rafmacalaba/datause-encoder-data huggingface dataset:
than zero (in case of negative exposure bias) by means of bootstrapping, as explained in Section 3.4.<sup>5</sup> For floods at the national‐level, under present‐day climate conditions, 35 out of the 52 countries show a significant result. Of these 35, about half (17) exhibit an over‐exposure of poor people to (riverine) floods. Another relevant question is whether there are more people (in absolute terms) living in countries with a positive exposure bias. Using country‐level population data from the World Bank’s World Development Indicators (World Bank, 2015b), we find that 60% of the analyzed population live in countries with a positive and significant exposure bias.

Moreover, regional patterns become visible. In particular, the countries in Southern Africa and the Horn of Africa (except for Ethiopia, Rwanda, Zimbabwe and Mozambique) and the Arab Republic of Egypt have a strong over‐exposure of poor people to floods, although not all countries show significant results (Tanzania and Democratic Republic of Congo). In Western Africa, the results show a mixed pattern, although in countries with larger rivers and delta areas (notably Benin, Nigeria and Cameroon), there appears to be a tendency towards poor people being disproportionally exposed to floods. Turning to Asia, Indonesia also gives a moderate but significant signal that poor people are more exposed to floods than average; the same can be seen for South America in Colombia and Guyana.

As discussed earlier, there are also a number of countries where poor people are found to be less exposed to floods than average. These include some of the Asian countries of our sample (Cambodia, Nepal and Philippines, although the PEB for the last is insignificant), some West African countries and most of the countries investigated in Central and South America.

These regional patterns suggest that the variability of the PEB across countries is not a random mechanism nor an artefact of the analysis, but may be a consequence of regional development patterns. It supports the idea that there are different mechanisms at play for regions at different development stages or with different characteristics (e.g., urbanization rates). The balance between these mechanisms may vary, resulting in different PEB values between regions.

It must be noted that the results may be uncertain because the hydrological and inundation model performance may regionally be poorer. In particular in semi‐arid regions with large inland wetland areas, such as the Niger River in West Africa, much of the discharge may evaporate within these wetlands. The hydrological model used in this study (as with most global hydrological models) does not accurately represent this river discharge loss, which may cause downstream overestimation of flood depth (see e.g. Neal et al., 2012; Ward et al., 2013).

> 5 The additional uncertainty due to uncertain geographic position of the households was found to be insignificant and is therefore not displayed here.

11
29

**Table 14: Madagascar, rural poverty by 'degree of remoteness'**

||_Headco_|_unt(Po)_|_Depth(P)_|
|---|---|---|---|
||_1997_|_1999_|_1997_<br>_1999_|
|**_Rural_**|**76.0**|**76.7**|**34.7**<br>**36.1**|
|_Quintileof'remoten_|_essindex'_|||
|Mostremote|78.0|82.8|34.8<br>42.4|
|2nd quintile|78.2|78.9|38.1<br>35.6|
|3<sup>rdquintile</sup>|74.5|78.9|32.7<br>37.7|
|**4 **<sup>**th**quintile</sup>|77.0|77.7|36.6<br>36.5|
|<br>Least remote|72.6|65.9|31.6<br>29.0|

Source: Paternostro, Razafindravonona and Stifel (2001).

# **_Distribution, poverty and private endowments_**

The experiences in Ethiopia and Uganda demonstrated that better-endowed households, particularly more educated households and those with more (fertile) land, were not only less likely to be poor, but also more likely to benefit from favorable changes in the macroenvironment. The importance of education for poverty reduction is echoed by the microeconometric evidence from Ghana, Madagascar, and Zimbabwe.<sup>19</sup> Both in Ghana and Madagascar, real consumption levels increase with educational attainment. And the returns to education across the different education levels increased from the first to the second survey year. These observations hold for both urban and rural areas. In Zimbabwe, a more precipitous increase in poverty following the economic decline was prevented because of previous investments in schooling that increased the educational attainment of the population in the 1990s (Alwang and Mills, 2001). That incomes fell and poverty increased despite household efforts to invest in human capital, assets and migration (see Figure 6, panel B) can only be attributed to a reduction in the _rates of returns,_ which Alwang and Mills relate to an overall deterioration of the economic and institutional climate.

