rafmacalaba/data-use-mentions-extended huggingface dataset:
informational asymmetries, as the likelihood of rescue lending decreases significantly with higher geographic and cultural distance. Besides economic exposure, political allegiance can help in overcoming these frictions.

# **6. Conclusion**

This paper quantifies and studies the scope and determinants of official, sovereign-to-sovereign lending over the past two centuries, based on an encompassing new database. We show that cross-border official lending has been an important force in international finance both in the previous two centuries and today. Official lending tends to spike during wars and severe financial crises with the most notable episodes in World Wars 1 and 2, under Bretton Woods and in the 1930s Depression.

Official cross-border loans and bailouts have also become much more institutionalized. Dozens of new official creditor institutions have been created since the 1970s, including, regional development banks. At the same time, bilateral and multilateral institutions lending continue to play an important role 35
(30) and (31) of chapter 3). This allows to consider variables which contain both stocks and flows in their definition. The approximate discrete analogue to the continuous model used for the estimation carried out in paragraph 3 was obtained as expounded in Gandolfo (1981) , chapter 3 paragraph 3.2.2

## A.2.2 Data used for the estimation of model (2.1)-(2.3)

The continuous time model (2.1)-(2.3) was also estimated (FIML) taking its approximate discrete analogue and data were treated as explained in Appendix 1. Following Fagerberg (1988), we introduce a measure of innovative activity including both inputs and outputs of innovative activity. We have computed a weighted average of two specialization indicators: one based on business enterprise R&D expenditures (BERD) and one based on patent counts. These two indicators are known as Revealed Technology Advantage (RTA) indicators, which correspond to the well known Balassa index used with trade data. This indicator, the index of "Industrial Technological Specialization" (ITS): is defined as follows 34
collect ideas and feedback on these issues. This paper should be considered work in progress and updated versions with more data and analyses will be published over the next months.

In this paper, we start by quantifying the number of people _exposed_ to extreme weather events globally today. We find that a 4.5 billion people are exposed to an extreme flood, drought, cyclone, or heatwave. Of those exposed, 2.3 billion are poor at the $6.85 poverty line (the median poverty line of upper middleincome countries) and 390 million are extremely poor at the $2.15 poverty line (the median poverty line of low-income countries).

Then, we identify the exposed households who are particularly _vulnerable_ and therefore _at high risk_ of experiencing severe economic impacts from these events. There are many determinants of vulnerability but we can broadly group determinants into two aspects: (i) the physical propensity to experience severe income, asset or health loss and (ii) the inability to cope with and recover from the losses through income or transfers (public and private). We use available data to provide some indicative estimates on these two aspects of vulnerability.

Physical propensity to experience severe losses is proxied using data on access to water and electricity. These are chosen to reflect the ability of a household to still access clean water in the face of lack of water availability or flood-contamination of water supplies, and the ability of households to access cooling appliances to cope with heatwaves.<sup>3</sup> However, more work on the right infrastructure indicators to use is needed and different dimensions of physical vulnerability are important for different types of shocks, for instance access to markets and emergency services, or coverage by early warning systems that help anticipate and mitigation impacts of extreme weather.

Inability to cope or recover is measured as not having income to manage the impact of a shock; not having education to switch livelihoods or access information and resources to recover; not having access to public support such as
of the F-test is 0.87, which shows that selection is not determined by a wide range of baseline observable characteristics.<sup>39</sup> I employ three different estimation approaches. I use OLS with control variables chosen by PDS Lasso in the main analysis, and employ a regression discontinuity design and a matching method in Section 4.4. The following is the main specification for outcome _Y_ of each selected or nominated operator _i_ in factory _f_ , while pooling both household survey waves _w_ = _{_ 1 _,_ 2 _}_ :

> 35Note that, for all checks in this subsection, I only include women whose data is also used in the analysis, i.e. who were interviewed in at least one household survey wave and had another adult decision-maker in the household. This is to ensure that attrition is not driving an imbalance.

> 36See Table C.4 in the Online Appendix for variable definitions.

> 37I use the “ritest” Stata package to implement the randomization inference tests, see Heß (2017).

> 38The normalized difference is calculated as the difference in means between the two groups, divided by the square root of the average of the sample variances of the two groups.

> 39See Table C.6 in the Online Appendix. In this paper, to include the variables only defined for married respondents, I follow two steps. First, I set these variables to zero if they are missing and, second, I include dummy variables that indicate whether the variables are missing in the regression. I also do not find that covariates predict selection if I include all covariates but limit the sample to married respondents, or if I only include covariates which are defined for all respondents in the regression. The p-values for the F-statistic of the regression in these cases are 0.96 and 0.92, respectively.

