rafmacalaba/data-use-mentions-extended huggingface dataset:
, that is new variables that are correlated with the endogenous covariates but are not correlated with the error term, would purge the parameter estimates of simultaneity bias. However, in the case of placement of community services, a valid set of instruments is not available from the EGSF data.'<sup>0</sup>

- s In the first stage regression, husband's earnmings and owned land are significant at the 10% level and 5%level respectively. The R<sup>2</sup> is 0.20. The EGSF collected no infonnation on either unearned income or the value of household assets, which would be other valid instruments for household resource availability.

- For instance, Frankenberg (1995) notes thal several studies of health policies in Indonesia suggest that the distribution of health facilities, both public and private, is related to factors such as population density, level of socioeconomic development, integration into transport networks, and administrative rank, all of which may be related to morbidity and mortality.

- **0** If we had data for more than one point in time, we could use fixed-effects estimates to control for the endogenous placement of community services by estirrating how the _change_ in local programs affects the _change_ in children's growth. Note that the panel data should not be too closely spaced because short-term program changes are likely to be small and their effect not significant. Rosenzweig and Wolpin (1986) and Pitt, Rosenzweig and Gibbons (1994) have adopted the fixed effects methodology to control for the nonrandom spatial distribution of government programs in the Philippines and Indonesia. Because governments allocated schools and health and family planning programs in more disadvantaged areas first, OLS estimates based on the assumption of exogenous program placement indicate a negative impact of such services on children's growth. Conversely, estimates that control for endogenous program placement by differencing across the two time periods reveal a substantial and significant effect of %vernment programs.

9
from similar analyses set in nations with a much higher rate of agricultural participation (and correspondingly, increased caloric requirements). As people in the Philippines have moved out of agricultural labor and into the industry and service sectors, poverty rates and the Gini coefficient have declined. But, despite these advances, individuals and families in the Philippines continue to struggle with undernutrition. There has been almost no reduction in the prevalence of undernutrition in the country in three decades (Mbuya et al., 2021).

The choice of using Philippines data is in large part due to the availability of individual-level consumption data, for all members of the household, as collected in the NNS in 2013. The NNS allows us to directly compare individuals’ within-household consumption, which is uncommon among household data, particularly in low-income countries. Despite the importance of food consumption and/or dietary data for understanding important questions of health and intrahousehold dynamics, there are very few examples of nationally representative individual-level dietary surveys outside North America and Europe. A notable exception to this is the Bangladesh Integrated Household Survey (BIHS), which has nationally representative data on dietary intake of surveyed household members (for work using these data, see Brown et al. (2021), D’Souza and Tandon (2019)). However, these sorts of data are still relatively rare. Coates et al. (2017) highlight a few of the challenges with collecting and using individual consumption surveys. These include the lack of infrastructure required to facilitate the collection of these data. This problem is compounded by the often-significant time and cost mandated in their collection. As a result, there are relatively few individual-level data sets which analyze consumption for all those within a household. For researchers, this lack of data can pose a serious challenge for assessing and monitoring individuals’ welfare across the world. Dang et al. (2019) create a typology of consumption data, which classifies household survey data as either entirely missing individual consumption data; partially missing such data, as in
but income may show an increasing then decreasing pattern if rich people move to clean areas. We see similar estimates in tabular results in Table 3. GDP per capita is positively correlated with SO2, but the quadratic fit shows an increasing and then decreasing pattern.

Besides these country-level results, we also assess the relationship between income levels and pollution exposure by conducting a pixel-level analysis. Gridded GDP, GDP per capita, and SO2 data enable us to consider a sample of over 11 million unique locations (i.e. pixels). This enables us to capture granular sub-national variations, thus distinguishing between areas with different pollution and income levels with high precision of estimation. The results, summarized in Table 4, are consistent with the findings of the country-level analysis. Across the world, higher-income areas tend to be exposed to higher SO2 pollution from coal-fired power plants. Every additional 10% increase in GDP per capita is associated with 1.64% higher SO2 pollution.

We use additional flexible fittings to explore the potential nonlinear relationship between air 14
# **Fig. 4: Unconditional convergence in urban shares**

<!-- Start of picture text --> Urban employment shares in 1994 Vs 2005<br>45 degree<br>Linear Fit<br>0 .2 .4 .6 .8 1<br>Urban employment share in 1994<br>1<br>.8<br>.6<br>.4<br>.2<br>Urban employment share in 2005<br>0<br><!-- End of picture text --> Notes: Author’s own calculations based on ASI and NSS survey data. In these plots, “bubble size” indicates the weight in the regression, that is, the log of district and industry size in 1994.
of pollution from motor vehicle emissions. Section 5 explores the implications for the distributional impact of air pollution, while Section 6 extends the analysis to identification of areas where reduction of traffic congestion would have the greatest distributional benefits. Section 7 summarizes and concludes the paper.

