rafmacalaba/data-use-mentions-extended huggingface dataset:
5 bulk metering was introduced for micro and small enterprises in 2009 and pre‐payment meters for households in 2012. An analysis of the bulk metering program by Never (2015) indicated that although this approach improved bill collection, there were still problems of theft and inability to pay.

This paper is part of a broader study examining the financial viability and related aspects of the power sector in Africa. The broader study focuses primarily on grid electricity for reasons of data availability at the regional level. The study examines quasi‐fiscal deficits—difference between the total cost of providing electricity and total cash collected by utilities—in 39 countries (Trimble et al. 2016), tariff structures in 39, and household use of electricity in 22.<sup>4</sup> The component on household use of electricity, which is the subject of this paper, takes household expenditure surveys to gain a better understanding of who uses electricity, what type of electricity, and how much. It asks the following questions:

- How widely is electricity used?

- What are the barriers to making electricity the main source of energy in Africa for lighting and powering appliances?

- Is electricity affordable?

- Is there potential evidence suggesting that female‐headed households are disadvantaged in some ways with respect to electricity use?

Although household expenditure surveys capture spending on all forms of electricity, as with the broader study, detailed analysis is carried out primarily on issues associated with grid connection.

# Scope and Methodology

The household expenditure surveys analyzed in this paper were carried out in 2008 or later. The survey period and the sample size are summarized in Table A.1 in the annex. This paper reports results calculated using household weights or population weights, depending on whether percentages of households or people are being examined. The only exception is when the sample size is discussed, primarily to point out that the number of households being interviewed was too small for meaningful results (for example, the number of rural households in the bottom 40 percent
who attended Screening 0 and visited them at home to administer the baseline survey. From now on, we refer to these 64 individuals as ‘main study participants’.<sup>9</sup> At the end of the survey, the main study participants received invitations to attend two other screenings organized in the two weekends following the interview: Screenings 1 and 2. Those in the ‘Friends treatment’(to be described in the next section) also received invitations to bring up to two friends of their choice. Note that invitees were not told in advance what they would watch, neither before Screening 0 nor before Screening 1.

Attendance to the screenings was relatively high: 78.4 percent of those invited attended at least one of the two screenings, with the shares being 77 percent in the treatment group and 81 in the control group (significantly different).<sup>10</sup> Note that in all our analysis we report

> 7 The condition for a household to be in the study was that at least one of the members should be in the target age range of our intervention, i.e., 18-25 years old.

> 8 We also compared our sample to the 2008 Nigeria DHS, restricted to the South-Western region, and found that the two were quite similar in terms of religion (a third Muslim and two thirds Christian), years of education (around 11 years) and television-ownership rates (around 90 percent).

> 9 Appendix Table A2 shows balance in observable characteristics between the people who attended Screening 0 and were selected into the baseline sample and those that were not.

> 10 70.5 percent of our sample attended Screening 1, 57.2 attended Screening 2, and 49.4 percent attended both. The likelihood of attending Screening 2 conditional on attending Screening 1 is 70 percent in both treatment and control.

12
,</mark> _<mark>28</mark>_ <mark>(3), 49-54.</mark>

Good, T. L., & Grouws, D. A. (1979). The Missouri Mathematics Effectiveness Project: An experimental study in fourth-grade classrooms. _Journal of Educational Psychology_ , _71_ (3), 355.

Greenberg, M. T., Kusche, C. A., Cook, E. T., & Quamma, J. P. (1995). Promoting emotional competence in school-aged children: The effects of the PATHS curriculum. _Development and Psychopathology_ , _7_ (1), 117-136.

49
in El Salvador is “unfair” or “very unfair”. Few Salvadorans are optimistic about current economic conditions (only 16% answered that the economic conditions of the country were better than a year before). A large share of respondents (i.e., 70%) thinks that democracy is the preferable to other kinds of governments, but only half of them (i.e., 35%) were satisfied with the democracy. Finally, the Latinobar ́ometro reveals a very high level of mistrust among Salvadorans since only 22% of respondent said that one could trust most people (as opposed to the alternative “you can never be too careful when dealing with others”).

