rafmacalaba/data-use-mentions-extended huggingface dataset:
Tolley, George S. "The welfare economics of city bigness." Journal of Urban Economics 1, no. 3 (1974): 324‐345.

- U.S. Environmental Protection Agency . (s.f.). _The Economics of Recyling in the Southeast: Understanding the Whole Picture. Archive Document._

UN Habitat. (2012). _Urban Patterns for a Green Economy: Clustering for competitiveness._ Nairobi: UNON, Publishing Services Section.

UN Habitat. (2016). Chapter 7: A City that Plans: Reinventing Urban Planning. En _WORLD CITIES REPORT._ United Nations. (2013). Chapter III. Towards Sustainable Cities. En U. Nations, _World Economic and social survey 2013. Sustainable Development Challenges._ Value of Water Campaign. (2017). _The Economic Benefits of Investing in Water Infrastructure._ van Donkelaar, A., R.V Martin, M.Brauer, N. C. Hsu, R. A. Kahn, R. C Levy, A. Lyapustin, A. M. Sayer, and D. M Winker, Global Estimates of Fine Particulate Matter using a Combined Geophysical‐Statistical Method with Information from Satellites, Models, and Monitors, Environ. Sci. Technol, doi: 10.1021/acs.est.5b05833, 2016.

World Bank. (2012). _Republic of Côte d'Ivoire. Côte d'Ivoire Urbanization Review._ The World Bank. GSURR. Africa.

World Bank. (2016). _Competitive Cities for Jobs and Growth. What, Who, and How._ Washington, DC: World Bank.

35
<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. CB refers to the cross-border transfers of data, whereas DR refers to the domestic data processing. Digital STRI denotes the OECD’s Digital Services Trade Restrictiveness Index for which is the grouping of Infrastructure and Connectivity is used. The OECD Digital STRI variable is taken for the importer’s side and is 25 Note however that in Ferracane and van der Marel (2018) the authors use a different identification strategy that is not based on the gravity model and uses imports as opposed to exports, as this paper does.

31
There are a number of technical questions that could be explored with further work. One such question is what level of disaggregation can be handled satisfactorily. Similarly, what time-gaps between estimation and application of the model can be handled; as the LFSs move further away from the last administered household consumption survey, the assumption that the consumption model has not changed over time becomes increasingly optimistic, in which case the imputation-based poverty estimates are expected to become less reliable. Other empirical questions include: Are upward and downward trends captured equally well? Can other moments of the income distribution, such as average income and inequality, be captured equally well? And, can we generally say something about the conditions under which the approach is expected to do well or not so well? Further empirical work is needed to answer these questions.

Finally, in order to strengthen the validation of the proposed approach some alternative tests could be explored. For example, instead of imputing household expenditure one could consider a model for a dependent variable that is readily available in the LFS (such as education, employment or durable assets). This has the important advantage that imputed data can be compared to actual observed data for all available rounds of the LFS.

# **9. Concluding remarks**

The paper has shown that Labor Force Surveys (LFSs) can be used effectively to estimate poverty when Household Expenditure Surveys (HESs) are not available. We have used cross-survey imputation methods to estimate poverty rates for Morocco and obtained a quarterly series for the period 20012010.

Results are very encouraging. We constructed first a consumption model based on the 2001 HCS data and a second model based on the 2007 HCS data. We then used the 2001 model to estimate poverty with the 2001 and 2007 HCS data and, vice-versa, used the 2007 model to estimate poverty for both the 2001 and 2007 years. The imputation-based poverty estimates are found to be close to the official poverty estimates, regardless of which model is used. Next, we used the 2001 and 2007
127 countries, we were unable to use any of the sources presented thus far. Many of these countries are small, such that these countries together account for roughly 18% of the global population. For this group of countries we use three other methods in the following order of preference.

