rafmacalaba/datause-encoder-data huggingface dataset:
with the mechanisms described above. Tax evasion is proxied by the amount of sales not declared for tax purposes. Distortions from the business environment are calculated based on the losses in annual sales due to power outages, corruption, etc. Finally, institutional quality is based on various variables related to the perceptions of the quality of public services and the credibility of the government. We find a positive and significant relationship between losses due to distortions from the business environment and tax evasion. On the contrary, institutional quality is negatively related to firms’ tax evasion.

However, these relationships are subject to omitted variable bias, potential measurement error, and reverse causality. As a result, we are not able to identify, using standard econometrics, the mechanisms through which distortions and institutional quality affect firms’ tax evasion behavior. Instead, we develop a general equilibrium model and use simulations to match the empirical evidence. Building on Restuccia and Rogerson (2008), the economic environment consists of (i) one representative household which maximizes his inter-temporal utility, (ii) a government which balances its budget and, (iii) heterogeneous firms which maximize their profits and make tax evasion decisions based on their idiosyncratic productivity level as well as their idiosyncratic level of distortions stemming from the business environment. We calibrate the model to the United States and treat that country as an economy with no distortions as is standard in the literature. This benchmark economy allows normalizing GDP per worker in both the model and the data. The model is then simulated for each of the 30 African and Latin American countries from the WBES sample, using the benchmark distribution of productivity and the idiosyncratic distribution of losses due to the distortions from the business environment of a specific country.

The model explains 35 percent of the variation of tax evasion and 49 percent of the dispersion of output per worker in the data. The simulation for each region provides similar findings. As robustness checks, we calibrate the model to the Chilean economy, which exhibits the lowest combination of distortions and tax evasion in the data. Moreover, the model is simulated using an alternative measure of the deterrence probability. These additional 3
<!-- Start of picture text --> IB socialism [IP Marxism 9} communism §§§ working class [jj proletariat<br>0.00200%,<br>0.00160% V7J<br>0.00140%)<br>0.00120%4)<br>0.00100%% :<br>0.00080%| y VAY,<> \<br>ff oy A<br>0.00080%%: gx{| y y<br>0.000404) = a Y J<br>0.00020% —-=<br>0.000005eyaap TB20 ——740 ——————————TED TB80 7300 ——7820 7340 7360 7380 2000<br><!-- End of picture text -->

<!-- Start of picture text --> I Keynesian economics J) Austrian School J Cambridge Schoo! (§§) Marxian economics [J Chicago School [J neoclassical economics | institutional economics<br>0.0000500%% XY<br>0.0000400% s] ~~<br>0.0000350% Z/<br>0.0000300.0000250%4 77<br>0.0000200%4<br>o.0000100% = — ~T\<br>0.0000050% aS eee a ~~ _<br>—e— ee eee—————<br>0.0000000F an 7310 7220 730 7840 7350 Td7360 770 780 790 2000<br><!-- End of picture text -->
Table 6: Origin Frauds: Downward Bias Least Squares Regressions

|Dependent variables|Quant|ity Discrep|ancies|Val|ue Discrepa|ncies|
|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)|(4)<br>|(5)<br>|(6)|
|AVEborder|0.691***|0.638***||0.571***|0.523***||
||(0.104)|(0.107)||(0.102)|(0.105)||
|Tari¤||2.026***<br>|||2.007***<br>||
|||(0.0669)|||(0.0699)||
|AVEborder+Tari¤|||1.706***|||1.670***|
||||(0.0559)|||(0.0576)|
|SPS/TBT NTMs|||-16.02***|||-24.97***|
||||(4.489)|||(4.501)|
|Other NTMs|||14.97**|||13.84**|
||||(5.843)|||(5.566)|
|Constant|95.41***|79.59***|83.19***|98.40***|82.63***|91.84***|
||(0.185)|(0.544)|(3.301)|(0.182)|(0.570)|(3.124)|
|Observations|251,184|244,689|244,689|246,373|239,854|239,854|
|R-squared|0.280|0.284|0.283|0.280|0.284|0.284|
|Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Product FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

