rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Large numbers of households in many less developed countries (LDCs) continue to cope without good quality water and sanitation services (WSS). As is true for many other services, the poor are more likely than the non-poor to receive poor quality WSS services. Bosch et al. (2000) find that fewer poor households are connected to water networks and many poor households have access to lower-quality services than non-poor households. Poor households bear a higher burden, as measured through time, health and energy, than non-poor households. WSS service provision in Sri Lanka is subject to similar problems. As discussed below and in more detail in Pattanayak et al. (2005a), contrasted with the rich, the poor consume less water per capita, and they are less likely to use safe water sources or engage in preventative measures such as treating their water or purchasing storage tanks. As a result, the poor are more likely to suffer from water-related diseases.

In the early 2000s, Sri Lanka, like many other LDCs at the time, was considering the possibility of inviting the private sector to operate WSS services, specifically around three coastal towns: Negombo, Kalutara and Galle. There were during the preparation process for these public-private partnerships concerns that such arrangements in WSS services may exacerbate the existing inequalities in WSS between poor and non-poor households. In 2004, the government abandoned its plan for setting up such a public-private partnership.

In this paper, we describe the results of a stated preference conjoint survey administered in Sri Lanka to assess demand for WSS services not just with respect to price, but with respect to important WSS service features. Our analysis contrasts the responses of the poor<sup>1</sup> and non-poor, providing valuable information for those who seek to improve WSS services in a cost effective manner. Without a detailed understanding of why and how people desire WSS services, it difficult to increase coverage of these services and achieve the resulting social, health and economic benefits as
., Chaurey, R., Fishman, R., & Malik, S. (2022). Land rezoning and structural transformation in rural india: Evidence from the industrial areas program.</mark> _<mark>World Bank Economic Review</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(2), 488-513.</mark>

- <mark>Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019).</mark> _<mark>A practical introduction to regression discontinuity designs: Foundations</mark>_ <mark>. Cambridge University Press.</mark>

CEMA and UNDP (2009), Reviewing the past responding to new challenges: Mid-term review of programme 135-Ii, 2006 – 2008, Committe For Ethnic Minority Affairs and the United Nations in Vietnam. Available at: <u>https://www.undp.org/sites/g/files/zskgke326/files/migration/vn/21361_P135_Report_E N.pdf</u>

- Chaurey, R., & Le, D. T. (2022). Infrastructure maintenance and rural economic activity: Evidence from India. _Journal of Public Economics_ , _214_ , 104725.

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poverty line, and a much stronger negative relationship between the poverty line and gross increases in poverty.

# **Comparison with the impacts of the 2008 food price crisis**

The recent surge in food prices, which has raised the World Bank food price index above its 2008 peak, raises an important question of how the poverty impacts of this surge might relate to those of the earlier food crisis in 2008. The poverty impacts of the 2008 food crisis were extensively analyzed by Ivanic and Martin (2008), who using the price change from 2005–08 found its average impact to be an increase in extreme poverty of about 105 million additional 12
# **References**

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Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). “New Trade Models, Same Old Gains?,” The American Economic Review, vol. 102(1), pp. 94–130.

Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2019). “The Elusive Pro-Competitive Effects of Trade,” Review of Economic Studies, 86, 46–80 Artuc, E., D. Lederman, and G. Porto (2015). “A Mapping of Labor Mobility Costs in the Developing World,” Journal of International Economics, vol. 95(1), pp. 28–41.

Artuc, E., G. Porto, and B. Rjkers (2019). “Trading Off the Income Gains and the Inequality Costs of Trade Policy,” Journal of International Economics, forthcoming.

Atkin, D. and D. Donaldson (2015). “Who’s Getting Globalized? The Size and Implications of Intranational Trade Costs,” mimeo.

Atkin, D., B. Faber and M. Gonzalez-Navarro (2018). “Retail Globalization and Household Welfare: Evidence from Mexico,” Journal of Political Economy, 126(1): pp.1–73.

