rafmacalaba/data-use-mentions-extended huggingface dataset:
21 Table 1: Data for the four empirical illustrations

||California school<br>districts|OECD Program for<br>International Student<br>Assessment (PISA)|OECD health systems|Vietnam district<br>hospitals|
|---|---|---|---|---|
|Sample coverage|313 unified school<br>districts in<br>California.|49 countries.|29 OECD countries.|795 district<br>hospitals in Vietnam<br>with 50-500 beds.|
|Time period|2003/04-2008/09|2000, 2003, 2006|1960-2005|1998, 1999, 2000|
|Sample size|1878|118|945|1039|
|Expenditure<br>measures|Current<br>expenditures in June<br>2000 prices, per<br>pupil|Secondary education<br>expenditure in<br>international dollars<br>at 2005 prices, per<br>secondary pupil|Total health spending<br>in international dollars<br>at 2000 prices, per<br>capita|Recurrent costs of<br>hospital, per case|
|Mean and standard<br>deviations of<br>expenditures|9742.44 (2662.77)|5492.25 (3637.27)|1294.55 (891.70)|262.83 (158.36)|
|Outcome measures|California Standards<br>Test (CST) scores<br>of grade 5 and grade<br>7 pupils in math and<br>science. Averaged<br>for one-outcome<br>exercise.|Average PISA test<br>scores in math,<br>reading, and science<br>of 15-year-old<br>students in grade 7<br>or higher. Averaged<br>for one-outcome<br>exercise.|Potential years of life<br>lost (
values (quintile 1) to highest asset index values (quintile 5).

_Flooding Impact._ Respondents were asked a series of questions about the impact of flooding on their day-to-day life in both 2022 and 2023. Households were categorized as being “impacted by flooding” if they answered “yes” to at least one question about whether flooding impacted their day-to-day life in 2022 or 2023.

_Domestic Violence._ Domestic violence experiences and attitudes were measured based on selfreport using three questions: (a) In your opinion, is a husband justified in hitting or beating his wife? (b) Did your husband ever hit or beat you? And (c) Are (were) you afraid of your husband: most of the time, sometimes, or never? These questions have been used in the Government of Pakistan Demographic and Health Survey 2017-2018. For the regression analyses, fear of husband was controlled for as it was the only variable that was correlated with all mental health outcomes and child development. Fear of husband was categorized from 1 to 3, with 1 indicating never afraid, 2 indicating sometimes afraid, and 3 indicating afraid most of the time.

_Community Safety._ Perceptions of and experiences of safety in the community (or lack thereof) were determined by exposure to crime (i.e., if anyone has taken or tried taking something from you by using force or threatening to use force in the last 12 months) and perceptions of safety in the community—specifically, feeling safe while home alone at night or while walking through the neighborhood alone after dark.

_Discrimination._ Respondents were asked if, in the past 12 months, they felt discriminated against or harassed on the basis of ethnic origin, immigration or refugee status, sex, sexual orientation, 8
national statistical agencies and corresponding national ministries.<sup>16</sup> To conduct robustness checks, we use two alternative measures of transport infrastructure: the length of the paved road network, collected from the same sources, and the combined total length of the road and railway network. The railway information is obtained from the World Bank‘s Railways Database and complemented with data from national sources.<sup>17</sup> As we shall impose constant returns to scale in the estimations (see below), the three variables underlying the index (phone lines, power generation capacity and the length of the road network) are measured in per-worker terms, and expressed in logs.<sup>18</sup> Their first principal component accounts for 82 percent of their overall variance and, as expected, it is highly correlated with each of the three individual variables.<sup>19</sup> More specifically, the correlation between the first principal component and main telephone lines per worker is 0.96, its correlation with power generation capacity is 0.97, and its correlation with the total length of the road network is 0.74. In addition, all three (log-standardized) variables enter the first principal component with approximately similar weights: where _z_ is the synthetic infrastructure index, _(Z1/L)_ is the number of main telephone lines (per 1,000 workers), _(Z2/L)_ is the power generation capacity (in GW per 1,000 workers), and _(Z3/L)_ represents the total length of the road network (in km per 1,000 workers).

