rafmacalaba/data-use-mentions-extended huggingface dataset:
per month is applied.|
|•<br>**Mobile distributors**: unit prices ranged from<br>$0.17/m<sup>3</sup>to $11.00/ m<sup>3</sup>for vendor-carters, within the<br>category of mobile distributors (MDs). The findings<br>support observations in the literature that water from a<br>mobile distributor may cost as much as 10 times that<br>from apiped supply<sup>12</sup>.|•<br>Individual**home-based systems**such as solar<br>panels range from US$200 to US$500 for sufficient<br>electricity for a few light points up to a mixture of a<br>small television, radio and several lights or other<br>appliances.|

This following section analyses unit prices, price differentials and connection charges<sup>13</sup> .

# **4.6.1. Unit Price Analysis**

The unit price of water per cubic meter was analyzed based on data from 47 countries and 93 locations according to the typology presented earlier in this report. The analysis showed wide variation in the prices offered by the type of small scale private service provider (SPSP) at global level. Unit prices charged by PNOs ($0.17/m<sup>3</sup> to $0.86/ m<sup>3</sup> ) were notably similar to those of formal utilities ($0.02/m<sup>3</sup> to $0.79/ m<sup>3</sup> )— for which data was presented as a benchmark. Despite the fact that many of the formal utilities in the 47 countries for which literature was reviewed, provide highly subsidized services, which may not account for full costs, prices charged by
2011) and Henderson et al. (2012). Error-measurement frameworks typically require the analyst to impose prior beliefs concerning data accuracy to overcome bias from errors-in-variables. We exploit the fact that NO2 and night lights are measured at different times to achieve identification through exogeneity in measurement errors. We scrutinize this identifying assumption at length, leveraging overidentifying restrictions from household surveys (Pinkovskiy and Sala-i Martin, 2016). Ultimately, we show the growth elasticity of NO2 is constant across nations. Finally, we substantiate our approach by showing that an NO2 composite estimate for economic activity is close to GDP in the U.S., a placebo in which data is widely thought to be accurate. However, it rebuts GDP from China, where official data has long been considered suspect (Rawski, 2001).

From the outset, we stress that NO2 is not infallible, and there are other key complementarities between NO2 and lights. In particular, NO2 captures a specific type of economic activity associated to the burning of fossil fuels. While combustion is responsible for about 84% of

> 1While there are instances where lights reflect abrupt downturns (Elvidge et al., 2020; Beyer et al., 2021), we show that such instances are the norm rather than the exception, as lights only predict annual fluctuations in 20% of countries, and have no additional explanatory power when ground level signals are available.

1
<!-- Start of picture text --> Land-Use Land Cover in Cox's Bazar, Bangladesh (Landsat Data)<br>(Using Random Forest Classifier)<br>Feb 2014 Feb 2017 Feb 2020<br>ee Saeea en See tS ae ey, as me<br>Land-Use Land Cover aa Mone Gt<br>Class Name ee} soe) Be<br>eA oi aes : eee<br>MMM Developed/Settlements Siam. : Me<br>QE Barren/Bare Grounds Bet aa eee<br>ME Forests ae. ¥, is, ae >-<br>MO Fish Ponds 4 N : a<br>Planted / Cultivated ¢ W .i E Es x<br>ees Kilometers<br>0510 20 30 40 Scale; 1:1,000,000<br><!-- End of picture text -->
are tight.<sup>13</sup> It is difficult to have a long-term perspective on these issues for reasons discussed

> 11 The Guy Carpenter Rate-on-Line index shows a 30 percent annual volatility over the past ten years. Premiums also differ markedly among perils that increase the concentration of risk to the reinsurers and perils which provide diversification. And it is not unusual to see reinsurance prices in a region increase by 20 to 50 percent after a major disaster. Catastrophe reinsurance prices in Florida increased by nearly 100 percent the year after Hurricane Katrina (Kunreuther and Michel-Kerjan, 2009, chapter 7).

12 Note that some reinsurers now provide collateralized reinsurance treaties as well, but those are more expensive than traditional reinsurance treaties.

