rafmacalaba/data-use-mentions-extended huggingface dataset:
based on the Brazilian Labor Force Survey “Pesquisa Mensal de Emprego” for our sample years indicate that the shares of formal employment, gender, age, and industry distribution in Belo Horizonte (the capital of Minas Gerais) is very similar to those in other urban areas (see table 1).<sup>2</sup> Our Uruguayan data consist of social security records from the Banco de Previsión Social (BPS). After the 1995 social security reform, the BPS started keeping employer and employee administrative records on a monthly basis. We exploit a random sample of the BPS records, which, like the Brazilian RAIS, contains information on establishments (number of employees, sector, region), workers (age, sex) and their jobs (occupation, weekly hours of work, monthly wages). Establishments and workers are uniquely tracked by an individual identifier and an establishment identifier. Each job within a workerestablishment pair is uniquely identified as well, which allows us to track year-to-year job stayers who have been neither promoted nor demoted. This job identifier, however, is not available in the Brazilian RAIS, so for the sake of comparability in our empirical analysis we define stayers as workers who are continuously employed in the same establishment for a year or more.<sup>3</sup> Regarding the compensation measures used in our analysis, the Brazilian RAIS reports monthly wages earned in December, which include extraordinary additions, supplements and bonuses, tips and gratuities, commissions and fees, contracted premia, overtime compensation for contracted extra hours, and, in general, all forms of payment that are taxable income or are subject to Brazilian social security contributions. The “thirteenth salary” -the special December payment that is made in some sectorsas well as severance payments for layoffs and indemnity payments are not considered wage components. Therefore, we construct a comparable monthly wage measure from Uruguay’s BPS that excludes severance payments and the special December payment. The number of hours worked per week is reported in both
each center at the same time. Both raters were present in the room with the class they were observing for three hours and followed this group if they left the room for outdoor play. Raters did not interact with staff or students during their observation. The two raters scored each center on a seven-point Likert scale, which ranged from inadequate (1), minimal (3), good (5) to excellent (7). For each center, rater one and rater two’s scores are averaged to construct a mean ECERS-R score.<sup>13</sup> We then computed village level averages of this ECERS-R score. In the analysis we divide the 310 villages in our sample in terciles – based on their average ECERS-R score. These are reported in Table 1 and show that girls and boys are equally likely to be found in villages with low, medium and high quality preschool centers.

# _Parenting practices_

The primary caregivers of the children in our sample (usually mothers) were asked to answer a series of questions about their parenting practices. These practices were measured using 24 items describing parent-child relationships adapted from the Longitudinal Study of Australian Children (LSAC) (Zubrick et al. 2008). The questions covered a range of possible practices that reflect three domains: parental warmth, consistency, and hostility.

Caregivers were asked how often they used each of a number of different parenting practices. A total positive parenting score was given to each child’s caregiver by adding together scores for each of the three parenting dimensions (with the negative items reversed). The higher the score, the more likely it is that parents have high levels of warmth and consistency, and low levels of hostility toward their children. These are reported in Table 1. On average, girls have a slightly higher total parenting practices score than boys but the magnitude of this difference is very small (0.91 points). In our analysis, we normalized this total parenting practices score to have a mean of 0
and insurgents.<sup>3</sup> The analysis is based on data from the 2007 Philippine mayoral elections, the

> 1For instance, Tucker (2007) argued that election fraud galvanized the public, triggered protests, and eventually led to the so-called “colored revolutions” in Eastern Europe.

> 2See Blattman and Miguel (2010) for a review of the extensive literature on civil conflict.

> 3The fact that this study examines organized violence in an already existing conflict sets it apart from most previous papers in the literature on the relationship between electoral manipulation and violence, which tend to focus on less organized forms of violence such as riots and protests.