_Use sectoral national accounts growth rates._ As with the HFPSs, we use the sectoral national accounts growth rates to estimate urban and rural growth rates. We apply those growth rates to the 2019 distribution, such that households could only experience two different growth rates in 2020. By design, this will only capture a small fraction of the within-country inequality changes that happened in 2020. This method is applied to 56 countries covering 13% of the global population (half the population in the Middle East, nearly a quarter in South Asia, and close to a fifth in SubSaharan Africa).

_Apply national growth rates._ For some countries we lack sectoral national accounts or information on whether a household resides in an urban or rural area. For those countries, we apply the growth rate in real GDP per capita to all households, which means that we do not allow for any within-country distributional changes. We use this method in 21 countries covering 2% of the global population.

13
losses in LICs. First, the extent of restrictions to mobility and economic activity, as measured by the OxCGRT stringency index, was somewhat lower on average in LICs. In addition, LICs have a much higher share of agricultural employment and a higher share of population residing in rural areas, which have not been affected as much by mobility restrictions as those living in urban areas. Finally, the HFPS data do not directly capture changes at the intensive margin, such as changes in hours worked which have been shown to have been affected significantly (ILO

> 15One possible estimate of recall bias within the HFPS is to compare a respondent’s recalled employment status for the previous wave and their employment status reported in that wave (in countries asking both questions). Another estimate of recall bias is possible by comparing the HFPS estimate based on recall with prepandemic LFS data. This is likely to be an upper bound estimate because several other sources of error are expected to add to any recall bias when making this comparison. Using these methods, we estimate prepandemic employment levels in HFPS include recall bias less than zero, on average, in the first case (24 countries), and up to 6 percentage points using the upper bound LFS method (26 countries). Upper bound recall bias estimates remain smaller than employment losses in 2021 estimated from HFPS for 16 of the 21 countries with both data sources, suggesting evidence of unrecovered employment is quite robust (Figure A4.4).

14
relating to income, is likely related to the supply of secondary school services. Disparities across indigenous and non-indigenous groups are sizable in Panama, Guatemala, and Honduras. An important result is that gender differences are relatively small, with females generally showing slightly higher enrollment rates than males across the secondary age group, with the exceptions of El Salvador and Guatemala where males show slightly higher rates.

In most Central American countries, schooling enrollment disparities across the household income distribution are relatively small in primary and start to expand in LS. As Figure 8 shows, at the primary level (first panel in the left) more than 90 percent of children in the relevant age group are actually enrolled in primary across the income distribution in most countries. The exceptions are Guatemala and Nicaragua, where enrollment rates for the poorest four deciles are between 80 and 90 percent. Enrollment rates reach over 95 percent in all countries as household income levels rise. In contrast, LS enrollment rates among individuals in the poorest decile of households are about 30 percentage points lower than those in the richest decile, with extreme cases like Honduras registering a distance of more than 50 points. By US, differentials between the poorest and richest decile reach 40 percentage points in most countries, with Panama showing the largest gap of 55

> 10 Some recent studies for Nicaragua (see for instance Ministerio de Educación de Nicaragua (2014)) and Guatemala (Ministerio de Educación de Guatemala (2014)  have argued that education coverage rates at the primary level after 2010 have declined, although there is controversy about the data sources from which this conclusion emerges. In particular, the calculations are based on school attendance rates from the administrative records of the Education Ministry while data on the number of individuals in the relevant age group (the denominator in the equation) is from extrapolations based on Census data. We do not find the same pattern in our household survey data.

> 11 Country-specific plots are in Appendix Figures A.1 to A.4.

