rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8922

# **Abstract**

Groundwater currently provides 98 percent of all the drinking water supply in Bangladesh. Groundwater is found throughout Bangladesh but its quality (that is, arsenic and salinity contamination) and quantity (that is, water storage depletion) vary across hydrological environments, posing unique challenges to certain geographical areas and population groups. Yet, no national-scale, multi-hazard groundwater risk maps currently exist enabling water resource managers and policy makers to identify areas that are vulnerable to public health. This paper develops, for the first time, groundwater risk maps at the national scale for Bangladesh that combine information on arsenic, salinity, and water storage, using geospatial techniques, linking hydrological indicators for water quality and quantity to construct risk maps. A range of socioeconomic variables, including access to drinking and irrigation water supplies and social vulnerability (that is, poverty), are overlaid on these risk maps to estimate exposures. The multi-hazard groundwater risk maps show that a considerable proportion of land area (5 to 24 percent under extremely high to high risks) in Bangladesh is currently under combined risk of arsenic and salinity contamination, and groundwater storage depletion. As few as 6.5 million (2.2 million poor) to 24.4 million (8.6 million poor) people are exposed to a combined risk of high arsenic, salinity, and groundwater storage depletion. The multi-hazard groundwater risk maps reveal areas and exposure of population groups to water risks posed by arsenic and salinity contamination and depletion of water storage.

This paper is a product of the Water 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 gjoseph@worldbank.org, _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
marginalized. In addition, if empowerment indicators become part of regular surveys generating panel data, changes in degrees of empowerment in different countries can be observed over time.

This working paper is an interim product of a series of in-country efforts to measure empowerment. Five of these measuring empowerment studies (Brazil, Ethiopia, Honduras, Indonesia, and Nepal) have been synchronized and the teams linked through the development of the analytic framework and exchanges. In this paper, examples of indicators have been drawn from two of these studies. A third example of measuring empowerment as part of a project monitoring and evaluation system in Mexico is also given. In each case, the ME framework has structured and informed the methodology design for the project’s impact evaluation. All five country cases that are part of the ME study have completed data collection and are currently finalizing data analysis. Country reports detailing findings and listing the empowerment indicators and instruments that were used are expected to be available in spring 2005. These will form the basis of a multi-country synthesis report and other dissemination products on measuring empowerment. Documentation and updates on these initiatives can be found on: <u>www.worldbank.org/empowerment.</u> 31
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- Nguyen, Trang. 2008a. 'Information, role models and perceived returns to education: Experimental evidence from Madagascar', _Unpublished manuscript_ , 6.

- ———. 2008b. 'Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar', Massachusetts Institute of Technology.

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624|0.018|

Notes: 1 High-Medium Tech countries: R&D (% GDP) > 1 (year 2002) 2 Medium-Low-Tech countries : R&D (% GDP) < 1 (year 2002) Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

United Kingdom’s economy has a large trade deficit in manufacturing and has become a net importer of energy.

11 Canada has moved from agriculture straight to services (this industry is very diverse and employs 75% of the total million working population). Manufacturing has never been a dominant sector of the Canadian economy, but it has been an important secondary industry.

12 The main economic activity of Georgia is agriculture. Mining, construction, financial services and communication are other sector making significant contribution towards Georgia’s GDP. Georgia has a good supply of hydropower, however it imports a major part of its energy resources. 13 Mexico has one of the largest economies in the world. The industrial sector in Mexico is very heterogeneous (the industrial sector combines technologically advanced businesses and antiquated industries). The agricultural sector is also an important part of the Mexican economy. The private sector has started assuming an increasingly important role in both the agricultural and the industrial sectors.

14 Manufacturing and creative media largely constitute the New Zealand economy. Some of the major industries of New Zealand include iron and steel, natural gas processing, printing, publishing and recorded media, wood processing, cement, and fishing. Other minor industries in New Zealand include paper, tanning, transport equipments, wine making, tourism, and timber trade. Manufacturing industries in New Zealand contribute over 15% of GDP and over 44% of export receipts. Agriculture also contributes significantly to the economic growth of New Zealand. 15 Iceland has a Scandinavian-type economy. This means that the main economic activity of Iceland is the fishing industry. Iceland also exports animal products and aluminum. New businesses in Iceland are tourism, software production, financial services and
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30
**Long-term and lasting impacts of personal initiative training on entrepreneurial success**<sup>**#**</sup>

Francisco Campos, Michael Frese, Leonardo Iacovone, Hillary C. Johnson, David McKenzie, Mona Mensmann _JEL Classification Codes:_ O12, O17, L26, J24, J16, D22.

