rafmacalaba/datause-encoder-data huggingface dataset:
40

**Figure 8: Official retail price of LPG sold in 12‐ and 15‐kg cylinders converted to U.S. dollars**

<!-- Start of picture text --> 0.60<br>0.40<br>0.20<br>0.00<br>Dec‐08 Dec‐09 Dec‐10 Dec‐11 Dec‐12 Dec‐13 Dec‐14 Dec‐15<br>Source: World Bank staff calculations based on data at http://www.infoleg.gov.ar/infolegInternet/anexos/240000‐<br>244999/240545/norma.htm.<br>US$/kg<br><!-- End of picture text --> difference between this price and domestic gas prices. Decree 786/2002 established a fund to subsidize residential LPG for low‐income households and natural gas expansion. The fund is paid for by specific charges on natural gas consumption. The decree limits the total amount of subsidies and compensation provided by the fund to 100 million pesos a year, although the Minister of Economy is granted the authority to increase the amount. It is clear from Table 4 that the annual budgetary transfers in 2013 and 2014 were far in excess of this limit.

**Table 4: Budgetary transfers for various energy subsidies**

|Recipient|2014<br>million|2013<br>pesos|2014<br>US$ m|2013<br>illion|
|---|---|---|---|---|
|Compañía Administradora del Mercado Mayorista Eléctrico<sup>a</sup>|71,333|36,229|8,799|6,623|
|Energía Argentina Sociedad Anónima<sup>b</sup>|53,450|31,188|6,593|5,702|
|Plan Gas|11,299|6,236|1,394|1,140|
|Yacimientos Carboníferos Río Turbio<sup>c</sup>|3,711|2,913|458|533|
|Fund forpower transmission|1,274|1,025|157|187|
|Fund for residential naturalgas subsidy|322|153|40|28|
|Fund for subsidizing low‐income consumers of LPG and natural<br>gas expansion|1,841|1,525|227|279|
|Provincial agencies|3,063|1,890|378|346|
|Nucleoeléctrica Argentina S.A.|6,608|4,882|815|892|
|Others|7,853|4,677|969|855|
|Total|160,755|90,717|19,828|16,584|

_Source:_ ASAP 2014.

a. Wholesale administrator of electricity.

b. State‐owned enterprise created in 2004 to engage in oil and gas exploitation and commercialization as well as the power sector.

c. State‐owned coal mining company.

A 2011 resolution removed subsidies for imported gas established under Decree 2067 of November 2008 for certain categories of non‐residential consumers. Resolution I/2851 issued in April 2014 allowed for three consecutive natural gas tariff hikes in April, June, and August, reducing price subsidies. In order to promote conservation, incentives were provided by making tariff increases dependent on the degree of consumption reduction—there are three tariff tables, corresponding to consumption reduction of more than 20 percent, between 5 and 20 percent, and less than 5 percent than that in the same period a year earlier. The differences in tariffs are substantial, amounting to hundreds of percent. Resolution
density of paved roads in good condition (km/million inhabitants) varies from 40 in low-income economies to 470 in middle and 8,550 in high-income economies. There is also a clear contrast between road infrastructure and income in low and middleincome economies in Africa: while the difference in average per capita GNP between the two country groups is 220 percent, the density of paved roads in good condition varies by about 370 percent from one group to the other, using 1989 data.

Causality is an issue highlighted for future research: Does an increase in road stock cause growth or is it the other way around? Assessing the impact of the supply and quality of road infrastructure on economic performance is a complex area of research with potentially important implications on the intemational infrastructure lending strategy to developing countries.

# Acknowledgements

The authors benefitted from comments of James Wright, Jean Doyen, Peter Morris, Charles L. Wright, Manuel Santos, Harold Young, Hiroshi Ueno, John Riverson, Antoine G. Hobeika, Merron L. Latta, Jeremy Warford, Ajay Chhibber, Arturo Israel, Peter Cook, John Roome and Mohsen Fardi.

11
representative of the adult population in each country.<sup>6</sup> A net sample size of approximately 1,000 households was selected in each of the countries, although in several countries this net sample size was boosted to 1,500 in 2010 (Serbia, Poland, Russia, Ukraine, Uzbekistan and Great Britain).

