rafmacalaba/data-use-mentions-extended huggingface dataset:
39 As far as the first question is concerned, relatively higher incomes, are, but not necessarily, associated with a nutritionally better diet. As shown in Table 3.2, in Indonesia higher income groups spend a smaller proportion of their food budget on rice, corn, wheat and cassava, and a higher proportion on fish, meats and poultry, eggs, dairy products, fruits and other foods which include soft drinks and convenience foods. This shift in composition is associated with an actual fall in consumption of corn, wheat, cassava and potatoes. Overall nutritional intake is higher among the higher Income groups nonetheless (Table 3.3).

With respect to the second question of how sources of income affect the diet, data from Indonesia suggest (Table 3.6) that, all other things being equal, including level of incomes, people whose main income source is agriculture do better almost across the board as far as consumption of nutrients is concerned than people whose main source of income is industry. (Exceptions are likely to be iron and calcium). This may be because people involved in agriculture can benefit first from technological change and are less prone to a reduction in real incomes as prices of produce rise. Another categorization of sources of income is by gender. There is some evidence that women's earnings have a greater HPC on food than men's earnings and that children (but not necessarily infants) of wage-earning mothers are nutritionally better off than children of non-wage earners. The phenomenon is particularly important when one considers that between 25 - 35%, of households in the developing world, are headed by women (Huffman, 1987).
using an accounting approach can double-count investment needs by ignoring synergies across different types of investment and across countries. For example, improvements in water and sanitation infrastructure can reduce child mortality. Improvements in health indicators can reduce poverty, and vice-versa. In South Asia, differences in seasonal patterns of energy supply and demand across countries implies that unrestrictive cross-border energy trade would reduce the need for building new power plants (Timilsina et al. 2015). In addition, benchmarking exercises do not attempt to optimize the supply of infrastructure, and typically do not consider the sensitivity of estimates to assumptions about growth and socioeconomic challenges, such as the effect of the migration of large numbers of people across national borders on demand for infrastructure services or the growing impact of natural disasters in many countries.

- _Operation and maintenance costs and discounting._ Many SDG costing exercises do not consider the operation and maintenance needs related to infrastructure, nor are the estimates discounted in a consistent manner. Operation and maintenance costs are significant, especially in the transport and water and sanitation sectors (Rozenberg and Fay 2019). These costs are expected to represent an even larger share of infrastructure investment needs as the number of unserved people falls in the future, and with it the cost of initial capital investments (Hutton and Varughese 2016).

> 3 The cost estimates provided by the WBG, IMF, and UN are not strictly comparable due to differences in country samples, subsectors (e.g., low-carbon transport in the WBG study—rail and bus rapid transit—versus only roads in the IMF study), and use of discounting, among other things. They also largely differ in their assumptions about initial conditions and other contextual factors under consideration, which do not allow a systematic comparison among various estimates.

3
Policy Research Working Paper 10153

# **Abstract**

Burning off the gas coming out of oil wells—gas flaring—is a common practice in oil-producing developing countries. This economically wasteful and environmentally damaging process occurs because infrastructure has been built with a focus on oil production rather than gas capture and because weak regulations and limited environmental monitoring make flaring an attractive choice for oil producers. Moreover, gas flaring is harmful to human health, especially because of pollutants. This research focuses on Nigeria, where over 10 percent of all gas produced is flared and about 2 million people in the Niger Delta live within four kilometres of a gas flare. While several studies from developed countries examine relationships between gas flaring and human (especially infant) health, a lack of data limits what research is possible in developing countries. This paper uses infant health data from Demographic Health Surveys, and satellite-detected data on gas flaring to examine the effects of flaring on disease incidence and infant mortality in oil-producing regions of Nigeria. The findings show a strong positive association between gas flaring and the incidence of respiratory diseases and fever among children younger than five years. The study contributes to the literature measuring the wider cost to society of oil and gas production and adds to a growing body of work using satellite data to understand well-being in places where conventional data sources are unavailable or unreliable.

