rafmacalaba/datause-encoder-data huggingface dataset:
countries.

Tax evasion is captured by the proportion of total sales not reported for tax purposes.<sup>5</sup> Distortions are measured as the sum of the losses (in percentage of total sales) due to power outage or surges from the public grid, insufficient water supply, unavailable main line telephone service, transport failures, crime (loss due to theft, robbery, vandalism or arson) and gifts or informal payment to public officials to "get things done".<sup>6</sup> Institutional quality is proxied using a measure of the effectiveness of public policies from the World Governance Indicators (WGI). More particularly, institutional quality captures the perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. We use the percentile distribution of these public policies effectiveness variables.

In the regressions below, we also account for a set of firm individual characteristics. Firms’ access to finance is measured as the share of working capital financed by commercial banks. Regulatory burden is measured by the percentage of time the senior management spends dealing with requirements imposed by government regulation. We include the percentage of the firm owned by foreign interests and the government, firm’s age captured by three categorical variables: young (1-5 years old), mature (6-15 years old), and older (more than 15 years old), and the percentage of the establishment’s sales exported. Finally, we account for the size of the firms using four dummy variables capturing firms’ size categories. Microenterprises have fewer than 10 permanent employees. Small and medium have between 11 and

> American countries, as the paper does not account for the distortions generated by informal firms and their impacts on tax collection.

> 5The question is: "Recognizing the difficulties many enterprises face in fully complying with taxes and regulations, what percentage of total sales would you estimate the typical establishment in your area of activity reports for tax purposes?"

> 6The questions related to the components of distortionary infrastructures are stated as follows. (i) "Please estimate the losses (as a percentage of total sales) of theft, robbery, vandalism or arson against your establishment in the last year." (ii) "What percentage of your total sales value was lost last year due to power outages, insufficient water supply, unavailable mainline telephone service, and transport failures?" (iii) "We have heard that establishments are sometimes required to make gifts or informal payments to public officials to "get things done" about customs, taxes, licenses, regulations, services, etc. On average, what percentage of annual sales value would such expenses cost a typical firm like yours?"

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_associated with higher unadjusted (observed) prices. In other words, firms only export the most expensive goods to the most distant markets_ ”.

In quality competition models with constant mark-up, firms sell at a constant mark-up over marginal cost in all markets, so that fob prices do not vary across destinations. Nevertheless, the most productive (qualitative) firms will use more expensive (quality) inputs and will charge higher prices. As a consequence, quality competition models predict differences in fob prices across firms, but not across destinations.

Lastly, quality competition models with variable mark-ups build on the Melitz and Ottaviano (2008) model, to which quality differentiation is included. Kneller and Yu (2008) are an example of a quality competition model with variable mark-ups: they simply assume that firms with higher marginal costs produce higher quality. Mark-ups are heterogeneous, so that firms that produce higher quality products will charge higher prices both because they use more expensive inputs and because they can charge higher mark-ups. In these models, the fob price charged by a firm will differ across destinations, according to the degree of faced competition.

From this review of the theoretical framework, it is clear that the theoretical models predict different relationships between export prices and gravity variables at the firm level. This finding is also true at the product level (not reported in Table 1): Melitz and Ottaviano (2008) predict a negative relationship between prices and distance, while Baldwin and Harrigan (2011) and Johnson (2007) predict and show the opposite (that prices increase with distance). Regarding the wealth in destinations, the framework developed by Hallak (2006) predicts a positive relation between (aggregate) export prices and the GDP per capita of the trade partner, while Melitz and Ottaviano (2008) predict a negative relationship.

With regard to the empirical literature, Manova and Zhang (2012) is the main reference for this paper. The authors analyze a custom database on Chinese firms and establish stylized facts on the variation in export prices and imported-input prices across firms, products and trade-partner countries. They also compare their findings with the literature and the expected results of the various models of trade and heterogeneous firms. They conclude that there is not a single trade model that can explain all the facts observed in the data and that future work on international trade models should incorporate quality differentiation both across firms and trade partners within firms.

An attempt similar to Manova and Zhang (2012) is made by Görg, Halpern and Muraközy (2010), who analyze the relationship between gravity variables and fob export unit values using a Hungarian firm6
probability of getting chosen, we correct for this by conditioning on the lotteries entered (Abdulkadiroglu et al, 2011).