In addition to information on opinions and attitudes in Europe and Central Asia, the LiTS also contains data on household welfare and individual characteristics. We construct, for the pooled sample and separately for each country, an asset index as a measure of household welfare (see Cojocaru and Diagne, 2014). The asset index is based on household ownership of the following items: a car, a secondary residence, a bank account, a debit card, a credit card, a mobile phone, a computer, access to internet at home, and household access to water, electricity, a fixed telephone line, central heating, public (piped) heating, and pipeline gas. The asset index is based on a linear combination of household asset ownership and housing characteristics. Principal components analysis (PCA) is used to derive weights (Filmer and Pritchett, 2001).

Table 1 shows summary statistics for the main variables considered in this paper.

> 6 The sample design for the two surveys was a two-stage cluster sample design within each country. In the first stage, primary sampling units (PSUs) were randomly selected systematically according to a probability proportionate to their size (population 18 and older). In the second stage, 20 households were randomly selected from each of the selected PSUs.

5
high polluted days.<sup>20</sup> In spite of that, the city has been outperforming the average GDP per capita growth of India and has managed to climb up the ladder from a low income to a lower‐middle income city. Despite this high performance, during 2015, Patna experienced a considerably lower rate of employment growth (from above 7% in between 2012 and 2014, to 0.16% in 2015)<sup>21</sup> while the population growth rate is still high. Pollution may be one of the factors affecting this low employment rate, for example, the city may be failing to attract new firms that could generate more jobs.

_Figure 10 Pollution and City Competitiveness in terms of Productivity Growth – South Asia and Sub‐Saharan Africa_ <!-- Start of picture text --> PM 2.5 Concentration and City GVA 2000−2015<br>Africa and South Asia<br>5.0<br>WHO Guideline<br>●<br>Chandigarh<br>●<br>2.5 Antananarivo ●<br>Bulawayo ●● Kano ● Abeokuta ● ●● ● ●● ●<br>● ●● ●●● ● ●● ●<br>● ● ●● Coimbatore ● ● ●● Allahabad Meerut<br>0.0 ●● ●●● ● ●●●●●● ● ● ● ● ● ●●● ●● ●● ●●● ●●●●● ● ●●●●●●●● ●●● ● ● ● ● ●●● ●●●● Amritsar ● ●●●●● ● ● ● Faisalabad ● Raipur Bhilai Nagar ●●● Cuttack ● ●●●●● JalandharLahore ●● Ludhiana Kathmandu ● ● Bokaro ● Dhanbad ● Moradabad ● Bareilly ● Aligarh ●● ● ●● ●●●● KanpurKolkataDelhi ● Lucknow ● Gorakhpur ● Patna ● Regionsaa ●● SARSSA<br>● ● ● ● ● ● ● ●● Bhubaneswar ●● Ranchi ● ● JamshedpurDhaka ● ●●● ● Agra ● Asansol ● GhaziabadVaranasi<br>● ● Gwalior Khulna Rajshahi Faridabad<br>−2.5 ● ●● ●<br>● ●<br>●<br>● ● ●<br>●<br>−5.0 ●<br>0 25 50 75 100<br>PM 2.5 in 2015<br>City GVA per capita compared to National GVA, 2006−2015<br><!-- End of picture text --> Dhaka is also struggling with high levels of air pollution, although levels are lower than what we can find in Patna for example. The Clean Air and Sustainable Environmental Project had certainly helped managing

> 20 Published in India Times, <u>https://timesofindia.indiatimes.com/city/patna/patna‐air‐pollution‐worse‐than‐that‐ of‐delhi/articleshow/61664183.cms, accessed on May 4, 2018.</u>

> 21 It is worth noting that, during 2015, productivity had increased approximately 2.5%. However, a similar rate was observed in 2009, although employment was growing at 5.5% at that time.

28
# I. Introduction

Hurricanes have become the icon of climate change with pictures from space parading the front covers of many climate change books including the journal of Climate Change Economics. The Intergovernmental Panel on Climate Change (IPCC) argues that hurricanes and other extreme events are an important reason to support greenhouse gas mitigation efforts. Several scientists report an increase in hurricane intensity over the last 30 years (Emanuel 2005, IPCC 2007a). The IPCC (2001) and Swiss Re (2006) report dramatic increases in hurricane damages over time.

