were interviewed at different dates.

One of the salient features of the COVID-19 pandemic has been the heterogeneous impact across industries. We construct a second indicator of potential exposure to the shock for narrowly defined sectors using publicly available aggregate data from the Small Business Pulse Survey in the U.S. (SBPS-US; ( _20_ )). We first compute the share of firms in each 3-digit NAICS sector reporting in the SBPS-US that they have experienced a “large overall negative effect from the pandemic” averaging the weekly averages published by the U.S. Census Bureau for the period April-June 2020.<sup>8</sup> We sort sectors into two categories for potential exposure, low and high, depending on whether the average fraction of firms reporting a large overall negative effect is below or above the U.S. national average (which we obtain for the period April-June 2020 also averaging the weekly averages publicly available). The list of less/more exposed sectors is available in appendix A (see tables A2 and A3).<sup>9</sup> In our specifications we include the interaction of this binary variable with fixed effects for broad sector and country.

# **2.4 Descriptive statistics**

Table 1 shows the characteristics of our sample across quintiles of the distribution of technology sophistication pre-pandemic (Q1-Q5). Our sample covers around 1,000 businesses. As documented in ( _17_ ), the right tail of the distribution of technology sophistication in our data is characterized by relatively larger firms; a higher management z-score; and managers compara-

> 8The exact wording of the question is “Overall, how has this business been affected by the COVID-19 pandemic? Select only one: large negative effect; moderate negative effect; little or no effect; moderate positive effect; large positive effect.” The SBPS-US is implemented weekly, and the U.S. Census Bureau makes publicly available the estimated fraction of businesses in each response for each question of the survey every week. The averages are available at the national level and for each 3-digit NAICS sector. Estimates are not available for July 2020. 9We matched the 3-digit NAICS list with the ISIC classification in our FAT data.

8
_4_ ) we find that unskilled labor in exports has a negative but statistically insignificant effect on backward and forward integration at the global level. However, in SSA, these negative effects are statistically significant for both forward and backward GVC participation. Thus, when a country is well endowed with skilled labor which could help produce sophisticated goods, this significantly affects the quality of its production and therefore its backward and forward integration. But for a country that is endowed with a high percentage of unskilled labor, its productive capabilities are limited especially for sophisticated production. This restricts the country’s ability to participate in forward and backward GVC.

**Table 3: Fixed effects panel estimation results – Baseline specification**

|VARIABLES|(1)<br>DVX LCX<br>Global|(2)<br>DVX<br>LCX SSA|(3)<br>FVA LCX<br>Global|(4)<br>FVA LCX<br>SSA<br>GVC|(5)<br>Participation LCX<br>Global|(6)<br>GVC Participation LCX<br>SSA|
|---|---|---|---|---|---|---|
|log_distance|-1.516***|2.879***|-2.293***|4.757***|-0.189***|0.0893|
||(0.163)|(0.633)|(0.170)|(0.696)|(0.0186)|(0.0890)|
|log_GDP|1.034***<br>(0.0189)|1.074***<br>(0.0678)|0.949***<br>(0.0217)|0.896***<br>(0.0681)|-0.0228***<br>(0.00260)|-0.0269***<br>(0.00932)|
|LACEX_sk|2.923*|20.16***|5.318***|24.24*|0.489|6.746**|
||(1.548)|(7.143)|(1.934)|(13.41)|(0.392)|(2.959)|
|LACEX_usk|-0.754|-4.428***|-0.791|-4.548*|-0.0975|-1.309**|
||(0.458)|(1.591)|(0.494)|(2.516)|(0.101)|(0.560)|
|Constant|-2.517|-43.51***|5.958***|-57.39***|2.872***|0.303|
||(1.675)|(5.713)|(1.756)|(6.450)|(0.195)|(0.825)|
|Observations|2,727|508|2,727|508|2,727|508|
|Number of panelid|982|205|982|205|982|205|
|Sector FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|
||||Robust standar<br>*** p<0.01,|d errors in parentheses<br>** p<0.05, * p<0.1|||

We also ran the regression using the GVC participation ratio as defined in _equation (6)_ , and all specification results (Table 3-5) are statistically insignificant for both skilled and unskilled labor in the global sample. However, in SSA, the positive impacts of skilled labor on overall GVC participation are large and significant while unskilled labor also exhibits significant negative effects (see Table 3-5). These results again confirm that skilled labor is a very important determinant for the integration of SSA countries in manufacturing GVCs, and its impact is larger in SSA countries than in other parts of the world given the region’s low human capital endowment.

