rafmacalaba/datause-encoder-data huggingface dataset:
PROVISION OF WATER TO THE POOR IN AFRICA

**Table 3.7   Regulation of water tankers**

|Country|City|Regulated|Regulated|
|---|---|---|---|
|Ethiopia|Addis Ababa|yes|1|
|Nigeria|Kaduna|no|2|
|Rwanda|Kigali|no|2|
|South Africa|Johannesburg|no|2|
|Ghana|Accra|no|2|
|Kenya|Nairobi|yes|1|
|Sudan|Great Khartoum|yes|1|
|Cape Verde|Praia|yes|1|
|Chad|N'Djamena|yes|1|
|Tanzania|Dar es Salaam|no|2|
|Uganda|Kampala|no|2|

<u>Source: AICD WSS database, 2007.</u>

# Small piped networks

The price charged by small piped networks is slightly higher than the prices charged by the utility for a household connection. In the cities studied, the prices charged by small piped networks range from $0.47/m3 in Madagascar to $2.11/m3 in the DRC. Since small piped networks are a relatively recent solution to inadequate water supply, less is known about the costs that they bear or their performance according to management by private individuals or by community groups.

Half of the cities do not have regulations that control the number of boreholes or restrict the amount of water withdrawn from them. For those cities in which regulations exist, regulations are unclear or rarely enforced. As utilities expand service into peri-urban areas, they are likely to increasingly operate in areas where these boreholes and small piped networks exist, and the pressure is likely to grow to either put them under the purview of the utility or to regulate them. Although there are limited data, analysis of SPNs shows most of the cities do not regulate these activities.

# An indicator for measuring the distortion in the water sector

Two main conclusions emerge from the overall price analysis. First, households without private connections or yard taps pay unit water prices significantly higher than those with these connections. Second, the prices for each water provider in the informal sector show a greater degree of variability than the prices for water offered by utilities to connected households. The standard deviation of the prices for each informal water service is 1.3 to 5 times higher than the price of water for connected households. There can be a great degree of variability in prices in different neighborhoods within the same city. Figure 3.5 shows the price ranges by water provider. In the case of the standpipes/kiosks, the Cape Verde example is highlighted because its high price is directly related to its very specific water production system; the red mark represents the upper value from comparison cities.

32
> Table 8. Employment Status. Matc in oup: **Age 17-65.** Three nearest neighbors base on propensity scores

|_Cohort_|_Control_<br>**_Gop_**|_Employer_<br>_CONALE_|<br> <br>**_Difference_**|_S_<br>_Control_<br>**_Gop_**|_elf-employ_<br>_CONALE_|_ed_<br> <br>_Difference_|_Control_<br>**_Gop_**|_Employee_<br>_CONALE_|<br> <br>_Difference_|_Coope_<br>_Control_<br>**_Gop_**|_rative mem_<br>_CONAL_|_bership_<br>_E_<br>**_DiffrneGop_**|_Wor_<br>_Control_<br>|_ker witho_<br>_CONAL_<br>_p_|_ut pay_<br>_EDiference_<br>**_Dfeec_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|90 - 93|1.0|2.8|1.9|8.0|9.3|1.4|85.6|85.8|0.2|0.0|0.0|0.0|5.4|2.0|-3.4|
|91 - 94|1.2|2.9|1.7|7.3|12.5|5.3|88.2|81.4|-6.8|0.0|0.7|0.7|3.4|2.5|-0.9|
|92 - 95|1.6|2.3|0.7|7.4|9.4|2.0|85.5|84.8|-0.8|0.0|1.2|1.2|5.4|2.3|-3.1|
|93 - 96|2.4|3.6|1.1|8.1|10.7|2.6|85.1|83.0|-2.1|0.0|0.8|0.8|4.4|2.0|-2.5|
|94 - 97|0.9|1.3|0.4|6.7|6.4|-0.3|88.1|87.2|-1.0|0.0|1.3|1.3|4.2|3.8|-0.4|
|Total'|1.4|2.6|1.2|7.5|9.8|2.3|86.5|84.3|-2.2|0.0|0.8|0.8|4.6|2.5|-2.0|
|_ENE 982_|4.3|||24.1|||60.2|||0.04|||11.4|||
|_ENE 98, LS'_<br>|0.2|||4.8|||77.9|||0.02|||17.1|||
|_ENE 98, US_<sup>_4_</sup>|2.5|||11.0|||77.8|||0.00|||8.7|||