13 This was suggested in the United States for the Hurricane Relief Fund in Hawaii in 2009. Another example relates to the U.S. Pension Benefit Guaranty Corporation. In the 1990s there were interest groups lobbying the PBGC to reduce premiums because they were “too high,” as evidenced by the fact that the PBGC was running a surplus.

23
their functional form is described in Annex 3. The coefficient β can be interpreted as the effect of seasonality.<sup>15</sup> To examine heterogeneity in the impacts of seasonality, the regressions can be augmented with interaction terms. Simply splitting the sample into different segments of the population provides a first check on heterogeneity in the results. However, to formally test whether the effects of seasonality differ for certain sub-populations, it is also helpful to run regressions of the form: where all variables are defined as above, Zijc gives the ‘cutting variable’ (such as urban-rural or different livelihoods), and δ can be interpreted as the differential impact of seasonality on certain types of households.<sup>16</sup>

# Section 3. Strategies for managing and coping with seasonality in the Sahel

Building on the literature described above, descriptive statistics from the EHCVM data immediately reveal three key characteristics of Sahelian households, which could limit their strategies for managing and coping with seasonality. First, their livelihoods are concentrated in agriculture and rearing livestock and are not well diversified. Second, financial inclusion appears to be low; holdings savings and taking out loans are relatively rare phenomena, even accounting for mobile money. Third, social assistance that could help households weather both seasonality and shocks does not appear to be widespread.

## Section 3.1. Livelihoods in the Sahel

Sahelian households, especially those in rural areas, depend mainly on rainfed agriculture, which is especially exposed to the effects of seasonality. In the absence of widespread irrigation systems, agriculture in the Sahel is mainly rainfed, so yearly swings in temperature and rainfall are crucial for determining output (see Annex 2 for further details). Pooling data from the EHCVM countries, 50.5 percent of the population live in a household where the head’s primary sector of work was agriculture and a further 5.0 percent of the population live in a household where the head worked primarily in raising animals or fishing (Figure 1).
control for education, another key driver of female labor force participation, educational attainment measured by average years of schooling at the country level, is included in the model.

Formal and informal institutions affect the gender roles in society and women’s decision to enter the labor market, especially in the MENA region. Three variables from the World Bank Women, Business and the Law dataset are used to control for such institutions. Regarding legislations, known as formal institutions, two variables are included in the model. The first formal institution variable is the number of days of paid paternity leave. Giving men access to paid paternity leave may challenge the traditional roles, as it suggests men and women will share care work responsibilities. So, in countries where the legislation states more days of paid paternity leave, women’s participation in labor market is expected to increase. The second formal institution variable is captured in a dummy variable that equals one if the law prohibits discrimination in access to credit based on gender, 0 otherwise. The informal institution included in the model is women’s freedom of mobility as a measure of values and norms. This dummy variable equals one if a woman can travel outside her home in the same way as a man, 0 otherwise.

The data used is a pooled cross section dataset of 13 MENA countries for years from 2018, to 2021. The dataset is constructed from the World Development Indicators, the Economist Intelligence Unit database of the Inclusive Internet index and the World Bank Women, Business and the Law database. The constructed dataset is used to estimate equation 1 using the generalized least square (GLS) method to control for heteroskedasticity of error terms. The error terms are clustered at the country level.

Year dummies are included in the model to capture the effect of the years of the COVID-19 pandemic. Three dummy variables are included for the years 2019, 2020, and 2021.