To account for initial human capital endowment, fragility and resource endowments, we include these indicators in our regressions in _Tables 4_ and _5_ . The effects of skilled labor on backward GVC 25
Mead, Donald, C. and Carl Liedholm. 1997. “The dynamics of micro and small enterprises in developing countries.” _World Development,_ 26(1): 61-74.

McKenzie, David J & Christopher Woodruff, 2006. "Do Entry Costs Provide an Empirical Basis for Poverty Traps? Evidence from Mexican Microenterprises," _Economic Development and Cultural Change_ 55(1): 3-42.

World Bank. 2007. _Tanzania Pilot Rural Investment Climate Assessment: Stimulating Non-farm Microenterprise Growth._ Washington DC: World Bank.

-23-
**Figure 6: Employment transition matrices for men by employment status, formality and sectors between 2012 and 2018, by age groups**

|||Men|||
|---|---|---|---|---|
||Private informal|**76**|10<br>6<br>5<br>3|56% (63%)|
||Private formal|48|**41**<br>7<br>3<br>1|11% (16%)|
|20-29|Public formal|14<br>14|**70**<br>1<br>1|7% (11%)|
||Unemployed|50|22<br>9<br>**8**<br>11|6% (5%)|
||Out of labor force|58|16<br>10<br>6<br>**10**|19% (5%)|
||Private infor<br>|mal<br>Private formal<br>|Public formal<br>Un-employed<br><br><br><br>|OLF<br>|
||Private informal|**77**|10<br>6<br>4<br>3|54% (53%)|
||Private formal|39|**40**<br>16<br>3<br>2|18% (16%)|
|30-39|Public formal|7<br>11|**80**<br>2|24% (26%)|
||Unemployed|67|8<br>18<br>**5**<br>2|3% (3%)|
||Out of labor force|46|13<br>14<br>5<br>**22**|1% (2%)|
||Private infor<br>|mal<br>Private formal|Public formal<br>Un-employed|OLF<br>|
||Private informal|**73**|14<br>3<br>3<br>7|45% (43%)|
||Private formal|43|**39**<br>11<br>3<br>4|16% (15%)|
|40-49|Public formal|7<br>5|**86**<br>2|36% (34%)|
||Unemployed|56|29<br>**5**<br>10|1% (2%)|
||Out of labor force|36<br>6|11<br>5<br>**42**|2% (5%)|
||Private infor<br>|mal<br>Private formal|Public formal<br>Un-employed|OLF|
||Private informal|**71**|10<br>3<br>1<br>15|33% (34%)|
||Private formal|43|**31**<br>9<br>1<br>16|14% (9%)|
|50-59|Public formal|5<br>2|**84**<br>1<br>8|45% (40%)|
||Unemployed|41|25<br>34|1% (1%)|
||Out of labor force|30<br>5<br>8|**57**|7% (15%)|
||Private info|rmal<br>Private formal|Public formal<br>Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to men who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by age groups. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

14
1

# **1.  Introduction**

Education policy-makers have long debated the relative benefits of social promotion versus grade retention. Social promotion is the policy of promoting students from one grade to the next, irrespective of their performance. Advocates claim that even low-performing students would benefit from staying with and learning from their peer group, whereas grade retention harms students’ selfesteem, does not improve their performance, and increases their likelihood of dropping out of school (Shepard and Smith 1989). To varying degrees, social promotion is practiced in countries such as Denmark, Japan, Korea, Norway, Sweden and many states in the United States. Countries such as France and most developing countries use grade retention extensively as a means to address student performance (Bonvin 2003): the practice of holding back underperforming students in the same grade until they attain minimum grade-appropriate skills. Proponents of grade retention believe that waiting until students have attained mastery of the curriculum will better prepare them for more advanced work at the higher grades whereas social promotion will doom them to falling ever farther behind their classmates.

