rafmacalaba/datause-encoder-data huggingface dataset:
strong distinctions to be made between a) what is reported to the regulator for supervisory purposes, b) what is to be shared between financial institutions to improve information on the debtors of the financial system and to improve the risk management practices of non-best practice banks and c) what is disclosed more widely to enhance creditworthiness assessments more generally and further sharpen “willingness to pay” incentives.

# **_IV. Conclusions_**

We believe that this is the first paper that has presented multi country empirical evidence on the value of Public Credit Registries. The empirical tests show that PCRs may improve credit access for borrowers for the same level of bank risk or reduce bank risk for the same level of credit access.

The empirical results also highlight several interesting features related to the design of PCRs. First, the empirical tests showed the added value of having positive as well as negative information on borrowers. These results were robust across countries. Second the results showed that small banks benefit even more than larger institutions from sharing credit information. Third, the results illustrated the importance of having a bank rating, as ratings were highly predictive in determining default risk.

While the three countries included in the empirical work (Argentina, Brazil and Mexico) all have private credit bureaus as well as PCRs, we argued that PCRs and private credit bureaus are complements and not substitutes. PCRs generally include positive information while banks may only be willing to share negative information voluntarily. PCRs typically cover all regulated financial institutions but tend to lack other voluntary data providers that private firms may capture. The private credit bureau industry is typically one of strong increasing returns to scale and hence prone to monopoly and restrictive practices; a PCR may lower entry costs and hence make the private industry more competitive. Finally, PCRs typically only share very basic information while private credit bureaus can add further information and supply higher valueadded services on a more competitive basis.

25
7

# **Appendix Table 4: Country Share of the Global Commodity-Specific WRI for Sugar, Milk, Rice, Beef and Cotton, 2000–04**

||Rice|Sugar|Milk|Beef|Cotton|
|---|---|---|---|---|---|
|WRIglobal average|**140.9**|**86.7 **|**72.8**|**68.1 **|**44.7 **|
|**Decomposition**||||||
|Argentina|||0.0|0.2||
|Australia|0.0|0.0|0.1|0.0|0.0|
|Austria||1.1|0.4|0.8||
|Bangladesh|0.0|2.8||||
|Benin|||||0.0|
|Brazil|0.0|0.1||0.2|0.4|
|Bulgaria||0.0|0.0|0.0||
|Burkina Faso|||||0.1|
|Cameroon|||||0.0|
|Canada|||4.3|0.0||
|Chad|||||0.0|
|Chile||0.1|0.0|0.0||
|China|3.9|2.4|0.4||8.2|
|Colombia|0.1|7.8|2.5|1.4|0.3|
|Cote d'Ivoire|0.0||||0.1|
|Czech Rep||0.9|0.3|1.6||
|Denmark||1.0|0.6|0.6||
|Dominican Republic|0.0|0.1||||
|Ecuador|0.4|0.1|0.0|0.1||
|Egypt|1.4|0.2|0.1|0.1|4.3|
|Estonia|||0.0|0.0||
|Finland||0.4|0.3|0.4||
|France|0.1|8.0|3.3|6.8||
|Germany||8.4|3.7|4.8||
|Ghana|0.0|||||
|Hungary||0.7|0.7|0.5||
|Iceland|||0.2|0.2||
|India|3.0|3.2|3.8||0.6|
|Indonesia|0.1|3.5||||
|Ireland||0.6|0.7|1.5||
|Italy|0.3|3.9|1.6|5.3||
|Japan|27.8|7.0|46.9|21.8||
|Kazakhstan||0.1|0.0|0.2||
|||||Co|ntinued over|
Table 4 Vietnam Household Living Standards Survey Sources of Income Panel Sample

||Mekon|g 4 (M4)|Mekon|g 6 (M6)|
|---|---|---|---|---|
||2002|2004|2002|2004|
|Catfsh|11.2|6.8|9.6|6.5|
|Other Aquaculture|1.0|1.0|1.2|1.3|
|Wages|26.7|28.1|25.7|27.4|
|Agriculture|42.5|43.2|44.3|43.4|
|sales|33.5|33.2|35.6|34.5|
|own|9.0|10.1|8.7|8.9|
|Livestock|9.5|10.4|10.6|11.6|
|Sylviculture|0.6|0.6|0.6|0.5|
|Farm Services|0.7|0.6|0.7|0.6|
|Other|7.8|9.3|7.4|8.8|

Note: Own calculations based on the panel sample of the Vietnam Household Living Standard Surveys, 2002 and 2004. Mekong 4 (M4) and Mekong 6 (M6) refer to two sets of Mekong provinces that specialize in catfish production: M4 includes An Giang, Can Tho, Dong Thap and Vinh Long, and M6 adds Soc Trang and Tien Giang.