Second, we use assignment to the treatment (winning the PAC lottery) as an instrumental variable (IV) for the actual treatment of migrating, in regressions like: where _Migratei_ is a dummy variable that equals one if person _i_ ever migrated to New Zealand and is zero otherwise. The IV estimator of <sup>ˆ</sup> is a Local Average Treatment Effect (IV‐LATE), interpreted in this case as the impact of migrating on people who would migrate only if they won the PAC lottery and not otherwise.

# **3. Impact on Risk and Time Preferences**

There are several channels through which international migration might be hypothesized to affect risk and time preferences. The very act of moving to another country to set up a new life is a risky decision, and successfully doing this may lead to more confidence in taking subsequent risks (Krueger and Dickson, 1994). To the extent that cultures differ in risk attitudes or time preferences (Falk et al., 2016), moving to a new country entails exposure to this new culture. Migration also can affect education and wealth levels, and lifespans, which in turn may affect time preferences and risk attitudes.

# **3.1 Existing Evidence of Migration Effects**

Risk is a key determinant of migration decisions and several studies have attempted to examine the association between risk preferences and migration, with an implied causal direction going from time‐ invariant risk preferences to migration. Jaeger et al. (2010) use data from the German Socio‐economic survey on an individual’s self‐reported propensity to take risks and find that individuals with higher risk propensities are more likely to migrate within Germany.<sup>6</sup> Gibson and McKenzie (2011) use the same risk measure and find that among high‐skilled people from three Pacific countries the more risk‐seeking individuals are more likely to have emigrated. They also find that individuals who are more patient in a discounting question are more likely to have emigrated. Three recent papers have also examined the association between risk‐seeking and internal migration within China. Akgüҫ et al. (2015) and Dustmann

> 6 In addition to their main analysis, which focuses on how risk preferences predict migration, they use panel data over two years to show that changes in migration status are not correlated with changes in self‐reported risk attitudes over this short period.

8
# **6 References**

- Atkinson, A. B. (2009). Stress-Testing the Welfare State. In B. Ofstad, O. Bjerkholt, K. Skrede, & A. Hylland (Eds.), Rettferd og Politik Festskrift til Hilde Bojer (pp. 31–39). Emiliar Forlag.

- Avellaneda, A., Chang, R., Collado, D., Jara, H. X., Mideros, A., Montesdeoca, L., Rodriguez, D., Torres, J., & Vanegas, O. (2021). Assessing the cushioning effect of tax-benefit policies in the Andean region during the COVID-19 pandemic. CeMPA Working Paper Series, No. 8/21.

- Paulus, A., & Tasseva, I. V. (2020). Europe Through the Crisis: Discretionary Policy Changes and Automatic Stabilisers. Oxford Bulletin of Economics and Statistics, 82(4), 864–888.

Barnes, H., Espi-Sanchis, G., Leibbrandt, M., Leolo, M., McLennan, D., Noble, M., Pirttilä, J., Steyn, W., van Vrede, B. & Wright, G. (2021). Analysis of the distributional effects of Covid19 and state-led remedial measures in South Africa. SA-TIED Working Paper #170, April 2021.

- Brewer, M., & Tasseva, I.V. (2021). Did the UK policy response to Covid-19 protect household incomes? The Journal of Economic Inequality 19, 433–458.

- Bronka, P., Collado, D., & Richiardi, M. (2020). The Covid-19 Crisis Response Helps the Poor: The Distributional and Budgetary Consequences of the UK lock-down. INET Oxford Working Papers 2020-17. Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.

- Cantò, S. C., Figari, F., Fiorio, C., Kuypers, S., Marchal, S., de la Cruz, M. R. & Verbist, G. (2021). Welfare resilience at the onset of the COVID-19 pandemic in a selection of European Countries: Impact on public finance and household incomes, Review of Income and Wealth 68: 293-322

- Christl, M., De Poli, S., Hufkens, T., Leventi, C., & Papini, A. (2022a). EUROMOD Labour Market Adjustment (LMA) Add-on - technical note, European Commission, Seville, 2022, JRC128274

- Christl, M., De Poli, S., Hufkens, T., Leventi, C., Papini, A., & Tumino, A. (2022b). Simulating labour market transitions in EUROMOD: EUROMOD LMA Add-on and COVID-related policies, European Commission, Seville, JRC128274

- Ebbinghaus, B., & Lehner, L. (2022). Cui bono – business or labour? Job retention policies during the COVID-19 pandemic in Europe. Transfer: European Review of Labour and Research, 28(1), 47–64.