9
# 3. Data and methods

# 3.1. Inputs

The conditions of data production in Nepal mirror the physical conditions of the roads themselves: fragmented, incomplete, and least comprehensive for the most remote areas (Bikas Udhyami, 2018). In this environment, gathering sufficient primary data to accurately assess travel times and accessibility is difficult. Our model manages these issues by using global and freely available datasets – Shuttle Radar Topology Mission (SRTM) 30 m terrain elevation model, High Resolution Settlement Layer (HRSL) raster population data sets, OpenStreetMap, national land cover products (ICIMOD 2013) – and core governmental datasets like roads, health care, government offices, and financial institutions that a partner government agency can be reasonably expected to possess. We employ the HRSL data set instead of the WorldPop data set used in Banick and Kawasoe (2019) because it represents uninhabited areas as “Null” instead of fractional values as in WorldPop, and thus better capture Karnali’s unpopulated expanses, illustrated in Figure 3(b). In all cases OpenStreetMap data can provide a suitable “backstop” data set for missing or inaccurate governmental datasets. The data sources that were used to model accessibility to services are summarized in Table 1.

Table 1: Data sources used in this research.

|Data|Source|Date|Resolution<br>or Type|
|---|---|---|---|
|Ofcial road network of Nepal|Survey Department of Nepal|2019|Vector (Lines)|
|Road and path network of Nepal|OpenStreetMap|Dec 2019|Vector (Lines)|
|Trail bridges|Trail Bridge Infrastructure<br>Support Program (TBIS)|Aug 2019|Vector (Points)|
|Proposed roads in Karnali province|Karnali provincial government<br>(via RAP)|Aug 2019|Vector (Lines)|
|Terrain|NASA Shuttle Radar<br>Topology Mission|Global|1 arc-second<br>(≈
On the other hand, the ongoing banking crisis and formal credit squeeze might be expected to have a larger direct impact on men than on women, since men are the majority of financial services users and formal sector borrowers. However, women are the majority of clients of microfinance institutions (MFIs). Women comprised 85 percent of the poorest 93 million clients of MFIs in 2006 (Microcredit Summit Campaign Report 2007), and as credit dries out, their earnings from micro-businesses are expected to drop. This should be especially true in Latin America and Eastern Europe and Central Asia, where MFIs obtain a significant portion of their lending resources from commercial rather than concessional (grant) sources (CGAP 2009).

# **3.2. Fertility**

Empirical evidence on how fertility is affected during economic crises is available mainly for middle income and developed countries and suggests that fertility rates are pro-cyclical. The research reviewed here examines the impact of business cycles (GDP growth rates, unemployment rates) on fertility (e.g., total or age specific fertility rate, or the probability of giving birth). Using panel data for 18 Latin American countries covering over 45 years and a separate analysis of transition to first, second and higher order births using Demographic and Health Surveys, Adsera and Menendez (2009) find that women postpone and in some cases even reduce childbearing during economic crises; this fertility response appears to in response to increases in unemployment rather than slowdown in GDP growth. They find fertility postponement to be stronger for younger, urban, and more educated women; however, the association between economic slowdown and likelihood of second or third childbearing is strongest among the least educated women. Further, with the spread of family planning in Latin America, the 13
Arab||Malawi|Sudan|
|Vietnam||Jamaica<br>Nicaragua|Republic<br>||Mozambique<br>Namibia|Tanzania<br>Zambia|

_Regression_ analysis confirms that countries that are vulnerable to gradual global warming also show macro‐financial vulnerabilities that could affect their ability to respond to the disruptions generated by climate change (Table A1.3). The “sensitivity” component of the Notre Dame Vulnerability Index is the main channel of transmission between climate change distortions and macro‐financial vulnerabilities, while the “adaptative capacity” component of the index offsets the effects of the “sensitivity” channel.

- **Fiscal position:** Countries with higher “sensitivity” to climate change impacts tend to have higher levels of public debt and lower sovereign ratings. This result means that countries that could be more affected by physical‐gradual risks of climate change have less fiscal space to respond to the challenges of that phenomenon. Countries with higher “exposure” also show higher public debt levels, while countries with more “adaptative capacity” show lower public debt.

