rafmacalaba/datause-encoder-data huggingface dataset:
million m<sup>3</sup> in 2008 (Table 3). The proportion of residential consumption grew substantially from only 11 percent of total consumption in 2002 to 33 percent in 2009. The pattern of household gas consumption before and during the crisis suggests gas demand is likely to increase with income growth, but less likely to decrease during income decline. In other words, household demand for gas is inelastic, i.e., the price increase would have large impact on household budget. However, note that the substantial decline in energy consumptions by the industrial and energy sectors in 2009, underscoring the significant slowdown in construction and manufacturing sectors in the aftermath of the global recession.

Armenia’s dependence on import for energy consumption increased over the last decade. Residential consumption, as a share of total energy import increased by nearly three-fold from about 10 percent to 30 percent in 2009. While total energy consumption as share of imports increased by 5 percentage points between 2002 and 2009, residential consumption grew by more than three times as fast as total energy import. This shows growing vulnerability of Armenian households to external energy sources and to frequent gas price hikes.

**Table 3.  Gas Import and Consumption in Armenia**

|||(_i_|_n millio_|_n cubic_|_meters_)|||||**2009/**|
|---|---|---|---|---|---|---|---|---|---|---|
||**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|<br>**2002**|
|Import Volume (mln m3)|1,070|1,201|1,333|1,685|1,716|2,055|2,400|1,814|2,100|2|
|Total Consumption (mln<br>m3)|952|1,034|1,222|1,516|1,586|1,869|2,080|1,661|1,980|2|
|Energy Sector|518|495|537|619|511|507|607|366|680|1|
|Industry|194|209|275|348|344|404|368|260|260|1|
|Residential|102|153|223|311|402|532|590|553|556|5|
|Fuel Stations|65|88|111|149|219|286|347|306|307|5|
|Other|73|89|77|90|110|140|168|176|177|2|
|Total Consumption<br>_(%, Import_)<br>Residential|89|86|92|90|92|91|87|92|94|1.0|
|Consumption_(%, Total_<br>_Consumption_)<br>Residential|11|15|18|21|25|28|28|33|28|3|
|Consumption_(%,_<br>_Import_)|10|13|17|18|23|26|25|30|26|3.2|

_Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia 6
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 24. Affordability in Sierra Leone better than in other low income countries, but access to services is very low**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Sierra Leone<br>LIC<br>Average cost of power is 7.75 USD/Month per 25 kwh or 15.5 USD/Month per 50 kwh<br>subsistance consumption<br>Average cost of water is 2.6 USD/Month per 4 m3 or 6.5 USD/Month per 10 m3<br>subsistance consumption<br>Power cost recovery tariff is 0.31 USD/kwh<br>household budget<br>Share of urban households whose utility bill would be less than 5 percent of the monthly<br><!-- End of picture text --> _Source_ : Banerjee and others 2009.

Operational inefficiencies of power and water utilities cost Sierra Leone a further $39 million a year, equivalent to 2.2 percent of GDP. The annual value of inefficiencies in the power sector (at $35 million) is significantly higher than of the water sector (at $4 million). Both power utility NPA and water utility GVWC can benefit from reducing distributional losses and improving bill collection. NPA‘s 2010 distributional losses of 45 percent are more than four times higher than the best-practice 10 percent benchmark, and result in $24 million in potential savings. Non-revenue water in the water sector stood at 40 percent of total water production in 2007, twice the best-practice benchmark of 20 percent. Nonrevenue water inefficiencies cost Sierra Leone about $2.9 million a year, equivalent to 0.17 percent of GDP. The power utility collects 88 percent of its billing; if 100 percent of bills were collected, NPA could receive additional $8 million a year. The water utility collects 77 percent of its billings, thus forsaking $0.9 million a year. Across both power and water sectors, the burden of utility inefficiencies in Sierra Leone is lower than for the benchmark countries (figure 25).

44
Girma, S., and Yu, Z., (2002), “The link between immigration and trade: evidence from the UK‟, _Weltwirtschaftliches Archive_ .

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Gould, D., (1994), “Immigration Links to the Home Country: Empirical Implications for US Bilateral Trade Flow”, _Review of Economic and Statistics_ 76 (2): 302–316.

Hanson, G., (2010), “International Migration and Development,” in Ravi Kanbur and A. Michael Spence, eds., Equity ina Global World, Washington, DC: The World Bank.

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Rose, A., (2000), “One money, one market: the effect of common currencies on trade”, _Economic Policy_ , 15, issue 30, p. 7-46 Rose, A., K., and van Wincoop, E., (2001), "National Money as a Barrier to International Trade: The Real Case for Currency Union", _American Economic Review_ , vol. 91(2), pages 386-390, May.