Table 1 shows descriptive statistics for output, physical and human capital, and the various infrastructure indicators, for the cross-section corresponding to the year 2000. Output and the capital stock are expressed in PPP US dollars at international 2000 prices, while infrastructure variables are expressed in per worker terms.

> 16 One caveat regarding these data
As a further validation of the mechanism at work, we conduct a series of checks. First, given that the FD Law also imposed limits to the indebtedness of municipal governments, we corroborate that the unwinding of crowding out of private firms is also present at the municipal level. That is, conditional on the public indebtedness of a state, we find that bank lending to the private sector increases more in municipalities with ex ante higher per capita public debt following the FD Law. Second, we use firm balance-sheet data to examine the evolution of real outcomes of firms after the Law and find that—consistent with the state-level GDP and employment results—firms headquartered in states with ex ante higher indebtedness grow more their liabilities, assets, and sales after the Law. For instance, a one-standard-deviation increase in a state’s public indebtedness is associated with an increase of 6 percent in firm liabilities, 7 percent in firm assets, and 1.2 in firm sales. These effects are stronger for firms headquartered in municipalities with branches of banks more exposed to local public debt. Third, consistent with the employment growth observed in the secondary sector, we document that in ex ante more indebted states, banks channel substantially more lending to firms in the secondary sector after the Law. Fourth, we corroborate that private-sector borrowers operating in sectors less dependent on public spending (i.e., less affected by the contraction in government spending) benefit relatively more from the unwinding of the crowding out.<sup>9</sup> Our paper contributes to the literature on local fiscal multipliers (Nakamura and Steinsson (2018); Adelino, Cunha and Ferreira (2017)) and, more generally, the literature on the impact of fiscal austerity.<sup>10</sup> Alesina, Favaro and Giavazzi (2019b) use cross-country panel data to analyze 9 To rule out that our results are driven by the most indebted states, we adopt a difference-in-difference approach where the treatment variable is discrete and equal to one
Blackburn, K., N. Bose, and S. Capasso. 2012. “Tax Evasion, the Underground Economy and Financial Development.” _Journal of Economic Behavior and Organization_ 83(2): 243–253.

- Bouvatier, V., and A. Delatte. 2015. "Waves of International Banking Integration: A Tale of Regional Differences," European Economic Review 80(C): 354-373.

- Capasso, S., and T. Jappelli. 2013. “Financial Development and the Underground Economy.” _Journal of Development Economics_ 101 (March): 167-178.

- Capasso, S., F. Ohnsorge, and S. Yu. 2022. “Financial Development and Informality: A Literature Review.” The Manchester School.

- Catão, L., C. Pages, and M. R. Rosales. 2009. “Financial Dependence, Formal Credit, and Informal Jobs: New Evidence from Brazilian Household Data,” IDB Working Paper 118, Inter-American Development Bank, Washington, DC.

- Chodorow-Reich, G., O. Darmouni, S. Luck, and M. Plosser. Forthcoming. “Bank Liquidity Provision Across the Firm Size Distribution.” _Journal of Financial Economics_ .

- Č ihák, M., A. Demirgü č -Kunt, E. Feyen, and R. Levine. 2013. “Financial Development in 205 economies, 1960 to 2010." National Bureau of Economic Research, No. w18946.

- Claessens, S., M. A. Kose, and M. E. Terrones. 2012. “How Do Business and Financial Cycles Interact?” _Journal of International Economics_ 87(1): 178-190.

- Csonto, B., A. Guerson, A. Myrvoda, E. Seworder, and S. Munoz. 2018. “Bank Network Analysis in the ECCU” IMF Working Papers 18/162, International Monetary Fund, Washington, DC.

