rafmacalaba/datause-encoder-data huggingface dataset:
while the household rankings are not the same when using these different subsets of variables, there is a large amount of overlap. The correlation in household rankings across these variants does not fall below 0.79.

Houweling et al. (2003) assess the sensitivity of analyzing inequalities in child survival and in measles immunization coverage using three variants of the full set of indicators available: first, excluding water and sanitation variables that they argue have a direct effect on morbidity; second, additionally excluding housing characteristics; and third, additionally excluding the indicator of whether the household had access to electricity. Analyzing data from 10 Demographic and Health Surveys they argue that the choice of the set of assets makes a difference. Indeed, they find that households are not always ranked in the same quintile when different subsets of the indicators are used. Nevertheless, fewer than 10 percent of households are ranked in a quintile more than one quintile away from the one they were assigned to on the basis of the full set of indicators. The authors also argue that statements about inequalities in outcomes differ by choice of the subset of indicators used. Again, their results do show some differences. However, they report remarkable consistency in the gaps they consider. For example, the ratio of under-5 mortality in the richest quintile to that in the poorest quintile differs by no more than 5 percentage points in the six out of the seven countries for which it is reported.<sup>10</sup> The ratio of measles immunization coverage between richest and poorest quintiles differs by no more than 10 percentage points in five of the seven countries for which it is reported.

Mukherjee (2006) compares an asset index approach that uses all indicators versus one that excludes non-durables such as the main sources of energy used for cooking and lighting, and the amount of land owned. He finds very small differences in the gaps in the fraction of children who had ever been to school (gaps of 0.31 versus 0.24) and in the fraction of adults who are literate (gaps of 0.40 versus 0.35).

In sum, the literature to date suggests that economic gradients in education and health outcomes are similar when these are based on per capita expenditures or on an asset index. On the other hand, the evidence suggests less than perfect agreement in ranking households—and thereby in identifying the poorest. In the remainder of this paper we add to this literature by

> 10 For example in Kenya, whereas the rich/poor differential is 42 percent using the full index, it is 47 percent using the reduced index—a difference of 5 percentage points.

11
Table 18: **Value added shares of exports by sector – United States**

|||1992|1995|1997|2004|2007|
|---|---|---|---|---|---|---|
|USA|Primary|0.367|0.364|0.348|0.351|0.314|
|Direct VA share|Textiles and Clothing|0.359|0.371|0.337|0.343|0.345|
||Machinery|0.453|0.465|0.413|0.328|0.327|
||other Manufacturing|0.358|0.423|0.390|0.385|0.372|
||Trade and Transport Services|0.611|0.596|0.586|0.566|0.552|
||Commercial Services|0.675|0.563|0.592|0.571|0.569|
||Public Services|0.843|0.777|0.776|0.693|0.688|
|||1992|1995|1997|2004|2007|
|USA|Primary|0.987|0.722|0.667|0.629|0.567|
|Total VA share|Textiles and Clothing|0.537|0.622|0.560|0.566|0.612|
||Machinery|0.530|0.579|0.512|0.434|0.435|
||other Manufacturing|0.712|0.890|0.831|0.757|0.713|
||Trade and Transport Services|1.124|1.189|1.369|1.975|1.834|
||Commercial Services|2.061|1.908|2.167|1.857|1.671|
||Public Services|2.120|1.008|1.216|0.875|0.855|

Table 19: **Relative intensity index based on forward linkages – USA**

|||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|---|
|USA:|Primary|0.126|0.085|0.068|0.061|0.074|0.082|
|RII|Textiles and Clothing|0.013|0.016|0.015|0.014|0.013|0.010|
||Machinery|0.223|0.251|0.246|0.247|0.209|0.196|
||other Manufacturing|0.146|0.190|0.184|0.193|0.204|0.200|
||Trade and Transport Services|0.217|0.192|0.179|0.161|0.173|0.166|
||Commercial Services|0.259|0.230|0.242|0.258|0.283|0.298|
||Public Services|0.015|0.037|0.066|0.066|0.044|0.048|

25
|Ab Kamari|7.5846774|25.656578|248|
|---|---|---|---|
|Bala Murghab|91.05668|283.35422|247|
|Ghormach|13.2|39.750008|20|
|Jawand|16.623077|65.459647|130|
|Muqur|21.822485|46.781577|169|
|Qadis|15.058824|31.127211|119|
|Qala-e-Naw|90.360294|204.55805|136|
|Total|41.689429|162.17494|1,069|

In 2020, 44,566 IDPs arrived in the Badghis province due to various reasons. Table A3 shows the breakdown of numbers of IDPs that arrived due to conflict, natural disasters, and their combinations<sup>17</sup> . It additionally specifies the numbers of IDPs due to conflict (9,504) and due to conflict and conflict-natural disasters (30,571).

