rafmacalaba/data-use-mentions-extended huggingface dataset:
ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. EAC stands for adultequivalent consumption. All dependent variables are in log units of monetary value adjusted for variation in living expense across regions. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 26
If the supply is unreliable or non-existent, households do not adopt LPG and use it regularly, limiting consumption. Because demand is low, marketers cannot achieve the requisite economies of scale to develop commercially viable markets for LPG and choose not to devote efforts to supplying these new markets, perpetuating the lack of supply reliability.

There is only a limited literature documenting _abandonment_ of polluting fuels and stoves by households. For example, there is a large literature on household use of LPG based on data from national household surveys or other surveys to study primary household energy sources, including adoption of LPG as the
15</sup> The lower panel (b) gives the rationing rate. (The demand rate shows a similar pattern to panel (a), though at higher levels given the rationing.) In both cases we also give the regression functions with controls for state effects, adding dummy variables for the state and using the PLREG procedure of Lokshin (2006).

> Jha et al.. 13 Household quintiles were drawn after correcting per capita consumption for cost-of-living differences across states using the price deflators implicit in the Tendulkar poverty lines. 14 The participation rate for the richest quintile is a lower proportion of that for the poorest than is the case for the corresponding demand rates. 15 Alternatively one can just use (say) log consumption per person. However, given the uneven spread of the data across levels of consumption, this can be deceptive. Using percentiles instead assures a uniform distribution of the data.

9
Harmonized core data is obtained from the World Bank’s Enterprise Surveys, where firms report on the quality of electricity infrastructure, including the average monthly frequency and duration of service disruptions, as well as self-reported estimates of the annual loss of sales that are caused by electricity outages. It should be noted that the latter variable may in principle suffer from measurement errors. Without strict monitoring, firms may not be able to provide accurate estimates of outage-induced annual sales losses. Nevertheless, the data provides an indication of the perceived burden of power outages on businesses – consistent across this global panel of businesses.

We further control for a series of variables at the firm and country levels, including country and sector fixed effects. For this purpose, the data is obtained from either the Enterprise Surveys or the World Development Indicators database by the World Bank. Specifically, we control for firms’ age, employment, inventory, state-ownership, export intensity, and generator ownership. Moreover, the model controls for the country-level output gap, electrification rate, water access rate, total population size, population density, urbanization and “Government Effectiveness”, as measured by the Governance Indicators (Kaufmann, Kraay, & Mastruzzi, 2010).

The model is fit to a pooled dataset of 43,829 firms from 106 predominantly middle- and low- income countries, sampled between 2007 and 2014. Estimation is conducted by weighted ordinary least squares using normalized sample design stratification weights that sum to unity for each survey wave. Standard errors are clustered at the regional level to account for regressor correlation with region fixed effects and uncertainty about regressors’ marginal effects being region-specific. The use of sectoral dummy interactions allows us to disaggregate slope coefficients by International Standard Industrial Classification (ISIC, revision 3.1) sectors, and thus assess sector-specific vulnerabilities.

Table 4: The impact of power outages on self-reported sales losses. Table 7 presents the full sectoral disaggregation of estimated coefficients.

|Dep.: Relative blackout sales losses|(I)|(II)|
Figures 1(a) and 1(c) strikingly illustrate the potential impact of climate change and sea level rise on species in salinity tolerance groups 1 and 2. For the two species in group 1 (tolerance range 0-2 ppt), stable habitat occupies a large swath of the eastern region (approximately 15,363 sq. km) in 2012 but practically disappears from that area by 2050 in the A2 (most change) scenario. The potential stakes are also high for the 25 species in group 2, which comprise 30% of all fish species consumed in the Sundarbans region. In 2012, almost the entire eastern part of the Sundarbans region is stable habitat for group 2. In the A2 scenario for 2050, however, maximum salinity has moved beyond the tolerance range of group 2 in broad north-south swaths at the eastern and western margins of the eastern part. By implication, poor communities in these swaths might face significant drops in fish supply by 2050.

# **4.  The Impact of Salinization on Fish Habitats**

We generalize the previous illustration using the digital salinity maps provided by Dasgupta, et al. (2015). For each of the 12 species salinity tolerance groups, we assign 1 to a pixel in the map for 2012 that satisfies the stable habitat criterion (pixel salinity range falls within the species tolerance range) and 0 otherwise. We add across 101,600 pixels to determine total stable habitat by salinity tolerance group in 2012.<sup>9</sup> We perform the same operations for all 27 salinity scenarios in 2050; calculate percent changes from 2012 to 2050 for each scenario and salinity tolerance group; and tabulate the results in Tables 2 and 3.

