rafmacalaba/data-use-mentions-extended huggingface dataset:
interactive, non-routine and analytic, and non-routine and manual. Since the 1980’s, the United States has been specializing more in tasks that are non-routine and either interactive or analytic, and less in the other three categories of tasks.

Spitz-Oener (2006) has conducted a similar exercise using German data. She ...nds that the pattern of specialization across tasks has evolved similarly in Germany as in the United States, except that Germany is performing more tasks that are non-routine and manual over time, unlike the United States. The evidence supports the plausible conclusion that routine tasks are migrating to low-income countries like China, India and Mexico, with the high-income countries specializing increasingly in the set of non-routine tasks. But the evidence also suggests that Germany is specializing in a di¤erent set of non-routine tasks than the United States, namely, those that are more manual in nature. Given that Germany is smaller than the United States in terms of aggregate output and it has lower wages, our theory predicts that it should specialize in tasks that are relatively easier to o¤shore. Our prediction accords with the available evidence to the extent that (non-routine) manual tasks can more readily be organized and coordinated from a distance than interactive or analytic tasks. This ranking of relative o¤shoring costs seems plausible to us, but we could ...nd no direct evidence to con...rm it.

Ideally, empirical research on task trade would begin by classifying tasks according to the relative ease of o¤shoring. More data on o¤shoring are becoming available as awareness of this phenomenon grows, so it may soon be possible to measure the o¤shoring costs for di¤erent tasks. Once that is possible, it will also be possible to study the pattern of specialization by task. We hope that our theory can help guide such e¤orts.

# 5 Appendix

In this Appendix, we prove Propositions 3, 4, 5, and 7.

Proof of Proposition 3. Equations (
average GDP per capita in the EU, but have grown slower than low growth regions that are already high income. Indeed, 17% of this group of 171 regions recorded a _negative_ average yearly GDP per capita growth rate over the period 2003-2017. Examples include the Greek region of Thesprotia which recorded an average growth rate of minus 2.4%, and the Greek region of Ioannina which recorded an annual growth rate of minus 0.02%. Figure 5 plots the growth rates against income level. Clearly the most variation in growth rate is among the lowest income regions: these regions have grown faster on average, but with very substantial heterogeneity.

_Table 7: Time average of yearly GDP per capita growth over the period 2003-2017._

|Taxonomy|Averae Yearl GDP Per Caita Growth|
|---|---|
|Income Level|g y p|
|High-Income<br>Transition|2.21<br>2.19|
|Less Developed<br>Low-Income|2.11<br>6.12|
|Long-Run Growth Rate||
|High-Income, High-Growth|2.83|
|High-Income, Low-Growth|1.11|
|Not High-Income, High-Growth|4.87|
|Not High-Income,Low-Growth|0.72|
|Decline in Average GDPper capita around the Great Recession||
|Not Declined|3.31|
|Declined|0.82|

Notes: Authors’ elaboration on Eurostat data. Time average of the yearly GDP per capita growth rate in percentage terms over the period 2003-2017. High-income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 90% of the sample average. Transition regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 75% of the sample average, but lower than the 90% of the sample average. Less developed regions are those with an average yearly GDP per capita over the period 2003-2017
adheres to T7 in that several products overlap and all have downward trajectories in the number of GDD per year. By contrast, Malawi, Niger, and Tanzania all deviate from T7, though each deviates in its own way. In Malawi, there is effectively no overlapping in the confidence intervals of the three products and while CPC and ERA5 have flat slopes, MERRA-2 produces a strongly divergent downward trajectory. Niger is similar to Malawi in that there is almost no overlap in confidence intervals and MERRA-2 has a strong downward trajectory. However, in Niger, CPC and ERA5 have strong upward trajectories. Further, in Malawi MERRA-2 produces the fewest GDDs while in Niger it produces the most. Finally, in Tanzania, there is almost perfect agreement between ERA5 and MERRA-2, both of which show no change in GDD over time. By contrast, CPC is in almost complete disagreement, producing fewer and fewer GDDs over time.

