rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>Table1:  Selected Rural Payphone Programs in Latin America, 1995-2000</sup>

||Chile|Colombia|Guatemala|Peru|
|---|---|---|---|---|
|_Country data_|||||
|GDPpercapita(1995), $|4,710|2,040|1,380|2,440|
|Ruralpopulation as % of total(1995)|15|27|59|28|
|Main telephones per 100 inhabitants|16|12|3|6|
|(1996)|||||
|_Program design _|||||
|Summary|200 projects.<br>7 bidding<br>rounds, 1995-<br>2000|7 regional<br>projects. Single<br>bidding round,<br>1999|17 provincial<br>projects. Bids<br>awarded until<br>7/2000|7 regional<br>projects. 3<br>bidding rounds,<br>1998-2000|
|Populationof localitiesto be served|>60|>250||>300|
|Service obligation, years|10|10|5|20|
|Priceregulation|yes|yes|no|yes|
|Exclusive operatingrights|no|no|no|no|
|Sources of subsidy funds|government<br>budget|levy on<br>operators,<br>fees|spectrum<br>license fees|levy on<br>operators, fees|
|Subsidy payout after service begins|in full|installments<br>|installments<br>|installments (5<br>|
|||(18 months)|(18 months)|years)|
|_Program results_|||||
|Localities served|6,059|7,415|1,598|4,420|
|population,million|2.2|3.7|1.3|1.6|
|% of total ruralpopulation|>90|35|20|24|
|Regional call charge per minute, US<br>cents|11|14||6|
|Annual revenue per payphone, $ (est.)|1,400|1,300||1,900|
|Subsidyawarded, $million|22|34|7|42|
|per locality, $|3,600|4,600|4,400|9,500|
|per inhabitant in localities served, $|10|9|5|26|
|as<br>%<br>of<br>maximum<br>subsidy<br>available|60|45|78|37|
|as % of total telecommunications<br>revenue|0.3|1||1|
|Private investment per $ of subsidy,<br>$|6||3|2|
|Localities perproject|30|1,200|94|630|
|Number of biddersper round|3-5|2-7|2-5|2-5|

Sources: Authors’ compilation from Izaguirre 1999, Cannock.2001, and ITU 1998 Competition among firms for subsidies is by now a well-established tool used to extend payphones to rural areas in middle-income countries. Table 1 summarizes the experience of Chile, Colombia, Guatemala, and Peru. In all these countries, basic voice communication has become accessible to large segments of the rural population previously excluded. Investments have been mainly financed by the private sector 7
# FIGURE 12 Global value chain participation and pass-through

A. Global value chain participation <!-- Start of picture text --> Percent<br>30 Weighted average Median<br>28<br>26<br>24<br>22<br>C. Global value chain participation and pass-<br>through from monetary policy shocks in EMDEs<br>50<br>40<br>30<br>20<br>10<br>0<br>0 0.2 0.4 0.6<br>Pass-through, ratio<br>percent<br>Global value chain participation,<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> C. Global value chain participation and passthrough from monetary policy shocks in EMDEs B. Global value chain participation and passthrough from monetary policy shocks <!-- Start of picture text --> Ratio<br>0.6 Interquartile range Median<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> D. Global value chain participation and average exchange rate pass-through in EMDEs <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-0.1 0 0.1 0.2<br>Pass-through, ratio<br>percent<br>Global value chain participation,<br><!-- End of picture text --> Source: OECD; World Bank.

Note: Global value chain data are from the OECD-WTO TiVA database. The selected indicator is foreign value added as a percent of gross exports. Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; OECD = Organisation for Economic Co-operation and Development; TiVA = Trade in Value Added; WTO = World Trade Organization.

B. Low and high value chain participation are defined as below or above the sample average.

C.D. The sample only includes EMDEs with floating exchange rate regimes according to the IMF classification. D. Shock-specific pass-throughs are aggregated using shares of currency movements accounted for by each type of shock as weights.