Table 2 displays all the results, ordered by local subsidence level, IPCC AR4 scenario and global circulation model. We include summary information in Tables 3 and 4 to aid interpretation.

> 9 We use pixels for numerical convenience, although pixel numbers are readily translated to areas.  In our mapping system, one
. Bernal (2008) estimates a dynamic model of employment, child care use, and production of child cognitive ability, which allows her to handle endogeneity in maternal employment and perform counterfactuals. She estimates the model on a sample of single mothers from the NLSY79, and finds that a child care subsidy would increase overall parental utility yet at the expense of child development. Also in the context of dynamic structural model of skill formation, Del Boca et al. (2014) use the Child Development Supplements of the Panel Study of Income Dynamics and exploit detailed data on parental time use with children rather than the usual, coarse measure of maternal employment. Consistent with the rest of the literature, they find that parental time inputs are relatively more important than material resources for children’s cognitive development.<sup>9</sup> Our paper also seeks to recover the structural parameters underpinning parents’ decision. In addition, it models the supply side of the market. To handle the inherent complexity of endogenizing both demand and supply, it uses a static rather than a dynamic framework.

The supply-side literature is much more limited in number of papers and scope. Blau and Mocan (2002) estimate the relationship between cost and child care quality with data from a US sample of child care centers. Using a center quality measure similar to ours, they show that the supply of quality is moderately elastic with respect to prices and child

> 8Cunha et al. (2010) and Agostinelli and Wiswall (2016) also estimate dynamic models but do not separate returns to material and time investments.

> 9Within this structural approach, Liu et al. (2010) is a rare contribution estimating a dynamic model which includes a location decision affected by K-12 school quality, in addition to labor supply and child quality production. This paper does not include maternal time as an input in child development.

5
on food insecurity.<sup>9</sup> Alternative household behaviors (e.g., receiving aid from family or friends, selling assets or using savings, etc.) also played an important role in buffering the impact of the disaster on household food consumption.

In order to assess the level of food insecurity after the earthquake and to learn more about the efficiency of emergency assistance programs and coping strategies, a second Rapid Emergency Food Security Assessment (EFSA II) survey was conducted in June 2010 by the CNSA and its partners. The survey methodology is similar to the previous one (EFSA I). Several features of this second survey are worth noting. First, although five months had passed since the earthquake, the distributions of pre-earthquake declared wealth—as obtained from both EFSA surveys (February and June surveys)—appear to be very similar. This suggests small recall bias. Second, as households were interviewed long enough after the disaster, the role of assistance programs and the dynamics of poverty can be better assessed. Indeed, we can assess, on the one hand, whether households are better off in June than in February and, on the other hand, whether assistance programs and other strategies have played a role in this recovery. Third, information on households in non-directly affected areas enables us to assess how the hosting of displaced people has impacted households.

According to these surveys, in the directly affected areas, food insecurity has dropped from 31% in February to 27% in June. In this paper, we will use the June survey in order to focus on how adequately assistance is allocated.

Prior to the 2010 earthquake, Haiti was already plagued by chronic poverty: over than three quarters of the population lived in poverty-ridden conditions and one half earned less than one dollar per day. Nearly half of the population had no access to healthcare services, and 58 percent did not have access to drinking water. Furthermore, risks are widespread in Haiti, as people regularly face various forms
The annual costs of being formal include a sanitary tax (R$53.40-106.76, depending on activity) and the TFLF, a municipal inspection tax (R$77.26) that firms need to pay within 30 days of formalizing and then again at the beginning of each year; and a revenue tax. The revenue tax is a flat rate of R$ 51.65 per month for individuals who qualify as a MEI, and otherwise under SIMPLES ranges from 4% to 8.21% depending on revenue level and industry<sup>4</sup> . In addition, it is mandatory for formal firms with two or more employees to use an accountant, who must prepare cash flow and accounting statements each month, ensure the firm makes the monthly tax payments, and each year submit a form to the federal tax authority. In Belo Horizonte the accountants charge on average R$300 per month for this service. Accountants are not required for MEIs with one or fewer workers and revenue under the MEI revenue threshold.