Another version of the model is estimated using the gender gap in internet access<sup>4</sup>
1093|2889|2125|0.00|
|Labor intensity of crop portfolio|0.27|0.07|0.28|0.07|0.00|
|Cost intensity of crop portfolio|21166|7374|26207|10097|0.00|
|Herfndahl index of crop portfolio|0.76|0.25|0.80|0.23|0.00|
|Number of crops<br>_Shocks_|2.04|1.03|2.08|1.33|0.65|
|Rainfall (deviation)|0.33|0.28|-0.06|0.16|0.00|
|Rainfall (deviation, lag)|-0.39|0.10|-0.12|0.10|0.00|
|Self-reported shock<br>_NREGS participation_|0.81|0.39|0.52|0.50|0.00|
|Household registered with NREGS|0.66|0.47|0.00|0.00|0.00|
|Household generated income from NREGS|0.54|0.50|0.00|0.00|0.00|
|Income, NREGS|1.24|2.39|0.00|0.00|0.00|
|Observations|750||337|||

Notes: All values in constant INR 1,000 (July 2006). One US$ is equivalent to 46.38 INR (July 2006). Variable definitions and sources are described in appendix B. Source: Author’s estimation based on the Young Lives data.

36
Air Quality Research_ , 14, 422–430.

- Jakiela, Pamela (2021), “Simple diagnostics for two-way fixed effects.” _arXiv preprint arXiv:2103.13229_ .

- Jayachandran, Seema (2009), “Air quality and early-life mortality evidence from indonesia’s wildfires.” _Journal of Human resources_ , 44, 916–954.

- Jayachandran, Seema (2021), “How economic development influences the environment.” Working Paper 29191, National Bureau of Economic Research, URL `http://www.nber.org/papers/w29191` .

- Jha, Raghbendra, Sambit Bhattacharyya, Raghav Gaiha, and Shylashri Shankar (2009), ““capture” of antipoverty programs: An analysis of the national rural employment guarantee program in india.” _Journal of Asian Economics_ , 20, 456–464.

- Jitendra, Shreeshan Venkatesh, Ishan Kukreti, Kundan Pandey, Deepanwita Gita Niyogi, and Polash Mukerjee (2017), “India’s burning issue of crop burning takes a new turn.” _Down to Earth_ , URL `https://www.downtoearth.org.in/coverage/agriculture/river-of-fire-57924` .

- Kant, Yogesh, Prakash Chauhan, Aryan Natwariya, Suresh Kannaujiya, and Debashis Mitra (2022), “Long term influence of groundwater preservation policy on stubble burning and air pollution over north-west india.” _Scientific Reports_ , 12, 1–11.

- Kelly, Morgan, Joel Mokyr, and Cormac Ó Gráda (2014), “Precocious albion: a new interpretation of the british industrial revolution.” _Annu. Rev. Econ._ , 6, 363–389.

- Korontzi, Stefania, Jessica McCarty, Tatiana Loboda, Suresh Kumar, and Chris Justice (2006), “Global distribution of agricultural fires in croplands from 3 years of moderate resolution imaging spectroradiometer (modis) data.” _Global Biogeochemical Cycles_ , 20.

- Kremer, Michael, Jessica Leino, Edward Miguel, and Alix Peterson Zwane (2011
investment to sales ratio for all countries available in the second wave of ICA data.<sup>13</sup> According to this dataset Egypt falls slightly above average on the investment to sales measure. The likely difference is in the sample composition – the ICA data contains mainly developing countries, while the listed data contains mainly developed countries and the largest of the emerging markets.

