rafmacalaba/data-use-mentions-extended huggingface dataset:
Of the 17 countries that generated more than 50 percent of electricity from hydropower in the reference year, 12 have multiyear data available in this study. The QFDs in all countries with a high dependence on hydropower are either declining or steady, with no significant movements in opex from year to year. One exception may be Mozambique, where performance improvements from 2011 to 2013 were driven in part by tariff increases and in part by increased availability of hydropower over the course of the three years in our analysis, while the decline in performance in 2014 was driven by a decline in bill collection rates.

# 7.2 Comparison with AICD

AICD data are available for 18 of the countries included in the present study. As mentioned earlier, the QFD is not directly comparable due to changes in methodology outlined in annex 1.1, so the analysis presented below should be treated with caution.

Detailed data are presented in annex 8. In general, T&D losses and bill collection rates found in this study are comparable to those reported by the AICD and show a similar order of magnitude. Where losses were high in the AICD, they are also high in the present study, and where they were low, they are also low in this study. In general, the multiyear QFD results in this study are steadier than some of the significant year-to-year changes estimated by the AICD, though the AICD generally covered longer time spans than the present study. Additional observations are noted in annex 8.

Selected countries are presented in Figure 14, which identifies three groups according to their trends in QFD. T&D losses and bill collection data are also shown. Comparable cost and tariff data are not available.

54
Policy Research Working Paper 7923

# **Abstract**

This paper presents the results of an investigation of selected census towns in northern India. Census towns are settlements that India’s census classifies as urban although they continue to be governed as rural settlements. The 2011 census featured a remarkable increase in the number of census towns, which nearly tripled between 2001 and 2011, from 1,362 to 3,894. This increase contributed to nearly a third (29.5 percent) of the total increase in the urban population during this period. Only part of this evolution can be attributed to the gradual urbanization of settlements in the vicinity or larger towns. Instead, the majority of census towns appear as small “market towns,” providing trade and other local services to a growing rural market. The case studies of representative census towns in Bihar, Jharkhand, Orissa, and West Bengal show the role of increased connectivity and growing rural incomes in driving the demand for the small-scale and non-tradable services, which are the main sources of nonfarm employment in these settlements. The case studies also reveal that the trade-offs between urban and rural administrative statuses are actively debated in many of these settlements. Although statistical comparisons do not show a significant impact of urban or rural administrative status on access to basic services, urban status is often favored by the social groups involved in the growing commercial and services sectors, and resisted by the residents still involved in the traditional farming sectors.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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://econ.worldbank.org. The authors may be contacted atamaria@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
Finally, it has been previously found that research on the African continent tends to be skewed to a few countries. The evidence base for local policy makers in neglected countries or “research deserts” is relatively small. Porteous (2020) documents statistics on economic research in Africa and shows that 87% of all published economics journal articles account for one-third of African countries and are highly skewed towards five countries.<sup>3</sup> The distribution is uneven and accounted for only 16% of the continent’s population. It is evident that the Comoros falls within the forgotten 21 countries that have an average number of publications of 0.2 per country (Porteous, 2020). Heterogenous characteristics (socio-economic and political) can limit external validity across countries, especially in Africa. Even before the pandemic, as highlighted above, the Comoros had one of the highest poverty rates in the world. It is also vulnerable to natural disasters and climatic shocks. It is thus important to understand how the pandemic has affected a small island state like the Comoros, which is already facing several development challenges but with a narrow evidence base. Our unique data consisting of pre- and post-pandemic observations provides an opportunity to make a meaningful contribution. To the best of our knowledge, this paper will be the first to evaluate the welfare consequences of the Covid-19 lockdown in the Comoros in a robust manner.

This paper aims to quantify the impact of direct lockdown measures on household welfare in the Comoros, a poor developing country and specifically an understudied developing country. The unexpected outbreak of Covid-19 coincided with data collection for the Harmonized Survey on Living Conditions of Households (EHCVM), lending itself to a quasi-natural experiment in which households interviewed prior to the lockdown could be considered as the control group and those interviewed after the lockdown as the treated group. First, this paper presents descriptive statistics, followed by ordinary least squares and probit regression analysis to control

> 3 These five frequently researched countries are South Africa, Kenya, Ghana, Uganda
/mark> ECD Economic Outlook, Interim Report September 2021: Keeping the Recovery on Trac_ _<mark>k</mark>_ <mark>, OECD Publishing, Paris.</mark>

- <mark>Roberts, M. (2021). Tracking Economic Activity in Response to the COVID-19 Crisis Using Nighttime Lights.</mark>

- <mark>Sampi, J.,and Jooste, C. (2020). N</mark> owcasting Economic Activity in Times of COVID-19 : An Approximation from the Google Community Mobility Report. Policy Research Working Paper No. 9247. <mark>World Bank, Washington, DC</mark>

15
information for listed firms. The data

> 15 Our merged data set comprises both financial and non-financial firms. The results in this paper are quantitatively and qualitatively robust to the exclusion of financial firms.

