rafmacalaba/data-use-mentions-extended huggingface dataset:
Distribution|0.005|0.05|0.06|0.13|0.14|X|||
|#6: Pure Formula 2006 +<br>Hold Harmless|0.03|0.04|0.06|0.12|0.16|0.01|X||
|#7: Population|0.04|0.07|0.05|0.09|0.03|0.05|0.07|**X**|

Source: Authors’ calculation In addition to overall mis-targeting, the analysis of fiscal transfer should be concerned with those individual districts that are under-resourced. The mirror image to under-resourced districts, given an overall vertical pool, is the districts that receive too much transfer. Localities where the problem of too little transfers is most chronic would also be those where public services may not be provided owing to an excessive budget constraint, given prevailing expenditure roles & responsibilities. We draw on the poverty literature, and construct a further measure akin to a Foster, Greer and Thorbecke poverty gap index by summarizing the deviations from ideal allocations (in Figure 3 the ratio is 1) for those below 1 (Deaton 1997):

# **Fiscal Resource Gap* = 1/N Σ (1 – Xi)**<sup>**2**</sup> **, where X≤1** (1)

With (i) being the district, X the ratio, and N the total number of localities. The squared implies a greater weight for outliers, but can be suitably increased or decreased. Table 4 presents the pair-wise fiscal resource gap measure for all comparisons across the 20
of the national household income and consumption expenditure survey, the Inquérito sobre Orcamento Familiar (IOF), conducted by Mozambique's National Institute of Statistics and (ii) farm level data from the 2015 Integrated Agriculture Survey (IAS) done by the Ministry of Agriculture and Food Security. A brief description of each data set and how they are used in the analysis is provided below. Later in this section we also discuss the data on market-level food prices, including trends across space and time.

12
To this end, a proper non-MLS comparison group (counterfactual) for projects _with_ MLS needs to be established.

To do this, a probabilistic model was first estimated to calculate the probability of receiving multilateral support for the full set of PPP projects. Second, a group of projects without MLS but with a very similar (statistically identical) probability of receiving MLS to the group that actually received MLS was identified. Details of the methodology and the results of comparing projects with MLS with their counterfactual are presented in the following sections.

# **_Methodology_**

In randomized controlled experiments, a randomly assigned intervention divides the individuals under study into two groups: those with intervention (treatment group) and those without intervention (control group). Because of the randomization, both groups have exactly the same likelihood of being treated and

> 6. To this point, it is important to recognize also that younger projects which are canceled are less likely to be recognized in the PPI Database due to lags in project status updates. As a robustness check, analysis was also performed on sets excluding PPPs with less than seven and less than five years of potential operability. See sensitivity analysis section and Annex 4 for more detail.

5
Table 1: Solutions in the literature for addressing MSB, NSB and interval data.

|Stage|Challenge|Solution in the literature|Main issues with the solution|
|---|---|---|---|
|Elicitation|MSB|Elicit a single switchpoint:<br>sMPL, iMPL|Error misattributed to WTP<br>variation, changed outcome<br>variance|
||MSB|Stop MPL questions after<br>frst switchpoint|Measurement bias; error<br>misattributed to WTP variation,<br>changed outcome variance|
|Analysis|Inconsistent<br>choices|Drop observations|Selection bias; loss of<br>information, reduced outcome<br>variance|
||NSB|Impose minimal/maximal<br>WTP|Measurement bias; potentially<br>changed outcome variance|
||Interval data,<br>NSB|Assign single point in the<br>interval, impose<br>min./max. WTP (NSB)|Measurement bias; reduced<br>outcome variance|
||Interval data,<br>MSB|Impose interval bounds<br>(MSB) and use interval<br>regression|Loss of information, potentially<br>changed outcome variance|

Notes: Overview of common approaches to addressing MPL data challenges arising from inconsistent choices (MSB and NSB with dominated choices) and interval identification (NSB and interval data). “Stage” refers to whether the solution is implemented during data collection (elicitation) or after (analysis). “Solution” and “Main issues” are discussed in more detail in the text.

interval identification when analyzing MPL data _ex post_ , at the analysis stage. However, we argue that these solutions do not full address the issues with both idiosyncratic and systematic choice error in MPL. They often misattribute the individual error to variation in WTP, fail to account for order biases, and potentially even introduce new sources
First, these numbers only reflect those exposed to severe weather events, as the thresholds used to estimate these numbers were selected to reflect weather events that cause significant damage. Lower intensity events can still cause substantial impacts on poverty, perhaps cumulatively larger given they occur with higher frequency (Hallegatte et al., 2020). The estimates presented here do not consider events of all intensities nor all types of events, so it is important to note they do not represent the total number of people whose welfare is impacted by weather events.