Table 7: Origin Frauds: Second Stage IV Regressions

|Dependent variables|Quant|ity Discrep|ancies|Valu|e Discrepa|ncies|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|AVEborder|1.413***|1.672***||1.367***|1.600***||
||(0.250)|(0.269)||(0.237)|(0.252)||
|Tari¤||2.007***|||1.990***||
|||(0.0668)|||(0.0698)||
|AVEborder + Tari¤|||2.272***|||2.306***|
||||(0.102)|||(0.102)|
|SPS/TBT NTMs|||-18.06**|||-29.03***|
||||(8.601)|||(8.398)|
|Other NTMs|||7.159|||15.92|
||||(11.12)|||(10.50)|
|Observations|251,184|244,689|146,609|246,373|239,854|144,111|
|Exporter Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Product Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

43
# **A Appendix: Robustness checks**

Table A.1: The decision to become an exporter

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Subj. fnancial||-0.015||-0.014||||
|constraint||(0.013)||(0.013)||||
|Unable to obtain a|||0.013||0.020|-1.337*|-1.391***|
|loan? (Yes=1)|||(0.024)||(0.024)|(0.706)|(0.504)|
|Foreign shock|-0.007|||-0.007|-0.007||0.019|
|(%∆GDP)|(0.009)|||(0.009)|(0.009)||(0.018)|
|Domestic shock|-0.011***|||-0.010***|-0.011***||-0.000|
|(%∆GDP)|(0.003)|||(0.003)|(0.004)||(0.007)|
|Capacity|-0.000|-0.000|-0.000|-0.000|-0.000|-0.003*|-0.003**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|
|Log(Labor|0.034***|0.040***|0.040***|0.034***|0.034***|0.003|0.005|
|Productivity)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.024)|(0.018)|
|Firm Age|0.002***|0.002***|0.002***|0.002***|0.002***|0.003**|0.003***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Small Firm? (Yes=1)|-0.272***|-0.272***|-0.272***|-0.271***|-0.270***|-0.386***|-0.389***|
||(0.024)|(0.024)|(0.024)|(0.024)|(0.024)|(0.074)|(0.060)|
|Foreign Ownership?|0.201***|0.197***|0.200***|0.199***|0.203***|0.124|0.123*|
|(1=Yes)|(0.034)|(0.035)|(0.035)|(0.034)|(0.034)|(0.076)|(0.073)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.326|0.326|0.326|0.326|0.326|0.326|0.326|
|F stat||||||18|15|
|Kleibergen-Paap LM test||||||0.146|0.013|
|Hansen J stat||||||0.563|0.561|
|Endog. test||||||0.001|0.000|
|N|15204|15204|15204|15204|15204|15204|15204|

The dependent variable is the _Prob_ ( _Exportit >_ 0). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

21
<!-- Start of picture text --> ay .<br>on : I<br>> : Ut ae<br>By 20 Dy -208<<br>a= : 2<br>E a ee<br>2 10- a -102 8<br>S ! _<br>2 aea<br>=:<br>2 : Il<br>jaa O- 4 1 - " 1 1 70<br>2014 2015 2016 2017 2018<br><!-- End of picture text --> — Anti-immigrant violence — Asylum applications
The estimates for new architectural and engineering designs are calculated as half of the architectural and engineering services industry Net Operating Revenue; these are then distributed over purchasing industries using sector coefficients from the input-output matrix.

Finally, the expenditure on R&D in social sciences and humanities is estimated by the same method used for science and engineering R&D, except that we replace the number of scientific-technical personnel by the number of social sciences and humanities researchers employed by each industry.<sup>16</sup> _C. Economic competencies._ Three basic asset types are included in economic competencies: ̳brand equity‘, firm-specific human capital, and organizational structure.

Brand equity expenditures consist of advertising expenditures and market research. For industrial sectors, the information on advertising expenditures is obtained directly from the Annual Survey of Industry; as for other industries such as services, commerce and construction, the values are estimated by distributing the net operating revenue of the advertising industry using sector coefficients from the input-output matrix. Outlays on market research are estimated as twice the industry net operating revenue and also distributed using the input-output matrix.

