rafmacalaba/data-use-mentions-extended huggingface dataset:
pronounced split _within_ exporters between the handful of large firms that drive trade volumes, trade growth, trade patterns, and diversification, and the rest.

This paper is organized as follows. In Section 2 we introduce the data utilized in the paper. Section 3 highlights the role of superstars in comparative advantage and characterizes those exporters we consider "Superstars". In Section 4 we analyze superstars’ contribution to export growth and diversification. Section 5 delves into the origin of superstars. Finally Section 6 concludes.

# **2. Data on Firm Exports in 32 Countries**

The data used for this paper are exporter-level information on non-oil exports from 32 countries in different regions of the world, mostly for the period 2004-2008.<sup>5</sup> This information has been gathered as part of the World Bank Export Dynamics Database.<sup>6</sup> In most cases, the data have been collected directly from Customs Authorities, Ministries of Finance or Commerce and National Statistics Institutes. In other instances, the data have been collected indirectly through think tank institutions (Egypt) or purchased from a private company based on inputs from customs authorities (Chile, Colombia and Ecuador).

> 5 Although exporter-level data is available for years before and after this period we focus on these years for two main reasons. First, this period is covered in all countries in our sample (whether fully or partially). Data for years before 2004 are scarce across countries and for years after 2008 they include the trade collapse in 2009. Second, for consistency purposes in our cross-country comparisons, it is important to have a similar period across all countries. However, in the analysis of the origin of superstars, which requires a longer period, we also use data from 2000 to2010 for three countries with available data.

> 6 See Cebeci, Fernandes, Freund and Pierola (2012) for a detailed description of the data and the cleaning process operated in it. This cleaning process includes the use of a “
- Edward Fulton Denison. _The Sources of Economic Growth in the United States and the Alternatives Before Us_ . New York: Committee for Economic Development, 1962.

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38
the ratio increases to 14 percent if the welfare aggregate is spatially deflated (results for Iraq and Rwanda are provided in the annex).

**Table 3. Percentage of population staying in the same decile regardless of welfare aggregate: spatially deflated or nominal**

|Deciles|Kazakhstan,2010|Iraq,2012|Rwanda,2010|
|---|---|---|---|
|bottom|89|86|90|
|2|72|66|72|
|3|62|57|64|
|4|57|54|60|
|5|56|49|58|
|6|57|49|57|
|7|60|55|61|
|8|65|59|69|
|9|76|68|81|
|top|91|88|93|

Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.

13
demand equation in Grossman’s (1972) model. The evolution of health capital over time reflects the time profiles of the depreciation rate and any other time-varying variables, but also the size of any random ‘shocks’. The latter, which are captured by ε 1, could be rationalized in terms of the depreciation rate being stochastic rather than deterministic as in Grossman’s original formulation (cf. Grossman 2000). Correlations between estimates of ε 1 and the other residuals provides information on the extent to which health shocks get transmitted into unexpected changes in income, and medical and non-medical consumption.

# <u>Data</u>

The data are from the two-wave Vietnam Living Standards Survey (VLSS). A representative sample of 4800 households was interviewed in 1992/93, and then again in 1997/98.

The focus is on changes in adult health, measured by the body mass index (BMI). This is equal to the individual’s weight in kilograms divided by the square of the individual’s height in meters. BMI is first and foremost a measure of nutrition. A person with a BMI score below 18.5 is considered ‘underweight’, while someone with a BMI score in the range 18.5-24.9 is considered ‘normal’. An individual with a BMI score in the range 25-29.9 is considered to be ‘overweight’, and someone with a score higher than 30 is considered obese. In Vietnam in the survey period, less than 1% of the sample in either year was classified as overweight or obese, while roughly a quarter of the sample were classified as underweight. While primarily a measure of nutritional status, BMI has been found to be a good predictor of mortality, with mortality risk higher at both the bottom and top ends of the scale (Calle, Thun et al. 1999). Account is taken below of the non-monotonic relationship between health and BMI. Beyond BMI, the VLSS contains very little by way of useful
and schools across the 65 countries available in the 2012 PISA dataset. This simple relationship suggests that students in schools in the bottom quartile of management within their country score are, on average, about 6 points lower than the PISA global mean, while students in schools in the top quartile of management within their country score, on average, about 5.5 points higher than the PISA global mean. To put this into context, 41 PISA points in math are the equivalent of a year of learning. The range of