34
**Table 2: The Territorial Scales for Water Resources Management in São Paulo State and in the Alto-Tietê basin**

<!-- Start of picture text --> Hydrographic Hydrographic unit for<br>Hydrographic region<br>basin management purposes<br>Mantiqueira<br>Sapucaí/Grande<br>Mogi Guaçu<br>Pardo<br>River Basin Pardo<br>Baixo Pardo/Grande<br>Turvo/Grande Juqueri-Cantareira (713 km 2 )<br>São José dos dourados Tietê-Cabeceiras (1,694 km2)<br>PARANA<br>RIVER<br>Alto Tietê (5,985 km2) Cotia-Guarapiranga (965 km2)<br>BASIN<br>(Part in Piracicaba/Capivari/Jundiaí<br>São Paulo State:<br>(212,197 km2) Tietê River Basin Tietê/Sorocaba Billings-Tamanduateì (1,025 km2)<br>(77,474 km2)<br>Tietê/Jacaré<br>Tietê/Batalha Pinheiros-Pirapora (1,588 km2)<br>Baixo Tieté<br>Aguapei<br>Peixe<br>Alto Paranapamema<br>Paranapanema<br>Basin Medio Paranapamema<br>Pontal do Parapamema<br>Paraiba do sul<br>COASTAL BASINS Litoral Norte<br>Baixada Santista<br>Ribeira Iguape e Litoral Sul<br><!-- End of picture text --> Source: Formiga Johnsson, 1998, adapted.

19
# **Export Destinations and Input Prices**<sup>_∗_</sup>

Paulo Bastos<sup>_†_</sup> Joana Silva<sup>_‡_</sup> Eric Verhoogen<sup>_§_</sup> JEL Classification: F12, O14, L11.

Keywords: Product prices, quality, heterogeneous firms, export destinations. Sector Board: EPOL.

——————————

> _∗_ We are grateful to Mine Senses, Michael Sposi and participants in several seminars for helpful comments and to Nicol ́as De Roux and Jonathan Dingel for excellent research assistance. Verhoogen thanks the New York Federal Reserve Bank for hospitality while the working paper was being completed. We remain responsible for any errors.

> _†_ The World Bank, pbastos@worldbank.org.

> _‡_ The World Bank, jsilva@worldbank.org.

> _§_ Columbia University, eric.verhoogen@columbia.edu.
29

### Table 3

## Inequality and growth

||Percent share of<br>income of<br>lowest 20 percent<br>(various years, 1980's)|Per capita income<br>1988|Per capita<br>income<br>lowest 20<br>percent|
|---|---|---|---|
|Bangladesh<br>|9.3<br>|170<br>|79<br>|
|India<br>|8.1<br>|340<br>|138<br>|
|Pakistan<br>|78<br>|350<br>|137<br>|
|Ghana|6.5|400|130|
|Sri Lanka<br>|48<br>|420<br>|101<br>|
|Indonesia<br>|8.8<br>|440<br>|194<br>|
|Philippines|5.5|630|173|
|Cote d'Ivoire<br>|5.0<br>|770<br>|193<br>|
|Morocco<br>|9.8<br>|830<br>|407<br>|
|Guatemala<br>|5.5<br>|900<br>|248<br>|
|Botswana<br>|2.9<br>|1010<br>|126<br>|
|Jamaica<br>|5.4<br>|1070<br>|289<br>|
|Colombia<br>|4.0<br>|1180<br>|236<br>|
|Peru|4.4|1300|286|
|CostaRica<br>|3.3<br>|1690<br>|279<br>|
|<br>Poland<br>|9.7<br>|1860<br>|902<br>|
|Malaysia<br>|4.6<br>|1940<br>|446<br>|
|Brazil<br>|2.4<br>|2160<br>|259<br>|
|Hungary<br>|10.9<br>|2460<br>|1341<br>|
|Yugoslavia<br>|6.1<br>|2520<br>|769<br>|
|Venezuela|4.7|3250|764|

Correlation Coefficien., per capita income and income of lowest quintile: 0.77

# Source: World Development Report .990

inequality was rather marginal. Lindert and Williamson (1984) present evidence against the Kaldor hypothesis that, because it is the rich that save, inequality is necessary for high saving and growth.

ratio The project would consider inequality in two ways. First, measures of inequality (e.g. the of the income share of the top to the bottom quintile) will themselves be tried as dependent variables as part of each policy task, to examine whether the same policies that affect growth also affect inequality. Second, inequality measures will be used as independent variables in growth equations to test whether higher inequality can make a given policy more or less damaging to growth. The synthesis task will explore further the empirical regularities between income growth and inequality to address the effectiveness of per capita growth as an instrument to reduce poverty.
5 smaller than those between urban and rural areas, but they may still matter, and we do report a robustness test with respect to cost-of-living differences in the only country in our sample for which data permits it, namely Brazil.

