rafmacalaba/datause-encoder-data huggingface dataset:
SYS-REVIEW/HI-LMIC

- a. Nationally managed and considered an extension of existing SHI

- b. Managed at the community level (limiting the risk-pooling population), either through a local government or nongovernmental organization (usually with government sponsorship), often called community-based health insurance

# **<<B>>** _Defining the Impact of Insurance_

We presume that the impact evaluation of a project should provide two essential pieces of information to policy makers: (1) Is the program implementable? (2) Once implemented, does the program achieve a set of desirable outcomes? In the case of a health insurance program, if the adoption of a program is high, then the program _at least_ approached proper implementation. Furthermore, policy makers are interested in the impact on those adopting the insurance, the average treatment on the treated, or the average impact on those who actually adopted the program. Insurance may affect those who do not adopt it by, for example, affecting the price of health care, which would be part of the total impact of the insurance program. Morduch (2006) **[[This reference is not in the reference list. Please complete the information at the place holder we have left there]]** reports that if richer individuals adopt a social program disproportionately at zero private cost, the program can be considered a large income transfer that may affect the prices of all goods within a relevant economy. The impact on prices is smaller when the poor adopt social goods because the adoption may not replace previous large expenditures. This type of general equilibrium impact has been largely ignored, probably because of the stable unit treatment value assumption where only those intended to 6
minigrid technologies. For single-household systems, we evaluate photovoltaic (PV) solar and wind, as well as diesel generators as a non-renewable alternative. For minigrid systems, we evaluate wind, a combined solar–wind system, biodiesel, and, again, conventional diesel generators. Solar and wind options include backup batteries for intermittency and the resulting issues of dispatchability.

Solar and wind resource potential information are drawn from a recent resource assessment by the Solar and Wind Energy Resources Assessment Project for Ethiopia, Ghana and Kenya (SWERA, 2004). We translate minimum and maximum daily solar insolation data to power potential using energy conversion and efficiency factors from the National Renewable Energy Laboratory (NREL) (see Appendix 1 for details on the computation of decentralized energy costs).

Wind power potential from SWERA and specific wind turbine characteristics yield turbine performance (power) at varying wind speeds (Ethiopian Rural Energy Development and Promotion Center, 2007). It is worth noting that in all three countries, feasible wind speeds over Class 3 (i.e. 11-13 m/s at a 10m hub height and 14-16 m/s at a 50m height) are limited to certain regions, sometimes in fairly remote areas. We estimate that areas with promising wind potential include 34.1 percent of households in Ethiopia, 6.3 percent in Ghana and 5.5 percent in Kenya. Wind power can be deployed both at an individual household level and, with larger turbine size, as a minigrid option. As an additional option, we evaluate a combined solar–wind system that can at least partially offset problems of intermittent supply (see KMOE 2008).

Power from the production of biodiesel in nearby agricultural areas is another promising option for more remote areas. There is some debate on whether biofuels represent a viable energy source or whether competition over land will jeopardize food production. In this study we assess biofuel potential using the production of _Jatropha curcas_ as a biodiesel minigrid fuel option and compare this with the other centralized and decentralized options. To estimate the potential of Jatropha, we identify non-agricultural areas proximate to population centers and assume that sufficient yields are possible to supply localized demand. Suitable areas are identified from land use data after removing 14
Ciccone and Hall (1996) find, from work on US states, that doubling employment density raises labor productivity by 6%. A recent survey by Rosenthal and Strange (2003) reports a consensus view that doubling city size increases productivity by around 3-8%. Authors who have tried to find the spatial range of such effects find that information spillovers attenuate rapidly over space (for example Jaffe and others 1993). Face-to-face communications remain important, despite technological changes (Gasper and Glaeser 1998).

As for the sectoral spread of spillovers, there is a long-standing debate in the economic geography literature between the relative importance of ‘Jacob’s externalities’ and ‘Marshall-Arrow-Romer (MAR) externalities’. The former derive from the diversity of a city, the argument being that innovation and creativity are stimulated by serendipitous interaction in a large and diverse economic environment, such as New York City. By contrast, MAR externalities are knowledge spillovers within narrowly defined sectors, as information on how to produce is transmitted from firm to firm. Detailed empirical studies of productivity spillovers in developing countries confirm the importance of MAR externalities. The positive dependence of productivity on employment levels in the same city and industry has been found by studies for a number of countries (e.g. for the US and Brazil, Henderson 1988; Indonesia, Henderson and Kuncoro 1996; and Korea, Henderson Lee and Lee 2001).

