rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6 Annex: Detailed RPSR Indices**

- A. Global reform index

The standard package of reforms prescribed by international donors in the 1990s included four principal components: restructuring (vertical and horizontal unbundling of power utilities); private sector participation; creation of an independent regulator; and competition in power generation.

In order to aggregate across the four dimensions of power sector reform considered in this study, a simple Power Sector Reform Index is constructed. The index gives each country a score on an interval of 0 to 100 on each dimension of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum (see tabulation below). The average of the four 0-100 scores is used to provide an overall summary of the extent of reform.

|**Regulation**|No regu<br>= 0|lator<br> <br>Regulator<br>= 100|
|---|---|---|
|**Restructuring**|Vertically integrated<br>= 0|Partial vertical<br>unbundling<br>= 33<br>Full vertical<br>unbundling<br>= 67<br>Vertical & horizontal<br>unbundling = 100|
|**Competition**|Monopoly<br>= 0|IPPs<br>= 25<br>Single Buyer<br>Model<br>= 50<br>Bilateral<br>Contracts<br>= 75<br>Competitive<br>market<br>= 100|
|**Private Sector**<br>**Participation**|0.5*(Percent<br>0.5*(Percent|age of generation capacity with private sector participation)<br>+<br>age of distribution utilities withprivate sectorparticipation)|

## B. Generation and transmission planning index for Pakistan and comparators, 2015

||**
|3.6|18.5|
||Thinking|1.2|0.7|0.1|3.9|
||Communication|5.3|1.9|1.0|10.3|
||Organization|4.2|1.5|1.2|9.2|
||**Socioemotional**|8.3|5.0|2.1|42.2|
||Emotions|0.5|0.5|0.0|4.3|
||Relationships|5.8|4.5|1.3|38.2|
||Personal growth|2.0|1.5|0.3|8.6|
||**Others**|68.2|10.6|28.6|84.3|

> 18 We use two sources to define similar occupations. ILO (2012) lists related occupations in ISCO-08. The O*NET Career Changers Matrix lists occupations in the US that have similar skills and experience and that workers could transfer between with minimal additional preparation (O*NET 2021). We only use these two sources to identify potential similar occupations. Once identified, the imputed occupations take the average share of skills requirements for each skills category of their similar occupations in the Malaysia data (or the skill profile of the similar occupation when the similar occupation is only one).

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Figure B.3: Missing verification identifiers <!-- Start of picture text --> CE MG<br>104 0.04<br>102 0.02<br>100 0.00<br>98 0.02<br>96 0.04<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>104 0.04<br>102 0.02<br>100 0.00<br>98 0.02<br>96 0.04<br>2009 2011 2013 2015 2017 2019 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>RS SP<br>60 0.20<br>50 0.15<br>40 0.10<br>0.05<br>30<br>0.00<br>20<br>0.05<br>10<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Null Verifications (%) Null Total Verified (%)<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of observations by year for which we were unable to build a unique identifier (id_liquidacao_bd) in the _verification_ table.

50
Second, the roles of non‐food expenditures, net remittance and public transfers in 2007 are examined. A large amount of non‐food expenditures implies that households can potentially reallocate expenditures to food consumption to accommodate price spikes. On the other hand, if they were connected with others through remittances and/or already receiving public transfers before the crisis, it is easier for them to insure the adverse impact of price spikes with remittances (private transfers) and/or public transfers. The results show that their roles were rather at minimum to mitigate the price impact on child nutrition intake.<sup>17</sup> The Paasche and Fischer indices are also used to compare with the Leypayres index (Table A3). The Paasche index uses the 2010 quantities (or calories) as weights, and the Fischer index is the square root of the product of the Lespayres and Paasche indices. Since the 2010 quantities (or calories) are part of the household’s optimal responses to the food price crisis, the Paasche index is clearly endogenous. Similarly, for the Fischer index though the problem is milder. As presented in Figure 4, the Paasche index is generally smaller than the Laspayres index. As expected, Table 5 shows that the price spike effect using the Paasche index is insignificant. The results using the Fischer index is a mixture of the Laypeyres and Paasche results.