Table A3. IDPs arrived in Badghis province in 2020.

|Districts|conflict,<br>conflict-natural disasters,<br>natural disasters, and<br>natural disasters-conflict|Arrival IDPs<br>due to:<br>conflict and<br>conflicts-natural disasters|conflict|
|---|---|---|---|
|Ab Kamari|1,881|1,503|154|
|Bala Murghab|22,491|16,715|9,005|
|Ghormach|264|257|84|
|Jawand|2,161|189|0|
|Muqur|3,688|2,207|240|
|Qadis|1,792|1,190|21|
|Qala-e-Naw|12,289|8,510|0|
|Total|44,566|30,571|9,504|

# Description of OCHA data on displacement

OCHA reports conflict-induced displacements for districts and provinces of Afghanistan. OCHA dataset additionally includes dates of displacement. According to OCHA, in 2020, there were 66 incidents (dates) of displacement in the Badghis province (Table A4). Table A4 presents summary statistics for conflict-induced IDPs based on displacement incidents (not comparable to IOM data because IOM’s summary statistics is for settlements).

Table A4. The summary statistics for conflict-induced IDPs in 2020.

|Districts|Mean|Std. Dev.|Freq.|
|---|---|---|---|
||||<br>(# of displacement incidents)|
|Ab Kamari|20|0|1|
|Qala-e-Naw|120.95385|93.139417|<br>65|
|Total|119.42424|93.251861|66|

As reported by OCHA, in 2020, conflict-induced IDPs settled in two districts of the Badghis province Qala-e-Naw (7,882 people) and Ab Kamari (20 people) (Table A5).

> 17The IOM questionnaire asks to indicate the percentages of IDPs that arrived at a settlement due to conflict and natural disasters, which then in dataset are grouped into 4 categories: conflict, conflict-natural disasters, natural disasters, and natural disasters-conflict.
# **References**

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43
Energy Sector Management Assistance Program (ESMAP) and World Bank (WB), 2008. Study of Equipment Prices in the Energy Sector..Washington, DC. April. 165pp. ESB (Electricity Supply Board). 2004. “Impact of Wind Power Generation in Ireland on the Operation of Conventional Plant and the Economic Implications.” February. 42pp. ESB National Grid, Dublin, Ireland.

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29
- While it is rational for individuals to be impatient, it is not clear that this should be the case for governments.

- Risks to society as a whole are much lower than risks to individuals within the society.

These arguments have merit and in fact most high income countries use a _social_ discount rate, lower than the opportunity cost of capital, in analyzing pension schemes. However, one reason for development institutions to use a relatively high discount rate is simply to ration the finance that they can provide. The capital base of development banks is limited, as is the annual quantity of concessional ODA<sup>9</sup> finance. This form of rationing has the benefit of biasing project choice toward those projects with relatively high economic rates of return, but at the cost of more heavily discounting longer-term future costs and benefits.

Suggesting that low carbon-emitting projects should qualify for economic analysis based upon lower social discount rates would seem to be a slippery slope. In the limit all project proponents would prefer to argue for the „special‟ properties of their project, which would merit the use of a lower rate of discounting the future. Markandya and Pearce (1991) suggest getting the valuations right (including, in this instance, the social cost of carbon or a carbon market price) rather than pleading for special discount rates, a theme that is echoed in Sterner and Persson (2007).