32
KOREA, REP. OF

## 2. Projections - NRR-1 by 2030

|Ya|PuAstio|SIP.<br>ratio|Pwid|anut<br>rote|||TFR|o<br>kt"|0<br>S<br>Femo|In|q5<br>xlOSO|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>|42790<br>|44.1|1990-94|0.90|16.0|6.1|1.75|67.8|74.0|21|25|
|1995|44770<br>|40.7<br>|1995-99<br>|0.85|15.5|6.2|1.75|69.4|7!.6|17|20|
|Z000<br>|46720<br> <br>|40.0<br>|2000-04<br>|0.77|14.6|6.3|1.60|71.0|77.1|14|17|
|2005<br>|<br>484<br>|41.7<br>|2005-09<br>|0.63|13.6|6.8|1.86|72.0|78.1|12|15|
|2010<br>|50107<br>|42.5<br>|2010-14<br>|0.52<br>|12.8<br>|7.3<br>|1.91<br>|73.0<br>|79.1|10<br>|13|
|2015<br>2020<br>|51417<br>52543|43.5<br>45.9|2014-19<br>2020-24|0.43<br>0J6|12.5<br>12.4|7.9<br>8.4|1.96<br>2.00|74.0<br>71.1|80.1<br>81.3|8<br>6|10<br>8|
|2025|53561|51.6<br>|2025-49|0.13|12.0|10.7|2.06|77.5|83.8|4|5|
|2050|55320|_n.8_|20o-74|-0.05|12.0|12.5|2.06|79.9|86.3|3|4|
|2075<br>|545<br>|71.2<br>|2075-99<br>|0.04|11.9|11.5|2.06|81.1|87.8|2|4|
|2100<br>|S5263<br>|n.6<br>|2100-24<br>|0.04<br>|11.8|11.3|2.06|82.0|88.9|2|3|
|2S<br>2150|SS862<br>56173|73.5<br>74.2|2125-49|0.02|11.7|11.5|2.06|82.4|89.3|2|3|

# **3. Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 756+<br>70-74<br>65-69<br>60-64<br>55.59<br>50-54<br>45-49<br>23; ||3 40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 810 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

## 4. Comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR e 0 Growth rate<br>Countr-y Region lnIn -ups Country Region Income grow~ Country ReIion lincome group<br><!-- End of picture text --> 69
Table A.3: Deviation from Montreal Protocol Targets after Entry into Force of RTA

|||_Dep_|_endent variab_|_le: IHS Ta_|_rget Deviat_|_ion_||
|---|---|---|---|---|---|---|---|
||CFCs|Halons|CFCs (B)|CTC|TCA|HCFCs|MB|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Post RTA|0.475<sup>_∗∗∗_</sup><br>(0.141)|0.347<sup>_∗∗∗_</sup><br>(0.096)|0.016<br>(0.031)|0.115<br>(0.075)|0.028<br>(0.050)|_−_0.403<sup>_∗∗∗_</sup><br>(0.088)|0.023<br>(0.094)|
|Post_×_ODS|_−_0.220<br>(0.295)|_−_0.267<sup>_∗_</sup><br>(0.147)|_−_0.042<br>(0.069)|_−_0.241<br>(0.200)|0.162<br>(0.118)|_−_0.134<br>(0.201)|_−_0.096<br>(0.236)|
|Observations|6,118|6,118|6,118|6,118|6,118|6,118|6,118|
|R<sup>2</sup>|0.562|0.579|0.408|0.485|0.661|0.645|0.566|
|Mean(ODP tons)|-821|-218|-3|-472|-19|-128|10|

FE triple-difference regressions on a stacked country-level panel with a _±_ 3 year event window around RTA entry into force. All outcomes are the inverse hyperbolic sine of the difference between ODS emissions and emissions targets under the Montreal Protocol. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report the mean deviation from target across the entire sample in tons of ozone depletion potential (ODP). Statistical significance from two-sided _t_ tests are denoted by _∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01.

iv
-48 -

# - V. IMPROVING **THE** ESTIMATES OF **AIR POLLUTION DAMAGES: UNCERTAINIE** AND FUTURE **NEEDS**

As indicated in the introduction, the current estimates of the benefits of reducing ambient levels of particulate matter are based on broad averages and fairly simple assumptions. Nevertheless, they do provide information about the health effects that can be quantified. These effects can, in turnt be valued in order to provide a range for the economic value of controlling these pollutants. These estimates can then form the basis for prioritization when choosing among several air pollution control strategies as well as for assessing the value of additional control efforts. Additional uncertainties arise when applying these numbers to other countries, particularly those that are less developed. This sectiorn provides a brief review of the major areas of uncertainty in the estimates, and describes tasks that could be undertaken to reduce this uncrinty. The greatest uncerainties are generated by the need to extrapolate the epidemiologic health effects from the U.S. and Western industrialized natons to other countries.