To sum, the above analysis confirmed potentially large negative impacts of food price spikes on child growth, measured by their heights, among food non‐producers. Differences by gender or age were not found. The point estimate in Column 1 of Table 3 shows that an exposure to the sample average Laspeyres index of 245.82 (based on quantities) leads to a reduction of the height z‐score by 1.9365. A similar exercise using calories in Column 2 of Table 3 shows a reduction of the height z‐score by 1.7723. Changes in the height z‐score by the above magnitude over 3
</sup> We find limited evidence that either aggregate socioemotional skills, intrapersonal skills, or interpersonal skills close the gap for women (Appendix Table A18). A similar pattern holds for pre-program income: women earn about 40% less than men and socio-emotional skills do not close the gap. Indeed, the results suggest limited returns to socio-emotional skills for tertiary educated women in the Rwandan labor market.<sup>34</sup> Appendix 2 explores whether the effects of the training were different for women and men.

> 31The authors identify a fourth set of stylized facts detailing how the skills gap varies with education. We are unable to speak to this point as all applicants to the soft-skills program had completed some form of tertiary education. 32Intrapersonal skills include emotional awareness, self-awareness, emotional regulation, self-control, perseverance, personal initiative, and problem solving. Interpersonal skills include listening, empathy, expressiveness, relatedness, influence, negotiation, and collaboration. We follow Ajayi et al. (2022) in the definition and construction of each index.

> 33The administrative records recovered 17 months after training show a significant gender gap in formal jobs: while around 70% of men in both treatment and control groups had had a formal job at some point in their lives, this was true for only 54% of women in the control group (the share in the treatment group was 58%, though the treatment effect is not statistically significant).

> 34We should take these results with caution since these are not causal estimates, and there is a literature pointing out the difficulty of measuring soft skills with self-reported questions (e.g., Laajaj and Macours (2021)).

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# **3 Methodological framework**

Extending earlier studies (Wang et al., 2017; Gage and Calixte, 2006), we use a mixed method design that combines findings from both quantitative and qualitative methods. The analysis of the quantitative data provides a general overview of factors that influence pregnant women’s decisions regarding institutional care while the qualitative data analysis explores indepth beliefs, perceptions, and attitudes. During the interpretation stage, findings from the econometric model and the behavioral sciences literature are brought together, with the same weight, for a deeper understanding of the interplay of structural and behavioral barriers that prevent women from accessing institutional care (Greene et al., 2001; Cullen et al., 2011). In addition, this design provides an opportunity for triangulation of the findings across methods.

## **3.1 Quantitative research data and analysis**

The quantitative data come from two datasets: the 2017 Demographic and Health Survey (DHS), called EMMUS ( _Enquete Mortalite, Morbidite et Utilisation des Services_ - Survey of Mortality, Morbidity and Service Utilization) in Haiti, and the 2017 Service Provision Assessment (SPA). The DHS dataset consists of a nationally representative sample of households interviewed between November 2016 and April 2017 by the Institut Ha ̈ıtien de l’Enfance with support from Institut Ha ̈ıtien de Statistiques et d’Informatique (IHSI). Besides information on household characteristics and an asset measure of wealth, typically collected in DHS-type surveys, it contains data from an in-depth survey conducted on a sample of 15- to 49-year-old women who answered questions about access to ANC, source, place, and type of assistance received during delivery for each child born within the five years prior to the survey. Surveyed women also answered questions related to decision-making in the household and agency.

In 2017, 14,371 15-49 year old women from 13,405 households were surveyed.<sup>8</sup> In addition to household- and individual-level characteristics, community characteristics can be
of facilities lacked both services. Figures 2 and 3 demonstrate the evidence of the gap in access to infrastructure for health facilities in Kenya, with some rural facilities far from the most populated cities lacking electricity and internet connectivity.