# **2.1.1 Recent Trends in Attitudes Towards Institutions**

Figure 1 depicts the temporal evolution of the aforementioned attitudinal measures over the 20042018 period. In Panel A I plot the evolution of trust measures from LAPOP. Two main patterns emerge from this figure. First, all measures but trust towards municipal government have experienced a steady reduction over the last years. Second, trust in the president seems to be the most volatile measure. It reached its highest value after the 2008 global financial crisis (almost 70% of respondents trusted the president in 2010) and then experienced a rapid drop reaching 27% in 2018 6
Temporal deflation was undertaken on the basis of the Brazilian consumer price indices IGP-DI (for 1976), and INPC-R for the three subsequent years. For 1996, the INPC-R was upwardly adjusted by 1.2199, so as to compensate for the actual price increases which took place in the second half of June 1994, and which were not computed into July's index, since the latter was already computed in terms of the URV. This adjustment is becoming the standard deflation procedure at IPEA when comparing incomes across June/July 1994. (See Macrometrica, 1994). In order to center the indices on the first day of the month, which is the reference date for PNAD incomes, the geometric average of the index for a month and for the preceding month was used as that month's deflator. Once again, this is now best practice for price deflation in hyperinflationary periods. Once the deflators were constructed in this way, the values to convert current incomes into 1996 Reais were as follows:

**Table A4: Brazilian Temporal Price Deflators (Selected Years)**

|**1976**<br>**1**4.115|1**1981**|**1**49.512|1**1985**|**1**2257.294|1**1996**<br>1.000|
|---|---|---|---|---|---|

A final possible adjustment to the PNAD data concerns deviations between survey-based welfare indicators (such as mean household income per capita) and National Accounts-based prosperity indicators (such as GDP per capita). The international norm is that household survey means are lower than per capita GNP, both because the latter includes the value of public and publicly
cities in the sample, providing a consistent mid-day activity benchmark for comparing CO2 concentration anomalies.<sup>3</sup> OCO-2 has an observation repeat time of 16 days. We have downloaded georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).

We filter the XCO2 data for local concentration anomalies, or differences between observed and background CO2 at each point. We calculate background CO2 using the methodology of Hakkarainen et al. (2019), which incorporates both temporal and geographic elements. As Hakkarainen notes, the available data are insufficient for estimating daily medians at resolutions higher than 10 degrees of latitude. We compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution. Following Hakkarainen, we use the median as the representative value because it is not skewed by extreme observations. We subtract this background value to compute the local anomaly for each observation. Then we compute monthly mean values of concentration anomalies for the 25-km grid cells in our database.

We use georeferenced facility-level global databases to obtain capacity measures and technology specifications for power plants (Byers et al. 2021), steel mills (GEM 2021), cement plants (McCaffrey et al. 2021), and oil refineries (Auch 2017). We convert capacity measures to annual CO2 emissions using standard emissions factors for power production by fuel source (USEIA 2021), steel mills (World Steel Association 2021), cement (IEA 2020), and refineries (Jing et al. 2020). Van der Werf et al. (2017) provide monthly estimates of carbon emissions from agricultural and forest burning at 25 km resolution.

Mistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km resolution.<sup>4</sup> We compute population at 25 km resolution by aggregating data from CIESIN (2021) at 5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We use two
# **5. Data and Sample**

# **5.1. Data**

We exploit different sources of school-level data. We exploit school census data between 2007 and 2011. The school census is conducted annually by MEC-INEP<sup>36</sup> and focuses on basic education offered by both private and public schools. In particular, we exploit data on school characteristics and student enrollments. Our sample includes only schools from the regular educational system. Private schools and schools that were not operating in a given year were excluded from our sample. We also excluded schools offering specialized education for children with disabilities.