_Keywords:_ Microentrepreneurship; Business training; Personal initiative; Firm growth.

# Campos: World Bank (email: fcampos@worldbank.org); Frese: Asia School of Business and Leuphana University of Lueneburg (email: : <u>michael.frese@asb.edu.my); Iacovone: World Bank (email: iacovone@worldbank.org);</u> Johnson: World Bank (email: <u>hjohnson1@worldbank.org); McKenzie (corresponding author): World Bank</u> (dmckenzie@worldbank.org); Mensmann: University of Cologne  (email: <u>mona.mensmann@wiso.uni-koeln.de).  We</u> thank Fréderic Cochinard and Jean-Luc Adzodo for excellent research assistance on this phase of the project, and thank seminar participants at Y-RISE, the IADB, and NOVAFRICA for helpful comments. We gratefully acknowledge funding for this round from the IPA SME Initiative, and from the World Bank through the Entrepreneurship for Development initiative and infoDev multi-donor trust fund. The original study is registered in the AEA RCT registry (AEARCTR-0000888) and we follow up on the same primary outcomes. The World Bank does not have an IRB, but instead has a Personal Data Privacy Protocol which governs the collection and use of survey data.
. We note that a number of alternative targeting approaches exist, such as targeting based on self-selection (Coady and Parker 2009), nutritional risk (Reber et al. 2019), social worker evaluation (Rossell et al. 2018), multidimensional poverty (Alkire and Seth 2013), and Poverty Scorecards (Skoufias et al. 2020), as well as community-based targeting (Alatas et al. 2012; Sabates‐ Wheeler et al. 2015) and geographical targeting (Van Domelen 2007), to name a few. However, PMTs have been widely used as a targeting system in several countries, due to their efficacy at accurately targeting the poor, as well as their relatively low cost and ease of implementation compared to alternative targeting methods.<sup>7</sup> Furthermore, a PMT can be combined with other targeting methods based on program goals, for example nutritional risks, environmental or hazard risks, or educational risk, to identify individuals who

> 6 As of 2020, Sudan’s population is estimated to be 43.8 million according to statistics from the World Development Indicators (WDI). Link here.

> 7 We direct the reader to Grosh (1994), Coady, Grosh, and Hoddinott (2004), Grosh et al. (2008), Kidd and Wylde (2011), and del Ninno and Mills (2015) for additional reviews of various program targeting approaches and the pros and cons of the PMT.

4
## _Taxes and Transfers and their Effect on Poverty_

The degree of poverty reduction from direct transfers is not sufficient to fully offset the poverty-increasing effect of taxes. Taxes have the strongest impact on poverty, and indirect taxes lead to higher overall increase in poverty than do direct taxes. VAT has the highest poverty-increasing effect, increasing poverty by about 1.76 percentage points. On the direct tax side, social security contributions and health insurance together have the highest poverty-increasing effect, increasing the poverty rate by respectively 0.13 and 0.33 percentage points. Despite direct transfers reducing poverty, the degree of poverty reduction is limited given the modest generosity of support through this channel. Direct transfers (pre-COVID) reduce poverty by only 0.06 percentage points.

Figure 15: Marginal contribution of taxes and transfers to poverty reduction <!-- Start of picture text --> Salary tax<br>SSC<br>HI<br>Property tax<br>MoT tax<br>Rental tax<br>Registration tax<br>All contributions<br>All direct taxes<br>All direct taxes and contributions<br>VAT<br>Specific tax, excise<br>Accommodation tax<br>Public lighting tax<br>All indirect taxes<br>Total direct transfers<br>Electricity subsidy<br>-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5<br>Point change<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **Heterogeneity of Effects across Deciles**

The burden of taxes and benefits varies across market income distribution deciles. In absolute terms, the burden of direct taxes, in particular salary tax, concentrates at the upper end of the income distribution (Figure 16A). In relative terms, the direct tax burden as a share of _market income_ does
dimensions. MENA did worse in all of them during the considered period. MENA even had a lower score in these last two dimensions than the much poorer Africa region.