Fourth, we do not test the robustness of our findings to variation in equivalence scales – which would matter if family sizes and composition varied systematically across city sizes. Fifth, we assess poverty in a fairly restrictive way: focusing on the share of the population with incomes or consumption levels below the poverty line. It is widely acknowledged that poverty can be viewed more broadly, reflecting multiple dimensions of wellbeing. We seek to mitigate this concern by reporting the association between city size and access to various publicly-provided services and, in one instance, by looking separately at a health outcome (child malnutrition). Nevertheless, it should be acknowledged that the patterns observed in these spaces need not be repeated when other dimensions of poverty are considered.

Each of the limitations of the analysis presented in this paper points to the need for additional research. This paper simply documents the existence of systematic differences in the intensity of poverty (and access to services) across city sizes in eight geographically diverse developing countries, and raises questions about the possibility that policies designed to reduce urban poverty may suffer from a metropolitan bias.

The paper is structured as follows. Section 2 provides an overview of the poverty mapping methodology which was used in each of the six countries, in order to generate reliable poverty estimates for every urban area captured in the population census. Section 3 describes the data sources to which this method was applied, in each country. Section 4 presents the consumption poverty profiles by city size in each country. Section 5 turns to the evidence on access to publicly provided services across city sizes. Section 6 looks at differences in poverty (and inequality) within specific cities in Morocco, focusing in particular on poverty and inequality differences between slums and non-slum areas in larger towns. Section 7 subjects our findings to some robustness checks. We first look to data from Brazil for evidence of spatial price variation across city-sizes. A second robustness check is applied to the case of Mexico, to examine whether an alternative dimension of deprivation, namely child malnutrition, exhibits the same gradient across city size categories as income poverty. In a final robustness check we show that, in India,
46 the value of the annuity, those using the nominal yield curve and the 5-year bond most likely overstate the true value, as they do not take into account the possibility of a well-diversified portfolio with a higher return.

**Figure 19  Nominal yield curves since 2000**

## **Yield Curves Nominal**

<!-- Start of picture text --> 4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>0 12 24 36 48 60 72 84 96 10 12 13 14 15 16 18 19 20 21 22 24<br>8 0 2 4 6 8 0 2 4 6 8 0<br>months<br><!-- End of picture text --> <!-- Start of picture text --> 2000 06 30<br>2001 06 29<br>2002 06 28<br>2003 06 30<br>2004 06 30<br>2005 06 15<br><!-- End of picture text --> Nominal yield curves since 2000 (June 30 of each year, June 15 in 2005). The corresponding inflation rates are 1.84 (2000), 1.58 (2001), 0.33 (2002), 0.55 (2003), 1.12 (2004), and 1.08 (2005).

Source: Swiss National Bank.

# **4.3 Money’s Worth Ratios in the Swiss System**

Recall that occupational pension benefits are strictly proportional to the accumulated retirement assets (retirement credits plus accrued interest). The accumulated capital _K_ is translated into a yearly pension _B_ using the conversion factor γ :<sup>45</sup>

# _B_ = γ _K_

The BVG/LPP mandates joint annuities for men, but not for women. The conversion factor is the same for everybody irrespective of gender, family status or income. Children under age 18 (or under age 25 if still dependent) of retired persons get an additional pension of 20 percent of the main claimant’s benefit. When a retired individual dies, his/her surviving spouse receives a benefit amounting to 60 percent of the previous pension, his dependent children a benefit of 20 percent each. These survivor benefits are not means tested.