We use administrative data from MEC/FNDE<sup>37</sup> to gather information on schools enrolled in _Mais Educação_ between 2008 and 2011. In particular, we identify for each school the program’s total costs, divided into variable costs (e.g., payments to tutors, meals, transportation), fixed costs (e.g., related to inputs/materials and school buildings), and fields of knowledge selected in each school, as well as the number of students enrolled in the program. An important drawback in MEC/FNDE’s dataset is that it provides the year that each school joined _Mais Educação,_ but there is no information on when exactly the program’s activities actually began. In our analysis, we assume a one-year lag between schools’ entering the program and the actual implementation of _Mais Educação._ Nevertheless, it is important to stress that the difference in exposure time among schools can lead to heterogeneous impacts, especially in the short-term analysis.

In addition, we exploit the demographic census, collected by the _Instituto Brasileiro de Geografia e Estatística_ (IBGE) in 2010 and data provided by _Instituto de Pesquisa Econômica Aplicada_ (IPEA), to capture characteristics of the municipalities (IDHM), population, etc.<sup>38</sup> We have information only from the demographic census in 2010,
|46.7%|60.0%|84.6%|76.9%|63.6%|100.0%|50.0%<br>|66.7%<br>|
|<br>Violence is a problem|27.6%|12.5%|20.0%|60.0%|61.5%|15.4%|36.4%|11.1%<br>|50.0%<br>|22.2%<br>|
|<br>Land invasions are a problem|35.6%|25.0%|26.7%|40.0%|84.6%|46.2%<br>|27.3%<br>|11.1%<br>|50.0%<br>|22.2%<br>|
|Theft is a problem<br>|60.9%<br>|37.5%<br>|66.7%<br>|40.0%<br>|100.0%<br>|69.2%<br>|54.5%<br>|66.7%<br>|100.0%<br>|55.5%<br>|
|No of projects|87|8|15|5|13|13|11|9|4|9|

Source: DLA Land Reform Beneficiary Survey
observed quality of electricity _Gi_ is calculated based on two collective indicators of continuity provided by ANEEL: the Equivalent

> 15This number comes from the World Bank PPI database: `ppi.worldbank.org` . Other sources (Paiva, 2010; BNDES, 2014) suggest very similar values.

> 16World Bank Data estimates that average electric power losses as a percentage of output from transmission and distribution are 14.6%. The series identifier is EG.ELC.LOSS.ZS.

21
communities (increased demand for economic goods, humanitarian assistance, improved infrastructure, increased agricultural production</mark><sup>4</sup> <mark>) might translate into better health care services and consequently positive children’s health outcomes. On the other hand, most studies have reported negative changes in welfare among host communities following refugees’ arrival. Disease outbreaks (K</mark> ibrea <mark>b, 1985), competition over natural resources and paid jobs, environmental damage, criminality and pressure on basic service delivery in schools and hospitals are among the most cited negative impacts of refugees on hosts. These could trigger poorer health, education and labor market outcomes of the first but also the second generation of children born years after the crisis.</mark> <mark>Baez (2011) uses secondary data from Tanzania and finds that, as compared to children living in areas not affected by refugees, children living in host communities have a higher incidence of infectious diseases, are more likely to die before the age of 5 and have a reduced stature (height) with an acute effect found for younger cohorts. However, building on Baez’s study, Mabiso et al. (2014) find no</mark>

> 4 <mark>Bharadwaj and Mirza (2019).</mark>

3
9,437|8,032|4,096|21,921|1|10|59|
|Land tax|1,173|1,997|1,355|2,498|9|41|14|
|patent tax|5,865|2,153|1,248|8,142|4|22|31|
|**Indirect taxes**|**953**|**2,898**|**1,022**|**3,304**|**4**|**26**|**33**|
|VAT|921|2,724|971|3,162|4|26|33|
|Import duties|767|2,546|627|2,220|5|29|29|
|Excise|1,195|2,096|951|2,779|5|25|37|

Notes: revenues are in mln. MDL SIC – social insurance contributions, MIC – medical insurance contributions/ National - below the national poverty line (1562.8 MDL per month per adult equivalent) International - below the international line (3.2 USD/day in 2011 PPP or 759.4 MDL per month per capita) International - below the international line (5.5 USD/day in 2011 PPP or 1305.2 MDL per month per capita) Income deciles were defined on the basis of disposable income plus tax for direct taxes and SSC and disposable income for indirect taxes. Source: authors’ calculations using HBS-2017 data.

