rafmacalaba/datause-encoder-data huggingface dataset:
Table 2. Dissemination of stress-test methodology <u>and results through FSR</u>

|Country|Most<br>Recent<br>FSR|that|Analyzed Risks|
|---|---|---|---|
||contains a stress test<sup>*</sup>|||
|Albania|2008 FSR||Credit and market risks|
|Austria|2009 FSR||Credit,<br>market,<br>liquidity,<br>and<br>contagion risks|
|Bosnia & Herzegovina|2008 FSR||Credit, market, and liquidity risks|
|Bulgaria|Does not publish FSR|||
|Croatia<br>|2010 FSR||Credit and market risks|
|Czech Republic<sup>6</sup>|2010 FSR||Credit, market, and liquidity risks|
|Greece|2009 FSR||Credit, market, and liquidity risks|
|Hungary|2010 FSR||Credit and liquidity risks|
|Kosovo|2008 FSB||Credit, market, and liquidity risks|
|Macedonia, FYR|2008 FSR||Credit and market risks|
|Montenegro|2006 FSR||Credit, market, and liquidity risks|
|Poland|2009 FSR||Credit risk|
|Romania|2008 FSR||Credit, market and contagion risks|
|Serbia|FSR does not present str|ess tes|t|
|Slovak Republic|2009 FSR<br>||Credit and market risks|
|Slovenia|2007FSR<sup>7</sup>||Credit,market, andliquidityrisks|

Note:<sup>*</sup> as of July 2010.

# **3. Macro Scenarios Formulation**

A key component of a stress-test procedure is the generation of a set of macroeconomic scenarios to which the resilience of the financial sector is analyzed. The set of scenarios considered needs to contain a baseline scenario, which reflects the current economic situation, and some adverse scenario(s), which describes the effects of extreme but plausible adverse events on macroeconomic conditions. To the extent possible, the scenarios should reflect country-specific circumstances. If short data history or data availability are an issue, international experience should serve as guidance for constructing relevant stress scenarios (Buncic and Melecky, 2010).

There are two distinctive approaches employed in the construction of scenarios: the judgmental approach and the model-consistent approach. The judgmental approach sets the relevant economic variables according to experts’ judgments. The model-consistent approach builds the macro scenario based on a model, which accounts for the interlinkages among macroeconomic variables, and eventually financial sectors’ variables. In practice, the macro scenarios used in stress-testing are often based on an intermediate combination of approaches utilizing both a macroeconomic model and expert judgments.

> 6 The Czech Central Bank, apart from the stress-test presentation in the annual FSR, publishes the quarterly aggregated stress-test results on the Czech National Bank’s web page.

> 7 The results of the 2010 EU wide stress testing exercise are published on the Slovenian Central Bank’s web page.

7
-69-

But reform of rural institutions and more infrastructure is insufficient to help the rural p or. A third area requires policy makers' attention: elimination of urban bias in the delivery of social services. The rural poor also need to improve their human capital through health and education. This implies not only tilting the balance of social spending towards the rural areas, but also allocating those resources where their social pay-off is high. In the short run improvements can be made by increasing attention to preventive medicine and bettering the quality of primary education. These are, again, the areas where the risks that the benefits will be appropriated by the non-poor are smaller and the externalities higher<sup>116</sup> .

Rural development is the key to poverty alleviation: this is where most of the poor are, and this is where in the short run growth is most needed. But what about the medium term? Can rural wages increase sufficiently to reduce poverty in the face of growing population? This is a difficult question to answer, but two points can be made. First, it is clear that over a longer time-span rural wages will increase only by increasing the land/labor ratio, which implies the need for further rural it migration. Second, however, given present distortions in input and output prices, land use and land tenure, it is very difficult to tell how much rural wages could increase in the short run if these distortions were removed<sup>11</sup> 7. Differently put, reform of the rural areas may allow for substantial increases in rural wages and reduce for some time the incentives for rural-urban migration 1161ssues of urban bias also call for decentralization of fiscal collections and switching of responsibilities from the federal to state governments; I abstract from these issues in this paper, but see World Bank, 1989a, for furthar discussion.