Evidence from Madagascar, the only other study which explicitly addresses the role of land holdings, confirms that consumption levels are higher for those who possess land, except for those with only a very small amount of land (less than 0.1 hectare per capita). Retums to land

> '9 One constraint these studies face is the absence of reliable price data (linked that is, to the household data), which would be needed to assess the direct impact of the reforms on consumption. Systematic changes in real producer prices are certain to have affected income distribution and poverty during this period. However, both the Madagascar and Zimbabwe studies control for rainfall shocks, an issue to which we return below.
# **<u>Table 9: Impact of a change in the price of cereals on producers- National</u>**

||**-25%**|**-12.5%**|**No change**|**12.5%**|**25%**|**50%**|**100%**|
|---|---|---|---|---|---|---|---|
|||||**Rice**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|48.25|48.02|**47.45**|47.04|46.94|46.42|46.02|
|Poverty gap|17.12|16.83|**16.66**|16.55|16.46|16.34|16.16|
|Squared poverty gap|8.33|8.10|**8.01**|7.96|7.91|7.85|7.75|
|**Poverty, rice producers**||||||||
|Headcount index of poverty|49.46|47.67|**43.31**|40.15|39.39|35.41|32.36|
|Poverty gap|19.15|16.95|**15.59**|14.75|14.13|13.17|11.78|
|Squared poverty gap|10.56|8.79|**8.08**|7.68|7.36|6.86|6.12|
||||**Millet a**|**nd Sorghum**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|47.78|47.65|**47.45**|47.45|47.15|46.92|46.20|
|Poverty gap|16.84|16.74|**16.66**|16.57|16.48|16.32|16.04|
|Squared poverty gap|8.13|8.07|**8.01**|7.95|7.90|7.80|7.64|
|**Poverty, millet and sorghum producers**||||||||
|Headcount index of poverty|69.92|69.11|**67.90**|67.90|66.04|64.64|60.18|
|Poverty gap|26.66|26.07|**25.52**|24.98|24.46|23.46|21.74|
|Squared poverty gap|13.77|13.37|**13.00**|12.65|12.32|11.73|10.76|
|||||**Corn**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|47.48|47.46|**47.45**|47.33|47.33|47.24|46.92|
|Poverty gap|16.81|16.73|**16.66**|16.58|16.52|16.41|16.23|
|Squared poverty gap|8.19|8.09|**8.01**|7.95|7.91|7.82|7.70|
|**Poverty, corn producers**||||||||
|Headcount index of poverty|80.13|79.95|**79.83**|78.27|78.27|77.09|72.96|
|Poverty gap|36.14|35.12|**34.12**|33.14|32.43|31.02|28.61|
|Squared poverty gap|21.22|19.94|**18.94**|18.21|17.62|16.58|14.98|
||||**All iden**|**tified cereal**|**s**|||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|48.50|48.25|**47.45**|46.92|46.44|45.60|44.35|
|Poverty gap|17.47|17.00|**16.66**|16.38|16.17|15.79|15.18|
|Squared poverty gap|8.64|8.24|**8.01**|7.84|7.71|7.48|7.14|
|**Poverty, food producers**||||||||
|Headcount index of poverty|61.69|60.83|**58.07**|56.22|54.58|51.67|47.35|
|Poverty gap|23.89|22.28|**21.07**|20.13|19.38|18.07|15.97|
|Squaredpoverty gap|12.71|11.32|**10.51**|9.94|9.47|8.68|7.51|

Source: Authors’ estimation using ELIM 2006.
comparative advantage in fossil fuels, as well as bringing foreign firms into pharmaceuticals, such as Slovenia’s Krka and Hungary’s Gedeon Richter. Similarly, German firms have introduced new construction services. Minority participation in Russia’s telecommunications sector has brought modernization through technology transfer, although there has also been significant round-tripping in this sector.<sup>6</sup> Certain sectors are more attractive for round-tripping rather than conventional FDI based on ownershipspecific advantages. For instance, 89 percent of the Russian FDI in basic metals and metal products originates in the round-tripping countries of Cyprus, Bermuda, and the Caribbean (Table 3). Similarly, close to half of FDI in services and construction is of the round-tripping type, with particularly high percentages in financial intermediation and real estate. By contrast, FDI in mining and quarrying and in most lines of manufacturing other than iron and steel is more likely to be “high-quality” FDI, proxied by the share that is not obvious round-tripping.

