rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: WEBS-De Loecker Comparison: Production Function Elasticities

|SectorDescrition||WBES|||De Loecke|r|
|---|---|---|---|---|---|---|
|p|_L_|_K_|_K/L_|_L_|_K_|_K/L_|
|Food & beverages|0.933|0.241|0.258|0.810|0.131|0.162|
|Textiles|0.925|0.206|0.223|0.562|0.165|0.294|
|Garments|0.911|0.251|0.276|0.833|0.152|0.182|
|Leather|0.735|0.364|0.495|0.542|0.356|0.657|
|Wood|0.868|0.160|0.184|0.885|0.063|0.071|
|Publishing, printing and|0.978|0.262|0.268|0.603|0.337|0.559|
|reproduction|||||||
|Chemicals|1.038|0.205|0.197|0.601|0.274|0.456|
|Rubber & plastics|1.071|0.204|0.190|0.669|0.142|0.212|
|Other non-metallic|0.974|0.254|0.261|0.614|0.255|0.415|
|products|||||||
|Basic metals|1.202|0.198|0.165|0.751|0.042|0.056|
|Fabricated metal prods.|1.097|0.184|0.168|0.666|0.194|0.291|
|Machinery and|0.991|0.225|0.227|0.700|0.199|0.284|
|equipment|||||||
|Electrical machinery|1.102|0.230|0.209|0.558|0.223|0.400|
|Furniture|0.877|0.307|0.350|0.709|0.146|0.206|

Notes. Table 1 presents the production function elasticities from estimating a value-added log-linearized Cobb-Douglas production function following De Loecker (2013). In this paper, value-added is a function of labor and capital. The estimating method is based on the Control Function approach by Ackerberg, Caves, and Frazer (2015). However, it departures from the latter by assuming an endogenous Markovian productivity process, which is a function of learning by exporting. WBES data covers a sample of 7,916 manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data fromDe Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient between the K-to-L estimated ratio using the WBES and De Loecker (2013) database is 0.55. It is also statistically significant at the 5 percent level.

Using the production function elasticities from Table 1, we calculate sector-specific factor intensities, defined as the capital-to-labor PF elasticity ratio, and examine the pairwise correlations between the results obtained using the WBES database and those from De Loecker (2013). We found a correlation coefficient of 0.55 between factor intensities, which is significant at the 5 percent level. The correlation coefficient between median productivitypremium from exporting is 0.36. This is high given that we only have 15 observations and there is a lot of cross-country variation in the WBES database.

20
decentralization, public expenditure and financial management (PFM), anti-corruption, tax policy and administration and other public sector governance issues. This breakdown allows us to estimate the effect of PSG conditionality on four CPIA cluster D components. The following dependent-independent variable pairs are tested: public expenditure and financial management conditions on CPIA’s quality of budgetary management, tax policy and administration conditions on CPIA’s efficiency of revenue mobilization, anti-corruption conditions on CPIA’s accountability and corruption in the public sector and civil service reform conditions on CPIA’s quality of public administration.