1. **Sample: Workers** in **the matching group.**

**2. Sample: All workers.**

**3. Sample: Workers with lower-secondary complete and 3 years of experience (18 and 19 years old).**

**4. Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).**

> Table 9. Employment Status. Matching group: Age **17-65.** Three nearest neighbors based on propensity scores

**_**

|_Cohort_|_Control_<br>**_Group_**|_Employer_<br>_CONALE_ <br>_P_|<br> **_Diference_**<br>_ifeec_|_S_<br> _Control_<br>**_Group_**|_elf-employ_<br>_CONALE_<br>_P_|_ed_<br> **_Difference_**<br>_feec_|_Control_<br>**_Group_**|_Employee_<br>_CONALE_<br>**p**|<br>_Difference_<br>_ffeec_|_Coope_<br>_Control_<br>**_Group_**|_rative mem_<br>_CONAL_<br>**p**|_bership_<br>_E_**_Diference_**<br>_feec_|_Wor_<br>_Control_<br>**_Group_**|_ker witho_<br>_CONAL_<br>**_p_**|_ut pay_<br>_E Diference_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|90 - 93|1.2|4.0|2.8|11.0|13.3|2.3|84.0|82.0|-2.0|0.0|0.0|0.0|3.7|0.7|-3.0|
|91 - 94|2.1|1.8|-0.4|7.0|13.6|6.7|86.1|81.7|-4.4|0.0|1.2|1.2|4.8|1.8|-3.0|
|92 - 95|1.2|2.9|1.6|8.6|10.0|1.4|85.2|85.7|0.5|0.0|0.7|0.7|4.9|0.7|-4.2|
|93 - 96|2.7|6.9|4.2|8.1|6.2|-1.9|81.2|84.6|3.4|0.0|0.0|0.0|8.1|2.3|-5.7|
|94 - 97|0.9|1.0|0.2|6.9|7.1|0.2|86.2|86.7|0.5|0.9|1.0|0.2|5.2|4.1|-1.1|
|Total'|1.7|3.3|1.7|8.4|10.5|2.1|84.6|83.8|-0.7|0.1|0.6|0.5|5.3|1.7|-3.5|
|_ENECE 992_|4.0|||24.4|||60.8|||0.03|||10.7|||
|_ENECE 99, LS'_|0.2|||4.4|||79.6|||0.00|||15.9|||
|_ENECE 99,US'_|<br>2.9|||9.2|||81.4|||0.02|||_6.5_|||

1. **Sample: Workers** in **the matching group.**

2. **Sample: All workers.** 3. **Sample: Workers** with **lower-secondary complete and** 3 **years of experience (18 and** 19 **years old).**

**4. Sample: Workers with upper-secondary complete and** 1-5 **years of experience (22-26 years old).**

31
not pay workers). To guard against outliers in the wage rates or ratios, we use the wage rate of the median employee in the district (and its urban and rural areas). We do not directly observe the full wage distribution, but instead calculate the metrics through plant averages and their relative employment levels. Wages are expressed in 2005 U.S. dollars. When discussing our results, we describe in greater detail the extent to which this factor may model other costs beyond labor factors for districts. We jointly model the average wage and urban-rural wage ratio variables as their correlation is small at 0.12.