Recent large cross-border mergers and acquisitions of Russian companies focus on a few sectors and are often carried out from round-tripping locations. The largest transactions since 2008 have involved electricity and heating, oil and natural gas, railroads, and telecommunications (Table 4). Buyers or sellers in six of the nine largest transactions were from either Cyprus or the Jersey Islands or firms from such locations were part of the chain of ownership. For instance, the gold mining company Polyus Zoloto was sold to a Cypriot company in 2009, and resold to a Kazakhstan company two years later. Russia’s largest rail freight operator, Pervaya Gruzovaya Kompaniya, is owned by another Russian transport company, which is owned by a Netherlands company, which is in turn owned by a Cypriot company.

# 3. Leading and Lagging Regions in Russia

Russia’s export performance by region has been uneven. Since the crisis in 2009, the export intensity of GDP has declined or remained steady in all Russian regions except in Moscow (Figure 17). Performance in Siberia and the Far East has been relatively low; exports from firms based in the North Caucasus, the region with the lowest per capita income, are especially low.

## **CHARACTERIZING LAGGING AND LEADING REGIONS**

Per capita gross regional product (GRP) varies considerably for Russia’s lagging, middle-range, and leading regions.<sup>7</sup> The highest-income regions of Russia would be considered high upper-middle- or highincome if they were separate countries, comparable to many OECD countries. The lowest-income regions are comparable to many countries in Sub-Saharan Africa. Ranked by decreasing real GRP, the six regions

> 6 For example, the acquisitions of OJSC “MegaFon” and OJSC “TelekomInvest” by Cyprus-based investment companies in 2012 (UNCTAD 2013, p. 225).

> 7 GRP is calculated using a deflator that takes into account important structural differences between Russian regions that likely affect purchasing parity power.

11
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Figure 2. Visualizing the stepping stone approach to infrastructure prioritization <!-- Start of picture text --> Selection Informed by Selection Informed by<br>Infrastructure Advanced Project<br>Prioritization<br><!-- End of picture text -->

||**Selection Informed by**|**Selection Informed by**|
|---|---|---|
||**Infrastructure**<br>|**Advanced Project**<br>|
|**Ad‐Hoc / Uninformed**<br>**Project Selection**<br>|**Prioritization**<br>**Framework**<br>‐<br>Limited institutional|**Appraisal**<br>‐<br>High technical and<br>institutional capacity|
|‐<br>Limited project‐level<br>|and/or technical|available|
|information available<br>|capacity|‐<br>Detailed project‐level|
|‐<br>Inconsistent use of<br>|‐<br>Partial project‐level|information available|
|information<br>fl|information available|‐<br>Extensive quantified|
|‐<br>Decisions requenty<br>bd|‐<br>Project costs known|and monetized social,|
|ase on non‐<br>technical or political<br>|‐<br>Some information on<br>social,|environmental,<br>financial and|
|considerations<br>|environmental, other|economic effects|
|‐<br>Subjective<br>|economic effects|known|
|assessment common|‐<br>Decisions based on|‐<br>Decisions based on|
||minimum relevant|extensive|
||information|information|

# **Current Approaches to Prioritization**

Prior to describing the mechanics of the Infrastructure Prioritization Framework, we begin with an overview of approaches for project prioritization. This naturally requires dealing with appraisal, which, while not the primary focus of this paper, influences the approach to prioritization and selection.<sup>6</sup> Project comparison based on cost‐benefit analysis (CBA) allows project comparison based on a single metric –that of monetized value. CBA essentially totals all costs and benefits of a project over its lifetime and discounts future flows to calculate present values. A key strength is that it allows decision makers to intuitively compare and rank diverse alternatives based on a single indicator (Thomopoulos, Grant‐Muller, & Tight, 2009). With Social Cost‐Benefit Analysis (SCBA), prioritization can be based on selecting projects that maximize the net present values for society overall.

Because SCBA assessments require quantification and monetization of positive and negative effects, extensive information about the projects and their projected impacts is required (Van Delft & Nijkamp, 1977). Since information in many contexts is limited and many costs and benefits are difficult to monetize, and since SCBA itself can be quite costly, this can make the standard a tall order for application across all proposed projects. This is particularly difficult when governments possess limited resources for appraising large sets of small‐ and medium‐sized projects. An extensive discussion of CBA/SCBA can be found in

> 6. A good discussion of the key elements of sound project appraisal may be found in _The Power of Public Investment Management_ (Rajaram, et. al, 2014, p. 76).

