rafmacalaba/data-use-mentions-extended huggingface dataset:
resolution satellite monitoring has begun to close the gap for developing-country cities. The most advanced example is provided by Heger et. al. (2018), whose research combines extensive air monitoring data for Cairo with vehicle counts obtained from ultra-high-resolution satellite imagery using a machine learning algorithm. Even for this study, significant problems remain: Spatial panels of vehicle counts must be pieced together from overlapping but non-identical satellite image frames; vehicle counts are sensitive to the technical characteristics of different satellite platforms; and pollution measures are limited to incomplete panels of particulate pollution at PM10 scale or low-resolution satellite measures of aerosol optical depth whose ability to measure particulate pollution remains controversial.

Differences in technical and institutional resources have produced bifurcated approaches to air pollution modeling in developed and developing countries. Some air quality modelers in developed countries have produced sophisticated atmospheric pollution transport models that link detailed emissions inventories from geo-located ground sources (Dommen et al. 2003) to pollution levels in three-dimensional atmospheric grids (Baertsch-Ritter et al. 2003; Keller et al. 2002; Khalid and Samson 1996; Jane et al. 1995). These exercises draw on scientifically-grounded models of atmospheric flow dynamics and photochemistry (Baertsch-Ritter et al. 2004; Russel and Dennis 2000; Gery et al. 1989). They also permit explicit incorporation of detailed meteorological data (Khalid and Samson 1996; Sillman and Samson 1995).

These models require so much data that their application is not widespread, even in high-income metropolitan regions. Where they are applied, they are not statistically fitted to local data because the available degrees of freedom (total observations minus the number of model parameters) are insufficient for robust nonlinear estimation. Even the most recent and sophisticated applications (e.g., Kim et al. 2018) are confined to verifying that predictions from imported atmospheric transport models are spatially correlated with gridded observations on local pollution.

Urban air quality modelers in developing countries have generally adopted more modest approaches tailored to limitations in data availability, technical resources and institutional constraints. Some of the more sophisticated statistical exercises
**|
|Mobile phone (jointly owned)|0.44***|0.37***|
|Financial account|0.14**|0.12**|
|Motorized vehicle|068***|053***|
|Livestock (exclusively owned)|.<br>0.16***|.<br>0.21***|
|<br>Livestock (jointly owned)|<br>0.23|<br>0.23|
|Residential land (reported, exclusively)|0.19**|0.22**|
|Residential land (reported, joint)|0.55|0.55|
|Non-residential land (reported, exclusively)|0.17|0.18|
|Non-residential land (reported, joint)|0.39|0.38|
|Observations|1,845|2,093|

Notes:(1) Estimates weighted by household sampling weight. Significant gender differences are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 . (2) Additional fixed effects for district, interview date, and enumerator were included (specification (A)).

21
The 2008 CNRS project team gathered detailed information on a wide number of variables covering many household activities. In particular, there were several blocks of the survey that focused on recording information on off-farm employment, wages and activities of respondents who did not have off-farm employment. Because we wanted to be able to estimate a counterfactual, “business as usual” (henceforth BAU) scenario, a nine-year employment history form was completed for each household member and each child of the household head. For each year between 2000 and 2008, the questionnaire tracks the individual’s participation in off-farm employment, the main type of off-farm work performed, the residence location while working within or outside the village, the location of off-farm employment, and whether or not each individual was self-employed or earning a wage.<sup>4</sup> For three reasons, we also collected detailed monthly labor histories for a 24 month period: a.) the timing of the financial crisis (started in September 2008, which was in the middle of the calendar year); b.) the nature of labor flows in China (which often are fluid and involve substantial job switching—even within a year or shorter time period); and c.) the timing of the survey (conducted in May 2009, which was also in the middle of the calendar year). Had we only collected data on a rural individual’s annual employment status, it is possible that we would have missed important employment/unemployment dynamics that occurred after the financial crisis, and which are central to this study. Therefore, enumerators also asked respondents to report their employment status month by month from May 2007 to April 2009. When used in conjunction with nine years of annual employment history data, these data enabled us to look at three types of trends: a.) within year employment trends, including the trend between the onset of the financial 6
numbers reached a peak due to the Delta variant, whereas other regions began to see Delta rise after June 2021.

# _2.3. Other Data_

**_CDD and HDD:_** Outside temperature is an important determinant of electricity demand, particularly in the building (residential and commercial) sector – with or without COVID (Jovanović et al., 2015; Alberini et al., 2019). Electricity generation fluctuates to balance the demand change induced by hourly, daily and seasonal temperatures. We capture the temperature using cooling degree days (CDD) and heating degree days (HDD).<sup>8</sup> For each country, data on daily temperature come from the weather stations in province capitals; we use the population of provinces as weights to calculate national-level CDDs and HDDs. The population data come from Eurostat and OECD regional databases, and the temperature data are from the NOAA Global Historical Climatology Network Daily (GHCN-D).