Measuring firm-specific human capital in Brazil is a major challenge because unlike countries such as the US and UK, there is no regular official survey in Brazil collecting information on worker training expenditures or on the wage and salary costs of employee time in formal and informal training. To obtain estimates for this item, we used a survey conducted by the National Confederation of Industry (CNI) that estimates the percentage of gross revenue spent in training programs in 2007. We use another survey from CNI, the Brazilian Service of Support for Micro and Small Enterprises (SEBRAE), and the Unions Department of Statistics and Socioeconomic Studies (DIEESE) which estimate the percentage of workers that participated in training and the distribution of the hours spent in training in 2003 in manufacturing firms. We combine this estimate of hours in training with wage data to estimate the wage and salary cost of employee time in formal training. Because these surveys were conducted only in the industrial sector, we do not have estimates for training expenditures for services. Therefore, when comparing our aggregate estimate of training investment to that from other countries, it is important to keep in mind that ours is an

> 16 As one would expect, this method suggests that business R&D in social sciences and humanities is insignificant.

14
we consider the three types of media capture tools measured by the _Freedom of the Press_ indicators: legislation that sets limits to reporting ( _Legal constraints_ ); political interference with information collecting, reporting and dissemination ( _Political constraints_ ); and economic media capture through positive inducements such as bribes, regulatory favors and market concentration de facto placing media into the hands of the state or government-connected oligarchs ( _Economic constraints_ ).

Table 6: Alternative mechanisms of media capture by total number of censorship events and types of targets (Panel data Poisson, country and year fixed effects)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||<br>Total<br>number of<br>censorship<br>events|<br>Total<br>number of<br>censorship<br>events|<br>Total number of<br>censorship events<br>targeting foreign<br>media|<br>Total number of<br>censorship events<br>targeting<br>TV<br>broadcasters|<br>Total number of<br>censorship events<br>targeting<br>daily<br>newspapers|<br>Total number of<br>censorship<br>events targeting<br>media<br>located<br>abroad|
|Political constraints||0.0458***<br>(0.00615)|0.0734***<br>(0.0192)|0.0679***<br>(0.0204)|0.000659<br>(0.0213)|0.0873***<br>(0.0265)|
|Economic constraints|-0.0270**<br>(0.00934)|-0.0353***<br>(0.00939)|-0.0440~<br>(0.0265)|-0.1000**<br>(0.0312)|0.0683*<br>(0.0304)|-0.0908*<br>(0.0421)|
|Legal constraints|0.0283***<br>(0.00798)|0.00788<br>(0.00815)|0.00517<br>(0.0278)|-0.0418<br>(0.0257)|-0.00574<br>(0.0255)|0.113**<br>(0.0385)|
|GDP pc (log)|-0.103|-0.0739|0.230|-0.445~|-0.330|0.478|
||(0.0718)|(0.0721)|(0.214)|(0.232)|(0.262)|(0.332)|
|Population (log)|0.365|0.309|0.386|0.522|1.091|-0.147|
||(0.344)|(0.345)|(0.832)|(0.885)|(1.034)|(1.043)|
|Democracy|-0.0397~<br>(0.0219)||||||
|Corruption|-0.512~<br>(0.290)|-0.659*<br>(0.294)|||||
|Country FE|√|√|√|√|√|√|
|Year FE|√|√|√|√|√|√|
|_N_|1918|1918|1473|1582|1438|1077|
|_Countries_|161|161|123|133|121|90|
|_Years_|12|12|12|12|12|12|

Standard errors in parentheses ~ p<0.10, * p<0.05, ** p<0.01, *** p<0.001 In Models 1 and 2 of Table 6, we tested the division of labor among such capture tools to verify whether coercion and economic inducements of media control are complementary or 42
Celsius. This relationship is more pronounced in urban areas and not significant in rural areas. For flooding, an additional flood is associated with 0.01 fewer employees in urban and rural areas.