We should note that acquisition and spread of knowledge are important in selling as well as in production. Roberts and Tybout (1997) establish the importance of learning effects in firms export behaviour, and Evenett and Venables (2003), using a fine commodity and geographical disaggregation, show that learning to export also has a destination specific component.

# **5. Structural Change in India**

One key objective of this paper is to argue that evidence building at the microeconomic level 22
**Figure 2: Output Impact in Different ‘Green’ Sectors: BBIN Scenario (% change)**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>‐5<br>Bangladesh India Nepal Pakistan SriLanka Bhutan<br>pdr wht gro osd c_b pfb ocr rmk wol frs fsh vol mil pcr sgr ofd wap NuclearBL WindBL HydroBL HydroP SolarP trd cmn ofi isr obs ros osg dwe<br><!-- End of picture text -->

# **7. Conclusions**

This is the first major attempt to develop a gender-differentiated data set for South Asian countries, within the widely used GTAP framework, coupled with the GTAP-POWER model that includes renewable sectors, to examine the nexus between trade, green economy, and gender. In order to generate gender differentiated employment and wages data in the current GTAP database, this study compiles gendered employment data and industry average wages across industries and occupations for each country and reconciles the data using the Yearbook of Labor Statistics from the International Labor Organization (ILO). The model and data set have been derived from a large number of survey data sets in South Asian countries. The data gaps for the select countries were filled using the data obtained from several published government sources. Female labor force participation and the gender wage gap were calculated using data disaggregated by economic activity according to the International Standard Industrial Classification of All Economic Activities (ISIC) at the 2-digit level of the classification and for the latest data available for that country. The study identifies the targeted value chains and economic activities, particularly among green trade sectors, that will benefit women in South Asia using the newly constructed model and database.

A few more innovations were introduced in the model; first, we introduce a multi-level nest in the labor demand, wherein perfect substitution happens between male and female composite labor while the different types of skilled labor substitute each other based on the standard GTAP elasticities; second, we introduce a labor supply curve based on calibrated elasticities from Indian data from 1981-82 to 2017-18, as well as other literature.

34
A fundamental premise underlying our work is that avoiding debt distress, especially in low-income countries, is desirable. We think this premise is justifiable, for three reasons:

- First, resolving debt distress is costly.  For example, costs associated so far with the debt reduction of the poorest countries of the world under the original and enhanced (Heavily Indebted Poor Countries) HIPC Initiative is estimated at $50 billion.    Moreover, the costs of excessive debt extend beyond the costs of debt reduction alone.  Excessive debt can also undercut support for policy reforms by political and civil society groups in debtor countries if they perceive that benefits from reforms will be directed to high debt service rather than deliver needed public services to the poor. The pressure to meet external debt service payments may also tempt debtor country governments to seek short-term solutions at the expense of fundamental, longer-term reforms. Creditors, as well, may be tempted to allocate resources according to resource needs rather than policy performance.<sup>1</sup>

- Second, non-repayment of  loans to multilateral lenders can have perverse distributional effects among borrowing countries.  Absent new resources from donors, the failure to repay concessional loans reduces the ability of development banks to provide new loans to other developing countries.  Moreover, to the extent that new lending is intended for countries with sound policies and institutions, but countries with poor policies and institutions are more likely to fail to service their past debts, this can result in a transfer of resources from countries with good policies to countries with bad policies.  The amounts at stake are nontrivial.  Consider for example the World Bank-administered International Development Association (IDA), which provides very substantial resources to the world’s poorest countries.  As of 2003, IDA’s portfolio consists of highly concessional loans with a face value of roughly $110 billion.<sup>2</sup> During the 2003

> 1   Birdsall, Claessens, and Diwan (2002).

> 2 The figures in this paragraph are taken from World Bank (2003).

3
It was not expected that any of the factors above by themselves would be able to fully explain why a particular RIA reform did not deliver on its goals. Consequently, this study attempted to capture the dynamic of these factors in the RIA reform context through the country case studies.

The country case studies were based on desk research / secondary sources, followed by field studies. Information was collected through semi-structured interviews with government officials, donors, and other stakeholders for two of the countries. Two of the studies were created relying on official World Bank documents and interviews with World Bank staff.

