rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

# **IV. DATA**

The data were collected through five rounds of household surveys carried out by the Ethiopia Road Authority (ERA), along the four main corridors that have been recently rehabilitated or are currently being rehabilitated. As discussed above, they are major Link Roads that share the similar functionality connecting major regional cities to Addis Ababa, the country’s capital. According to the preliminary screening, these four roads share certain common characteristics and are broadly comparable with each other ( **Table 2** ). The baseline survey was conducted in 2012 before the first major construction work was started along Aposto-WendoNegele Road. Each round of the survey was targeted at the same, approximately 250 households along each main RSDP road. For each road, 15 to 20 villages were selected around the starting, middle and ending point of each road. The villages were also stratified based on distance to the project roads.<sup>3</sup> Attrition turned out fairly small over time ( **Table 3** ).

**<u>Table 2. Household characteristics (sample means) by RSDP road</u>**

|<br>Variable|<br>Aposto-Wendo-<br>Negele|Mekenajo-<br>Dembidolo|Kombolicha-<br>Bati-Mille|Ankober-<br>Awasharba|
|---|---|---|---|---|
|Size of household|6.00|5.08|5.02|4.24|
|Dummy variable for male household head|0.81|0.84|0.68|0.74|
|Age of household head|44.0|44.6|46.3|44.0|
|Dummy for the level of education attained by hou|sehold head:||||
|1 to 6 grade|0.27|0.14|0.22|0.13|
|7 to 8 grade|0.08|0.19|0.06|0.08|
|9 to 12 grade|0.18|0.19|0.10|0.11|
|Technical/ vocational certificate|0.02|0.00|0.00|0.01|
|University/ college diploma|0.06|0.08|0.02|0.03|
|University/ college degree|0.02|0.04|0.02|0.02|
|Adult education program/ Non-formal|0.05|0.02|0.05|0.06|
|Dummy variable for infrastructure services:|||||
|Electricity for cooking|0.02|0.01|0.02|0.01|
|Electricityfor lighting|0.54|0.78|0.49|0.63|

> 3 The normal cutoff point was originally set to 5km, which is considered to be a normal distance allowing people to walk to a road in Ethiopia. In the actual sample, as shown in the above figure, half of the sample households live less than 2km of the project roads, and another half live 5 to 20km from the roads.
In an analysis of results from a study by the International Forestry Resources and Institutions (IFRI) research network of the conditions of forests used by 178 forest user groups located in 12 countries, Gibson, Williams, and Ostrom (2005) found that the level of local monitoring varies substantially across groups. One of the measures obtained in this study is the frequency with which a local group monitors and sanctions rule-breaking behavior in the forest. We examined the impact of this variable on appropriators’ assessment of forest conditions (as well as on a forester’s assessment). We also examined the impact of a group’s social capital, the group’s dependence on forest resources, and whether the group was formally organized or not. The result of the analysis is that regular monitoring by a local group is more important than the other three variables in enhancing forest conditions. Regardless of the levels of social capital, forest dependence, and formal organization, regular monitoring and sanctioning are strongly and statistically associated with better forest conditions (see Hayes and Ostrom, 2005; Coleman, 2009). Chhatre and Agrawal (2009) have just completed a major empirical study of how local autonomy in rule making and forest ownership are positively associated with increased carbon storage.

Some local utilities in the United States are now seeking to reduce energy consumption by developing local monitoring systems whose results are then reported on the bills that customers receive. The Sacramento Municipal Utility District, for example, has tried various techniques, including rebates for energy-saving appliances, but recently found a more effective technique.

Last April (2008), it began sending out statements to 35,000 randomly selected customers, rating them according to their energy use compared with that of neighbors in 100 homes of similar size that used the same heating fuel. The customers were also compared with 20 neighbors who were especially efficient in saving energy.

37
households in CPHS over 23 months from January 2019 to November 2020. These mean incomes were then used to divide the income distribution into ten “objective” income deciles, and each household was categorized in one of these deciles. The mean and standard deviation of monthly income corresponding to each decile, and the number of households interviewed by decile, is given in supplementary Table S7 and Figure S9.

In the June-August 2021 wave of the CPHS, an additional module on inequality was administered to the panel. In this round of the CPHS, interviews were successfully completed with 116,061 households, which comprise the sample for our analysis.<sup>5</sup> Half of these households were randomly assigned to the treatment group, and the other half to a control group. This randomization was stratified by state and whether the household was in a rural area. We compare the treatment and control groups on a set of observable characteristics and find no discernible difference between the groups (see supplementary table S4).