> 16 We deflate the nominal variables (measured in US dollars) by the US Consumer Price Index (CPI).

> 17 Given the focus of this paper on firm size and growth, we explore three variables for robustness purposes. Each of these measures captures different conceptual aspects of the firm dynamics and can be influenced by different factors. For example, sales and total assets are affected by inflation and exchange rate dynamics, whereas the number of employees is not. Another example is an increase in productivity, which can lead to sales growth without parallel increase in employees or assets. Total assets are arguably important for the capital-intensive firms, the number of employees might be relevant for labor-intensive ones, while sales might depend on the value of intermediate inputs.

9
should be implemented when governments try to tackle energy poverty.

**In addition, we show that different subsets of the population face different dimensions of energy poverty.** This means that the government might want to include a different population set for interventions that target energy poverty in the short and long-run, for example. Those needing renovation programs and interventions that increase their energy efficiency might differ from those who cannot afford their energy bills.

**Based on our literature review, we recommend moving away from single-indicator energy poverty metrics toward multiple-indicator approaches, which are consistent with previous studies.** We show that the incidence rate of energy poverty fluctuates significantly depending on the underlying methodology. Based on this pattern of results, we recommend moving away from single-indicator energy poverty metrics towards multiple-indicator approaches. This recommendation is in line with recommendations made previously in the literature (see, for example, Herrero (2017) and SiksnelyteButkiene (2021)). It is also consistent with the latest EC recommendation developed by the Energy Poverty Advisory Hub, which emphasizes the importance of utilizing a mixed indicators approach that covers all contributing factors and accurately identifies vulnerable populations, given the complex nature of energy poverty<sup>45</sup> . One example would be to follow previous pilots like the Belgium Energy Observatory or the French National Energy Poverty Observatory (ONPE), which both track more than one indicator. The operationalization of energy poverty measures should involve a cooperative approach between several ministries, academics, and local communities. In addition, big data sources

> 44 Importantly, results differ from some of the results detailed in Rude and Robayo-Abril (2024) because we use income from the EU-SILC after a statistical matching procedure in this case, while we rely on income measures reported in the HBS in the work at hand.

> 45 Source: Energy Poverty Advisory Hub (2023).

32
Budget Survey (EBCNV). Moreover, Section 4 is devoted to the presentation of the new sample design in the case of the 2021 Household Budget Survey in Tunisia. Three scenarios are taken into account: i) PSUs are selected with Simple Random Sampling (SRS) within the 24 governorates (explicit strata) in Tunisia; ii) PSUs are, first, stratified among the 24 governorates and then further stratified (implicitly) with systematic sampling by ordering the lists according to some economic and geographical variables, which consequently make the list a **geographical serpentine** ; iii) PSUs are, first, stratified among the 24 governorates, and then further stratified (implicitly) with systematic sampling by ordering the lists of PSUs according to the average per-capita consumption expenditure estimated by the poverty mapping method presented in Section 3.

This third scenario represents the primary, original contribution of this paper, and the design effect gain is compared with other scenarios. Such gain in the design effect has an impact on both the estimate variance (for higher precision in the estimates), and in the optimal sample size (i.e., for a smaller budget).

Finally, Section 5 concludes the paper. After summarizing the main results of the paper, the section aims to briefly present some variants of the proposed methodology when Census and/or Household Budget Surveys are not available for recent years in order to perform the poverty mapping exercise.

# **2. Theory of design effects, variance estimation, and resampling techniques**

This section aims to present the theory behind the estimation of standard errors in complex surveys such as HBSs. Since the _implicit stratification_ proposed is based on poverty estimates and per capita consumption expenditure performed with the World Bank ELL method (described in further detail in Section 3), here the notation adopted is a mixture of the notation for sampling techniques in Wolter (1985) and the notation for poverty mapping in ELL (2003), as follows.

We have a _population_ divided in L strata
the accuracy of remote-sensing-based poverty maps. One logical entry-point in this regard is an assessment of how closely a poverty map obtained using remote sensing data tracks a poverty map derived from population census data. Such assessments are currently missing from the literature. Existing studies typically validate their small area poverty estimates by comparing them against direct-estimates obtained from the household survey. However, these direct estimates are subject to large sampling error and cover only a small subset of the total number of areas. They thus fail to confront what motivates the use of small area estimation methods to begin with. Engstrom et al. (2021) is a notable exception. That study uses the census-based small area estimates of poverty obtained using the ELL approach as the dependent variable in their model employing predictors derived from remote sensing data as independent variables. This does not allow, however, for a validation of a poverty mapping approach that does not use any population census data (i.e. an approach that combines household survey with remote sensing data).

