rafmacalaba/datause-encoder-data huggingface dataset:
Fires being used more intensively following MNREGA would also explain a larger increase in emissions than raw fires. While the MODIS satellite can detect fires as small as 100m<sup>2</sup> it does not distinguish between pixels that have one fire and those that have many fires (Korontzi et al., 2006). As a result, if the implementation of MNREGA induces the use of fires in areas that already had frequent fires, MODIS will underestimate the increase in fires. Using more finely resolved data on fires in the period after 2014 from the VIIRS satellite platform, I show in the appendix that MODIS does indeed under-count fires relative to VIIRS (appendix Figure A14a and A14b).

Translating these increases in emissions rates into precise changes in pollutant concentrations is difficult and would require a model of pollution dispersion. I do not create such a model here. Rather, I estimate the impact of the change in emissions on pollution concentrations in two ways. First, I estimate the correlation between emissions rates of black carbon, organic carbon, and SO2 using data on emissions concentrations from MERRA at the district _×_ month level.<sup>28</sup> These estimated correlations suggest that in months with emissions rates that are higher than average, PM2 _._ 5 concentrations are also higher than average.

Consistent with this correlational evidence, I show that the implementation of MNREGA is associated with an increase of approximately 11% in the number of months in which PM2 _._ 5 levels exceed the annual ambient standard set by the Indian government.<sup>29</sup> I look specifically at PM2 _._ 5 as it has the largest negative impacts on health (Behrer and Mauter, 2017; Muller and Mendelsohn, 2007; Nel, 2005; Chen et al., 2016). The annual threshold for ambient concentrations is set by the Central Pollution Control Board at 40 _μg/m_<sup>3</sup> . I calculate the share of district _×_ months that have an average PM2 _._ 5 level that exceeds this threshold and use a linear fixed effects regression in the difference-in-differences framework to determine if the implementation of MNREGA increased the share of months in which the threshold was exceeded.

Column 4 of Table 6 shows that implementation of MNREGA increases share of months that exceed the threshold by 0.014 percentage points. This represents an 11% increase relative to the preMNREGA baseline rate.<sup>30</sup> The magnitude of this effect is consistent with pollution levels exceeding

> 28 MERRA provides estimates of the monthly concentration of black carbon, organic carbon and SO4. I convert these into a measure of PM2 _._ 5 using the formula described in He et al. (2019).

> 29The Air (Prevention and Control of Pollution) Act 1981 set standards for Annual and 24hour concentrations in the ambient air of a number of pollutants. They are detailed here: http://www.arthapedia.in/index.php?title=Ambient_Air_Quality_Standards_in_India

> 30When I use the 24-hour threshold of 60 _μg/m_ 3 I observe a similar increase, 0.007 percentage points or roughly 10% on the pre-MNREGA baseline rate.

23
# Figure 1: Bottom-Up Analysis Results

# A. Marginal abatement cost curves

<!-- Start of picture text --> Armenia<br><!-- End of picture text --> <!-- Start of picture text --> Georgia<br><!-- End of picture text --> <!-- Start of picture text --> Residential Televisions<br>100 Household Televisions 100 Residential Washing Machines<br>Commercial Air Conditioning Residential Wall Insulation<br>50 Public Lighting 50 Commercial Wall Insulation<br>Household Air Conditioning 0<br>0<br>‐50<br>‐50 Residential Windows<br>‐100<br>Residential Roof Insulation<br>‐100 Commercial Windows<br>‐150<br>‐150 Household Insulation Commercial Roof Insulation<br>‐200 Public Lighting<br>‐200 Household RefrigeratorsIndustrial Insulation ‐250 Residential LightingResidential Refrigerators<br>‐250 Household Lighting ‐300 Commercial Lighting<br>0 100 200 300 400 500 600 700 800 0 1000 2000 3000 4000 5000 6000 7000<br>Discounted Potential ktCO2e saving Discounted Potential ktCO2e saving<br>Discounted Marginal Abatement Cost ($/tCO2e) Discounted Marginal Abatement Cost ($/tCO2e)<br><!-- End of picture text -->