> 45 This conversion also applies to defined benefit plans; the fund has to make sure that enough capital is accumulated to cover the claims made based on previous income.
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.92*** 0.95***<br>H<br>Kendall tau-b 0.78*** 0.78*** 0.83***<br>Spearman 0.97*** 0.95*** 0.9***<br>A<br>Kendall tau-b 0.89*** 0.89*** 0.78***<br>Spearman 0.93*** 0.95*** 0.95***<br>M0<br>Kendall tau-b 0.83*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C6. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Possession of assets indicator removed (weights within the Resilience dimension<br>were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C6.1. FOSD: Counting Vector C6.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C6.3. SOSD: Social Sustainability Index C6.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.88*** 0.67***<br>H<br>Kendall tau-b 0.81*** 0.73*** 0.54***<br>Spearman 0.82*** 0.84*** 0.83***<br>A<br>Kendall tau-b 0.65*** 0.68*** 0.76***<br>Spearman 0.97*** 0.88*** 0.66***<br>M0<br>Kendall tau-b 0.87*** 0.73*** 0.53***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8<br>0<br>.6<br>CCDF -.05<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C6. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Possession of assets indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C6.1. FOSD: Counting Vector C6.2. Dominance Analysis 53
<!-- Start of picture text --> World<br>1.6<br>= 1.4<br>"<br>Oo<br>a<br>@<br>modaP<br>3<br>£<br>1.0<br>0.8<br>1990 1995 2000 2005 2010 2015<br>—— Enmission intensity effect —— population effect<br>—— Economic growth effect — co2<br><!-- End of picture text --> <!-- Start of picture text --> 3.5 High income 3.5 Upper middle income 35 Lower middle income 3.5 Low income<br>3.0 3.0 3.0 3.0<br>= 2.5 = 25 = 25 = 25<br>ul iT Il tH<br>& 2.0 & 2.0 S 2.0 8 2.0<br>= 2 2 2<br>Zz 15 Z15 Z15 15<br>oO a oD oO<br>= 1.0 2 10 Z 1.0 2 1.0<br>0.5 0.5 05 0.5<br>0.0 0.0 0.0 0.0<br>1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 #2000 2010<br>—— Emission intensity effect _—— population effect —— CQ2<br>— Economic growth effect<br><!-- End of picture text -->
The main data needed for the study includes a social accounting matrix (SAM) of Thailand and elasticity parameters as implied by Figure 1 to Figure 3. The SAM was taken from Timilsina and Shrestha (2002) and elasticity values were taken from Timilsina and Shrestha (2006).

# **3. RESULTS FROM MODEL SIMULATIONS**

# **3.1. Impacts on economic welfare**

Welfare impacts of the DSM program are presented in Figures 4(a) to 4(c). In each figure, the curve designated by “No CDM” represents welfare effects of the DSM program in the absence of the CDM, whereas the other curves represent the welfare effects of the DSM program under the CDM with varying CER prices. Figure 4(a) assumes that the unit cost of electricity savings is 40% of the price of electricity (i.e., CPR =0.4). Figure 4(b) and 4(c) assume CPR to be equal to 0.6 and 1.0, respectively.

**Figure 4: Welfare Effects of the DSM Option**

<!-- Start of picture text --> 0 20 40 60 80 0 20 40 60 80<br>0 0<br>-0.2 -0.2<br>-0.4 NO CDM -0.4 NO CDM<br>US$5/tCO2 US$5/tCO2<br>US$10/tCO2 US$10/tCO2<br>-0.6 US$25/tCO2 -0.6 US$25/tCO2<br>US$50/tCO3 US$50/tCO3<br>-0.8 -0.8<br>Replacement rate (%) Replacement rate (%)<br>(a) η = 0.4 (b) η = 0.6<br>Welfare change (%) Welfare change (%)<br><!-- End of picture text --> 19
<!-- Start of picture text --> Figure 2<br>Adjusted Weekly Hours Worked, 1996<br>50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Primary Primary L-Secon. L-Secon. U-Secon. U-Secon. The Mix- The The Low-<br>Teacher Pub. Teacher Priv. Teacher Pub. Teacher Priv. Teacher Pub. Teacher Priv. Skilled Group Agricultural Skilled Group<br>Sch Sch Sch. Sch Sch Sch Group<br>Urban Rural<br><!-- End of picture text --> Source: Own estimates based on ENIGH survey

# _Secondary Occupation_

As mentioned above, teachers work fewer hours than the other occupational groups. It is relevant to examine how many hours on average teachers devote to their secondary occupation. Table 4 shows the secondary occupation shares by occupational categories, or the percentage of people that have a secondary occupation. On the whole, for all occupational groups the secondary occupation share is larger in the rural areas than the share of a secondary occupation is for the urban areas, excepting upper secondary teachers in public schools. In particular, note that the secondary occupation share for teachers in basic public schools is significantly larger than the secondary occupation share for the teachers in basic private schools. Yet if one compares the secondary occupation share for the teachers in basic public schools with regards to the upper secondary teachers occupation share, the pattern is the opposite, especially in the urban areas.