# **2.  Prior Research**

# **2.1 Air Pollutants and Health**

This paper uses data from the European Space Agency’s Sentinel-5P platform that have been published since July 2018. Sentinel-5P measures four air pollutants that are generated by vehicular emissions: nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO) and sulfur dioxide (SO2) Extensive research has analyzed their significance as sources of health damage, with particular attention to respiratory problems for children and cardio/pulmonary problems for the elderly. For this pilot exercise, we focus on NO2, which prior research has assigned the greatest impact among the four. NO2 impacts have been extensively studied, both singly and in combination with other air pollutants (Brauer et al. 2002, 2007; Cesaroni et al. 2008; Chen et al. 2012a; Gauderman et al. 2005; Gehring et al. 2002, 2006; Hoeck et al. 2002; Morgenstern et al. 2007; Naess et al. 2007; Nafstad et al. 2004; Sunyer et al. 2006). NO2 emissions also have significance as a source of particulate matter less than 2.5 micrometers in diameter (Hodan and Barnard 2004), which is strongly associated with health problems but not measured by Sentinel-5P.

# **2.2  Modeling Traffic and Air Pollution**

Empirical research on spatial relationships between traffic and air pollution falls into three broad categories. In the first category, metropolitan-scale assessments of traffic and pollution employ engineering models calibrated for developing-country cities with plentiful information about the spatial distributions of vehicle traffic, emissions, and atmospheric conditions (Jerrett et al. 2005). For urban researchers and planners in developing countries who use these imported models, the
Figure 5: Difference between the Youth Female and Youth Male Inactivity Rates (Target 2), in percentage points <!-- Start of picture text --> 40%<br>36%<br>35%<br>29%<br>30%<br>25% 21%<br>19%<br>20%<br>15% 12%<br>10%<br>5% 5%<br>3%<br>5% 2%<br>0%<br>-5% -1%<br><!-- End of picture text --> Source: Authors’ computations based on data from ILO KILM and WDI Figure 6: Cost of Girl Inactivity and Joblessness, if Equal to Adult Female Inactivity or Joblessness as % of annual GDP (Target 1) <!-- Start of picture text --> 6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>Inactivity Jobless-lower bound Jobless-upper bound<br><!-- End of picture text --> Note: Jobless-lower bound reduces the inactivity rate by gross upper secondary enrolment rates thereby dropping students . Jobless-upper bound reduces the inactivity rate by gross upper secondary and by partial net lower secondary thereby dropping students.

Source: Authors’ computations based on data from ILO KILM and WDI 28
# **Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis**

Hai-Anh H. Dang, Talip Kilic, Kseniya Abanokova, and Calogero Carletto<sup>*</sup>

**Keywords:** consumption, poverty, survey-to-survey imputation, household surveys, Viet Nam, Ethiopia, Malawi, Nigeria, Tanzania, Sub-Saharan Africa.

**JEL Codes** : C15, I32, O15.

> * Dang (hdang@worldbank.org; corresponding author) is a senior economist in the Living Standards and Measurement Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Kilic (tkilic@worldbank.org) is the senior program manager of the Living Standards Measurement Unit, Development Data Group, World Bank; Abanokova (kabanokova@worldbank.org) is an economist in the Living Standards Measurement Unit, Development Data Group, World Bank and is a junior research fellow at the Higher School of Economics, National Research University, Russia; Carletto (gcarletto@worldbank.org) is the senior manager of the Strategy and Collaboratives Unit, Development Data Group, World Bank. We would like to thank Madeleine Gauthier, Nathan Ives, Hoa Nguyen, Lars Osberg, Anne Swindale, Petra Todd, and seminar participants at Australian National University, the Center for the Study of African Economies conference (Oxford), IARIW-TNBS conference (Arusha), Econometric Society’s Summer Meeting (Vanderbilt), and Statistics Canada’s International Methodology Symposium for their helpful discussion and feedback on the earlier drafts. We are grateful for the funding from the United States Agency for International Development (USAID).
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While there are undoubtedly many factors affecting the establishment of an SIF, the widening of the financing gap for long-term investment that followed the 2008 financial crisis is likely one of them. Indeed, 17 of the 26 SIFs presented in appendix A were established after 2008. SIFs like InfraCo Africa and the Indonesia Infrastructure Guarantee Fund were set up to crowd in private sector financing to infrastructure. Others, such as the Macquarie Mexico Infrastructure Fund and PINAI, funded by a combination of public and private capital, aim to improve government capacity to efficiently invest in public-private partnerships (PPPs). PPPs have historically been used to engage private capital in infrastructure projects that are too complex or too expensive to be financed by public capital alone. Global PPP investment in infrastructure reached $1.2 trillion in 2015 (PPI database). Although SIFs may be responsible for a significant portion of this investment, the lack of publicly available information on investment deals makes it difficult to estimate their contribution.

