rafmacalaba/datause-encoder-data huggingface dataset:
Newman, J. et al (2002) “An Impact Evaluation of Education, Health and Water Supply Investments by the Bolivian Social Investment Fund.” World Bank Economic Review, 16(2), 241-74.

- Rosengrant, M, X. Kai and S. Cline (2002): World Water and Food to 2025. Washington, D.C.: International Food Policy Research Institute.

- Rout, Satyapriya, University of Hyderabad, Institutional variations in practice of demand responsive approach: evidence from rural water supply in India, Water Policy, July 2014.

Sara, J. and Katz, T. (1997) Making Rural Water Supply Sustainable: Report on the Impact of Project Rules. Washington D.C.: UNDP-World Bank Water and Sanitation Program.

Seckler, D, R. Barker and U. Amarasinghe (1999): ‘Water Scarcity in the Twenty-first Century’, International Journal of Water Resource Development, 15 (1/2), 29–42.

Sen, Amartya K. 1999. Development as Freedom. New York: Knopf Press.

Sugden, S. (2001). Assessing sustainability-the sustainability snap shot. Paper presented at the 27th WEDC conference, Lusaka, Zambia.

Taylor, B. (2009) Addressing the Sustainability Crisis: Lessons from research on managing rural water projects. Available online at: <u>http://www.wateraid.org/~/media/Publications/sustainability-crisisrural-water-management-tanzania.pdf</u>

- United Nations. (2010). the Millennium Development Goals Report 2010. Retrieved from <u>http://www.un.org/millenniumgoals/</u>

- United Nations. (2011). The Millennium Development Goals Report 2011 Retrieved from <u>http://www.un.org/millenniumgoals</u>

United Nations. (2015). Transforming Our World: The 2030 Agenda for Sustainable Development. Retrieved from http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E Voluntary Organization for Social Action and Rural Development (VOSARD), Conservation of Natural Resources, Drinking Water Management, Jalanidhi Project, Video online at: <u>http://www.vosard.org/conservation%20of%20natural%20resources.html</u> World Bank 2013 A -“World Development Indicators”, Data, Rural population (% of total population), 2010-2014, online at: <u>http://data.worldbank.org/indicator/SP.RUR.TOTL.ZS</u> World Bank 2013 B- World Bank, India: Getting Water on Tap in Rural Kerala, August, 2013, Online at: <u>http://www.worldbank.org/en/news/feature/2013/08/12/india-getting-water-on-tap-in-ruralkerala</u>

- World Bank 2013 C- Project Performance Assessment Report, Republic of India, Kerala Rural Water Supply and Environmental Sanitation Project “Jalanidhi”, June 27, 2013, Independent Evaluation Group Public Sector.

- World Bank 2009- Implementation Completion and Results Report, Republic of India for the Kerala Rural Water Supply and Environmental Sanitation Project, March 9, 2009, South Asia Sustainable Development Unit, The World Bank Group.

- World Bank 2006 - Background Paper, Rural Water Supply and Sanitation, India Water Supply and Sanitation, Bridging the Gap between Infrastructure and Service, January 2006, the World Bank Group.

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INTRODUCTION XXiii Figure 6 Projected Population (billions) In Less Developed.Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections <!-- Start of picture text --> 7. 5 -<br>7 0<br>6.5 ~~~~~~~~~~~~~~~~~~~U. N.<br>6.0 - ~~~~~~~~~~~~~~~~Prev ious<br>5.5 - C u rr e n~~~~~~~~~Curet<br>5.5-<br>5.0- 6.0-<br>4.5-<br>4.0<br>3.5 I , I I<br>1985 1990 1995 2000 2005 2010 2015 2020 2025<br><!-- End of picture text --> Figure 7 Projected Population (billions) in More Developed Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections <!-- Start of picture text -->
1.36  -<br>1.35-<br>1.34-<br>1.33  M<br>1 .32  -Peiu<br>1.31-<br>1.3029rrent<br>1.29-<br>1  .28  -<br>1.27<br>1.295<br>1.25<br>1 .24-<br>1.23-<br>1.22-<br>1.21-<br>1.20-<br>. 19<br>. 18<br>1.71985  19I90  19I95  20100  2005  2010  20115  20120  2025<br><!-- End of picture text -->
females and families maintained by single women. In other words, there were specific vulnerabilities at the intersection of gender and other identities.