Our remaining variables are covariates to contrast with the above factors. We first consider district land area. This metric is calculated by the World Bank using Geographic Information System (GIS) data files of district boundaries in India. Conditional on the other variables specified, we would not anticipate district scale playing an important role. Our focus is instead on the urban build-up of a district. Following Schneider et al. (2009), we consider the share of land in the district that is built-up for urban use. This fraction is calculated using 2001 MODIS satellite information at 500 meter spatial resolution. Built-up area, focusing on building structures, is a good proxy for urban area. Greater built-up area in a district can allow for stronger urbanization growth and is our best proxy for local land markets. A second proxy, termed land use intensity, is calculated as total land value of organized sector plants per unit of shipments.

For our core estimations, we do not model the overall level or change in population urbanization for a district, as this may be endogenous/follow the industry movements we consider. Robustness checks do consider, however, the change in population urbanization from the 1991 Census to the 2001 Census as a control. Thus, these regressions will be considering the movement of industry over-and-above that of the underlying population base. Section 2 noted that some sub-areas of districts change their urbanization status in 2001 and that this could affect our urbanization measures even if a plant’s location remained fixed. Including this change provides a quantitative check on whether this is important as the redefinition would apply to the sub-area’s population, too.

# **_Basic District Analyses_**

Our base estimation for district d and industry i is specified as: ΔUrban Shared,i = γ∙Initial Urban Shared,i + β∙Xd + ηi + ηs + εd,i.

The dependent variable, ΔUrban Shared,i, is the change in urban share in employment across a time period. Initial Urban Shared,i is the initial urban share in employment at the start of the period. This initial condition is important as we consider shares that are bounded. Thus, highly urbanized areas like Delhi have limited potential to further urbanize.

12
# **1 Introduction**

In the last decade, Mali has become the epicenter of one of the world’s worst civil conflicts (ACLED, 2020), generating more than 346,000 internally displaced peoples (IDPs) (UNHCR, 2020). Real and perceived inequalities between and within communities have helped fuel this conflict (Pezard & Shurkin, 2015). The plight of IDPs in Mali is severe, which has been recognized and they are actively aided by national and international organizations. Hoogeveen, Rossi, and Sansone (2019), which studies the dynamics of intentions to return after the early part of Mali’s conflict subsided, shows that some but not all IDPs intend to return home. IDPs fleeing this conflict and locating in other Malian towns potentially affect social cohesion by changing economic conditions including wealth, poverty, and inequality among the population in host communities. Policymakers dealing with an internal displacement crisis are often challenged to mitigate shocks caused by a sudden inflow of people on host populations, while providing adequate support for incoming displaced peoples. If IDPs foster increased inequality or negatively affect local wealth, then they may affect social cohesion in ways that exacerbate the conflict. This work tries to answer how does the arrival of IDPs in host communities in Mali affect wealth, consumption, poverty, and inequality?

We analyze the effects of IDP presence and numbers on consumption, poverty, and inequality of host communities in Mali. We use comprehensive nationwide household survey data on over 36,000 households from 2013 - 2019 combined with data on IDP populations to estimate econometric models of the effects of IDP presence and numbers. Our modeling strategy uses multiple econometric methods, difference-in-difference, instrumental variable, and propensity score matching to provide the most robust results to concerns about the endogenous location choices of IDPs.

A now burgeoning literature analyzes the economic impact of displaced peoples on host communities, see Verme and Schuettler (2020) for a systematic review. While the literature review finds a low probability of a decline in well-being for households in host communities, there is little evidence on what hosting displaced peoples does to inequality. A positive 1
7 percent in recent years. Domestic borrowing remained within reasonable levels and external debt was cut substantially through debt reduction measures agreed with major lenders in the early 2000s. In 2009, the total stocks of external and domestic public debt were at 20.6 and 15.8 percent of GDP, respectively, with the government being able to service these debts without difficulty.