_Zit_ is a vector of control variables. As other donors also might be engaged in supporting policy change, we include total aid over GDP as a control variable. Next, we also add the logarithm of GDP per capita as a regressor. High levels of per capita income could improve the quality of governance by increasing tax revenues if government funds are a binding constraint. Higher income levels could also reflect a greater volume and size of transactions, increasing the benefits of developing policies for contract enforcement. Furthermore, as many factors influence a country’s welfare, GDP per capita also serves as a general control. Following Besley and Persson (2011) among other studies, we also include a measure of democracy, i.e., the Freedom House index of political freedoms. Finally, country and year fixed effects are added as regressors. Cumulative conditions are positively correlated with time, so the inclusion of year effects is important to control for any time trend or transitory events that could otherwise bias the coefficient on cumulative conditions. In section 4.3, we also test whether results are robust to including several additional controls. We have drawn upon the public sector reform literature to select the additional covariates. It is often argued that fiscal pressures and crises are engines for reform (see, e.g., Krueger, 1993; Ranis and Mahmood, 1992). On the other hand, crises in and by themselves do not necessarily entail deep, structural change, and could actually decrease the political capital for engaging in costly reform (Brinkerhoff, 2000; Tommasi, 2004). To control for any crisis8
On the other hand, by combining CET<sup>harmonization with removal of export subsidies Turkey can expect</sup> to obtain significant welfare gains from<sup>EC tariff harmonization. This result follows from our analysis of the joint</sup> effects of removing tariffs and export subsidies<sup>in Figure 2, along with our interpretation of CET harmonization</sup> as a _defacto_ across-the-board reduction<sup>in tarffs.</sup> Irrespective of the final policy package, our<sup>main policy lesson here is that the welfare effects of quibbling</sup> over the proper interpretation of CET<sup>harmonization are in the second-order of smalls. This conclusion is</sup> particularly true in relation to the foregone<sup>benefits of more substantial liberalization or uniformity packages.</sup>

# 5. SENSITIITY ANALYSIS

How robust are our major policy conclusions<sup>to the many assumptions of our numerical model? We answer</sup> this question partially by considenng a systematic<sup>sensitivity analysis of the main results with respect to all of the</sup> elasticities of the model.<sup>3 3</sup> Our sensitivity analysis emplovs the procedures<sup>developed by Harrison and Vinod [ 1992]. Essentially these</sup> procedures amount to a Monte Carlo simulation<sup>exercise in which a wide range of elasticities are independently and</sup> simultaneously perturbed from their benchmark<sup>values. These perturbations follow prescribed distributions, such</sup> as a t distribution with a specified standard<sup>deviation and degrees of freedom, or a uniform distribution over a</sup> specified range.<sup>3 4</sup> For each Monte Carlo run<sup>we solve the counter-factual policy with the selected set of</sup> elasticities. This process is repeated<sup>until we arrive at the desired sample size, in our case 1000. The results are</sup> then tabulated as a distribution, with<sup>equal weight being given (by construction) to each Monte Carlo run. The</sup> upshot is a probability distribution<sup>defined over the endogenous variables of interest. In our case we focus solely</sup> on the welfare impacts of each policy.

**33** We appreciate that there are many **other** assumptions<sup>that remain fixed as we just vary elasticities, but regard those extensions as</sup> beyond the scope of the present study. For example,<sup>an important question is how our results might change as we</sup><sup>**examinse**</sup> alternative market structure assumptions, or move from<sup>a static framework to a growth setting. We plan to examine these extensions using the</sup><sup>**SOE**</sup> model. but do **not** believe that they<sup>can be appropnately treated briefly enough to include here.</sup>

**34 The** exact distributional assumptions used are<sup>documented in Appendix C (available on request).</sup><sup>**The**MPSS software used to</sup> implement the Monte Carlo simulations<sup>**as**documented in Harrison (19901.</sup>

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As much of the micro-decomposition literature, this methodology suffers from path-dependence, in other words the order in which the cumulative effects are calculated matters<sup>4</sup> . One of the major contributions of this paper is that we apply the best known remedy for path-dependence, which is to calculate the decomposition across all possible paths and then take the average between them following the method proposed by Azevedo, Sanfelice and Nguyen (2012a). This involves calculating the cumulative decomposition in every possible order, and then averaging the results for each component. Since we have seven variables, this adds up to 5,040 potential decomposition paths (the result of 7!). The average effect for each variable is also known as the Shapley-Shorrocks estimate of each component.<sup>5,6</sup> There is one remaining caveat to this approach: the counterfactual income distributions, on which these decompositions depend, suffer from equilibrium-inconsistency. Since we are modifying only one element at a time, the counterfactuals are not the result of an economic equilibrium, but rather a fictitious exercise in which we assume that we can in fact modify one factor at a time and keep everything else constant.