2
successful livelihood systems around it—“living with floods”). The use of panel data provides the cleanest way to protect welfare estimates of natural disasters from (time invariant) unobserved heterogeneity across communities. However, sufficiently long panels of communities are usually not available for large geographical areas, let alone countries. Within community estimators are usually also less efficient given the lower signal‐to‐noise ratio, especially when panels are short. This in turn may lead to the disproportionate acceptance of the null‐ hypothesis of no welfare effect.

Here, repeated cross sections are used instead. To mitigate potential bias from unobserved community heterogeneity, a comprehensive set of socio‐economic and agro‐ecological characteristics of the locality is included. Most importantly the likelihood of the occurrence of different disaster types is controlled for, omission of which is identified as the major cause for biased estimation of the coefficients on natural disasters. Explicit inclusion of disaster exposure further permits exploration of the occurrence of adaptation. In particular, disaster incidence is hypothesized to mitigate the immediate effect of a particular disaster if households have reverted to low‐risk, low‐return activities. They are likely to suffer less when disasters hit, and benefit less if there is a rebound, for example due to accelerated replacement of the capital base with more productive equipment.

Empirical analysis of the effects of shocks on household welfare is often predicated on the assumption that the observations are independent from each other, thereby also abstracting from spatial correlation. But in many cases, it seems more likely that observations that are "near" in a geographic sense either influence each other more so than an observation that is far, or that they are more influenced by the same unmeasured force than an observation that is far. This seems especially relevant in a study of natural disasters, where their effects on welfare may permeate across locations especially when product (e.g. food) and/or factor (e.g. labor) markets are
Cameron, C., J. Gelbach, and D. Miller. 2008. “Bootstrap-based Improvements for Inference with Clustered Errors.” _Review of Economics and Statistics_ 90(3): 414-27.

Carter, M., R. Laajaj, and D. Yang. 2014. "Subsidies and the Persistence of Technology Adoption: Field Experimental Evidence from Mozambique." _NBER Working Paper_ w20465.

Corral, C., X. Giné, A. Mahajan, and E. Seira. “A Multiple Intervention Approach to Improve Agricultural Yields.” In Preparation.

Dulfo, E., M. Kremer, and J. Robinson. 2008. “How high are rates of return to fertilizer? Evidence from field experiments in Kenya.” _American Economic Review: Papers and Proceedings_ 98 (2): 482-88.

Duflo, E., M. Kremer, and J. Robinson. 2011. “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya.” _American Economic Review_ 101(6): 2350-90.

Feder, G., R. E. Just, and D. Zilberman. 1985. "Adoption of Agricultural Innovations in Developing Countries: a survey." _Economic Development and Cultural Change_ 33(2) 255-298.

Goyal, A., and J. Nash. 2017. _Reaping Richer Returns: Public Spending Priorities for African Agriculture Productivity Growth._ Africa Development Forum series. Washington, D.C.: World Bank.

Harou, A., N. Amuri, M. Madajewicz, C. Magomba, H. Michelson, K. Tschirhart, J. M. Semoka, and C. Palm. “When does information make a difference? A field experiment with farm-specific soil information in Tanzania..” In preparation.

Holden, S., and R. Lunduka. 2012. “Do fertilizer subsidies crowd out organic manures? The case of Malawi.” _Agricultural Economics_ 43 (3): 303-14.

Jack, B. K. 2013. "Private
survey to measure employment. Simulations suggest that the rural female labor force participation rate in Honduras is underestimated by 6 to 23 percentage points. Significant discrepancies arise once one compares the household survey responses with tasks completed by rural women. Women reported activities ranging from agricultural production, to commerce and services for pay even though they had previously responded negatively to the questions on economic activity. This shows that there is a disconnect between the definition of employment as meant to be captured in the labor force survey and the definition used by women (and men) in the rural communities visited for this research.