Governments have also established SIFs to support the domestic capital market. Particularly in EMDEs, local financial markets may lack the range of financial products or intermediaries required to sustain economic development, or the density of financial intermediaries may be too low to ensure effective competition between providers. As a government-sponsored financial intermediary, an SIF may offer financial services and products that are not yet being commercially provided, either for the economy as a whole or for certain sectors. The Indonesia Infrastructure Guarantee Fund is an example of this type of SIF.

In advanced economies and EMDEs alike, governments are increasingly preoccupied with enhancing the competitiveness of the local private sector, by nurturing the creation and growth of innovative SMEs. According to a recent Organisation for Economic Co-operation and Development (OECD) study, young SMEs are globally the primary source of net job creation. The disproportionate contribution of young firms to employment creation holds across all economies, sectors, and years considered (Criscuolo, Gal, and Menon 2014). In countries with underdeveloped capital markets, SMEs frequently lack access to financing.
the poorest education outcomes.<sup>6</sup> The selection of districts for the program was based on the literacy rate (among those age 10 and above), as measured by the 1998 Pakistan Population and Housing Census. Of the 34 districts that Punjab had at the time, the 15 with literacy rates below 40 percent were selected for the program (table 2). Over time, as the number of districts in Punjab increased from 34 to 36,<sup>7</sup> the number of program districts increased to 16, and the number of nonprogram districts to 20.

Districts selected for the program had poor school participation outcomes (see table 1). Measured in 2003, preprogram school participation rates for children of primary and secondary school ages were lower in program districts than in nonprogram districts, for both boys and girls. Moreover, the gender difference in preprogram school participation rates was greater in program than in nonprogram districts, particularly among children of secondary school age. In program districts, 59 percent of boys of secondary school age were enrolled in school, compared with 41 percent of girls—a difference of 18 percentage points. In nonprogram districts, the corresponding gender difference was only 4 percentage points. By 2014, near the end of our observation period, school participation rates had increased for both boys and girls and in both program and nonprogram districts, but large gender differences in school participation rates remained for children of secondary school age in program districts (figure 3).

When initiated in 2004, the program covered girls enrolled in government schools in grades 6–8. In 2005, it was expanded to also cover girls enrolled in grades 9–10. Under the program, households were offered a cash benefit of 200 Pakistan rupees (PRs) a month ($3.40) for each girl enrolled in grades 6–10 in a government school, conditional on an

> 6 The first distribution of cash benefits was initiated in January 2004, based on enrollment
they are moving from. Changes in short-term migration rates after an individual storm are not by themselves evidence of adaptive migration, nor is it a requirement for adaptive migration to be occurring in general. General long-run trends in migration may be adaptive, even if the immediate response to a particular storm shows no evidence of adaptation. As a result, we also examine how population-level exposure has changed over the full 25-year period in our panel.

To perform our analyses, we assemble data from the IRS Statistics of Income (SOI) on aggregate county-to-county migration in the United States and data on the paths of every Atlantic basin hurricane from 1992 to 2017. These data allow us to examine how migration patterns change in counties that are impacted by hurricanes following a storm, relative to the normal patterns of migration from those counties. They also have the advantage of capturing only individuals who file taxes in their new county and so do not include temporary migrants who might relocate only for the length of time it takes to rebuild their home before returning. We use multiple definitions of exposure to hurricanes based on the incidence of flood warnings, wind speeds, and Federal Emergency Management Agency (FEMA) disaster damage determinations. Our results are broadly consistent across all of these definitions. We also collect data on county-level 4
‐off groups are those most vulnerable to disasters; moreover, their condition is exacerbated by disasters. Thus, disasters are a great concern from an equity, or fairness, standpoint. Second, lagging social economic groups or lagging regions have been found to represent a drag on economic growth and development. Third, identifying the impacts on various stakeholders provides insight into the motivations of government decision‐makers and the likelihood of support or lack of support for DRM policies. Distributional information can better inform stakeholders and thus enhance the public participation process, as well as serving as a predictive tool for the decisions the process is likely to yield. Used appropriately, distributional data can fill in many needed informational gaps and help lead to a more enlightened citizenry, and hence DRM more attuned to the needs of the public (Rose et al. 1989).

