rafmacalaba/data-use-mentions-extended huggingface dataset:
sup>12</sup> While night lights do have some explanatory power for cyclical fluctuations, statistical significance is less than when using NO2. For context, in Columns 3-5 we repeat the exercise again, but now using ground level signals which have less broad availability than the satellite signals. Column 3 uses ground station measured CO2 emissions from combustion sources, as reported by the International Energy Agency. As previously noted, while satellite data is not subject to politically-motivated manipulation, this data may be. Moreover, at the time of writing, it was available for a slightly smaller sample of countries, and only until 2019. Column 4 contains log electricity consumption (units are kWh). Column 5 uses household survey response means from the World Bank’s PovcalNet database. These means aggregate income or

> 12See Online Appendix B.1 for details, and correspondence with the baseline estimates in Henderson et al. (2012).

15
conflict, leading to a national holiday and making it difficult for paperwork to get processed toward the end of May. We therefore decided to give all four treatment groups an extra month to carry out their registration if they desired, with a new deadline of the end of July 2009. Any firm which had completed its registration between the previous deadline and the new deadline was also eligible to receive the payment.

# **3.4 Quasi-registration and Actually Receiving the Treatment Offer**

Owners of the firms assigned to treatment were asked to confirm their registration status on a follow-up visit in which we made the offer corresponding to their treatment group. We were surprised to learn that a non-trivial number of the firms were already registered with the DS. Recall that all of the firms stated they were not registered with the DS in the baseline survey. A handful of these firms had registered in the 3 to 4 months since the baseline survey (this was confirmed by examining the BRC, which shows the date of registration), but the majority of those registered had either misunderstood the question in the baseline survey or were what we term ―quasi-registered‖. The latter refers to a number of cases where the business had been registered in some form, but the registration did not match the current circumstances of the business. For example, in some cases the business had been registered by another family member, who was no longer running it. In other cases the registration had been for another location. The firm thus had a BRC, but it didn’t match the firm in all details. This BRC could still presumably be used to access government contracts or in dealing with a bank. We therefore chose not to offer the treatment to firms in this category. We subsequently also re-visited the control group firms to determine which of them also fell into this quasi-registered status.

In total, 152 out of the 415 firms which were assigned to one of the four treatment groups did not receive the registration offer. The majority of time
the end of the field experiment. Of a total of 126 mentors between the _Original_ and _Plus_ modalities, enumerators were able to interview 107 of them. The attrition of survey participation of mentors is unrelated to the treatment assignment ( _p_ -value = 0.514). For further details on the survey of mentors, please refer to Appendix A.2. Mentors with enhanced training are more inclined to foster attitudes that are centered on educative parenting styles, such as communicating with the child (first column), as well as learning activities (second column). The overall educative style GLS-weighted index (third column) shows a sizable and significant effect (across the three inference procedures) of the _Plus_ modality, with an increase of 0.49 standard deviations in the promotion of educative parenting styles to parents during the home visits. Other aspects of the parent-child relationship that are focused on emotional practices do not seem to systematically vary across the two program modalities.

The evidence presented in this section points toward cross-modality variation in the quality of both the parent/mentor interactions and parent/child interactions as a potential mechanism behind the observed difference in children’s outcomes. While we cannot separately quantify 17
usage in Rwanda, in this Appendix we refer to any official receipt generating system as EBM, and delineate versions when referring to any specific form of EBM (EBM I, EBM II). In the body of the paper, for parsimony we refer to EBM II as EBM, both because we do not use EBM I data for our analysis and also because of the relatively broad coverage of EBM II.

> A2Although EBM I offers more coverage before November 2019, these receipts are not comprehensively stored in a machine readable format.

A1
**Residential Electricity Subsidies in Pakistan**<sup>**1**</sup> Targeting, Welfare Impacts, and Options for Reform

Thomas Walker<sup>*</sup> Ezgi Canpolat<sup>*</sup> Farah Khalid Khan<sup>*</sup> Adea Kryeziu<sup>*</sup> _Keywords:_ Electric Utilities, Subsidies, Welfare, Safety Nets, Targeting, Pakistan. _JEL codes:_ L94, H22, D6, I38.

> * Social Protection and Labor Global Practice, The World Bank Group.