And yet despite these findings, the link between climate change and hurricane damage remains controversial. Partly this is due to the fact that highly destructive hurricanes are rare events and so it is difficult to detect changes in underlying frequencies and severity (Landsea et al. 1999; 2006). Over the last 50 years, 111 hurricanes have struck the United States (NOAA 2009). Many things changing over time can influence the damages from storms, most noticeably population and income (Pielke et al. 1998; 2008). Accounting for changes in the vulnerable population and capital in the path of storms, it is not clear that there is any trend in hurricane damages (Pielke et al 1998; 2008). The historic record may simply not be long enough and clear enough to detect how climate may be affecting hurricanes.

Estimates of current hurricane damages in the US are $9 billion/year (0.06% of GDP) (Nordhaus 2010). Using simulations of future hurricanes, Emanuel (2006) predicts that average hurricane intensity in the US would increase with warming. Assuming that every hurricane increases in intensity, several authors have predicted US damages would increase. Assuming that hurricane damages increase with the cube of wind speed, Hallegatte (2007) predicts US damages would increase 54 percent. However, other authors have calculated that damages increase by the fifth power of wind speed or higher and so predict that US hurricane damages would double (Nordhaus 2006; 2010; Pielke 2007.

In this paper, we take a different approach to estimating the impact of climate change on hurricanes by relying on a geographically detailed Tropical Cyclone Integrated Assessment Model (HIAM). The model begins with the A1B SRES global emissions 2
# **<u>Table 3: Double difference estimates of rural road kilometer increments during project period (total km)</u>**

|||**Total samp**|**le**||**Treatment sup**|**port**||**Common sup**|**port**|
|---|---|---|---|---|---|---|---|---|---|
||**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|<br>**PS weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched DD**|**PS weighted**<br>**DD**|
|**Total km**|4.88|5.76|6.76|4.64|5.75|6.76|3.77|4.57|5.88|
||(1.68)**|(2.45)**|(2.39)**|(1.70)**|(2.30)**|(2.39)**|(1.51)**|(2.27)**|(2.29)**|
|**_Of which:_**||||||||||
|<br>**Built**|2.43|2.77|2.89|2.42|2.76|2.89|2.09|2.38|2.56|
||(1.04)**|(1.37)**|(0.94)**|(1.04)**|(1.17)**|(0.94)**|(1.02)**|(1.03)**|(0.92)**|
|**Rehabilitated**|2.45|3.00|3.88|2.23|2.99|3.87|1.68|2.19|3.32|
||(1.48)|(1.87)|(2.13)*|(1.46)|(1.85)|(2.14)*|(1.38)|(1.89)|(2.10)|
|**Allocated by RTP1**<br>(Project database)||4.58|||4.58|||4.50||
|<br>**p-value for test**:||||||||||
|<br>rehab=allocated|0.16|0.40|0.74|0.12|0.40|0.74|0.05|0.23|0.32|
|total=allocated|0.86|0.64|0.37|0.97|0.60|0.37|0.60|0.98|0.58|

Note: (i) Standard errors clustered at the district level are given in parentheses; PS kernel matched standard errors are obtained by bootstrapping (100 repetitions). (ii) ** and * indicate significance levels of 5 and 10%; (iii) The total sample has 194 observations (96 comparison and 98 project communes); treatment support has 191 observations (93 comparison and 98 project communes); common support has 185 observations ( 93 comparison and 92 project communes).

25
- 30 -

- **Table 6: Market capitalization portfolio distribution**

|**Variables**|**Mo**<br>**Coeff.**|**d5**<br>**P-val.**|
|---|---|---|
|e|-1.6290|0.072|
|**_r_**|-1.1447|0.335|
|p|0.0861|0.273|
|**cssh**|0.8245|0.000|
|**nish**|5.2434|0.031|
|**vt**|0.8793|0.000|
|**duml**|-0.6998|0.000|
|**psim**|-0.8647|0.006|
|**constant**|0.8267|0.000|
|**obs**|229||
|**R2/1**|0.85||
|**JS /2**|89.41|0.000|
|**HS/3**|2.02|0.959|

Notes: In Mod5 we used **the** same instruments as in Modl. **_/L_** The R2 **statistics** reported is the squared correlation between fitted and actual values. /2 The joint significance test is distributed as a Chi2(8). **/3** HS is the Hausmann specification test and it is distributed as a Chi2(7).

