rafmacalaba/data-use-mentions-extended huggingface dataset:
trade effects through ad-valorem equivalents, feeding them into computable general equilibrium models, and hoping to cut them down through inter-governmental negotiations. However, little progress has been achieved so far. While political-economy factors may well have contributed to the lack of progress, this paper argues that trade-based approaches may also be going down the wrong alley. Our results suggest that NTMs should be viewed as a competition-policy issue as much as a trade one. They are not mere surrogates to tariffs, contrary to what a large part of the literature typically assumes, and they may affect market structure in terms of large vs. small firms more than home vs. foreign.<sup>7</sup> Considering them through this perspective would help shift the policy perspective away from doomed trade negotiations to more constructive approaches emphasizing cooperation between regulatory and antitrust agencies (within and between countries) in the design of regulations.

Our results also suggest that trade policy is an important element to consider when trying to understand the transmission of shocks across countries through prices. In the last section of the paper, we indeed show that our results also hold at the product- and country levels, i.e. that tariffs and NTMs affect aggregate pass-through. By impacting overall competition, market structure and markups, trade policy is also likely to have important effects on aggregate welfare (Edmond _et al._ , 2018).

Our work relates to various strands of the literature. Our paper is close to a number of papers that examine the role of trade policy and trade reforms on market power and pricing behavior. For instance, looking at the U.S. steel industry and using plant-level census data, Blonigen _et al._

> 7See e.g. Feenstra (1984), Leamer (1990), Anderson and Neary (1994), Kee _et al._ (2009), Carrere and de Melo (2011).

3
Table 2 shows that the common factor explains more than half of the variation in the prices of Copper, Silver, Wheat and Sugar, and a substantial degree of the variation in the prices of Maize, Palm Oil, Rubber, Silver and Tin. The prices of Tobacco, Hides and Lamb, however, seem to be less tied to the common factor and, hence, show more idiosyncratic behavior. While this finding points to some degree of heterogeneity in the co-movement of commodity prices, as also suggested by the SVR statistic, nevertheless a large degree of the historical variability of the majority of commodity prices seems to be determined by a non-stationary common factor.

We next investigate to what extend this common factor can be related to macroeconomic fundamentals

# **5. The Common Factor in Commodity Prices and Its Determinants**

In this section, we examine the relationship between the principal component of the 24 commodity prices and the macroeconomic determinants discussed in the literature. Since Frenkel (2008) and Svensson (2008) refer to real prices, we deflate the identified principal component using the US CPI. This transformation also ensures that our non-stationary principal component becomes stationary in real terms, as it can be seen from Table A2. While we focus on the role of the real interest rate and risk as determinants of commodity prices, we also investigate the role of global demand, as proxied by the growth rate of US real GDP, and of real crude oil prices, interpreted here as a measure of supply shocks.

# _Bivariate Scatter Plots_

Figure 3 provides bivariate scatter plots between these four variables and the real principal component in commodity prices. While a negative relationship seems to 18
specifically, among other things, convergence is found conditional upon a country’s initial human capital stock. Therefore, a poor country on average may grow faster, but only if the poor country’s human capital stock is above the amount initially expected at the level of per capita income.

# **_Reverse Causality_**

Most of the evidence of some sort of relationship between education and growth is based on statistical _correlations_ . From these correlations, it has been generally inferred that higher levels of education cause higher growth. One critique of these findings comes from Bils and Klenow (2000) who suggest reverse causation. Based on a calibrated micro-foundation model, they claim that faster growth induces people to stay longer in school. In other words, the channel from schooling to growth that is assumed to dominate in many models cannot explain all the observed correlation between education and growth. However, the reverse channel provides some explanation. Therefore, in an econometric framework, schooling should be treated as an endogenous input with respect to income. This implies making use of econometric methods for dealing with this problem.

# **_Few Individual Case Studies_**

Recent research has mainly relied on cross-country regression analysis. However, the original motivation of studying economic growth focuses on the time-series dynamics of macroeconomic variables. In addition, the cross-section focus may be inadequate if rates of return to education or the quality of education differ substantially across countries. Unfortunately, with respect to human capital, there are very few studies that analyze a single country over a certain time period.