There is mixed evidence regarding the relative merits of the two policies. Reviewing a large number of studies of U. S. schools, Holmes (1989) and Jimerson (2001) conclude that retained children perform about one-third of a standard deviation below promoted students on achievement measures in subsequent school years.<sup>1</sup> The adverse effect of grade retention appears larger on test scores than on emotional or behavioral outcomes (Jimerson 2001). Using a regression discontinuity design to get a better match between the retained students and comparison groups, Roderick and Nagaoka (2005) find that third-grade students who were retained under Chicago’s high-stakes testing policy do not yield higher language test scores two years after the retention, and that retained sixth graders had lower achievement growth. Focusing instead on dropout rates, Grissom and Shepard (1989) find that grade retention increased dropout rates by as much as 20-30 percent, even after controlling for achievement, socioeconomic status and gender, although Eide and Showalter (2001)
Japan: Savings-Investment Balance, Average Percentage K#i.io to GNP

||Ne|t Savings|||
|---|---|---|---|---|
|Period (PY)|Private<br>Sector|Covernment<br>Sector|Trade<br>Surplus|Discrepancy|
|1960-74-1.17|4<br>13|+0.36|+0.06||
|1975-80+4.23|-4,15|+0.32|-0.23||
|1981-85+4.22|-2.80|+1.90|-0.48||

(Source: Lincoln, 1988, pp. 72-77) In short, tntil early 1970s, a persistent negative net saving in the private sector was marginally more than offset by a positive net saving in the government sector with the current account running a marginal surplus. In the second half of the 1970s, however, the private sector balance turned to a substantial surplus (because of a sharp fall in the investment level), while the government sector balance turned to a deficit of almost equal proportion, with the current account balance remaining marginally positive. In the 1980s, the government deficit was substantially reduced while the private sector surplus remained high. At the same time, the current account surplus expanded dramatically. One important part of the story here is the liberalization of external financial transactions since 1978, which has permitted a growth in capital outflows. 1/ 6. The above "explanation", however, is not to be interpreted to meaI that the "oversaving" has caused the current account balance surplus. The relationship is not a unidirectional, causal relationship. The identities only indicate ex post accounting relationships between the internal and external 1/ See Lincoln (1988).
_20_ _Estimates and Projections of Mortality by Cause_ Table 10. rredlcted and reported percentage distributions of deaths by cause for reporting countries grouped by region, 1985

||_Indust_|_rial (24Y_|_Nonm_|_arket (8)._|_LA_|_C (26)_|_As_|_ia (7)_|
|---|---|---|---|---|---|---|---|---|
|_Cause_|<br>_Predicte_|<br>_d Reporte_|<br>_d Predicte_|<br>_d Repored_|<br>_Predicte_|<br>_d Reporte_|<br>_d Predicte_|<br>_d Reported_|
|Percent of all causes|||||||||
|Infections<br>|8<br>|8<br>|20<br>|11<br>|23<br>|20<br>|31<br>|32|
|Neoplasms<br>|17<br>|22|12|12|11|11|9|9|
|Circulatory|51|51|38|50|31|32|25|20|
|Pregnancy|0|0|0|C|0|0|1|1|
|Perinatal|1|1|2|2|7|7|6|8|
|Injury|6|6|6|6|7|10|6|11|
|<br>Other|17|13|22|19|21|b|22|19|
|Percent of infections|||||||||
|Diarrhea|7|1|16|3|22|28|25|9|
|Tuberculosis<br>|4|2|8|5|6|6|5|8|
|Acute respiratory|42|49|43|30|41|31|43|33|
|Measles|1|0|2|0|2|1|3|1|
|COPD|33|45|19|17|16|b|12|16|
|Polio|0|0|0|0|0|b|0|0|
|Yelow fever|0|0|0|0|0|0|0|0|
|Malaria|0|0|1|0|1|0|1|2|
|Schistosomiasis<br>|0<br>|0<br>|0<br>|0<br>|0<br>|0<br>|0<br>|0|
|Entestinalparasites|0|0|1|0|1|1|1|1|
|Other Infections|12|4|10|44|12|17|9|31|
|Percent of circulatory|||||||||
|Ischemnlc<br>heart|30|38|29|43|29|28|27|17|
|Cerebrovascular|24|24|24|28|23|19|23|42|
|Other cardiovascular|<br>34|29|34|22|34|37|34|23|
|Diabetes|6|3|6|1|6|8|6|3|
|Certain degenerative|<br>7|6|7|5|9|13|9|15|
|<br>Percent of other causes|||||||||
|Mentaldisorders|2|4|1|1|1|c|1|1|
|Oralhealth diseases|0|0|0|0|0|c|0|0|
|hicronutrient|3|2|3|3|3|c|3|2|
|Malnutrition<br>|1|1|2|2|2|c|3|5|
|Unspecified|94|93|94|94|94|c|93|91|