117In fact, the information problem is quite severe. Under the present set of regulations it is very difficult to tell what is the comparative advantage of the rural areas. It is plausible to argue that given Mexico's endowments this would be in crops with high value per unit of land and low value per unit of labor. But issues like access to marketing, weather upcertainty and the like are also central. (This provides yet another reason why the government should not take decisions witn regards to the output mix; this should be decided. uy individual producers who have the incentive and ability to gather this '.aformation.)
Policy Research Working Paper 10787

# **Abstract**

Small Island Developing States (SIDS) are a group of 39 United Nations (UN) member states and 18 dependent territories mostly located in the Caribbean and Oceania. They are among the most vulnerable countries to losses and damages from climate change globally, and they are especially exposed to tropical cyclones and sea level rise. This paper evaluates the climate change-related vulnerabilities of the SIDS. It uses data from the International Disaster Database (EM-DAT), to examine the magnitude of damages incurred over the last three decades and conducts an event study analysis to examine the fiscal impacts of large tropical cyclone disasters in the last decade. SIDS need to invest substantial resources over the long term for climate change adaption. The paper reviews the limited studies that have been conducted so far, on the potential long-term costs of effective adaptation investment programs. Using the most recent Debt Sustainability Analyses of SIDS, the paper discusses the challenges countries with high public debt levels face and whether a general program of debt relief might be a feasible way to fund the adaptation investment requirements of SIDS. Finally, the paper discusses the type of assistance that SIDS need to strengthen their resilience to climate change in a cost-effective manner, and the role development partners can play in supporting climate-resilient development.

This paper is a product of the Climate Change 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://www.worldbank.org/prwp. The authors may be contacted at scanagarajah@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
# **Financial Stability Implications**

The last step is to calculate the implications of these stress tests for financial stability. The pertinent question is how important are vulnerable households for the loan portfolio of banks. The focus is no longer on the share of all households that are vulnerable (or the share of households with debt that are vulnerable), but on the total debt held by these households. Implicitly, it can be assumed that the loans held by vulnerable households will eventually enter into default. In this regard, the exposure to households ( _EH_ ) is given by where _p_ denotes the probability of default and it is assumed that this probability equals 1 if a household is vulnerable and 0 otherwise, and _D_ is the level of debt held by household _j_ .

But this is an extreme case. It could well be the case that loan restructuring efforts might make these loans once again viable. As already noted, a large share of the debt held by vulnerable households is at risk at loan origination. Assets held by households, which are not captured in HBS, might serve to reduce the default risks faced by households.

More importantly, the recovery given default might lead to more nuanced conclusions regarding financial stability. Unlike other countries in emerging Europe, Croatia did not experience a sharp increase in real estate prices during the pre-crisis period (Figure 7); real estate prices have declined by about 22 percent from the peak reached back in 2007. It can thus be assumed that bank losses could be limited to about 20 percent of the debt value held by households. In this context, the loss given default ( _LGD_ ) would be given by where _p_ again denotes the probability of default that equals 1 if a household is vulnerable and 0 otherwise, _D_ is the level of debt held by household _j_ , and _RE_ is the value of real estate; it is assumed that _D_ > _RE_ and that _RE_ = 0.8 _Dj_ . Some caveats are needed, however. On the positive side, a loan-to-value ratio of 100 percent is implicitly assumed. In all likelihood this is too high, thus facilitating banks’ recoveries on bad loans. On the negative side, losses given default in non real estate-related loans might be higher and, in addition, housing prices could enter a vicious declining path as NPLs rise—in particular if banks vigorously pursue foreclosure procedures.<sup>15</sup>

> 15 A few caveats. The decline in housing prices shows a marked difference between price developments in the Adriatic coast and those that have taken place elsewhere within the country. Indeed, central bank data suggests that much of the decline since the 2008 peak has occurred in Zagreb. Also, as noted by Kraft (2007), ever-growing competition in the banking sector in the one or two years just before the global financial crisis points to, for example, increases in loan-to-value ratios. But these factors were short-lived and brought to an abrupt end by the crisis; i.e., they are likely to affect a small share of the outstanding household debt stock. Of greater concern might be the situation in commercial real estate, but this is not a subject examined in this paper.

13
Based on the possible values of the three varying parameters we derived three estimatescentral, upper and lower bounds- of the increase in the poverty line, or conversely the reduction in real income, resulting from higher food prices.<sup>8</sup> Table 2 summarizes results of three scenarios. The higher the relative price change, the higher the food share in total consumption, and the lower the price elasticity, the higher the decline in real income, in our case, 20 percent.

**Table 2 Estimates of chan** **<mark>g</mark> e in real household income as a result of the chan** **<mark>g</mark> e in relative food** **<mark>p</mark> rices**

||Relative price<br>change (%)|Share of food<br>consumption (%)|Price<br>elasticity|Change in real<br>income (%)|
|---|---|---|---|---|
|Upper bound scenario|30|70|0.1|-20|
|Central scenario|20|60|0.2|-11|
|Lower bound scenario|10|50|0.3|-4|

Notes: The figures in the first three columns capture possible parameter values determining the real income effects of price changes among poor and near poor households (i.e households near the poverty line). The upper and lower bound estimate can be interpret as the worse and best case scenario, respectively. The figures are based on the stylized facts shown in Figure 1 and Table 1. The change in real income is estimated using equation 5.

