rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6: Corporate Debt at Risk**

<!-- Start of picture text --> (US$ Billion)<br>US$ billion<br>4000<br>100%<br>3500<br>3000<br>2500<br>58%<br>2000<br>1500<br>35%<br>1000<br>500 12%<br>4%<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total Debt DaR 1 indicator DaR 2 indicators DaR 3 indicators<br>DaR 4 indicators DaR 5 indicators DaR 6 indicators DaR 7 indicators<br><!-- End of picture text --> Note: DaR refers to ‘Debt at Risk’. Source: Bloomberg, own calculations At the global level, corporate vulnerability has increased sharply since 2013 according to our CVI<sup>10</sup> , although the speed of deterioration has moderated, especially in 2016 (see Figure 7). The global increase in corporate vulnerability has been driven by a rise in leverage ratios, and deteriorations in profitability and debt service capacity. Corporate vulnerability has also deepened: the country median value of DaR>=1 increased by 10.3 percentage points from 2013 to 2016, while DaR>=2, DaR>=3, and DaR>=4 rose by 8.6, 2.7, and 0.14 percentage points, respectively (Figure 8).

> 10 See Appendix for a CVI ranking at the country-level.

14
pregnancy may also lead to substitution of other foods for breastmilk, since other foods can be prepared and given to the child by individuals other than the mother.

# child death

) next Dregnancv _figure 5_

# (5) Child death on next pregnancy [figure 51.

Direct: The direct effects of child death on the risk of a next pregnancy are due to behavioral responses to the death, such as changes in contraceptive practice and in coital frequency. These changes may be motivated by a conscious desire to replace the deceased child or to accelerate the arrival of the next child.

Indirect: The effect of death on the risk of the next pregnancy is typically assumed to operate primarily through the cessation of breastfeeding. However, in behavioral terms, child death does not cause weaning; rather, child death censors breastfeeding. Thus, we do not allow for an indirect effect of child death on next pregnancy through weaning. But since we estimate all five effects as components of one overall system, the effects of child death on next pregnancy are estimated allowing for weaning effects on next pregnancy.

Our discussion of the mechanisms underlying the five effects has implicitly assumed a natural fertility, as opposed to a controlled fertility, population. The distinction is most salient with reference to effects on the next pregnancy: weaning on next pregnancy (see (2)), and child death on next 11
Policy Research Working Paper 6425

# **Abstract**

This paper tests whether structural or firm-specific characteristics contributed more to (labor) productivity growth in the European Union between 2003 and 2008. It combines the Amadeus firm-level data on productivity and firm characteristics with country-level data describing regulatory environments from the World Bank’s Doing Business surveys, foreign direct investment data from Eurostat, infrastructure quality assessments from the Global Competitiveness Report, and credit availability from the World Development Indicators. It finds that among the 12 newest members of the European Union, country characteristics are most important for firm productivity growth, particularly the stock of inward foreign direct investment and the availability of credit. By contrast, among the more developed 15 elder European Union member countries, firm-level characteristics, such as industry, size, and international affiliation, are most important for growth. The quality of the regulatory environment, measured by Doing Business indicators, is importantly correlated with productivity growth in all cases. This finding suggests that European Union nations can realize significant benefits from improving regulations and encouraging inward and outward foreign direct investment.

This paper is a product of the Finance and Private Sector Development Department, Europe and Central Asia Region. 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://econ.worldbank. org. The author may be contacted at adallolio@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
Table 5.<sup>13</sup> ) Rail remains the choice for long distance commutes, especially for households with incomes between Rs. 7,500 and 20,000 per month: One-quarter of commuters in this income range report rail as their main commute mode. Indeed, it is commuters in the Rs. 7,500-20,000 income range who are the largest users of public transit. In the highest income category (Rs. 20,000 or more), the share of walking declines to 15% and is replaced by motorcycles and cars. For commuters in the highest income category, the modal shares are 20% for two-wheeler and 24% for car.

The modal share for bus is highest for commutes between 3 and 10 km. For motorcycles, the share is highest for trips of 5 km or less. The relationship between income and mode choice is what one would expect: The poor rely heavily on walking (61% for commuters in households earning less than Rs. 5000) but take rail for long distances (5 km or more) and bus for intermediate distances (3-10 km). Overall, the modal shares for rail and bus are 16% and 15% for the poor; however, these shares are higher in the suburbs than in zones 1-3 (see Annex Table A.1).