16
0.005)|(0.004)|(0.006)|(0.003)|
|FDR q-value|0.860|0.399|0.860|0.860|0.860|0.860|0.055|
|Observations|226,400|226,400|226,400|226,400|226,400|226,400|226,400|
|Mean|0.046|0.001|0.030|0.143|0.104|0.177|0.073|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain occupation category indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 7 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. See Table 6 for a list of top3 occupations per category.

52
m (Eq.1) ii,mm,tt ii,mm,tt ii,mm,tt mm,tt mm,tt+3 mm,tt+6 bbhtt = αα+ ββ XX + ββ XX + ββ XX + δδ + δδ mm,tt+9 mm,tt−36 mmtth mm ii,mm,tt The terms δδ p p p + δδp ( _j=3,6,9_ pp) are the exposure to violence in each pregnancy trimester. I + γγ + γγ + γγ + εε estimate the terms separately, even though there is no full consensus on whether the impact of a mm,tt+jj stress shock is heterogeneous across the length of gestation (Persson and Rossin-Slater 2018) nor on the stage for which impacts should be visible.<sup>36</sup> The last trimester ends at birth, and because pregnancies are typically viable from the 24th week of gestation, or the end of the second trimester, the last one can be absent. The specification accounts for early births by interacting exposure to violence in the third trimester (p p p p ) with an indicator of the pregnancy not reaching such trimester ( ).<sup>37</sup> This allows to keep those pregnancies in the sample but without mm,tt+9 misrepresenting their exposure to violence, that is, without assuming that they had no exposure to ii,mm,tt an event in the last trimester, or that their exposure corresponded to the events that occurred in the mmppttp mm3 municipality in the months after their birth. The base coefficient ( 3) continues to be the parameter of interest for exposure to violence in this trimester.<sup>38</sup> δδ

> 35 The municipal controls vary by year or by survey round because of limited historical municipal data reported monthly, and sometimes even yearly. Yearly variables are linked with DHS on the year and month of birth: if the child was born after June, the
**Table 5:  Household PM10 Concentrations (ug/m**<sup>**3**</sup> **) by Fuel**

|Fuel|Sample<br>Households|Mean|Minimum|1<sup>~~st~~</sup><br>Quartile|Median|3<sup>~~rd~~</sup><br>Quartile|Maximum|
|---|---|---|---|---|---|---|---|
|Dung|95|291|60|172|231|380|755|
|Firewood|159|263|84|161|201|323|1165|
|Jute|68|190|72|136|165|219|727|

using “dirty fuels” such as firewood, dung and jute have PM10 exposures resembling those for natural gas, while others face concentrations at extremely high levels.

Of course, part of the variation across households is also determined by fuel use. Table 6 summarizes the MiniVol data for cooking areas across all households, by fuel type. While these descriptive statistics provide some insight, they can be misleading because fuel choices may be correlated with other variables in the sample: cooking practices, structural characteristics and ventilation practices. In Table 6, the cleanest fuels are natural gas (101 ug/m<sup>3</sup> ) and kerosene (134). As we have previously noted, these concentrations are not far above urban ambient pollution levels. Among biomass fuels, dung seems to be the dirtiest fuel (291 ug/m<sup>3</sup> ), followed by firewood (263), sawdust (237), straw (197), jute (190), and twigs and branches (173). In our sample, the relatively few households using LPG/LNG have an
This paper has responded to these observations by developing new approaches to measurement, as well as a simple theoretical framework that captures key features of education systems in Latin America. The first application of our new measurement approach used publicly available data from the school principal surveys conducted by PISA to construct a school management index spanning 65 countries. This PISA-based school management index can be well-validated against the more detailed (though also much more expensive) index based on the WMS. As such, it has clear value in settings where cross-country coverage is important, enabling researchers to study and compare the (within-country) correlation between management and student/school-level outcomes for a far wider set of countries than was previously possible.

Our second application used publicly available data from a national administrative survey to construct a school management index spanning all public schools in a single country: Brazil. This Prova Brasil-based index was also well-validated against the WMS. This second application has value in settings where within-country coverage, and the availability to merge with other administrative data sets, is important.

It is striking that both of our new school management indices confirm the strong positive correlation of school management scores with school-level student outcomes first reported in Bloom et al. [2015a]. A positive relationship holds for the global PISA sample, and in the census of schools in Brazil. Our theoretical framework, for the first time, formalizes the possible causal mechanisms in one of these regions: Latin America. We argued that strong people management practices may be improving student learning through a combination of teacher selection and incentive effects, and that schools could be encouraged to adopt practices that reward good performers, develop good performers, and create a good employee value proposition. Looking to operations management, we argued that strong operations practices may be improving student learning through a combination of teacher selection and household incentives, and that schools could be encouraged to adopt practices promoting quality of delivery in the classroom and adopt processes to review school performance and drive change. We also provided a suggestive set of evidence for
the spread of AD activity worldwide has been widely examined, but nothing is known about domestic exporters’ responses to the threat of AD retaliation. To our knowledge, this is the first paper to address this issue by documenting exporters’ price adjustments due to this threat.