# **Figure 5.  Investment to Capital, Listed Firms Data**

<!-- Start of picture text --> 0.6 IK , Listed, Estimated (Mean)<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Year<br>Source: Datastream, staff calculations<br>Figure 6. Investment to Sales, ICA Data<br>0.3 IS, ICA (Mean)<br>0.2<br>0.1<br>0<br>NigeriaCzech RepublicSlovak RepublicChile ArgentinaColombia Slovenia Mexico Finland Thailand JapanVenezuela, RBSwitzerland PortugalJordan TurkeyPhilippinesPeru ItalyLuxembourgPakistan Greece Egypt, Arab Rep.HungaryBelgiumIreland SpainTaiwan MalaysiaKorea, Rep.Netherlands Austria France Sri Lanka Indonesia Brazil SingaporeNew Zealand GermanyChina Bahrain Lithuania Denmark Estonia Saudi Arabia Hong Kong, ChinaSweden Israel Russian Federation United KingdomIndia Canada Iceland South Africa Morocco Bermuda Kuwait Poland Australia United Arab ... Virgin Islands (UK)QatarCayman Islands<br>MexicoSenegalColombiaUruguayPanamaDRCArgentinaMauritaniaSouthAfricaUgandaPeruHondurasElSalvadorGhanaAngolaMozambiqueEcuadorNicaraguaChileParaguayGuatemalaNamibiaRussiaHungaryRwandaNigerBoliviaBotswanaGuineaSlovak RepublicGuineaBissauCzech RepublicTanzaniaUzbekistanMalawiBelarusPhilippinesSloveniaUkraineLatviaLithuaniaBulgariaBurkinaFasoPolandBrazilTurkeyCameroonBosnia and ...CroatiaEstoniaKazakhstanIndoneshiaSwazilandEgypt BurundiIvory CoastGambiaNepalKyrgyz RepublicChadGeorgiaMadagascarRomaniaVenezuelaArmeniaSamoaMoldovaVietnamSerbiaMauritiusFyr MacedoniaAzerbaijanTogoMicronesiaCapeVerdeMongoliaTajikistanBeninBhutanAlbaniaMontenegroVanuatuLaoPDRTimor LesteAfghanistanEritreaKosovo<br><!-- End of picture text --> Source: Datastream, staff calculations

> 13 Unfortunately there is no data on stock of PPE in the second wave of ICA data, and thus we chose to scale investment by sales.

15
characteristics in the analysis, because they can directly affect both the level of women’s empowerment and the probability of electricity expansion. Although the survey was carried out in both urban and rural areas, village characteristics were collected only for the rural sample. We therefore use only the rural sample, consisting of 28,446 households and 21,896 eligible women in the 2012 survey.

Table 1 shows the distribution of sample households across six geographic regions as well as the union territories as a group. The rural electrification rate varies widely by region. The union territories and the rural vicinity of the national capital have the highest electrification rate (almost 100 percent). East India, which includes the states of Bihar, Jharkhand, Orissa, and West Bengal, has the lowest rate (less than 65 percent). For rural India as a whole, the electrification rate was 77 percent in 2012.

Table 2 presents summary statistics of various observed indicators of women’s empowerment. Almost all the variables indicate stronger empowerment for women in households with electricity supply from the grid and the differences are statistically significant. For example, compared with women in off-grid households, women in grid-connected households are more likely to have decided alone on their work, traveled out of state, had a bank account, and participated in women’s self-help groups.

# **IV. Measuring empowerment and the effects of electrification**

# _a. Measuring latent indicators of empowerment_

Empowerment is an abstract concept and manifests itself in multiple aspects of life. For example, whether a woman is empowered to make decisions may be reflected by whether she can decide on her own work, medical treatment or how many children to have. In such a case, it is difficult to measure empowerment directly using one single indicator. One way to resolve this issue is to use an indexing or scoring approach in which individual attributes are combined to form an index or score.<sup>4</sup>

> 4 The use of indexes is common in
there has been rising inequality within the rural and (especially) urban sectors, growth within sectors has delivered sufficient gains to poor people to mitigate the higher inequality. Population urbanization played a role, but not in the way assumed by the Kuznets process. Instead we find that urbanization came with distributional changes within sectors; these were pro-poor in the pre-1991 period, but that have not been so since 1991. Another difference is that the sectoral pattern of growth in NDP mattered less to progress against poverty post-1991 than was the case in the pre-1991 period.

> 22 The period of analysis in Ravallion and Datt (1996) ended at about the time (1991) when India’s process of economic reform started in earnest. Datt and Ravallion (2011) updated Ravallion and Datt (1996) by incorporating an extra 14 rounds of the National Sample Surveys (NSS) taking the series up to 2006.  This paper extends the series to 2012.

> 23 Datt and Ravallion (2011) did not study the sectoral structure of NDP or identify the role played by the Kuznets process.

6
The concession also guaranteed the investor 14 percent ROR, so the government increased tariffs to reflect costs. The impact of utility governance reforms on cost-recovery is robust also when looking at broader indicators of sector-wide private-sector participation and comprehensive indicators of utility governance (see Figure 17a-b). This is in line with the (still relatively limited) empirical literature on the impact of power sector reforms on utility finances, which finds correlations between independent regulators, vertical unbundling, competition, and private sector participation on the one side and tariffs and cost recovery on the other (Huenteler et al., 2017).