# _Road connectivity_

We use two sources of road data: the Global Friction Surface 2015 and georeferenced road network data from government sources (Kenya Road Fund 2018). The Global Friction Surface 2015 enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2015.<sup>7</sup> It was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying data sets used to produce the map include roads (comprising all Open Street Map and Google roads data sets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. A speed of travel in terms of time to cross is allocated to each pixel. The final map represents the travel speed from this allocation process, expressed in units of minutes required to travel one meter. We also use a road network from the Kenya Road Board for 2018 that relies at least partially on actual road surveys. The road network data set features surface type and road conditions.

We use the Global Friction Surface 2015 to calculate the catchment area of population for each health facility, which represents the number of people that can be reached in a certain amount of time given the time allocated on the Global Friction Surface. We use the road network to calculate the orthogonal distance of each facility to the nearest fair- or good-quality road.<sup>8</sup>

# _Socioeconomic factors_

We use local estimates of deprivation scores, a proxy for poverty, calculated by the Poverty Global Practice at the World Bank. These calculations were done in small areas for the whole country, providing helpful information
The distinct challenges across urban and rural areas in education are non-binary, with great variation within both urban and rural areas, depending on population density, infrastructure, distances, and other factors. The solution, likewise, is not merely to provide more of the same services in rural areas that are offered in urban areas. Global data sets like the ones provided by Moreno-Monroy and co-authors (2020) for FEAs, or by Cattaneo and co-authors (2021) for URCAs open new possibilities. Matching them with improved geo-located data—as was done for poverty in Section 3.3—that identify and describe the contexts in which children and youth live, the constraints they face, and their educational participation, is crucial. It will enable policies that are better designed and better targeted so that every child has equal opportunities to fully realize their potential.

# 5. The way forward

With this review, we revisit rural–urban connectivity, emphasizing the bidirectional nature of rural– urban relationships, and the difficulty of discussing social and economic processes in one area without acknowledging the other. We bring together two different approaches that address territorial issues – functional areas and the rural–urban continuum – and provide a framework for their adaptation and operationalization based on recently published data and the methods to compute it. This area of research is significant for all countries regarding access to employment, education, health services, and opportunities for civic engagement. For low income countries, it is particularly relevant for poverty reduction strategies, organizing city–region food systems, and mobility transitions towards more commuting and less migration. We suggest directions to shift the discussion on development policy towards a more territorial perspective that accounts for interlinkages between different size cities as well as their surrounding rural areas. We argue that the recently developed URCA global data set is an important step forward, especially if complemented with studies, to understand how spatial relationships operate in a variety of social, economic, and developmental contexts. Similarly, the global delineation of metropolitan areas (Moreno-Monroy, Schiavina
study: Cˆote d’Ivoire (2003-2012), Ethiopia (2011), Ghana (2003), and Kenya (2010). These countries provide comprehensive and comparable census data. The censuses are nationally representative and both small and large firms in the formal sector are adequately included in all countries. In all four countries, the data are restricted to manufacturing sector (ISIC Rev 3 15-37).

In what follows, we describe each country’s datasets.

**Cˆote d’Ivoire** The data source for Cˆote d’Ivoire is balance sheets and income statements associated with tax reporting. The data is available for all registered firms in the country and contains detailed balance sheet information on firms’ revenue, employment, cost of labor, book value of fixed assets, intermediate inputs and other firm characteristics. All registered firms are required to report their financial statements to the National Statistics Institute (INS), the tax administration (DGI), the court of justice, and the Central Bank (BCEAO), which are reported under the West Africa accounting system standards, Systeme Comptable Ouest Africain (SYSCOA). INS processed the data after firms hand in hard copies of their forms between March and June following the closing of the fiscal year in December. The Cˆote d’Ivoire data cover 2003 to 2012.

> 7Following Hsieh and Klenow (2009), we use an establishment’s total wage bill (including benefits) instead of employment in order to account for differences in the quality of labor across establishments.

