rafmacalaba/datause-encoder-data huggingface dataset:
are at particular risk of attack in and around toilet facilities located some distance from their homes (Cornman-Levy et al., 2011; McIlwaine, 2013; Sommer et al., 2015) Other costs associated with water disruptions are incurred through the direct cost of alternative source of water. During long water interruptions, people have no choice but to rely on alternative sources of water, which are all more expensive than piped water. Some households may be able to use their own well, but energy for pumping can be expensive. Also, many households subject to IWS use some form of water storage, what increases the amount of water consumed, poses health risks due to poor storage, and carries some costs. In most cities, people without reliable access to water have to rely on water kiosks, street vendors, or tanker trucks. All these solutions come at a higher cost than piped water. As documented in Kjellen (2000) for Dar es Salaam and UN-Habitat (2003) for 19 cities, these alternative sources of water can be ten to 100 times more expensive than piped water (Figure 7).<sup>9</sup> These additional costs, combined with negative health outcomes, have a direct impact on the economy: in Lusaka, Zambia, financial transactions decrease in times of water outages, with particularly adverse effects on the poorest (Ashraf et al., 2017).

**Figure 7: Ratio of water prices: comparison of water vendors and house connection prices**

<!-- Start of picture text --> 120<br>96.5<br>100<br>85.3<br>80<br>60<br>50.9<br>40<br>20.9<br>20 14.5<br>11.4<br>9.5<br>7.4 7.1 7.4<br>2.2<br>0<br>Ratio = 1 (reference)<br>Bandung, Indonesia Karachi, Pakistan Kathmandou, Nepal Manila, Philippines Mumbai, India Phnom Penh, Cambodia Chonburi, Thailand Malé, Maldives Seoul, South Korea Bangkok, Thailand Port Vila, Vanuatu<br><!-- End of picture text --> _Source: (UN-Habitat, 2003). Based on consumer surveys by Asian Development Bank._

> 9 Detailed tables on water prices in Dar es Salaam and in Asian cities may be found in the Appendix section (Table 6 ; Table 7).

15
# Figure 3: Risks to LIC debt sustainability

# (A) Private sector debt

<!-- Start of picture text --> Percent of GDP<br>Non-LIC EMDEs<br>50<br>LICs<br>40<br>30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

## (B) Median export concentration

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Non-LIC EMDEs LICs<br>Export concentration index<br><!-- End of picture text -->

# (C) Growth

<!-- Start of picture text --> LICs Oil-exporting Metals- Non-resource-<br>LICs exporting LICs intensive LICs<br>2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020<br><!-- End of picture text -->

# (D) Global commodity demand growth

<!-- Start of picture text --> Percent<br>5 Baseline projection<br>2010-16<br>4<br>3<br>2<br>1<br>0<br>Metals Energy Agriculture<br><!-- End of picture text --> Source: BP Statistical Review, Haver Analytics, United Nations, United States Department of Agriculture, World Bank, World Bureau of Metals Statistics.

A. Domestic credit to the non-financial private sector provided by commercial banks and, if data are available, by other financial corporations. Median debt, based on 148 EMDEs and 29 LICs.

A.B. Non-LIC EMDEs= Emerging market and developing economies excluding LICs; LICs= Low-income countries.

B. Orange lines indicate interquartile ranges of Herfindahl-Hirschmann concentration index, which measures the degree of product concentration, with values closer to 100 indicating a country’s exports are highly concentrated in a few products.

D. To ensure comparability, 2010-16 is model-predicted commodity demand growth.

8
since it determines who benefits from any economic, developmental or spillover effects associated with the project.