Direct taxes and social contributions are more efficient in tax collection considering the poverty and inequality increase effects (Table 16). PIT is the most efficient direct tax in terms of redistribution effects among the direct taxes. PIT decreases inequality and has the highest tax collection for one point of poverty increase. In other words, it causes the lowest increase
days per month). 35.0 percent of subjects operate their own business enterprise; among these businesses, only 11.6 percent have any employees. 16.7 percent of participants have bank savings accounts,<sup>17</sup> and 52.8 percent are members of rotating savings and credit associations (ROSCAs).<sup>18</sup> Most experimental subjects in our sample live amongst their kin, and are embedded in interhousehold transfer networks. The median participant has seven relatives living outside her household but in the same village: two close relatives (parents, parents-in-law, adult children, grandparents, siblings, aunts, and uncles) and another five more distant relatives. 43.8 percent of subjects had received a transfer in the last three months, while 89.8 percent report making a transfer to another household over the same period.<sup>19</sup> The median household making a transfer had given 363 shillings, while the median household receiving a transfer had been given 600 shillings. In the three months prior to being surveyed, 42.4 percent of subjects’ households had been asked for a gift or loan, and 89.7 percent of households had contributed money to a “harambee,” a local fundraising drive.<sup>20</sup> Table 1 reports tests of balance across our six experimental treatments.<sup>21</sup> The randomization was successful, generating minimal differences in observables across treatments. Of 68 tests (34 for each gender) reported, we observe only five significant differences in observables across treatments.

> 17Dupas and Robinson (forthcoming) found that less than 3 percent of the daily wage earners sampled in Bumala, Kenya, had savings accounts. While Bumala is just a few kilometers from the region where the present study took place, their data were collected over two years before our household survey. The daily wage earners (primarily market vendors and bicycle taxi drivers) included in their
i) the brain drain raises the expected return on education; ii) this induces additional investment in education (a brain gain); iii) this may result in a ‘beneficial brain drain’ or _net_ brain gain, i.e., in a brain gain that is larger than the brain drain; and iv) a _net_ brain gain raises welfare and growth.

These results are said to hold independently of other potential effects of the brain drain on the level of education, whether through remittances or through the skills return migrants might have acquired in the destination countries.

Seminal papers in the new brain drain literature include Mountford (1997) **,** Stark et al. (1997, 1998), Stark and Wang (2002), Stark (2004), Stark et al. (2004), Beine et al. (2001, 2003), Docquier and Rapoport (2004), and Vidal (1998).<sup>4</sup> Their work has led to a reconsideration of the impact of the brain drain on the number of skilled individuals and on economic welfare and growth in the source country.

Most studies in that literature are theoretical, though empirical results are slowly emerging thanks to the work of Carrington and Detragiache (1998, 1999), Adams (2003) Docquier and Marfouk (Chapter 6 in this volume; 2004), and Dumont and Lemaitre (2005).<sup>5</sup> These studies have estimated the stock of skilled migrants from developing countries who are living in OECD countries.<sup>6</sup>

> 4 Commander, Kangasniemi and Winters (2004) provide a survey of the brain drain literature.

> 5 An analysis of regional differences in the brain drain is provided by Docquier et al. (2005).

> 6 Carrington and Detragiache used the 1990 US census data to estimate the brain drain for a number of developing countries in 1990. Docquier and Marfouk (Chapter 6 in this volume)
that incorporate uncertainty about the errors in our income model. The bounds are highly likely to contain the true value of the probability estimates, as was confirmed in other settings by Dang et al. (2014) and Hérault and Jenkins (2019). In addition, sample sizes for each round are large, so that the standard errors of our estimators are generally small.