# **3. Data**

The data used in this paper are from a harmonized database of household surveys from 14 Latin American countries compiled in the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a joint effort of the _Centro de Estudios Distributivos Laborales y Sociales_ of the Universidad Nacional de La Plata and the World Bank’s Poverty, Gender and Equity group for Latin America. The countries included in this particular analysis are Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay. So as to make the time periods comparable across time, we use the circa criteria for the years 2000 and 2010. Table A1 in the Annex provides more details of the countries, years and surveys included in this study.

# **4. Results**

The first result that is clear is that changes in labor income were the most important contributors to the decline in inequality across countries in Latin America as measured by the Gini coefficient (Figure 7). On average, 54 percent of the decline in income inequality in Latin America is related

> 4 Path dependence is common in the micro-decomposition literature. See Essama-Nssah (2012), Fortin et al (2011) and Ferreira (2010) for recent reviews of the literature.

> 5 See Shapley (1953) and Shorrocks (1999).

> 6 The decomposition implement in this paper used the user written Stata ado ADECOMP written by Azevedo, Nguyen and Sanfelice (2012b).

10
# **Table 6. Continued**

|||||Tariff Rate_(%)_||_<br>_<br>_|
|---|---|---|---|---|---|---|
|Exporter|Value**($**million)|Applied 1992|MFN 1992|Applied 1999|MFN 1999|Preference<br>Margin Reduction|
|Rwanda|127.0|0.01|3.60|0.00|0.22|3.57|
|Sao Tome & Principe|12.0|0.00|6.45|0.00|3.85|2.60|
|<br>Senegal|466.9|0.13|10.40|0.10|8.58|1.79|
|<br>Seychelles|121.8|0.00|21.24|0.00|20.78|0.45|
|<br>Sierra Leone|190.2|0.33|1.87|0.32|1.12|0.75|
|Somalia|52.4|0.00|14.29|0.00|1.61|12.68|
|South Africa, Republic of|17,358.8|0.93|1.01|0.53|0.58|0.02|
|<br>Sudan|311.6|0.00|0.85|0.00|0.55|0.30|
|Togo|145.7|0.00|1.68|0.00|0.58|1.10|
|Uganda|284.4|0.00|3.03|0.00|0.02|3.02|
|Tanzania, United Republic|258.9|0.11|3.23|0.09|1.33|1.88|
|Zaire|1,331.6|0.00|0.74|0.00|0.14|0.60|
|Zambia|751.1|0.00|0.29|0.00|0.22|0.07|
|Zimbabwe|930.7|0.88|6.58|0.61|4.50|1.81|
|ALL ABOVE COUNTRIES||0.31|4.56|0.21|2.68|1.78|

The averages shown below include Uruguay Round "tariffied" rates of protection for agricultural nontariff barriers. The 1992 applied tariffs are the average of the GSP, Lome, Least Developed or MFN rates actually applied to the African country's exports. The 1999 applied rates reflect the Uruguay Round reductions in any MFN tariffs that may have faced African exporters. The 1992 and 1999 MFN rates show the average Most-Favored-Nation tariff applied to the products exported by each African country. The preference margin reduction shows the decline in the average preference margin each country received due to the Uruguay Round MFN tariff cuts.
# The Changing Way Governments Talk about Poverty and Inequality:

Evidence from Two Centuries of Latin American Presidential Speeches<sup>*</sup> Oscar Calvo-González<sup>†</sup> , Axel Eizmendi<sup>‡</sup> , Germán Reyes<sup>§</sup> JEL Classification: I32, D63, D72, N16 Keywords: Poverty, Inequality, Text Mining, Presidential Speeches, Latin America

> * The authors would like to thank Laura Chioda, Alexandru Cojocaru, Peter Hakim, Luis Felipe Lopez-Calva, Bill Maloney, Ambar Narayan, Mariano Tommasi, Roy Van der Weide, and participants at an internal seminar for fruitful discussions and suggestions. We also thank Sandra Pastrán, Evangelina Cabrera, Yalile Uarac, and César Polit and Martha Narvaez, for compiling and forwarding to us multiple speeches from Argentina, Paraguay, Chile and Ecuador, respectively. Natalia García-Peña Bersh helped compile data from Colombia. We are grateful to the Research Support Budget from the World Bank for financial support.