A number of studies have looked at the composition of project location under alternative policy settings; Haites (2004) provides a review as do recent IPCC evaluations.<sup>67</sup> These studies roughly indicate country or regional potential for Kyoto-project offsets since they are driven by abatement costs. In general, most models suggest that Asia has the largest potential for CERs. For example, Jakeman et al. (2000) place about 62 percent of the predicted CDM market in Asia, Sijm et al. (2000) suggest 71 to 78 percent and Jotzo and Michaelowa (2002) estimate 72 percent. For studies that provide country detail, China is usually the largest source of potential offsets.<sup>68</sup> Asia’s dominance is partly due to the large population but also because of the composition of the region’s industrial base and its reliance on coal and oil.

There is less agreement as to differences in sectoral potential. In their synthesis of sector studies, Barker et al. (2007) suggest large potential savings in all regions based on improving energy efficiencies in residential and commercial buildings, accomplished through, for example, improved lighting and insulation, gains in small appliance efficiency, and the use of alternative coolants. Sijm et al. (2000) suggest that potential gains in energy efficiency account for most potential emission reductions. In developing countries, agriculture and forestry projects are significant when carbon prices remain under $20 per tCO2e. This includes the use of better soil management techniques to improve soil sequestration and adding to sinks through afforestation and reforestation projects. At higher prices, the composition of potential projects expands to include more industry-based (the use of more efficient equipment, the control of non-carbon-dioxide emissions, etc.) and energy-supply projects (renewables, fuel-switching, etc.)

# **The geographic distribution of Kyoto-project credits**

To date, the supply of issued and potential CERs remains firmly centered in Asia. Nearly 75 percent of the pipeline projects accounting for about 79 percent of potential first-period CERs are hosted in Asia.<sup>69</sup> This is largely due to population and also the relatively high consumption levels of oil and coal in Asia. Latin America accounts for 21 percent of the projects and 15 percent of pipeline CERs and, on a per capita basis, hosts a larger share of the first-period pipeline than Asia. Sub-Saharan Africa accounts for about 2.6 percent of the pipeline potential. Across all regions, the least-developed countries host few projects and account for about 1 percent of potential first-period CERs. A

> 67 See especially Barker et al. (2007).

> 68 For example, Chen (2003) estimates that roughly 55% of the potential for CDM projects is in China alone, with another 10 percent in India.

> 69 UNEP/RISOE, March 2008.

44
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and the national operator Air Congo provide services. Regional destinations include Douala, Libreville, Cotonou, Abidjan, Lomé, Luanda, and Kinshasa.

## **Challenges**

According to ICAO audit, the Republic of Congo’s air transport safety is well below international standards. Safety in air transport would greatly improve if Congo’s civil aviation authority were to acquire proper capacity to regulate the sector’s security and safety. However, considering the small size of its aviation sector, gain in regulatory capacity will be hard to sustain and Congo might envisage teaming with larger civil aviation authorities such as Cameroun to maintain and/or improve its overall regulatory capacity in key areas such as aircraft and air crews licensing.

# Water supply and sanitation

## **Achievements**

The Republic of Congo compares favorably with other African countries in access to piped water: 26 percent of the population has access to piped water, compared with the regional average of 17 percent (table 6). But access is skewed to urban areas, with an access rate of 46 percent compared with rural access of only 11 percent. Two-thirds of the country’s rural population (one-third of the total population) still uses surface water as its main water source (figure 8).

**Table 6. Benchmarking access to water and sanitation and water utility performance**

||Unit|Republic of Congo|Resource rich|Sub-Saharan Africa|
|---|---|---|---|---|
|Access to piped water*|% pop|25.8|12.8|16.6|
|Access to stand posts*|% pop|23.5|12.6|15.6|
|Access to wells/boreholes*|% pop|15.3|49.0|41.5|
|Access to surface water*|% pop|30.3|23.7|32.7|
|Flush toilets*|% pop|5.3|11.2|9.8|
|Improved latrines*|% pop|15.1|6.4|9.2|
|Traditional latrines*|% pop|69.8|54.8|52.4|
|Open defecation*|% pop|9.5|27.6|34.2|
|Domestic water consumption**|liter/capita/day|25|78.9|102.9|
|Collection ratio**|% sales|88.0|69.7|94.4|
|Distribution losses**|% production|45|43.9|33.4|
|Cost recovery of tariffs**|% total costs|51.6|59.3|44.2|
|Tariff (US cents per m3) **|Republic of Congo|Scarce water resources|Other|developing regions|
|Residential tariff (at 30 m3) **|48.6|60.26||30600|
|Non-residential tariff (at100 m3/mo**|48.6|120.74||.–.|

Sources: * Household survey, 2001; refers to national access. ** AICD database, 2005; refers only to SNDE.

