rafmacalaba/data-use-mentions-extended huggingface dataset:
literature in economics. Autor et al. [2003], Costinot et al. [2011] and Acemoglu and Autor [2011], have argued that globalization and advances in technology and computerization have increased the comparative advantage of individuals who perform non-routine tasks requiring problem solving, intuition, persuasion, and creativity.

To obtain measures of human capital, following Ayyagari and Maksimovic [2017] we use O*NET, a database maintained by the U.S. Department of Labor that provides data on occupation-specific descriptors that define the key features of an occupation such as worker abilities, technical skills, job output, work activities, etc. We focus on the following three measures of human capital: _CPS_ (Complex Problem Solving) which is identifying complex problems and reviewing related information to develop and evaluate options and implement solutions; _NRCOG_ (Non-routine Cognitive Analytical skills) from Keller and Utar [2016] which is the sum of Mathematical Reasoning, Inductive Reasoning, Developing Objectives and Strategies, and Making Decisions and Solving Problems; and _RMAN_ (Routine Manual) from Keller and Utar [2016] which is the sum of Spend time making repetitive motions, Pace Determined by Speed of Equipment, Manual Dexterity, and Finger Dexterity. We merge the occupation-level scores with the Occupational Employment Statistics (OES), a US establishment level dataset from the Bureau of Labor Statistics, where workers are classified into occupations on the basis of the work they perform and skills required in each occupation. We compute a weighted average across occupations in each firm weighting by the number of employees in each occupation to obtain a score for each establishment. We then take weighted averages across all establishments in an industry to compute industry-level skill scores.

We separate our sample into high and low skill industries based on the _CPS_ , _NRCOG_ , and _RMAN_ scores where high skill is defined as greater than or equal to the median value for each of the skill measures and low skill is defined as less than the median value for each of the skill measures. In Figure 2,
Agriculture|61.1|21.3|14.4|21.5|1.3|46.7|
|developingcountries||Manufactures|31.7|17.2|9.0|8.9|1.1|22.7|
|**Memo:**|||||||||
|Average: Developed||Agriculture|17.3|9.3|6.2|12.2|2.3|11.1|
|countries||Manufactures|6.6|5.5|3.2|4.5|1.9|3.4|

Note: The classification of sector is defined as agricultural products HS 1-24 and manufacturing products HS 25-97.

Sources: UNCTAD TRAINS (applied tariffs) and WTO IDB (bound tariffs) databases.

groups with the exception of applied tariffs in agriculture, where there is little difference between the average for the low and middle-income developing countries. Similarly, the average differences in the margins between applied and bound tariffs tend to be highest in the low-income countries and lowest in the highest-income ones.

10
<br>consumption)|30.7*|20.4**|

Note: Calculations are based on the World Bank Open Data. Middle-income countries are defined based on the World Bank country classification for 2021.<sup>*</sup> Latest data available for 2018.<sup>**</sup> Latest data available for 2015.

The government has recently adopted and proposed several tax policies to promote the uptake of hybrid and electric vehicles. First, starting in 2017, the sales tax on new vehicles was reduced from 19 percent to 5 percent for hybrid and electric vehicles; the sales tax for gasoline vehicles remains at 19 percent. In 2017, Colombia also reduced import tariffs from 35 percent to 0 for electrics and 5 percent for hybrids; tariffs for gasoline vehicles remain at 35 percent. Initially, the

> 11 See https://www.worldenergy.org/news-views/entry/electric-mobility-is-a-reality-in-colombia [last accessed: February 2022].

13
(1): 1–30.

- Hangartner, Dominik et al. (2019), “Does Exposure to the Refugee Crisis Make Natives More Hostile?” _American Political Science Review_ 113(2): 442–55).

- Hartman, Alexandra, and Ben Morse (2017), “Violence, Empathy, and Altruism: Evidence from the Ivoiran Refugee Crisis in Liberia.” _SSRN Electronic Journal_ . https://papers.ssrn.com/abstract=2769889 (May 10, 2021).