> 12Independent researchers conducted the WMS in Colombia and Mexico during 2015, with guidance and supervision from the original WMS team. These data were not available to Bloom et al. [2015a]. India is not included in Figure 1 because it did not participate in PISA in 2012.

> 13We show comparisons between the WMS and PISA sub-indices for operations management in Figure B.1 and people management in in Figure B.2 in Appendix B and confirm that the distributions are consistent for both sub-indices.

9
)|Employm|ent weigh|ted||(3) Sales|weighted||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||cdfat a|distance of||||||
|Year|10km|50km|100km|500km|10km|50km|100km|500km|10km|50km|100km|500km|
|1990|0.020|0.076|0.139|0.420|0.021|0.083|0.151|0.449|0.022|0.086|0.156|0.453|
|2009|0.013|0.056|0.107|0.373|0.015|0.063|0.121|0.397|0.017|0.068|0.126|0.403|
|Mean (1990–2009)|0.015|0.064|0.121|0.394|0.017|0.073|0.136|0.422|0.019|0.077|0.141|0.428|
|% change|-36.0%|-27.1%|-22.6%|-11.3%|-28.7%|-23.3%|-20.3%|-11.4%|-21.5%|-21.2%|-19.3%|-11.0%|

_Notes:_ Authors’ computations based on the Annual Survey of Manufacturers Longitudinal Microdata file, 1990–2009. We report the values for the starting and the end years only since
# Figure 4: Poverty Rates: $3.20 Per Day

<!-- Start of picture text --> 74.6<br>69.7<br>60.4<br>53.4<br>2004.5 2009.5 2014.5<br>Year<br>National Urban<br>Rural Year of Survey<br>Sources: India National Sample Survey (NSSO) Surveys.<br>90<br>80<br>70<br>60<br>50<br>Percent 40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Figure 5: Poverty Rates: $5.50 Per Day <!-- Start of picture text --> 95.7 94.3<br>89.7<br>85.6<br>2004.5 2009.5 2014.5<br>Year<br>National Urban<br>Rural Year of Survey<br>Sources: India National Sample Survey (NSSO) Surveys.<br>90<br>80<br>70<br>60<br>50<br>Percent 40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> 29
- Sahn, D., Alderman, H., 1988. The Effect of Human Capital on Wages, and the Determinants of Labor Supply in a Developing Country. Journal of Development Economics 29, 157–83.

- Schultz, T. P., 1990. Testing the Neoclassical Model of Family Labor Supply and Fertility. Journal of Human Resources 25 (4), 599–634.

- Sen, A. K., 1984. Family and Food: Sex Bias in Poverty. In: Sen, A. K. (Ed.), Resources, Values, and Development. Basil Blackwell, Oxford.

- Sen, A. K., Sengupta, S., 1983. Malnutrition of Rural Children and the Sex Bias. Economic and Political Weekly Annual Number.

- Skoufias, E., McClafferty, B., 2003. Is PROGRESA Working? Summary of the Results of an Evaluation by IFPRI. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Smith, L. C., Ramakrishnan, U., Ndiaye, A., Haddad, L., Martorell, R., 2003. The Importance of Women’s Status for Child Nutrition in Developing Countries. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Standing Committee on Nutrition, 2000. Fourth Report on the World Nutrition Situation. United Nations System, Geneva.