Summarizing the descriptive evidence: there can be substantial differences in the weather reported by each EO product. Again, this is not surprising since each product uses different data and different methods to produce gridded weather data. If all that these descriptive figures showed was one EO product consistently reporting higher levels of rainfall and temperature than other products, researchers could easily adjust their methods to account for this over reporting. However, Figures 4 through 7 document not just differences in cardinality (one product reports a few more mm of rain) but in ordinality. In most countries ERA5 reports the most rain but in Niger it reports the least. In most countries TAMSAT falls in the middle in terms of how many days are without rain, but in Uganda it reports the driest conditions. And while compared to rainfall, there is much more agreement among the EO temperature products, there are still significant deviations, particularly in the number of GDD produced by the products. In Malawi, MERRA-2 produces the fewest GDD but in Niger it produces the most. In most countries, CPC is in agreement with ERA5 but in Tanzania CPC disagrees
# 2. **The facts on capital flows to Central and Eastern Europe and Former Soviet Union**

We start with providing some simple raw statistics for the various capital flows. In principle, one can distinguish capital flows by destination (e.g., public versus private); by type (e.g., debt, of which long-term and short-term, FDI, portfolio, of which bonds and equity) and by origin (e.g., commercial, i.e., private versus official creditors). One can also combine the three distinctions, e.g., by splitting debt-type flows into public and private debt, with the latter further into long and short, and by origin, e.g., commercial versus official. For our purposes, and given the data we have at hand and the patterns in capital flows we observe, we create five categories of capital flows: public debt (official) flows; commercial long-term (LT) debt flows; commercial short-term (ST) debt flows; FDI-flows; and portfolio (bond and equity) flows. For some purposes, it would be useful to further split commercial debt flows into those going to the banking system versus to other sectors of the economy, but it turns out that this can not be done for most of the countries given the data available. Our focus is on net flows; however, while we occasionally also discuss "capital flight" (other than that captured through short-term flows), we do not net out capital flight from our net flow measures. We group countries in two regions: Central Europe and the Baltics; and the rest of Eastern Europe, the Caucasus and Central Asia.<sup>3</sup> The group of countries in Central Europe is relatively homogeneous. The second category groups countries with more diverse economic characteristics. Data for individual countries are reported in Annex 1, where we provide both absolute figures and figures relative to GDP.<sup>4
Fourth, we contribute to the literature on scarring effects of recessions, mass layoffs, and plant closures: Jacobson et al. (1993); Kletzer (1998); Davis & von Wachter (2011); Krolikowski (2017); Flaaen et al. (2019); Lachowska et al. (2020); Bertheau et al. (2023), and Schmieder et al. (2023). Instead of focusing on the variation of shocks across US regions, we directly link firm foreign shocks to their workers’ outcomes. Also, we show, for a developing country, long-lasting effects of negative foreign shocks even for non-displaced workers.

Fifth, we add to the thin US-focused literature on the role that income support programs yield in mitigating worker adjustment to shocks: Autor et al. (2013, 2014) and Yagan (2019). We show only limited replacement for lost earnings for Brazil from unemployment insurance, complementing similar evidence obtained for the US by Yagan (2019), also as a result of the GFC. The income replacement of Bolsa Familia we estimate is lower than that of welfare programs in the US found by Autor et al. (2013, 2014).

The paper proceeds as follows. Section II describes the data and provides summary statistics. Section III presents the empirical design to study the long-run impacts of GFC foreign shocks on workers and the baseline results as well as results for heterogeneity across worker characteristics. Section IV identifies the driving mechanisms for the persistent effects on workers, studying the reallocation of workers across firms and sectors and estimating various margins of adjustment by firms in response to foreign shocks. Section V examines the mediating role of informality and lack of competition for the impacts of GFC foreign shocks. Section VI evaluates the ability of assistance programs to buffer workers’ crisisinduced earnings losses. Section VII examines responses in a different labor market, using data from Ecuador. Section VIII concludes.

# **II. Data and Summary Statistics**

The analysis in this paper draws on
control for contiguity, common language, common legal origins and colonial relationship. As Baldwin and Taglioni (2006) explain, country-pair fixed effects are more suitable to deal with the estimation bias in a gravity model with panel data. A drawback of using the country-pair fixed effects is the inability to observe the effects on time-invariant bilateral variables such as distance but this will not affect the variable of interest – Internet adoption – which are time-varying and specific to exporter and importer. The underlying identification assumptions of our empirical specification are that, conditional on the GDP controls and the country-pair fixed effects, the Internet adoption is exogenous to exports and changes in Internet adoption are not correlated with other unobserved time-varying characteristics that can affect bilateral exports. Thus, changes in trade can be attributable to changes in the levels of Internet adoption.

We will also examine how the similarities of Internet adoption affects trade by grouping the importers and exporters according to their Internet adoption levels. The countries are divided into two groups depending on their Internet adoption rates for each year: a country has high (low) Internet adoption if it is above (below) the median. Dummy variables are created for each of the pairwise combinations: if both countries in the bilateral pair have high Internet adoption, if only the exporter has high Internet adoption, if only the importer has high Internet adoption, and if both countries have low Internet adoption.