The study identifies two main explanations behind this observed discrepancy. First, women and men each identify with specific spheres of life, with women commonly identifying with the private and hence, the domestic sphere, while men identify with the public sphere and thus the agricultural production sphere. Even in those cases where women perform activities within the agricultural production sphere, they define those as ‘supporting’ activities, as ‘helping’ their husbands and consequently, not as their own economic activity. The results suggest that women identify themselves (and are identified by others) primarily as housewives, and the concepts of housewife and employment are taken as mutually exclusive. As such, their economic activities are considered to be household tasks.

A second explanation identified through this research relates to women defining ‘employment’ based on a set of necessary characteristics. Those characteristics may not all be in place in their own individual cases. The necessary characteristics suggested by interviewees’ responses are as follows: work needs to i) be conducted _outside_ the home; ii) be in exchange for money immediately or in the very short term; and iii) entail sufficient and regular time commitment. Importantly, given the gendered identities and their perceived relationship to work, these conditions are not binding constraints for men to identify their own activities as economic activity. That is, agricultural activities for own consumption and those on crops that 25
In the top panel of Table 1a, the base case is the 148 large cities in developed countries. Relative to these, Sub-Saharan African cities have higher measures across the board, including in particular the coefficient of variation and covariance terms, where they are respectively 44 and 27 per cent higher. Moreover, terms for Africa are higher than the rest of the developing world terms, including those for the coefficient of variation and covariance terms. With no separation into nocturnal and diurnal populations, we do not know if this involves greater clustering of residences or workplaces, or both; it is the ambient population as the measure of economic density. The bottom panel adds a control for ln GDPpc. This reduces the Africa terms making them smaller absolutely and relative to the rest of the developing world. Now the differentials on the coefficient of variation and covariance terms are insignificant. In summary, in the raw data Sub-Saharan African cities have higher coefficients on the coefficient of variation and covariance terms, which contradicts the presumption of the literature. Moreover, greater clustering seems to be negatively related to GDPpc, with developed countries having the lowest degree of clustering, perhaps where automobile cities like Atlanta and Houston form the stereotype.

In terms of just developing countries, Table 2, shows that relative to Asia, the outlier with lower clustering is Latin America even controlling for income. Sub-Saharan Africa, as well as North Africa and the Middle East, have similar measures of density and clustering as Asia. Overall compared to the rest of the developing world, Sub-Saharan African cities have (not controlling for GDP per capita) higher average densities of people, but no different degree of economic density as measured by PPD or RPA and no different degree of clustering. Controlling for income, Sub-Saharan African cities are similar to others in the developing world in all measures.

# **4 How are differences in economic density across the spatial hierarchy related to income differences?**

This section first describes the data on income and wages and then the characteristics of the
, logistics might not be exogenous, so we take potential endogeneity seriously. Further, unlike bilateral distance, the logistics index is a country-speci...c variable which precludes the use of ...xed e¤ects to control for MR in estimation. Instead, we proxy MR terms using an adaptation of Baier & Bergstrand’s (2009) method. Our approach allows us to implement this method together with Helpman, Melitz & Rubinstein’s procedure to account for ...rm heterogeneity. While the application in this paper is to logistics, the implications fall on a wide class of country-speci...c international trade costs.

Section 2 provides an overview of the existing literature on logistics and international trade before expanding on the importance of MR and the selection issues associated with ...rm heterogeneity. Section 3 describes the data. The World Bank constructed its logistics performance index (LPI) using a principal component analysis of six indicators. We describe those indicators and describe how we extract only those that are relevant to international trade (as opposed to within-country trade) to produce what we call the International Logistics Index (ILI).

Section 4 formalizes our gravity modelling framework, which accounts for both ...rm heterogeneity and MR, and derives the Baier & Bergstrand (2009) approximation for country-speci...c trade costs in this context. In deriving the full comparative statics that take account of MR, this section illuminates how the trade impact varies with country size. Section 5 discusses estimation issues. Because trade ‡ows may a¤ect investments in logistics, logistics may be an endogenous regressor. To deal with this, 3
can aggravate the region’s existing vulnerabilities and inequalities.