The exception is a study from Jenkins (1995) using time series data from 1971-1992 for the United Kingdom. Still, the limited size of her time series sample makes it difficult to draw firm conclusions that can be generalized. Also Pissarides (2000) summarizes single case studies for India, Egypt, Tanzania and Chile. Part of an OECD project, these studies were to provide a more thorough test of the relation between human capital and growth in a single country context
|||||||||
|Not in labor force|0.26|0.20|0.48|0.02|0.17|0.02|0.29|0.08|
|Unemployed|0.04|0.17|0.06|0.00|0.00|0.00|0.09|0.01|
|Employed in Agriculture|0.24|0.19|0.06|0.66|0.31|0.69|0.36|0.54|
|Employed in Industry|0.20|0.07|0.07|0.07|0.12|0.06|0.05|0.09|
|Employed in Services|0.26|0.37|0.32|0.25|0.40|0.23|0.21|0.29|
|**Access to basic services**|||||||||
|Improved water|0.86|0.61|0.87|0.62|0.85|0.66|0.71|0.77|
|Improved sanitation facility|0.45|0.51|0.52|0.15|0.63|0.13|0.28|0.22|
|Access to electricity|0.41|0.46|0.93|0.21|0.74|0.10|0.56|0.39|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the WorldPop 250m dataset and the DOU method.

41
) 2016, Sudan Labor Market Panel Survey (SLMPS) 2021, and Tanzania National Panel Survey (TZNPS) 2018/19. In each survey, the LSMS+ has supported the respective national statistical office to operationalize the latest international recommendations for individual-disaggregated survey data collection on asset ownership and labor. For more information on survey methods and fieldwork implementation protocols followed by the LSMS+ supported national surveys, please consult Hasanbasri et al. (2021a).

> 9 The CSES 2019-20 covers a nationally representative sample of 10,080 households and 1,080 EAs.

> 10 In each EA, the Cambodia LSMS+ household sample was selected from the universe of households that did not include the households that had been selected for the CSES 2019/20.

> 11 For more information on the survey sampling and methodology, see Hasanbasri et al. (2021b).

> 12 See Daum et al. (2019) for an example of smartphone-based real-time time use data collection in a low-income context.

> 13 A tabulation of secondary activities, however, did not reveal much information.  Common responses were conducting one unpaid activity combined with another (cooking with childcare for example), or leisure with sleep.

6
# Economic Integration, Industrial Structure, and Catch-up Growth: Firm-Level Evidence from Poland<sup>_∗†_</sup>

Paulo Bastos<sup>_‡_</sup> Stefania Lovo<sup>_§_</sup> Gonzalo Varela<sup>_¶_</sup> Jan Hagemejer<sup>_∥_</sup>

- _Keywords:_ Economic integration, industrial structure, foreign direct investment, catch-up growth, firm performance.

_JEL Classification_ : F11; F14; F15; F23; F63; L25; O47

> _∗_ We thank several anonymous referees for very helpful comments. We are also grateful to participants at various conferences and seminars for helpful comments and suggestions. Research for this paper was supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Program on Economic Development. The views expressed herein are those of the authors only and not those of the World Bank. Bastos is also affiliated with the CEPR and REM. We remain responsible for any errors.

> _†_ Data Availability Statement. The data that support the findings of this study are available from the from the Central Statistical Office of Poland and the Orbis database of Bureau Van Dijk. Restrictions apply to the availability of these data, which were used under license for this study. The data from the Central Statistical Office of Poland are confidential, but are available to other researchers. The data from Bureau Van Dijk require a subscription. We stand ready to provide interested researchers with the necessary information on how to obtain the data from the Central Statistical Office of Poland and Bureau Van Dijk; as well as the programs, and details for the computations necessary for replication.

> _‡_ Development Research Group, World Bank. E-mail: pbastos@worldbank.org

> _§_ Department of Economics, University of Reading. E-mail: s.lovo@reading.ac.uk

> _¶_ World Bank. E-mail
# **3.3 How effective is Zoom training?**

Did business owners learn anything from online training, implement what they had learned, and experience changes in business outcomes? We answer these questions using our 2- month follow-up survey, presenting intention-to-treat estimates in Table 2. There is a statistically significant, but small impact of 2.4 percentage points on business knowledge as measured by an 11-question test. This is consistent with the small effects on test-assessed knowledge seen in in-person business training and financial education programs (Carpena et al., 2019; McKenzie and Puerto, 2021).

To measure the impact on business practices, we implement a slightly modified version of the practices in McKenzie and Woodruff (2017). We measure 22 business practices consisting of 9 marketing practices (e.g. monitors competitor’s prices, uses special offers), 10 accounting practices (e.g. keep written records, separates household and business accounts), and 3 planning practices (e.g. has a written budget, sets sales goals). The control group are doing 50.2 percent of these practices, and we find that being assigned to treatment results in a statistically significant improvement of 5.4 percentage points. The magnitude of improvement is similar to the impact of an in-person course like the ILO SIYB course (McKenzie and Woodruff, 2017). The largest improvement comes in planning practices (11.7 percentage points), followed by accounting practices (6.9 percentage points). In contrast, the impact on marketing practices is small (1.6 percentage points) and statistically insignificant. We also find a small and statistically insignificant improvement in an index of personal initiative based on Campos et al. (2017). These results accord with our qualitative interviews, where personal initiative and marketing were the topics least remembered by participants, while finance and planning had the highest recall.