6
**_Table 1: Summary statistics_**

||N|Mean|Median|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Credit constrained|17,902|0.246|0.000|0.431|0.000|1.000|
|Green management|17,902|-0.019|-0.394|0.973|-1.908|6.980|
|Firm age (log)|17,902|2.719|2.833|0.727|0.000|5.263|
|Firm age (log squared)|17,902|7.920|8.027|3.722|0.000|27.700|
|Exporter|17,902|0.193|0.000|0.394|0.000|1.000|
|Percentage of employees with a<br>university degree|17,902|30.850|22.220|27.310|0.000|100.000|
|Publicly listed|17,902|0.060|0.000|0.238|0.000|1.000|
|Sole proprietorship|17,902|0.197|0.000|0.398|0.000|1.000|
|Audited|17,902|0.394|0.000|0.489|0.000|1.000|
|No. bank branches|17,902|247.600|87.000|417.100|1.000|3649.000|
|Local banks' average asset size in<br>2007 (log)|17,902|15.220|15.670|1.614|10.250|18.450|
|Leave-one-out mean credit<br>constraints|17,902|0.238|0.182|0.203|0.000|1.000|
|Change in local average Tier 1 ratio<br>(\% points)|17,902|1.702|1.529|7.684|-35.880|44.600|
|Leave-one-out mean green<br>management practices|17,902|0.193|0.000|0.697|-1.317|6.980|
|No data on leave-one-out mean<br>credit constraints|17,902|0.007|0.000|0.081|0.000|1.000|
|No data on leave-one-out mean<br>green management|17,902|0.118|0.000|0.323|0.000|1.000|

Source: Authors’ calculations based on Enterprise Surveys and BEPS II.

# **IV. Firms’ mitigation strategies**

Scope and strategic preferences of mitigation The number and quality of green measures that a firm adopts communicates its green investment strategy. The scope of firms’ transition to a low-carbon future describes the degree of adjustment required (Smit and Wandel, 2006) or the extent to which the firm reduces its environmental impact by, for example, pursuing several climate mitigation measures simultaneously. However, the mitigation measures pursued show different strategic preferences that reflect climate objectives and the level of ambition. A firm has a choice to implement multiple of these strategic measures at the same time, possibly with different intensities. Therefore, a mitigation strategy can be conceptualized as the combination of mitigation measures with possibly distinct strategic preferences that a company has. A first fundamental distinction between mitigation measures that a firm seeks to implement to reduce its energy cost and, in parallel, its carbon footprint was made by Thollander et al (2007).

Based on the work of Thollander et al (2007), we develop a framework by which all mitigation measures can be assigned to one strategic preference. For such a framework, two different aspects are considered in terms of how the mitigation measures can affect firms’ carbon performance in line with Kalantzis and 14
Policy Research Working Paper 8300

# **Abstract**

In 2009, the G20 embarked on an ambitious financial regulatory reform agenda to address the fault lines that caused the global financial crisis. Although the global benefits are expected to outweigh the overall costs, these reforms could produce cross-border adverse spillover effects to individual emerging markets and developing economies that are not required to implement the reforms themselves, but are affected by their implementation elsewhere. To improve the evidence base on such potential adverse impacts, the World Bank has undertaken qualitative surveys of senior officials at regulatory agencies, local banks, and global banks that are active in seven emerging markets and developing economies. While important caveats prevent the formulation of definitive conclusions, the survey finds that banks and regulators routinely have different perspectives on the impacts. Most banks claim adverse effects on financial products, services, and markets; regulators broadly expect the effects to be positive over the longer term, but some recognize they may be negative during the transition phase. Regulators tend to agree that the (potential for) spillover impacts demand stronger home-host coordination, impose a higher supervisory burden, and require a stronger role for the international community to monitor and evaluate the impacts. The findings also emphasize the need for regulatory consistency within and between jurisdictions to ensure a level playing field. Taken together, more work remains to better understand the nature of these spillover effects, how they shape the provision of commercial financing to meet developmental objectives, and what action can be taken to mitigate any adverse impacts.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice Group. 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 authors may be contacted at efeijen@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
98 Figure A3.5: Ratio of retail kerosene prices to retail diesel prices in July 2012