40
TABLE 1: POLICE AND CRIME AGAINST FIRMS (LOSSES DUE TO CRIME/SALES)

||% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|
|---|---|---|---|---|
||1|2|3|4|
|Police per 100,000 persons|-0.0011**|-0.0020**|0.0052*|0.0004|
||[0.0005]|[0.0009]|[0.0027]|[0.0006]|
|Police x GDP per Capita Growth||0.0003**<br>[0.0001]|||
|Police x GINI|||-0.0002**<br>[0.0001]||
|Police x Large City or Capital||||-0.0022***<br>[0.0005]|
|GDP per Capita Growth|-0.0808***|-0.1679***|-0.0706***|-0.0821***|
||[0.0208]|[0.0471]|[0.0230]|[0.0199]|
|Firm with Female Owner|0.1181*|0.1078*|0.1143*|0.1069*|
||[0.0632]|[0.0630]|[0.0616]|[0.0622]|
|Real GNI per capita (in 100s)|-0.0058***|-0.0061***|-0.0056***|-0.0060***|
||[0.0012]|[0.0012]|[0.0011]|[0.0010]|
|GINI|-0.0065|-0.0066|0.0455*|-0.0069|
||[0.0113]|[0.0114]|[0.0258]|[0.0101]|
|Population in Millions, Total|0.0034*|0.0025|0.0022|0.0042***|
||[0.0017]|[0.0019]|[0.0015]|[0.0014]|
|Small firms|0.4023***|0.3979***|0.3955***|0.4040***|
||[0.0940]|[0.0937]|[0.0936]|[0.0937]|
|Medium firms|0.1664**|0.1654**|0.1592**|0.1631**|
||[0.0658]|[0.0654]|[0.0655]|[0.0654]|
|Large or Capital city|0.0331|0.0454|0.0398|0.6432***|
||[0.0953]|[0.0944]|[0.0982]|[0.1681]|
|Manufacturing|-0.2238**|-0.2312***|-0.2225**|-0.2219**|
||[0.0813]|[0.0815]|[0.0822]|[0.0827]|
|Number of Countries|27|27|27|27|
|Number of Observations|12274|12274|12274|12207|

* significant at 10%; ** significant at 5%; *** significant at 1%, Standard errors in brackets clustered at the country level

24
- Rozo, S. V. and J. F. Vargas (2021). Brothers or invaders? How crisis-driven migrants shape voting behavior. _Journal of Development Economics 150_ , 102636.

- Sears, D. O. (1983). The persistence of early political predispositions: The roles of attitude object and life stage. _Review of Personality and Social Psychology 4_ (1), 79–116.

- Spear, L. P. (2000). Neurobehavioral changes in adolescence. _Current Directions in Psychological Science 9_ (4), 111–114.

Torney-Purta, J. V. (2017). _The development of political attitudes in children_ . New York: Routledge.

17
United States and the United Kingdom, also have experienced a significant decline in public listings. On the other hand, trends in developing countries are less obvious. Some countries, like China, have seen rapid growth in both public company listings and private equity activity, while other countries, like Brazil, have seen volatility in private equity activity concurrent with declines in public listings. This paper attempts to explain these changes in global capital markets by exploring three main hypotheses that may explain trends in global PE investment: i) country and industry macroeconomic conditions, ii) financial market development, and iii) institutional and regulatory environment. The hypotheses we examine are not mutually exclusive, and consequently, we seek to also understand the relative importance of different determinants of PE investment. Our ultimate goal is to better understand current and future trends in capital formation through financial intermediation by uncovering the historical determinants of PE investments at the country and industry levels.