# B. Capital-energy substitution

# Armenia Georgia

<!-- Start of picture text --> 1 1<br>HH Building Shell HH Building Shell<br>0.9 HH Lighting 0.9 HH Lighting<br>HH Appliances HH Appliances<br>0.8 HH HVAC 0.8 Com. Bldg Shell<br>Com. HVAC Com. Lighting<br>0.7 Com. Lighting 0.7 Ind. Mechanical<br>Ind. Insulation Ind. High Temp.<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>1 2 3 4 5 6 1 3 5 7 9 11 13<br>Fractional change in capital input from baseline ( ��/� ) Fractional change in capital input from baseline ( ��/� )<br>�̃/� )(Fractional change in energy input from baseline �̃/� )(<br>Fractional change in energy input from baseline<br><!-- End of picture text -->

# C. Base-year technology input costs ($ M) and implied capital-energy elasticities

||||Armen<br>|ia<br>|||||Georgi<br>|a<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Gas|Coal|Elec.|Capital|_ξθ_||Gas|Coal|Elec.|Capital|_ξθ_|
|||||||Residential||||||
|Bldg. Shell|||376.1|79.0|-2.15|Bldg. Shell|300.7|||88.6|-0.77|
|Lighting|||97.0|2.5|-0.16|Lighting|||63.7|4.6|-0.11|
|Appliances|||114.5|73.4|-1.50|Appliances|||73.7|111.1|-1.28|
|HVAC|||22.4|9.8|-1.13|||||||
|||||||Commercial||||||
|HVAC|||8.0|3.9|-1.16|Bldg. Shell|||57.2|8.4|-0.76|
|Lighting|||9.1|8.6|-1.31|Lighting|||15.6|9.0|-0.24|
|||||||Industrial||||||
|Insulation|93.7|||0.4|-0.19|Mechanical|||69.2|4.8|-0.07|
|||||||High Temp. Proc.|57.1|66.3||38.1|-0.60|

13
_40_ Table 7—Teacher Salaries and Composition

||School Cost|s (monthly)|Teach|er Roster|Teache|r Salaries (m|onthly)|
|---|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Wage Bill|(3)<br>Total|(4)<br>Num New|(5)<br>All|(6)<br>New|(7)<br>Existing|
|High|3,147.48*|2,741.83*|0.42|0.46**|519.52**|580.05**|492.01*|
||(1,894.67)|(1,510.50)|(0.32)|(0.18)|(257.94)|(265.80)|(284.29)|
|Low Treated|-1,127.41|-838.26|0.32|0.27|-175.63|-89.45|-223.10|
||(1,716.66)|(1,520.25)|(0.33)|(0.24)|(273.11)|(406.49)|(246.45)|
|Low Untreated|-302.25|65.14|0.25|0.25|194.48|89.47|253.39|
||(1,374.56)|(1,106.67)|(0.29)|(0.18)|(202.53)|(236.07)|(201.69)|
|Baseline|0.88***<br>(0.07)|0.85***<br>(0.08)|0.77***<br>(0.05)|||||
|R-Squared|0.69|0.63|0.50|0.19|0.20|0.23|0.20|
|Observations|1,470|1,470|1,590|1,645|11,725|3,903|7,818|
|# Schools (Rounds)|797 (2)|797 (2)|816 (2)|840 (2)|802 (2)|723 (2)|793 (2)|
|Mean Depvar|25,387.0|19,491.2|6.7|2.0|2,676.6|2,665.5|2,681.9|
|Test pval (H=0)|0.10|0.07|0.19|0.01|0.05|0.03|0.08|
|Test pval (_L_<sup>_t_</sup>=0)|0.51|0.58|0.33|0.25|0.52|0.83|0.37|
|Test pval (_L_<sup>_t_</sup>=H)|0.05|0.05|0.78|0.45|0.04|0.13|0.04|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 a) This table looks at impacts on teacher salaries and composition from the intervention. The dependent variable in column 1 is monthly variable costs, which includes utilities, rent, teaching and non-teaching sta↵salaries, over two years of the experiment. Column 2 shows the impact on the teaching salary component of variable costs. Data used in the first two columns are from school survey data. The remaining columns use teacher level data from the teacher roster. Columns 3-4 collapse data at the school level to understand changes in teacher composition; cols 5-7 decompose teacher salaries by employment status at the school before and after treatment. Whether a teacher is new or existing is determined by their start date at the school relative to baseline. Closed schools are coded as missing in all columns, except cols 3-4 where they are coded as 0.