- Davidson, R., and J. MacKinnon. 2006. “
Figure 3: Payments to Key Stakeholders across Industries <!-- Start of picture text --> Real estate<br>Energy<br>Textiles, apparel, & luxury goods<br>Financials<br>Communication services<br>Materials<br>Auto & components<br>Consumer staples<br>Information technology<br>Other consumer discretionary<br>Industrials<br>Hotels, resorts, & cruise lines<br>Health care<br>Multiline & specialty retail<br>Other transport<br>Airlines<br>Restaurants<br>0 20 40 60 80 100<br>Labor expenses Accounts payable Interest payments<br><!-- End of picture text -->

**Note** : This figure shows the share of operating expenses owed to workers, suppliers, and creditors across U.S. listed firms. The figure shows 2000-2016 averages by industry.

**Source:** Authors’ calculations based on Compustat data.

and seek investors that might be better suited for their financing needs. Firms also have long-term relations with customers that have become loyal to their products and services. These relationships or matches, and the knowledge embedded in them, can be thought of as an important intangible asset or organizational capital of firms. Survey evidence for 500 large firms in ten emerging countries suggests that firms value these crucial relationships (Beck et al., 2020). The surveyed firms were negatively affected by the pandemic shock and their immediate response was to reduce investment while protecting their long-term relationships with stakeholders.

Pushing viable firms into bankruptcy during the COVID-19 pandemic would mean that the different relationships would need to be reconstructed in the recovery following the crisis. This churning process of destroying and then recreating relationships and contracts is far from efficient. For instance, maintaining employment in firms during the temporary negative shock could enable firms to keep specific human capital within the firm and avoid the costly processes of separation and then of re-hiring and training when the
governments). To rule out this hypothesis, we adjust our estimates for the ideology of the government (Table A12). Our main results are about the same (both in magnitude and in statistical significance).

These augmented models cannot entirely rule out endogeneity concerns. To mitigate concerns about reverse causality and third variables bias, we follow the CBI literature to address these concerns (J ́acome and V ́azquez, 2008; Bodea and Hicks, 2015 _a_ ; Garriga and Rodriguez, 2019).

First, following Bodea and Hicks (2015 _b_ ), we verify that removing observations for the two years prior to CBI reform or the following two years does not a↵ect our estimates (Table A4 and A5). Likewise, our results remain qualitatively similar when including a dummy variable that captures whether a government has implemented CBI reform in the past 5 years (Table A6).<sup>10</sup> Second, we implement two separate instrumental variables approaches. Following J ́acome and V ́azquez (2008) and Bodea and Hicks (2015 _a_ ), we use the lagged values of the CBI Index. The first stage F-value is well above the conventional level of 10. Our results remain virtually identical when compared to baseline (Table A7). Acknowledging the limitation of this approach, we construct another instrument from the interaction of the lagged value of CBI and the average CBI level in neighboring countries. The rational is that CBI displays a regional di↵usion pattern (McNamara, 2002; Polillo and Guill ́en, 2005; Garriga and Rodriguez, 2019), which should not necessarily impact domestic inequality. However, policy change often follows a regional pattern which is important in our context (e.g., Simmons and Elkins, 2004).<sup>11</sup> To account for potentially competing e↵ects, we form a compound instrument and interact this variable with the lagged value of CBI (e.g., Nunn and Qian,
# **3.1.2 Patterns of Cyclicality**

According to Keynesian principles, counter-cyclical fiscal policy provides a means of smoothing out trends in economic fluctuations over time. Most studies in the literature support the economic cyclicality of fiscal policies, but the patterns differ according to a country’s stage of development. Government spending has typically been countercyclical in industrial countries and procyclical in developing economies (e.g. Alesina et al, 2008; Gavin and Perotti, 1997; Kaminsky et al., 2004). One strand of the literature relates this phenomenon to the lack of access to international credit markets (Calderón and Schmidt-Hebbel, 2008) by developing countries (especially during recessions) that might constrain their ability to spend during economic downturns. While another strand of the literature attributes this to restrictions on access to domestic markets (Caballero and Khrisnamurthy, 2004) and institutional and political structures (Alesina et al., 2008; Lane, 2003; Talvi & Végh, 2005). In addition, empirical studies indicate the differences in fiscal cyclicality between regions (Gavin and Perotti, 1997), between countries in different income groups (Ilzetzki & Végh, 2008; Kaminsky et al, 2004; Talvi & Végh, 2005), and across time (Fatás & Mihov, 2009; Thornton, 2008).