1 The authors thank Pablo Gottret, Gul Najam Jamy, Amjad Zafar Khan, Iftikhar Malik, Lucian Pop, Mohammad Saqib and Richard Spencer for their support. The authors also appreciate the assistance and advice of officials in the Government of Pakistan, specifically the Ministry of Water and Power, Ministry of Finance, Benazir Income Support Programme, NADRA, and Planning Commission. In addition, the authors are appreciative of the generosity of officials at IESCO, FESCO and GEPCO for sharing consumer billing data analyzed in this report. Qualitative analysis referenced in this paper was carried out by SoSec International. The work was financed by a grant from the United Kingdom Department for International Development, under the _Towards Integrated Social Protection Systems in Pakistan_ trust fund.
|0.38|0.000|
|Technical [=1]|0.33|0.008|0.13|0.000|
|University [=1]|0.24|0.007|0.22|0.000|
|Postgraduate [=1]|0.12|0.006|0.08|0.000|
|**Economic Strata**|||||
|Strata 1 [=1]|0.09|0.005|0.09|0.000|
|Strata 2 [=1]|0.35|0.008|0.41|0.000|
|Strata 3 [=1]|0.39|0.008|0.36|0.000|
|Strata 4 [=1]|0.12|0.006|0.09|0.000|
|Strata 5 [=1]|0.04|0.003|0.03|0.000|
|Strata 6[=1]|0.01|0.002|0.02|0.000|

_Notes:_ We use the CNVP 2018 _(National Population and Housing Census 2018)_ to construct the following variables: male, age and education levels for Bogot ́a, Colombia. To construct the economic strata data, we use the EM 2017 _(Multipurpose survey 2017)_ . Both, CNVP 2018 and EM 2017 were designed and implemented by the National Administrative Department of Statistics (DANE) from Colombia.

**Table (A.2)** Order of survey modules in treatment and control groups

|**Treatment**|**Control**|
|---|---|
|1) Sociodemographics|1) Sociodemographics|
|2) COVID-19|2) Attitudes towards migrants|
|3) Crisis questions|3)
this point. The study by Di Falco and colleagues focuses on long-term migration instead, and thus individuals are proxied as migrants if households report them to have left between two visits or waves of the survey and who were not observed to return to their household during the time of analysis. In this specific case, the assumptions made are even stronger than those implied by Mueller et al. (2020), as the definition of migrants includes individuals that left the household because they married.

**_Sample and statistical issues._** Migration is, statistically speaking, a rare event. As reported below, the only five climate-migration studies using LSMS-ISA data have very small samples. This is unsurprising, as in a normal clustered sample design typical of multi-topic surveys, the expected number of households associated with emigration may be very low (Carletto et al., 2014). To better identify rare events, two potential approaches are disproportionate sampling and two-phase sampling. However, both sample designs require some prior knowledge of migration in the population and are not easy to implement as

24
Policy Research Working Paper 10005

# **Abstract**

Air pollution is one of the leading causes of death worldwide, especially affecting poorer people who tend to be more exposed and vulnerable. This study contributes (i) updated global exposure estimates for the World Health Organizations’s 2021 revised fine particulate matter (PM2.5) thresholds, and (ii) estimates of the number of poor people exposed to unsafe PM2.5 concentrations. It shows that 7.28 billion people, or 94 percent of the world population, are directly exposed to unsafe average annual PM2.5 concentrations. Low- and middle-income countries account for 80 percent of people exposed to unsafe PM2.5 levels. Moreover, 716 million poor people (living on less than $1.90 per day) live in areas with unsafe air pollution. Around half of them are located in just three countries: India, Nigeria, and the Democratic Republic of Congo. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. The findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

This paper is a product of the Sustainable Development Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at jrentschler@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
# **Data collection**

Quantitative surveys were administered on tablets and paper questionnaires with all enrolled participants at two time points (wave 1, August 2018 - February 2019 and wave 3, June 2021 - January 2022). Surveys included questions on employment and income, client history, sexual behavior, HIV/STI risks, mental health, and gender-based violence. Surveys at the end of the study period included additional questions about changes in work and sexual activity related to COVID19. An additional survey was administered via phone to a subset of participants in the control arm only (wave 2, July-October 2020), and included a reduced set of survey questions focused on wealth and income, income shocks related to COVID-19, food security, and sexual history.

Qualitative interviews were conducted with a subset of 20 wave 3 participants using a semistructured interview guide to elicit views on the intervention and to understand the effects of COVID-19 on employment, income, client type, and work conditions. The interview guide was informed by preliminary findings from the wave 3 quantitative surveys which indicated that participants had been experiencing changes in income and work activity related to COVID-19, thus the interviews served to gather additional perspectives and insights into the experiences of FSW during COVID-19. Interviews were approximately 60 minutes in duration and audio recorded.

All data were collected in Kiswahili by trained research assistants at Innovations for Poverty Action Tanzania.