Thanks to rising oil prices, export earnings and foreign exchange reserves expanded during the period 2002-2009; in 2009, reserves were sufficient to cover imports for 7.5 months (IMF, 2010).<sup>4</sup> Up to and including 2009, Yemen gradually liberalized trade, with tariff rates on most items cut to single digits and most non-tariff barriers removed. However, oil reserves are expected to be depleted before 2020 (EIU, 2008, p. 3). In addition, Yemen is also facing the depletion of its groundwater. These challenges are compounded by population growth at rates exceeding 3 percent, poor infrastructure, weak institutional capacity, a fragile security situation, and the widespread use of qat.<sup>5</sup> In addition, rising food prices pose a particularly difficult problem, given the very slow increase in domestic food production since unification, with the country currently dependent on imports for over 80 percent of its basic food staples.

As an element of its inflation control policy, the Central Bank of Yemen has been maintaining a fairly stable nominal exchange rate of the Yemeni rial relative to the US dollar during the period 2002-2009, allowing an annual depreciation of 3-5 percent. Nevertheless, given an average inflation rate of around 10 percent during the same period, the real exchange rate has appreciated, boosting imports and undermining non-oil exports, a development that has been made possible by still high oil revenues.

> 4 In the context of the ongoing conflict, reserves of foreign exchange declined drastically during the first half of 2011, according to IMF’s Arab News on 3 August 2011, downloaded from: < _http://arabnews.com/economy/article482640.ece_ >.

> 5 In addition to accounting for over one–third of agricultural production, as said, the production of qat requires a quarter of total water resource use. Furthermore, according to the Yemen Poverty Assessment (Government of Yemen and others, 2007: p. 43), ―qat impacts the economy in a negative manner through the opportunity cost of lost savings as well as lost work-hours. Although the authorities have developed public awareness campaigns, citing the drawbacks of qat consumption, discouraging qat consumption is a complex and difficult task; it is far too well integrated into the Yemeni economy and social structure to be eliminated on a short-term basis without adverse effects‖.

9
_Table 2: School construction programme summary statistics_

||**Panel A: A**|**lldistricts**<sup>**(a)**</sup>|**Panel B: IF**|**LS districts**<sup>**(b)**</sup>|
|---|---|---|---|---|
||**Mean**|**Standard**<br>**deviation**|**Mean**|**Standard**<br>**deviation**|
|Total number of districts in<br>sample|293||222||
|INPRES schools constructed<br>(1973/74 - 1978/79)|222|173|252|182|
|INPRES schools constructed<br>per 1000 children<br>(1973/74 - 1978/79)|2.3387|1.2621|2.2105|1.0706|
|Fraction of population that<br>were children in 1971|0.2868|0.0772|0.2714|0.0176|
|Enrolment rate in the<br>population in 1971|0.1777|0.0969|0.1689|0.0606|
|<br>Allocation of water and<br>sanitation programmes in<br>1973/74|0.7064|0.5680|0.5525|0.2959|

Notes: (a) All districts for which school construction data were available (b) Districts with school construction data represented in IFLS survey (districts of birth) Sources: Data on school construction programme collected by Esther Duflo from INPRES instructions, Census (1971) and Ministry of Education and Culture.

# **6. Analytical framework**

## **Economic demand model**

Empirical analysis of health care choices is generally based on the human capital and household production literature. Grossman (1972a; 1972b) presented a model in which health affects the total time that can be spent productively, instead of having a direct impact on market or non-market productivity. Health, rather than health care, enters the utility function. Health care is included in the production function, along with time and other intermediate inputs including education. The demand for medical care represents the rational response to a health shock, which leads individuals to shift resources away 18
Policy Research Working Paper 7846

# **Abstract**

Rationing car use based on license plate number has become a popular policy in several cities around the world to address traffic congestion and air pollution. This paper studies the effects of the moderate and drastic driving restrictions imposed as part of the _Pico y Placa_ program on car use and air pollution in Bogotá. Using data on ambient carbon monoxide, gasoline consumption, and vehicle sales and registrations, no evidence of an improvement in air quality or a reduction in car use is found in either phase of the program. On the contrary, there is some indication that, relative to the moderate phase, gasoline consumption, vehicle ownership, and carbon monoxide in the morning peak tended to increase slightly when drastic restrictions were implemented.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 jobonill@uniandes.edu.co.