Empirical works using firm-level data are scarce in the AD literature. One strand of the literature focuses on the productivity effects of AD duties on domestic firms (Konings and Vandenbussche 2008; Pierce 2011). In contrast, Belderbos (1997) investigates the relationship between AD enforcement and foreign direct investment. Konings and Vandenbuscche’s (2009) study is the only one to focus on the effect of AD duties on export sales using firm-level data. To our knowledge, this study is the first attempt to use disaggregated firm-level data to analyze the pricing effect of AD duties.

In terms of the effects of a country's own AD activity on its domestic exports, our analysis of the pricing effects of retaliation threats most closely complements the study by Konings and Vandenbussche (2009), which examines the impact of France’s AD duties on the export sales of protected firms. Using French exporters’ firm-level customs data, they show that foreign export sales decreased dramatically among the protected firms. They also note that this decrease might be a consequence of the limitations of price setting in the international market, stemming from AD protection in the home market.

Like Konings and Vandenbussche's (2009) study, our AD threat analysis focuses on the effect of a country's AD activity on its export markets rather than its domestic market. However, the two works differ in two key respects. First, Konings and Vandenbussche (2009) use a difference-in-differences approach to determine the effect of AD on the exports of protected firms compared with a control group of unprotected firms. In contrast, we focus on the threat of retaliation resulting from ongoing AD investigations, regardless of their tentative outcomes, 4
implications for welfare. Specifically, we show that the auto-correlation coefficient of the AR(1) process (the catching-up parameter) measures “good mobility” in the sense that a reduction in this parameter increases both mobility and welfare. In contrast, social welfare is (almost) unaffected by measurement error or transitory income shocks even though mobility increases with the variance of the i.i.d. component of labor income. Finally, the variance of persistent income shocks (income risk) increases mobility, but decreases social welfare. This implies that two societies with the same initial distribution of income and the same level of measured income mobility and aggregate growth may experience quite different social welfare changes depending upon the different combinations of the underlying income parameters.

We present a quantitative implementation of our framework that underscores the importance of decomposing income dynamics into its components, as we have discussed. Specifically, an application using data on individual incomes from Mexico yields striking results. Most of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral, and only a small part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals. However, despite the small mobility effects, (idiosyncratic) persistent income risk has significant negative effects on social welfare – eliminating or insuring it would generate welfare gains that are equivalent to an increase in lifetime consumption by about 10 percent even if workers are only moderately risk-averse (log-utility).<sup>9</sup> Eliminating the catch-up of low income individuals with high income individuals yields a loss in social welfare of similar magnitude. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

# II. Income and Mobility

## II.1. Income Process

Consider a large number of workers indexed by i. For notational ease, we focus on one cohort of workers who enter the labor market for the first time in period t = 0 so that t =
|**atment**|**t‐test**|
|---|---|---|---|---|---|
|Variable|N|Mean/SE|N|Mean/SE|p‐value|
|PHQ‐ADS score|117|7.215|118|6.678|0.639|
|||[0.816]||[0.769]||
|PHQ‐ADS prevalence|117|29.9%|118|22.0%|0.170|
|||[0.043]||[0.038]||
|Transformed WHO‐5 score|116|77.931|117|82.393|0.096*|
|||[1.984]||[1.786]||

_Note:_ The value displayed for t‐tests are the p‐values of difference across the groups. Standard errors in brackets. ***, **, and * indicate significance at 1, 5, and 10 percent levels respectively. Source: Saraf, 2019; World Bank Group, 2018

# 5.5 Intervention Implementation, Take‐up, and Attendance Rates

A total of 118 participants received the intervention in 12 groups at 9 locations . Training was organized in the form of ` _affinity groups_ ` based on the gender and location of the participating entrepreneurs based on inputs from the rapid needs assessment. Fourteen trainers participated in the _training of trainers,_ and each group (with 10 participants on average) was led by one of these trainers and a co‐facilitator. The five interactive sessions were, on average, approximately three hours each.

Overall, workshop attendance exceeded the usual rates seen in FCV contexts.<sup>6</sup> However, attendance was lower than the assumed 75 percent, with 63.5 percent of the
1%)<br>Quantile w/ 1% Cum. Growth<br>1.0% Quantile w/ 40% Cum. Growth<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Year<br>Adult Equivalent<br>% Change in Household Consumption per<br><!-- End of picture text --> Note that the initial and final years of the simulation will match the “true” distributions in the survey data, and consequently the simulation’s cumulative change in mean consumption will match the change implied by the survey data. If cumulative growth in GDP per capita differs from 7 The “two survey” approach has a natural relationship to growth incidence curves: the simulations scale growth for each quantile to match the average growth rates that are plotted in the GICs.
pressure, but MNEs themselves may possess higher market power if they possess significant economies of scale to undercut local firms, force firm exit, and eventually reduce competition. To date, data availability has limited existing studies to advanced economies, and conclusions have been widely heterogeneous based on the country of interest.