The validation exercise presented here provides a clean comparison between poverty maps obtained with and without population census data. Employing data for Malawi, we first obtain small area estimates of poverty that combine the Malawi household

> 2More recent examples of empirical studies that use remote-sensing data to track local economic activity include Nhu and Noy (2020), Marx et al. (2019), and Van der Weide et al. (2018), among others.

4
Figure 1 continued Attainment profiles for ages 15 to 19, by economic group: Eastern and Southern Africa <!-- Start of picture text --> Comoros 1996 Kenya 1993 Malawi 1992<br>- - -. ---------- -. - -.--- ..... --.- .----------- -------------<br>0~8 0.8 -0.8-<br>s 0~6 ;0 eg 06- 0g.6<br>024 02 4 02<br>0 20- 2 0 2<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>t.~~~~~~~ f~~~~~~~~~~~ I<br>Namlbia 1992 Rwanda 1992 Tanzania 1991-92<br>ll I~ ~ ............................................................---- .r....= '<br>0.8- 08- 0.8 -<br>O S606 \0 6<br>IC 4 0 404<br>02 - 0.2 0.2<br>O 0 0-<br>1 2 3 4 5 6 7 8 9 I 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>a. 04 -- - - 0.04
9 -1 1 3 5 7 9<br>GDP growth (%) GDP growth (%)<br>Poverty Change (ppts) Poverty Change (ppts)<br><!-- End of picture text --> Source: LAC Equity Lab tabulations using SEDLAC data (World Bank and CEDLAS). Note: Commodity-boom countries are identified as those whose terms of trade grew by more than two percent in annualized terms between 2003 and 2013 as defined in World Bank (2015). Commodity boom countries are Chile, Bolivia, Colombia, Peru, Ecuador, Brazil, and Argentina; the other countries included are Dominican Republic, Guatemala, Honduras, Mexico, Paraguay, Uruguay, and El Salvador. Since growth rates in 2009 are significant outliers, they are excluded from the first period.

This convergence in poverty reduction is largely explained by the change in growth performance among countries that benefited from the commodity boom. Figure 6 shows the relationship between poverty change and economic growth during the commodity boom period (2003-11) and the slowdown (2012-14). The best performers in each period are those countries in the bottom right of the figure. Commodity-boom countries have jointly moved to the upper left during the slowdown period, reducing the poverty reduction gap with the non-commodity countries.

8
differences are probably small enough to be explained by varying data coverage.

**Following the literature, we assume that employees report their total income in survey data, including envelope payments and income earned in the informal economy** . The higher value of aggregate compensation of employees in the EU-SILC than in the tax data indicates that this is indeed a fact. Then, we compare the "true income" reported in the EU-SILC with the "reported income," which is determined by the tax income imputed in the EU-SILC dataset based on common characteristics of individuals available in both data sources. The comparison outcomes indicate the possible degree of underreporting of income in tax data.

13
such results. On the other hand, if there is one country in which we would expect this reaction, it is Norway. Asatryan and Peichl, 2017 underscored that in developing countries, where formal institutions might be weaker, the effect of audits could be different from that in developed economies. This is precisely the context we explore in our analysis of Indonesian data.

The second class of mechanisms behind the increase in tax compliance is often referred to as "tax morale". It represents a more recent body of literature that has investigated the role of nonpecuniary motives more broadly Kirchler, 2007, Luttmer and Singhal, 2014, Besley et al., 2023. In that sense, the visitation program would increase the intrinsic value of being compliant.

The last class of information explains compliance behavior through "information frictions" Bhargava and Manoli, 2015, H. J. Kleven and Kopczuk, 2011, Cox et al., 2020. Here, evasion could originate from taxpayers who are not aware of their obligation or who do not know how to be compliant. The visits would increase compliance to the extent that tax authorities inform the taxpayers how to fulfill their obligations Kirchler, 2007 and Slemrod et al., 2001. Recently, De Neve et al., 2021 explored this hypothesis in an experiment in Belgium using quantitative methods similar to those used in this paper.

# **2.2. Use of Machine Learning to Investigate Heterogeneous Effects**

The advent of Machine Learning (ML) techniques signifies a pivotal advancement in the toolkit available to applied researchers across various fields. It encompasses a broad spectrum of statistical learning methods such as Random Forest, Neural Networks, and Penalized Regression, which have revolutionized prediction and pattern recognition, especially in highdimensional settings (Athey and Imbens, 2019; Hastie et al., 2009;Bishop, 2006).