In turn, for a given change in real households’ income the country-specific information on income distribution allows us to estimate the change in the poverty deficit. The computations are based on the micro dataset part of the Global Income Distribution Dynamics (GIDD) model.<sup>9</sup> The GIDD data includes 73 household surveys for low and middle income countries, adding up to 1.2 million households and over 5.1 million individuals. This large dataset together with aggregate information for China accounts for 88 percent of the population of the developing world in 2005. The advantage of having such a rich dataset is that we can compute the initial and final PD taking full account of household heterogeneity, without relying on simple average characteristics of the poor or on parameterizations of the Lorenz curve.

# **III. Results**

This section reports our estimates of the impact of food inflation on urban poverty at US$1 and US$2 a day. Given the large number of country- and scenario-specific estimates, this section shows detailed results for those countries with the higher change in the poverty deficit under the central scenario. The results for all countries under each of the three scenarios are presented in Annex II.

# **A. US$1 a day urban poverty**

> 8 The use of three different values for the three parameters yields 27 possible combinations. For the sake of clarity, we only report here the lower bound, the central, and the upper bound estimates. 9 For an explanation of the GIDD and an application on the ex-ante changes in global income distribution see Bussolo et al. (2007).
viation, and some others rely on ranks in the schooling distribution.<sup>8</sup> Second, even when based on the same measures of education and mobility, comparability across countries is often compromised by a lack of standardization of the data. Some surveys include data on years of schooling while others only categorical variables such as primary, secondary, etc. These dierent measures of schooling are likely to contain dierent degrees of measurement error and a study based on such data may not yield comparable estimates of intergenerational educational mobility across countries.

To deal with these challenges, we use sibling correlation in education as a measure of mobility, and take advantage of data from Demographic and Health Surveys (DHS). Sibling correlation stands out as a measure on both conceptual and empirical grounds. Conceptually, sibling correlation is a broader measure of intergenerational mobility compared to most of the other measures used in the literature.<sup>9</sup> First, the standard measures such as correlation between parents and children's schooling attainment usually rely solely on father's education because of missing data on mother's education. Sibling correlation reects the eects of both father and mother along with other family members residing in the household such as grandparents, uncles, and aunts. Second, similarity between sibling's educational outcomes does not only reect observable family characteristics, but also unobservable (to the researcher) factors shared by siblings such as family culture. It also captures other factors such as parenting style, aspiration, and risk attitude, even when the data sets do not contain any information on these variables. In this sense, sibling correlation is broader than the IOP and LW approaches which can only use the observable characteristics measured in a survey.<sup>10</sup> Third, sibling correlation captures broader neighborhood eects (including school and peers) that are not correlated with parental education but shared by the siblings growing up together in the same neighborhood.

An important advantage of sibling correlation as a measure is that it is less susceptible to coresidency bias because we want to capture the factors shared by siblings when growing

> 8For a comparative analysis of these alternatives measures of intergenerational educational mobility with a focus on possible conicts among them, please see Ahsan et al. (2022). They use data from China, India, and Indonesia.

> 9For discussions on this point, see Bjorklund and Salvanes (2011), Bjorklund et al. (2010), Deutscher and Mazumder (2022).

> 10IOP can include variables that represent factors not shared by the siblings. See the discussion by Bjorklund and Jantti (2020).

3
The main purpose of this paper is to investigate the connection between the creation of regulatory agencies and the performance of electricity operators in developing and transition countries. Our hypothesis is that, compared to selfregulation or control by a Ministry, a regulatory agency can do a better job at monitoring electricity distribution companies and can take remedial action if necessary. More specifically, regulatory agencies are expected to set tariffs that are in line with efficient costs, to ensure that minimum quality-of-service standards are met, and to enforce the targets for connection of new customers imposed by the governments.

The hypothesis of potential performance improvements associated to the creation of an independent regulator for infrastructure industries has been debated for over 10-15 years now (see Kessides, 2004). The empirical literature on the impact of reforms on the performance of individual operators, however, has mainly focused on the impact of privatization.<sup>2</sup> We investigate the connection between regulatory agencies and the performance of operators in the electricity distribution sector. Our empirical analysis takes advantage of a unique dataset that allows disentangling the impact of establishing a regulatory agency from the impact of private participation in a context in which increased private participation has been quite significant.

We first focus on the impact of regulatory agencies on firm efficiency as approximated by a labor requirement function. We then check the robustness of our

> 2 Megginson and Netter (2001) provide a survey of empirical studies on privatization. More recently, Andres et al. (2006) propose a very thorough assessment of the impact of privatization on various dimensions of performance of Latin American electricity distribution companies. Gassner et al. (2006) evaluate the connection between reforms and performance in developing and transitional countries (emphasizing the impact of privatization) by using partial performance indicators. Estache and Rossi (2005) focus on the impact of regulatory regime rather than institutions. Zhang et al. (2005) study the impact of reforms in developing countries in the electricity generation sector, emphasizing on the sequencing of reforms. Guasch (2004) studies the impact of regulatory agencies on the odds of renegotiation.