unumber of countries included in the comparison. "With no reported data for this category, it was assumed to be equal to the predicted value to permit comparisons for other categories.

'No data.

diseases, they range from 8 to 24; and for specific causes under other causes, they range from 1 to 3. Generally, the distributions for nonmarket economieF are more poorly predicted than those for other regions.

fwor particular causes, predicted percentages are higher than reported percentages in some regions, lower in others. For circulatory system diseases, for instance, predictions are too high in Asia, too low in nonmarket economies. One might draw implications from such comparisons about the possible directions of bias in the estimates for nonreporting countries, but only if one assumes similar mortality patterns in nonreporting as in reporting countries.
Policy Research Working Paper 10850

# **Abstract**

This paper explores the potential correlations between financial development and state fragility, using a sample of 137 countries observed over the period from 1998–2019. The countries are grouped into clusters that capture the different joint states of financial development and fragility. The paper introduces a new switching methodology to further allow for a qualification of the evolution of countries in terms.

of fragility scores with and without controlling for other variables. Irrespective of the precise methodology and state fragility measure as used in this paper, the findings indicate a negative correlation between financial development and state fragility, after controlling for several forms of observed and unobserved heterogeneity.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pcalice@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
28 Evaluation Results. As indicated in Table 5, the evaluation design<sup>differs sharply between the</sup> Downriver and Buffalo projects and the Texas WAD and New Jersey projects. The two earlier projects use a treatment group/comparison group design, where the two groups of laid off workers are drawn from different plants. In contrast, the two later programs implemented a true experimental design in which program participants were randomly assigned to a treatment group or to the control group. Beginning with the Downriver project, the net impact estimates presented in the Abt<sup>Associates report vary</sup> substantially depending the phase of the program and on the plants from<sup>which treatment and</sup> comparison group members were selected. The net earnings estimate of<sup>$111 per week shown for Phase</sup> I (July 1980-September 1981) is the 'best estimate reported in terms of its magnitude and stability across alternative comparison group plants. The incremental effect of CT above that of JSA Is not reported, but Kulik, Smith, and Stromsdorfer (1984:92) conclude that classroom training did not significantly improve participants' post-program reemployment rates.

Shown in Table 5 for the Buffalo project are net Impact estimates<sup>obtained for the "target</sup> plant sample.09 The Buffalo project is noteworthy for the relatively<sup>large percentage of OJT slots it</sup> provided, and the Mathematica Policy Research report supplies estimates of the incremental effect of OJT as well as CT (see Coron, Long, and Maynard 1985). Unfortunately, there is little Indication that either type of training substantially improved participants' labor market opportunities above those available to workers who undertook a JSA-only program. Note also that the cost<sup>per participant for the JSAICT and</sup> JSA/OJT treatments is more than three times that of JSA-only. It<sup>is clear that JSA is the only potentially</sup> cost effective treatment of the three evaluated.

The superior evaluation design and more broadly based sample available<sup>for the Texas</sup> WAD projects do not substantially alter the pessimisti **c** onclusion reached for skill training from the two earlier projects. Judging from Table 5, only for women In El Paso is there<sup>evidence that the Texas WAD</sup> program had a permanent effect in increasing earnings and reducing Ul<sup>benefits. For these women, the</sup> program's effect on eamings at the end of the first year slightly exceeds<sup>program costs. WAD also</sup> reduced Ul benefits by an average of $227 per participant measured<sup>over a 30-week period.</sup>