b) Regressions are weighted to adjust for sampling and tracking where necessary and include strata and round fixed e↵ects, with have standard errors clustered at village level. The number of observations may vary across columns as data are pooled across rounds and not all outcomes are measured in every round. We thus also report the unique number of schools and rounds in each regression; any variation in the number of unique schools arises from attrition or missing values for some variables. The mean of the dependent variable is the baseline value or the follow-up control mean. c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.
Policy Research Working Paper 5143

# **Abstract**

Female education levels are very low in many developing countries. Does maternal education have a causal impact on children’s educational outcomes even at these very low levels of education? By combining a nationwide census of schools in Pakistan with household data, the authors use the availability of girls’ schools in the mother's _birth_ village as an instrument for maternal schooling to address this issue. Since public schools in Pakistan are segregated by gender, the instrument affects only maternal education rather than the education levels of both mothers and fathers. The analysis finds that children of mothers with some education spend 75 minutes more on educational activities at home compared with children whose mothers report no education at all. Mothers with some education also spend more time helping their children with school work; the effect is stronger (an extra 40 minutes per day) in families where the mother is likely the primary care-giver. Finally, test scores for children whose mothers have some education are higher in English, Urdu (the vernacular), and mathematics by 0.24–0.35 standard deviations. There is no relationship between maternal education and mother’s time spent on paid work or housework—a posited channel through which education affects bargaining power within the household. And there is no relationship between maternal education and the mother’s role in educational decisions or in the provision of other child-specific goods, such as expenditures on pocket money, uniforms, and tuition. The data therefore suggest that at these very low levels of education, maternal education does not substantially affect a mother’s bargaining power within the household. Instead, maternal education could directly increase the mother’s productivity or affect her preferences toward children’s education in a context where her bargaining power is low.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand the long-term impacts of female education in low-income countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jdas1@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
# **2. Pensioner Risks and Retirement Products**

## **2.1 Pensioner Risks**<sup>9</sup>

2.1 The main risk faced by pensioners is the risk of outliving their savings. This is often defined as longevity risk, although the two risks are not identical (see below). Depending on how their savings are invested, pensioners are exposed to investment and inflation risks as well as liquidity and bequest risks. Bankruptcy risk, which relates to the fate of the institution providing a particular product rather the product itself, is also an important risk that requires regulatory action to ensure that providers are financially sound.

2.2 Bankruptcy risk is present in all types of financial products, but is particularly important in the case of life annuities, which in principle are long-term contracts that are neither revocable nor portable. In recent years, it has become increasingly possible for fixed life annuities to be transferred among providers, magnifying the risk exposure of annuitants, who have no control over the transfer process. This places a clear responsibility on the regulatory authorities to adopt an effective and robust system of prudential regulation and supervision.

2.3 Except for the bequest risk and to a lesser extent liquidity risk<sup>10</sup> , all the other risks relate to the risk of pensioners outliving their savings. This is not identical to longevity risk because retirees may outlive their savings for several reasons even at a relatively young age. Their savings at the time of retirement may be too low, their rate of consumption in retirement may be too high, they may incur large medical costs, their savings may be exposed to a high investment risk, or they may be depleted by high inflation. Longevity risk is the risk of living longer than anticipated at the time of retirement. If this happens, even very large savings may be exhausted and prove inadequate. Linked to the risk of outliving their savings is the risk of a substantial decline in consumption and living standard since retirees experiencing a significant erosion of their savings will take action to cut their consumption spending to delay the moment of crisis when their savings are fully depleted.