12
**Table 13: Heterogeneous Treatment Effects - Subsample of urban schools**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Grade 5|Grade 6|Grade 7|Grade 8|
|Stipend District|||||
|2004|-0.790|6.578***|-0.994|-0.400|
||(0.604)|(0.857)|(0.736)|(0.926)|
|2005|0.128|7.892***|3.462**|0.545|
||(0.701)|(1.066)|(1.052)|(1.136)|
|2006|-1.068|7.546***|2.163*|1.679|
||(0.727)|(1.093)|(0.980)|(1.142)|
|2007|-2.616***|3.012**|0.0145|-0.582|
||(0.672)|(1.123)|(1.083)|(1.185)|
|2008|-2.141**|1.704|0.116|0.695|
||(0.659)|(0.926)|(1.001)|(1.155)|
|Stipend District * 2004|2.237**|4.196**|3.771***|3.167**|
||(0.841)|(1.514)|(1.118)|(1.212)|
|Stipend District * 2005|1.478|11.87***|7.582***|5.316***|
||(0.899)|(1.937)|(1.712)|(1.484)|
|Stipend District * 2006|4.268**|12.92***|14.08***|9.816***|
||(1.353)|(2.117)|(1.784)|(1.813)|
|Stipend District * 2007|3.445***|14.85***|15.59***|14.95***|
||(0.911)|(1.953)|(1.881)|(1.798)|
|Stipend District * 2008|2.773**|12.00***|13.16***|12.98***|
||(0.996)|(1.708)|(1.725)|(1.778)|
|Constant|25.65***|53.10***|51.68***|48.68***|
||(0.358)|(0.603)|(0.564)|(0.621)|
|Observations|10057|10494|10492|10493|
|Number of Schools|1791|1791|1791|1791|
|Adjusted R-squared|0.006|0.047|0.035|0.024|
|R-squarewithin|0.00694|0.0480|0.0361|0.0246|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable in each column is girls’ enrollment in a particular grade. The sample excludes mosque and primary schools as well as two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment. All columns report estimates from a fixed effects specification at the school level. The excluded group is no facilities. The excluded year is the baseline year 2003.

51
## **Integrated Urban Upgrading for the Poor: The Experience of Ribeira Azul, Brazil**

## **Judy L. Baker**

## **Summary**

This study looks at the experience of integrated urban upgrading in a low-income neighborhood of Salvador, Bahia, Brazil. Infrastructure and social investments have been made in the community through a government program, with community participation playing a major role in the design and implementation. This approach is now perceived to be highly successful in terms of its implementation and positive impact on living conditions, and will provide the basis for a major state-wide program. This paper analyzes the lessons learned from the experience, with implications for scaling up as well as applications for other urban upgrading programs.

# **I. INTRODUCTION AND STUDY APPROACH**

An integrated approach to urban upgrading has been implemented in the neighborhood of Ribeira Azul in Salvador, Bahia, Brazil since 1999 under the _Viver Melhor_ program. This program combines infrastructure and social interventions and has been perceived as highly successful in terms of its implementation and positive impact on living conditions. The integrated and participatory approach has been implemented by the state with the assistance of grant funding from the Italian government through Cities Alliance for the provision of TA and social development activities. This integrated approach is now the basis for the State’s urban development strategy and will be implemented state-wide through a World Bank Project. The state program is called _Viver Melhor/IBRD_ .

In preparation of the Integrated Urban Development Project, the World Bank carried out Poverty and Social Impact Analysis (PSIA) to assess the potential impacts of the policy reforms and investments under the project. The approach to the PSIA work was to draw on the lessons from the existing experience in Novos Alagados II within the Ribeira Azul Program, implemented from 1999-2004 with analysis of the implications for scaling up.

Ribeira Azul is a relatively small area in Salvador, Bahia, Brazil, covering approximately 4 square kilometers along an inlet with 40,000 families and 135,000 individuals. This represents 6 percent of Salvador’s municipal population. Ribeira Azul is part of a broader area which has been characterized as ‘high risk’ situated in a flood prone area, with a large number of squatter settlements, insecure land tenure, a highly polluted environment by household and industrial waste, poor social indicators (the worst in the city), and very limited access to infrastructure and basic services.

1
# The Consequences of the COVID-19 Pandemic for Children in Kenya

Emma Cameron Antonia Delius Amanda Devercelli Utz Pape Samuel Siewers Originally published in the Policy Research Working Paper Series on _April 2022._ This version is updated on _July 2022._ To obtain the originally published version, please email prwp@worldbank.org.