This paper is a product of the Office of the Chief Economist, Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at oalimi@worldbank.org, omoniyi.alimi@waikato.ac.nz, and john.gibson@waikato.ac.nz.

_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
imputed poor population is larger in the rest of the country than in the National Capital District.<sup>14</sup> But in addition to imputing an estimate of monetary poverty, we also estimate the World Bank’s MPM. The MPM includes 6 different indicators across three welfare dimensions- monetary poverty, education, and access to basic services (World Bank 2018). The monetary poverty measure is the international poverty line using the $ 2.15 (2017 PPP) per day poverty line; the indicators capturing access to education include indicators addressing the educational attainment of adults and the attendance of schoolaged children; and access to basic services include indicators capturing access to proper sanitation, clean drinking water, and access to electricity.<sup>15</sup> Although monetary poverty is an indicator in the World Bank’s MPM and is not directly able to be estimated from the 2016-18 DHS, we use the imputed poverty rate for each household described above. See Table 2 for a summary of the dimensions and the indicators captured in the standard World Bank MPM.

For each of these individual indicators, we report the share of the population that is deprived relative to the thresholds of the MPM. We also report the share of the population that is multidimensionally poor, which is a measure of the degree to which deprivations overlap in individual households.<sup>16</sup> Specifically, the measure is defined as the share of people that fall short of the MPM threshold in at least one dimension, or a combination of indicators equivalent in weight to a full dimension.<sup>17</sup><sup>_,_18</sup>

> 14In results not reported, the difference is statistically significant at the 1 percent level.

> 15A detailed list of the indicators that were calculated for PNG from the 2016-2018 DHS are available from the authors upon request.

> 16This figure is referred to as the multidimensional poverty headcount ratio. We refer to this figure throughout the
**Figure 3. Financial Policy Response Activity Index Across Regions (September 1**<sup>**st**</sup> **, 2020)**

<!-- Start of picture text --> HInc EAP ECA LAC MENA SA SSA<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database. Note: Boxes show the median and the interquartile range. Dots show outliers.

Note: World Bank regional classification. High Inc. = High income; EAP = East Asia Pacific; ECA = Europe & Central Asia; LAC = Latin America & Caribbean; SAR = South Asia; MENA = Middle East & North Africa; and SSA = Sub-Saharan Africa. WBG regions exclude highincome countries.

**Figure 4. Financial Policy Response Activity Index and Selected Country Characteristics (September 1**<sup>**st**</sup> **, 2020)**

**Panel A. Log cumulative COVID-19 cases per Panel B. Log population size 100,000 people**

<!-- Start of picture text -->
-2 0 2 4 6 8 10 15 20<br>Log cumulative cases per 100,000 Log Population size<br> High Inc  EAP  ECA  LAC   High Inc  EAP  ECA  LAC<br> MENA   SA   SSA   MENA   SA   SSA<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>Financial Policy Response Activity Index Financial Policy Response Activity Index<br>0 0<br><!-- End of picture text -->

15
contactless payments up to 2020:12 (the control group).<sup>10</sup> To ensure that the merchants in my sample are actively in business and have not been directly affected by the measures, I require them to have positive card sales for 24 months (all months from 2019:01 to 2020:12) and to be an essential sector including supermarkets, groceries, bakeries, pharmacies, health, tobacco stores, and fuel. Finally, I exclude all online stores from my sample because the increase of contactless payment limit should only affect offline sales. After this step, I am left with 94,199 unique merchants in total during the two-year period. My data set covers 75,668 merchants in the treatment group and 18,531 merchants in the control group.