The module on inequality began by eliciting respondents’ beliefs about their position on the income distribution, _“Suppose we divide the households of India into 10 equal parts, where the poorest households are in the first part and the richest households are in the last part. See this bar_ [respondents were shown a long rectangle composed of 10 equally sized blocks] _. The first box contains the poorest households. The second box contains the next poorest households. The last box contains the richest households. Which box do you think your household would fall into?”._ Following this question, respondents in the treatment group were informed if their estimates coincided with those of the research team. Respondents were told, _“Based on your answers, you belong to Group_ [X] _. In reality, you belong to Group_ [Y] _. Hence, you are poorer than what you thought / richer than what you thought / correct.”_ The bias in their perceptions was thus explicitly pointed out to respondents in the treatment group. This

> 5 Since interviews were not completed with a significant fraction of the CPHS sample, this sample on which our analysis is based is not nationally representative. More generally, the representativeness of the CPHS sample has been debated by scholars and analysts—a limitation that we acknowledge.

6
- Kanbur, R., R. Chambers, et al. (2001). Qualitative<sup>andQuantitative PovertyAssessment:</sup> Complementarities, Tensions and the Way Forward. New<sup>York, Cornell</sup> University.

- Lofgren, H. and S. Robinson (1999). "To Trade or<sup>Notto Trade:Non-separableFarm</sup> Household Models in Partial and General Equilibrium",<sup>TMD Discussion Paper</sup> 37, IFPRI, Washington D.C..

- Matusz, S. J. and D. Tarr (1999). "Adjusting to Trade<sup>Policy Reform."World Bank</sup> Working papers 2142.

- McCulloch, N. and B. Baulch (2000). "Simulating the<sup>Impact of Policy uponChronic and</sup> Transitory Poverty in Rural Pakistan." Journal of Development<sup>Studies 36(6):</sup> 100-130.

- McCulloch, N. and M. Calandrino (2002). Poverty Dynamics<sup>in RuralSichuan between</sup> 1991 and 1995. Brighton, Institute of Development Studies.

- McCulloch, N., Winters, L.A. and Cirera, X. (2001).<sup>"TradeLiberalisationand Poverty:</sup> A Handbook", Centre for Economic Policy Research, London.

- Minot, N. and F. Goletti (2000). "Rice Market Liberalization<sup>and Poverty in Viet Nam",</sup> IFPRI Research Report 114, IFPRI, Washington D.C.

- Mundlak, Y. (1963), "Estimation of production and behavioural<sup>functions froma</sup> combination of cross-section and time-series data", in _Measurement_<sup>_in_</sup> _Economics: Studies in Mathematical Economics and Econometrics_<sup>_in memory of_</sup> _Yehuda Grunfeld,_ ed. Carl F. Christ, Stanford University Press.

- Narayan, D., R. Chambers, et al. (2000). Voices of the<sup>Poor:Crving out for Change.New</sup> York, Oxford University Press for The World Bank.

- Nicita, A. (2002). Behavioural responses of poor and<sup>non-poor households.Estimation</sup> of income, quality and price elasticities for Mexico. July<sup>2002.WorldBank.</sup>

- Nicita, A., M. Olarreaga and I. Soloaga (2002). A simple<sup>methodology to assess the</sup> poverty impact of economic policies using household<sup>data.An application to</sup> Cambodia. World Bank.

Niimi Y., Vasudeva-Dutta, P. and Winters, L.A.<sup>(2002)."Trade Liberalisationand</sup> Poverty Dynamics in Vietnam". paper presented at<sup>Globalisation, (In)Equality,and</sup> Regionalism Conference at the University of Warwick,<sup>March15-17,2002.</sup>

- Pritchett, L., A. Suryhadi, et al. (2000). Quantifying Vulnerability<sup>to Poverty:A Proposed</sup> Measure with Application to Indonesia. Washington DC,<sup>World Bank.</sup>

- Rich, K. and M. Lundberg (2002). "Multimarket models<sup>and policyanalysis:an</sup> application to Malawi", Personal Communication.

- Reimer, Jeffrey J. (2002). Estimating the Poverty<sup>Impactsof TradeLiberalization,</sup> Purdue University.