Second, the people exposed or at high risk from extreme weather events are not the same as those exposed to climate change risks, even though there is a strong overlap, especially over the next decades. The approach based on extreme event proposed here may not capture the impacts of changes in average conditions, for instance when water scarcity makes it impossible to produce certain crops, even during good years, or when labor productivity is reduced by heat even during normal days. Also, this indicator will capture the vulnerability to droughts, even in places where climate change makes drought less likely. Replacing the historical hazard data by climate scenarios is relatively straightforward but would require agreement on the scenarios and models to be used. Our approach is justified by the fact that many climate risks will be experienced first as a change in the frequency and intensity of extreme events (e.g., sea level rise will first be experienced as more frequent coastal floods, even though it will eventually lead to permanent land loss). Capturing the risks linked to changes in average conditions but not extreme event would require a deeper adjustment to the methodology.

Third, the data choices and challenges that this paper sets out also highlights the need to invest in data and methods to increase our ability to identify and count who is vulnerable. Some of the data limitations have already been highlighted, and throughout the paper we highlight additional areas where further data investments are needed. Since reducing the number of people at high risk of
# **Electrification and Women’s Empowerment:**

# **Evidence from Rural India**<sup>**1**</sup>

Hussain Samad, Fan Zhang

**Key Words: Electrification, Women’s Empowerment, Intra-household bargaining power, India**

**JEL:  J16, I14, I3**

1 Hussain Samad is a Consultant and Fan Zhang is a Senior Economist in the World Bank. This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia conducted by Office of the Office of the Chief Economist of the World Bank’s South Asia Region. Financial support from the South Asia Umbrella Facility for Gender Equality Multi-Donor Trust Fund is greatly appreciated. Authors are grateful to the University of Maryland and the National Council of Applied Economic Research (NCAER) for allowing them to use the IHDS data. Views expressed in this paper are entirely authors’ and do not reflect those of the World Bank or its affiliated organizations.
and damages it sustained during the hurricane events. Particularly useful in this instance is a detailed assessment (New York Power Authority et al. 2017) of individual power generation stations, transmission line failures and design recommendations for recovery efforts. Puerto Rico provides an ideal case study for assessing power system vulnerabilities because of its isolated grid, its diversity in fuel generation technologies and the availability of information following the event. The recent hurricanes here can also be compared directly with impacts in the US Virgin Islands, a much smaller set of islands 180 kilometers east of Puerto Rico. The US Virgin Islands were hit by the same hurricanes, at similar or higher strength, and saw similar devastation to the power systems. Despite this, the rebuilding efforts, time, planning and other factors differ notably with those of Puerto Rico (e.g. U.S. Department of Energy 2018a). These events then present a unique opportunity to compare the recovery efforts and identify best practices related to preparation and building back better.

Drought and heat waves often coincide and may exacerbate the severity of both events. High temperatures can make it difficult for water to reach temperatures cool enough to be discharged and, due to permitting restrictions, may require generation curtailment (Galbraith 2011). A case study on the 2011 Texas heat wave was performed and augmented with facility-specific data for curtailments at several facilities. The impacts on power generation were similar across all data reviewed: water availability and temperature affected generation systems that rely on cooling water. This was also seen in France in August 2018. Here four nuclear reactors were shut down because river temperatures were too high to allow for water discharge (Shugerman 2018). There are examples of adaptation to these challenges, highlighting both policy and design considerations. During the 2011 heat wave in Texas, water typically allocated to farmers in the area was directed to power plants despite regulations detailing the opposite prioritization (Faeth 2013). Partially due to the aggressive installation of wind power in years prior, the increased demand was
(2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Another important trend concerns the Ethiopia and Sudan samples, which surveyed refugee and/or IDP households in camps, whereas the Nigeria, Somalia, and South Sudan data sets surveyed IDP populations distributed throughout the country. Of the five countries, Ethiopia and Sudan have the largest differences in MPI and indidence between the refugee/IDP populations and their host communities. Ethiopia has a nearly 33 percentage point difference in incidence between refugee households in camps and host community peers, while Sudan has a 34 percentage point difference in incidence between the IDP households in camps and their host community neighbors. Curiously, only in Ethiopia and Sudan do we see higher intensities of poverty among the refugee and IDP populations, whereas in Nigeria, Somalia and South Sudan, we observe, on average, a higher intensity of deprivations among the poor in the host community populations. More, the included areas of Ethiopia and Sudan also have the lowest rates of multidimensional poverty for the host communities, with incidences of 12.8% and 9.4%, respectively.