The trade data are obtained from the UN COMTRADE database for the period 2001-2013. We exclude oil products from the aggregated export flows as these products experience a lot of price volatility during the study period.<sup>3</sup> The gravity variables are obtained from the CEPII and the GDP data is from the World Bank World Development Indicators. The Internet adoption rate is defined as the number of individuals using the Internet per 100 persons in the country and is obtained the International Telecommunication Union (ITU). Table 1 reports summary statistics for trade, Internet penetration and GDP.

> 3 We exclude all products in “Section
sup>_33_</sup> can indicate if credits that have recently been granted are displaying an abnormally high delinquency rate. This, for example, could lead to conclude the convenience of activating the countercyclical add-on.

- **Evaluation of systemic importance** . Recognizing that the macroprudential approach has also an important cross-section aspect, Basel III will also include particular – more stringent – provisions for Systemically Important Financial Institutions (SIFIs). A precondition for such a framework is counting with sound elements for their identification.

PCRs can provide a valuable input for measuring the degree of systemic importance of banks that, as a result of their size, inter-linkages or lack of substitutability, must be more conservatively regulated and intensively supervised. Examples include banks that are the unique creditors of certain geographic regions or business sectors, or are extensively interconnected with other financial institutions.

> 32 For a thorough explanation of the mechanics of the countercyclical capital framework, see BCBS (2010b).

> 33 _Vintage analysis_ partitions the credit portfolio by date of origination and compares the evolution of the respective cumulative delinquency rates – i.e. _delinquency maturation curves_ –. The steeper a curve is towards delinquency, the riskier the vintage is. If recent vintages display a steeper pattern, they can be indicating deterioration in lending standards or in the credit environment. This kind of analysis is typically used as a portfolio management tool in the banking industry, but should also be used by bank supervisors and can be readily performed with PCR data.

14
# 1. Introduction and context

Excessive exposure to air pollutants can have severe negative health impacts and is a leading cause of death (WHO 2021), and emissions from fossil fuels burned by vehicles and for domestic use are known to substantially contribute to urban air pollution (Andrade et al. 2010; Parrish et al. 2009; Zhang and Smith 2007). As a result, many cities adopt low-emission zones, clean-burning fuels, and other measures to combat adverse effects on health and well-being. But although such policies have been found to be effective, they can incur substantial upfront costs for consumers and require enforcement (Zhai and Wolff 2021; Jones et al. 2012). In his seminal work, _The Economics of Welfare,_ Arthur C. Pigou (1920) introduced an alternative mechanism applicable to anthropogenic emissions, proposing the use of taxes—and thus price incentives—to curb activities that cause negative externalities to a socially optimal level.

The relationship between fuel prices and consumption has been studied extensively in economic research and one would expect price changes to have follow-up effects on air quality. Although the exact magnitude of impact depends on price elasticity, lower fuel prices result in higher consumption, which, intuitively, should increase levels of anthropogenic fine particulate matter (PM2.5) pollution. This paper expands on available research on the price-pollution channel—which mostly focuses on city- and state-level analysis—by looking at the global relationship between fuel prices and consumption. Thus, our main contribution to the literature is an investigation into whether higher fossil fuel prices are tied to lower urban pollution levels globally.

To properly examine this notion, we estimate the relationship between PM2.5 concentrations, fossil fuel prices, fuel consumption, and country-specific variables using the Common Correlated Effects (CCE) estimator by Pesaran (2006) and a novel panel dataset that spans 133 countries over 20 years. Our findings suggest that, at the global level, transport fuel prices are significantly negatively correlated with air quality. Coefficient estimates for gasoline and
|0.126<br>(0.222)|_−_0.178<br>(0.475)|
|Post_×_ODS_×_DS|0.434<br>(0.399)|0.269<br>(0.182)|0.304<sup>_∗_</sup><br>(0.170)|_−_0.484<br>(0.298)|0.193<br>(0.133)|_−_0.367<br>(0.291)|0.114<br>(0.492)|
|Observations|6,118|6,118|6,118|6,118|6,118|6,118|6,118|
|R<sup>2</sup>|0.562|0.579|0.409|0.485|0.661|0.645|0.566|
|Mean(ODP tons)|-821|-218|-3|-472|-19|-128|10|

FE triple-difference regressions on a stacked country-level panel with a _±_ 3 year event window around RTA entry into force. All outcomes are the inverse hyperbolic sine of the difference between ODS emissions and emissions targets under the Montreal Protocol. DS is an indicator equal to 1 if the the ODS provisions are binding with dispute settlement under the RTA. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report the mean deviation from target across the entire sample in tons of ozone depletion potential (ODP). Statistical significance from two-sided _t_ tests are denoted by _∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01.

v
**Figure 15. Most Advantaged and Least Advantaged Simulations for Social, Emotional, and Cognitive Development by Year (Percentages)**

<!-- Start of picture text --> 2006/7 2011 2012<br>100 99<br>95<br>100<br>90 83<br>79<br>80 74<br>70<br>60<br>50 45<br>36<br>40<br>30<br>18 16<br>20<br>8<br>10 4<br>0<br>ECCE Development Violent Discipline Work & Domestic<br>Activities Work<br>Percentage<br>Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged<br><!-- End of picture text --> _Source:_ Authors’ calculations based on MICS 2006/07, ENPSF 2011, and ONDH panel 2012.