- Robilliard, A.-S., F. Bourguignon, et al. (2001).<sup>Crisisand Income Distribution:A</sup> Macro-Micro Model for Indonesia, International Food Policy<sup>ResearchInstitute</sup>

33
km<sup>2</sup> (20%) where 21 million (15%) people are currently at combined low risk of arsenic, salinity and groundwater storage depletion.

In the groundwater risk map under the model 2 (Figure 4b), the mapped area within the zone of extremely high risk is substantial (18,297 km<sup>2</sup> ; 13% of total land area) and it covers most parts of southeastern Bangladesh where nearly 13 million (9% of total population) people are exposed (Tables 1 and 2). The high-risk zone covers an area of 15,147 km<sup>2</sup> (11%) and an estimated 12 million (8%) people are currently exposed. The medium-risk zone covers an area 13,160 km<sup>2</sup> (9%) and an estimated 9 million (6%) people are currently exposed. Finally, an estimated land area of 18,797 km<sup>2</sup> (13%) and 13 million (9%) people are located in an area where there is low groundwater risk due to relatively less contamination of arsenic and salinity. Considering arsenic and salinity individually, we find that approximately 33 million (23% of total population) people are currently at variable risk of elevated (>50 μg/L) arsenic; and nearly 26 million (18%) are exposed to very high (>1500 μS/cm) salinity in shallow groundwater.

# **3.3 Groundwater risks and access to drinking and irrigation water supplies**

In addition to demographics of the population, we estimate exposure of poor people and household access to water supplies within the mapped groundwater risk zones under both models. Results are summarized in Tables 1 and 2. Groundwater risk mapping reveals that 3 million to 6 million households with an access to tubewells are currently at risk of various degrees under model 1 and 2, respectively. Furthermore, an estimated 237,000 to 418,000 households with access to tapwater supply are at risk of arsenic and salinity contamination as well as depletion of shallow groundwater. However, it is noteworthy that tapwater supplies in towns and metropolitan cities often derive from deep groundwater that may not be at immediate risk to arsenic or salinity contamination.

# **3.4 Groundwater risks and socially vulnerable population**

Our analysis shows that poor people are exposed to groundwater risks at various degrees. According to the groundwater risk map (model 1), approximately 5 million poor people are currently exposed at various degrees of risks with a potential of another 6 million poor people being affected in future. The groundwater risk map based on model 2 shows a greater number of poor people (11 million) are currently exposed to shallow groundwater with elevated arsenic and salinity contamination. Our analysis shows that 9,740 (extremely high risk) to 229,192 (high risk) children are underweight, and 10,206 (extremely high risk) to 274,545 (high risk) 10
# **Table 20: Consumer protection and financial literacy**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-0.190|-0.191|-0.185|-0.114|-0.126||-0.069|-0.069|-0.050|||
||(0.129)|(0.130)|(0.125)|(0.114)|(0.111)||(0.118)|(0.118)|(0.117)|||
|Population density (log)|-0.010<br>(0.053)||||||-0.009<br>(0.053)|||||
|Expected GDP (log)|0.068|0.066|0.061||||0.074|0.072|0.068|0.062||
||(0.053)|(0.052)|(0.047)||||(0.051)|(0.050)|(0.052)|(0.048)||
|# deposit accounts/1000 adults (log lagged)|-0.073|-0.079|-0.079|-0.088|||-0.164|-0.169|-0.166|-0.171|-0.174|
|, ,|(0.107)|(0.101)|(0.101)|(0.102)|||(0.116)|(0.110)|(0.107)|(0.104)|(0.107)|
|# loans/1000 adults (log lagged)|0.277**|0.282**|0.272**|0.221**|0.180**|0.081*|0.315**|0.318**|0.269**|0.234**|0.255***|
|, ,|(0.128)|(0.128)|(0.118)|(0.113)|(0.088)|(0.042)|(0.129)|(0.127)|(0.121)|(0.095)|(0.099)|
|Deosit insurance|-0.045|-0.041|||||-0.400**|-0.397**|-0.374**|-0.367**|-0.283*|
|p|(0.179)|(0.176)|||||(0.177)|(0.177)|(0.187)|(0.184)|(0.161)|
|Credit rank|0.002<br>(0.002)|0.002<br>(0.002)|0.002<br>(0.002)||||0.002<br>(0.002)|0.002<br>(0.002)||||
|Number of Observations|58|58|58|58|61|61|58|58|58|58|58|
|Log-likelihood|-36.25|-36.26|-36.30|-37.99|-39.88|-40.51|-34.24|-34.26|-34.77|-34.84|-35.75|
|Pseudo R<sup>2</sup>|0.0953|0.0948|0.0940|0.0518|0.0553|0.0402|0.147|0.147|0.134|0.133|0.110|
|Robust standard errors in parentheses, *** p<0<br>Dependent variable: FI CP (effective) for colum<br>otherwise. FI FL (effective) for columns (23) -<br>Table reports marginal effects of probit estimat|.01, ** p<0.<br>ns (17) - (2<br>(27) equals<br>ion.|05, * p<0.1<br>2) - equals 1<br>1 if the finan|if the financ<br>cial regulato|ial regulato<br>r oversees f|r oversees fi<br>inancial capa|nancial cons<br>bility/educ|umer protec<br>ation/literacy|tion and has<br>and has a d|a dedicated<br>edicated tea|team, equal<br>m, equals 0|s 0<br>otherwise.|