**Table 4.  Main Mode to** **<u>Work</u>**

|<br>**Transport Mode**|**T**<br>|**otal**<br>|**Incom**<br>|**e <Rs. 5k**<br>|
|---|---|---|---|---|
|<br>|**Freq.**|**Percent**|**Freq.**|**Percent**|
|On foot|2649|45.32|796|62.68|
|Bicycle|175|2.99|76|5.98|
|Rail|832|14.23|127|10.00|
|Public Bus|813|13.91|143|11.26|
|Rail + Bus|462|7.90|63|4.96|
|Auto-Rickshaw|101|1.73|16|1.26|
|Taxi|8|0.14|0|0.00|
|Own Two-Wheeler|488|8.35|10|0.79|
|Own Car|153|2.62|1|0.08|
|Other’s car|12|0.21|2|0.16|
|Other|152|2.60|36|2.83|
|**Total**|**5845**|**100.00**|**1270**|**100.00**|

Source: Authors’ calculations

> 13 In Table 5, “rail” refers to “rail” and “rail+bus.”  The samples used to construct Tables 4 and 5 differ slightly; hence modal shares differ slightly between the two tables.

8
# 11. **Concentration of exports in DRC exposes the country to potentially high fluctuations**

**in export earnings.** Examining the relationship between export concentration and export revenue volatility for the period 2000–2013 (Table 1) shows that increased export concentration is strongly correlated with higher export earnings volatility: countries (depending on their level of development) with a higher export concentration ratio have a higher relative standard deviation in export earnings. The Least Developed Countries (LDCs) that have the highest export market/product concentration ratio as a group also have the highest relative standard deviation in export earnings. DRC’s export concentration ratio is even higher than LDCs’ as well as SSA’s averages (and medians), with a corresponding higher relative standard deviation of export earnings (63.1 percent, compared to 56.8 for LDCs and 50.8 For SSA).

**Table 1. Export Concentration and Export Revenue Volatility, 2000-2013**

|**Country/Development level**|**Export**<br>**Concen**|**Market**<br>**tration**|**Export**<br>**Concen**|**Product**<br>**tration**|**Relative Standard Deviation**<sup>**2**</sup><br>**of Export Earnings **|
|---|---|---|---|---|---|
||Mean|Median|Mean|Median||
|**DRC**|0.46|0.432|0.811|0.807|63.1|
|**Least developed countries**|0.41|0.429|0.69|0.692|56.8|
|**Sub-Saharan Africa**|0.393|0.419|0.599|0.601|50.8|
|**Developing economies**|0.163|0.166|0.235|0.238|44.4|
|**Developed economies**|0.067|0.066|0.162|0.166|29.5|

Source: Calculated using data from UNCTAD database.

12. **A more diversified exports basket plays in favor of smooth earnings and reduces volatility.** A more diversified export portfolio will have a more stable stream of export revenues (Samen, 2010). Indeed, if a country’s exports are perfectly concentrated in one product, then export earnings will fluctuate with the international prices of that product. Such fluctuations, especially in the case of commodities,<sup>3</sup> can be very large with strong macro-fiscal consequences. Countries

> 2 The equation of the relative standard deviation, given as a percentage is as follows: %���� �̅� ∗100, where _s_ is equal to the standard deviation, and �̅ is equal to the mean. A lower percentage indicates a lower variability in the data set. Equally, a higher percentage indicates the data set is more varied.

3 Commodities include the following HS numbers: 0201, 0202, 0203, 0204, 0206, 0207, 0209, 0210 (meats); 03 (fish, excluding 030110); 0803 (bananas and plantains); 080510, 080512 (oranges); 0901 (coffee); 0902 (tea); 1001, 1003, 1005, 1006 (grains); 1201, 1020, 1026 (oil seeds / groundnuts); 1507, 1509, 1510, 1511, 151211, 151219, 151321, 1514 (vegetable oils); 1701 (sugar); 1801 (cocoa); 230120 (fishmeal); 2304 (soybean meal); 2601, 2603, 2604, 2606, 2607, 2608, 2609, 261210 (ores); 2701 (coal); 2709 (petroleum oils); 271111 (natural gas); 284410, 284420, 284430 8
**<u>Table 11: Probit regression modelling poverty impact of health status and utilization (marginal effects reported)</u>**