> † Poverty and Equity Global Practice, World Bank. Mail: ocalvogonzalez@worldbank.org

> ‡ University of Oxford, Oxford, UK. Mail: axel.eizmendi.larrinaga@gmail.com

> § Cornell University, Ithaca, NY, USA. Mail: gjr66@cornell.edu
Ugandan Ministry of Education and Sport and UNESCO IIEP Pôle de Dakar. (2014). _Teacher Issues in_ _Uganda: A shared vision for an effective teachers policy._ United Nations Statistics Division. (2007). International Standard Industrial Classification of All Economic Activities Revision 4, Series M: Miscellaneous Statistical Papers, No. 4 Rev. 4, New York: United Nations. Retrieved from <u>https://unstats.un.org/unsd/classifications/Family/Detail/27</u> UNESCO. (2009). _Universal primary education in Africa: the teacher challenge._ Retrived from <u>https://unesdoc.unesco.org/ark:/48223/pf0000186643.</u> UNESCO. (2020). Institute for Statistics Education Statistics. Retrieved from: <u>http://data.uis.unesco.org/</u> UNESCO. (2016). _The World Needs Almost 69 Million New Teachers to Reach the 2030 Education_ _Goals_ . Retrieved from <u>http://uis.unesco.org/sites/default/files/documents/fs39-the-world-needsalmost-69-million-new-teachers-to-reach-the-2030-education-goals-2016-en.pdf</u> <mark>U.S. Bureau of Labor Statistics. (2018). May 2018 National Occupational Employment and Wage</mark>

- <mark>Estimates United States. Retrieved from</mark> <u><mark>h</mark> ttps://www.bls.gov/oes/current/oes_nat.htm#25-0000</u>

- <mark>U.S. Bureau of Labor Statistics. (2021). Labor Force Statistics from the Current Population Survey.</mark>

<mark>Retrieved from https://www.bls.gov/cps/cpsaat18.htm</mark> <mark>World Bank. (2018).</mark> _<mark>Learning: To Realize Education's Promise</mark>_ <mark>. World Bank Group.</mark> <mark>World Bank. (2019a). Learning Poverty.</mark> <u><mark>h</mark> ttps://www.worldbank.org/en/topic/education/brief/learningpoverty</u> <mark>World</mark> Bank. (2019b). World Bank Country and Lending Groups.

<u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-</u> - <u>lending groups</u> <mark>World</mark> Bank. (2019c). World Development Indicators. <u>https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators</u> <mark>World Bank. (2020). The Human Capital Project (HCP).</mark>

- <u>https://www.worldbank.org/en/publication/human capital</u>

<mark>World Bank.</mark> (2021). World Development Indicators. <u>https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators.</u> 28
# **Covid-19 and Food Protectionism**<sup>**1**</sup>

**The Impact of the Pandemic and Export Restrictions on World Food Markets**

Alvaro Espitia, Nadia Rocha, Michele Ruta<sup>2</sup> World Bank

# 1. **Introduction**

Could Covid-19 (coronavirus) lead to a global food crisis? As the virus spreads around the world, there are concerns that global food security could come under pressure (Laborde, Martin and Vos, 2020; Torero, 2020; Voegele, 20020). Food production is currently high, but it could be negatively impacted by increased workers’ morbidity, disruption in supply chains, and containment measures. Governments’ attempts to restrict food exports to meet domestic needs could make things worse. This paper analyzes how world trade in food could be affected by Covid-19. It shows that, similarly to the global food crises in the late 2000s, uncooperative trade policy actions could magnify the disruptions in global food markets caused by Covid-19, leading to spikes in world food prices.