46
Policy Research Working Paper 5692

# **Abstract**

In many emerging democracies women are less likely to vote than men and, when they do vote, are more likely to follow the wishes of household males. The authors assess the impact of a voter awareness campaign on female turnout and candidate choice. Geographic clusters within villages were randomly assigned to treatment or control, and within treated clusters, some households were left untreated. Compared with women in control clusters, both treated and untreated women in treated clusters are 12 percentage points more likely to vote, and are also more likely to exercise independence in candidate choice, indicating large spillovers. Data from polling stations suggest that treating 10 women increased turnout by about 9 votes, resulting in a cost per vote of US$ 2.3. Finally, a 10 percent increase in the share of treated women at the polling station led to a 6 percent decrease in the share of votes of the winning party.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group; and the Poverty Reduction and Equity Team, Poverty Reduction and Economic Management Network. 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 xgine@worldbank.org and gmansuri@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
# **6. Access to Services and Assets**

The problem of poverty and inequality in Bahia largely reflects disparities in opportunity. The distribution of key productive assets – labor, human capital, physical assets, financial assets, and social capital – is highly unequal. These disparities are most prevalent between the poor and non-poor, but also manifest themselves differently by geographic area. Also access to services is unequal. This section addresses a few of these areas, namely labor, education, basic infrastructure services, and social assistance and analyzes the coverage of selected social programs by income quintile, which shows the share of the population (or subgroup of population) in each quintile that receives a given service in Bahia.

## **LABOR**

Labor is the poor’s most abundant asset and it accounts for nearly all of their total income. Nonetheless, the poor are constrained in their use of this asset in a number of ways: (1) high level of unemployment, (2) a strong correlation between informal sector employment and poverty, and (3) black and female workers face probable wage discrimination. Table 6.1 shows that formal sector employment has not been able to keep up with population growth in Bahia.

The poverty analysis reveals that many workers in Bahia, particularly those in the informal sector, are poor (see Sections 5 and 7). The challenge of creating employment is therefore to increase the number of jobs that are able to provide sufficient income to lift the employee’s household out of poverty or cushion against it. Creating jobs regardless of quality is not enough—people need good jobs. As the labor market, particularly the informal one, is relatively flexible, the worry is about generating sufficient income via employment rather than simply having a job. The trend in this regard, as reflected by decreasing real wages of informal workers, is discouraging.

The demographic change that demands the most urgent policy response is the sizeable growth in the economically active population. During 1992–1999 the number of those aged 14 and older rose by more than 3.2 million. Some of the growth in the economically active population is from in-migration, but most of it is natural growth. The high rate of growth of this age cohort—along with increasing female participation in the labor force—means that a high number of good new jobs are needed each year to keep pace, and better jobs are needed by those already in poor jobs. This is a major challenge.

27
new innovations, and Kerr (2008) finds foreign countries realize manufacturing gains from stronger scientific integration, especially with respect to computer-oriented technologies. Multiple studies document specific channels sitting behind this heterogeneous diffusion.<sup>1</sup> As invention is disproportionately concentrated in the United States, these ethnic networks significantly influence technology opportunity sets in the short-run for following economies. This study uses heterogeneous technology diffusion from the United States to better quantify the importance of technology differences across countries in explaining trade patterns. Trade between the United States and foreign countries is excluded throughout this study due to network effects operating alongside technology transfers. Attention is instead placed on how differential technology transfer from the United States—particularly its industry-level variation by country—influences exports from the foreign country to other nations. Said differently, the study quantifies the extent to which India’s exports, for example, grow faster in industries where technology transfer from the United States to India is particularly strong. This provides an important complement in the migration literature to the typical focus on how ethnic networks boost bilateral trade.