2.4 Retiring workers also face annuitization risk, i.e., the risk that at the time of their retirement financial markets may be depressed, lowering the value of accumulated balances, especially those invested in equities and real estate, while long-term interest rates may be low, implying a high cost of fixed annuities.

2.5 An important characteristic of the risks faced by pensioners is that they often pull in opposite directions. Thus, the bequest risk works counter to the longevity risk and the risk of outliving one's savings. In a similar vein, the investment risk points in an opposite

> 9 This section draws on Chapter 3 of Rocha and Thorburn (2007).

> 10 The bequest motive becomes weaker as people reach advanced old age since children and even grandchildren will have reached maturity and independence by that time. Liquidity risk is present throughout a person's life and relates to the inability to use in an emergency a person's annuitized wealth.

9
- Sala-I-Martin, X., Subramanian, A., 2003. Addressing the natural resource curse: an illustration from Nigeria. National Bureau of Economic Research. Working Paper 9804, Cambridge, MA.

- Siddig, K., Luckmann, J., 2013. GTAP 8 Data Base Documentation - Chapter 7: Nigeria. Center for Global Trade Analysis, Purdue University. Center for Global Trade Analysis, 2013.

- The Economist, 2012. Protests in Nigeria let them have fuel. The president loses his nerve and brings back a controversial subsidy. The Economist January 21, 2012, page 41.

- The Economist, 2011. Nigeria’s subsidies: End them at once! The president will be a brave man if he fulfils his promise to end cheap petrol. The Economist December 28 2011.

Umar, H. M., Umar, M. S., 2013. An Assessment of the Direct Welfare Impact of Fuel Subsidy Reform in Nigeria. American Journal of Economics, 3(1): 23-26.

- Victor, D. G., 2009. The Politics of Fossil Fuel Subsidies. Global Subsidies Initiative and International Institute for Sustainable Development, November.

- Walmsley, T., Minor, P., 2013. MyGTAP Model: A Model for Employing Data from the MyGTAP Data Application—Multiple Households, Split Factors, Remittances, Foreign Aid and Transfers. GTAP Working Paper Series, #78, Center for Global Trade Analysis, Purdue University, 2013.

World Bank, 2009. Tapping a Hidden Resource: Energy Efficiency in the Middle East and North Africa. Sustainable Development Network, Middle East and North Africa Region, Energy Sector Management Assistance Program (ESMAP). Washington, D.C. 20433, USA.

World Bank, 2013. World Development Indicators.

27
of the training would be that trained traders cross the border at official hours, with an increase in the non-payment of bribes and a drop in the incidence of gender-based violence. Such a scenario would occur outside the trader-led pathway in Figure 2.

# **4 Experimental design, data, and empirical approach**

## **4.1 Experimental design and baseline data**

We rely on the random assignment of individual traders to examine the impact of the empowerment training program on corruption and gender-based violence (GBV) at the borders between Eastern DRC and Rwanda. The sample frame was generated based on a listing exercise at the Goma/Gisenyi and Bukavu/Cyangugu border crossings between Eastern DRC and Rwanda. Among the 628 crossborder traders included in the sample, 314 were randomly selected to be invited to the training and 314 selected to be control traders.

There were two rounds of survey data collection conducted by an international survey firm with support from the Catholic University of Bukavu. The baseline survey took place prior to the empowerment training workshops in August 2011, and the end-line survey was carried out in August 2013. The survey instrument was a multi-topic questionnaire covering the trader’s demographic characteristics, housing characteristics and assets, expenditure, trade (border crossing, trading activity, corruption, and income), GBV, and access to mobile technology. The questionnaire was piloted prior to the survey, with a particular focus on adapting the survey to the local cultural context. All field staff received training on data collection consisting of instructive lectures and demonstrations followed by practice sessions with non-sampled cross-border traders, and a final briefing about field data collection techniques. A team of researchers from the Catholic University of Bukavu and the World Bank’s Gender Innovation Lab closely monitored all field activities.