- Helbling, Marc, Liv Bjerre, Friederike Römer, and Malisa Zobel (2017), “Measuring Immigration Policies: The IMPIC Database.” _European Political Science_ 16(1): 79–98.

- Helbling, Marc, and David Leblang (2019), “Controlling Immigration? How Regulations Affect Migration Flows.” _European Journal of Political Research_ 58(1): 248–69.

- Horiuchi, Yusaku, Zachary D. Markovich, and Teppei Yamamoto (2021), “Can Conjoint Analysis Mitigate Social Desirability Bias?” _Political Analysis, forthcoming_ .

- Hvidtfeldt, Camilla, Jørgen Holm Petersen, and Marie Norredam (2019), “Prolonged Periods of Waiting for an Asylum Decision and the Risk of Psychiatric Diagnoses: A 22-Year Longitudinal Cohort Study from Denmark.” _International Journal of Epidemiology_ 49(2): 400–409.

- International Organization for Migration (2015), _How the World Views Migration_ . Geneva. http://publications.iom.int/system/files/pdf/how_the_world_gallup.pdf.

- Jeannet, Anne-Marie, Tobias Heidland, and Martin Ruhs (2021), “What Asylum and Refugee Policies Do Europeans Want? Evidence from a Cross-National Conjoint Experiment.” _European Union Politics_ .

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comparability of the composite wealth score, I constructed it at once using the entire household-round observations. In particular, in constructing the wealth score, I include whether a household lives in its own house, and a household’s number of members per number of sleeping rooms in the main dwelling, access to

> 13The (25) food items that are included in the food consumption aggregate are cereals ( _teff,_ wheat, barley, maize, sorghum, and millet), pulses (horse beans, chickpeas, field peas, lentils, and haricot beans), oil seeds (Niger seed and linseed), fruits and vegetables (onions and bananas), tubers and stems (potatoes and _kocho/bula_ ), stimulants (coffee and chat) and others (meat, milk, cheese, eggs, sugar, and salt). These items are comprehensive enough to capture the most commonly consumed food items not just nationally but also regionally for each of the 11 regions of the country.

> 14These items could be purchased, own produced, or gifted from others. The value of purchased items is directly collected while the median price from the lowest geographical unit for which there are at least 10 purchase price observations is used to construct the value of food items that are own produced or gifted from others.

> 15Before calculating the total consumption expenditure, a few adjustments are made to food consumption expenditure. First, households with zero reported aggregate consumption from all of the food items are excluded. Next, food consumption expenditure per capita for each of the food items is winsorized at the 98th percentile in the ESS1 to correct for outliers while the winsorization is at the 99th percentile for the other two rounds of surveys, as these rounds have fewer outliers. Finally, the bottom 2nd percentile of aggregate food consumption expenditure per capita is winsorized in each round of the survey.

> 16I constructed the ESS wealth index in the same fashion as the wealth index in the Demographic and Health Surveys (DHSs) - ( <mark>
Countries such as the Russian Federation were, in general, not exposed to market forces until the beginning of the 1990s and state-owned enterprises (SOEs) still continue to play a significant role in the domestic economy.<sup>1</sup> Thus, it is not clear that the link between management practices and firm performance can be found in transition countries as well. Following the four core management areas from Bloom and Van Reenen (2007), the European Bank for Reconstruction and Development (EBRD), in cooperation with the World Bank, conducted a survey on Management, Organization and Innovation (MOI) for 10 transition economies, including Russia.<sup>2</sup> The results of the face-to-face interviews conducted with 1,874 factory managers were benchmarked against Germany and India. Using the MOI survey on all transition economies, Bloom et al. (2011) suggest that an improvement in management practices scores from the lower to the upper quartile of the distribution is associated with a 9 percent increase in operating revenue,<sup>3</sup> an over 85 percent increase in profit margins<sup>4</sup> and an increase in the return on total assets<sup>5</sup> by almost 20 percent.<sup>6</sup> Friebel and Schweiger (2012) specifically use the MOI survey data on 311 Russian firms to assess the importance of managerial quality on firm performance in Russia.<sup>7</sup> Their work suggests that differences in managerial quality, as measured by the four core management areas, explain relatively little in terms of variation in overall firm performance, however, it does explain some of the regional differences between firms in Russia’s Far East and the rest of Russia. Furthermore, they argue that exposure to foreign competition, both in the product market and the labor market, explains the variation in adoption of good management practices across firms and broad regions.