Benjamin, D. and A. Deaton (1993). “Household Welfare and the Pricing of Cocoa and Coffee in Cˆote d’Ivoire: Lessons from the Living Standards Surveys,” The World Bank Economic Review vol. 7, pp. 293–318.

Bergquist, L. (2018). “Pass-Through, Competition, and Entry in Agricultural Markets: Experimental Evidence from Kenya” mimeo.

Caliendo, L. and F. Parro (2015). “Estimates of
Country Level, Night Light per Capita Growth**

|sample|number of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|2055|6.01|44.40|-76.12|1641.00|
|civil war countries|1869|6.04|30.97|-63.94|564.25|
|civil war countries during peace|1558|6.62|32.30|-54.99|564.25|
|violent countries during violence|311|3.13|23.01|-63.94|116.33|

“Conflict” is defined as a year in which the number of battle related deaths is higher than 0.008 per 1000 population. GDP per capita growth rate are computed using data from Penn World Tables and World Bank Dataset. Growth rate of night light per capita are computed using data from National Oceanic and Atmospheric Administration (NOAA).
deviations below that of a reference population, increases dramatically from about the age of 6 months to 24 months, and stabilizes (but does not recover) thereafter (Shrimpton et al. 2001). Are there comparable patterns in other dimensions of child wellbeing? Do specific health insults, inadequate resources, or low levels of stimulation have especially large negative effects on outcomes if they occur at a particular age in the life of a child? What deficits can be made up later in life? These are hard questions to answer with a single cross-section of data, both because it is not possible to disentangle age and cohort effects, and because a variety of child outcomes tend to be correlated over time. There are therefore important benefits to the collection of panel data that span the life of children from birth onwards. Indeed, because in-utero conditions are likely to have an effect on subsequent child development, panels would ideally begin during a mother’s pregnancy.

Descriptive work is a critical building block for more ambitious attempts to combine economic theory and empirical analysis in an attempt to understand the causal pathways whereby characteristics of households or children affect a given dimension of development in early childhood. In the literature on the United States, there is considerable controversy about _how_ low incomes lead to poor outcomes in early childhood. Low income is often associated with a lack of resources that can affect child development—for example, toys or reading material that stimulate cognitive development, or high quality day care. Children in low income households also tend to have worse health and nutritional status, and generally have parents with lower levels of education. In addition, lower incomes are associated with higher levels of maternal depression and home environments which are less nurturing, both of which are believed to have direct causal effects on outcomes. If there is a gradient between socioeconomic status and the formation of skills in early childhood in Latin America and the Caribbean, as seems likely, careful descriptive work informed by sound economic theory about how (and why) households make choices
. Only 28 percent of refugees are employed compared to 64 percent among Ugandans. Unemployed refugees are more likely to be young, with low levels of education, who arrived in Uganda less than two years ago. One in five refugee households own a non-agricultural enterprise which is slightly less than among Ugandans. Among the employed, seven out of ten hosts and half of refugees work in the agriculture industry. As mentioned earlier, refugees are less likely to own land than hosts and mostly have user rights instead of owning the land (World Bank 2019).

The COVID-19 shock had a substantial negative impact on refugees who demonstrated a much slower recovery than Ugandans. According to results from phone surveys conducted since onset of the COVID-19 pandemic (Atamanov et

> 3 These are mostly Somalian refugees living in Kampala and who are not getting aid.

4
coverage increased significantly in recent decades. Access in the region is still low, however, at 2 percent of the population in Somalia, 19 percent in Ethiopia, 22 percent in Kenya, and 56 percent in Djibouti in 2019.

# **3 Data**

This paper uses household survey data that have been georeferenced, new spatial infrastructure data, and district characteristics in order to study links between access to infrastructure and the structure of local economies as well as the complementarities between types of infrastructure.