33
attending elementary school. In addition, we show (using a different sample) that our distance variable is uncorrelated with test scores (Math, Bahasa, Science, and Social Studies) in elementary school. These are very important dimensions of the pre-secondary school experience that are measures of early ability and early home environments, and which we would expect to be correlated with distance to the nearest secondary school if this variable was endogenously determined.

Our instrumental variable estimates of the returns to schooling are higher than the returns to schooling for Indonesia found in Duflo (2000) with the qualification that the dataset, the instrumental variable, and the time period are not the same. Petterson (2010) finds similar rates of return using the same year, same data but a different sample and a different instrument variable.

This paper proceeds as follows. Section 2 discusses the data. Section 3 reviews the econometric framework. Section 4 presents our empirical results. Section 5 concludes.

# **2. Data**

We use data from the third wave of the Indonesia Family Life Survey (IFLS) fielded from June through November, 2000<sup>3</sup> . The IFLS is a household and community level panel survey that has been carried out in 1993, 1997 and 2000. The 2000 sample was drawn from 321 randomly selected villages, spread among 13 Indonesian provinces containing 83% of the country‟s population containing over 43,000 individuals. The sample we used consists of males aged 25-60 who are employed in the labor market and who have reported non-missing wage and schooling information. We consider only salaried workers, both in the government and in the private sector. We exclude females from the analysis because of low labor force participation, and we exclude self-employed workers because it is difficult to measure their earnings. The dependent variable in our analysis is the log of the hourly wage. Hourly wages are constructed from self-reported monthly wages and hours worked per week. The final sample contains 2608 working age males.

> 3
While state capture in SSA remains difficult to measure, corruption scandals, selected audits and investigations, and anecdotal evidence provide basic empirics on its scope and patterns. State capture takes different forms, ranging from embezzlement to rent-seeking and patronage. It plays out through the privileged access of politically connected elites to state resources, public employment as well as policy decisions, including privileged access to permits and licenses, land, public contracts, grants/subsidies or tax breaks (Fiebelkorn 2019). For example, external audits and investigations during the 2014 Ebola Crisis revealed the misuse of large sums of Red Cross funds in Sierra Leone and Liberia, which amounted to up to 2.7 million dollars in the latter case. A stark example was the relationship between South Africa’s former President and leader of the African National Congress, Jacob Zuma and the Gupta brothers. Over several years, the parasitic relationship between the President and donors resulted in widespread looting and state capture, which the country is still coming to grips with.<sup>13</sup> Perhaps one of the most daunting challenges faced by SSA countries is capital flight, a large share of which is estimated to be illicit financial flows – this is financial flows that are either illegally earned or used (e.g. embezzlement, inflated payments through fraudulent invoices), and/or illegally moved or diverted (e.g. to evade taxes),<sup>14</sup> Empirical evidence about the relations between offshore centers, African elites and capital flow remains limited, but the release of the Pandora Papers, among others, showed the African connection to be robust.<sup>15,16</sup> Ndikumana and Boyce (2021) provide estimates of capital flight from 30 African countries over the period 1970-2018 and show that these countries lost a combined $2 trillion (in 2018 dollars), representing 94 percent of their total combined GDP in 2018. More worryingly, they show that capital flight from African countries has been
<br>Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0)<br>Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0)<br><!-- End of picture text --> Figure A5: Actual and predicted mobility trends around transit stations. Selected countries.

<!-- Start of picture text --> BGR BRA CIV<br>20 20 20<br>0 0 0<br>-20 -20 -20<br>-40 -40 -40<br>-60 -60 -60<br>-80 -80 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>PAK VNM ZAF<br>20 20 20<br>0 0 0<br>-20 -20 -20<br>-40 -40 -40<br>-60 -60 -60<br>-80 -80 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>Predicted<br>Actual data<br