The instrumented elasticity of export growth on the intensive margin with respect to the exporter’s productivity growth is 2.4 in unweighted estimations. The elasticity is 1.6 when using sample weights that interact worldwide trade volumes for exporters and importers in the focal industry. Thus, the study estimates that a 10% increase in the labor productivity of an exporter for an industry leads to about a 20% expansion in export volumes within that industry compared to other industries for the exporter. This instrumented elasticity is weaker than Costinot et al.’s (2012) preferred estimate of 6.5 derived through producer price data for OECD countries in 1997, but it is quite similar to their 2.7 elasticity with labor productivity data that are most comparable to this study. The two analyses are also qualitatively similar in terms of their relationships to uninstrumented elasticities. This study does not find evidence of substantial adjustments

> 1. Channels for this technology transfer include communications among scientists and engineers (e.g., Saxenian 2002, Kerr 2008, Agrawal et al. 2011), trade flows (e.g., Rauch 2001, Rauch and Trindade 2002), and foreign direct investment (e.g., Kugler and Rapoport 2007, Foley and Kerr 2013). The supplement provides further references to the role of international labor mobility and other sources of heterogeneous technology frontiers (e.g., Eaton and Kortum 1999, Keller 2002).
# **Table 2. The macroeconomic effects of a 5 percent climate-induced shock to capital stock without reconstruction**

||Year 0|Year 1|Year 2|Year 3|Year 4|Year 5|Year 10|Year 20|Year 30|
|---|---|---|---|---|---|---|---|---|---|
|**Economic Activity**||||||||||
|GDP|0.0|-1.9|-2.1|-2.1|-2.1|-2.1|-2.3|-2.1|-1.8|
|Private consumption|0.0|-2.1|-2.3|-2.2|-2.1|-2.0|-2.0|-2.0|-1.8|
|<br>Government consumption|0.0|-0.4|-1.0|-1.5|-1.8|-2.0|-2.6|-2.8|-2.3|
|Total investment|0.0|-2.9|-2.9|-2.9|-3.0|-3.0|-2.9|-2.0|-1.4|
|Exports|0.0|-0.1|-0.2|-0.3|-0.5|-0.6|-1.1|-1.0|-0.6|
|Imports|0.0|-2.2|-2.3|-2.2|-2.1|-1.9|-1.3|-0.6|-0.6|
|Current account balance<br>(pp of GDP)|0.0|0.4|0.4|0.4|0.4|0.4|0.3|0.1|0.0|
|**Prices**||||||||||
|Producer prices|0.0|0.0|0.1|0.1|0.2|0.3|0.4|0.2|0.1|
|Consumer prices|0.0|-0.0|-0.0|0.0|0.0|0.0|-0.0|-0.1|-0.1|
|Real exchange rate|0.0|0.1|0.2|0.4|0.6|0.8|1.4|1.3|0.8|
|Import prices|0.0|-0.1|-0.1|-0.2|-0.3|-0.5|-1.0|-1.2|-0.8|
|Export prices|0.0|-0.0|0.0|0.1|0.2|0.2|0.3|0.1|0.0|
|**Fiscal**||||||||||
|Revenues|0.0|-1.9|-2.0|-1.9|-1.9|-1.8|-1.9|-1.9|-1.7|
|Expenditures|0.0|-0.7|-0.9|-1.0|-1.0|-1.1|-1.3|-1.7|-1.7|
|Deficit (pp of GDP)|0.0|-0.2|-0.2|-0.1|-0.1|-0.1|-0.1|-0.0|-0.0|
|Debt (pp of GDP)|0.0|0.7|0.8|0.9|0.8|0.8|0.8|0.6|0.3|
|<br>**Supply Side**||||||||||
|Potential GDP|0.0|-3.0|-2.9|-2.8|-2.8|-2.7|-2.5|-2.0|-1.6|
|Capital stock|-5.0|-4.9|-4.8|-4.7|-4.6|-4.5|-4.1|-3.3|-2.5|

_Sources:_ MFMod, World Bank; Authors’ calculations. _Note:_ Table shows deviations as percent from a baseline scenario _._ 23