Delving further into the depth of poverty, Table 4 shows a breakdown of the poor population by intensity, ranging from 50% to 100%. In Sudan, 72% of the poor in host communities and 62% of the poor IDPs are in the lowest intensity band, relatively close to the poverty line. In Somalia, the country with the highest average intensity across both groups, the distribution is less concentrated, with only one-third of IDPs in the lowest band. Nearly 18% are deprived in 70% to 79% of the weighted indicators, and a further 6% of poor IDPs face deprivation in 80% or more of the weighted indicators.

_Table 4: Intensity of deprivation among MPI poor by displacement status (%)_

||**Eth**|**iopia**|**N.E Ni*
Agreement, especially provisions on advance rulings (Articles 3) and border agency and Customs cooperation (Articles 8 and 12) should significantly increase predictability and reduce the dispersion of border clearance times. Third, supporting the modernization of public border entities other than Customs, such as SPS agencies or the Police, would significantly contribute to lowering supply chain unreliability by shortening the import process as most import clearance delays usually originate from these agencies. In particular, incentivizing them to adopt risk management systems aimed at reducing physical inspections for low-risk consignments without compromising their mandate of protecting the domestic market from phytosanitary threats or illegal trade is key to enhancing predictability in import times. Fourth, incentivizing the expansion of the Authorized Economic Operator (AEO) Program could open a fast-track and predictable clearance channel for compliant firms. Inviting public agencies other than Customs to join is key for the program to be attractive to the private sector. Accepting a wide range of private stakeholders, including importers, exporters, Customs brokers and transport and logistics services providers that comply with a list of security, tax and Customs requirements is also key to facilitating the trade process without compromising its safety.<sup>37</sup> Finally, our findings call for a revised methodology to quantify time to trade by using transaction-level trade data. Nowadays IT systems adopted by public and private operators involved in the trading process, such as ASYCUDA for Customs, Navis for port operators and GPS devices for transport operators, allow moving away from perceptions-based indicators of time to trade, usually derived from expensive surveys administered to truckers, to produce transaction-based objective measures at low cost based on time stamps collected from these IT systems for any container in movement along the logistics supply chain, from port arrival to cargo delivery at the importer’s warehouse. Constructing measures of unreliability in times to trade using Big data opens new directions of research on trade costs and their impact on firm trade performance.

> 37In exchange, benefits for AEOs include lower clearance time, higher predictability and lower logistics costs due to
evidence suggests that, despite their shortcomings, uctuations in global unit values are good proxies for uctuations in international prices.

The BLS data also shed some light on the validity of the assumption that the correlation between nominal exchange rate growth and the growth of the sectoral prices at monthly frequency is proportional to the correlation at annual frequency. Using the monthly BLS data it is possible to compute and compare the correlations at monthly (within year) and annual frequency, as well as the variance of relative prices ( _p_ ˆ _h − p_ ˆ _i_ ) and the standard deviation of sectoral prices ( _p_ ˆ _h_ ) at these two dierent frequencies. The results, summarized in Table 2, show that, within a country, the correlations, variances, and standard deviations at these two dierent frequencies are positively and signicantly related. Not surprisingly, the weakest relation is for the correlation measures, where the relation between the within and across year measures is signicant only at the 2 percent level. It is also the case that all the estimated coecients are much smaller than 1, which suggests that the coecients of the specication described in equation (7) estimated using annual frequency proxies will be smaller than the true coecients associated with the within year measures of correlations, variances, and standard deviations. Furthermore, it is clear from the gure that the relationship between annual and monthly correlations, while signicant, is imprecise and annual correlations explain only a fraction of monthly correlations ( _R_<sup>2</sup> is only 0.05). This means that the annual proxies

> 8I attempted using US import price indexes from BLS as an alternative, but doing so signicantly reduces the number of goods covered because of a higher level of aggregation and to an imperfect match between BLS sectors (based on SITC rev 3) and Feenstra sectors (based on SITC rev 2).

14
of excluding observations with productivity above the 99th percentile or below the 1st percentile, leading to a 3,936 households data set of which 517 are headed by women (13.1 percent), while 3,419 (86.9 percent) are headed by men. The share of female-headed households is thus held constant between 2008 and 2016 in our data.

As in 2008, some differences in education, household male labor, input use and export crop adoption are observed between households excluded and those remaining in the sample. All descriptive statistics and regressions are weighted by the sample weight which is the inverse probability of a household to be included in the sample. The sample design exploited two-stage clustered sampling methods. At the first stage, a certain number of EAs were randomly selected with equal probability, and with implicit stratification by urban/rural and region while in the second stage 12 households were randomly selected within each EA.

