rafmacalaba/datause-encoder-data huggingface dataset:
reports of sibling survival status for siblings who survived until age 15 (2.8 million of these are for Sub-Saharan Africa). Of these, 2.9 million are reports on women (including respondents) and 2.1 are reports on men (corresponding figures for Sub-Saharan Africa are 1.6 million and 1.2 million).<sup>11</sup>

# **2.2) Determining socioeconomic characteristics**

The DHS include a large number of variables that could be used to characterize respondents’ socioeconomic status. However for the task at hand, we face two challenges. First, the data we have describe the socioeconomic characteristics of the respondent, and not necessarily their sibling whose mortality we are analyzing. Second, the data collected pertain to the time of the survey, and not necessarily to the timeframe for which we are estimating mortality.

We address the second of these potential problems by focusing on relatively stable indicators of socioeconomic status. We address the first by using information about the respondents to proxy the socioeconomic status of their siblings. We limit our analysis to two variables: urban/rural residence and educational attainment. It is impossible using the DHS to conclusively determine the extent to which these variables are correlated across siblings. Two sets of facts give us confidence that the approach is legitimate. First, some DHS datasets include a variable that indicates whether a respondent lived in a rural or urban area as a child. It is very likely that siblings would share the same residential location in childhood. We find that the results we get are extremely similar whether we use the current or childhood residential location (a discussion we return to below). Second, Conley and Glauber (2005) indicate that in the United States the correlation in the educational outcomes of siblings is 0.576. Dahan and Gaviria (2001) report that this correlation is substantially higher in a sample of 16 countries in Latin America, suggesting that using the educational status of a sibling as a proxy might work even better for developing countries. We also investigated the extent to which the variation in schooling attainment is primarily within or between households in the DHS surveys included in

> 11 Appendix 2 describes two datasets we have created for this project and which are freely accessible to analysts who wish to use them for their own work.  The first dataset includes mortality estimates for all subgroups at the level of each DHS survey that was carried out (that is, for each country and each survey).  The second dataset includes mortality estimates for all subgroups at the level of each country (that is, after we have averaged multiple countryand period-specific mortality rates).

8
The HIES 2010 is a balanced survey with a ...xed number of households (20) in each psu, making estimation of spatial disparity index simple. The metropolitan strata of the 2000 survey has fewer households (10) relative to rest of the survey stratum. Since a psu with 20 households contains more information than one with 10 households, one can use an appropriate weighting scheme to correct for the unbalanced nature of 2000 survey. Solon, Page and Duncan (2000) proposed three such weighting schemes. As the proportions of our sample from metropolitan stratum are small, the results are not sensitive to weighting schemes. Thus we use equal weighting for all psus in both of the survey rounds.

The survey collected a wealth of information on many aspects of living standards including detailed household level expenditure, demographics, employment, education and housing characteristics. We utilized these surveys to de...ne employment composition, education levels, and other community level infrastructure and service provision variables. We also constructed several spatial price indices from the survey data. While HIES data can be used to generate many community variables, the datasets do not have information on geographical attributes of the communities. We collected information on agro-climatic conditions and distances to cities from other data sources. Rainfall data are drawn from Bandyopadhyay and Skou...as (2012). The original data on rainfall come from the Climate Research Unit (CRU) of the University of East Anglia. The CRU reported estimated monthly rainfall for most of the world at the half degree resolution from 1902 to 2009. The CRU estimation combines weather station data with other information to arrive at the estimates.<sup>4</sup> To estimate the sub-district (upazila/thana) level rainfall from the CRU data, Bandyopadhyay and Skou...as (2012) uses area weighted averages.<sup>5</sup> Travel times to di¤erent

> 4 Previous versions of the CRU data were homogenized to reduce variability and provide more accurate estimation of mean rain at the cost of variability estimation. The version 3.1 data is not homogenized and thus allows for better variability estimates. The estimates of rainfall near international boundaries are not less reliable as compared with those in the interior of the country, as the CRU estimation utilizes data from all the weather stations in the region.

> 5 For example if an Upazila/thana covers two half degree grid cells for which CRU has rainfall estimates, then upzila/thana rainfall is estimated as the average rainfall of the two grid-cells, where the weights are the proportion of the area of the upazila/thana in each grid-cell. For details, please see Bandyopadhyay and Skou...as(2012).