This is done in regression Mod5 in the Table 6. Although the sample size is now smaller with only 23 countries and 229 observations, results are directly comparable to Modl. In fact, similarly to what was obtained in Modl, the portfolio distribution of contractual savings and non-life insurance companies has also a positive and significant impact on market capitalization at almost any significance level.

# **_Contractual savings and value traded_**

The model we presented focused on one dimension of stock market development: market capitalization. In this section, we would like to present preliminary results on the impact of contractual savings and non-life insurance development on stock value traded. The issue we would like to explore is whether the different natures of liabilities between contractual savings and other institutional investors has any influence on the choice of markets these institutions are active. In other words, whether there is any evidence in the data collected to support the assertion that contractual savings, because of their more illiquid liabilities, should be more active in the primary market when it comes to buying and holding long term securities, whilst more liquid institutions like mutual funds and non-life insurance companies are more active in the secondary market. Thus, whether there is evidence supporting the idea that contractual savings have a stronger impact on market capitalization while non-life insurance companies have a stronger impact on value traded.

We already found in the paper that both contractual savings and non-life insurance companies have a significant impact on market capitalization. In this section, we explore whether the same relationship between the two types of institutions exists with respect to value traded as well. In order to analyze these propositions, we modeled the impact of the development of institutional investors in terms of financial assets over GDP on the liquidity of capital markets expressed by stock value traded over GDP. Like
WPS3956

# **Discordant couples**

**HIV infection among couples in Burkina Faso, Cameroon, Ghana, Kenya, and Tanzania**

## **Damien de Walque**<sup>∗</sup>

<u>ddewalque@worldbank.org</u>

**Development Research Group The World Bank**

## **Abstract**

Most analyses of the determinants of HIV infection are performed at the individual level. The recent Demographic and Health Surveys which include results from HIV tests allow studying HIV infection at the level of the cohabiting couple. This paper exploits this feature of the data for Burkina Faso, Cameroon, Ghana, Kenya and Tanzania. The analysis yields two surprising findings about the dynamics of the HIV/AIDS epidemic which have important implications for policy. First, at least two-thirds of the infected couples are discordant couples, i.e. couples where only one of the two partners is infected. This implies that there is scope for prevention efforts among infected couples. Second, between 30 and 40 percent of the infected couples are couples where the female partner only is infected. This is at odds with levels of self-reported marital infidelity by females and with the common perception that unfaithful males are the main link between high risk groups and the general population. This study investigates and confirms the robustness of these findings. For example, even among couples where the woman has been in only one union for ten years or more, the fraction of couples where only the female partner is infected remains high. These results indicate that extramarital sexual activity among cohabiting women, whatever its causes, is a substantial source of vulnerability to HIV that should be, as much as male infidelity, targeted by prevention efforts. Moreover, this paper uncovers several inconsistencies between the sexual behaviors reported by male and female partners, suggesting that, as much as possible, prevention policies should rely on evidence including objectively measured HIV status.

### World Bank Policy Research Working Paper 3956, June 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ I thank Quy-Toan Do, Timothy Johnston, Mead Over, Albertus Voetberg and seminar participants at the World Bank and at the Secrétariat Permanent du Comité National de Lutte contre le SIDA et les Infections Sexuellement Transmissibles (SP-CNLS) in Burkina Faso for useful discussions.

1
<!-- Start of picture text --> 0 500 1000 1500 2000<br>Woll DS SSSoceeeeweeeereereeeeaL—)<br>Sub-Saharan Africa Jj 3<br>i =o > beid &<br>LAC 20 re-tax subsidy Externalitiesa VAT c><br>L.Sia | of<br>Z_SSSanam —<br>ccs =i 5<br>Advaned (IGG =<br>in<br><!-- End of picture text -->
led to continuous conflicts and ended up costing governments enormous sums. The lack of transparency of the whole process of privatization, contract award and renegotiation contributed to fuel popular discontent with private participation.

To a large extent, the drive towards private sector participation was prompted by the severe governance difficulties and incentive problems posed by public sector provision of infrastructure services. Almost two decades later, the experience of Latin America shows that the governance and incentive problems posed by private participation are no less thorny.