To quantify the importance of management
_Early childhood education and development in poor villages of Indonesia: Strong foundations, later success_ . The World Bank.

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Fifth, data-constraints prevent from adequately covering several dimensions or properly accounting for joint distribution. The issue arises in our design for the nutrition indicators considered as part of the health dimension. We do not have data on bad nutrition outcomes except between 15 and 49 years old and below 5 years old. For the large fraction of the population outside of these two age windows, we conservatively assume that their nutrition status is normal. Moreover, because nutrition outcomes come from a separate survey, we had to input the nutrition outcomes from DHS to NLSS (see Appendix 3 for details on the imputation model). This imputation is not expected to have a large impact on results given the small fraction of the population for which we have bad nutrition status (see Table A1 in Appendix 1). However, one should bear in mind that our results suffer from this limitation.

## **Aggregating quality with quantity of life**

For the reasons exposed in Section 2.2, we account for the direct impact of mortality on quantity of life using PALE. There only remains to select a value for its normative weight , which defines the trade-off between quantity of life and quality of life. θθ There are several ways one could select a value for . For instance, the selection could be grounded in the views of the individuals themselves. Such views should be collected through dedicated surveys. θθ Alternatively, parametric values for could be deduced from assuming a particular Bernouilli (period) utility function, as illustrated in Appendix 2. θθ For our purposes, we simply assume two different values for the baseline and preferred designs. We conservatively assume for the baseline design. From a policy making perspective, this choice provides the smallest plausible weight to mortality. The reason is that implies that being dead θθ = 1 provides the same well-being level as being (multidimensionally) poor. Arguably, no policy maker would θθ = 1 explicitly ground its decisions on the assumption that poor individuals would be better-off dead. Providing the smallest plausible weight to
A growing body of empirical work has documented the superior productivity of exporting plants and firms relative to those producing solely for the domestic market. I argue that, besides this superior productivity, firms that sell in foreign markets are positively associated with firms that use signaling and technological marketing tools. I find some weak evidence that suggests that these tools might help firms become exporters; however, there is a need for panel data to be able to do a more comprehensive study of the direction of causality.

In the next section I discuss the existing literature that is relevant to the study of the impact of signaling and technological marketing tools on the export behavior of firms. In Section 3 I describe the data and the variables that are used in the empirical analysis. In Section 4 I confirm that the productivity premium between exporting and nonexporting firms exists in the sample of firms used in this study as it has been previously found in the literature. Section 5 describes the econometric framework and Section 6 explains the main results. Section 7 verifies the results from Section 6 through a number of robustness checks. Finally Section 8 summarizes the main findings and explains future work.

# **2 Literature Review**

There are two main strands of literature that are relevant to the current study. First and foremost is the growing body of work related to the extensive margin in international trade. Of particular relevance are the empirical studies that document the superior performance characteristics of exporting plants and firms relative to non-exporters. According to this strand of literature, exporters are larger, more productive, more capitalintensive, and more technology-intensive than non-exporter firms. In addition, exporters 3
million.<sup>4</sup> East Asia has 83 million people living on less than $1.90 a day, South Asia 231 million, and Sub‐Saharan Africa 347 million. Put another way, during the last 25 years East Asian countries reduced extreme poverty by more than 30 million—each year!