_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
28 (1)

# (2)

# Variable

# Betaa,h/ Coeff.-/ S.E./ Beta Coeff. S.E.

|Teachers, teaching:||||
|---|---|---|---|
|Teacher experience||.06<br>.04|.03|
|Teadher sex(female)||.00<br>.00|.60|
|Use, published material||-. 03<br>-. 10|.15|
|Time,instnxtional<br>tasks||.11*<br>.01|.00|
|Time, students listening||.26*** -. 04|.01|
|Time,administrative<br>tasks||*.07<br>-. 01|.01|
|Time,addin., squarede/||.14<br>.00|.00|
|Constant|17.98|21.65||
|R2|.05|.16||

Note:

- (a) These are %tandardized regression coefficients, equivalent to adjusted correlations between the independent variable after having taken the other irnkependent variables in the model into account. We interpret these as effect sizes.

- (b) Nbninal sigificance levels of regression coefficients are as follows: *h.p,.05; *k-p(.Ol: ***k-p<.001. Detenmired as t-statistics computed by dividing the unstardardized regression coefficient by its standard error.

- (c) Ihese are unstandardized regression coefficients.

- (d) Standard error;of unstandardized regression coefficients.

- (e) Ifie relationship beteen math score and the teacher's time spent on admnistrative tasks had both linear and quadratic ccmponents. Thi.; variable represents the quadratic carporent.
The coefficients oif the error-correction process are not all well-determined, but sometimes there is also an indication of a stronger impact of world interest rates after the break. All iin all, it the empirical patterns detected seem to confirm the a _priori_ belief that this method would capture a significant date in the liberalization of wholesale interest rates.<sup>23</sup> The same approach was extended to 8 of the 10 money market rates (Argentina and Brazil excluded because of the difficulty of defining a satisfactory smoothed inflation series). Here a further three break points were detected as shown in Table 4, again with high volatility ratios. The indications were that most of the remaining countries may have crossed that threshold before 1980.24 Also included in Table 4 are the liberalization dates provided for these countries by other recent studies.<sup>25</sup> The differences between the dates reflect differences in the concept of liberalization date being used. They should thus be considered as complementary to the dates obtained bv approaches (a) and (b) here. Galbis (1993) study uses dates at which preferential lendiing rates, or controls on key bank deposit or lending rates were removed. Demirguic-Kunt and Detragiache (1998) use deregulation of bank interest rates as the observable policy change to date liberalization. Williamson and Mahar (1998) are looking at a wider concept of financial liberalization and provide two dates: 'start of liberalization' and 'largely liberalized'.

> 23 We also carried out the same exercise for the thirteen industrial countries. Break points within the sample period were also found for four of these. (An unusual situation arose for one country, Ireland, where a break existed, but the post-break residual variance was lower than the pre-break. In fact the identified break was in this instance related to the EMS crisis of 1992-93 rather than to liberalization.)

> 24 Liberalization is not irreversible; the data from Malaysia (late 1981), and to a lessor extent India (early 1984) and Pakistan (late 1985) provides some indications of a reversal to a narrower range of fluctuation. 25 Appendix Table A gives the dates from these studies for additional developing countries.

15
38

# **Appendix Figure 2 (continued). Global Welfare Reduction Indexes, 1960 to 2004**

(percent)

# (c) Tropical crops

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Sugar Cotton Coconut Coffee Rubber Tea Cocoa<br><!-- End of picture text -->