# 4. Household budget surveys in the MENA region

# <u>Availability, timeliness, and access</u>

## _Regional picture_

Household budget surveys (HBSs) collect detailed information about individual/household expenditure, consumption, and income, and also collect household demographic and socio-economic characteristics. This information is used to measure monetary poverty. Most HBS surveys last one full year to allow accounting for seasonal variations in consumption/income. Once primary data collection is finished, data cleaning and poverty measurement can take one year to be completed. It is advisable to conduct HBSs regularly at least once every three to five years (Serajuddin et al., 2015). Given rapid changes in the MENA region, every three-years is a more advisable benchmark, but we use the more conservative five-year gap below.

Table 1 demonstrates that the vast majority of MENA countries fall short of this benchmark. It summarizes for every region the number of countries that conducted HBSs suitable for constructing a monetary poverty metric over the past 15 years.<sup>8</sup> The first panel of Table 1 reports the number of countries in the region that conducted no surveys, one to two surveys, and three and more. The second panel reports the share of countries by number of surveys conducted. The third panel reports the share of the population in the respective region covered by the number of surveys conducted (as opposed to share of countries). Countries with fewer than three surveys are considered “data deprived” according to the benchmark of conducting a survey at least every five years.

Between 2004 and 2018, seven of 13 MENA countries, or more than half, conducted fewer than three surveys. This falls short of areas suitable for socio-economic comparison to MENA, such as Latin America and the Caribbean (LAC) and Europe and Central Asia (ECA
**Livelihood zone**|**Hazard z-score < -3**|**Hazard z-score < -2**|**Negative hazard z-score**|
|---|---|---|---|
|Soil moisture (GS)||||
|Maize|0|0.01|0.63|
|Highland|0|0.05|0.64|
|Roots and lowland|0|0.03|0.63|
|NDVI (RS)||||
|Agropastoral|0|0.00|0.40|
|Pastoral|0|0.01|0.38|

_Source:_ World Bank. _Note:_ GS = growing season; RS = rainy season. We generated the alternate draws of the hazard distribution, k , a in two ways. The first used the historical weather distribution to select k for all households. This provided a first assessment of what an EP curve would look like but is based on a limited set of historical data points—in the case of soil moisture, only 13 years of data. The second way RR used the data set of 10,000 possible weather simulations for Malawi developed by the risk modeling firm AIR, which RR was based on the historical pattern of rainfall in Malawi. This catalog is available only for Malawi but suggests the degree of precision that modeling the weather offers as compared to using historical weather data. The generated k are used with parameter estimates from tables 4.1 to 4.5 to simulate what consumption would be for each household under these weather conditions. Once consumption has been simulated for a given draw of the weather, variables that describe the simulated consumption distribution can be generated. We focus particularly on: (i) RR the poverty headcount rate—the share of households whose simulated consumption falls below the extreme poverty line of $1.90—and (ii) the total
Policy Research Working Paper 6443

# **Abstract**

In large parts of the world, a lack of home tap water burdens households as the water must be brought to the house from outside, at great expense in terms of effort and time. This paper studies how such costs affect girls’ schooling in Ghana, with an analysis based on four rounds of the Demographic and Health Surveys. Using Global Positioning System coordinates, it builds an artificial panel of clusters, identifying the closest neighbors within each round. The results indicate a significant negative relation between girls’ school attendance and water hauling activity, as a halving of water fetching time increases girls’ school attendance by 2.4 percentage points on average, with stronger impacts in rural communities. The results seem to be the first definitive documentation of such a relationship in Africa. They document some of the multiple and wide population benefits of increased tap water access, in Africa and elsewhere.

This paper is a product of the Environment and Energy Team, Development Research Group. 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 author may be contacted at jstrand1@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
the purchasing power parity of food and nonalcoholic beverages relative to the nominal exchange rate for the year 2005. Information is from the World Bank (2008b). _Note_ : *, **, and *** indicate significant at the 10 percent, 5 percent, and 1 percent levels, respectively. “Europe-plus” includes Eastern European countries plus North America and Australasia. Note that self-reported food insecurity data are measured in 2005 or 2006, whereas the food price ratio is measured in 2005.