We contribute to the literature on the distributional implications of climate change. Many studies find that the poor are impacted more severely by extreme heat, floods and other climate shocks.<sup>1</sup> (Kahn 2005; Hallegatte et al. 2016; Triyana et al. 2024). But as noted in a recent review (Triyana et al. 2024), evidence on differential exposure to climate shocks is comparatively limited and mixed. Research into this question has been constained by limited spatial detail in available data on household poverty and wealth. In effect, most existing studies have used poverty or wealth estimates that only vary across admistrative units such as districts and subdistricts. This is a significant drawback because there is sizable varation in wealth within districts and subdistricts.

Recent work has benefited from the growing availability of more spatially detailed data on poverty. Notably, Park et al. (2018) use 0.5 by 0.5 degrees (approx. 50 by 50 kilometers at the equator) resolution geo-referenced household survey data to examine the exposure of poor households to extreme heat in 52 countries and find that the poor tend to be more exposed to heat than the nonpoor in hot countries. Using data at a similar spatial resolution, Winsemius et al. (2018) find that globally, the poor are more exposed to flooding.

Use of the RWI allows our study to exploit even more granular (2.4 by 2.4 kilometers resolution) variation in relative wealth. Moreover, as it is constructed using a machine learning algorithm, the RWI is a standardized measure which does not rely on researcher discretion in the choice of survey questions used to index wealth. Additionally, we use a definition of urbanicity that is comparable across countries (Nelson et al. 2019). These comparable definitions allow for an analysis such as ours to be replicated in other settings across the globe.

We also contribute to the literature on the differential incidence and impact of climate shocks on firms. A growing body of research examines how extreme heat, floods and other natural disasters affect productivitity, investment and survival among firms.<sup>2</sup> But only a few papers examine if climate shocks have unequal impacts on firms. In the United States, agricultural firms (Nath 2021) and firms that predominantly serve local markets (Gallagher, Hartley, and Rohlin 2023) suffer

> 1 Kahn 2005; Stéphane Hallegatte et al. 2016; and Triyana et al. 2024 review this evidence.

> 2 See Goicoechea and Lang (2023), Rexer and Sharma (2024) and Grover and Kahn (2024) for recent reviews of this literature.

4
# WPS3493

## The Clash of Liberalizations:

## Preferential versus Multilateral Trade Liberalization in the European Union<sup>∗</sup>

Baybars Karacaoval ı<sup>†</sup> Nuno Limão<sup>‡</sup> University of Maryland

### Abstract

There has been an explosion in the number of preferential trade agreements (PTAs) in the last decade. PTAs are characterized by liberalization with respect to only a few partners and thus they can potentially clash with and retard multilateral trade liberalization (MTL). Despite this important concern with PTAs, there is almost no systematic evidence on whether they actually affect MTL. We model the effect of PTAs on MTL and show that PTAs slow down MTL unless they have a common external tariff and allow for internal transfers. Next, we use detailed data on product-level tariffs negotiated by the European Union in the last two multilateral trade rounds to structurally estimate our model. We con fi rm the main prediction—the European Union’s PTAs have clashed with its MTL–and fi nd that the effect is quantitatively signi fi cant. Moreover, we also con fi rm several auxiliary predictions of the model and provide new evidence on the political economy determinants of multilateral liberalization in the European Union.

JEL Classi fi cation: D78; F13; F14; F15.

Keywords: Preferential trade agreements; customs unions; multilateral trade negotiations; MFN tariff concessions; reciprocity.

> ∗We thank, without implying, Stephanie Aaronson, Kishore Gawande, Bernard Hoekman, Patricia Tovar-Rodriguez and Alan Winters for helpful discussions and comments and Marcelo Olarreaga and John Romalis for generously sharing some data. The views expressed here are those of the authors.

> †University of Maryland, Department of Economics, MD 20742. E-mail: karacaov@econ.umd.edu.

> ‡Corresponding Author: limao@econ.umd.edu. University of Maryland, Department of Economics, MD 20742. I acknowledge the fi nancial support of the World Bank Research Group which hosted me during the completion of part of this research.
# **Export Survival: The Role of Banks and Stock Markets**<sup>_∗_</sup>

Melise Jaud<sup>_†_</sup> Madina Kukenova<sup>_‡_</sup> Martin Strieborny<sup>_§_</sup> JEL classification: G10, G21, F14.