- **Banking sector:** Countries that are more vulnerable to climate change show lower bank provisions (% of NPLs). Even though we find no statistical association between NPLs (% total assets) and the Notre Dame vulnerability index, provisions show a negative and significant relationship with the vulnerability index via its “sensitivity” component.

- **Macro‐financial risks:** Countries with higher vulnerability to climate change also show higher macro‐financial risks, particularly in the public sector bucket, based on the EIU Index. Consequently, the ability to respond to the challenges of climate change is limited in countries with weaker macro‐financial conditions. At the same time, there is a negative correlation between country risk and countries’ readiness to adapt to climate change (Figure 9). The negative correlation between country risk and readiness to adapt may partly reflect the fact that there is some overlap in what
IMF survey of the Indian economy suggests a robust 7-8 percent growth in the next few years in spite of a global economic slowdown, it will be necessary, according to the IMF, to focus on reinvigorating the structural agenda, rather than relying on monetary and fiscal stimulus to ensure sustainable growth. Measures to facilitate infrastructure investment, reform the financial sector and labor markets, and address agricultural productivity and skills mismatches stand out. Also according to IMF, reorienting expenditure toward social areas is vital to make growth more inclusive (which, in turn, would boost growth).<sup>8</sup> 7. Second, an "environmental baseline" was constructed according to our estimations of PM10 and other small particles.<sup>9</sup> Third, a health module was developed outside 5 Another CGE model that looks at the carbon impacts of different growth paths for India is Ojha, 2005, 2008. His model is much smaller (11 sectors) and does not look at local pollutants such as PM10.

6 The standard version of the model represents the world economy in the form of 57 sectors/economic units trading with each other for 113 countries/regions. In this study, India is disaggregated from the rest of the regions and from the other South Asian countries.

7 IMF (2011). World Economic Outlook: Slowing Growth, Rising Risks, September 2011.

8 IMF (2012). India: 2012 Article IV Consultation-Staff Report.

> 9 From the literature, the contribution to the costs of environmental degradation traditionally include not only PM10 and poor water supply and sanitation, but also groundwater depletion and soil degradation, which play a significant role in agriculture.  These are not included in this study due to data and modeling constraints.

4
significant impact in increasing enrollment, except when using the measure asking parents to show evidence of enrollment (p-value: 0.135).<sup>16</sup> In the comparison between conditional and unconditional transfers (Panel B), the coefficient on “treatment” can be interpreted as the impact of conditional cash transfers. Again, these coefficients are all positive and significant, indicating that conditional cash transfers encouraged school enrollment. The coefficient on the UCT indicator is negative for the 3 measures and significantly so for the first two, indicating than unconditional cash transfers were less effective than conditional transfers in increasing enrollment. This finding is confirmed when looking at the p-values reported got “Treatment + UCT”: only for the school-based measure does the p-value suggest a combined coefficient statistically different than zero.

The impact of the cash transfers on enrollment at round 2, one year into the experiment, is not as strong or significant. Only using the school-based measure (column 1), can we detect a significant impact of cash transfers (transfers given to fathers and both conditional and unconditional cash transfers). One likely explanation of this more limited impact of cash transfers at round 2 is that due to logistical reasons, the cash transfer program was unexpectedly launched late by Burkina Faso’s government in the 2008-2009 school year. The first cash payment was only made at the end of November/early December 2008, while the school year started October 1, 2008. This meant households did not receive the transfer in time to pay school fees due at the start of the academic year and this might have reduced their ability to enroll their children during the program’s first year.