23
OED (described earlier) as dummy variables to control for the reform areas covered by the intervention. In this way, we can test whether specific reforms have, on average, met with more success than others (other relevant factors held constant). In particular, we can test whether the relatively poor performance of post- 1990 interventions can be traced to the increased emphasis on particular reforms (privatization, re-capitalization, and supervision). In addition, to test whether FSALs are intrinsically distinct from other Bank interventions, we include an FSAL dummy. Finally, to test whether the comprehensiveness of an intervention affects its success, we include a continuous variable measuring the total number of reform areas covered. On an a priori basis, it is difficult to know whether comprehensive interventions are beneficial, or whether they overly complicate matters.

# (B). **Results**

It should be emphasized at the outset that, given data limitations and the myriad factors that may affect financial sector performance in the years immediately subsequen **t** o an intervention, one might not expect results to be as robust as recent ones from the growth literature; there, growth rates and some explanatory variables are averaged over long time periods.<sup>32</sup> In addition, whereas most of those studies use samples of 50-100 countries, the sample here is much smaller (twenty-three observations). As noted above, because the IFS data run only through 1995 (for those countries whose data is complete),

**32** Robust growth results, the focus of Levine and Renelt (1992), are also found in the institutional work of Knack and Keefer (1995). Knack and Keefer employ a number of the same variables as Levine and Renelt including average annual GDP per capita, average annual investment as a percentage of GDP, and average annual government consumption as a percentage of GDP, all measured over the period 1974-89.

24
global CGE model. This study focusses in particular on distributional effects of FFS reform, considering redistribution of reform revenues through cash transfers or food subsidies. Siddig et al. (2014) use the CGE model by the Global Trade Analysis Project (GTAP) to analyse subsidy reform in Nigeria. They distinguish 12 households, each representing a region rather than an income group. Using this set-up they consider the impact of FFS reform on several standard macro-economic parameters, such as consumption and GDP. For Pakistan, Feltenstein and Datta (2020) assess electricity subsidy reform and revenue redistribution through targeted transfers, and find increases in the real incomes of poor households. Shehabi (2020) develops a general equilibrium model for Kuwait featuring an oligopolistic industrial structure and studies how subsidy reform affects sectoral diversification.

While the above mentioned models differ in the complexity of their modeling set-ups, they coincide in their focus on standard macro-economic parameters, in particular output, consumption, trade and fiscal balances, and income distribution. They do not consider illicit activities in any way.

# _2.3. The double dividend_

There is a prominent strand in this literature suggesting that there is a “double dividend” – i.e., that if environmental taxes are increased, but other distortionary taxes are reduced (while maintaining revenue neutrality), then not only can environmental benefits be reaped, but also fiscal efficiency can be increased (Goulder, 1995 _a_ ; Fullerton and Metcalf, 1997; Bovenberg, 1999). However, Parry and Williams (2010) also show that there is a considerable trade-off between efficiency and distributional effects. That is, income tax reductions are economically efficient, but regressive, while cash transfers are progressive, but less efficient.

Since FFS are (at least theoretically) equivalent to a negative carbon tax, the double dividend argument deserves thorough consideration in the context of FFS reforms. Especially given the significant economic efficiency costs of labour and consumption taxes (Bovenberg and Goulder, 1996; Parry, 1997; Goulder, 1995 _b_ ), there may be a rationale for using FFS reform revenues to reduce these taxes. However, instead of reducing pre-existing taxes, past reforms and associated analyses have focused almost exclusively on cash transfers for revenue redistribution.