Such descriptive patterns have been documented in education outcomes for ethnic minorities as well. Lewis and Lockheed (2007) show that inter-group disparities are common phenomena in countries with diverse ethno-linguistic populations. Testing the link between girls’ school participation and ethno-linguistic fractionalization across 120 countries, they show that countries with multiple ethnic and language groups have lower primary school completion for girls, a larger gap between male and female primary school completion rates, and lower overall achievement. Similar findings have been shown in specific country cases as well. For example, Das et al. (2010) find that educational attainment among tribal women in India is lower compared to tribal men and other groups, including women in the disadvantaged castes. Further, rural tribal women face additional disadvantages due to their remoteness from rural and urban centers, which translates into triple disadvantages based on their tribal affiliation, gender and place of residence. Similarly, King and van de Walle (2007) report that indigenous women in Lao PDR are five times more likely to never attend school compared to nonindigenous women. Whereas 34 percent if non-Lao-Tai women never attended school in 2003, the share among LaoTai girls was only 6 percent. Further, they find disparities within the non-Lao-Tai groups, especially in rural areas, where only 30 percent of rural Chine-Tibetan girls aged 6-10 were enrolled in primary school, compared to 56 percent of rural Mon-Khmer boys and 53 percent of Mon-Khmer girls.

To sum up, the existing literature on intersectionality and descriptive evidence from the peripheral literature point to a few conclusions and directions for future research. Although the evidence is limited, it indicates that development outcomes falter at the intersection of identities. This is an important finding, not least because it highlights the importance of identifying the specificity of development challenges faced by different groups and takes researchers one step closer to understanding overlapping roots of exclusion. It is also important from a policy perspective, since failing to recognize the role of intersectionality can misinform the design, targeting and resource allocation decisions in development interventions. Going forward, the empirical base needs to be broadened, especially for developing countries. Currently, censusbased studies that have the potential to portray systematic patterns of exclusion are concentrated 8
_Table 3: Selected Plot-Level Means by Plot Distance to Household (Above versus Below 1 Kilometer)_

|||**Malaw**|**i(IHS3)**|||**Ethiop **|**ia(ESS2)**||
|---|---|---|---|---|---|---|---|---|
||**Entire**<br>**sample**|**Sample**<br>**[d < 1km]**|**Sample**<br>**[d > = 1km]**|**x[d <1km] -**<br>**x[d >= 1km]**|**Entire**<br>**sample**|**Sample**<br>**[d < 1km]**|**Sample**<br>**[d > = 1km]**|**x[d <1km] -**<br>**x[d >= 1km]**|
|Observations (Plots)|18,256|13,161|5,095||19,966|15,352|4,614||
|**_Plot Areas_**|||||||||
|GPS-based plot area (Ha)|0.394|0.383|0.420|-0.037***|0.197|0.177|0.261|-0.084***|
|Farmer-reported plot area (Ha)|0.414|0.403|0.440|-0.036***|0.193|0.175|0.251|-0.075***|
|**_Yields_**|||||||||
|Maize yield (Kg/Ha)|1,693|1,694|1,692|2|||||
|Value of output/Ha|||||29,303|31,575|22,447|9,128|
|**_Plot Manager Characteristics_**|||||||||
|Female †|0.261|0.267|0.246|0.021|0.153|0.158|0.137|0.021|
|Age (Years)|43.147|43.511|42.273|1.238***|46.817|47.220|45.472|1.748*|
|Education (Years)|5.028|4.934|5.252|-0.318***|1.874|1.952|1.614|0.338|
|**_Household Characteristics_**|||||||||
|Household size|4.934|4.871|5.086|-0.215***|6.476|6.491|6.427|0.064|
|# of HH members - [0,5]|0.981|0.974|0.998|-0.024|0.916|0.920|0.904|0.016|
|# of HH members - [6,14]|1.396|1.369|1.461|-0.093**|1.932|1.930|1.940|-0.010|
|# of female HH members - [15,39]|0.901|0.879|0.953|-0.074***|1.111|1.102|1.143|-0.041|
|# of male HH members - [15,39]|0.837|0.819|0.881|-0.062**|1.212|1.210|1.217|-0.007|
|# of female HH members - [40,59]|0.270|0.270|0.268|0.002|0.386|0.394|0.363|0.031|
|# of male HH members - [40,59]|0.269|0.262|0.287|-0.024|0.391|0.387|0.404|-0.017|
|# of HH members – 60 & above|0.280|0.297|0.238|0.059***|0.527|0.548|0.456|0.092*|
|Household consumption expenditures per capita|50,431|48,494|55,087|-6,593***|5,723|5,804|5,453|350|
|Number of plots in the holding|2.374|2.359|2.410|-0.051|15.857|16.086|15.091|0.995|
|**_Plot Characteristics_**|||||||||
|Owned by household †|0.904|0.917|0.872|0.045***|0.866|0.888|0.794|0.094***|
|Use of hired labor †|0.223|0.195|0.290|-0.095***|0.057|0.050|0.082|-0.032***|
|Use of organic fertilizer †|0.116|0.122|0.101|0.021***|0.183|0.213|0.085|0.128***|
|Use of inorganic fertilizer †|0.618|0.623|0.607|0.016|0.404|0.415|0.369|0.047|
|Irrigated †|0.005|0.005|0.006|-0.001|0.016|0.017|0.011|0.006|
|Soil quality good †|0.467|0.453|0.503|-0.050***|0.327|0.329|0.319|0.010|
|Soilquality poor†|0.113|0.112|0.116|-0.004|0.173|0.168|0.188|-0.019|