Overall, the results reported above present a mixed picture of the validity of cross-country patterns in the Gallup data. On the one hand, there are certainly some worrying outliers in the GWP indicator (particularly in the 2005–06 round). On the other hand, the data as a whole are plausibly patterned across countries and strongly correlated with other welfare indicators and relative food prices. However, we acknowledge that many social scientists are wary of subjective 14
some special issues related to road concessions and its application in Chile. Finally, the paper ends with some conclusions and lessons that can be extracted from the Chilean experience.

# **2 Infrastructure deficit**

Between 1980 and 1994 the vehicle stock in Chile increased from 746,782 to 1,499,904. It is now expected to reach over 2 million vehicles by the year 2000. In Santiago, the number of vehicles per capita almost doubled in less than two decades (see Table 2.1).

**Table 2.1: Average number of vehicles per thousand residents, Greater Santiago**

|**Year**|**Vehicles per 100 residents**|
|---|---|
|1977|5.8|
|1991|9.1|
|1995|11.9|

Source: Trip Origin and Destination Survey, 1991. The 1995 figure is an estimate by the Ministry of Public Works.

The problems created by the increase in the vehicle stock were compounded by the poor condition of the road network. During the eighties, maintenance investment reached only 30% of the amount required. The portion allocated to the paved network was even lower. Estimates showed that by the middle of the decade close to 50% of the road network was in poor condition (see Table 2.2).

**Table 2.2: Conditions of the road network (km)**

|Concrete|**Good**<br>1,611|**Fair**<br>1,726|**Poor**<br>498|**Total**<br>3,835|
|---|---|---|---|---|
|Asphalt|5,157|2,157|1,802|9,116|
|Gravel|4,191|22,244|5,803
|Age|Age inyears|
|Sisben|The household’s Sisben III score|
|Woman|A binary variable indicating gender. It is omitted from estimations by<br>gender.|
|Widowed<br>Divorced<br>Single|Categorical variable indicating each of the three civil states. The omitted<br>variable is married.|
|Potential beneficiaries,<br>number<br>Other adults, number<br>Minors,number|The number of household members in each group: members old enough<br>to qualify for Colombia Mayor; other household members 18 years of age<br>or older; members younger than 18|
|Number of women|The number of women household members|
|Primary<br>Secondary<br>Postsecondary|Set of categorical variables indicating the highest attained certificate or<br>degree. The omitted category is no completed level of education.|
|Incapacitated|A binaryvariable indicatingthat aperson ispermanentlyunable to work.|

20
||$3.2 PPP|3.0%|1.7%|2.8%|
||$5.5 PPP|4.9%|3.3%|5.6%|
||60% of median equivalized disposable||||
||income|6.5%|4.9%|8.1%|

Source: Own estimations using HBS and SILC data <mark>The finding that fiscal policy is estimated to increase poverty in Serbia is similar to that in most available analyses in Europe and Central Asia. Only in some countries in the CEQ sample, such as Russia and several countries in Latin America, poverty is reduced or remains mostly unchanged after accounting for direct and indirect taxes and transfers.</mark><sup>13</sup> <mark>Poverty, measured as per-capita income below $5.5/day in 2011 PPP, is estimated to increase by about 7 percentage points in Serbia, from 15.3 percent in market income (including pensions as deferred income) to 22.4 percent in consumable income. In Armenia, poverty increases by 6.6 percentage points while in Romania 1.5. Other studies using slightly different international poverty lines also indicate poverty increases after fiscal interventions in Albania, Montenegro, Croatia, and Poland.</mark>

# **V. Progressivity and marginal effects of individual taxes and transfers**

This section discusses how each component of the tax and benefit system contributes to the system’s overall impacts on poverty and income inequality. The influence of individual fiscal interventions may be different from the influence of the overall fiscal system. Both the size and progressivity of a tax or expenditure item determine its marginal effects on inequality and poverty measures. For example, a highly progressive but small government transfer may have a small marginal effect on inequality and, therefore, small redistributive impacts. We first show how taxes and transfers affect household income across