# _2.4. Tax evasion and smuggling_

More recent studies have contributed an additional perspective, which strengthens the double dividend argument. Liu (2013) observes that energy (or carbon) taxes are more difficult to evade than labour or income taxes. Using a simple CGE model, the author shows that a revenue neutral shift from labour to carbon taxes can substantially reduce tax evasion and the welfare cost of climate change mitigation policy<sup>2</sup> . Especially when pre-existing tax evasion is high, this argument can significantly strengthen both the environmental and fiscal cases for carbon taxes (Liu, 2013; Bento et al., 2013; Carson et al., 2014).

> 2Liu (2013) shows that costs can be lowered by 89% in China and 97% in India.

5
reported at baseline) drive the response to illness shocks.<sup>16</sup> A male worker who is the highest earner in the household works 0.76 days and 7.6 more hours in response to the illness of another worker compared to another male. The results with usual hours of work lose statistical significance at conventional levels, but are still large quantitatively and point in the same direction as the earnings results.

Again, there is no differential effect for women who are the highest wage earners or have the highest usual hours of work (as reported in the baseline survey) in their households. This is not because too few women report the highest earnings or usual hours of work in their households to give us the power to detect these effects. Indeed, table A9 shows the joint distribution of gender and status as the highest earner and member with the longest usual hours of work in the household among the individuals employed at baseline. While it is more common for men to be the highest earners or have the most usual hours of work in the household – mirroring the result in table 1 that men on average have somewhat larger average hours of work and greater earnings than women – 42 percent of women are the highest earners in their household and 48 percent of women have the highest usual hours of work.<sup>17</sup> Instead, it appears that even (relatively) high-earning women have caregiving and other duties around the home that prevent them from increasing their labor supply when another worker is ill.

We now turn to another dimension of heterogeneity: risk aversion. The more risk averse an individual, the more he or she will dislike fluctuations in consumption, and thus, the greater the incentive to increase labor supply in order to smooth consumption. Survey respondents

> 16 Table O1 in the online appendix (http://faculty.washington.edu/rmheath/onlineappendix_HMR.pdf) shows that these results are robust to alternate measures of hours and earnings, namely, to a dummy variable for above median hours/earnings within the household, and to the ratio of the respondent’s hours/earnings to the household average. 17 When considering only the sample of households with two or more employed workers at baseline (within which the sole employed worker is not automatically the highest earner or has the highest usual hours of work at baseline), the percentages naturally drop, but are still non-trivial: 37 percent of women have the highest regular hours of work and 30 percent of women are the highest earners.

25
# **Table of** Contents

|1. Introduction ..|**(**..1.......<br>)|
|---|---|
|The Education System..<br>...................................|**()**|
|Schooltynes ....................................|(2)|
|II. Data and Method..<br>...<br>...|(3)|
|Sample..<br>._......._|_.._<br>(3)|
|Variables ..........................................|(4)|
|Pupil Level Differences ....................................|_(5)_|
|Class Level Differences ....................................|_(5)_|
|School Level Differences ....................................|(7)|
|M ethodology .............................................|(I 1)|
|1Il. Results: Modeling English and Mathematics Achievement ..............<br>.|(11)|
|Variance Components Models for Ernglish and Mathematics Achievement ..|.....<br>(12)|
|Schooltype Differences in English and Mathematics Achievement ....<br>....|....<br>(13)|
|Testing Variables in Individual Regressions ..........................|(14)|
|Progression Toward the 'Best' Model of English Achievement ..............|(20)|
|Progression Toward the 'Best' Model of Mathematics Achievement ....<br>..|.....<br>(22)|
|Analysis of Residuals ......................................|(26)|
|IV. Conclusions<br>_............................_|(29)|
|References ......................<br>........................<br>.|...<br>(31)|
|Armnex A: Glossary of Variable Names ...........<br>..<br>..............|(32)|