# **5. Conclusions**

Poor infrastructure is commonly viewed as a key obstacle to economic development. Across Latin America, there is an increasing perception that inadequate infrastructure is holding back growth and poverty reduction. As a result, infrastructure has become a major priority in the policy agenda. This paper has offered an overview of the trends in Latin America’s infrastructure sectors over the last quarter-century, and an evaluation of the potential contribution of improved infrastructure to growth in the region.

We document the evolution of infrastructure availability, quality and accessibility across the region, in comparison with other benchmark regions. This is done for four basic infrastructure sectors: telecommunications, electricity, land transportation, and water and sanitation. In spite of the progress made in some specific cases, on the whole Latin America and the Caribbean still lags significantly other MICs and East Asian countries both in terms of quantity and quality of infrastructure. The same holds for the universality of infrastructure access in the region: it is still well behind that of East Asia and other MICs.

We also offered an empirical evaluation of the potential contribution of infrastructure to growth in the region. The quantitative assessment is based on the estimation of infrastructure-augmented growth regressions using a large time-series cross-country data set. The empirical approach encompasses different core infrastructure sectors, considers both the quantity and quality of infrastructure services, and employs 35
Weighting larger distances from the poverty line more heavily, through the P<sup>2</sup> measure, poverty increased from 1981 to 1999 everywhere in the Northeast, except Rio Grande do Norte and Maranhão (Figure 6). The impact of the Real Plan on poverty was largest in the Northeast, translating into a 6.7-percentage points reduction in poverty during the 1993 to 1995 period (from 0.21 to 0.14). In Rio Grande do Norte, P<sup>2</sup> fell from 0.18 in 1993 to 0.11 in 1995, but reached nearly 0.13 in 1999 – after an all time low of 0.10 in 1998.

What happened to _income inequality_<sup>_17_</sup> ? One interesting observation is, that the data show that the Real Plan brought a slight increase in inequality followed by a fall. This finding contrasts with findings of Barros and Mendonza (1999) and Amadeo and Neri (1999) that show a slight decrease in inequality.

**Figure 7: Inequality in Brazil: Theil's L Index**

<!-- Start of picture text --> 0.64<br>0.63<br>0.62<br>0.61<br>0.6<br>0.59<br>0.58<br>0.57<br>0.56<br>0.55<br>0.54<br>1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1990 to 1999.

The reason for this is that we focus on per-capita household income, while Barros and Mendonza (1999) and Amadeo and Neri (1999) analyze individual income. This indicates slightly different developments in inequality at individual and household income level. However, taking methodological differences into account, by and large, inequality has widely remained at the same level since 1995.

> 17 Tables B8 and B9 display different inequality measures for Brazil and the Northeastern states.

15
from Barth, Caprio, and Levine (2001). The number of countries for which this information is available is smaller than in our previous estimations. This might explain why this variable is significant for Italian and Spanish banks, but not for U.S. banks.

In columns (3)-(6) of Table 6, we present estimations where real GDP growth, inflation and property rights, treated so far as measures of default, price and expropriation risks, respectively, are replaced by one composite country risk measure. As with the individual measures, we find that risks do not seem to consistently affect the mix of foreign bank claims.

Finally, the last three columns in Table 6 show results controlling for the total level of foreign bank claims each country receives. These estimations try to address the point that perhaps local presence is larger in countries where banks are willing to have a larger exposure. We find no consistent evidence of this.

Our second set of results, reported on Tables 7 through 9, examine the implications of the mix of local versus cross-border foreign claims for the stability of foreign bank financing across countries. In each table, we include seven different specifications from least to most general. In the first column of each table, we include only the variable of interest: the share of local to total foreign claims.<sup>17</sup> The remaining columns include other controls that might also affect total foreign bank claims volatility. In particular, we control for price and output volatility, the level of financial sector development (as proxied by the ratio of private credit to GDP), and the behavior of foreign claims vis-à-vis other countries. Furthermore, in some estimations, we include a composite measure of country risk.

> 17 We treat as outliers and, therefore, we exclude from the regressions cases where the volatility of the share was very high due to some unusual event or transaction. This meant dropping Croatia, Poland, and the Slovak republic from our sample of countries in the estimates for Italian banks, since these countries experienced an abrupt change in the share of local Italian claims due to mergers and acquisitions.

23