Evidence on the cyclicality of infrastructure spending specifically has been thin due to data limitations. The purpose of this section is to fill this gap with recourse to the BOOST database, which provides data on public investment on infrastructure in the form of capital formation by central and general government. One important limitation of the database for this type of analysis is that it only covers a 13-year time period, which is relatively short for the study of economic cycles. The cyclicality of infrastructure spending patterns is first explored visually, with more sophisticated statistical analysis subsequently applied to examine the evidence on cyclicality. In particular, this shifts the primary focus from the relationship between the business cycle and associated fiscal responses to a dynamic relationship
Incidence of COVID-19 and Connections with Air Pollution Exposure

# 6.2.4 Re-estimation using March 31 confirmed hospital admissions per 100,000 inhabitants

Model 5f in table 7 below, re-estimates Model 2 then uses the covariates in the same specification as Model 5 using confirmed hospital admissions from March 31. This is to evaluate whether the relationship with PM2 _._ 5 is robust to possible measurement error in the confirmed cases. The correlation between confirmed cases per 100,000 inhabitants on March 22 and March 31 hospital admissions is approximately _._ 80, the correlation using March 30 confirmed cases is _._ 88. It is clear from the estimation results that using confirmed admissions instead of cases does not alter the main conclusions. In particular, the parameter estimate for PM2 _._ 5 remains highly significant.

Table 7: Dependent Variable: Confirmed COVID-19 hospital admissions per 100,000 inhabitants.

|**Variable**|**Model 5f**|
|---|---|
|(Intercept)|-256.68***<br>(82.20)|
|Population density|-2.48<br>|
||(1.82)|
|Share from 14 to 24|1.55|
||(1.24)|
|Share from 25 to 44|2.25*|
||(1.20)|
|Share from 45 to 64|0.40|
||(0.84)|
|Share above 65|3.18***<br>(1.15)|
|Share of unmarried|2.63***|
||(0.74)|
|Share of single households|-1.90***<br>(0.48)|
|Share of non-western immigrants|-0.47<br>(0.36)|
|Share of water surface|14.89*
# _Excess mortality and years of life lost_

The impact of COVID-19 on mortality has been so severe that global life expectancy declined for the first time since at least 1950 (Heuveline 2022) and the World Health Organization (WHO) estimates that approximately 14.9 million excess deaths occurred between January 1, 2020, and December 31, 2021 (WHO 2022a). Excess mortality estimates are widely viewed as a more complete measure of pandemic mortality impacts than nationally certified COVID-19 deaths due to incomplete national death registers in many countries (Msemburi et al. 2023). Furthermore, and relevant to the inquiry here, excess mortality is due not only to the direct mortality effects of COVID-19 infection but also the net effect of disruptions in the availability of medical care and care-seeking behaviors as well as declines in real income.<sup>8</sup> To estimate the total years of life lost ( _YLL_ ) due to the excess mortality witnessed over the 2020-21 period we first take the WHO estimates of excess deaths in each country, disaggregated by age, over the period 2020–21 (WHO 2022b). The number of years of life lost from an excess death will depend on a country’s residual life expectancy at the age at which the excess death takes place. We use pre-pandemic reported life expectancies for 2019, conditional on surviving age, from the Population Division of the United Nations (United Nations 2022).<sup>9</sup>

# _The increase in poverty over 2020-2021_

We adopt poverty estimates for 2020 from Mahler et al. (2022), updated using fall 2023 data in the Poverty and Inequality Platform, who employ various data sources and methods to estimate poverty globally in 2020.<sup>10</sup> For 2021, we utilize the poverty nowcasting methodology described by the World Bank (2022). In short, this approach takes the prior year’s income or consumption distribution and adjusts this distribution forward using distribution-neutral growth from national account sources.<sup>11
sample frame. An alternative approach matches survey data reported by the household head to all phone numbers associated with the household. As shown in Table S7, the predictive accuracy of these models is slightly attenuated relative to the benchmark results (Table S3).