Most of the dose-response functions provided in the literamre are based on research conducted primarily in the U.S. Extrapolating these results to countries in very different stages of development adds additional uncertainty to any esdmate of benefits associated with reduced air pollution. These uncertainties arise since we must assume that the population characteristics that relate to the sensitivity to air pollution in the U.S. will bc similarly distnrbuted in other countries. This may include factors such as: age and sex distribution, smoldng rates, general health status, exercise and diet, medical access and use, competing risks, avening behavior, and activity patterns.

It may be instuctive to consider a few cases that indicate the importance of these differences when extrapolating from the U.S. studies. For example, consider fte effects of activity patterns on the effective dose. Ideally, the dose would reflect the ambient air quality and the amounL of time spent outdoors. Studies of populations in the U.S. suggest that people spend about 90 percent of their time indoors. Many of these indoor environments are well sealed and dramatically reduce the penetration of
better investor protection and greater information availability facilitates better monitoring. Second, information asymmetry provides a potential channel in which shocks can be propagated through the banking system (a number of papers have used constrained information asymmetry framework to explain risk contagion and crises; see for instanceGenotte and Leland 1990, Kodres and Pritsker 2002, Hong and Stein 2003, and Barlevy and Veronesi 2003, Yuan 2005). If greater information availability provides a substitute to competition in reducing systemic fragility, then we would expect the impact of greater information to be stronger in less competitive markets. However, it is also possible that information asymmetry affects stability through different channels than competition. For instance, competition may incentivize banks to take on more diversified risks, while greater information availability may help reduce contagion of a macro shock to the banking system.

The baseline results are provided in columns (6), (7) and (8) in Table 4. Consistent with our expectations, both depth of credit information and the existence of private information collection agency significantly reduce systemic risk after controls. In columns (6), (7) and (8) of Table 5, we present results from regressions including the interaction between private monitoring variables and the Lerner index. The results indicate that the effect of investor protection and depth of credit information in reducing systemic risk is lower in less competitive markets.

# **3.3. Robustness Checks**

In this section we show that our results are robust to alternative regression specifications as well as alternative definitions of competition and systemic stability. A number of previous studies have used concentration, measured as the percentage of total assets held by top three or five bank in a given country, as a proxy for competition. While concentration is correlated with bank competition, the recent literature also emphasizes the differences between the two, suggesting concentration is a less reliable indicator of competition (Beck, Demirguc-Kunt, and Levine, 2006; Demirguc-Kunt and Martinez Peria, 2010). Nevertheless, as an alternative measure of competition, we use the three bank asset concentration ratio from the Financial Structure Dataset to measure banking concentration. We run the same regressions specified in equation and (9) replacing the Lerner index with bank concentration. Unlike the Lerner index, bank concentration is measured at the country level each year. All other control variables are the same as those used 17
on their kin obligations (even though they face hurdles to entering the market economy) but indeed facilitate the migration of other members; and the kin group, recognizing the long-term benefits from having economically successful members in the modern sector, facilitates the process of integration. If that happens, the kin system will turn out to be a beneficial institution not only in the pre-modern economy, but also during the process of transition to a modern economy. It will be a case where the institution adapts in response to structural changes in the economy in a way that benefits all its members − a case where “social customs follow the money,” as some sociologists put it.

However, we show that the kin system can become harmful to all (or at least a majority of) its members in the process of modernization and nevertheless fail to be dismantled by its members. As Akerlof (1976) has argued in analyzing the caste system in India, an important reason for this is the presence of network effects that cause a _coordination failure_ in individual decisions.<sup>2</sup> In studying the behavior of kin group members facing a modern economy, we identify the nature of the network effects that may cause them to take collective decisions that are _ex ante_ inefficient. Our formal analysis delineates conditions under which the kin system will (or will not) become a _dysfunctional institution_ .

The rest of this chapter is organized as follows. We begin by presenting a view of culture as a set of control mechanisms through which a kin group can enforce mutual assistance arrangements. We document anecdotal evidence of the experiences of members of kin systems when they face expanding market opportunities, and indicate how the evidence relates to the formal analysis that follows. We then formalize the entry barriers that kin members may face

> 2  A long line of work starting with Rosenstein-Rodan (1943) has emphasized the role of coordination failure in explaining why certain countries or social groups remain poor.  See Ray (1998, ch. 5) and Hoff (2001) for surveys of this literature.