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.13|0.07|0.18|0.10|0.09|0.20|
|Increased use of any digital|1,063|0.47|0.24|0.45|0.53|0.54|0.62|
|Change in mobility|1,063|-33.81|-56.92|-41.61|-22.81|-17.28|-27.20|

Table 1: Descriptive statistics of FAT and COV-BPS data. Computations do not use sampling weights. Quintiles (Q) are based on technology sophistication pre-COVID-19. Q1 = Bottom 20%; Q2 = Second quintile; Q3 = Third quintile; Q4 = Fourth quintile; Q5 = Top 20%.

10
the importance of protecting the sizable share of informally employed workers in Malaysia, the government introduced voluntary schemes for retirement savings in 2010, and for workplace injury and disability insurance in 2017. However, take-up for both schemes remains low. The voluntary retirement savings scheme, i-Saraan, covered 10.7% of all selfemployed workers (defined as own account workers and unpaid family workers) in 2022, while the SelfEmployment Social Security Scheme (SESSS) covered 16.4% of all sell-employed workers in 2021. The reasons behind the low take-up rates for these schemes are unclear.

The objective of this paper is to assess digital platform workers’ assessments of social insurance coverage (that is, retirement pensions, insurance for workplace injuries) and unemployment insurance, as well as their willingness to pay for such coverage. We deploy a novel vignette experiment in an online survey of digital workers. The experiment involves the randomized allocation of fictitious job descriptions that offer alternate combinations of social insurance coverage and earnings to elicit digital workers’ preferred job description.

Overall, we find that the majority of digital workers value social insurance highly, and also report that they do not have sufficient social insurance coverage in their current work. They are willing to trade off a part of their earnings to receive social insurance coverage. This willingness is particularly high in the case of unemployment insurance, followed by retirement income and insurance for workplace injuries. The low prevalence of social insurance coverage despite a high willingness to pay for it suggests that current policies are inadequate in ensuring adequate coverage, calling for better and potentially more customized design and implementation of social insurance policies for gig workers, and not an emphasis on the affordability of insurance contributions alone.

2
Palma ratio (Chandy et al., 2013), or the income share of the bottom 40% (Ncube et al., 2014). A previous version of this paper used differences in growth rates of the bottom 40% and the mean to project poverty towards 2030 (Lakner et al. 2014), similar to Hoy and Samman (2015).

While our focus is on the impact of the distributional nature of future growth, we also develop our own baseline distribution-neutral growth scenarios. Two main approaches are used in the literature, which can produce quite different results for global poverty (Dhongde and Minoiu, 2013; Edward and Sumner, 2014). First, scenarios based on historical survey growth rates (e.g. Yoshida et al., 2014). Second, scenarios derived from national accounts either through growth models (Birdsall et al., 2014; Hillebrand, 2008), or projecting historical or forecasted growth rates into the future (Karver et al., 2012). Similar to our approach (explained in more detail in Section 4), Chandy et al. (2013) use Economist Intelligence Unit (EIU) and IMF’s World Economic Outlook (WEO) growth rates adjusted to survey growth using factors from a cross-country regression. We base our projections on both country-specific historical growth rates and forecasted growth rates, adjusted for observed differences between household survey growth and national accounts growth. The distribution-neutral global poverty projections remain at around 6.5% in 2030 regardless of which growth scenario we use.

We model distributional changes and growth rates in GDP independently of each other. Although the famous Kuznets Hypothesis (Kuznets, 1955) would predict that higher growth in low-income countries would tend to increase inequality, the empirical support for this hypothesis is weak. Ferreira and Ravallion 3
>(0.083)|0.667<br>(0.076)|0.553<br>(0.082)|0.463|0.793|0.311|
|Form 2 pass rate in 2015 for English|50.99|50.108|51.582|51.351|0.861|0.88|0.978|
||(3.342)|(5.724)|(6.088)|(5.849)||||
|Form 2 pass rate in 2015 for Math|44.153|38.796|47.474|47.194|0.33|0.309|0.974|
||(3.477)|(6.023)|(6.445)|(5.541)||||
|Form 2 pass rate in 2015 for Science|48.731|46.033|47.703|53.597|0.85|0.329|0.504|
||(3.411)|(5.43)|(6.901)|(5.428)||||
|Average teaching experience in month|150.491<br>(6.724)|139.479<br>(13.055)|149.542<br>(9.991)|162.742<br>(11.91)|0.541|0.19|0.397|
|Observations|187|62|64|61||||