_Hurdle prices, switching prices, and the discount rate._ Under reasonable assumptions it turns out that the hurdle price varies inversely with the discount rate _r_ . Define the sum of discount factors _Z_ from period 1 to period N as follows: 1 It is clear that _Z_  . _r_

> 9 Official Development Assistance

17
**Table 3: Daily wages for farm and non-farm casual labor in Bihar from 2009/10 NSS, by**

||All op|**sub-**<br>Agricultu<br>erations|**round**<br>ral work<br>Culti|vation|Manu<br>agric|al non-<br>ultural|
|---|---|---|---|---|---|---|
|Sub-Round|Mean|Median|Mean|Median|Mean|Median|
|1 July-Sept 2009|74.8|77.5|72.4|73.3|86.9|80.0|
|2 Oct-Dec 2009|72.8|80.0|67.6|70.0|103.4|95.0|
|3 Jan-Mar 2010|84.6|80.0|80.2|80.0|97.1|90.0|
|4 Apr-June 2010|86.0|81.4|78.9|80.0|120.7|100.0|
|Total|79.0|80.0|74.4|76.7|100.2|90.0|
|Percentage<br>change 1-4|15|5|9|9|39|25|

_Source_ : Authors’ calculations from NSS 66th round.

**Table 4: Total person days of non-PW work by operation and sub-round 2009/10**

|Sub-round|Manual<br>agricultural|Manual<br>non-<br>agri|Non-<br>manual<br>non-agri|Missing<br>operation<br>code|All<br>operations|Household<br>own farm<br>enterprise|
|---|---|---|---|---|---|---|
|1 July-Sept 2009|1340.0|440.0|150.0|21.0|1951.0|2354.0|
|2 Oct-Dec 2009|1284.5|473.0|324.0|32.0|2120.5|2406.0|
|3 Jan-Mar 2010|973.0|577.0|235.0|7.0|1792.0|2728.0|
|4 Apr-June 2010|1035.0|399.5|297.0|7.0|1759.5|2280.5|
|Total|4632.5|1889.5|1006.0|67.0|7623.0|9768.5|

_Note_ : Authors’ calculations from NSS 66th round. Public works excluded. The total for all operations includes some minor omitted categories. _Source:_ Authors’ estimates based on BREGS Survey.

**<u>Table 5: Forgone work opportunities identified by BREGS participants</u>**

|% BREGS participants who say that,|if<br>Rou|nd 1|Rou|nd 2|
|---|---|---|---|---|
|not for BREGS,theywould have ...|Men|Women|Men|Women|
|Migrated forwork|10.7|2.9|14.0|1.0|
|Type of paid work:|||||
|Casual work (agricultural)|22.4|24.8|19.4|23.6|
|Casual work (non-agricultural)|23.8|5.7|25.7|5.0|
|Other|1.0|0.8|2.1|0.0|
|Searched for work or remained<br>unemployed|44.0|35.7|37.6|13.2|
|Worked on own land or house|8.8|33.1|14.9|58.2|
||100|100|100|100|

_Notes_ : The questions were asked of BREGS participants by work episode. The means are formed over all work episodes such that an individual who worked more than once is also counted more than once. Would be migrants are included in totals according to the type of activities they would have done. _Source:_ Authors’ estimates based on BREGS Survey.

35
Our findings may also be used for identifying the scope of the problem of managing climate variability today and in the future, and may thus be interpreted as a sort of baseline estimate for what may be to come, if we do not manage emission reductions and adaptation to climatic risks better. Comparing our estimates of the costs of financing high loss layers of sudden onset events with the at least as uncertain numbers currently available regarding the costs of adaptation to both slow and sudden onset climate variability and change, we arrive at the lower end of the estimates of the prominent UNFCCC (2007) report. For example, for adapting infrastructure alone to climate change impacts (including extreme events), the UNFCCC calculates a broad range of 8 –130 billion USD necessary in 2030 (in 2030 values) (UNFCCC, 2007). Others have even put their estimates a magnitude higher, yet essentially these, as well as our numbers are associated with uncertainty, and remain imprecise. However, as societies have found many ways for coping with uncertainty, such quantitative estimates may be sufficiently precise to at least start a process for thinking about appropriate options and courses of action for absorbing current and future extreme event risks.