Using nearly comprehensive country-industry-level data on international private equity activity, we study the determinants of buyout investments across 61 countries over the period 1990 – 2017.<sup>5</sup> Our results indicate that macroeconomic conditions, financial development, and regulatory environment all play some role in determining the level of buyout activity at the country level. For macroeconomic conditions, we find that buyout activity increases more during economic expansions (as measured by a declining unemployment rate). We also find similar evidence at the industry level: industries receive more buyout investment following expansions in industry-wide

> 5 The countries included in our study are Argentina, Australia, Austria, Belgium, Bulgaria, Brazil, Canada, China, Croatia, Colombia, Costa Rica, Cyprus, Czech Republic, Denmark, the Arab Republic of Egypt, Finland, France, Germany, Greece, Hong Kong SAR, China, Hungary, Indonesia, India, Ireland, Iceland, Israel, Italy, Jamaica, Jordan, Japan, Kazakhstan, Mexico, Malaysia, Nigeria, Netherlands, Norway, New Zealand, Peru, Philippines, Poland, Portugal, Qatar, Romania, the Russian Federation, Saudi Arabia, Singapore, the Slovak Republic, Slovenia, the Republic of Korea, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, Uruguay, United Arab Emirates, United Kingdom, United States, Vietnam, South Africa.

3
|justafterthepandemic,whenstudentscouldfinallyreturntoschoolsforin-personlearning,thethe|
|---|

|Thelong-term consequences ofthese trends are significant, as diminished educational outcomesand<br>lower these barriers (Ozden and Wagner, 2020). Further, specific interventions aimed at removing<br>obstacles to education for children on the move can contribute significantly to better integration<br>Thispaperbenefitsfromkeyinformationcomingfromadministrativedataoneducationalrecordsof|
|---|

|Resultshighlightthatdespitegradualimprovements,enrollmentratesremainsignificantlylower|
|---|

proficiency such as Italian and English. However, good performance in mathematics suggests
# **Introduction**

As efforts to decarbonize progress, it is vital to ensure the reliability of power systems within the context of the challenges of phasing out fossil fuels, aging transmission and distribution infrastructure, and increasing weather-induced fluctuations on energy supply (i.e., wind, solar, and hydro-power) and demand (i.e., heating and cooling demand). Even without these increasing challenges, the economic costs of unreliable critical infrastructure are already costly, particularly in less developed countries. A 2019 World Bank report estimated that the cost of unreliable infrastructure in 137 low- and middle-income countries (a cohort of countries that represents about 80 percent of the GDP of all low- and middle-income countries) was nearly $300 billion a year (about 1.5% of GDP), with over half of that cost associated with electricity outages and the subsequent losses to sales and the costs of self-generated electricity (Rentschler et al. 2019 _)_ . A 2019 survey found that power outages in a typical month in Kosovo, North Macedonia, Albania, and Serbia were estimated to occur about 8, 5, 4, and 2 times each month, respectively (Dalloshi et al. n.d.).

Extreme demand for cooling is among the many reasons why power systems can fail to maintain reliable supply. High temperatures heat the land surface and infrastructure, trigger the usage of air conditioning where available, and cause some of the highest electricity demand spikes that systems experience (Franco et al. 2007, Waite et al. 2017). As such, periods of elevated electricity demand driven by hot temperatures are critical times for electrical grids and can lead to soaring energy prices and, most critically, power outages or rolling blackouts (Hatvani-Kovacs et al. 2016). Examples include the blackouts triggered in California in July 2018 when spikes in electricity demand during a heat wave could not be met by the power system (Khan 2018). The July heat wave of 1995 was an even more dire example, where power failures were partially to blame in the deaths of over 800 people in the United States (Changnon et al. 1996). Furthermore, anthropogenic warming is expected to exacerbate these power system reliability issues (Allen et al. 2016, Li et al. 2012, Franco et al. 2007, Miller et al. 2008) via rising temperatures, and the anticipated increased adoption of air conditioning will lead to more energy systems being stressed during hot times (Waite et al. 2017).

In addition to climate change’s effects of exacerbating the magnitude of extreme heat, the trend of increasing urbanization can also increase the magnitude of extreme cooling demand events for two 5
Figure 4: South Asia Per Capita Income 2006 <!-- Start of picture text --> 3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Countries<br>Per capita income<br>Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka<br>Nominal US Dollars<br><!-- End of picture text --> _Source_ : World Bank 2007.

The income gap at the national level carries through at the sub-national level (figure 5). During the period 1993-2004, GDP growth in the leading states in India grew at twice the rate compared to the lagging states. The average annual growth rate for the leading states (A.P., Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, and West Bengal) was 5.9 percent. The average growth rate for the lagging states (Bihar, M.P., Orissa, Rajasthan, and UP) was 3.0 percent per annum. In Sri Lanka, the leading regions grew at an annual average rate of 6.5 percent during the period 19962005, while the lagging regions (Sabaragamua, Central, Uva, and North Western) grew at an average rate of 1.5 percent per annum. In Pakistan, the difference in the growth rates between the leading and lagging regions is less striking. The leading regions of Punjab and Sindh experienced an average annual growth rate of 2.3 percent during the period 1991-2000, while the lagging regions of Baluchistan and NWFP grew at an average annual rate of 1.8 percent. In Bangladesh, the leading regions (Dhaka and Chittagong) grew at an annual average rate of 3.15 percent annum while the lagging regions (Barisal, Rajshahi, Khulna, Sylhet) grew at an average annual rate of 2.73 percent during the period 1990-1999. Nepal’s growth since 2000 has averaged a paltry 3%, around half of the South Asian regional average. Conflict, poor road connectivity, and urban bias associated with earlier growth spurts have resulted in a clear divide between lagging regions and the Kathmandu valley.