To address this gap, this paper evaluates the effect of FDI on market power within industries using firmlevel panel data for Vietnam. We first apply the methodological work by De Loecker and Warzynski (2012) to estimate firm-level production functions and compute markups. Then, we estimate the impact of FDI on markups via a difference-in-differences estimation. We also analyze the impact of foreign ownership on individual firms’ markups by looking at differences in markups between foreign- and domestic-owned firms and analyzing the impact of acquisition and divestment by foreign investors.

Our findings suggest that the presence of MNEs in a given industry decreases markups in that industry. We find that a 10 percent increase in foreign-owned firms’ share of output within a given two-digit manufacturing sector is associated with a 0.4 to 1.2 percentage point decrease in the ratio of price to marginal cost. This pro-competitive finding holds despite MNEs charging higher markups on average than domestic competitors. Furthermore, the results show that, while the markups of both foreign- and domestic-owned private firms tend to decrease with greater FDI presence, state-owned enterprises (SOEs) may be relatively insulated from FDI-driven competitive pressures.

Our contribution is twofold. First, our work contributes to the scarce literature that evaluates the effects of FDI on market power in host countries, especially low- and middle-income contexts. Most country-level studies on this topic rely on accounting-based price-cost margins, concentration, or entry and exit dynamics to measure market power and focus on high-income settings (Abolhassani & Danakol, 2019; 2
All fiscal instruments are equalizing except for indirect subsidies, which have an effect of approximately 0. The largest reduction in inequality comes from direct taxes, which cut the Gini coefficient by 1.4 points, almost 40 percent of the total reduction. In-kind transfers and indirect taxes also reduce inequality considerably, lowering the Gini coefficient by approximately one point, and 0.73 Gini points respectively. The effect of direct transfers is only 0.1 Gini point.

The redistributive effect of the fiscal system in Uganda for 2016/17 (3.2 Gini points), is slightly higher than in Ethiopia (2.3), approximately equal to Ghana (3.5), and lower than in Tanzania (5.3), Kenya (6.5), and South Africa (17.6).

_Figure 6. Inequality Before and After Fiscal Policy a. Gini Coefficient_ <!-- Start of picture text --> 0.434 0.434<br>0.420 0.419 0.419<br>0.411 0.411<br>0.402<br>Market Market Net Market Disposable Disposable Disposable Consumable Final Income<br>Income (PGT) Income plus Income Income Income + All Income - All Income<br>Pensions (PDI) indirect indirect Taxes<br>Subsidies<br>b. Redistributive effect, Sub-Saharan African countries<br>17.6<br>6.5<br>5.3<br>3.2 3.3 3.5<br>2.3<br>Ethiopia (2010) Uganda (2016) Uganda (2012) Ghana (2012) Tanzania (2011) Kenya (2015) South Africa<br>(2010)<br>Gini coefficient<br>Gini pooints<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank
Mexico (Santos-Burgoa, et al., 1998, 52 households), and Gambia (Campbell, 1997, 12 households. Recently, a larger sample of houses has been studied in rural India (Balakrishnan, et al., 2002; Parikh, et al., 2001). In Section 9, we will compare the India results to those obtained by this study.

Because monitoring studies are costly, IAP exposure analyses frequently use biofuel consumption data to proxy the degree of exposure to fine particulates, and extrapolate to estimates of ARI prevalence and mortality (Smith, 2000). Although fueluse data are widely available, this approach implicitly assumes a constant relationship between fuel combustion and indoor air pollution across households. However, the previously-mentioned studies indicate that IAP levels in households with identical fuel use are affected by factors such as the location of cooking (inside/outside), ventilation through windows and doors, and air flow through building materials. Additional information could have a large social payoff in this context, since simple alterations in structures, ventilation practices, building materials and cooking locations may be much less costly than switching to cleaner fuels or investing in clean stoves.

This paper provides evidence on PM10 and PM2.5 concentrations in poor households, using new air monitoring data from Bangladesh. Recent technical advances have significantly increased the power, portability and durability of equipment for monitoring particulate pollution. Our study has used two types of equipment: air samplers that measure 24-hour average PM10 concentrations, and real-time monitors that record PM10 and PM2.5 at 2-minute intervals for 24 hours. Each device has advantages that we will describe in the paper. Together, their readings provide a detailed record of 4