28
Policy Research Working Paper 6886

# **Abstract**

Cash transfers have been demonstrated to improve education and health outcomes and alleviate poverty in various contexts. However, policy makers and others often express concern that poor households will use transfers to buy alcohol, tobacco, or other “temptation goods.” The income effect of transfers will increase expenditures if alcohol and tobacco are normal goods, but this may be offset by other effects, including the substitution effect, the effect of social messaging about the appropriate use of transfers, and the effect of shifting dynamics in intra-household bargaining. The net effect is ambiguous. This paper reviews 19 studies with quantitative evidence on the impact of cash transfers on temptation goods, as well as 11 studies that surveyed the number of respondents who reported they used transfers for temptation goods. Almost without exception, studies find either no significant impact or a significant negative impact of transfers on temptation goods. In the only (two, non-experimental) studies with positive significant impacts, the magnitude is small. This result is supported by data from Latin America, Africa, and Asia. A growing number of studies from a range of contexts therefore indicate that concerns about the use of cash transfers for alcohol and tobacco consumption are unfounded.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at devans2@worldbank.org and apopova@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
# Figure 18. SAR: Output growth and drivers of potential growth

<!-- Start of picture text --> A. GDP growth B. Contributions to potential growth<br>Percent SAR Median Percent TFP Capital Labor Potential growth<br>10 8<br>8 6<br>6 4<br>4 2<br>0<br>2<br>0<br>-2<br>2000-21 2000-10 2011-21 2022-23 EMDEs SAR<br>C. Investment growth D. Potential TFP growth<br>Percent Percent<br>SAR Median SAR Median<br>14 4<br>12<br>3<br>10<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-2021 2000-2010 2011-2021<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent<br>100 SAR Median 3 SAR Median<br>80<br>2<br>60<br>40<br>1<br>20<br>0 0<br>2000-20 2011-20 2000-2021 2000-2010 2011-2021<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Population Prospects; World Bank; World Development Indicators database.

Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A.C.-F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of the six EMDE regions. Orange whiskers show max-min range.

B. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 3 economies are from SAR (Bangladesh, India, and Pakistan).

C.D. Sample includes three SAR economies (where potential growth estimate is available for both investment growth and TFP growth measures for the period 2000-21).

E. Period averages of simple annual averages. Percentage of population ages 25 and above that completed at least lower secondary education. Sample for SAR includes Bangladesh, Pakistan, and Sri Lanka.

F. Working-age population refers to population ages 15-64. Sample includes three SAR economies.
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 12. ENERCA’s average revenue and total costs, 2002–09**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Total cost Average revenue Operating cost Long Run Marginal Capital cost<br>Costs<br>US cents per KmH<br><!-- End of picture text --> _Source:_ Based on Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Historical cost (both total and operational) is a simple average of ENERCA’s cost between 2002 and 2009.

ENERCA’s revenue collection ratio has worsened over time, decreasing from 88 percent to 62 percent between 2006 and 2010. A striking 50 percent of the unpaid bills were for public sector entities and administrations related to state and local governments (including the town hall/mayor’s office) (ENERCA 2011). The municipality of Bangui accumulated arrears of almost CFAF 7 billion ($7 million) to ENERCA. As of 2009, the government needed to repay its arrears and assume the domestic commercial bank debts of ENERCA. These payments were valued at CFAF 1.7 billion or almost $4 million (IMF 2009). Paying off this balance will allow ENERCA to strengthen its finances.

The large system leakages—compounded by ENERCA’s inability to recover power production costs and its undercollection of revenues—have led to vast hidden costs,<sup>11</sup> which have worsened over time both in absolute and relative terms (table 10). The increase in operating costs from 2008 to 2009 was largely circumstantial due to the breakdown of the Boali power plant.

**Table 10. Large inefficiencies drain significant potential revenues from ENERCA**

|Year|Power<br>delivered|System losses|Collection<br>ratio|Average<br>total costs|Average<br>effective<br>tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(GWh/year)|(% of<br>production)|(% of<br>billings)|($/kWh)|($/kWh)|($ million/year)|(% revenues)|
|2004|64|50|88|0.14|0.14|5|48|
|2005|77|57|88|0.16|0.14|8|84|
|2006|65|55|88|0.15|0.14|7|90|
|2007|66|53|83|0.12|0.15|6|63|
|2008|64|57|74|0.15|0.15|7|76|
|2009|72|48|64|0.20|0.15|14|117|

_Source:_ ENERCA 2005, 2006, 2007a, 2007b, 2008, 2011; World Bank 2009f. _Notes:_ Collection ratios for 2004–05 were not available, so the 2006 ratio was used.

> 11 Hidden costs were defined in the WSS section.

28