# 5. Co-Benefits of Public Sector Investment

# **5.1. Co‐Benefits to Society**

Not adequately considering co‐benefits of DRM is a major reason for underinvestment (Vorhies 2012). Public officials in general need to be better informed of these co‐benefits and need to do a better job of communicating them to their finance ministries and to the business community.

To expand on the discussion in the previous section, there are several reasons why even the knowledge of co‐benefits may not be sufficient for adequate public sector action. Such reasons come under the 16
the following web-link: https://databank.worldbank.org/source/world-development-indicators (last accessed: November 2024).

> 4 See: Dimension data matrix, version 2015 12 08 0-100, available at the following link: <u>https://geerthofstede.com/research-andvsm/dimension-data-matrix/ (last accessed: June 2024). In our database, cultural data begin in 2015.</u>

> 5 CO2 emissions monitoring data is available for the years 2020-2023 for 49 countries.

> 6 See: Galindo-Rueda and Verger (2016).

7
to the North East quadrant), and the policies associated with them. But as democratic transitions from other regions demonstrate (see Box 1), major regional political transition episodes can yield a significant diversity of associated policies and institutional evolutions. These may also have implications for future violent conflict resilience, and well as PSM/PSD reform prospects and priorities. As with any governance indicators, these are subject to significant measurement error and issues of interpretation. However, we argue they provide a useful benchmark to assess potential forward looking trajectories. The highly oil endowed and institutionalized MENA countries of Bahrain, Oman, and Saudi Arabia are based squarely in the hegemonic quadrant. This in part is also a reflection of their monarchical institutions. By these measures, Kuwait is actually just placed under programmatic pluralism, suggesting that potential ―beacons‖ do exist in the region. The fragility of Yemen, particularly around inter-temporal credibility, places it in the patrimonial government setting, although it rates relatively highly on inclusiveness.

10
. A requirement in such a case would be that the household expenditure survey is also geo-referenced, i.e. that the enumeration areas (or primary sampling units) can be located on a map. While this would have been a demanding requirement a decade ago, geo-referencing of household surveys is now increasingly becoming standard practice. To preserve the anonymity of sampled households, the geographic coordinates of the primary sampling units in public use datasets are not exact, i.e. some random offsets are applied. Our secondary objective is to investigate whether these random offsets undermine this alternative approach of obtaining a poverty map. The primary sampling unit, such as a village or a neighborhood in a city, will be the unit of observation (rather than the household). Correspondingly, the level of poverty in the primary sampling units will now serve as the dependent variable. Examples of possible predictors of local poverty rates include local Night-Time-Lights (NTL), road network, urban footprint, vegetation, elevation, surface temperature, rainfall.

The use of remote sensing data in the economics literature has expanded dramatically in recent years, as these data have become richer and more accessible, see e.g.

> 1See e.g. Alderman et al. (2002) and and Bedi et al. (2007). Recently, the approach has also been extended to the small area estimation of child malnutrition (Fujii, 2010). For an overview of the literature on small area estimation we refer the reader to Elbers and van der Weide (2014).

3
br>40 Egypt Ethiopia<br>Turkey<br>30<br>0 10 20 30 40 50 60 70 80<br>% of Population Below Poverty ($1.25/day, PPP)<br>Source: OECD, 2009. World Development Indicators, 2010. ILO KILM.<br>Note: 48 countries with available data shown. Chart uses latest available data on informal share of employment<br>(1995-99 or 2000-07).<br>employment<br>informal employment share of non-agricultural<br><!-- End of picture text --> Certain caveats are in order. The links between working informally and being poor are not always simple. Recent empirical work (Chen 2006) has emphasized heterogeneity within the informal sector. On the one hand, not all jobs in the informal economy yield paltry incomes. Many in the informal economy, especially some self-employed, in fact earn more than unskilled or low-skilled workers in the formal economy. There is much innovation and many dynamic growth-oriented segments in the informal economy, some of which require considerable knowledge and skills. One of these is the fast-growing information and communications technology (ICT) sector in the large cities of India. There is also some evidence that 16