Keywords: COVID-19, Education, Kenya, Inequality, High-frequency phone survey JEL Codes: I18, I24, O15 Cameron: World Bank (Consultant); Delius: World Bank; Devercelli: World Bank; Pape: World Bank and University of <mark>Göttingen;</mark> Siewers: World Bank (Consultant) and University of <mark>Göttingen.</mark> The corresponding author is Samuel Siewers: samuel.siewers@uni-goettingen.de. The authors are grateful to Rabia Ali (World Bank), Nduati Kariuki (World Bank), and Adelle Pushparatnam (World Bank) for helpful comments.
households participating in each survey round and create imperfect panels to both minimize survey fatigue for each household and prevent communicators from focusing on just a few households, after observing who we survey. This yields samples of 3,685, 3,496 and 3,314 households of ‘regular’ (non-communicator) farmers and 467, 474, and 329 communicators at baseline, midline and endline, respectively. The baseline was conducted in August-September 2009, a midline in July – September 2010 at the end of the first agricultural season, and an endline in July – September 2011 after the second agricultural season. We augmented our midline and endline instruments with a spouse/additional farmer module, increasing the total number of respondents to 6,006 and 4,693 at midline and endline, respectively.

These surveys capture a rich set of measures of knowledge and adoption of the promoted techniques, along with agricultural production, household demographics, individual characteristics, perception, and social network relationships. To test respondents’ knowledge about the technology introduced in their village, we ask seven questions regarding the details of how to apply the technology. The questions are based on the material used by AEDOs to train communicators in treatment villages.<sup>12</sup> We use these questions to construct a [0,1] knowledge index where 0 indicates a respondent could not correctly answer any question, and 1 that a respondent answered all questions correctly. Adoption of the technology is self-reported in the survey, and validated through direct observations during the on-farm monitoring.

The social network module records farmers’ interactions with and perceptions of (shadow or actual) communicators and other farmers in their village. Social network relationships with other non-communicator farmers were recorded as follows. For each respondent, we drew a list of six randomly-picked farmers in the community from our baseline listing. We then asked questions about the nature of the respondent’s interactions with each of these farmers, as well as their perceptions about those farmers. Appendix B details the construction of all main outcome variables.

> 12 See Appendices B and C for a detailed record of the knowledge exam, and training manuals, respectively.

13
# <mark>A.2. Base metals (6 series)</mark>

<mark>This group includes aluminum, copper, lead, nickel, tin, and zinc. T</mark> he metals (aluminum, copper, lead, nickel, tin, and zinc) are widely used in commercial and industrial applications. **Aluminum’s** largest uses are in transport, followed by construction, packaging, and electrical grids. **Copper’s** main application is in the electrical sector, including power cables, generators and motors, as well as in construction and electronics. **Nickel** is one of the main <mark>components</mark> of stainless steel, while **zinc** is mostly used as an anti-corrosion agent to galvanize iron and steel while some is alloyed with other metals (e.g., combined with copper to produce brass). **Tin** is heavily used in electronics in the form of solder. **Lead** , which was once used in various chemical applications (most of which have been banned) is still widely used in car batteries, ammunition, and in storage of corrosive liquids. <mark>Prices are taken from the London Metal Exchange. As notable exception is iron ore, a key input to steel production. For most of the sample period, iron ore prices were not determined on a competitive basis. The prices, all taken from the London Metal Exchange (LME) are defined as follows:</mark>

1. **Aluminum** (LME): Unalloyed primary ingots, standard high grade, physical settlement.

2. **Copper** (LME): Standard grade A, cathodes and wire bar shapes, physical settlement.

3. **Lead** (LME): Refined, standard high grade, physical settlement.

4. **Nickel** (LME): Cathodes, standard high grade, physical settlement.

5. **Tin** (LME): Refined, standard high grade, physical settlement.

6. **Zinc** (LME): Refined, standard special high grade, physical settlement.

# <mark>A.3. Precious metals (3 series)</mark>

<mark>Precious metals include gold, platinum, and silver. To various extents, all three precious metals have been used as store of wealth, hedge against uncertainty, jewelry, and industry. Prior to the collapse of Bretton Woods in 1972,</mark> **<mark>gold</mark>** <mark>was used as a currency and store of wealth. Then, it was used as a hedge against inflation but more recently it has been used as a hedge against uncertainty.</mark> **<mark>Silver</mark>** <mark>, in addition to being considered as precious metal, it also enjoys various industrial uses, including in the electronics industry, jewelry, and silverware.</mark> **<mark>Platinum</mark>** <mark>has various industrial uses as well, including the auto industry (as a catalytic converter to reduce exhaust pollution) and various petrochemical applications. The precious metal prices are defined as follows:</mark>

1. **Gold** (U.K.): 99.5 percent fine, London afternoon fixing, average of daily rates.

2. **Platinum** (U.K.): 99.9 percent refined, London afternoon fixing.

3. **Silver** (U.K.): 99.9 percent refined, London afternoon fixing.

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