13
between Dhalkebar-Muzaffarpur (90 kms) and Hetaua-Duhabi (40 kms) (partly funded by the World Bank), coupled with the operation of a combined 20,000 MW capacity of hydropower projects undergoing feasibility study in Western Nepal is expected to increase the electricity trade between India and Nepal.

Generation capacity addition in Nepal significantly lags behind growing electricity demand, making Nepal a net importer of electricity from India (Figure 1). Electricity imports now account for nearly 20% of the total availability. Given the existing demand-supply mismatch in Nepal, it may continue to import electricity from India through the upcoming interconnections in the short-term. However, it can become a net exporter of electricity as the country develops its vast hydroelectric potential.

Figure 1: Changing Balance of Electricity Trade between Nepal and India Source: NEA (2013) <!-- Start of picture text --> 900 25<br>Import of electricity from India<br>800<br>Export of electricity to India 20<br>700<br>600 Share of electricity import in total<br>availability<br>15<br>500<br>400<br>10<br>300<br>200<br>5<br>100<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>(P)<br>So: NEA (2012, 13) Year<br>(GWh)<br>Percentage share<br>Import and Export of Electricity from India<br><!-- End of picture text -->

# **_2.2. Bhutan-India Electricity Cooperation_**

Bhutan conducts 80% of its total trade with India; electricity trade constitutes 45% of Bhutan’s total exports. Electricity is Bhutan's principal export commodity and the largest revenue earner (ADB, 2013). In 2011-12, earnings from export of electricity to India accounted for 11.4% of the country’s GDP (Singh et al., 2013). The bilateral arrangements between India and Bhutan started with the Jaldhaka Agreement in 1961. The first hydroelectric plant (Joshina) of 360 kW capacity was commissioned in 1967 6
WPS3705

# **Fiscal Determinants of Empowerment**

## **Uri Raich**

## **Abstract**

Empowerment of local governments and citizens is a primary object of decentralization. Using the analytic lens of empowerment, this paper explores the nature of decentralized governance and how this type of structure is likely to be more or less empowering. The primary concern of this work is fiscal decentralization, specifically the association between fiscal determinants and the degree of empowerment of both citizens and local governments.

The paper’s main argument is that both the revenue and expenditure characteristics of local public finances have an effect on the degree of empowerment. The hypothesis is that in a context of decentralized local governance, empowerment is most likely to occur when three conditions prevail: low costs of participation; large and flexible budgets; and a high proportion of tax revenues from a local base. However, these ingredients for empowerment will not necessarily produce outcomes that are progressive and pro-poor. This paper contrasts the intrinsic and the instrumental approaches to empowerment, but it does not assess the impact of empowerment on instrumental outcomes. For this hypothesis to support an instrumentalist perspective on empowerment, further empirical work must be conducted.

## World Bank Policy Research Working Paper 3705, September 2005

_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._
# **Table A1: Summary Statistics on Industry-Year Cells**

|Variables|Number of<br>Observations|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log US Manufacturing Sector Employment|6,675|10.09|1.48|4.63|13.34|
|Log of low income affiliate employment|6,615|9.52|1.39|4.97|12.80|
|Log of high income affiliate employment|6,635|10.47|1.06|6.07|13.14|
|Log of price of investment using 1979 weights|6,437|4.68|0.13|3.60|4.86|
|Total factor productivity level using 1979 weights|6,583|1.01|0.32|0|5.31|
|Export share using 1979 weights|6,583|0.11|0.09|0|0.58|
|Import penetration using 1979 weights|6,583|0.13|0.13|0|0.83|
|Log of real price of shipments using 1979 weights|6,437|8.94|1.13|5.70|13.14|
|Education level|6,675|2.97|1.40|1|5|

_Source_ : Affiliate (or offshore) employment data are taken from the Bureau of Economic Analysis annual survey of US firms with multinational affiliates for 1983-2002. Low income countries are defined according to the World Bank income categories. Employment data are taken from all workers in the Current Population Surveys Merged Outgoing Rotation Groups for the same period. Import penetration and export share are taken from Bernard et al. (2006). Investment good prices, total factor productivity measures, and the real price of shipments are taken from the NBER productivity database.
that the decision to select countries into the 15-country reference group is based on mean per capita income of the country and not the number of poor people. The 15 countries represent about 11 percent of the total poor in 2005, and about 13 percent of the estimated poor in 2011.