Analysis of the BPS data shows that policy support can be effective in changing the firm’s outlook towards future and in lowering financial fragility, especially for firms with lower level of public support access. To this end, our analysis also underscores the importance of effective and welldesigned policy measures to help build back better for resilient recovery. _First_ , the low access to public support among South Asian firms, particularly for smaller and non-exporting firms, highlights the information gaps and the importance of networks and inter-organizational capital in times of crisis. South Asian governments can play a significant role in promoting linkages between smaller and more organized larger companies to overcome information gaps and allow for productivity spillovers via business networks. _Second_ , targeting of support in the form of external attributes such as firm size and sector categorization is neither precise nor desirable, especially in the presence of large heterogeneity within these groups. Policy makers should consider a range of instruments that customize support based on the dominant channel of transmission of shock within the size and sector groups. Clearly, supply-side disruptions should be met with a substantially different support program than that appropriate for demand or finance-related shocks. A deeper understanding of the various ways in which COVID-19 affects firms will be key to developing effective interventions.

19
control for country characteristics (e.g., macroeconomic growth, exchange rate changes, etc.) that might be changing over time that can affect loan growth. Furthermore, in some specifications, we allow for interactions of the crisis dummies with bank characteristics to control for the possibility that bank-level variables other than ownership have different effects in crisis and non-crisis episodes.<sup>13</sup>

# **3. Data**

Our main source of bank-level data is Bankscope, a comprehensive commercial database of banks' financial statements produced by Bureau Van Dijk. In particular, for the period 20042009, we collect information from this source on bank ownership, total loan volumes, and loan amounts by type of loan (corporate, consumer, and residential mortgages). We also gather data on bank size, capitalization, liquidity, profitability, and funding structure. Table 1 contains definitions of all the variables we include in our analysis along with descriptive statistics.

We classify banks into foreign, government-owned, and domestic private banks depending on whether 50 percent or more of banks’ shares are owned by foreigners, central or local governments, or domestic private actors, respectively. On average, between 2004-2009, 70 percent of banks in Eastern Europe were foreign-owned and foreign banks accounted for 81 percent of total bank assets over this period. Only 6 percent of banks were government-owned in Eastern Europe, accounting for 11 percent of total assets. In Latin America, 35 percent of banks were foreign-owned and they represented, on average, 38 percent of banking system assets.

> 13 In some estimations, we drop the ownership dummies and we replace them with bank fixed effects (these results are available in Table 3). This does not change our main results. We prefer to include the foreign and government ownership dummies in our baseline regressions rather than the bank fixed effects because these dummies allow us to assess the extent to which foreign and government bank lending grew faster than domestic private bank lending prior to the crisis.