In Table 2, we present the 2016 results for the overall sample average and for female-headed and maleheaded households separately. Similarly, to 2008, we report in the table the significance levels of all differences, using a t-test. The simple difference shows no significant difference in overall agricultural productivity between female-headed households and male-headed households. Note that the summary statistics presented in Table 2 are fairly sensitive to winsorization, while our gender gap results are not, remaining substantially unchanged regardless of how the data is trimmed. Female-headed households cultivated far less land than male-headed households. As in 2008, female household heads in 2016 tend to be older, less educated and have a higher incidence of disability. Households headed by women use less hired labor and substantially less male household labor, though they use more female and child household laborers. The dependency ratio in female-headed households is approximately 39 percent higher; they also use less fertilizer and pesticide and are 38 percentage points less likely to be engaged in export crop cultivation.

6
“covid effect” in these data, since the most notable variation is in 2019 and the annual series for 2020 looks very similar to the series in 2021. Although some monthly fluctuation remains in 2022, the previous first-semester “peaking” is not present.

This exercise aims to mobilize global GGU-level information like the Madrid data for monitoring methane emissions trends in high-priority areas. To date, satellite-based estimation of physical emissions volumes from methane plumes has relied on relatively sophisticated models that utilize local atmospheric data for specific periods (Maasakkers et al. 2022; Varon et al. 2019; Cusworth et al. 2021; Zhang et al. 2020.). Such tailored exercises are undoubtedly critical for monitoring the performance of individual methane emitters that commit to specific reductions in emissions. However, their data- and model-intensities have precluded global scale-up to date.

> 17 Per Section 3.3, this is the 20-km-buffered convex hull surrounding all GGU EDGAR points in the top 5 deciles for global methane emissions.

17
Longitudinal Study (CHARLS) pilot conducted in 2008, we note 45 percent of urban men aged 60 to 64 were still employed at least one hour per week (Panel A), but in rural areas nearly 86 percent of men in this age range were still working. The difference in employment of urban and rural women aged 60 to 64 was even wider, with only 16 percent working in urban areas and nearly 57 percent still employed in rural areas.

If anything the CHARLS pilot, with relatively small sample sizes in urban Zhejiang and Gansu Provinces and representative of only two provinces, may overstate the employment rates of older men and women. Also presented in Panel A of Figure 1 are the 1991 and 2009 estimates from the China Health Nutrition Survey (CHNS), which show a substantially lower employment rate of 31 percent for urban men in the 60 to 64 age range. When comparing employment rates across the age distribution over time using the CHNS, one observes declines for both men and women in urban areas, but less pronounced declines in employment rates in rural areas.<sup>8</sup> Rural women between age 45 and 65 are somewhat less likely to be working in 2009 than in 1991, but this does not appear to be true for older women or for rural men.<sup>9</sup> One of the sharper changes from 1991 to 2009, as viewed from the CHNS, lay in the decline in employment rates of women over age 45 in urban China. Reviewing representative evidence from census data (Figure A.1) this effect appears strongest among women from age 45 to 55, as the population census and one-percent sample suggest modest increases in the employment of women over 55. Examining further evidence from three waves of the China Urban Labor Survey (CULS), which allows one to carefully distinguish employment (under our definition) from labor force participation, it is clear that China’s booming economy and a tight labor market has increased women’s employment across
firms do tend to have poorer management practices for a sample of 10,000 public and private sector manufacturing firms across 35 economies. Lemos and Scur (2019) also find a similar relationship in a sample of 2,710 firms across 18 economies and implement an instrumental variable identification strategy for 912 firms.

We build on this literature by using unique data from the 2019 EBRD-WIB-World Bank Enterprise Surveys (WBES). This data has a special module on family-managed firms and political connections on top of the comprehensive information on internal and external operating environment of businesses found in the regular World Bank Enterprise Surveys. These surveys were rolled out across a similar timeframe (20182019) for developing and advanced economies across Europe, the Middle East and North Africa, and Mongolia. The module defines our sample and enables this study to make a unique contribution by examining the relationship between family managers and management practices for a set of economies, many of which have not been explored before. This study additionally contributes to the literature by leveraging the surveys to uncover firm characteristics and business environment factors that may strengthen or weaken the effects of family management on management practices.

For around 9,000 medium and large firms across manufacturing and service sectors in 41 countries, we largely confirm the findings of Bloom et al (2015) that family management is negatively correlated with the quality of management practices. This is an important finding given the differences of our sample – we explore both manufacturing and service firms, while Bloom et al (2015) only study manufacturing firms. Our sample also consists of firms in Eastern Europe and the Middle East and North Africa that are largely omitted in the Bloom (2015) sample.

The extensive nature of the WBES allows us to enrich the analysis in several ways. First, the surveys collected information on the share of managers that are family, providing some nuance on whether any family management or all family management is detrimental to management practices. We find evidence towards the latter. Second, the surveys allow for the exploration of whether