In addition to the weak support, the average poverty line estimated from these 15 countries is quite sensitive to seemingly innocuous changes in the time series of inflation data for these countries. The sensitivity of the average to changes in inflation data stems both from the small sample of 15 and also from the fact that the poverty lines from these countries need to be updated from the year of data collection to the reference year (2011 in our case). Table 2 reveals that on average, the 15 reference national poverty lines date from 1997. The poverty line from Mali comes from 1988-89 and requires 22 years of CPI data to bring it to the 2011 reference period. Given that many of these 15 countries have limited capacity for statistical production and some have experienced very high levels of inflation, a reasonable concern is that even small errors or relatively minor updates when compounded over decades can have potentially large effects on the estimated value of the global poverty line.<sup>20</sup> As one example of the sensitivity of the average poverty line to inflation data, in our initial analysis we updated the 15 poverty lines to 2011 values based on official national CPI data as reported in the World Development Indicators database. This updating produced a global poverty line of $1.70 using 2011 PPPs and about 976 million poor people. In the current analysis, we use the inflation data provided by PovcalNet (to ensure internal consistency) and this changed the average value of the poverty lines to $1.82 and increased the count of the poor by more than 60

> 20 Sandefur (2013) shows that there is a large discrepancy between official estimates of inflation in Tanzania (one of the 15 reference countries) and household survey-based estimates of price changes. He also illustrates how these differences in inflation estimates have significant effects on the trend in poverty headcounts. Gimenez and Jolliffe (2014) document significant discrepancies between official measures of inflation in Bangladesh, survey-based measures of inflation for the poor, and implicit measures of inflation as assessed through official poverty lines.

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<mark>WPS3580</mark>

# On the Contribution of Demographic Change to Aggregate Poverty Measures for the Developing World

## Martin Ravallion<sup>*</sup>

Development Research Group, World Bank <u>mravallion@worldbank.org</u> _Recent literature and new data help determine plausible bounds to some key demographic differences between the poor and non-poor in the developing world. The paper estimates that selective mortality — whereby poorer people tend to have higher death rates — accounts for 10-30% of the developing world’s trend rate of “$1 a day” poverty reduction in the 1990s. However, in a neighborhood of plausible estimates, differential fertility — whereby poorer people tend also to have higher birth rates — has had a more than offsetting poverty-increasing effect. The net impact of differential natural population growth represents 1050% of the trend rate of poverty reduction._

### World Bank Policy Research Working Paper 3580, April 2005

_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._

* Helpful comments on an earlier version of this paper were received from Kathleen Beegle, Monica Das Gupta, Michael Lipton, Lant Pritchett, T. Paul Schultz, Damien de Walque, Dominique van de Walle and Adam Wagstaff.  For useful discussions on this topic and helpful comments on earlier versions of this paper the author is particularly grateful to Etienne van de Walle.
- _Dis_ tan _ceij_ is the distance between the capitals of countries i and j.

- _RTAijt_ is a dummy variable that is equal to 1 when countries i and j have an active

preferential trade agreement in year t.

- _Landlockedi_ is 1 when country i is landlocked.

- Border _ij_ is 1 when countries i and j have a common border.

- Common_Language _ij_ is 1 when countries i and j have the same language.

- Colonial_Relationship _ij_ is 1 when countries i and j have the same colonizers.

- Common_Colonizer _ij_ is 1 when countries i and j have the same colonizers post-1945.

- Ij and<sup></sup> _t_<sup>are two vectors with importer-specific and year-specific dummies.</sup>

-

- Ɛijt is a random error term satisfying the usual assumptions.

Regarding the selection estimation, we assume that _X ijt_ is observed when the following condition is met:  0  1 ln(Border_Transport_Efficit )  2 ln(Business_ Environment _it_ )  3 ln( _ICTit_ )   4 ln(Infrastructure _it_ )   1 ln  1  _tijt_   2 ln( _GDPit_ )   3 ln( _Populationit_ )   4 ln  _Dis_ tan _ceij_   5 _RTAijt_   6 _Landlockedi_   7Border _ij_   8Common_Language _ij_   9Colonial_Relationship _ij_  .  10Common_Colonizer _ij_   _t_  _I j_   1 _EntryCostsijt_  _uijt_  0 (2) where: _EntryCostsijt_ is a binary indicator that equals 1 if the sum of the number of days and procedures to start a business is greater than the median for country i and country j, or if the sum of the relative costs of starting a business (as percentage of GDP per capita,) are greater than the median for both countries, and 0 otherwise. Using Doing Business data on the regulations to start a business, we updated the indicator for fixed entry costs constructed by HMR (2008) to cover more countries and more years. By construction, this variable reflects regulation costs that should not depend on a firm’s volume of exports to a given country, and satisfies the exclusion restrictions of the two-stage Heckman estimation method, as it is excluded from the outcome equation.