4
only marginally alters the PIPs, switching to the UIP prior leads to slightly lower inclusion probabilities and model size. Overall, these findings indicate that our baseline results are robust.

Figure 2. Model Comparison with Private Credit, Model 1=hyper-g,birthdeath; Model 2=hyper-g,reverse-jump; Model 3=UIP,birth-death; Model 4=UIP,reverse-jump <!-- Start of picture text --> Model 1<br>Model 2<br>Model 3<br>Model 4<br>1.0<br>0.8<br>0.6<br>PIP<br>0.4<br>0.2<br>0.0<br>LifeExp GDP60 Mining Confucian BlMktPm RFEXDist SubSahara stdBMP EquipInv Buddha LabForce French Muslim English NequipInv LatAmerica RuleofLaw Hindu EthnoL Abslat Foreign Catholic Brit WorkPop PublEdupct YrsOpen Privatecredit Spanish Jewish PrScEnroll Protestants EcoOrg Age OutwarOr HighEnroll Area RevnCoup CivlLib Popg WarDummy PrExports PolRights<br><!-- End of picture text --> The beta-binomial (“random”) model prior offers meaningful insights. This setting allows for a less restrictive selection of model priors around the prior expected model size and limits the risk of imposing any particular one (Ley and Steel 2009b). Thus, if the true model size is lower than that expected by the prior (21), we should expect the mean model size to decline in this setting. We present the results of the estimation using this model prior in figure A1 in the Appendix. In the first setting with a hyper-g prior, the mean size declines to 15.05 and the PMP correlation between analytical and MC<sup>3</sup> sampler likelihood achieves a satisfactory value of 0.96. The most important variables according to their PIPs remain nearly unchanged, although their relative positions adjust. One significant change is the 29
Policy Research Working Paper 9860

# **Abstract**

This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth—a method that departs slightly from current World Bank practice—performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.

This paper is a product of the Development Data Group, Development Economics and the Poverty and Equity Global Practice. 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 dmahler@worldbank.org, acastanedaa@worldbank.org, and dnewhouse@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
# **Inequality in Latin America: Determinants and Consequences**

## **J. Humberto Lopez and Guillermo Perry**<sup>*****</sup>

The World Bank

> * Paper prepared for the conference “Paradigma y Opciones de Desarrollo en Latin America”, Santiago de Chile, Junio 2007. We would like to thank Maria Fernanda Rosales for excellent assistance. The views expressed here are ours only and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.
_Africa’s Macroeconomic Story_

# **Table 3: Public Debt Levels for Five HPIC/MDRI Recipients**

_Percent_

||_Public_|_Debt / GDP _<sup>_2_</sup>|_2013-17 a_|_verage_|
|---|---|---|---|---|
||**2007**|**2012**<br>**2017 **|**Primary Bal./ GDP**|**Real GDP Growth**|
|The Gambia|62.7|77.2<br>51.5|0.8|7.1|
|São Tomé and Príncipe|60.0|75.5<br>25.6|4.2|21.3|
|Guinea-Bissau<sup>1</sup>|51.7|59.8<br>39.1|0.5|5.7|
|Ghana|31.0|56.5<br>55.0|n.a.|6.4|
|Malawi|35.4|54.9<br>33.3|1.3|6.3|

_Sources:_ April 2013 IMF _World Economic Outlook_ database and IMF Country Reports No. 12/216 and 12/221. _Notes:_

1. Completion point attained in 2010.

2. Estimates for 2012; projections for 2017.

# _Large fiscal imbalances have narrowed significantly_

Once again, data limitations prevent us from examining the evolution of fiscal balances over the past few decades.<sup>16</sup> Data available from the mid-1980s indicate a steady pace of fiscal consolidation from the early 1990s up until the onset of the global financial crisis in late 2008. The median cyclicallyadjusted deficit declined from around four percent of GDP in the early 1990s to just one percent in 2005-07 (Figure 15). Budget balances then deteriorated in the wake of the global financial crisis. In addition, the sharp increase in commodity prices in 2008 and 2011 adversely affected fiscal positions in countries with large food and fuel subsidies. The number of countries with a cyclically-adjusted deficit exceeding four percent of GDP increased from 4 in 2007 to 15 in 2012. The deterioration in fiscal balances was most prominent in countries that had comfortable surpluses in 2007, many of which are commodity exporters (Figure 16). Those countries were able to allow automatic stabilizers absorb some of the impact of the shocks.

> 16 Data on fiscal balances dating back to 1980 is only available for five countries in the region; only 18 countries report data back to 1990.

24