There is some evidence that this improvement in business practices is accompanied by shortterm improvements in business sales
the possibility of an economic recovery in later years, which is projected to bring poverty back to its initial level, marking a period of five years without additional poverty gains.

Figure 4 also shows that using an aggregate measure of inflation (headline CPI) underestimates the projections of poverty rates compared to using disaggregated COICOP-level data on inflation (subject to availability). As indicated by the dashed line for FY19 to FY23, accounting for differential price trends and consumption baskets when projecting inflation, produces poverty rates that are consistently higher than those based on average national inflation. This is because the approach does not capture how lower-income households and those close to the poverty line whose food consumption is a large share of the total consumption basket, likely experienced higher inflation rates than the national average. This is important given that higher food prices mostly drove inflationary pressures.

Ignoring inflation heterogeneity across income groups results in an underestimation of poverty rates. Similar results are found for income inequality. The comparison between inequality (Gini 12
# **1 Introduction**

Estimates of income inequality are conventionally derived from household income and expenditure surveys. Due to the sizeable cost of collecting accurate data on household standards of living the sample size of these surveys generally constitutes less than half a percent of the total population. Unfortunately, the rich are often missing or under-covered, either due to non-response or under-reporting of income or both; see the recent literature on top income shares (e.g. Atkinson et al., 2011). Surveys still permit accurate estimation of median income and measures of poverty, even when data on top incomes are poor or are missing altogether, since the rich make up a small percentage of the total population. For the estimation of income inequality however, having good data on top incomes is crucial.

A remedy that has gained considerable traction recently is to estimate the top tail of the income distribution using data from income tax records. This estimate of the top tail can then be combined with an estimate of the bottom part from the household survey to obtain an estimate of the complete income distribution (Atkinson, 2007; Alvaredo, 2011; Alvaredo and Londo ̃no V ́elez, 2013; Diaz-Bazan, 2014; Anand and Segal, 2015).<sup>1</sup> Income tax records denote the ideal source of data as far as top incomes are concerned. For lower incomes tax records may be less reliable, here the household income survey arguably denotes the ideal data. When household survey and tax data are combined in this way, the Gini index for (i) the United States in 2006 increases from 59 to 62 (Alvaredo, 2011), (ii) Colombia in 2010 from 55 to 59 (Alvaredo and Londo ̃no V ́elez, 2013), and (iii) Korea in 2010 from 31 to 37 (Kim and Kim, 2013).

For all the pros of income tax records, the availability of the data is unfortunately rather limited, particularly in developing and emerging
# **VI.   CONCLUSION**

By combining as many national household surveys as possible, this paper has studied the distribution of consumption expenditure among all individuals in (developing) East Asia. It thus complements analyses of inequality that are typically focused on inequality among individuals within the same country. The East Asian Gini index increased by more than 4 points (or almost 11%) between 1988 and 2012. The increase in inequality has been most pronounced during the initial decade (between 1988 and 1997).

Regional inequality can be decomposed into differences among individuals residing in the same country, and differences between countries. The within-country component has increased over this period, driven by increasing national inequality in some populous countries – China initially and Indonesia a decade later. On the other hand, differences between countries have declined, i.e. average incomes have converged across countries. This can be explained by the rapid growth in China closing the gap with the regional average income, which counteracted the inequality-increasing effects of some lagging countries such as the Philippines. China thus had both equalizing and disequalizing effects on East Asian inequality, through its growth in average consumption and national inequality, respectively. But the rapid growth in average income dominated, thus leading to a net equalizing effect.

As a result of these changes, the share of regional inequality that is explained by within-country inequality increased from two-thirds to close to 90%. At the global level, there has also been an increase in the within-country share, but at a much lower level – it increased from 20% to 35% over the same period (World Bank, 2016). Therefore, the situation within East Asia is already very close to an internalization of regional inequality within countries, in contrast to the global level.<sup>24</sup> However, in the most recent period (2007-2012), which spans the Great Recession, regional inequality appears to have fallen slightly, consistently across different inequality measures. For the first time, the level of
We adapt the procedure to account for our quality assessment of the data and for auxiliary data. We first review the quality of the data used to construct our initial estimate of the input-output table. To assess the quality, we evaluate whether we believe the data are representative of what they are supposed to report. That is, to what extent do we consider the underlying data complete and accurate?