- European Commission (2020). Employment and Social Developments in Europe: Leaving no one Behind and Striving for More: Fairness and Solidarity in the European Social Market Economy. Luxembourg: Publications Office of the European Union.

- Figari, F., & Fiorio, C.V. (2020). Welfare resilience in the immediate aftermath of the covid-19 outbreak in Italy. EUROMOD Working Paper.

- Gasior, K., & Rastrigina, O. (2017). Nowcasting: Timely Indicators for Monitoring Risk of Poverty in 2014-2016. EM 7/17. EUROMOD Working Paper Series

Jara HX, Montesdeoca L, & Tasseva I. (2022). The Role of Automatic Stabilizers and Emergency Tax-Benefit Policies During the COVID-19 Pandemic: Evidence from Ecuador. European Journal of Development Research 34(6):2787-2809.

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foreign factors of production within the MNCs’ internal network.

In 2016, about one-third of the Korean manufacturing firms in our sample had foreign affiliates, and the majority of foreign affiliates were located in East Asian countries whose 1. comparative advantage lies in an abundance of cheap labor, as shown earlier in Table As long as an increase in labor costs associated with changes in labor market policies in Korea implies a decrease in _relative_ operating costs abroad, it is likely to have induced Korean MNCs to save costs by reducing domestic production tasks through task substitution: that is, using already established foreign networks to concentrate R&D activities and non-production tasks, for example.<sup>29</sup> In this section, we test this mechanism (MNCs’ task reallocation abroad) in detail as follows. First, if task substitution occurred, then we would expect to see a reduction in domestic tasks in addition to a decline in employment. Second, the negative employment effects should be most pronounced between parent-foreign affiliate pairs that perform similar tasks and/or those firms whose subsidiaries were located in low-wage countries close to Korea (e.g., China and the ASEAN members). In both cases, firms would be relatively easier to substitute tasks in response to the increase in labor costs.

# **5.1 Reducing Production Tasks in the Domestic Market**

To investigate the task substitution channel, we first check whether Korean MNCs’ domestic production contracted more than other firms as a result of the task reallocation to foreign affiliates in response to the labor market policy changes. For this, we replace the dependent variable in equation (2) with real value added in the log, the results of which are summarized in Table 7. As earlier, we begin by including firm fixed effects only, and adding year fixed effects, region-year fixed effects, and industry-region- year fixed effects, respectively, from columns (1) through (4). In column (4), the preferred specification, the coefficient estimate is -0.0459 and statistically significant at the 5% level. Quantitatively, the real value added of the MNC group contracted by 4.6% more than that of the nonMNC This corroborates our earlier that Korean MNCs reduced group. finding finding domestic employment more than other Korean firms by reallocating tasks to foreign affiliates, which would have also led to a disproportionate drop in domestic production among Korean MNCs.<sup>30</sup>

> 29Korean MNCs with foreign production networks do not need to spend money on setting up new plants as additional fixed costs (Markusen, 1984; Helpman, Melitz and Yeaple, 2004), so task substitution is easier than it is for firms with no foreign affiliates.

> 30Appendix Figure B.2 illustrates corresponding event study analyses and dynamic treatment effects, confirming the robustness of the results summarized in Table 7.

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statistic of 22.07 also rejects the null hypothesis that the regression is under-identi...ed. For completeness and ease of comparison, Column (3) presents the second-stage cross-sectional regression LS estimates which are smaller in magnitude than the IV estimates. Finally, in Column (4) the ...ve IVs are directly included in the second-stage cross-sectional regression and we cannot reject the null hypothesis that jointly these IVs are not signi...cant and do not have a direct impact on GVC participation.