||**Albania**|**Bosnia**|**Montenegro**|**Serbia**|**Kosovo**|
|---|---|---|---|---|---|
|Chronic illness|0032|007|-0011|-0004||
||.<br>(2.67)***|.<br>(3.52)***|.<br>(0.62)|.<br>(0.43)||
|Acute illness|0.016||0.139|0.041||
||(1.25)||(5.98)***|(3.79)***||
|Health use|-0.045|-0.046|0|-0.054|-0.006|
||(5.94)***|(2.60)***|(2.94)***|(6.84)***|(0.69)|
|Health insurance|-0.022<br>|-0.074<br>|0.088<br>|||
||(2.87)***|(3.31)***|(5.74)***|||
|Age|0.005|-0.001|0.003|0.003|-0.002|
||(6.90)***|(0.65)|(2.52)**|(3.93)***|(2.72)***|
|Age squared|0|0|0|0|0|
||(6.78)***|(0.4)|(2.57)**|(3.21)***|(2.59)***|
|Sex (female)|0.007|-0.01|0.014|-0.001|0.009|
||(1.04)|(0.66)|(1.83)*|(0.1)|(1.12)|
|N. of infants in the hh (0-5)|0.081||0.097|-0.016|0.027|
||(1832)***||(127)|(268)***|(797)***|
|N. of children the hh (6-18)|.<br>0.067|0.034|.<br>0.099|.<br>0.025|.<br>0.028|
||(24.42)***|(2.38)**|(1.3)|(6.99)***|(12.36)***|
|N. of adults in the hh (15-64)|0.025<br>|0.041<br>|0.078<br>|0.013<br>|-0.011<br>|
||(11.65)***|(7.10)***|(1.02)|(4.71)***|(5.06)***|
|N of elderly hh members (65+)|0.017|0.072|0.075|0.043|0.034|
||(3.24)***|(10.48)***|(0.99)|(9.76)***|(5.41)***|
|Education level (A):||||||
|Primary edu.level|-0.078||0.015|-0.025|0.048|
||(6.24)***<br>||(1.02)<br>|(2.37)**<br>|(1.19)<br>|
|Secondary edu.level|-0.147<br>(11.24)***||-0.022<br>(1.34)|-0.085<br>(6.78)***|0.049<br>(1.19)|
|Vocational edu.level|-0.168<br>||0.006<br>|-0.06<br>|0.06<br>|
||(12.42)***||(0.3)|(2.96)***|(1.36)|
|University and higher edu.level|-0.191||-0.078|-0.089|0.054|
||(10.97)***||(4.64)***|(7.09)***|(1.24)|
|Ethnicity (B):||||||
|Roma|0.454||0.469||0.467|
||(8.16)***||(16.33)***||(12.03)***|
|Croat|||||0|
||||||(0.00)|
|Serb|||0||0.146|
||||(0.02)||(9.23)***|
|Moslem/B|||0.064||0.052|
||||(3.93)***||(1.78)*|
|Macedonian|-0.175|||||
||(3.66)***|||||
|Vllahe|0485|||||
||.<br>(5.14)***|||||
|Turk|||||-0.182|
||||||(4.35)***|
|Albanian|||0.01|||
||||(0.23)|||
|Other|0.107||-0.1||-0.036|
||(1.4)||(2.10)**||(0.19)|
|No answer|||0.091|||
||||(515)***|||
|Region (C):|||.|||
|<br>Other urban|0.097|-0.036||0.095||
||(672)***|(181)*||(754)***||
|Rural area|.<br>0.136|.<br>0.066|0.011|.<br>0.104|0.032|
||(11.35)***|(4.02)***|(1.4)|(8.50)***|(3.71)***|
|Observations|15434|2325|8205|7871|15697|

Absolute value of z statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% (A) None education is omitted in each country regression.

(B) Albanian ethnicity is omitted in Albania; Montenegran in Montenegro; Albanian in Kosovo. (C) Tirana is omitted in Albania; city is omitted in Bosnia; Belgrado is omitted in Serbia; urban is omitted in Montenegro and Kosovo.