The above picture suggests that despite the differences in economic size and population, South Asian countries face similar development challenges arising from large spatial disparities. South Asia’s leading regions are leaving lagging regions behind, as exemplified by the phrase “two South Asias”. The leading regions are characterized by rapid GDP growth, urbanization and integration with the global economy. The lagging regions remain rural, rely on low value activities, and are not well integrated with the national, regional, and global markets.

5
# PPR Working Paper Series

||Title|Author|Date|Contact|
|---|---|---|---|---|
|WPS70|Vocational<br>Education and Economic<br>Environments: Conflict or||||
||Convergence?|Arvil V. Adams<br>Antoine Schwartz|August 1988|T. Hawkins<br>33678|
|WPS71|School Effects on Student Achievement<br>In Nigeria and Swaziland|Marlaine Lockheed<br>Andre Komenan|August 1988|T. Hawkins<br>33678|
|.PS72|The Relative Efficiency<br>of Public<br>Schools In Developing<br>Countries|Emmanuel Jimenez<br>Marlalne Lockheed<br>Vicente Paqueo|August 1988|T. Hawkins<br>33678|
|WPS73|Taxation and Output Growth In Africa|Jonathan Skinner|August 1988|A. Bhalila<br>60359|
|WPS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalla<br>60359|
|WPS75|Welfare Dxominance<br>and the Design of<br>Excise Taxation in the Cote dlIvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
|WPS76|On the Shadow Price of a Tax<br>Inspector|Shlomo Yitzhaki<br>Yitzhak Vakneen|||
|WPS77|<br>Incentive Policies and Agricultural<br>Performance in Sub-Saharan<br>Africa|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS78|Economists, Institutions<br>and Trade<br>Restrictions: A Review Article|J. Michael Finger|||
|WPS79|Quantitative<br>Appraisal of<br>Adjustment Lending|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS80|Emerging Issues of Privatization<br>and the Public Sector|Samuel Paul|September 1988|E. Madrona<br>61711|
and the Tanzanian time lost on importing food from Kenya is due to Tanzanian roads, ports and customs procedures that are under the control of Tanzania. Then the distortions would be independent and should both be included. We have chosen to include the distortions on both imports and exports, but take modest cuts in both recognizing that there is a potential for double counting.

# References

Djankov, Simeon, Caroline Freund and Cong S. Pham (2010), “Trading on Time,” _The Review of Economics and Statistics_ , MIT Press, vol. 92(1), pp. 166-173, February.

- Hummels, David L. and G. Schaur (2013), “Time as a Trade Barrier,” _American Economic Review_ , vol. 103, 1-27.

- Hummels, David L., Peter Minor,  Matthew Reisman and Erin Endean  (2007),  “Calculating Tariff Equivalents for Time in Trade,” Arlington, VA: Nathan Associates Inc. for the United States Agency for International Development (USAID). Available at: <u>http://www.krannert.purdue.edu/faculty/hummelsd/research/tariff_equivalents.pdf</u>

- Hummels, David L. (2007), "Transportation Costs and International Trade in the Second Era of Globalization," _Journal of Economic Perspectives_ , 21(3), 131-154.

- Minor, Peter (2013), “Time as a Barrier to Trade: A GTAP Database of ad valorem Trade Time -

- Costs,” ImpactEcon, Second Edition, October. Available at: http://mygtap.org/wp <u>content/uploads/2013/12/GTAP%20Time%20Costs%20as%20a%20Barrier%20to%20Tr ade%20v81%202013%20R2.pdf.</u>

- World Bank (2014), _Doing Business Report, 2014_ . Available at: http://www.doingbusiness.org/reports/global-reports/doing-business-2014.