Machine Learning (ML) methods excel in conducting research within observational studies, particularly in scenarios where significant sample imbalances exist between treated and nontreated groups. Traditional approaches in
As displayed in Table 2, country-specific annual real income growth rates are applied in the OECD project. These numbers are derived from the OECD _Medium-term Baseline_ , which is prepared by the OECD Economics Directorate, based on historical data and short-term projections, and extended to the medium term based on assumptions about the growth of potential output in each country.<sup>8</sup>

**Table 2. Annual real income growth rate, discount rate and remaining working years in OECD project**

||CAN|FRA|ISR|ITA|KOR|NLD|NZL|NOR|POL|ROU|ESP|GBR|USA|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Annual real<br>income growth<br>rate (%)|1.39|2.18|1.32|2.29|3.98|1.23|0.77|1.82|3.04|1.32|2.78|2.14|1.30|
|Annual real<br>discount rate<br>(%)|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|4.58|
|Remaining<br>working years|26.1|26.1|28.9|25.1|27.5|25.2|26.8|25.8|27.1|27.5|26.4|26.0|26.6|
|Source: Liu (20|11)
5_|_0.6_|_0.7_|_9.8_|
|Singapore|# of Issues|5|16|3|24|
||_Volume ($bn.)_|_2.8_|_17.9_|_5.5_|_26.2_|
|Other countries|# of Issues|2|1|9|12|
||_Volume ($bn.)_|_0.4_|_0.5_|_4.9_|_5.8_|
|Unreported|# of Issues|11|87|13|111|
||_Volume ($bn.)_|_3_|_54.1_|_8.1_|_65.2_|
|**Total**|# of Issues|140|470|62|672|
||_Volume ($bn.)_|_81.2_|_360.5_|_55.2_|_496.9_|

Source: Authors’ calculations based on Dealogic data. Excludes bonds issued by tax havens and offshore financial centers.

**Figure 3: Volume of dollar-denominated central government debt issued under U.K. and**

**U.S. governing laws**

<!-- Start of picture text --> $ billions<br>50<br>45 U.K. Law<br>40<br>U.S. Law<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Authors’ calculations based on Dealogic data.<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> 5
regress the annualized growth in the survey means on the annualized growth in real GDP/capita, under the constraint that the intercept is zero, s s G , and use as the fraction of growth in GDP/capita that is passed-through to the welfare observed in surveys. Using a sample of 1,351 spells for which we can calculate changes in ssyy GG/c cciic gg = ββ∗gg + εε ββ the survey mean suggests that . Each spell relies on two adjacent surveys from the same country with welfare measured in the same way (either income or consumption). ββ= 0.83 Yet there is no reason to believe that is constant across different contexts. It may differ by geographical region, by income level, by whether income or consumption is used as the input into poverty ββ measurement, over time, etc. Although interactions for these additional covariates can easily be accommodated in the equation, it is not clear which variables should be used to define the interactions and using all possible interactions will likely overfit the data. Applying a selected number of interactions is common practice in adjusting between household survey growth and national accounts growth rates (see for example Birdsall et al., 2014; Chen and Ravallion, 2010; and Chandy et al., 2013), but it is not entirely clear on what basis the interactions were selected.

To circumvent this issue, we apply a machine learning algorithm, Model-based Recursive Partitioning, to determine when there is reason to believe that the passthrough rate varies in different contexts (Zeileis et al. 2008). This algorithm can take as input all potential variables that might matter for the passthrough rate. In our case, as input variables we use geographical region (we use two versions, the official World Bank geographical regions, and the regions from PovcalNet, where most high-income countries form a separate region), a dummy for whether consumption or income is used, mean consumption, median consumption, the Gini index, population, GDP/capita, and the
fixed assets. These variables can be further broken down into type of investment – (a) land and buildings and (b) equipment and machinery. Summary statistics can be found in Table 1.

The main variable of interest is central government debt as a percentage of GDP at the country level. This data is obtained from the IMF Global Debt Database (Mbaye et al., 2018). The data is available for virtually the entire world. The integrity of the data has been checked through consultations with IMF country desks.

> 1 See Appendix A5 for the list of countries and years.

7
0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.004)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Education|No|No|Yes|Yes|No|No|Yes|Yes|
|Employment|No|No|No|Yes|No|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.266|0.286|0.372|0.791|0.211|0.227|0.317|0.751|
|Panel B Adj.R<sup>2</sup>|0.232|0.254|0.352|0.789|0.183|0.201|0.303|0.750|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|119276|119276|119276|119276|119276|119276|119276|119276|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. GHSPOP 1km is used for the DOU and DB method. The dependent variable is a share of household workers employed in a respective sector. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in