**Note:** † denotes a dummy variable. *** p<0.01, ** p<0.05, * p<0.1. Sample of plots within a 1-kilometer radius is the comparison group for the tests of mean differences.

10
_Restrictions._ Third, in order to control for restrictions on bank activities and on financial FDI, we include the _Index of Banking Freedom_ produced by the Heritage Foundation. This index takes into account the extent of restrictions on the ability of foreign banks to open branches and/or subsidiaries, how heavily regulated the financial system is, the presence of state-owned banks, whether the government influences the allocation of credit, and whether banks are free to offer all types of financial services. This index takes values from 1 to 5, where higher values represent less freedom in the banking sector.

Bank profits arising from cross-border activities are taxed at the rate prevalent in the banks’ country of origin, while taxes on their FDI activities or local claims depend on the hosts’ tax rates. Other things equal, we expect to find relatively lower levels of FDI activity or a lower share of local claims in countries with higher corporate taxes. In our study, _taxes_ refer to the top corporate income taxes in each country as reported by the Heritage Foundation. Ideally, we would have also liked to control for taxes on financial intermediation, but such data is not available on a cross-country basis.

Studies on both, cross-border claims and financial FDI, document that economies of scale, profit opportunities, and risks might also affect banks’ decisions to expand internationally. Thus, the degree to which each of these factors might affect the share of local claims is an empirical question. However, a priori we could speculate that since the returns from cross-border lending are limited to the initial claims extended plus an interest rate, while extending local claims often implies an equity participation in a local affiliate (bringing the potential for unbounded gains and losses<sup>13</sup> ) scale economies, profit opportunities, and risks might be more important in driving FDI or local claims relative to cross-border loans.

> 13 This might be different for branches and subsidiaries due to the financial independence but also differences exist depending on the banks’ country of origin.

17
community. The first and in many ways most interesting development is not in the further expansion of CDD _per se_ , but in the adoption of CDD principles to improve the performance of other types of poverty programs. It has been a struggle to avoid having social protection programs not think of each other as competitors for the same pot of poverty funds. But in a growing number of cases, governments are starting to use the principles of community targeting for household transfer programs. In Indonesia, for example, Alatas, et al. (2012) have shown through a randomized control trial that using community targeting systems produces nearly as accurate a targeting of results as highly sophisticated statistical systems do, but with much higher rates of local legitimacy and satisfaction due to the abilities of communities to articulate why some people receive benefits over others. Afghanistan ’ s new Citizen ’ s Charter program uses the CDD foundation built up under the 2002-2014 National Solidarity Program to coordinate ministry delivery of agriculture, health, education, and rural energy.

A second area that is only now getting underway is expanding the use of social media to reduce CDD ’ s management and transaction costs and to intensify the dialogue between community members and government policy makers. Because of its national coverage, CDD generates very large data sets on topics as diverse as local disputes and conflict to large-scale survey data on village-level choice making. Capitalizing upon these large data sets and making use of more savvy information technology forms of monitoring and communications opens up a large potential area of research and citizen ’ s feedback.

Now that a growing number of countries are operating national CDD programs and financing them from their own budget, expertise from donor agencies is increasingly being replaced by homegrown specialists. This trend can be nurtured. Developing countries are increasingly building their own networks of universities, civil society groups, think tanks, and cadres of officials who understand and can disseminate CDD approaches. In East Asia, the World Bank and Australia ’ s Department of Foreign Affairs and Trade (DFAT) have launched a regional community that brings together policy makers and specialists to discuss shared needs and experiences, and this approach can be launched in other regions as well.