**_Fuel prices_** : Monthly fuel prices by major energy hubs (Coal, Australia; Coal, South Africa; LNG, Asia; Natural Gas, EU; Natural Gas, United States; Henry Hub Gas; Dubai Crude; Brent Crude; WTI Crude) are obtained from IMF Primary Commodity Prices. Figure A. 12 illustrates how closely the changes in fuel prices and the stringency index are linked. At the beginning of the pandemic, fuel prices fell quickly due to depressed economic activities directly and indirectly caused by the pandemic. Crude oil prices dropped by 75% from US$80/bbl. to US$20/bbl. As shown in  Figure A. 12, the first round of COVID deaths was at their peak when oil prices were at their bottom, most likely because of uncertainty about the virus and its impact on the economy. The price of oil later rebounded.

Visual inspection suggests that it is the stringency (lockdowns and other measures), rather than the number of cases, which drove the energy prices. Like
children‘s education. She has also managed to put aside some money for emergencies.

### **Attitudes toward energy reforms**

The respondent stated that she does not have sufficient knowledge regarding the reforms in the energy sector. In her opinion, the government is responsible for making such decisions. However, she was aware that the price of electricity had been increasing.

Participants perceive future price increases as unjustified without improvements in service reliability and quality. Most of the respondents—in particular, BISP beneficiaries and _katchi abadi_ residents—mentioned that they could not bear further increases in electricity tariffs. Upon further probing, interviewees agreed that price increases would be acceptable only if bills are made credible, load shedding is reduced and governance issues are addressed. However, the majority of households voiced their skepticism during the discussions as they doubt that such improvements will ever happen. This contrasts with findings from the SDPI study, in which 63 percent of respondents indicated that they would be willing to accept a Rs. 100‐500 increase in their utility bills if they were provided 24 hours of uninterrupted service. When asked whether they would be willing to pay an increase greater than Rs. 500, only 19 percent of respondents agreed. SDPI reported that all respondents would accept an increase in electricity charges if it was guaranteed that there would be no load shedding in the future (SDPI, 2014).

# **V. Policy Recommendations**

## **Moving beyond general price subsidies**

As we have demonstrated throughout this paper, the existing price‐based subsidy fails to protect low and middle‐income households from high electricity costs while disproportionately benefiting rich households. Price‐based subsidies will only favor the poor 17
simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.<sup>5</sup> In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In selected economies where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method.<sup>6</sup> At least three attempts are made to reach a person in each household, spread over different days and times of day.

# **Data weighting**

Data weighting is used to ensure a nationally representative sample for each economy. First, base sampling weights are constructed to account for oversamples and household size. If an oversample has been conducted, the data are weighted to correct the disproportionate sample. Weighting by household size (number of residents age 15 and above) is used to adjust for the probability of selection, as residents in large households will have a disproportionately lower probability of being selected for the sample. Second, poststratification weights are constructed. Population statistics are used to weight the data by gender, age, and, where reliable data are available, education or socioeconomic status. Finally, approximate study design effect and margin of error are calculated. The average country-level margin of error for the account penetration indicator is plus or minus 3.9 percent.

All regional or income group aggregates are also weighted by country population (age 15 and above).

**9**

MEASURING FINANCIAL INCLUSION
comprehensive list that includes access to finance, business licensing and permits, tax rates and tax administration, and an inadequately educated workforce. Another survey that can be used as a reference for the design of the new survey is the RAND + AKATIGA (R+A) SME Survey. What differentiates the R+A Survey from other existing enterprise surveys are the explicit open-ended questions such as why respondents chose (not) to register their business, the effects of licensing on their growth prospects, and specific steps taken to expand their business (Burger et al., 2015). _Informal employment_ Of the various data sources available, SAKERNAS remains the best option to study informal employment in Indonesia. As discussed earlier, the addition of new questions in 2016 gives SAKERNAS a distinct edge over other existing surveys as it enables informal employment to be measured in line with ICLS definitions. In addition, SAKERNAS is nationally representative, and is conducted regularly by BPS. These features allow us to accurately measure informal employment (particularly from 2016 onwards), and to document its overall growth or decline on a national scale.

As highlighted previously, the main disadvantage of SAKERNAS is the limited set of questions about the non-pecuniary characteristics of informal employment. For instance, the survey does not contain any questions capturing the benefits that have frequently been associated with informality, such as autonomy and flexibility over working arrangements. This limitation has important implications for the kinds of hypotheses that can be tested using SAKERNAS. Without data on these benefits or an overall measure of worker utility (e.g., job satisfaction), it is difficult to determine whether the benefits of being informal outweigh its costs. Moreover, the fact that SAKERNAS was not designed to be a panel survey means it is not an ideal data source to study the dynamics of informality at the individual level. Altogether, these limitations prevent us from making definitive statements about the nature of informal employment – a point that we return to in the next and final section.