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# Absentee Landlords and Land Tenancy

Melanie Khamis, Annemie Maertens and Siddharth Sharma<sup>∗</sup>

## **Keywords: migration, land tenancy, sharecropping, land rent, India JEL Classification: O1, J61, Q13, Q15**

> ∗Corresponding Author: Annemie Maertens (a.maertens@sussex.ac.uk) at Sussex University; Melanie Khamis (mkhamis@wesleyan.edu) at Wesleyan University and IZA; Siddharth Sharma (ssharma1@worldbank.org) at the World Bank. We acknowledge excellent research support from Deepika Pingali, Maika Schmidt, Sakshi Bhardwaj, Claudia Isabel Medina Lopez, Aditi Guha and Morgan Usen, and support in obtaining the data from Dipendra Das and Cyriac George, as well as Eswaran Jagadeesh and Anupama at ICRISAT. Michael Norton at the World Bank generated the travel time data set using scanned maps provided by Treb Allen. We are grateful to Sanjay Jain, AV Chari, Shingyi Wang, Laura Schechter, Anukriti, Rachel Heath, Adriana Kugler, Ira Gang, Cory Smith, Amrit Amirapu, Agyris Sakalis, Larry Blume and seminar participants at the 2019 AAEA, 2021 16th Annual Conference on Economic Growth and Development (ISI), 2022 97th Annual Conference WEAI, 2022 MLPortal 1st Workshop of Applied Microeconomics, 2022 100 Years of Economic Development Conference Cornell University, 2023 Southern Economic Association, Fordham University, CUNY Baruch and at Sussex University for excellent comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank or its Executive Directors.
<br>conflict; 1 represents a medium-intensity conflict; and 0 indicates the absence of<br>conflict.|The World Bank FY20<br>List of Fragile and<br>Conflict-affected<br>Situations|
|Fragile and Conflict-affected<br>situations|This variable was developed by the authors through the World Bank 2020 List of<br>Fragile and Conflict-affected Situations. A value of 3 corresponds to a high-intensity<br>conflict; 2 represents a medium-intensity conflict; 1 indicates the absence of conflict<br>but high institutional and social fragility; and 0 indicates the absence of both conflict<br>and high institutional and social fragility.|The World Bank FY20<br>List of Fragile and<br>Conflict-affected<br>Situations|
|Government effectiveness|Government Effectiveness captures perceptions of the quality of public services, the<br>quality of the civil service and the degree of its independence from political<br>pressures, the quality of policy formulation and implementation, and the credibility<br>of the government's commitment to such policies. Estimate gives the country's score<br>on the aggregate indicator, in units of a standard normal distribution, i.e., ranging<br>from approximately-2.5 to 2.5.|Worldwide<br>Governance Indicators|
|Developing countries’ dummy|It takes 1 if the country is a developing country (or not a high-income country) and 0<br>otherwise.|World Bank country<br>classification (WDI)<br>and authors’<br>computation|
|GovTech Africa|It takes 1 if the country is an African country having implemented a GovTech platform<br>and 0 if it is an African or a non-African country without GovTech platform.|Authors’ computation<br>based on GTMI 2022<br>and World Bank<br>country classification<br>(WDI)|

25
fact that the documented disadvantage of Natives survives adding location fixed effects does not mean that the disadvantage is constant across locations. Table 4 shows the differences in outcomes between Natives and non-Natives in rural and urban areas. While for a majority of outcomes the disadvantage is significantly greater in rural areas, this is not the case for all outcomes. The difference in disadvantage between rural and urban areas is not for formal and unem- statistically significant employment ployment. In addition, Natives in rural areas are more likely to own the house and land in which they live than non-Natives, contrary to what is observed in urban areas.

> 6 This comparison is limited by the differences across censuses of different countries on the questions on the use of or access to computers.

> 7 Table A2 in the Appendix shows that this is also the case for the US.