# **Data analysis**

Quantitative data were analyzed in STATA version 15.1. Descriptive statistics were generated to 1) assess the demographic characteristics of participants at enrollment (wave 1, 2018) and to compare the overall study population with those who were reached for phone surveys (wave 2, 2020) and at the end of the study period (wave 3, 2021); 2) examine self-reported data on employment and income before and during COVID-19. Linear first difference regression with robust standard errors and chi-square tests were used to generate p-values for differences in total income, sex work income, price per client, and clients
Costa Rica, Cuba, Dominican Republic, Guatemala, Mexico, Nicaragua, Panama, Paraguay, Trinidad and Tobago, and Uruguay.

MENA includes Arab Republico f Egypt, Israel, Malta, Oman, Qatar, and United Arab Emirates. NA includes Canada and the United States. SA includes Afghanistan and Sri Lanka.

SSA includes Botswana, Cabo Verde, Ethiopia, Guinea, Madagascar, Mauritius, Senegal, Seychelles, South Africa, Tanzania, and Zimbabwe. Source: ILO Stats Figure 7: Employment (%LF) over time, by region

> 6 Our total sample consists of 88 countries, but central government employment data are reported for only for 56 countries, while public sector employment data are reported for 113 countries.

10
high informality. Some of these studies also find that women and young workers were disproportionately affected.

More recently, researchers have shifted the focus to other dimensions of the effects of Syrian refugee inflows into Turkey. Altindag et al. (2020) argue that refugee inflows had a positive impact on firm production, both in terms of the volume of production and the introduction of new product varieties. Akgunduz and Torun (2020) examine other channels by which Turkish workers and firms accommodated the inflows of Syrian refugees. The authors find that skilled native workers increased their specialization in complex tasks, moving away from manual tasks, and domestic companies took advantage of the increased abundance of labor by reducing capital intensity. As shown in earlier studies, both mechanisms contribute to mitigate the effects of immigration on the wages of the receiving country (Lewis (2005), Peri and Sparber (2009), Gonzalez and Ortega (2011) and Dustmann and Glitz (2015)).

In the last few years, some researchers have begun to analyze the economic effects of the exodus of Venezuelans on the surrounding countries but progress has been slow due to the difficulty of analyzing Venezuelan migrants equipped solely with governmentprovided data or the standard labor force surveys. The existing work has so far focused on the labor market effects in Colombia, the main receiver of Venezuelan migrants (e.g. Caruso et al. (2019) and Penaloza-Pacheco (2019)). To our knowledge, the only existing study concerning the labor market effects of Venezuelan migration to Ecuador is Olivieri et al. (2020). This paper relies on Ecuador’s labor force survey to document adverse effects of the inflows on the wages and employment quality of young, low-educated Ecuadoran workers in the main receiving areas within the country.

Relative to these studies, our paper provides the first analysis of the labor market conditions of Venezuelan migrants in a Latin American context on the basis of a survey specifically designed for this purpose. We also use this information to analyze policies that are tailored to
ideal setting for the application of this methodology. With a wide range of agro-ecological zones, there is considerable variation in agricultural productivity and climate across the sub-continent. Also, Indian agriculture is expected to be especially hard-hit by warming temperatures in the coming decades, although not necessarily uniformly (World Bank, 2009). While agriculture contributes only about a fifth of GDP, productivity growth in this sector has been the major driver of poverty reduction in India, largely through increases in agricultural wages (Datt and Ravallion, 1998; Eswaran, et al. 2008; Lanjouw and Murgai, 2009). Data for India are relatively abundant as well, our main source being the nationally representative National Sample Surveys (NSS). The 2002-03 NSS round collects detailed farm-level information and the 2004-05 round includes both a huge household expenditure survey and an employment survey. These data are supplemented with a district-level panel of agricultural output and weather.

Past empirical literature focuses on predicting the impact of a changing climate on national or global agricultural product, largely ignoring the question of how damages will be distributed across the relevant population. There are three distinct approaches to making such predictions. Agronomists base crop damage assessments on temperature and, perhaps, precipitation responses taken from agricultural field trials. More sophisticated crop modeling (e.g., Parry et al. 1999) also accounts for certain adaptations to climate change, such as shifts in planting dates, increased irrigation, and changes in crop varieties. However, even this limited set of adaptations is _assumed a priori_ , rather than necessarily reflecting how future farmers would behave. Economists prefer to use data that reflect actual farmer behavior. The hedonic or so-called Ricardian approach, pioneered by Mendelsohn, et al, (1994), infers the impact of climate change on future agricultural productivity from the present-day cross-sectional relationship between climate and land values. This allows for as much (but no more) adaptation than is revealed in the data. Predictions based on reduced-form Ricardian regressions presuppose that the technological envelope along which farmers will adapt
percentage points in urban areas. For urban areas, there are probable signs of a turnaround with a marginal recovery; the FLFP rate increased from 19.4 percent in 2009-10 to 20.5 percent in 201112. Based on these participation rates calculated from NSSO data, Table 4 shows that the estimated total FLFP for the age group of 15 years and above had increased from 116.9 million in 1993-94 to 148.0 million in 2004-05. Thereafter, it dropped to 128.8 million in 2011-12.