100
**Table 3.2. Macro indicators in 2012 and for base simulation by period (Percent of nominal GDP)**

||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|
|**Absorption**|184.2|123.9|118.3|144.9|119.5|126.6|
|**Consumption - household**|83.9|73.5|75.4|76.5|73.8|74.5|
|**Consumption - UNMIL**|25.4|0.0|0.0|7.2|0.0|2.0|
|**Consumption - government**|21.2|24.8|23.1|23.2|25.4|24.8|
|**Fixed investment - private**|38.8|11.7|17.2|25.0|15.7|18.3|
|**Fixed investment - government**|14.9|13.9|2.7|13.1|4.6|7.0|
|**Exports**|37.8|65.5|72.6|54.9|69.9|65.7|
|**Imports**|-121.9|-89.5|-90.9|-99.8|-89.4|-92.3|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|11.3|8.6|8.3|9.7|8.3|8.7|
|**GDP at factor cost**|88.7|91.4|91.7|90.3|91.7|91.3|
|**GNI**|91.2|92.0|89.2|89.1|89.7|89.5|
|**GNDI**|151.9|118.3|112.5|125.0|113.7|116.8|
|**Foreign savings**|32.2|5.7|5.8|19.9|5.9|9.8|
|**Gross national savings**|21.4|20.0|14.0|18.2|14.5|15.5|
|**Gross domestic savings**|-30.5|1.7|1.5|-6.9|0.8|-1.3|
|**Foreign government debt**|8.6|15.2|20.9|12.2|19.2|17.3|
|**Domesticgovernment debt**|15.1|14.1|18.4|14.1|16.3|15.7|

**Note:**

**Columns show values for single years or averages for multiple years (as indicated). Gross domestic savings equals gross national savings minus the sum of net transfers and net factor income from abroad.**

19
**Table 3.10: How far away is universal coverage?**

<u>(Anticipated date of universal coverage)</u>

||Present effort levels<br>sustained|Present effort<br>levels doubled|
|---|---|---|
|Electricity|2006|2003|
|Water|2007|2004|
|Basic sanitation|2003|2002|
|Improved sanitation|2014|2007|

_Note_ : It is assumed that population growth remains at historically observed rates of 2.6% per annum and that household size remains constant.

Based on typical unit costs for service expansion, the total cost of meeting universal coverage across the electricity, water and sanitation services is estimated at US$1.5 billion (Table 3.11). The electricity service, owing to its relatively high unit cost of US$1,000 per household, accounts for over 40% of this total expenditure, compared with 25% for piped water. In the case of sanitation, two levels of universal service are defined. The first level is universal _basic_ sanitation, which basically entails providing latrines to the 288,807 households that currently have no form of sanitation, and would cost less than US$15 million to achieve. The second level is universal _improved_ sanitation. This entails providing sewerage to all households in conurbations with greater than 50,000 population (notably the Metropolitan area, Quetzaltenango, and Escuintla)<sup>19</sup> , and upgrading all other households to a flush toilet with a septic tank. This is a very much more expensive proposition, accounting for almost a third of the overall expansion costs.

**Table 3.11: Cost of reaching universal coverage**

||Coverage gap<br>(connections)|Unit cost<sup>♣</sup><br>(US$ per connection)|Total cost<br>(US$)|Share of<br>total cost|
|---|---|---|---|---|
|Electricity|585,933|1,000<br>|585,933,000|40.1%|
|Water|686,893|500<br>|343,446,500|23.5%|
|Basic sanitation|288,807|100<br>|28,880,700|2.0%|
|Improved sanitation|1,148,702|250<br>|287,175,500|19.6%|
|•Large cities|205,193|750<br>|153,894,750|10.5%|
|•Elsewhere|585,933|1,000<br>|585,933,000|40.1%|
|Public telephones|12,730|5,000<br>|63,650,000|4.4%|
|Total|||1,462,980,450|100.0%|

Finance, Private Sector and Infrastructure Division of the Latin America and Caribbean Region of the World Bank In the case of water and sewerage services, international experience suggests that the costs of universalizing access could be reduced by as much as 40% if innovative ‘condominial’ designs are adopted and implemented through community participation (Foster, 2001). The ‘condominial’ approach to water and sewerage networks was pioneered in Brazil in the 1980s and has recently been applied with some success in Bolivia. The approach involves altering the engineering design of the water or sewerage network so that instead of providing a separate branch from the main network to each household, a single branch is provided to a whole block (or ‘condominium’) of households, who then make their connections along this common branch. This saves costs by reducing the length, diameter and depth of

> 19 For the purposes of this analysis it was only possible to estimate the coverage deficit for the Metropolitan region.

25