21
**Annex: Secured Transaction Regimes for Movables in the EU 8**

|**Annex: Secured**<br>**Hungary**|**Transaction**<br>**Yes**|**Regimes for Movables in the EU 8**<br>**OVERVIEW**<br>• Has a modern and efficient regime for secured transactions (EBRD<br>2003 Transition Report).<br>• The Charge Registry is kept at the Hungarian National Chamber of<br>Public Notaries.<br>• The registry is accessible for anybody to inspect.<br>• The registry is centralized and computerized.<br>• The secured transaction system is defined in the Hungarian Civil Code<br>of 1959. Modifications of the code in 1996 amended the secured<br>transaction system and have led to an increased use of security over<br>movables. The law was further updated in September 2001.<br>**TYPES OF PLEDGES**<br>• The charged asset may be any tangible object, any transferable right<br>or claim.<br>• A charge on rights and receivables may be created by agreement.<br>• A floating charge may be created on the whole of a business associa-<br>tion without legal entity or on a part of it that operates as a separate<br>economic unit without having to define the objects, rights and receiv-<br>ables constituting the asset, by concluding the charge agreement in<br>form of a notary instrument and registering the charge in the charge<br>registry.|
|---|---|---|
|**Latvia**|**Yes**|**OVERVIEW**<br>• Commercial pledges are registered in the Register of Commercial<br>Pledges, a database on commercial pledges.<br>• The register is fully accessible to the public.<br>• The system is centralized and electronic.<br>**TYPES OF PLEDGES**<br>• The object of the commercial pledge may be:<br>• a movable tangible or intangible object which belongs to a legal<br>person engaged in business („enterprise” or „business company”).<br>• a pool of objects (as mentioned above);<br>• the complete assets of an enterprise.<br>• Aircraft and land mechanical means of transportation and their trailers<br>as well as an enterprise as a pool of objects; capital shares of an<br>enterprise; shares, stocks of a closed issue; and bonds of a closed<br>issue may be the object of the commercial pledge irrespective of their<br>ownership.<br>• The commercial pledge where the object is a pool of objects shall in-<br>clude the existing as well as future parts of the pool if it is not explicitly<br>clear that the intent of the pledgor of the commercial pledge has been<br>to pledge only the part of the pool as it was at the moment of creating<br>the pledge right.|

**36**

**THE WORLD BANK**
Policy Research Working Paper 8978

# **Abstract**

The purpose of this work is to analyze the performance of the Ecuadorian pension system, its challenges, and available policy options. Therefore, the study analyzes coverage, financing sufficiency, and sustainability indicators that were created based on information from the _Encuesta Nacional de Empleo, Desempleo y Subempleo_ (National Employment, Unemployment and Underemployment Survey) that was carried out over 2003–16. Likewise, actuarial simulations are made by using the World Bank pension reform options simulation toolkit. The findings show that, although in the latest 13 years there has been coverage extension, resulting from an increase in reported employment and an extension of noncontributory pensions, current coverage is still insufficient. In addition to the challenge posed to coverage extension, in the medium term, population aging would exert some pressure on financial sustainability that, within the current framework, would imply a deficit trend starting in the mid-2030s. However, some public policy areas, parametric as well as structural, have been identified that, together with an extension of noncontributory coverage, may provide a more supportive and sustainable scheme.

This paper is a product of the Social Protection and Jobs 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 author may be contacted at iapella@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
# **Annex III: Regulation and Convergence – Regression results**