Equation (1) sets the determinants of the volume of trade, provided that one of the partners has positive exports to the other, while Equation (2) defines the selection criteria.

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studies, for those concerned with health planning and evaluation mortality is only one aspect of the health situation. Information on morbidity and on coverage and use of health progra_es and services is probably of more significance.

Against the background of disillusionment with questions about recent deaths in the household, particularly among investigators with a background in medicine, it is important to emphasise that alternative methodologies for the retrospective collection of information on mortality have performed far better. Questions addressed to respondents about deaths among clearly-defined categories of their kin normally yield useful data. In particular the World Fertility Survey convincingly demonstrated that collecting detailed birth histories from women and asking about the ages at which deaths of their children occured could provide upexpectedly high-quality estimates (Hobcraft 1984). Such birth histories also provide information about the constitutional factors affecting early mortality (such as the age and parity of the mother and the length of birth intervals) and represent a framework within which dated information on child rearing practices that influence health and mortality (such as breastfeeding and weaning) can be collected. To minimise costs a WHS core questionnaire might collect truncated birth histories covering only the 5 years before the survey. A full birth history could be an optional module. Obtaining reliable data about adult mortality is more difficult. Questions about orphanhood and widowhood often, but not always, perform well (see, for example, Timaeus 1987). Methods for measuring long-term trends in mortality are now well established (Brass and Bamgboye 1981). In addition a number of approaches have been suggested that can define the period of exposure to risk much more closely. These include the supplementary questions proposed by Brass and by Chackiel and Orellana (1986) and procedures for analysing intersurvey changes in orphanhood (eg. Timaeus 1986).

In **some** countries the WHS could provide an opportunity to develop methodologies for assessing causes of death. This information is valuable both for health impact evaluation and for setting health priorities. It is generally agreed that open-ended questions addressed to relatives of a dead person do not provide reliable 25
54 percent before the 2017 influx. An estimated 1,337 ha of forest cover has transitioned to settlements since 2017. However, our estimates of forest cover losses within the camp boundaries is lower than estimates from earlier studies, in which estimates range from 1,619 ha (4,000 acres) to 2,286 ha (5,650 acres) (Hassan et al., 2018; Mukul et al., 2019; Tallis et al., 2019). These differences in estimated forest loss within the camp boundaries could be due to the ongoing reforestation efforts by the UNHCR’s Energy and Environment Team working with stakeholders to restore and regenerate green cover in the area.

Our econometric results suggest that the factors driving deforestation in response to camp openings go beyond the direct impact of refugees. We find significant forest losses 0-1 km from camp boundaries that we believe can be attributed to the human activities of camp residents. But in the 1-5 km area from camps, we find considerable evidence that forests are being cleared for settlements. Given extensive limitations on refugee mobility, this settlement expansion most likely represents Bangladeshis moving towards camps in pursuit of camp-related economic opportunities and/or improved access to services. This case study highlights the fact that camps can stimulate forest loss, but for reasons beyond refugee forest product extraction.

In the prior literature, forest loss in Cox’s Bazar has been attributed to several mechanisms. These include (a) settlement expansion, (b) development of economic activities, facilities, services, and infrastructural projects, (c) high fuelwood demand leading to loss of forest, and (d) increased population and growing in-migration flow (Bdnews24.com, 2017; Paul, 2017; Tani & Rahman, 2018; UNDP & UN Women, 2018).

First, forest transitions to settlement, including the expansion of refugee camps, is the primary determinant of LULC change, particularly in the district’s southern area. Tallis et al. (2019) reported that the camp areas increased by 835 percent between 2016 and 2017, while Hassan et al. (2018) indicated an increase of 774 percent between the same period from 175 to 1,530 ha. For example, the largest refugee campsite, the Kutupalong–Balukhali compound, gained a net settlement growth of 1,219 ha between 2016 and 2017 (from 146 ha to 1,365 ha).

Second, the development of essential facilities, services, and infrastructure projects such as roads, water, sanitation, and hygiene structures are alleged to have contributed to forest cover loss in the region (UNDP & UN Women, 2018). The delivery of these services sometimes comes with direct and indirect impacts on forest cover and landscape alternations. For example, road construction often leads to forest cover loss in many cases.

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