# 4.3 Relative Importance of the Determinants

Overall, the between and the cross-sectional IV estimates paint the same picture that tari¤s and FDI in‡ows are important in determining backward GVC participation of a country. In addition, factor endowments, political stability, geographical proximity to GVC hubs, and domestic industrial capacity are crucial in a¤ecting backward GVC participation. To assess the marginal contributions of the di¤erent determinants, we conduct a thought exercise described below, based on the IV cross-sectional estimation results in Column (2) of Table 4, and we report the results in Table 5. First, we partial out all the included exogenous variables such as distance, political stability, and factor endowments for a regression that includes only the endogenous variables: tari¤s and FDI. The R-squared of 0.056 gives the marginal contribution of tari¤s and FDI in explaining backward GVC participation. Next, we add back the other determinants one at a time and we assess the changes in the R-squared. After adding distance to GVC hubs, the R-squared increases to 0.145, indicating the marginal R- squared contribution of distance is 0.089 (=0.145-0.056). Adding all factor endowments, the new R-squared is 0.238, suggesting that their marginal contribution is 0.182 (=0.238-0.056). Overall, factor endowments can explain 43% of the backward GVC participation shares in the IV cross-sectional regression, which is the most important determinant, followed by distance (21%), political stability (18%), tari¤s and FDI (13%), and domestic industrial capacity (4%), while exchange rate appreciation has no explanatory power.

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**26 |** P a g e

exports increased from 4 percent in 2003 to 8 percent in 2007 with the shares of other categories of labor slightly contracting. Medium‐skilled blue collar jobs appear to have been more affected, as their share overall fell from 29 percent in 2003 to 25 percent in 2007. The high‐skill labor intensive sectors have posted the strongest export growth. The value of their EU‐oriented exports increased from US$ 35 million in 2003 to US$ 237 million in 2007, with exports of industrial equipment (SITC 74) leading the pack: the value grew from US$ 22 million to US$ 193 million in this period.

# **Concluding observations**

In spite of weaknesses in the investment and business climate, BiH exporters have posted one of the strongest growth rates and the largest increase in the share in world trade among SEE‐6 economies during the current export expansion phase. They outperformed in world markets not only suppliers from SEE but also from other countries.

Traditional manufacturing and resource‐based industries continue to tower over BiH’s export basket and display strong comparative advantage. Yet, there are some signs of the emergence of more sophisticated export‐oriented activities pointing to an increased participation of BiH manufacturers in global supply chains. BiH export growth performance during its current expansion phase has been characterized by:

- Strong growth across all sectors of the BiH economy. It was broadly based with all double–digit SITC sectors having had expanded exports of their products, albeit at a different pace.

- Greater use of skilled labor, especially visible in EU imports from BiH, indicating that the gap between factor content of BiH exports and its endowments in skilled labor force, characteristic of post‐war recovery phase, appears to have been partly closed.

- Increase in the export coverage of goods in terms of their imports. As a result, BiH has made a huge step towards improved financial sustainability of external performance.

- The growing diversity in export offer. This points to the depth of industrial restructuring and increased level of processing embodied in exports, as indirectly revealed in trading partners’ import statistics.

- Strong growth of capital and skilled labor‐intensive products associated also with the entry of BiH firms into global production networks (almost exclusively automotive).

- And an increased presence of firms with foreign capital. Like in other transition economies, they tend to display, as a rule, strong export orientation and higher technology content.

The current expansionary phase bears some resemblance to the second wave of export growth of Central Europe, albeit with two caveats. Both have significant implications weakening benefits associated with the presence of foreign firms. The first is that Central European economies at this stage had mostly zeroed customs duties on industrial imports from the EU. In consequence, firms had to
inequality data, we will present estimates based on the Gini income inequality coefficient and income shares of the 1<sup>st</sup> , 2<sup>nd</sup> , 3<sup>rd</sup> , 4<sup>th</sup> , and 5<sup>th</sup> quintiles provided by WDI (2014).

**Other Data.** Data on real GDP per capita and investment are from the Penn World Tables (Heston et al., 2012). Data on the average years of schooling and the share of population with secondary and tertiary education are from Barro and Lee (2010). Descriptive statistics for the above variables are provided in Appendix Table 1. For a list of the countries in the sample, see Appendix Table 2.