# 2.2 Trend

Comparing the two periods between 1993-94 to 2004-05 and 2004-05 to 2011-12, most of the drop in FLFP is found to have occurred during the second period, and it was most pronounced in rural areas (Table 4). In terms of change, our estimation suggests that there were an additional 31 million females in the labor force during the eleven-year period between 1993-94 and 2004-05. In contrast, during the later seven-year period between 2004-05 and 2011-12, there was a significant drop in the female labor force by 19.2 million individuals (Panel II, Table 4). Of the 31 million additional female workers during 1993-94 to 2004-05, 25.9 million had joined the labor force during the period 1999-2000 to 2004-05 (Panel I, Table 4, second row, and third column). Of the 19.2 million drop, 19.13 million female workers had left the labor force during the short period of 2004-05 to 2009-10 (Panel I, Table 4, fourth row, and third column).

**Table 4: Change in Male and Female Labor Force Participation Across Rounds**

||Ru<br>|ral<br>|Urban<br>|<br>|Tota<br>|l<br>|
|---|---|---|---|---|---|---|
|Panel I|Male|Female|Male|Female|Male
# Ghana’s Infrastructure: A Continental Perspective

## **_Vivien Foster and Nataliya Pushak_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Ishac Diwan (country director), Katherine Bain (country manager), Peter Kristensen (sector leader), Ventura Bengoechea and Emmanuel Nkrumah (water and sanitation), Sunil Mathrani (power), Mavis Ampah (ICT), John Richardson (roads), and Sebastien Dessus (macro).
|Bihar|0.121|18|Bihar|0.304|18|Bihar|0.395|18|
|---|---|---|---|---|---|---|---|---|
|C.V.|47.1|||41.8|||32.7||

Source: Author’s Calculations. C.V- Coefficient of Variations

**_Financial Infrastructure:_** Table 4 compares the performances of different states in terms of the financial infrastructure index, where we find large inequality among the states. Though the variations across states in financial infrastructure over time have narrowed down, the CV is still high. The CV is higher for financial infrastructure than physical and social infrastructure for all three-time points.  Additionally, the relative positions of states have not changed much from 2005 to 2019. Exceptions include Haryana and Chhattisgarh experienced significant improvement in their relative positions, whereas Andhra Pradesh witnessed deterioration in its position and is among one of the worst-performing states along with Uttar Pradesh and Bihar.

**<u>Table 4: Relative Performances of States for Financial Infrastructure Index</u>**

||**20**<br>|**05**<br>||**2010**<br>|||**2019**<br>||
|---|---|---|---|---|---|---|---|---|
|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_States_**|
|HP|1.505|
revenue per capita results in 0.37 R$ more per capita education spending—close to education spending’s average 30% share of total expenditure. Column (10) shows that education spending as a fraction of revenue is increasing in revenue; a standard deviation revenue increase leads to 2.7 percentage points more revenue going to education.

OLS estimates are sufficiently different from IV estimates to suggest bias. Both suggest that higher revenue is associated with higher pre-primary enrollment and higher education spending, but the OLS estimates are smaller in magnitude than the IV estimates. This is consistent with the previously-described channels of downward bias. If revenue is higher precisely where there are fewer child care needs or a mayor with a high discount rate, then OLS would mute the effects of revenue on pre-primary enrollment and education spending due to its correlation with these unobservables. Under OLS, revenue has a near-zero effect on both teacher and infrastructure quality (columns 4 and 6), which is statistically insignificant in the case of infrastructure quality.

# **6.3 The roles of income and inequality**

According to the predictions, median income and inequality moderate the effects of a reforminduced revenue increase. One expects higher median income and higher inequality to result in: (i) a smaller increase in public pre-primary enrollment, (ii) a smaller decrease in public pre-primary quality, (iii) a smaller increase in public education expenditure, and (iv) a larger increase in public expenditure on goods without private sector substitutes, like infrastructure. In this section, I test these predictions and find empirical support for each.

Table 8 shows the results for public pre-primary enrollment. Per capita revenue has a positive effect on enrollment, but this effect is statistically significantly smaller (at the 0.01 level) in richer and more unequal municipalities. By column (5), if two otherwise identical municipalities have Gini coefficients at the 25th and the 75th percentiles (0.37 and 0.41), a revenue increase that would lead