As expected, given the fragile context, the field survey team confronted a number of challenges during data collection linked to the mobility of traders and their distrust of outsiders. At baseline, some respondents provided fake names to survey enumerators, making it particularly difficult to track them for the follow-up survey. In addition, even among those traders who provided accurate names and contact information at baseline, it was difficult to track respondents who were frequently 18
- 29 -

K. Schmidt-Hebbel, S. Webb, G. Corsetti, "Households Saving in Developing Countries: First-Country Evidence", The World Bank Economic Review, vol.6, 1992 C. Schultz, "The Value of the Ability to Deal with Disequilibria", Journal of Economic Literature, 13, 1975 P. Tan and A. Mingat, "Education in Asia: A Comparative Study of Cost and Financing", The World Bank, Regional and Sectoral Studies, 1992.

H. White, "A Heteroskedasticity-Consistent Covariance Matrix Estimator and Direct Test for Heteroskedasticity", Econometrica, 48, 1980
Figure 3: Average Household Characteristics and Municipality Size for Compliers and Other Students

# (a) Household level

<!-- Start of picture text --> Household Income: <2 MW<br>Share of students<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>All BP All SCP All Male Female<br>Students Students Compliers Compliers Compliers<br><!-- End of picture text --> <!-- Start of picture text --> Mother's level of education: Primary<br>Share of students<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>All BP All SCP All Male Female<br>Students Students Compliers Compliers Compliers<br><!-- End of picture text --> <!-- Start of picture text --> Mother's level of education: Secondary Mother's level of education: Higher Education<br>Share of students Share of students<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>All BP All SCP All Male Female All BP All SCP All Male Female<br>Students Students Compliers Compliers Compliers Students Students Compliers Compliers Compliers<br><!-- End of picture text -->

- (b) Municipality size (population)

<!-- Start of picture text --> Medium city (0.4-2.2 million) Big city (>2.2 million)<br>Share of students Share of students<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>All BP All SCP All Male Female All BP All SCP All Male Female<br>Students Students Compliers Compliers Compliers Students Students Compliers Compliers Compliers<br><!-- End of picture text --> Note: The figure shows average characteristics and municipality size of bachelor’s program (BP) students, short-cyle program (SCP) students, compliers, and compliers by gender.

32
sustainability which exerted further downward pressure on the demand for Brazilian sovereign bonds and pushed their prices even lower in a vicious circle.

<!-- Start of picture text --> Figure 4 Figure 5<br>Brazilian Spreads and Exchange Rate Capital Flows to Brazil<br>Jan 2000 – April 2003 (ratio to international reserves<br>4.0 2800<br>.3<br>$R/US Exchange rate bps<br>3.6 (left scale) 2400<br>.2<br>3.2 2000 .1<br>2.8 1600 .0<br>2.4 1200 -.1<br>2.0 800 -.2<br>Sovereing spread<br>1.6 400 -.3<br>2000 2001 2002 2003 96 97 98 99 00 01 02 03<br><!-- End of picture text --> Source: Bacen and JP Morgan 20. The fall of Brazilian government securities’ prices and capital outflows also occurred because of a global phenomenon: the rise in uncertainty and risk aversion due to the growth slowdown of the industrialized nations, the terrorist attacks in the United States, and the corporate corruption scandals of the more mature capital markets around the world. This fact exerted additional downward pressure on Brazilian government paper, and made those prices move in tandem with asset prices worldwide (Figure 6). Favero and Giavazzi (2003) show how Brazilian spreads depend both on domestic factors, in particular the stance of fiscal policy, and on global conditions. The relationship between external factors and Brazilian sovereign spreads is non-linear: when domestic fundamentals are sound, this relationship is not as clear, but when fiscal fundamentals are weak, the effect of global factors is amplified.

10