# **4. Estimation Framework**

Following the empirical literature on the impact of income inequality on economic growth (e.g. Forbes, 2000), the baseline econometric model is:

- (1) ln(y)it - ln(y)it-1 = ai + bt + θ1Inequalityit + θ2Inequalityit*ln(yi1970) + φ ln(y)it-1 + uit

where ln(y)it stands for the natural logarithmn of real GDP per capita in country i and period t; ln(yi1970) is the natural logarithmn of country i's GDP per capita at the beginning of the sample, i.e. in 1970<sup>2</sup> ; ai are country fixed effects; bt are time fixed effects; uit is an error term. We note that this equation can be re-written as:

- (1') ln(y)it = ai + bt + θ1Inequalityit + θ2Inequalityit*ln(yi1970) +(1+φ)ln(y)it-1 + uit

We estimate equation (1') with 5-year non-overlapping panel data. The panel comprises 104 countries during the period 1970-2010. This is the largest possible 5-year non-overlapping sample given the availability of data on GDP per capita and income inequality. Our measures of income inequality are the Gini coefficient and the income shares accruing to the 1st, 2nd, 3rd, 4th, and 5th quintiles.

The parameter φ captures the convergence rate over a 5-year period. The contemporaneous effect of a within-country change in inequality on the natural logarithmn of GDP per capita is captured by θ1+θ2*ln(yi1970). If φ is significantly negative, so that 1+φ is below unity in absolute

> 2 The term, ln(yi1970), does not appear in the econometric model because it is implicitly contained in the country fixed effects, ai.

6
originated dropped by more than 50% between the first half of 2014 and the first half of 2015.<sup>38</sup> Revenues and profits of firms dropped and many firms no longer managed to cover their costs and exited the market. As the HCST market is small (outstanding HCST credit is less than 0.2% of GDP), these developments did not impact the wider financial system in the United Kingdom.

**Figure 17: Changes in HCST loan number, value and revenues**

<!-- Start of picture text --> Number of loans originated Value of loans originated Value of loan revenue<br>0%<br>‐10%<br>‐20%<br>‐30% ‐23%<br>‐28%<br>‐40%<br>‐50%<br>‐60% ‐54% ‐54% ‐51%<br>‐70%<br>‐80% ‐73%<br>Change H1 2014 (pre cap) to H1 2015 (post cap)<br>Change H2 2014 (pre cap) to H1 2015 (post cap)<br><!-- End of picture text --> _Source:_ FCA To analyze the impact on consumers, the FCA distinguishes between consumers that obtained a loan following the introduction of the cap and those consumers that had obtained an HCST credit before the cap, but lost access thereafter. It shows that the former group benefitted from the implementation of the cap. The average cost of credit has declined and borrowers are more able to repay the loan on time. Default rates have declined and fewer late fees are charged. Consumers also benefit from the increased availability of longer‐term loans.

However, a report by the Consumer Finance Association (CFA) highlights that from the start of 2014 until mid‐2015 the acceptance rate at the final stage of the loan application fell from 50% to around 30%.<sup>39</sup> This led to many consumers losing access to the credit that they had in the past. Declined loan applicants were on average younger, more likely to be

> 38 The decline in borrowers cannot be linked entirely to credit supply reductions as also fewer people applied for loans due to improved financial performance, less marketing by lenders, etc.

> 39 http://cfa‐uk.co.uk/wp‐content/uploads/2017/03/030317‐HCSTC‐and‐market‐functioning‐Oxera.pdf

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**Figure 2: Matched counties and cities by blocks of 1994 propensity scores**

|**Blocks of PScores**|**County**<br>**(comparison)**|**City**<br>**(treatment)**|**Total**|
|---|---|---|---|
|0|2,953|1|2,954|
|0.003125|470|3|473|
|0.00625|501|4|505|
|0.0125|522|16|538|
|0.025|541|14|555|
|0.05|532|34|566|
|0.1|387|73|460|
|0.2|239|92|331|
|0.4|64|57|121|
|0.6|6|19|25|
|0.8|0|2|2|
|**Total**|**6,215**|**315**|**6,530**|

**Figure 3: Kernel density distribution comparison between cities and counties**

<!-- Start of picture text --> Kernel Density Estimate<br>0 .2 .4 .6 .8 1<br>Estimated propensity score<br>City County<br>kernel = epanechnikov, bandwidth = 0.0552<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> 8