Although the COVID-19 crisis is exogenous to merchants and allows me to compare both groups using the difference-in-differences approach, merchants who accept contactless payments may not be directly comparable to those who do not. Indeed, merchants may decide when to accept the contactless payment technology, thereby making the acceptance of contactless payments a non-random experiment. To avoid possible bias, I use a score matching approach to compare merchants with similar pre-treatment characteristics. These include merchant characteristics and its city characteristics. A merchant may indeed for instance be more willing to accept contactless payments as it is a supermarket and the contactless payments is high in its city. Therefore, in addition to score matching, I use difference-in-differences setting and rely on the untreated merchants to investigate the resilience to shock as well as the effect of the staggered shocks on the substitutions between payment methods. This approach requires the control group to have the same sales patterns as the treatment group in the pre-treatment period so their sales after the shocks constitute a valid counterfactual.

Table 1 provides summary statistics on merchant and its city characteristics for the treatment and control groups in my sample. It highlights that the control group (non-NFC equipped merchants) is not directly comparable with the treatment group (NFC equipped
look at addressing causality concerns using firm-level panel data, propensity score matching, and the Rajan and Zingales (1998) methodology.

# **_A. Enterprise Survey sample_**

We begin our analysis by running firm-level regressions of employment growth on access to finance across 70 countries in the ES as specified in equation (1) below: _Employment Growthi_ ,j,k,t= α _Access to Finance_ i,j,k,t-1 + β _X_ i,j,k,t + γZ j,t-1 + Cj+Yt+ ηi,j,k,t (1) Where i identifies firms, j countries, k sectors/industries and t years. _Employment Growth_ refers to the annual employment growth. _Access to Finance_ is an indicator variable equal to 1 for firms that have an outstanding loan. _X_ is a matrix of firm level characteristics such as firm size, ownership (foreign or government owned), exporter status, and age. Z captures country level variables that can affect employment growth such as inflation and GDP growth. Cj and Yt are a vector of country and year fixed effects, respectively. With the repeated cross-sections in ES, we obtain within-country estimates that control for non-time-varying country controls in the empirical specification. The regressions are estimated using ordinary least squares with standard errors clustered at the survey level.

We first estimate equation (1) for all firms to understand the overall correlation between financial access and employment growth. We follow this up with separate estimations over subsamples of MSMEs, SMEs and large firms, and test for differences in the impact of financing for these groups.

13
who examines whether peer effects generate changes in education investments in the areas where deported criminals are located in Central America, using administrative data on El Salvador. Our paper also contributes to the general analysis of how repatriations affect origin locations, with a special focus on firm behavior.

# **II Conceptual Framework**

There are two direct channels through which repatriations can affect labor markets in an economy with no market segmentation (i.e., no formal and informal divisions): a positive supply and demand shock. Ultimately, the effects that we observe empirically on wages and employment will be a combination of both shocks and their relative strength.

1. _Supply Shock:_ if repatriated individuals spent a long time abroad, they would increase the overall supply of labor upon their return. On the other hand, if they were deported within a short time period after leaving, it is unlikely that this would be the case since labor markets may still have not adjusted since their departure.

If the supply shock prevails, a simple supply and demand model predicts that repatriated individuals who spent a significant amount of time abroad would lead to _lower wages_ and _higher employment_ .

2. _Demand Shock:_ there are many channels through which repatriated individuals can prompt a positive demand shock. The most obvious one is through higher consumption levels that are being pushed by the larger population shock. This channel is likely to be weak if deportees return with fewer resources than before departure.

A second, more indirect channel, through which repatriated individuals can affect firms’ demand for labor is through intrinsic changes that they can cause to firms production processes if they are employed by those firms. For example, repatriated individuals can bring 5
.79|34.61|0.32|74.84|107.21|43.28|18.10|
|Paraguay|7,499,000,000|1,327,000,000|2,536,386|4,052.64|18.93|137.76|1.52|22.25|56.01|0.09|58.82|81.36|21.06|25.45|
|Peru|56,480,000,000|11,610,000,000|12,200,000|12,417.64|8.12|141.56|3.22|31.38|29.31|0.11|60.51|227.77|33.12|30.46|
|Uruguay|22,490,000,000|3,050,000,000|1,567,053|2,879.18|8.85|114.26|2.21|26.00|31.69|0.27|65.15|290.09|34.43|16.98|
|Venezuela, RB|19,600,000,000|24,710,000,000|10,400,000|57,239.00|4.50|97.75|2.90|50.03|44.50|0.37|45.46|157.55|39.74|30.46|
|Total|119,279,187,500|21,787,343,750|12,265,342|34,465|11|115|4|30|50|0|59|205|28|19|

Source: Authors‘ calculation from the World Bank‘s World Development Indicators (WDI).