For the median firm in our baseline survey (described below), which reports having one worker and earning R$30,000 in annual revenues and R$1,200 in monthly profits in our baseline surveys, the annual costs of being registered would therefore be approximately R$750 or 5.2% of their annual income. For a firm at the 75<sup>th</sup> percentile of reported annual revenues (R$57,000) and monthly profits (R$2,000), with the median of 2 employees, annual costs would therefore be approximately R$6,580 since they would need an accountant, or 27.4% of annual income. This assumes formal firms report their full revenues. Our baseline survey asks firms what percentage of revenues they think other firms report, and the mean (median) is 46% (50%). Firms may also underreport workers, especially if this
than across different RTAs. This heterogeneity in the effect of RTAs means that a sound estimation of globalization dynamics requires controlling for the country-specific effects of RTAs. An analysis that estimates the different effects of the existing RTAs in the world economy is out of the scope of this paper, but I focus on one interesting example to show how to empirically disentangle the role of RTA from the overall globalization dynamics. I use the example of Spain’s integration into the European Union, going from autarky toward the end of the 1950s to the current high levels of trade integration. Taking into account that RTAs tend to have a phase-in effect over several years, the empirical strategy, in this case, is as follows: where I disentangle the globalization dynamics of the Spanish economy from the world average, and at the same time, I disentangle the dynamic effects of the RTAs signed by Spain with other countries. In equation (7), when the terms _RTAij_<sup>_ESP_</sup> , _t_ are not included, the overall globalization dynamics for Spain are obtained. When the terms _RTAij_<sup>_ESP_</sup> , _t_ are included, the globalization dynamics for Spain are obtained but without including the RTA’s effect. Equation (7) shows how to disentangle the role played by RTAS for one specific country, but it also shows how to do the same for any other countries and economic factors of interest.

# **2.3 Data**

One of the main objectives of this paper is to cover a period as long as possible to understand where globalization stands today. Also, as discussed in the previous sections, using both international and domestic trade flow data is key for measuring globalization. Additionally, covering most countries in the world economy is a priority to contribute to economic policy both in developing and developed economies. The best way to do this is to use international input-output tables. I use three input-output tables databases to cover an extended period, starting in 1965, and
<sup>2</sup> =0.94). Based on our new estimates, the projected value for 2015 is 23.5% (standard error=1.05%), which is well over half the 1990 value of 37% (Table 2). So past trends do not suggest that the developing world as a whole outside China is on track to reaching the MDG for poverty reduction.

Our new estimates suggest only slightly less progress in absolute terms for the $2 per day line than $1.25 (though less in proportionate terms). The poverty rate by this higher standard has fallen from 70% in 1981 to 43% in 2008 (Table 3). The trend is also about 1% per year (a regression coefficient on time of -0.97; standard error=0.09); excluding China, the trend is only 0.4% per year (a regression coefficient of -0.44; standard error=0.07%). Clearly, in proportionate terms, however, the rate of progress has been lower for the higher poverty line.

The number of people living below $2 per day has fallen over 1981-2008, but only because of the progress since 1999 (Table 3). The number of people living between $1.25 and $2 a day has almost doubled from 648 million to 1.18 billion. Most of the 649 million fewer poor by the $1.25 per day standard over 1981-2008 are still poor by the standards of middle-income

> 26 Our preliminary estimate for 2010 using survey data representing about 80% of the population of the developing world indicates that the first MDG was in fact achieved in that year.

14
# **ASSESSMENT OF WORLD BANK IMPACT EVALUATIONS CONTAINING VFM ANALYSES**

We developed a framework to analyze the frequency, type and transparency of VFM analyses found in the sample of 168 IEs produced by the World Bank. Information from the subset of 30 IE’s found to contain any kind of VFM IE’s was tabulated in a Microsoft Excel spreadsheet for analysis by Sector Board, business line, method, and transparency of reporting.

The analysis generated three outcome variables corresponding to the type of VFM analysis observed in the studies: CBA, CEA, or general efficiency discussion (includes some VFM, but no indicators meeting the CBA or CEA criteria, and no VFM (a small number of tagged studies were found to contain no VFM analysis).

An impact evaluation was classified as having a cost-benefit analysis (CBA) if it included a comparison of estimates of Costs and Benefits (with data of costs and benefits) and one or several of the following indicators: Benefit to Cost Ratio, Cost to Benefit Ratio, Economic Rate of Return/Internal Rate of Return, Financial Internal Rate of Return, Present Value, or Net Present Value. The studies classified as CBA vary in the extent of their reporting from quick, one paragraph “back of the envelope” calculations, to more elaborate exercises. For example, we classified as a CBA the analysis in “Evaluating Preschool Programs when Length of Exposure to the Program Varies: A Nonparametric Approach” implemented in Bolivia. This IE includes benefit-cost ratios in the range of 2.28 and 3.66 resulting from sensitivity analysis of the program’s benefits under different assumptions. We classified “Does Management Matter? Evidence from India” as a “back of the envelope” CBA. Firms included in the study did not want to report internal accounts information. The evaluation’s authors reported a 130 percent rate of return in one year based on an analysis of the firm’s main cost (cost of the consultancy firm’s services) and an
(although insignificant) would bias us against finding a decline in under-5 EFM because, in the post-ultrasound cohorts, the marginal survivors of the neonatal period are negatively selected (i.e., in the pre-ultrasound period they would have succumbed to neonatal mortality, but now they “just survive”).