**Infrastructure** We collected new information on road network expansions, access to the electricity network, and access to Internet fiber backbone from various sources (Table 1).

Table 1: Summary of Infrastructure data

|Infrastructure|Country|Year|Source|
|---|---|---|---|
|Roads|Ethiopia<br>Kenya|1996, 2006, 2016<br>2003,2009,2016-18|Ethiopian Roads Authority (ERA)<br>Kenya Road Board|
|Electricity|All|varyacross countries|Nighttime lights / Population raster|
|Electricity grid|All<br>All|around 2006<br>most recent|Foster and Briceno-Garmendia (2010)<br>gridfnder.organd Arderne et al.(2020)|
|Internet(fber backbone)|All|2009-2019|Africa Bandwidth Maps 2009-19|

We gathered geospatial maps of road expansion using government sources as well as previously harmonized collections of road networks (Foster and Briceno-Garmendia, 2010; Jedwab and Storeygard, 2020). The quality of the network and associated features and the frequency of updates vary across countries. For Ethiopia and Kenya, we used a rich panel of data on road network expansions complemented by details on road conditions (a panel of GIS data and maps for 1996, 2006, and 2016 for Ethiopia and 2003, 2009, and 2018 for Kenya that rely at least partially on actual road surveys).<sup>3</sup> Panels
used is taken from Shepard (1968), a commonly used method which takes into account relative distance and direction between the meteorological stations and the centroids (see Appendix 5).

We carried out an independent interpolation for every day between 1950 and 2007, for each municipality. Since not all meteorological stations existed throughout the entire period and

> 12 MxFLS collects information on the value spent purchasing various categories of goods—food, dining out, healthcare, transportation, personal items, education, recreation, cleaning services, communications, toys/baby articles/childcare, kitchen items and bedding, clothing, tobacco, gambling, appliances and furniture, and other expenses—as well as the value of goods consumed from own production or received as gifts.  Unfortunately the value of goods consumed from own production versus the value of goods received from others cannot be separated. 13 Although the MxFLS also collected anthropometric measures for the household members, we choose not to use them as we can only get accurate height-for-age information for the first wave observations and of the potential under 36 month olds we lose about 30% due to non-measurement  and an additional 20% due to other missing information.

> 14 We took INEGI‘s 2005 catalogue of localities, which contained 2451 municipalities.

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less effective in developing countries. Maloney and Taskin (2020) analyze the determinants of social distancing and economic activity across countries. They conclude that much of the social distancing behavior was voluntary rather than a result of repressive restrictions. Azevedo et al. (2020) and Psacharopoulos, Collis, Patrinos and Vegas (2020) focus on school closures. They estimate significant negative effects on years of schooling adjusted for quality, which will significantly depress future earnings of affected cohorts. Demirg ̈u ̧c-Kunt, Lokshin and Torre (2020) estimate the effects of non-pharmaceutical interventions across countries in Europe & Central Asia and find that countries that implemented restrictions sooner had better short-term economic outcomes. Similarly, the International Monetary Fund (2020) concludes that mitigation measures have been successful in bringing down infections and set the stage for an eventual recovery from the downturn.

The analysis most closely related to this paper is International Labour Organization (2020), which specifically monitors labor market impacts, in particular the effect of workplace closures, working hours losses and labor income losses derived from labor force surveys. These reports find that a large share of the world’s workers live in countries with workplace closures (with a peak of 97 percent in April 2020). In their analysis, working-hour losses are high and translate into substantial losses in labor income. This work complements that analysis in two ways. First, it looks at a wide variety of labor market outcomes, including work stoppage, income loss, income and job changes. Second, this study considers a larger set of developing countries, particularly in Sub-Saharan Africa, that are not reporting labor statistics during the pandemic. Considering the literature as a whole, this paper makes three main contributions. First, it considers developing countries instead of developed countries, which have been the focus of most existing studies. Second, it compares the labor market impacts across a variety of developing countries spanning multiple regions. This paints a richer and more accurate picture of how the crisis affected workers than existing global or
database.