Column 4 reports the cash transfers’ impact on unconditional school attendance. Given that attendance conditional on enrolment is very high (98.3%) since once enrolled children tend to attend classes, our approach consists of estimating the impact on average school attendance rates of all school age children, unconditional on their enrollment. This is a broad measure
5

# **III. Interpreting the Coefficient**

Adding a residual to equation (6) and estimating the regression equation, the coefficient of schooling is an estimate of the average percent increase in earnings per year of schooling.' Note that the coefficient of S is not the rate of return from schooling, but rather is rK. If the parameter K is known and the regression coefficient (b) is estimated, the rate of return from schooling is _r = b/K._ The parameter K = 1 if the investment in schooling equals the full-year potential earnings if there were no further investment. This assumption was made to simplify the exposition in the orginal formulation of the specification (Becker and Chiswick, 1966). This assumption is also made in later treatments (see, for example, Mincer 1974, Willis 1986, Rosen 1987, Psacharopoulos and Mattson 1996). In most estimates of the rate of return from schooling using the HCEF there is no acknowledgmen **t** hat this assumption is made; the coefficient of schooling is just accepted as the rate of return from schooling.

There are certain circumstances in which K= 1. Ct = El 1 can occur, for example, if there are no out-of-pocket costs (Cd = 0) and the foregone earnings or opportunity cost (Cf = Et,1<sup>) is a full year's earnings. It would also occur if, for example, opportunity costs were</sup> 75 percent of full-year potential earnings and it just so happened that direct costs were equivalent to 25 percent. However, K need not equal unity. Consider a case in which the direct costs of school are fully funded by the govermment, including books and school 3 Mincer (1974) shows that the coefficient of schooling (S) is biased downward if years of labor market experience is not included in the equation.
# **3. Data and descriptive statistics**

# **a. Data and key variables**

World Bank lending data come from the Business Intelligence (BI) unit.<sup>10</sup> For each fiscal year 19612016, the data list all loans made by the Bank (including DPFs, IPFs and P4Rs). For each loan, the data also reports the borrowing country, the Global Practice (GP) that administered the loan, the loan amount (including if the loan is from IDA or IBRD funds), and the date the loan was committed. For this paper, we map the loans to fiscal years, and construct a yearly data set where each observation is a country-year.

Although the organizational structure of Bank changed over time (with two major reorganizations in 1997 and 2013), the purview of three HD units – Education (EDU), Health, Nutrition and Population (HNP) and Social Protection (SPL) – has remained intact. For the remaining units, we could map current and historical units into current vice-presidencies (VPs – Human Development (HD), Sustainable Development (SD), and Economic and Financial Institutions (EFI)) with accuracy. We use the GP and VP mapping of loans to construct several variables. Our first variable of interest is if a country borrows at all in a given year and if it borrows for HD. Similarly, for both overall lending and lending by HD GPs, we compute the number of projects and total lending for each country-year. We also use the GP mapping to compute the “share of HD GPs” variable, which is defined as total lending for HD in a year divided by total lending for all GPs. Finally, we construct the “share the HD projects” variable, which is the ratio of number of HD projects to number of total projects. These variables are the key outcomes for our analysis.

Two issues warrant caution in the interpretation of the “share of HD GPs.
**Table S30. Educational activity and food insecurity**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Education|||
|Activity|-0.075***|-0.033|
||(0.024)|(0.023)|
|Ethiopia|-0.118***|-0.054**|
||(0.029)|(0.024)|
|Nigeria|0.112***|0.102***|
||(0.029)|(0.027)|
|Uganda|-0.195***|-0.147***|
||(0.028)|(0.021)|
|Ethiopia-Nigeria|0.000|0.000|
|Ethiopia-Uganda|0.005|0.000|
|Nigeria-Uganda|0.000|0.000|
|Observations|5,978|5,978|
|R-squared|0.089|0.074|

_Note_ : Each column reports results from a single regression of a continuous variable that measures the prevalence of moderate or severe food security, or just severe food insecurity among adult household members. The variable is regressed on indicators for each country, with Malawi as the base case, and a binary for educational activity. Educational activity is a binary variable equal to 1 if the household has a school-aged child that has been engaged in some educational activity since schools closed and 0 otherwise. The question is asked conditional on the household containing a school-aged child. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 2 in