|_Tables_|||
|---|---|---|
|Table 1.|Breakdown of Sample by School Type, Classes and Pupils<br>.<br>.(4)||
|Table 2.|Mean Values of Student-Level Variables by Schooltype .<br>.(6)||
|Table 3.|Mean Values of Class-Level Variables by School Types<br>..|(8)|
|Table 4.|Mean Values of School-level Variables by School Type<br>..|(10)|
|Table 5.|Frequency of Resource Availability by Type of Primary School .<br>.(12)||
|Table 6.|Variance Components Models for English and Mlathematics Achievement .<br>.(13)||
|Table 7.|Schooltype Differences (Base Model) for English and Mathematics Achievement . . .|(14)|
|Table 8.|Coefficients of Student-Level Variables Estimated in Individual Multi-Level<br>Regressions on English and Mathematics Achievement ...<br>.<br>.......... . . . .<br>.|(15)|
|Table 9.|Coefficients of Class-Level Variables Estimated in Individual Multi-Level Regressions<br>on English and Mathematics Achievement .......<br>. .<br>..........<br>I<br>. . . . . .<br>.|(17)|
|Table 10.|Coefficients (Standard Errors) aind Variance Explained for Combinations of Student-<br>level and School-t-pe Variables Regressed on English Achievement . .......<br>. .<br>.|(19)|
|Table 11.|Coefficients (Standard Errors) and Variance Explained for Combinations of Student-<br>level and School-type Variables Regressed on Mathematics Achievement .......<br>.|(24)|
|Table 12.|Bottom and Top Schools Ranked by Standardized School Level Residuals for<br>English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|(27)|
|Table 13.|Bottom and Top Schools Ranked by Standardized Class Level Residuals for<br>Mathematics ..............<br>..<br>.<br>.........................<br>..<br>.|(28)|
# **Looking Back on Two Decades of Poverty and Well‐Being in India**

Ambar Narayan and Rinku Murgai<sup>*</sup>

**JEL Codes** : D30, D63, I31, I32

**Keywords:** poverty, shared prosperity, inequality, growth, India

> * The authors gratefully acknowledge the contributions of Christina Wieser and Swati Puri in data analysis and research support, and Urmila Chatterjee for inputs and comments.
# **Figure 7: Sri Lanka Data P-Values on Difference in Outcome Variable at Follow-up vs. Baseline**

<!-- Start of picture text --> 30 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>100 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>300 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br><!-- End of picture text -->

# **Figure 8: ENE Data P-Values on Difference in Outcome Variable at Follow-up vs. Baseline**

<!-- Start of picture text --> 30 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>100 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>300 observations sample<br>0 .2 .4 .6 .8 1<br>P-value at baseline<br>10th percentile 50th percentile 90th percentile<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>P-value at follow-up<br>.2<br>0<br><!-- End of picture text -->
E. Figure shows fiscal multipliers two years from expansionary measures based on estimates from the IPVAR model of Huidrom et al. (2019). An economy is considered to have low debt when government debt is below 40 percent of GDP and high debt when it exceeds 60 percent of GDP. Orange lines represent 16-84 percent confidence bands. F. An economy is considered to implement a fiscal rule if it has one or more fiscal rules on expenditures, revenues, budget balance or debt.
- vii -

and agricultural data, disaggregated by gender; farming systems research or similar approaches; and feedback from extension.

- **e** Develop the linkage with the research institutions to ensure that the research agenda covers farming activities of women, and that technologies are developed to meet the needs of women farmers. The scope of the research should include crops and livestock produced by women, technologies and farming systems suitable to the circumstances of women farmers (such as alley cropping), technologies for water and fuelwood collection and use, and food processing, preservation and storage.

- Design tools and machines suitable for women. These should be portable, inexpensive, multi-functional, suited to women's size and strength, and have use patterns compatible with other activities. To the maximum extent possible, they should be produced locally.

- Improve the availability of new tools and machines, using women's groups as the channel for distribution wherever possible.