> 1 We are grateful to Paul Brenton, Jack Carlson, Sara Carlson, Cristina Constantinescu, Jakob Engel, Carmen Estrades Pineyrua, Simon Evenett, Michael Ferrantino, Caroline Freund, Madhur Gautam, Ian Gilson, Bernard Hoekman, Hiau Looi Kee, Will Martin, Aaditya Mattoo, Antonio Nucifora, and Gianluca Orefice for their inputs and helpful suggestions. Errors are our responsibility only.

> 2 World Bank, 1818 H Street, Washington DC, USA. Alvaro Espitia, Email: <u>aespitiarueda@worldbank.org; Nadia Rocha, Email: nrocha@worldbank.org; Michele Ruta, Email: mruta@worldbank.org. The findings, interpretations, and conclusions expressed in</u> this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Policy Research Working Paper 6718

# **Abstract**

Survey data from 120 developing countries are used to examine the relation between establishment size and age in the formal sector. Existing research suggests that manufacturing establishments in developing countries do not grow over time, most likely because of market imperfections and regulations. To the contrary, this paper finds that the average plant in developing countries that is more than 40 years old employs almost five times as many workers as the average plant that is five years old or younger. The analysis finds consistent evidence when it looks within a large country, India, based on detailed manufacturing census data over 23 years. It also finds that differences in financial development across Indian states, while substantial, have a minor effect on firm growth, consistent with inefficiency of state-owned financial systems. These results hold controlling for differences in labor regulations across states, capital intensity, labor regulations, and firms born before and after the major reforms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ademirguckunt@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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TABLE 3: Genders of Depositors and Money-Keepers 1988

|Depositors<br>Interviewed|Mal|Mon<br>e|ey-Ke<br>Fem|epers<br>ale|Bot|h|
|---|---|---|---|---|---|---|
|Male|16|(80X)|<br>4|(20Z)|20|(100?)|
|Female|18|(64Z)|<br>10|(36Z)|28|(1002)|
|Total|34|(71Z)|<br>14|(292)|48|(100Z)|
In the process of computing the ADD a government must aggregate the results of comparisons between the normal value and export prices. Hundreds or even thousands of individual transactions are aggregated to produce a single ADD. The ADA provides rules for how such calculations should be done. Zeroing refers to one particular step in the calculation. Zeroing is the practice of replacing the actual amount of dumping that yield negative dumping margins (i.e., export transactions for which the export price exceeds the calculated normal value) with a value of zero prior to the final calculation of a weighted average margin of dumping for the product under investigation with respect to the exporters under investigation. Because the zeroing method drops transactions that have negative margins, it has the effect of increasing the overall dumping margins.<sup>14</sup> In practice zeroing is much easier to understand than the formal definition suggests. In Table 1 we present an example of a foreign firm’s home and export sales in a given month.<sup>15</sup> We assume that the data in Table 1 represent net prices for separate transactions on a series of dates in the month of September.<sup>16</sup> To keep the example as simple as possible we will assume that each transaction is for the same volume, i.e., one unit. Governments compute dumping margins on a weighted average basis, but for the purposes of our illustration, the introduction of different quantities on different dates just 14 There are two zeroing methods, simple and model. For purposes of this paper, we limit our discussion to just simple zeroing. Readers interested in the fine details of both methods should consult Prusa and Vermulst (2009).

15 Example is drawn from Prusa and Vermulst (2009).

16 Net prices are the exporter’s prices following a series of adjustments. For example all expenses incurred to promote, sell, store, and transport the products are deducted from both export price and domestic price. In addition, various other adjustments, such as level of trade and accounting for physical differences are made.

– 14 –