## **<u>RE-FE Baseline Comparison, ETCR Index</u>**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||FE|RE GLS|RE GLS|RE GLS|baseline RE|
||lrgdppc_ppp|lrgdppc_ppp|lrgdppc_ppp|lrgdppc_ppp|lrgdppc_ppp|
|L.lrgdppc_ppp|**0.7335*****<br>**[0.0522]**|**0.8718*****<br>**[0.0259]**|**0.8777*****<br>**[0.0332]**|**0.8772*****<br>**[0.0328]**|**0.9472*****<br>**[0.0109]**|
|linv_gdp|**0.1495*****|**0.1663*****|**0.1640*****|**0.1557*****|**0.1721*****|
||**[0.0337]**|**[0.0353]**|**[0.0360]**|**[0.0361]**|**[0.0356]**|
|L.linv_gdp|**-0.1360*****|**-0.1614*****|**-0.1664*****|**-0.1589*****|**-0.1684*****|
||**[0.0311]**|**[0.0332]**|**[0.0340]**|**[0.0336]**|**[0.0345]**|
|L.lropenness|0.0345|0.0078|**0.0098***|**0.0087***|**0.0189*****|
||[0.0216]|[0.0051]|**[0.0051]**|**[0.0051]**|**[0.0040]**|
|lyagri_empl|**-0.0514*****|-0.0035|-0.0002|0.0001|0.0015|
||**[0.0172]**|[0.0054]|[0.0067]|[0.0066]|[0.0034]|
|lyrule_law|0.1007|0.0067|-0.0085|0.0056|-0.0206|
||[0.0629]|[0.0244]|[0.0235]|[0.0252]|[0.0196]|
|lpatent_labor|-0.0074|-0.0019|-0.0016|-0.0014|**-0.0040***|
||[0.0084]|[0.0022]|[0.0027]|[0.0027]|**[0.0022]**|
|lfin_risk|0.0346|0.0255|0.0298|0.0321|0.0215|
||[0.0235]|[0.0227]|[0.0246]|[0.0241]|[0.0237]|
|lempl_priv|0.0156|**0.0770*****|**0.0711*****|**0.0697*****|**0.0978*****|
||[0.0606]|**[0.0193]**|**[0.0228]**|**[0.0215]**|**[0.0192]**|
|lyfertility|-0.0085|**0.0708*****|**0.0680*****|**0.0662*****|**0.0488*****|
||[0.0274]|**[0.0116]**|**[0.0125]**|**[0.0123]**|**[0.0102]**|
|lfis_burden|**0.0347****|0.0102|0.0103|0.0103|0.0058|
||**[0.0142]**|[0.0114]|[0.0113]|[0.0114]|[0.0107]|
|lave_yr_sch|-0.0458|**0.0406*****|**0.0383*****|**0.0359*****|**0.0293****|
||[0.1565]|**[0.0135]**|**[0.0138]**|**[0.0138]**|**[0.0124]**|
|L.lelect_pc|**-0.0298***|0.0052|0.0055|0.0073|**0.0155*****|
||**[0.0174]**|[0.0066]|[0.0068]|[0.0065]|**[0.0060]**|
|etcrave|**-0.3832*****|**-0.1709*****||||
|_|<br>**[0.0939]**|<br>**[0.0599]**||||
|etcr_lrgdp|**0.0388*****<br>|**0.0176*****<br>||||
||**[0.0094]**|**[0.0059]**||||
|etcr_pub_ownership|||-0.0531<br>|-0.0088<br>||
||||[0.0882]|[0.0883]||
|etcr_pub_lrgdp|||0.0054<br>|0.0009<br>||
||||[0.0087]|[0.0087]<br>||
|etcr_other_pub||||**-0.1632****<br>||
|||||**[0.0749]**||
|etcr_other_lrgdp||||**0.0167****<br>**[0.0075]**||
|etcr_entry_barrier|||-0.1168|||
||||[0.0748]|||
|etcr_entry_lrgdp|||0.0119|||
||||[0.0075]|||
|Constant|**2.6194*****|**0.6356****|0.6244|0.6161|**-0.2297***|
||**[0.7630]**|**[0.3060]**|[0.3897]|[0.3824]|**[0.1294]**|
|Observations|132|132|132|132|132|
|Number of id|16|16|16|16|16|
|R-squared|0.9912|||||

Robust standard errors in brackets; * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies are excluded in the table.

90
response to an unexpected, radical minimum wage hike in Korea. Contrary to the conventional wisdom that MNCs would be the least likely to be affected by the minimum wage increase, we find strong evidence of their relative reduction of employment by about 3% after the shock to non-MNCs. Further reveal that MNCs re- policy compared analyses allocated manufacturing tasks to their foreign affiliates, particularly located in nearby low-wage countries. Our results thus highlight that the cross-country network within MNCs lead to unintended of domestic labor market may consequences policies. Policy makers need to be aware of the possibility and be better prepared with labor adjustment programs, such as retraining, to mitigate the negative effect.

The results also provide a new insight into the ongoing minimum wage discussion. Previous minimum wage studies have typically focused on employment effects for young and unskilled workers; and they have not found consistent results (Card, 1992; Neumark and Wascher, 1992; Neumark et al., 2014; Allegretto et al., 2017). In this study, we find negative employment effects for MNCs, arguably the most productive and largest firms (Bernard and Jensen, 2007; Bernard et al., 2007). An important implication of the finding is that a relatively swift decline in employment by MNCs may imply a deterioration in aggregate labor productivity. Put differently, the jobs specifically vulnerable to being replaced by foreign workers due to minimum wage hikes may be "better" paying jobs in an MNC-prevalent economy. This idea is somewhat different from the general view in the literature that minimum wage increases have the greatest effect on the lowest-paid workers, causing them to move from less to more productive establishments (Dustmann et al., 2022). Further research is warranted for these seemingly contradictory views.

33
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