**Figure 1: Where the extreme poor are, by region – 1990 and 2015**

<!-- Start of picture text --> 1990 2015<br>Number of extreme poor: 1.9 billion Number of extreme poor: 702 million<br>5%<br>6%<br>12%<br>15% EAP<br>SAS<br>51% 33% SSA<br>49%<br>30%<br>Rest of World<br><!-- End of picture text --> With a quarter century of experience, it is reasonable to ask some questions: Does the 2.5‐point plan work everywhere? If it does not, what should be done instead? If it does work, have we learned to implement it efficiently in places where the incidence of extreme poverty is as high today as it was in China and Vietnam in 1990, say in Nigeria and the Democratic Republic of Congo? Where it has worked, should the strategy be changed as extreme poverty is reduced to low levels?

3 The international poverty lines of $1.25 and $2.00 were updated to $1.90 and $3.10 in 2015 to reflect improvements in the measurement of purchasing power parity conversion factors. The new lines maintain the real purchasing power of the earlier lines in poor countries, and as such, the revised poverty estimates do not change drastically as a result of this update. See Ferreira et al. (2015) for a detailed discussion on the topic.

4 This number is statistically projected from the 2012 global poverty figures.
Gini as predicted by the short-term hypothesis discussed above. Interestingly, there is a clear regional pattern to the increases and decreases. All but one of the increases in inequality during the crisis are in Latin America, all of the decreases are elsewhere (Africa and Asia). The small number of observations militate against worthwhile regression analysis: as illustrated in Regression Table 2, only the Africa dummy is significant.

# _Distributional effects on poverty near crises_

A third approach to selecting and sorting the cross-country information is to move to the careful decomposition prepared by Kraay (2004) of changes in poverty between the part attributable to changes in mean income (at a constant Lorenz curve) and the part attributable to distributional shifts. Kraay has conducted this decomposition for over 200 non-overlapping intervals for which sufficient data is available, and I am very grateful to him for making this data available to me before its publication. Of these intervals, some 42 span the onset of a banking crisis recorded in the Caprio et al. database in the sense that the first year of the crisis either coincides with the start of the data interval, or is after that start date and strictly before the end date of the data interval. Note that this choice of intervals is rather different to that employed above, but is conceptually somewhat close to the short-term individual crisis calculation. Nevertheless, this approach gives us a larger set of observations, 41 of which 16 relate to transition economies.

Regression analysis of the 25 non-transition cases suggests that, even when poverty increases with a fall in overall income following crisis, the impact of _distributional_ 15
adoption is more important in the internationalization process of high-tech firms rather than low-tech firms (classified based on the share of expenses devoted to R&D). To this, Gal et al. (2019) add that productivity gains from ICT in the EU do not depend on firm size but rather on productivity level and type of technology. They find that gains are higher in high productivity firms, increasing the productivity gap between frontier and laggard firms. For instance, the authors find that, on average, a 10 percentage-point increase in adoption of cloud computing services increases a firm’s productivity level by 3.5 percent, but that these productivity gains are more than doubled for high productivity firms. This difference could be explained by the dependence on ICT specialists which is higher in less productive firms (Berlingieri et al., 2018). In terms of type of technology, Gal et al. (2019) find that cloud computing has bigger productivity effects on smaller firms, while Enterprise Resource Planning (a type of IT applications, designed to automate business processes) is more beneficial for bigger firms.

In general, the literature points to multiple channels of impact on productivity, and when considering ICT as a determinant, there is a consensus that their impact is amplified when accompanied by other factors such as employees' skills. Colombo et al. (2013) study how the adoption of broadband internet has affected the productivity of 799 Italian SMEs using data from 1998 to 2004. The authors find that the impact of internet adoption on productivity is negligible, but that broadband application fosters productivity if combined with complementary strategic and organizational changes such as adjustment of internal routines to maximize the potential of ICT, cultural adaptation to the use of technologies, and good organizational skills.

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shares. Although Auer and Schoenle don’t discuss pass-through of competitors’ exchange rates — and I don’t discuss market shares — one might expect that many of the firms responding to competitors’ prices in Auer and Schoenle are the same firms responding to competitors’ RERs here.