The transition matrix in Table 1 shows, for example, that 0.53 million hectares (Mha) of physical units of land of the Cerrado biome in the state of Mato Grosso, which was natural vegetation in 2002, became Pasture in 2010, while 13.16 Mha remained as natural vegetation. In the same state and period, 0.82 Mha of Pasture was transformed into Crops. The model has, therefore, for each biome in every state, a complete transition matrix. The data observed in the period mentioned above was processed to show the Markov probability that each hectare under given use in a certain year will be in another use the following year.

These transitions are also price influenced. Transition from pastures or forests to crops, for example, accelerates with the growth of the relative prices of agricultural products. Moreover, the model is flexible enough to allow exogenous projections of the level of deforestation according to desired patterns. In this case, the Transition Matrix ensures information consistency, that is, the increase of pasture, crop and forestry area in a given year must respect the increase in the available area given by deforestation in the previous year.

The transition matrices, then, determine the total land available for each broad land use group (Crops, Pasture, Forestry and Natural Forests or Unused). Once the amount of each aggregate category is determined, the model will allocate land among the activities within each category. Crop area, for example, will be allocated among the eleven agricultural activities of the model, through a CES (Constant Substitution Elasticity) function, based on the relative prices of the products of these activities.

An emissions matrix in LUC is associated to the transition matrices described above. This matrix accounts for emissions in LUC, and has the same dimensionality. The GHG emissions matrix associated to the LUC module (MCT, 2015), then, shows observed emissions on LUC transitions, by state and biome. This allows a detailed accounting of emissions on land use transitions
1) natural resource countries that include Chile, Colombia, Peru, and Costa Rica from LAC; (2) simple processing or assembly economies with Guatemala, Dominican Republic, El Salvador, and Nicaragua; and (3) countries with a broad export manufacturing base, including Brazil and Mexico.<sup>16</sup> The natural resource countries were chosen based on net exports of natural resources during 1980-2005 (see Lederman and Maloney, 2012).<sup>17</sup> The simple processing countries have high shares of exports of apparel and textiles in their total exports,

> 16 The comparators selected are based on the availability of data on export entrepreneurship in the Exporter Dynamics Database. 17 It is noteworthy that most LAC countries are net exporters of energy, mining or agriculture. In the case of Costa Rica, the economy is a net exporter of various agricultural commodities. It is also a major exporter of superconductors due to an INTEL plant. However, we work with data that excludes INTEL following existing literature (e.g., Lederman, Rodriguez-Clare and Xu 2012), partly because the story of INTEL is well known. Furthermore, although Brazil and Mexico are also net exporters of commodities, they have much more diversified export structures (as measured by the standard indicators, such as the Herfindahl index of export-revenue concentration) as well as high shares of manufactured exports over total merchandise exports.

12
Figure 20 confirms that rising fuel prices would be detrimental to the fight against poverty, while the decrease in remittances would not cause as high an increase in poverty, as a considerable share of remittance-receiving households are relatively well-off. In addition, given the manner in which the transfers are financed under the scenario _trnsfrpoor_ , the aggregate poverty rate and the poverty gap do not decrease by much. However, our scenario fares considerably better in terms of reducing income inequality. (Therefore the main message of the _trnsfrpoor_ scenario is clear: without sacrificing much in terms of growth, it is feasible to increase the welfare of poorer households.

# **Figure 20. External shock and transfer scenarios: Poverty and inequality in 2010 (%-age point deviation from** **_base_ )**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>fuelpwm<br>0.5 remitdecr<br>0.0 trnsfrpoor<br>-0.5<br>-1.0<br>-1.5<br>Headcount poverty Poverty gap (P1) Gini<br>(P0)<br><!-- End of picture text -->

## **4.4 Combination of scenarios with and without additional transfers**

The final two scenarios are combinations of changes included in the scenarios discussed above. The scenario _allgrw_ is a combination of _agrpweff_ , _indupweff_ and _transeff._ Thus, productivity is enhanced across most of the economy – the only exceptions are the public sector and part of private services -- and almost all goods (agricultural and industrial) export prices are higher than under _base_ .