22
** . The list of products targeted by import bans and their time coverage are hand-coded from Sri Lanka’s Extraordinary Gazettes on Imports and Exports. The fertilizer import bans started in May 2021, with most bans lifted by July 2021 or by November 2021 at the latest. We also use novel high-frequency granular data covering the universe of Sri Lanka’s export and import transactions, relying on machine learning and text analysis to clean firm names and assign unique firm identifiers to create a panel of firms involved in exports.

**Agricultural production** . We complement administrative data on crop-district-level yields with remote sensing estimates of rice and tea yields at a highly granular level. Remote sensing provides a spatially granular view of crop cultivation, allowing a detailed assessment of the heterogeneous effects of the fertilizer import ban within Sri Lanka. For rice, we apply an expert-based image classification algorithm to satellite data to identify rice-growing areas using the specific growing conditions for paddy rice in Sri Lanka (Ozdogan et al., 2024).<sup>22</sup> To estimate rice yields, a statistical

> 22The presence of rice-growing areas is determined by factors related to climate, water availability, and topographic position, along with farmer decision-making and technical expertise. The methodology monitors rice-related vegetation
|||||
|Fired or laid off|2.8|7.4|3.5|0.6|5.1|1.5|
|No other work available|5.2|10.7|15.2|13.2|22.0|15.7|
|To be independent|52.5|41.0|41.8|59.2|42.9|44.6|
|Family tradition|12.7|8.4|9.9|14.4|3.6|13.3|
|Complement family Y|10.8|13.3|13.8|10.9|16.1|12.9|
|Higher pay than salaried|33.3|23.3|27.8|24.7|22.4|24.1|
|Flexible hours|1.7|2.3|3.7|2.9|3.5|_3.5_|
|Total Observations|640|309|1409|174|1092|802|
|%of sample|14.5|7.0|31.8|3.9|24.7|18.1|

' Includes family responsibilities or moving. 2 Multiple'responses possible.

Table7: **Main Business Problems (in percent)'**

|Problem|1|Cl<br>2|uster<br>3|4|5|6|Total|
|---|---|---|---|---|---|---|---|
|Lack of clients|53.6|48.2|63.7|56.9|62.3|61.7|60.2|
|Lack of credit|12.5|19.4
**Figure 3: Exports, imports and average job incomes**

<!-- Start of picture text --> Exports Imports<br>Total labor income Total labor income<br>15,000,000<br>15,000,000<br>10,780,209 12,001,126<br>10,000,000<br>10,000,000<br>High exports High imports<br>5,000,000 4,099,168 Low exports Low imports<br>5,000,000<br>2,878,250<br>0 0<br>Average labor income Average labor income<br>6,000 6,000<br>5,196<br>4,888<br>5,000 5,000<br>3,996<br>3,688<br>4,000 4,000<br>High exports High imports<br>3,000 3,000<br>Low exports Low imports<br>2,000 2,000<br>1,000 1,000<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median. (3) Total and average labor incomes are expressed in 2015 Mexican pesos.

Fourth, it is also possible that trade induces a shift from informal to more formal work which is not reflected in higher employment rates. We therefore inspect the informality channel and find a strong difference in
Another way of illustrating the heterogeneity of changes in test scores along different parts of the distribution of learning outcomes is through a growth incidence curve (GIC). Originally developed for incidence analysis in the poverty and inequality literature, the GIC in the case of learning outcomes computes changes in average test scores by percentiles of the test scores distribution (for methodological details see Ravallion and Chen, 2003). Therefore, a GIC with a positive slope would indicate a regressive change in test scores, that is, students with initial lower scores improve less (or decrease more) than those with higher ones. The GIC summarizing the changes in reading test scores for the period 2000-12 is presented in Figure 2.<sup>6</sup> Between 2000 and 2006, average test scores declined along the entire distribution of learning outcomes. However, the decline in reading test scores was substantially larger among students in the bottom 20 percent of the distribution of scores in 2006 vis-à-vis their peers in 2000. The decline in learning outcomes is lower as one moves to better-performing percentiles. The reverse is true for the period 2006-12. Except for students around the 95th percentile of the test score distribution, everybody did better in 2012 than in 2006. Not only did test scores increase during this period, but those that gained the most were the most disadvantaged students in the bottom part of the test score distribution.