Larger reductions in under-5 than in neonatal mortality are consistent with the increases in postnatal investments that we document below, given that neonatal survival is less dependent upon postnatal investments and is more closely linked to maternal health and delivery conditions. Our findings are also congruous with previous work; for instance, The Million Deaths Study (2010) in India shows that only 3.2 percent of neonatal deaths were caused by diarrhea—a function of clean water and nutrition—in contrast to 22.2 percent of post-neonatal deaths. The significance of our results is enhanced by the fact that reductions in post-neonatal mortality also improve later life circumstances, predicting adult height, a marker of health (Bozzoli et al. (2009)) and cognitive performance (Chay et al. (2009)).

**Results by birth order.** Estimates by birth order are in Table A.9. The only statistically significant reductions in under-5 EFM are among second births; although there are sizable but less precisely estimated reductions among third births too. This may reflect the common finding that parents are particularly averse to having more than two girls (which applies at order three and above),<sup>36</sup> so there remains a girl-boy differential in under-5 mortality at higher orders.

**Equalization of inheritance rights by gender.** We also test if our results are biased by state-level legislative changes during our sample period that equalized inheritance of ancestral property between men and women. Recent work shows that this led to an increase in sex-selection (Bhalotra et al. (2018a)). We find that our results hold even if we restrict the sample to states that did not experience changes in inheritance rights,
scientific challenges prohibiting analyzing the influence of conflicts on disaster impacts. This paper seeks to address this gap and support further quantitative analyses on the additional impact on households’ welfare and nations’ economic growth in countries experiencing these compounded crises.

# Empirical strategy

# Data

density, and administrative boundaries, to econometrically analyze the specific effects of flood events in Mozambique as well as Nigeria's conflict and non-conflict affected regions.

## _Nightlights data_

Furthermore, we utilize composite images of nighttime radiance data captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NASA-NOAA Suomi satellite. These monthly composites are available since 2012 at a resolution of 15 arcseconds by 15 arcseconds (approximately 463 meters at the equator). VIIRS Day Night Bands (DNB) data exclude grid cells affected by lightning, stray light, lunar illumination, and cloud cover (Elvidge et al., 2017). We favor VIIRS data over traditionally used data from the Defense Meteorological Satellite Program (DMSP) 3
7 line (Boateng _et al,_ 1992). For instance one-third and two-third of means have been regularly used. In this document we use the latter approach and construct a relative poverty line since there is no absolute poverty line available for Nigeria.

The poverty lines that have been constructed for Nigeria include the following: The International Labor Organization (ILO) under the JASPA study (1982) constructed poverty lines on the basis of 1978 income data which amounted to 65 Naira per month per family in urban area and 35 Naira per month per family in rural areas. The poverty line assumed in the case of urban households 50 Naira per month per family for food and the rest for housing, clothing and the rest, and set the rural prices to be 40 percent below urban level. According to these guidelines it was estimated that 34 percent were poor in urban areas (instead of 15 percent in 1974), while 40 percent were below the poverty line in rural area. The other study is the World Bank Food Security Study of 1989 which draws its results on the basis of unprocessed 1985/86 FOS data.4/ Poverty lines are set at NI 50 for urban and NI 10 for rural using only food expenditures. This leads to 22.2 percent urban households and 17.2 percent of rural households falling below the poverty line or are food insecure, resulting in around 18 percent of all households nationally being poor.

Bevan et al (1988) drew an absolute rural poverty line based on 1952/53 data dividing the bottom 20 percent and 40 percent of the population. They concluded that in 1983/84 there were 17 percent and 58 percent respectively under those lines. This indicates that "moderate" poverty has worsened although the "hard core" poverty has not changed dramatically.

Both UNICEF and ILO have been concerned with developing poverty analyses. The former has been mainly interested in the qualitative side of the poverty analysis, while the latter has done some of the early work on poverty using survey