|0.65<br>Angola||||
|---|---|---|---|
|Argentina|0.77|||
|Bangladesh||1.00||
|0.<br>Bolivia<br>|73|||
|Cambodia||1.04||
|0.67<br>Chil<br>Cameroon<br>||105<br>||
|Costa Rica<br>e|0.89|.||
|Côte d'Ivoire||1.00||
|Dominican Republic|0.91|||
|Egypt, Arab Rep.<br>||1.00||
|Ethiopia<br>|0.82|||
|0.59<br>Gabon<br>||||
|053<br>Ghana||||
|.<br>Gl<br>||||
|uatemaa|||1.20|
|Honduras|0.85|||
|0.56<br>0.36<br>Indonesia<br>India||||
|Iraq||0.97||
|Jamaica||101||
|Jordan<br>||1.00<br>.||
|Kenya|0.78|||
|Liberia||1.00||
|0.38<br>0<br><br>Malawi<br>Madagascar|.75|||
|<br>Mozambique|0.76|||
|Nepal||1.00||
|Nicaraua||102||
|Ni<br>g|086|.||
|0.30<br>Nigeria<br>ger|.|||
|Pakistan|0.90|||
|Philippines|||1.22|
|Senegal<br>|0.78|||
|0<br>South Africa<br>|.74<br>|||
|Sri Lanka|0.9|2||
|Ti||||
|anzana||0.99||
|Thailand|||1.19|
|Togo|0.78|||
|0.68<br><br>Tunisia<br>||||
|Turkey|0.81|||
|Uganda<br>|087<br>|||
|<br>Uruguay<br>|0.76<br>.|||
|Vietnam||1.00||
|Yemen, Rep.||1.00||
|0.68<br>Zambia||||
|0.00<br>0.20<br>0.40<br>0.60<br>0.|80<br>1.0|0<br>1|.20<br>1.40|

_Sources:_ Figures A3.2 and A3.3. _Note_ : The ratio for Jamaica is that of ex-refinery prices and taxes but excludes distribution margins.
# **Egypt and the Uruguay Round'**

Bernard Hoekman The World Bank and CEPR Arvind Subramanian International Monetary Fund

*Presented at a University of Cairo Conference, The Impact of the Uruguay Round on Egypt, Cairo, January 13-15, 1996. We are grateful to Ahrned Galal and participants in the conference for helpful comments and suggestions. Section 2 of the paper draws upon Shiells, Subramanian and Uimonen (1995). The views expressed are personal and should not be attributed to the World Bank or the International Monetary Fund.
2018 (age, gender, whether the student has children of her own, family of origin’s socioeconomic score, and mother’s education level.) Note that the subset of students to which we were given access may not be a representative sample of all students who took the test. Further, the scores of the _Ser Bachiller_ test were not shared with us.

**_Labor Market Outcomes._** Their source are individual-level records from the Ecuadorian Social Security Institute ( _Instituto Ecuatoriano de Seguridad Social,_ IESS) and the Ecuadorian Internal Revenue Service Unit ( _Servicio de Rentas Internas,_ SRI), which together yield the universe of individuals formally employed or self-employed, with monthly records of individual employment status and earnings between January 2018 and December 2020. We use these to construct labor market outcomes for the 12-month periods before and after graduation.

The final dataset for Ecuador is a sample of 2019 SCP graduates. It includes individual characteristics (gender, age, whether the student has children), socioeconomic background (mother’s education level and socioeconomic index), pre-graduation labor market outcomes, and post-graduation formal employment status and wages. We merge the individual-level dataset with information at the program and HEI levels from the WBSCPS and administrative sources. The resulting dataset includes 1,239 individuals and 92 programs (relative to the 245 programs with effective surveys in our sample).<sup>18</sup> It is worth emphasizing that our individual-level datasets for Brazil and Ecuador are different in that, for Brazil, we have information on all students who entered our survey programs in 2014

> 18 Some programs do not match to individual-level data for reasons similar to those in Brazil. In addition, some programs match but only have one student. Table A9 presents descriptive statistics for the subsample of the 92 surveyed programs that match to individual-level data and shows t-tests for mean differences between these programs and the 153 unmatched ones with 2+ students. Some differences are statistically significant. Matched programs are more likely to update the curriculum based on government standards and HEI labor market perceptions, require a professional test for graduation, or have agreements with firms to hire their graduates. They have more workshops for practice, charge a higher tuition, and have more faculty members. They are more likely to be taught by private, large HEIs, and have high-quality accreditation.

19