We also explore the extent to which CDR can be used to predict other measures of socioeconomic status. Our main analysis focuses on the household’s ultra-poor designation as the ground truth measure of poverty, since this label was both carefully curated and the actual criterion used to determine TUP eligibility. In Table S8, we report the accuracy with which CDR (obtained from the household head, who is typically male) can predict consumption and asset-based wealth (elicited from the primary woman of each household).<sup>11</sup> In general, these machine learning models trained to directly predict consumption or asset-based wealth do not perform well. This result contrasts with prior work documenting the predictive ability of CDR for measuring asset-based wealth (e.g. Blumenstock et al., 2015). We suspect a key – difference in our setting aside from the fact that we are matching CDR to socioeconomic – status at the _household_ rather than the _individual_ level is the homogeneity of the beneficiary population: whereas Blumenstock et al. (2015) uses machine learning to predict the wealth of a nationally-representative sample of Rwandan phone owners, our sample consists of 535 individuals from the poorest villages of a single province in Afghanistan, where even the relatively wealthy households are quite poor.

# **4 Discussion**

Our key finding is that, in a sample of 535 phone-owning households in poor villages in Afghanistan, machine learning methods leveraging phone data are nearly as accurate at identifying ultra-poor households as standard asset- and consumption-based methods. Further, we find that methods combining survey data with CDR perform better than methods using a single data source. However, as we demonstrate empirically, low rates of phone ownership — or the inability to access data from all operators — can undermine the
is the soil moisture index. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, agroecological zone indicators, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district. Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

# 4.3. Exploring food and nonfood consumption impacts separately

Four of the nine countries (Ethiopia, Malawi, Mozambique, and Niger) had separate aggregates of food and nonfood consumption in the data sets available for analysis. We conducted the same analysis separately on food and nonfood consumption for these countries to assess whether the main impact was on food consumption or whether households protected food consumption by reducing nonfood consumption; the latter result is found, for example, in Kochhar and Knippenberg (2023) using a similar method. Tables 4.7 and 4.8 present results for the maize and highland livelihood zones respectively. Results are shown using just the soil moisture variable. For both livelihood zones, the impacts of the hazard variables on food consumption are larger than impacts on nonfood consumption. Nonfood consumption is impacted only among landed households. Further work with data from additional countries would be informative in understanding whether this pattern holds.

25
observation in the sample.<sup>12</sup> With this outlier detection technique, we remove outliers throughout the entire distribution, but mostly concentrated on the left- and right-hand tail.

Given the numerous data quality issues outlined above, we consider the second avenue of outlier removal, the more restrictive technique, most appropriate for our purposes and hence use the multivariate outlier removal technique to obtain the _final data set_ . All other data sets are contained for robustness check purposes and to illustrate the data processing only (cf. Table 1). Price estimations and predictions are only performed on the _final data set_ .

# Data Sets Illustrating Data Processing

Table 1 summarizes the property price data of the five economies for the different stages of the data processing. In the _original_ and _truncated_ data set, the means of price, size, and per square meter price are (much) greater than their medians as the distribution is positively (and in some cases strongly) skewed by outliers.

While more robust statistics such as median and the interquartile range (IQR) stay relatively consistent across data sets, the standard deviation and mean drop significantly from the _original_ data set to the _final_ data set. This pattern remains constant throughout the five countries and provides some suggestion that the outlier removal process, while comprehensive on distance metrics, does not significantly alter the balance of the right and left tail and the order of the distribution.

In Albania, the difference in standard deviation between the _original data set_ and the _final data set_ is stark despite the relatively low number of outliers being removed. In Morocco, the _right censored data set_ is the same as _the truncated data set_ as the maximum price listed in Numbeo is smaller than the maximum price in the truncated data set, hence, no right censoring is applied here. In South Africa, the _truncated data set_ – particularly if compared to the other four countries – excludes a relatively larger share of data entry errors and potential rental data. This might be attributed to large farms being