Lastly, _allgrw-tr_ scenario is identical to _allgrw_ except for one addition: government transfers increase according to the specifications of the _trnsfrpoor_ scenario.

Table 14 confirms that the combination of shocks that _allgrw_ represents would bring
Source: authors. Note: The sources listed above are: Bertelsmann Stiftung’s Transformation Index (BTI); Global Monitoring Database (GMD); Barometers which comprise Afrobarometer (AF), Arab Barometer, Asian Barometer; and Latinobarómetro; World Values Survey (WVS); Varieties of Democracy (V-DEM); World Governance Indicators (WGI).

# **5. Application to Peru and South Africa**

We now present an empirical application of our measure of social sustainability in the context of Peru and South Africa. These two countries are good candidates for piloting an appraisal of social sustainability and its related fragilities in the form of multiple social gaps: both are upper middleincome economies, with high levels of income poverty (30 and 57 percent, respectively). South Africa is meanwhile the most unequal country in the world—with race playing a significant role—while Peru is one of the most unequal countries in Latin America, itself a highly unequal region. Poverty in Peru is disproportionally high among Indigenous peoples (Busso and Messina 2020, IMF 2020). Discrimination, lack of societal cohesion (including extreme hostility towards immigrants), regular episodes of social unrest, and lack of accountability are notorious features of both countries (World Bank 2018, 2022a).

To assess social sustainability multidimensionally, we use Peru’s 2019 National Household Survey (ENAHO) and South Africa’s 2018 Social Attitudes Survey (SASAS). Both are nationally representative at the department and province levels, respectively. SASAS uses face-to-face, threestage-stratification data collection to gather information on relevant demographic, behavioral and attitudinal characteristics of a representative sample of 3,500 adult individuals aged 16 and older in households spread across the country’s nine provinces.<sup>7</sup> The 2018 survey collects information on democracy and governance, intergroup relations, education, crime and security, poverty, the labor market, household characteristics and assets. The ENAHO likewise uses face-to-face interviews and a probabilistic three-stage sampling method to collect information on personal
schooling attainment remains low in rural areas of both Bangladesh and India at the time of the survey years. The mean and median years of schooling are 4.97 and 5.00 respectively for Bangladesh, and 6.23 and 7 for India. The relatively lower education attainments in Bangladesh compared with India were present during parents’ generation as well: median years of father’s education was 2 years in Bangladesh compared with 2.50 years in India. The average number of children per household in Bangladesh is about 5.74 compared with 3.53 in India. This difference probably reflects the fact that Bangladesh data include information on children from other marriages while India data do not. There are some differences in the age distribution of children also: median age for Bangladesh data is 30 years compared with 33 years for India. The gender gap in education between boys and girls is about 1 year in Bangladesh in contrast with 2 years in India.

Table A.1 also reports the ratio of standard deviation of parent’s education to that of children’s education for both all and coresident children in columns 3 and 7. The ratio is unambiguously smaller in the full sample (including both coresident and non-resident children) compared with that in the coresident sample. This is consistent with the observation noted earlier in the introduction that a higher estimate of this ratio in a coresident sample is likely to partially offset the biases in IGC estimates.

Figures 1.A (Bangladesh) and 1.B (India) plot the probability of nonresidency at the time of the survey against the schooling of children. The graphs in both Bangladesh and India show that probability of nonresidence is higher in the tails. Also, the probability of nonresidence is higher for girls at any given level of schooling, although the gender gap closes substantially at the right tail in the case of Bangladesh.

# **4. Empirical Results**

We begin the discussion with a graphical presentation of the data, following the classic analysis of truncation