10
Asia Sub-Saharan Europe & Central Asia<br>Asia Asia<br>Eastern Europe Central Europe Central South Caucasus Western Balkans Eastern Europe Central Europe Central South Caucasus Western Balkans<br>OECD<br>Africa Pacific<br>Sub-Saharan East Asia & Europe & Central Asia Middle East & N. Africa South Asia Latin America & Caribbean<br><!-- End of picture text --> Sources: EBRD (2015a), Haver Analytics, World Bank, World Economic Forum. A. The score is overall quality of infrastructure. The score from 1 to 7 (best). Investment is the share of fixed capital formation as a percent of GDP. OECD average is the average investment share of OECD countries from 1990 to latest.

B. Sum of container port traffic (TEU: 20 foot equivalent) per current U.S. dollar GDP in millions in 2014. C.D. The score is from 1 to 7 (best). The OECD and EMDE average are the simple average of all the countries in the respective subgroupings.

E. Regional aggregates. Data are for 2014 or latest available. GDP data are in constant 2011 “international dollars.” F. Share of renewable energy consumption as percent of total final energy consumption in 2012. EMDE averages are the simple average of all the countries in the respective subgroupings.

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profits (profits net of dividend distribution).

In Figure E1a, we present the distribution of total income in the tax records, excluding the imputed undistributed profits. As we discuss in section 3, one key limitation of the administrative tax records we observe is the lack of information on wages and other labor income below the minimum threshold for filing income taxes - not only individuals receiving wages below that level are not required to file, but employers also we do not inform or withhold annualized wages below that threshold. In the figure, observe a peak of tax records precisely at the level of the filing threshold (right-most dashed line). We also illustrate, in the left-most dashed line, that the annualized minimum wage was significantly below that threshold, meaning that there is a large mass of workers receiving wages between these two levels that are not observed in the tax records. The smallest peak in the income distribution to the left of the minimum wage is comprised mostly of individuals receiving small amounts of income from dividends

> 5The large number of individuals receiving dividends is mostly driven by a few large corporations, often in the financial sector, distributing very small amounts of dividends to thousands of taxpayers. In that sense, the majority of these individuals are not active shareholders with meaningful participation in corporations.

59
particular, we are able to investigate disruptions in economic activities and food market supply chains, mechanisms through which violent conflicts can affect food security and related welfare outcomes.

We focus on three sets of household outcomes: (1) food security, measured using the Food Insecurity Experience Scale (FIES); (2) access to food and food markets; and (3) household participation in major economic and livelihood activities (farming, non-farm business, and wage employment). To quantify the impact of violent conflict on these outcomes, we use a Differencein-Differences (DID) approach, where the periods (months) before and after the outbreak of the war are collapsed into pre-war and post-war-onset periods for aggregate analysis, and a two-way panel fixed effects model is implemented for disaggregated analysis using (monthly) householdlevel exposure to conflict data. The first stage of the war (November 2020 to June 2021) was mostly confined to Tigray, spilling over to neighboring Afar and Amhara regions thereafter. Thus, our analysis focuses on the first period of the war, so that we can precisely define the spatial extent of the conflict. To account for potential intermittent conflicts elsewhere, we relax this treatment assignment in the analysis by using monthly household-level exposure to conflict from the ACLED database.

The high-frequency nature of the data facilitates our identification strategy in two useful ways. First, the monthly follow-up and hence comparison of outcomes across affected and unaffected regions allows us to minimize compounding trends. The existence of multiple pre-war and post-war-onset waves also allow us to indirectly test for parallel trends, the main identifying assumption for a DID estimation strategy. Second, the relatively large sample size due to highfrequency data allows us to probe the robustness of our results using alternative definitions of control groups. For example, in some of our estimations, we restrict our sample to the Highland regions of the country, with Amhara, Oromia and SNNP regions assigned as the controls.

We find that the outbreak of the civil war increased the probability of moderate or severe food insecurity by