Finally, while CDD projects by their very nature are concerned with community-level decision making, it is far more realistic to view communities as one end of a spectrum that extends into national and regional networks. CDD projects are only now starting to come to grips with how CDD programs can help improve poor people ’ s access to higher-paying labor 30
Chinn-Ito index-in 1999, followed a steeper trend in liberalizing their capital accounts compared to countries that started with a more open capital account. The faster pace of liberalization for initially more closed countries suggests that the level of capital account openness has been converging over time.

**Table 5: Trend in the Cumulative Index of CFMs and Initial Openness**

||Resi<br>Inflows|dent<br>Outflows|Non-r<br>Inflows|esident<br>Outflows|
|---|---|---|---|---|
|Time trend|0.052**|0.119***|0.050***|0.019*|
||[2.59]|[4.65]|[3.35]|[1.79]|
|Time trend*Chinn Ito|-0.034**|-0.055***|-0.025**|-0.024***|
||[2.59]|[3.29]|[2.38]|[3.06]|
|Observations|3,290|3,290|3,290|3,290|
|R-squared|0.169|0.335|0.197|0.159|
|# of countries|47|47|47|47|

Note: The table shows the coefficient of a panel regression of the respective country specific cumulative indices on a linear time trend and the interaction between this time trend and the initial level of capital account openness, measured by the level of the Chinn-Ito index in 1999:Q1. All regressions include country fixed effects. Robust t-statistics are reported in parenthesis. ***, ** and * indicate statistical significance at the 10%, 5% and 1% level.

The overall trend in the liberalization of capital flows holds also for each type of flow separately. The cumulative change indices displayed in Chart 2 are all upward sloping, indicating a trend toward liberalization. CFMs on resident portfolio flows have been liberalized most often, while CFMs on non-resident FDI and banking sector flows have been changed the least.

**Chart 2: Cumulative change indices by type of flow**

<!-- Start of picture text --> Capital flow measures on residents Capital flow measures on non-residents<br>2000q1 2005q1 2010q1 2015q1 2000q1 2005q1 2010q1 2015q1<br>FDI Portfolio flows FDI Portfolio flows<br>Banking sector flows Banking sector flows<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br><!-- End of picture text --> _Note_ : The chart displays the index of cumulative changes to inflows and outflows of CFMs over time. The blue line shows changes to restrictions on FDI, the red line to restriction on portfolio flows and the green line to restrictions on 12
<!-- Start of picture text --> a<br>. -0.01805 JV -0.018<br>-0.04649 Vay, 0.029<br>; -0.03227 7 ly<br>-0.06072 KY<br>. -0.07494 > -0.041a<br>-0.052<br>—<br>La -0.064<br>4K . -0.075<br>0.4 |KKVby 0.4<br>0.3 A\y 0.3<br>0.2 0.2<br>o (non-food errors) 0.1 0.1 o (food errors)<br>00<br><!-- End of picture text -->

<!-- Start of picture text --> 0.008<br>-0.019<br>" 0.007875<br>-0.02576 -0.046<br>a<br>- -0.05939 -0.073<br>1 -0.09302-0.1267 -0.100<br>-0.127<br>-0.4 -0.4<br>-0.3 -0.3<br>-0.2 -0.2<br>w (non-food errors) -0.1 -0.1 $ (food errors)<br>00<br><!-- End of picture text -->
control, and where the risks of fertilizer use were relatively low but the expected returns were higher (Gautam 2015). In addition, farmers faced domestic prices for fertilizers below world market prices, suggesting that fertilizer use was profitable in the absence of subsidies (Rashid et al., 2013). In contrast, drought-prone rainfed areas in Asia had relatively low fertilizer use comparable to many areas in Africa (Jayne and Rashid, 2013).

After eight years, the NAIVS program was discontinued in 2016 due to lack of funding, two years after the closing of the World Bank loan. Of the 10 Sub-Saharan African countries with an existing ISP in 2010 (Jayne et al., 2016), only the ones in Zambia and Malawi are currently still active.

# **References**

Alatas, V., A. Banerjee, R. Hanna, B. A. Olken, and J. Tobias. 2012. "Targeting the Poor: Evidence from a Field Experiment in Indonesia." _American Economic Review_ 102 (4): 1206-40.