_Figure 2: Growth Incidence Curve for the Changes in PISA (Reading Test Scores)_ <!-- Start of picture text --> Change between 2006<br>and 2012<br>Change between 2000 and<br>2006<br>0 20 40 60 80 100<br>Percentiles of PISA Distribution<br>Source: authors' own computation with data from PISA<br>40<br>20<br>0<br>-20<br>Change in Reading Test Scores<br>-40<br>-60<br><!-- End of
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m is a dummy variable that takes a value of unity if an NTB is applied to the item in year m and zero otherwise. 9/ If N and m are zero the index is based on current trade values, otherwise it is expressed in a base year trade weights. Holding n constant and varying m will measure the effects of changes in protection with constant trade weights. In the present analysis, however, _-_ we employ current year trade weights (i.e., m N) given the major structural changes that occurred in the developed countries over 1966-1986.

Before turning to the empirical results, it would be useful to consider some of the ways pressures for protection differed between the 1960a and 19809. In the 19609, rapid market penetration by "newly industrialized" developing countries was a problem in the textile and clothing sector, but was not yet a major factor in most other labor intensive industries. 10/ Pressures for protection in the 1970s and 1980s would be heightened by a rapid

- 2/ This measure suffers from the familiar problem of any "own" trade weighted index in that products facing very restrictive NTBs will enter the calculation with zero or low weights. There have been attempts to reduce this source of bias in aggregate data through the use of "world" trade weights, but factors such as demand differences or A high degree of sectorial correlation in OECD protection make this approach suspect. Also, equation (2) tells nothing about the "restrictiveness" of NTBs, but merely how much actual trade encounters these measures. While recent applications of equation (2) are based on tariff line level records of NTBs and trade data, we have utilized Walter's concept of "affected" products for the empirical analysis in this study. That is, the coverage ratios in this report show the value of trade in "affected" four-digit SITC products as a percentage of total imports.

- 10/ It could be argued that conditions for a trade liberalization were more favorable
Different strands of the literature have examined the varied claims about the positive impacts of microcredit, but the evidence continues to be mixed. While most studies find that access to microcredit enables households to better smooth and enhance consumption, the picture around other claims remains murky.

We evaluate microcredit in its purported income-enhancing role – Do small loans enable the poor to make productive investments? In this context, we document the low use of microcredit for business purposes in Sub-Saharan Africa (SSA).. We discuss the potential reasons behind this low usage, and examine how SSA fits in with the patterns and predictions of the academic evidence on microcredit. We also evaluate a new avatar of microfinance – microsavings. We review the literature on savings as well as the evidence from the ground to show how savings might be positioned to yield the gains that were expected from credit. Finally, we discuss the behavioral and institutional challenges that the poor face in saving money and the policy prescriptions for overcoming these challenges.

The paper proceeds as follows: in Section 2 we review the related literature and motive for empirical analysis; Sections 3 and 4 discuss financial inclusion data from SSA, in the context of the microcredit and microsavings movements, respectively; Section 5 concludes.

# **2. Review of the Literature on Microfinance and Returns to Capital**

Rigorous experimental evidence establishing a causal link (or lack thereof) between access to microcredit and growth of microenterprises is hard to come by. Selection bias prevents direct comparisons between those who borrow from microfinance institutions (MFIs) and those who do not. Similarly, the lack of a counterfactual makes it impossible to gauge how the borrowers would have fared in the absence of credit. The first quasi-experimental study in this 3
_δ h,t_ = mortality rate of the human capital stock.

The mortality rate for the economically active population aged 15 and over is estimated from: and assumes that the mortality rate (which also includes the exit of the economically active population due to retirement or inactivity) is independent of the level of schooling attained, which is not entirely correct. The term _Lt–L15t_ describes the number of survivals from the previous period, which are subtracted from _Lt-1_ in order to estimate the total number of missing persons. Equation (5) as such describes the proportion of the labor force which did not survive from the previous period. The formulas can be rearranged to create the final equations that were used to generate the attainment ratios, _hrj_ , for the four broad levels of schooling for the economically active population aged 15 and over: The procedure requires school enrollment ratios that are crucial for exact calculations, but the proper accounting for Guatemala is not easy. Even though net enrollment ratios would be more precise for estimating the accumulation of human capital, gross enrollment ratios are used, as only this data is available. As reported in the Appendix, the ratios are taken from various yearbooks of the Guatemalan Ministry of Education (MINEDUC) for the 1990s, the United Nations Educational, Scientific and Cultural Organization (UNESCO) for earlier periods, and other sources available for Guatemala. The data for primary, secondary and tertiary enrollment ratios have been found consistent over time. Interpolation techniques were used to fill gaps in the data, but the use of this approach was kept to a minimum. The tertiary enrollment time series were more difficult to compile and required greater use of interpolated estimates.

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