# (d) Livestock products

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Pigmeat Milk Beef Poultry Egg Sheepmeat Wool<br><!-- End of picture text --> Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).
INTRODUCTION IV Table 12 **FenIlity** and Mortality Rates by Geographic Region and by Income GroLtp, 1985-90, 2000.05, and 2020-2E

||||1985-9|0||||2000-0|5|||2|020-2|5||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region or||||||||||||||||
|income group|CBR|<br>TFR|CDR|e(O)|INR|CUR|<br>TFR|<br>CDR|eCO)|IMR|<br>CUR|<br>TFR|COR|e(O)|IMR|
|World|27.1|3.43|9.7|64.9|70|22.6|2.91|8.3|68.2|48|18.7|2.48|8.4|70.6|35|
|Less developed (a)|31.0|3.92|9.6|62.1|78|25.0|3.13|7.9|66.2|52|19.9|2.55|7.8|69.3|38|
|More developed (a)|14.7|1.90|9.9|74.1|15|12.9|1.89|10.0|76.0|10|12.5|2.03|11.6|77.0|9|
|Africa|44.4|6.14|14.2|53.4|107|38.1<br>|5.09|10.2|58.8|73|27.8|3.36|7.6|64.0|52|
|East Africa|46.1|6.31|14.4|53.0|110|59.<sup>9</sup>|5.39|10.4|58.4|78|29.7|3.62|7.6|63.8|54|
|West Africa|47.0|6.52|16.1|50.2|113|40.8|5.52|11.5|56.1|78|*29.1|3.51|8.2|62.2|55|
|Worth Africa|37.1|5.19|10.4|59.4|<br>84|29.6|3.80|<br>7.4|64.6|50|<br>20.7|2.46|<br>6.6|68.5|36|
|Amertca|23.4|2.84|<br>8.1|70.2|<br>44|17.9|2.28|<br>7.1|73.0|27|<br>15.3|2.11|<br>8.2|74.6|19|
|LatinAmerl:a<br>and||||||||||||||||
|Caribbean|28.6|3.57|7.4|66.6|56|20.6|2.46|6.1|70.5|33|16.6|2.15|7.1|73.0|23|
|Northern America|15.4|1.87|9.0|75.7|10|12.9|1.90|8.7|77.4|8|12.5|2.03|10.5|78.0|7|
|Asia|27.7|3.47|8.9|63.9|71|22.0|2.76|7.7|67.8|45|17.7|2.32|8.1|70.7|32|
|East and Southeast Asia|22.6|2.66|7.1|68.4|40|17.5|2.21|7.0|71.8|21|15.1|2.10|8.5|73.9|16|
|South Asia|34.6|4.70|11.6|57.0|102|27.1|3.38|8.8|62.3|67|20.0|2.47|8.0|66.9|46|
|Southwest Asia|36.6|5.11|8.8|63.4|73|30.3|4.00|6.4|67.9|43|23.8|2.94|6.0|70.8|31|
|Europe and U.S.S.R.|14.9|1.96|10.7|72.8|18|13.1|1.91|10.6|75.0|12|12.7|2.04|11.7|76.2|10|
|Oceania|20.1|2.55|8.1|72.1|28|17.8|2.41|7.9|73.7|17|15.0|2.15|9.0|74.8|14|
|Income group (b)||||||||||||||||
|Low|30.8|3.89|10.2|61.1|<br>84|25.2|3.18|<br>8.5|65.3|58|<br>20.1|2.58|<br>8.2|68.5|41|
|Lower-middle|31.5|4.05|8.4|64.0|63|24.5|3.00|6.7|68.1|38|19.2|2.41|6.9|71.0|28|
|Upper-middle|26.7|3.39|8.1|67.3|<br>53|21.6|2.73|6.9|70.9|32|18.0|2.39|7.5|73.0|23|
|High|13.9|1.79|9.4|76.1|12|12.7|1.90|9.6|77.5|10|12.6|2.10|11.6|77.9|9|
|Nonreportingnormember|19.6|2.52|10.6|69.1|32|16.4|2.28|9.9|7c.1|24|15.3|2.22|10.2|73.8|20|

Note: Crude birth rates (CUR) and crude death rates (COR) are per 1000 population; total fertility rates (TFR) are per woman 15-49 years of age; life expectancy at birth, e(O), is given in years; and infant mortality rates (IMR) are per 1000 tive births. (See definitions in World Bank 1989.) a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan.

"Less developed" comprises the rest of the world.

- b. Criteria for income groups are given in World Bank 1989. See listing in Exhibit 1.