# 4. Data

Our study analyzes six rounds of cross-sectional data from the Household Income and Basic Amenities Survey (HIS) collected by the Department of Statistics Malaysia. The data are for the years 2004, 2007, 2009, 2012, 2014 and 2016; we consider the five pairs of adjacent survey rounds.<sup>16</sup> One requirement for analyzing welfare changes over time is that the measure of welfare, income in our case, is measured in a consistent and comparable manner. The Malaysian data are of high quality and satisfy this requirement. In addition, the sample sizes of the surveys are large, ranging from 36,000 to over 80,000 household-level observations per survey year (see Table 1).

At the start of our analysis, the official Malaysian poverty line was very low; poverty was virtually non-existent by those standards.<sup>17</sup> Given that this line had not been updated in real terms since 1977, it was not an appealing yardstick for the purpose of our analysis. Present-day discussions about poverty and mobility in Malaysia often refer to the living standards of the bottom 40 percent of the income distribution. Accordingly, we have chosen to employ a poverty line corresponding to the income level of the 40th percentile in the first year of our analysis, 2004. This line is then kept constant in real terms for the subsequent years. Hence, it is initially set as a relative line, but is treated as absolute poverty line thereafter. In 2016 prices, the line stands at MYR 527 per capita per month. This is roughly equivalent to MYR 17 per day, or
Table B13: Mechanism at play, the role of uncertainty on Mergers and Acquisitions

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|
|---|---|---|---|---|---|
|Uncertainty (standardized)|-0.379|-1.381|-1.442|-1.327|-0.048|
||(0.508)|(0.965)|(0.960)|(0.888)|(0.040)|
|Uncertainty _×_ Developing||1.715|1.784*|1.997*|0.103**|
|||(1.051)|(1.048)|(1.029)|(0.049)|
|Observations|1,370|1,370|1,352|1,149|1,149|
|R-squared|0.794|0.795|0.793|0.802|0.695|
|Controls|No|No|Yes|Yes|Yes|
|Period|2003-2022|2003-2022|2003-2022|2003-2019|2003-2019|

Notes: The unit of observation is the destination-year level, _it_ . ‘CR5” stands for the concentration ratio of the top 5 investing firms; countries with fewer than 5 foreign investors on average are excluded. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts words from EIU reports for 143 countries since the 1950s. A higher “T3” value indicates greater uncertainty. Uncertainty in year is approximated as
the sensitive statements is simply the difference in means, in terms of the number of statements, between the treatment and control groups. In the context of the endline survey, the sensitive behaviors pertained to violent behavior, including domestic violence, as well as cattle raiding. Direct questions were asked to the control group alongside the list question without the sensitive item, so as to compare results obtained through the list‐method. Below we report the list of sensitive items included in the experiment.

_Figure A4.1 List of sensitive statements included in the list experiment_

# **Cattle raiding:**

1. I know someone who has participated in cattle raiding, including myself.

2. I have participated in cattle raiding.

# **Violent behavior:**

1. I have had a verbal disagreement in the last month where the other person threatened me with violence.

2. I have had a verbal disagreement in the last month where I threatened the other person with violence.

50
We provide a first application of survey imputation methods to obtain poverty estimates for the Syrian refugees living in Jordan. Our results show that imputation-based poverty estimates are statistically not different from the non-predicted consumption-based poverty rates, and this result is robust to various validation tests. These estimates are found to perform better or have smaller standard errors than other poverty measures based on asset indexes or proxy means testing, and our imputation models are rather parsimonious and use variables that are already available in the UNHCR’s global registration system. These encouraging results are consistent with the findings in recent studies for imputation-based poverty estimates for regular populations.

The estimation results also point to the need for further research on an alternative and promising method of obtaining poverty estimates for refugees where it is expensive or logistically challenging to implement a large-scale survey. We provide both theoretical and empirical evidence for Jordan that relatively small surveys may be fielded for refugees, and data from this survey can be combined with those from the census-type registration system to provide cost-effective and updated estimates of poverty. While these results are encouraging, they are not definitive and should be replicated in other contexts, possibly using surveys that have a more detailed consumption module. If further validated in other contexts, these findings can potentially lead to significant reductions in data collection costs in the context of refugee operations.

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