In addition, one can compare the teacher secondary occupation shares and the hours-worked in secondary occupation relative to _the mixed-skilled_ group. The table below shows that the secondary occupation shares of teachers in public schools are larger than the shares of secondary occupation for _the mixed-skilled_ group. Nevertheless, one must consider that: (a) _The mixed-skilled_ group has the longest hours worked among all the occupational categories, and (b) the relative difference of hours worked in the main occupation and the total hours worked (including the hours worked in the secondary occupation) between _the mixed-skilled_ group and these kinds of teachers remains almost unchanged in urban areas and increases in rural areas.

11
# **Are banks too big to fail or too big to save? International evidence from equity prices and CDS spreads**

Asli Demirgüç-Kunt<sup>1</sup> World Bank Harry Huizinga<sup>1</sup> Tilburg University and CEPR

**Key words** : Banking, Financial crisis, Credit default swap, Too big to fail, Too big to save

**JEL classifications** : G21, G28

> 1 Demirguc-Kunt: World Bank (Ademirguckunt@worldbank.org); Huizinga: Tilburg University and CEPR (Harry.Huizinga@ecb.int). We thank seminar participants at the European Business School for comments. Financial support from DFID is gratefully acknowledged.  This paper was in part written while Harry Huizinga was a Duisenberg Research Fellow at the European Central Bank.  This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the European Central Bank and the World Bank, its Executive Directors, or the countries they represent.
However, there is considerable variation across villages, ranging from no land to all land allocated to non-cereal crops.<sup>8</sup> Our fi nal measure relates to commercialization of agriculture which captures the structural change away from home to market production emphasized in the recent literature (see, or example, Gollin et, al. 2002, 2006). Speci fi cally, the shares of production of all crops and non-rice crops sold in the market are taken as measures of commercialization.<sup>9</sup> According to Table 1, on average, about 14.7% for rice output, and 21% of non-rice output are sold in the market. Overall, 19% of total crop output (median=15%) is sold for cash, rest being consumed at home or handed out as in kind payments. Despite the relatively low average degree of commercialization, in a number of villages, more than 50% of output are sold even in the case of rice, the main staple crop in Nepal.

Panel B of Table 1 reports the summary statistics for access to and size of the market located nearest to the surveyed villages. Despite its comprehensive data coverage, the NLSS 1995/96 lacks information on access to urban centers as well as market size in each urban centers. We complement the NLSS data by constructing measures of both access to urban centers and urban income. The Population Census of 1991 identi fi es 34 towns and cities in Nepal where a town is de fi ned as a settlement of more than 10, 000 inhabitants. We fi rst compute the distance between each surveyed ward/village and each of these towns. Distances are normally taken along existing roads, except when roads do not exist, in which case we calculate the shortest arc distance to the nearest road, and then the distance to various cities along the road. Distances are then converted into travel time using available information about trucking and walking speeds along various types of roads in Nepal.<sup>10</sup> O ff the road

> 8 Both the Her fi ndahl Index and share of land devoted to non-cereal crops have a maximum of 1. There is, however, only one village with H=1. There are only 5 villages where all of the land is allocated to non-cereal crops. This should ameliorate any concerns for censoring in our data.

> 9 The regression analysis focuses on all crops and non-rice crops sales because there is no rice sales in a large number of villages (about a third of the villages). This causes the problem of censoring in the case of regression for rice sales. The di ff erence between the rice and non-rice sales can be highlighted by focusing on all vs. non-rice sales as well while avoiding the censoring problem.

> 10 Travel speeds are calculated for various terrains and types of road. The travel times on di ff erent terrains and road types were obtained through discussion with various transportation experts and South Asia operations sta ff at the World Bank. Travel on highways and provincial roads is assumed to take place by truck; travel on secondary roads is assumed to be by cart. For details on the construction of the travel times, see Fafchamps and Shilpi(2003).

11