We distinguish three levels of quality: high, medium and low. The purpose is to have more adjustments in data of low quality and fewer adjustments in data of high quality. This is one way (albeit indirect) of correcting for problems in data.

We begin with data of high quality. The first are data on imports and exports, which in turn are based on customs data. These data are typically collected with lots of detail and in a format consistent with input-output tables. The second are the data on total GDP. This data point can be and is calculated in several ways, and should be identical independently of the method used. This provides a check on the data. Moreover, GDP data is widely used and thus frequently reviewed.

As data of medium quality we include industrial structure, measured as output by sector. These data are in part based on commodity balances and on wage data by sector, both of which are typically collected with lots of details.

As data of low quality we include the structure of intermediate demand, and household, government, and investment expenditure. These data are to some extent based on surveys. In this sense they are less complete and must subsequently be scaled appropriately to become representative. Also, the surveys used are not always consistent with the classifications of the input-output table.

We account for data quality in the objective function by assigning weights to the data elements depending on quality. High, medium and low quality data are assigned the weights of 5, 3, and 1, respectively.

This setup implies that a given adjustment in data carry different penalties depending on what
# **5 Coping Mechanisms, Fishing Recovery and Industry Spillover Effects**

In this section, we turn to focus on the different coping mechanisms of the victimized fishery workers. We corroborate evidence from both the satellite and labor force data. First, we discover a large distributional impact to fishery incomes by location. We also show that, dependent upon where fishers were located, these individuals likely responded differently to the shock. Those who could feasibly travel to safe grounds likely did so to sustain fishing activities. In contrast, fishers who were likely “trapped” inside the contaminated zone chose to substitute fishery work-hours with having secondary jobs. Next, we look at disaster recovery, examining the _Formosa_ impacts on both fishing intensity and fishery income by quarters. We find a gradual reduction in disaster damage to both outcomes, even though the impact on earnings still remained sizable in the last quarter of 2016. Lastly, we study potential spillover effects of _Formosa_ on related industries, and find evidence suggesting an increase in freshwater fishery earnings.

## **5.1 How did the victims cope with the shock?**

To motivate our discussion on the coping mechanisms of fishery workers following _Formosa_ , we first provide evidence that the impact of the crisis is highly heterogeneous. In Table 4, we split the treatment provinces into two separate groups by geographic location: Ha Tinh and Quang Binh (i.e. the “upstream” group), and Quang Tri and Hue (i.e. the “downstream” group). Immediately, we discover a stark difference on how _Formosa_ affected fisheries. In terms of the impact on incomes, the estimated ATE size (i.e. the size of average income reduction) for the downstream fishers is found to be between 10 and 15 percentage points larger than that for the upstream counterparts, suggesting a more severe impact of _Formosa_ on the economic well-being of fishers living downstream.

In order to investigate the reason underlying such discrepancy in the geographic distribution of earning impacts,
28 the case not only for small cities, but also for many large cities, which are often seen as “engines of growth” in developing countries (World Bank, 1999, 2009).

Furthermore, we can use our city-level measures of _production/consumption city-ness_ (PCC; Section 1.) as well as population data for each city to obtain the populationweighted average level of PCC for each country c. 2000 (see Panel A of Web Appx. Table D.8 for the full list). While some developing economies have high aggregated PCC values, for example China, Costa Rica, Lesotho, Mauritius and Vietnam, other developing economies have low aggregated PCC values, especially Colombia, Indonesia, Nigeria, Tanzania and the Rep ́ublica Bolivariana de Venezuela. Some countries such as Brazil, India, Malaysia, Mexico and Turkey have both production cities and consumption cities, as indicated by a (city population-weighted) Gini index of PCC calculated for each country (Panel B for the full list).<sup>37</sup> Either their urban system is chronically “dualistic” or it is transitioning, whether away from production cities as in Latin America or towards them as in Asia (Section 1.).

**Urban Human Capital.** These facts pose the question of whether having an urban hierarchy dominated by consumption cities might have economic implications for a country, “beyond” the economic structure of its cities. If consumption cities are not as growth-enhancing as production cities, are they doomed to remain so or is it possible that they evolve into production cities in the future? An important factor that can shed light on these questions is the stock and quality of _urban human capital_ . Indeed, cities with a bigger stock of high-quality human capital may be better able to adapt to future economic conditions and grow regardless of their current economic structure.

We assess how urban human capital correlates with our structural variables of interest by obtaining from IPUMS the average number of years of education of adults