9
that the reform was welfare improving as long as the government values giving R$0.90 to BF households in a non-distortionary manner more than spending R$1 on their next best alternative. On the other hand, the reform was welfare decreasing if the government values spending R$1 on their next best alternative more than giving R$1.12 to BF households in a non-distortionary manner.<sup>42</sup> But what is the government’s best alternative use of funds? In general, this is a difficult question to answer. Lindert et al. (2007) show that BF performs better (in terms of targeting performance) than other anti-poverty programs in both Brazil and other countries in Latin America; this suggests that there is not a better alternative anti-poverty program that the government could finance. Instead, for expositional simplicity we assume that the next best use of funds is a non-distortionary UBI. Under this assumption, we can perform a conservative back-of-the-envelope calculation to argue that the 2014 BF reform was almost certainly welfare improving. Suppose the government is utilitarian and that the true income distribution is log-normal with parameters taken from the World Bank’s PovcalNet database for Brazil in 2016.<sup>43</sup> Conservatively, we assume that the BF household per-capita income distribution matches the distribution for the bottom 50% of income earners in Brazil.<sup>44</sup> Moreover, we assume utility of consumption is CRRA: _u_ ( _c_ ) = _c_<sup>1</sup><sup>_−γ_</sup> _/_ (1 _− γ_ ). Based on these assumptions, the government prefers giving R$0.90 (in a non-distortionary manner) to BF households more than spending R$1 on a non-distortionary UBI as long as _γ >_ 0 _._ 14. While estimates for _γ_ vary widely, typically estimates fall between 1 and 10 (Outreville, 2014). Alternatively, if _γ_
(10miSq) FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|monthXyear FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|provinceXmonth FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Control Groups|All Other|Provinces|Rest|ricted|All Other|Provinces|Rest|ricted|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the impact of Formosa on fishing activity in the fishing-restricted region (i.e. 20nm near-shore of Formosa-affected provinces), using VIIRS’ monthly-aggregate boat detection data. Each observation is a grid-month. Sample includes all monthly observations between 2013 and 2016. The grid size is 10-mile-square. A grid is considered near-shore when it is located less than 20 nautical miles away from the coast line. The reported outcome variables include log-transformed monthly-aggregate boat counts in each grid (columns 1-4), and 0/1 probability that the grid was detected with at least a boat in that month (columns 5-8). The control group ”All Other Provinces” refers to all coastal provinces in the country except for the four Formosa-affected central provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). The control group ”Restricted” refers to all coastal provinces located south of Phu Yen (i.e. distant from the Formosa-affected region).
For the analysis in this paper, expected deaths are estimated using a simple regression using 2015-2019 historical data, run separately by country and age-group, where observed deaths are modeled as a linear function of year and an error term.

The second issue often neglected in COVID-19 excess mortality discussions is that excess mortality represents a combination of effects. Specifically,

# _Net excess deaths_ =

# _[Direct COVID-19 deaths]_ + _[Indirect COVID-19 deaths]_ - _[Averted deaths]_

_Direct COVID-19 deaths_ are those that are due to infection with the virus. _Indirect COVID-19 deaths_ are those not due to the virus itself but in part due to factors such as congestion in the health system, delays in care-seeking, or mental health issues caused by the pandemic. _Averted deaths_ are those that would have taken place in the absence of the pandemic but did not occur as a result of pandemic-induced measures. These could include deaths due to other infectious diseases, violence, or traffic injuries that were avoided due to voluntary or mandated social distancing. The possibility of non-zero indirect deaths and averted deaths makes the interpretation of excess deaths more complex than is often recognized.

# _Slope calculation_

The shape of an age-mortality curve can be summarized in terms of the percent rate of increase in probability of death per year of additional age after age 45, calculated by fitting a line to the age-specific mortality rates as follows:

# log(r(x, i)) = αi + βix

D(x,i) where the age-specific mortality rate is r(x, i) = for the ten-year age group age _x_ to age _x_ N(x,i) + 9, ( _x_ = 45, 55, 65, 75, 85) in country _i_ , D(x, i) is the number of deaths in the age group and N(x, i) is the population of the age group in country _i_ .<sup>2</sup> The _x_ = 85 points correspond to the group
between labor types.

Poverty declines substantially and the other social indicators improve as well. In the baseline scenario, the poverty rate falls to about 24 percent in 2025, down from 51 percent measured by a household survey in 2005 (see Table 6). The under-5 mortality rate is reduced significantly from 128 to 67 per 1,000 live births. In the education sector, the performance is improved according to all indicators, i.e. promotion, repetition, drop-out and continuation rates. The only exception is a decrease in the gross primary completion rate, which, however, does not indicated worse performance, but rather that fewer older and out-of-cohort students enter primary school.

# 4.2 <u>Front-loading versus back-loading</u>

Under an alternative set of scenarios, the government is scaling up infrastructure spending even more than currently planned. It is assumed that an additional CFAF 2,000 billion (about USD 4 billion) are spent either over 3 years (2013 to 2015), equaling CFAF 667 billion additional investment each year, or over 7 years (2016 to 2022), amounting to CFAF 286 billion annually. We assume that the delay in 9