16
2010). Shadow Economies All over the World: New

- Estimates for 162 Countries from 1999 to 2007 (Revised Version). _World Bank Policy Research Working Paper_ No. 5356.

Schneider, F. and D. Enste (2000). Shadow Economies: Size, Causes, and Consequences, _Journal of Economic Literature,_ 38(1), 77-114.

StataCorp (2005). Stata Statistical Software: Release 9. College Station, TX: StataCorp LP.

Statistics Estonia (2010). Employees by Indicator, Place of residence, Sex, Type of employment relation and Year. Online statistical database: Social life/Labour market /Employees: ML262.

Stock, J.H. and J.H. Wright (2000). GMM with Weak Identification. _Econometrica_ 68(5): 1055-1096.

- Torgler, B. (2007). _Tax Compliance and Tax Morale: A Theoretical and Empirical Analysis_ , Cheltenham, UK: Edward Elgar

Torgler, B. (2010). Tax Morale and Compliance: Review of Evidence and Case Studies for Europe. Background paper for _In from the Shadow: Integrating Europe’s Informal Labor_ , a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries (Task number P112988).

- Torgler, B. and F. Schneider (2009). The Impact of Tax Morale and Institutional Quality on the Shadow Economy, _Journal of Economic Psychology_ . 30: 228-245.

Torgler, B., M. Schaffner, and A. Macintyre (2010). Tax Compliance, Tax Morale, adn Governance Quality, in: Alm, J., J. Martinez-Vazquez, and B. Torgler (eds.) (2010). _Developing Alternative Frameworks for Explaining Tax Compliance_ . London: Routledge: 56-73.

37
52

|**Country E R**|**Pricing policy and strategy**|**Fuel supply conditions**|
|---|---|---|
||time, for example the price of 92 RON gasoline in Astana between<br>August 2011 and March 2012. Because wholesale prices are not<br>controlled, recently wholesale prices—governed in part by import<br>prices—have exceeded retail prices. In Aug 2011, the oil ministry<br>and oil companies signed a memorandum to limit wholesale prices<br>to 87% of maximum retail prices. There is a preferential lower price<br>for diesel supplied to farmers. In 2012, the preferential price was<br>10–15% below the non-agricultural diesel price level.<br>**Use of export ban to lower domestic prices**: Regional shortages of<br>gasoline and diesel have prompted government to introduce seasonal<br>export bans on petroleum products to ensure sufficient domestic<br>supplies. In May 2010, government banned exports for six months.<br>The expiration of this ban has since been repeatedly extended, from<br>Nov 2010 to May 2011, July 2011, Dec 2011, Jul 2012, Dec 2012,<br>and most recently to end-Jun 2013.<br>**Use of export tax to lower domestic prices**: Although not as high<br>as those in Russia, export duties are sizable, and were increased to<br>US$114.05 /tonne in Aug 2011 for light products.<br>**Information**: The Statistical Agency of Kazakhstan posts monthly<br>average prices of gasoline and diesel in 18 cities on its Web site.|<br> <br> <br>sector to boost capacity and improve<br>product quality over the next several years<br>for self-sufficiency.
poverty or prevent those households from engaging in costly risk coping mechanisms such as selling-off assets or migrating away from employment that could result in these households joining the ranks of the chronically poor.