3
Figure 2: Conglomeration by Type of Financial Entity (latest available) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Banks CFCs Fiduciarias Comisionistas Insurance Pensions<br>Domestic Mixed-Activity Conglomerate Foreign Financial Conglomerate<br>State-owned Domestic 'Pure' Financial Conglomerate<br>Market Share (%)<br><!-- End of picture text --> _Source: Superintendencia Financiera and own analysis. Note: Percentages are market shares of all firms controlled by a group for each type of financial vehicle (the Government of Colombia is classified separately here). They are based on total assets (banks and CFCs), total debt and equity buying volumes for 2005 (comisionistas de bolsa), total assets under management (fiduciarias), total life and non-life assets (insurance), and total AFP assets under management (pensions)._

**Table 7: Classification of Financial Conglomerates (latest available)**

|**Financial Conglomerate**|||**Ma**|**rket Share**|||**Non-Financial**<br>|**Banking Sector Presence**|
|---|---|---|---|---|---|---|---|---|
||**Banks**|**CFCs**|**Fiduciarias**|**Comisionistas**|**Insurance**|**Pensions**|**Activities**||
|Grupo Aval|29%|19%|25%|4%|9%|27%|Construction and<br>real estate|4 banks (Bogota, Occidente,<br>Villas, Popular)|
|Grupo Sindicato|20%|41%|25%|8%|22%|24%|Cement, retail, food|<sup>Recently consolidated activities</sup><br>into one bank (Bancolombia)|
|Grupo Bolivar|7%|3%|3%|1%|9%||Construction|Davivienda|
|State-owned|12%|2%|22%||6%|||Banco Agrario, Granbanco|
|BBVA|11%||3%|0%|3%|17%||BBVA acquired Granahorrar in<br>privatization tender in late 2005|
|Citigroup|3%|1%|6%|||14%||Citibank|
|Santander|3%||2%|0%||11%||Santander|
|Skandia|||1%||2%|7%||N/A|
|Mercantil Colpatria|3%||1%||7%||Construction|Red Multibanca Colpatria|
|Helm Financial Services|2%|8%|2%|0%||||Banco de Credito|
|Fundacion Social|4%||0%||2%|||Banco Caja Social|

_Source: Superintendencia Financiera and own analysis. Note: Percentages are market shares of all firms controlled by a group (the Government of Colombia is also included here) for each type of financial vehicle. They are based on total assets (banks and CFCs), total debt and equity buying volumes for 2005 (comisionistas), total assets under management (fiduciarias), total life and non-life assets (insurance), and total AFP assets under management (pensions). Foreign and domestic entities that are primarily active in only one financial sector (e.g. Banistmo and Banco GNB Sudameris in banking, AIG and RSA in insurance) are excluded from the definition of financial conglomerates._ 15
21 interventions, by Ministry, expenditure level, and number of beneficiaries, and World Bank estimates of total nutrition and health expenditures are reproduced in Appendix table B2. About 90% of the financing for nutrition interventions is from central government, and 10% from local government (Appendix table B4). The central government contribution is divided into receipts from taxes (80% of the total expenditures on nutrition), with the other 10% contributed by central government coming about equally from user fees and from foreign contributions (Appendix table B4). As regards local government contributions, about three quarters is from city governments, with the rest from provincial governments. (Municipal governments contribute a very small amount: Appendix table B3).

Appendix tables B5-7 present some comparative cost information for USAID-supported programs. There are differences by operating agency: CARE tends to report higher costs than CRS (Catholic Relief Services), however CARE tend to document costs better than most other NGO's. Of the 3 intervention types (school feeding, MCH and food for work), MCH programs are the cheapest.

ARE reports the highest cost program is food for work, whereas CRS report the highest cost program is school feeding. Food for work and school feeding interventions cost 1.5-2 times as much as MCH programs. However once targeting is taken account of, they cost 4 to 6 times as much as MCH programs, per beneficiary with 2nd or 3rd degree malnutrition. Further information on programs in the Philippines is available in USAID (1982) and Aguillon (1986).

Data for Malawi are much more scanty. The majority of expenditures are financed externally. Domestic funding goes to the relatively new Food Security and Nutrition Institute, and as a contribution towards the WFP-financed feeding programs. Appendix table Cl presents information on WFP and UNICEF expenditures. There are few published sources describing nutrition interventions in Malawi, other than World Food Program project documents.