Amiti et al (2016) use a new firm-level dataset matching Belgian domestic manufacturing prices, costs, imported intermediate input usage and competitors’ prices, which allows them to control for firm-level marginal costs, and instrument competitors’ prices (which they argue are endogenous due to the price-setting game). They find 2/3 cost pass-through and 1/3 competitors’ price pass-though into Belgian domestic manufacturing prices, with higher rates of competitors’ price pass-through in larger firms. They also find a 30% pass-through rate of the TWI into domestic prices, and use a calibrated model to argue that most of this is through the cost of intermediates. This is consistent with my finding of lower ER pass-through into the PPI after controlling for materials prices.

> 3Low bilateral pass-through is important because it underlies a disconnect between exchange rates and macroeconomic aggregates and, in many cases, a departure from the Law of One Price (Obstfeld and Rogoff 2000). Since Gopinath and Rigobon (2008) and Gopinath and Itskhoki (2010) found low pass-through conditional on a price change and over long horizons, research has focused on models including real rigidities.

> 4This finding is related, in an abstract sense, to Anderson and van Wincoop’s (2003) argument for the inclusion of “multilateral resistance” terms in gravity regressions to remove omitted variable bias due to aggregate price indices.

> 5Specific mechanisms include industry aggregation (AB), additive local distribution costs (Corsetti and Dedola 2005), nonCES demand (Melitz and Ottaviano 2008, Kimball 1995), consumer search (Alessandria 2009), and customers-as-capital (Drozd and Nosal 2012a). In the final two cases, the firm’s price can be written to reflect two forces: a marginal
9|-2.7|-7.1|-36.1|
|Fiscal cost (USD) per ton of CO2|0.0|671.3|374.4|580.6|-833.6|
|Welfare cost (USD) per ton of<br>CO2|0.0|65.0|149.7|339.8|-545.6|

Note: This table shows the counterfactual simulations of emissions and cost-effectiveness for the five scenarios in 2019 and 2020, including emissions coming from electricity generation. The "No Policy" scenario’s calculations are in levels. All other scenarios’ calculations are increments from the "No Policy" scenario. The Lifetime CO2 emissions are computed by multiplying the emissions produced per vehicle times the average mileage driven per year in Colombia times the expected lifetime of a vehicle. We assume that the average mileage driven in Colombia is 12 thousand miles, while a vehicle's lifetime is ten years. The "Fiscal cost per ton of CO2" is computed by dividing the fiscal cost by the difference in lifetime emissions in each scenario. The "Welfare cost per ton of CO2" results from dividing the difference in total welfare by the difference in lifetime emissions in each scenario. Emissions from electricity generation are calculated based on International Energy Agency data for Colombia’s electricity generation mix in 2019 as 211.09 t/GWh.

38
65 and 1.48; iii) skewness = 0.99 and 1.76; iv) standard deviation<br>deviation = 2.55 and 9.87. Estimated number of persons living in extreme poverty deviation = 0.55 and 1.07. Estimated number of persons living in extreme poverty = 1.30 and 1.59. Estimated number of persons living in extreme poverty corresponding<br>corresponding to the median headcount = 87 and 127 million people. corresponding to the median headcount = 635 and 531 million people. to the median headcount = 90 and 18 million people.<br>96% confidence interval 80% confidence interval<br>40% confidence interval Median<br>Poverty headcount of 3 percent Year = 2030<br>) ) )<br>(percentage) (percentage) (percentage)<br>( ( (<br><!-- End of picture text --> _Source:_ Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates.

<!-- Start of picture text --> FIGURE A 5. PREDICTED GINI COEFFICIENTS BY INCOME GROWTH COUNTRY TYPOLOGY<br>Panel A. LOGCs Panel B. MOGCs Panel C. HOGCs<br>(Base period: 1980–2014) (Base period: 1980–2014) (Base period: 1980–2014)<br>70 70 70<br>57.5 57.5 57.5<br>45 45 45<br>32.5 32.5 32.5<br>20 20 20<br>1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040 1980 1990 2000 2010 2020 2030 2040<br>Years Years Years<br>Aggregated Gini index sta ts derived from 500 ra ndom dra ws per year in each of the 8 Aggregated Gini index sta ts derived from 500 r andom dr aws