Keywords: banks versus stock markets, transmission from finance to real economy, finance and export survival

> _∗_ The previous version of this paper circulated under the title ”Financial dependence and intensive margin of trade”. We would like to thank Tibor Besedeˇs, Xin Huang, Jean Imbs, Beata Javorcik, Rafael Lalive, Luis Santos-Pinto, Pascal St-Amour and the seminar and conference participants at University of Lausanne, Lund University and the 37th AFFI conference for very useful comments and suggestions. Melise Jaud appreciates the financial support from the Swiss National Science Foundation. Madina Kukenova appreciates the grant received from the Swiss School of Higher Education. Martin Strieborny appreciates the financial support from the Handelsbanken Research Foundation. The most recent version of this paper including an accompanying Online Appendix can be downloaded from www.martinstrieborny.com

> _†_ World Bank; E-Mail: mjaud@worldbank.org

> _‡_ Swiss School of Higher Education; E-Mail: madina.kukenova@sshe.ch

> _§_ University of Glasgow, Adam Smith Business School, Room 469, Main Building, University Avenue, Glasgow, G12 8QQ, United Kingdom. E-Mail: martin.strieborny@glasgow.ac.uk
Policy Research Working Paper 6478

# **Abstract**

This paper proposes a new quantitative implementation of Balassa’s idea that export composition and revealed comparative advantage inform the relationship between endowments in domestic factors of production and exports. It proposes that the export composition of countries is close to a low-dimensional manifold or “Product Space” within the space of export composition, which has as many dimensions as product lines. The Product Space corresponds to a few latent endowments explaining the structure of the trade matrix. The model uses non-linear techniques to identify the product space from the 2010 export matrix of 128 countries and 61 products, and to estimate the latent factors of endowments by country. It formalizes a concept of latent comparative advantage, which has practical country specific applications, relevant for “trade competitiveness” policies. Compared with classical revealed comparative advantage, the model assesses how well countries are matching their potential implied by the latent variables, and also identifies products for which the latent advantage is not yet revealed (extensive margin). The data suggests that the degree of overlap between latent and revealed advantage is a metric of “trade competitiveness.” This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. 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 author may be contacted at jarvis1@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
# **1 Introduction**

Policies introduced to control the spread of COVID-19 have caused unprecedented economic upheaval throughout the world, particularly for firms in low- and lower-middle-income countries that typically received little to no government support. Mobility and economic activity restrictions, alongside higher transaction costs and disruptions in cross-border trade, have hindered the allocation of resources within countries and across industries, lowering aggregate productivity growth (Apedo-Amah et al. 2020). Additionally, the pandemic has substantially shifted consumer demand, which has forced fundamental changes to business practices that are likely to persist in the medium term (Barrero et al. 2020). Therefore, it is imperative to develop a greater understanding of how firms have coped in this extremely challenging environment, especially in lower-income settings.

We examine this issue by drawing on a combination of survey and tax administrative data to analyze how formal firms in Zambia have recovered from COVID-19.<sup>1</sup> In general, the composition of the formal sector in Zambia is similar to many other medium-sized SubSaharan African countries, except there is a greater prevalence of firms in the mining sector (World Bank 2022). As is the case in most countries around the world, at the outset of the pandemic the Government of Zambia introduced restrictions to control COVID-19, which considerably curtailed normal economic activity. We investigate how firms have fared since this initial shock by using three follow-up surveys to a standard World Bank Enterprise Survey (ES) covering a representative sample of 601 formal firms that was conducted immediately prior to the pandemic and by drawing on the universe of monthly value-added tax (VAT) and pay-as-you-earn (PAYE) returns filed by around 20,000 firms throughout 2020 and the first half of 2021.<sup>2</sup> Both data sources have strengths and weaknesses. A key advantage of the survey data is that firms provide extensive details about their operations when completing the question-

> 1For the purposes of this study, formal firms are defined as firms that are registered with the Zambia Revenue Authority.

> 2In Zambia, personal income tax is recorded in PAYE returns.

1