_Notes_ : This table reports the balance test for various school level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group.
<mark>Muller, K and C Schwarz (2020a), “Fanning the Flames of Hate: Social Media and Hate Crime”,</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>.</mark> <mark>Muller, K and C Schwarz (2020b), “From Hashtag to Hate Crime: Twitter and Anti-Minority Sentiment”, SSRN Working Paper.</mark> <mark>Ortony, A, G L Clore and A Collins (1990),</mark> _<mark>The cognitive structure of emotions</mark>_ <mark>, Cambridge University Press.</mark> Paltoglou, G and M Thelwall (2012), “Twitter, MySpace, Digg: Unsupervised Sentiment Analysis on Social Media”, _ACM Transactions on Intelligent Systems and Technology_ 3(4).

<mark>Prati, A, M G Onorato and C Papageorgiou (2013), “Which reforms work and under what institutional environment? Evidence from a new data set on structural reforms”,</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>95(3): 946-968.</mark> Rodrik, D (2010). “Diagnostics before Prescription.” _Journal of Economic Perspectives_ 24(3):33-44 Tetlock, P (2007), “Giving Content to Investor Sentiment: The Role of Media in the Stock Market”, _Journal of Finance_ 62(3): pp 1139-1168.

Turney, P D (2002), “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews”, arXiv preprint cs/0212032.

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World Bank (2018), “Gulf Economic Monitor: Staying the Course on Reforms”.

World Bank (2020), “Doing Business 2020”,
ii "tablita" (little tableau), was a preannounced schedule of devaluations, each smaller than the last and all significantly less than the difference between world and domestic inflation rates.

The initial impact of the tablita was to induce large capital inflows that brought real interest rates back to negative levels not seen since the years of regulatory ceilings (-20 U). But this boom period ended abruptly two years later when it became clear that the stabilization program was unsustainable. Capital inflows ceased, and peso interest rates soared to record heights. Simultaneously, external demand for Uruguayan goods rapidly dropped with maxi-devaluations in Argentina. Our sample period ends in the year of these developments, on the eve of Uruguay's own maxi-devaluations and financial crisis.

# B. The Data

To link the changing Uruguayan macro environment with industrial sector growth and financial structure, we fit our model to panel data on Uruguayan firms (augmented by several macro time series). The raw data for this exercise describe 74 manufact.uring firms on an annual basis from 1972 through 1981. They were collected with surveys in 1975, 1980, and 1981; then corrected for inflation bias using a variant of the "general purchasing power" system of adjustment. 1/ The sample, which r_presented roughly 65% of the total manufacturing labor force during the period of analysis, was stratified on che basis of employees (see Pascale, 1982 for details). In cases where

- 1/ General patterns that emerge from the data are described in de Melo, Pascale, and Tybout (1985). Details of the inflation adjustment procedure are provided in the Wo':ld Bank Staff Working Paper version of this same study, and in Tybout (1988).
and low-density settlement areas much further north of Nairobi. And it would place the center of Nairobi well outside its true central core. A higher cutoff of, say, 750 people per sq. km. (light blue areas) may be too stringent and exclude satellite cities around Nairobi that are very likely to be within the commuting zone. Obviously, other arguments about drawing boundaries can be made. In the figure we also outline in green the independent LD settlements. Some are very spatially distinct, but some follow ribbons (roads) to the north outside Nairobi, where rural areas are interspersed with urbanized settlements. In the figure everything in yellow or the Google Earth background is rural.