_4.4 Circumstances Contributing to Inequality in ECD over Time_ The D-indices indicate the extent of inequality of opportunity in ECD in Morocco due to children’s early circumstances. An important element of rectifying that inequality is understanding the different risk and protective factors driving it. The Shapley decompositions (Table 5) show the relative contributions of different circumstances (wealth, mother’s education, father’s education, region, urban versus rural, and gender) to this inequality, as a percentage. While the contributions of different background characteristics vary over time and across the measures of ECD, a number of consistent patterns stand out. First, wealth contributes substantially and the role of wealth in inequality appears to have been rising slightly over time. Mother’s education also plays an important role in inequality, although less so in nutrition inequality than other dimensions of inequality. Father’s education contributes to inequality but less so than mother’s education. Regional and urban/rural differences contribute to inequality, often substantially, but to
potentially informal firms in 574 census blocks. In terms of listed sector, 48% were in commerce, 45% in services, only 1% in manufacturing, and 6% undefined. The large number of firms to be matched in a short timeframe, requiring firms to be on both formal lists, and, possibly the inexperience of the survey firm in doing such a matching exercise, meant that, as we will see, a number of formally registered firms remained in this listed sample.

Since part of our design involves looking at spillovers within blocks we wanted to minimize the risk of spillovers across treatment blocks. To do this, we used the address of each listed firm and, from this, obtained the GPS latitude and longitude, and calculated the number of firms in other census blocks that lay within 100 meters of the listed firm in a straight-line. We then examined in detail the 239 census blocks in which at least 10 percent of the firms were close to at least one firm in another census block. We were most concerned with adjacent census blocks where firms on one side of the street that was a block boundary were in one block, and those on the other side of the street were in a different block. We then used an algorithm to reclassify these into new blocks<sup>8</sup> , giving a total of 662 geographic blocks. Out of these blocks, 57 contained only one potentially informal firm each, and so we dropped these blocks to end up with a sample of 605 geographic blocks containing 7,795 firms.

# **4.2. Randomization into Treatments at the Geographic Block Level**

We randomized these 605 geographic blocks into three groups: control blocks, communication blocks, and inspector blocks. Randomization was stratified by the 9 sub-districts in Belo Horizonte, and by whether the block had above or below the median firm density (measured by the number of listed informal firms divided by the area of the block).<sup>9</
business owners from 50 countries as we restrict the sample to those countries with sufficient sample size to conduct gender-disaggregated analysis in each survey wave.

> 13The country level aggregated data set from the Future of Business Survey is provided for free through a Creative Commons license and is available on the World Bank Data Catalog as well as on Humanitarian Data Exchange.

> 14The bi-annual FoB surveys typically are done June and December of every year and cover a larger set of over 100 countries. While 84 countries were surveyed for the COVID-19 high-frequency waves in 2020, only 50 countries had sufficient sample size to conduct gender-disaggregated analysis.

> 15Each survey wave was targeted in the last week of every month over a six-month period in 2020. The first wave (Wave 1) of the FoB COVID-19 survey was conducted in May 28th to 31st 2020, Wave 2 June 24-30, Wave 3 in July 23-31, Wave 4 in Aug 24-31, Wave 5 September 23-30, and Wave 6 October 24-31.

10
decades, especially because the role of top earners in inequality seems to have increased in the period (Medeiros et al., 2014, 2015). However, an underestimation bias of inequality in usual official sample surveys such as PNAD is not necessarily true if the cases of omitted income reporting are equally distributed across the distribution. For instance, Barros et al. (2007) argue that poorer families may omit non-monetary income or occasional income transfers from the government (such as unemployment insurance) or from relatives. In fact, Barros et al. (2007) compute similar or smaller measures of inequality using different sources of information than the traditional Brazilian Household Survey (PNAD). Hence, more research is needed on this topic to clarify the role of top earners in inequality and its dynamics.

# **5.2 What to expect in the coming years**

In the last years, the decline in inequality in Brazil has stalled, displaying a small increase in the last quarters. Should we expect a halt in the process of inequality reduction in the coming years, even an increase? Also, what could be done for Brazil to return to the previous path of inequality reduction?

Real minimum wage increase, one of the main factors behind the decrease in wage 29