Before the pandemic, social safety net targeting in Africa was mostly oriented towards identifying chronically poor households rather than vulnerability to shocks. Most social safety net programs developed in Africa in the first decade of the twenty-first century employed Proxy Means Testing (PMT) for targeting beneficiaries (del Ninno and Mills, 2015). PMT targeting provides a standardized and objective way to identify chronically poor households at relatively low cost. The targeting accuracy of the PMT approach is moderately effective in general (Coady et al., 2004; Brown et al., 2018), but because it is based on household assets at some point in the past, it is least effective at identifying households that are most vulnerable to shocks such as the COVID-19 pandemic. Furthermore, collecting household surveys during 24
0.293|0.266|0.249|0.241|0.260|0.262|0.241|
|Share of informal workers|0.302|0.279|0.259|0.281|0.250|0.224|0.229|0.211|0.210|0.195|0.206|0.211|0.216|0.203|
|Share of self-employed|0.715|0.754|0.702|0.847|0.703|0.713|0.705|0.774|0.731|0.791|0.719|0.680|0.635|0.703|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Nominal hourly wages are converted into real terms using national consumer price index deflators and expressed in dollars using 2005 purchasing power parity. The 1st and 99th percentiles of real hourly wages in each country-year were trimmed. If a specific country did not have information available for a particular year, a simple interpolation was applied. The sample was restricted to individuals between ages 18 and 65 years.

62
# **1. Introduction**

Traditionally, except for the United States, for which data on metropolitan statistical areas (MSAs) are readily available, urban economists have relied on data for cities as defined by their administrative boundaries. However, administrative boundaries often fail to adequately delineate the “true” boundaries of a city, leading to cities being, sometimes substantially, _“under-”_ or _“overbounded”_ (administrative boundaries under- or, respectively, overstating the true city area). This issue has been highlighted for developing and developed countries alike, especially in situations where urbanization has been rapid, and cities have been growing quickly in terms of not only population, but also in the land area which they cover (see, for example, Ellis and Roberts, 2016).

In reaction to this, there have been a growing number of attempts in recent years to develop and apply algorithms that enable the better delineation of cities and metropolitan areas. These attempts have been led not just by economists, but also by both geographers and the remote sensing community, in which there is a very long tradition of using satellite imagery to help define a city’s “true” extent (see, for example, Danko, 1992; Elvidge _et al_ ., 1996). Moreover, many of them have been driven by international organizations such as the Development Bank of Latin America (CAF), The European Commission (EC), the Organisation for Economic Co-operation and Development (OECD), and the World Bank. The ambition of these organizations has been to construct consistently defined global data sets of cities to facilitate the uniform measurement of urbanization.<sup>5</sup> While the preferred approach of economists to defining cities and metropolitan areas tends to be rooted in a labor market perspective based on the use of commuting flow data, as with the definition of MSAs in the US, such data is hard to come by for many countries in the world, especially for many
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 11. Rates of expansion of access to safe water and improved sanitation are above the African average**

<!-- Start of picture text --> a. Water b. Sanitation<br>4.0 4.00<br>3.0 3.00<br>2.0 2.00<br>1.00<br>1.0<br>0.00<br>0.0<br>-1.00<br>-1.0<br>-2.00<br>-2.0<br>Senegal SSA Senegal SSA<br>% population getting access every year<br>% population getting access every year Piped water Stand posts Flush Toilets<br>Well/boreholes Surface water Improved latrines Traditional latrines Open defecation<br><!-- End of picture text --> _Source:_ World Health Organization (2010a and 2010b) from Demographic and Health Surveys for 1993, 1999, and 2005. _Note:_ SSA = Sub-Saharan Africa.

**Table 10. Evolution of operational indicators associated with the urban water utility SDE**

|Year|Water<br>delivered<br>(million<br>m<sup>3</sup>/year)|System losses<br>(%)|Collection ratio<br>(%)|Average cost<br>($/m<sup>3</sup>)|Average<br>effective tariff<br>($/m<sup>3</sup>)|Total hidden<br>costs<br>($ million/year)|Total hidden<br>costs<br>(% revenues )|
|---|---|---|---|---|---|---|