# **2 Defining Economic Density**

Figure 4 illustrates issues about urban density definitions.<sup>6</sup> All hypothetical cities in Figure 4 have the same total population (180) in thousands and average density (5). City 1 has no clustering. Cities 2 and 3 have the same degree of within grid square clustering, with half the grid squares with no population and half with 10 people per grid. The 10 means greater within grid square possibilities for intersecting with others (the pairwise possibilities for meetings for example, ((n-1)!). However city 2 allows for more possibilities for interactions with neighbors. Ignoring the boundaries in city 3, on average a grid squares has 40 queen neighbors, while in city 2 a grid square has 80 queen neighbors.

We now turn to two measures which reflect these differences, personal population density and De La Roca and Puga (2017) density [RPA]. For a given city area, personal population density is a weighted, rather than simple, sum of own cell population densities. So in Figure 4, that gives a value of 5 for city 1 and 10 for cities 2 and 3. The RPA measure further makes a distinction between cities 2 and 3. It does a sum of grid square measures, where each grid
poverty lines for Russia, Ravallion and Lokshin (2002) find some striking differences in the properties of the equivalence scale. The objective poverty lines had an elasticity of 0.8 to household size, while the subjective indicator called instead for an elasticity half this size.<sup>21</sup> Subjective data have thrown new light on the long-standing debate on whether poverty is ―absolute‖ or ―relative.‖ The position one takes on this issue is crucial to whether one believes that economic growth reduces poverty. If the poverty line has an elasticity of zero to the poverty line then growth tends to reduce poverty, but this ceases to be true at an elasticity of one (Ravallion, 2003). However, one finds little support for the idea of a relative poverty line set at a constant proportion of the current mean income. Poverty lines calibrated to subjective welfare tend to rise with mean income but with an elasticity less than unity, suggesting that they are more like the ―weakly relative poverty lines‖ defined by Ravallion and Chen (2011). Hagenaars and Van Praag (1985) estimated an elasticity of 0.51 for eight European countries. For the US, Kilpatrick (1973) estimated an elasticity of about 0.6 for subjective poverty lines and De Vos and Garner (1991) found an own-income elasticity of the US subjective poverty line of 0.43.

A number of papers have reported evidence of effects on subjective welfare that can be interpreted as indicative of ―relative deprivation,‖ meaning that self-assessed well-being tends to fall as social comparators become better off, at given ―own income.‖<sup>22</sup> Luttmer (2005) reports regressions for subjective welfare in the US that imply a particularly strong relativism, whereby own income does not matter to subjective well-being independently of income relative to the mean in the area of residence. (The next section points to some reasons to question this finding.) The bulk of the evidence has been for relatively rich
Human Capital Index score in 2020, it still ranks ahead of only six other countries. The fluctuating nature of budgetary allocations of the Federal Government in areas like health, education, water and sanitation, in Naira terms and in terms of the share of the total government budget, do not seem to be encouraging or indicative of strong commitment to improve on the condition of children or on human capital in general. The additional availability of data on state and local government budget allocations in these areas, as well as the donor communities’ initiatives, may allow more focused impact evaluations of these initiatives on monetary and multi-dimensional deprivation-based child poverty across the country.

Further analysis will also be needed to find whether the adoption of the Child’s Rights Act is correlated with the level of deprivation. Its adoption though indicates a higher level of commitment and also creates an enabling environment for interventions to address different areas of deprivations of children. The Federal Government along with the States that have adopted the Act should therefore make a concerted effort to encourage remaining states to adopt the Act and to live up to the obligation of the constitution and ensure uniform protection for the children in Nigeria.

# 7 References

<mark>Abdu, M., & Delamonica, E. (2018). Multidimensional child poverty: From complex weighting to simple representation.</mark> _<mark>Social Indicators Research</mark>_ <mark>,</mark> _<mark>136</mark>_ <mark>(3), 881-905.</mark> <mark>Abramovsky, L., Augsburg, B., & Oteiza, F. (2015).</mark> _<mark>Sustainable Total Sanitation - Nigeria: Baseline Report</mark>_ <mark>. Institute for Fiscal Studies, Centre for the Evaluation of Development Policies: London</mark> <mark>Adebowale, N. (2019). UPDATED: 11 states in northern Nigeria yet to