Alatas, V., A. Banerjee, R. Hanna, B. A. Olken, R. Purnamasari, and M. Wai-Poi. 2019. “Does elite capture matter? Local elites and targeted welfare programs in Indonesia.” _AEA Papers and Proceedings_ 109: 334-339.

Alderman, H. 2002. “Do local officials know something we don’t? Decentralization of targeted transfers in Albania.” _Journal of Public Economics_ 83 (3): 375-404.

Bardhan, P., and D. Mookherjee. 2005. “Decentralizing antipoverty program delivery in developing countries.” _Journal of Public Economics_ 89 (4): 675-704 ____. 2006. “Decentralization and Accountability in Infrastructure Delivery in Developing Countries.” _Economic Journal_ 116: 101–127.

____. 2011. "Subsidized Farm Input Programs and Agricultural Performance: A Farm-Level Analysis of West Bengal's Green Revolution, 1982-1995." _American Economic Journal: Applied Economics_ , 3(4): 186-214.

Bold, T., K. C. Kaizzi, J. Svensson, and D. Yanagizawa-Drott. 2017. “Lemon Technologies and Adoption: Measurement, Theory and Evidence from Agricultural Markets in Uganda.” _Quarterly Journal of Economics_ 132 (3): 1055-1100.

Bueno de Mesquita, B., S. Smith, R. M. Siverson, and J. D. Morrow. 2003. _The Logic of Political Survival._ Cambridge, MA: MIT Press.

Burke, W. J., T. S. Jayne, and J. R. Black. 2017. “Factors explaining the low and variable profitability of fertilizer application to maize in Zambia.” _Agricultural Economics_ 48: 115-126.

29
## **Figure 4. When did households build their toilets?**

<!-- Start of picture text --> 45%<br>39.74<br>40% 38.71<br>35.10<br>35%<br>32.26<br>30%<br>25%<br>20%<br>15%<br>Control Treatment<br>10%<br>7.26<br>5.65<br>4.64 4.64 4.84 4.64<br>5% 3.23 2.65 2.42 2.65 2.65 2.65<br>2.42<br>0.81 0.81 0.81 0.66 0.81<br>0%<br><= 1 2 3 4 5 6 7 8 10 11 12 24<br>month<br>Number of months prior to endline survey<br><!-- End of picture text -->

# _8.2 Open Defecation_

Households were also directly asked whether household members ever engage in open defecation (on a daily, seasonal, or occasional basis). This question was asked regardless of the type of sanitation facility households had access to and where they reported they normally defecate. These data were reported separately for male household members, female household members, and children under five years of age, and are presented in Table 10. The table shows that 51 percent of households reported that at least one household member engages in open defecation. Women engage in open defecation slightly less often than men and children. Most open defecation in East Java occurs in rivers (95.36 percent of households that do not have access to sanitation report defecating in rivers). In villages that are within 20 minutes of a river, 54 percent of households had at least one member who defecates in the open at least occasionally. The figure is much lower (13 percent) for households in villages that are more than 20 minutes from a river. The fraction of households reporting that at least one member engages in open defecation is systematically lower in treatment communities than in control communities. The percentage of households reporting that a household member defecates in the open is 4.4 percentage points lower in treatment villages (significant at the 5 percent level). Among 17
<!-- Start of picture text --> Poverty Rates, 2006-2012<br>(percentages)<br>20.00 15.0%<br>4 j 15.0%<br>70.0 -—Am-ws bom<br>4 60.0 - eS re ee é<br># 90.0 | 3.0%<br>2400 00% §)<br>3 300 poe =)<br>20.0 - 90% &<br>10.0 | @=$8 SSee 12.0% 8<br>0.0 | _ 45.09%<br>2006 2007 2008 2009 2010 2011 2012<br>- 6 -$2.5PPP —=—$5,0PPP<br>--+--$10.0PPP ——GDP pe growth<br><!-- End of picture text -->

<!-- Start of picture text --> Inequality, 2006-2012<br>0.405 15.0%<br>2 | 12.0%<br>; 9.0%<br>0395 . 6.0% ¢<br>= 93900 \ Pp. ,- 3.0% 9<br>2 \ ° oo% 2<br>3 0385 aa 3.0%<br>§ 0.380 6.0% :<br>0375 9.0% &<br>; -12.0%§<br>0.370 -15.0%<br>2006 2007 2008 2009 2010 2011 2012<br>—> Gini ——GDPpc growth<br><!-- End of picture text -->