rafmacalaba/datause-encoder-data huggingface dataset:
# **C Data Appendix**

The main outcome variable of interest in this paper is juvenile catch: the number of individual anchoveta that vessels catch that are less than 12 cm. There are two challenges in calculating juvenile catch in an unbiased and accurate manner.

First, vessels may underreport percentage juvenile in the electronic logbook data in order to avoid triggering a closure in the area they are fishing. If I only used raw electronic logbook data to calculate juvenile catch and underreporting is correlated with closures declared by the regulator, my treatment effect estimates would be biased.

Second, even if percentage juvenile reported by vessels in the electronic logbook data is unbiased, percentage juvenile and tons caught are not sufficient for calculating juvenile catch because the number of individuals caught depends on the length distribution of those individuals. For example, consider two sets that both catch 40 tons of anchoveta that are 20% juvenile. In the first set, 20% of individuals are between 11.5 and 12 cm and 80% of individuals are between 12 and 12.5 cm (actual length distributions are much more diffuse; see Figure C1b). In the second set, 20% of individuals are between 10 and 10.5 cm and 80% of individuals are between 14 and 14.5 cm. The weight of an anchoveta in grams equals _._ 0036 _length_<sup>3</sup><sup>_._238</sup> (IMARPE, 2019). Therefore, 683,137 juvenile anchoveta are caught by the first set and 469,685 are caught by the second set, even though both sets caught the same tons and percentage juvenile.<sup>43</sup> Recall from Section II that vessels report percentage juvenile to the regulator in the electronic logbook data, but not the length distribution. I obtained a supplementary electronic logbook dataset for a group of vessels that report length distribution data to their owners. These vessels represent 56% of landings and I received their data from Sociedad Nacional de Pesquer ́ıa (SNP), a consortium of fishing companies, in January 2020 (Englander, 2022).

To calculate juvenile catch for each set, I first use the length distribution values from sets in the SNP electronic logbook data to impute length distributions for non-SNP sets, based on

> 43My results are robust to measuring juvenile catch in terms of tons of juveniles caught (Figure A6).

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Eaton, Eslava, Kugler, and Tybout (2008) show that in any given year more than half of Colombian exporters are new entrants but total export growth results mostly from the intensive margin (i.e., the expansion of incumbent exporters). The interpretation is that new firms export small amounts and face high export failure rates. Lederman, Rodriguez-Clare, and Xu (2011) find that during 1997-2007 the main driver of total export growth in Costa Rica is the introduction of new products by incumbent firms but the contributions of new exporters and new products explain less than a tenth of total export growth. Freund and Pierola (2009) examine exporter entry into products and destination markets not previously served by focusing on nontraditional agriculture in Peru during a period of very rapid growth. They show tremendous exporter entry and entry into new destination markets also accompanied by high exit rates (interpreted as trial and error) but they find lower entry rates into new products.

There is no consensus in the literature on the definition of the extensive margin of exports or export entrepreneurship (as discussed in Lederman, Rodriguez-Clare, and Xu, 2011) or entry channel (as designated in Besedes and Prusa, 2010). This lack of consensus often leads to different and non-comparable results across studies. Some equate the extensive margin with changes in the number of exporters, others focus on changes in the number of products exported, and yet others assess the number of export destinations (countries). This study takes a broad approach covering multiple dimensions of the extensive margin of national exports.

As far as we know, this is the first ever LAC comparative study on export entrepreneurship with comparable data and a uniform methodology across countries. In addition, there are no other papers that cover the effects of the global financial crisis of 2008-09 on export entrepreneurship in LAC (or any other country). Furthermore, this is the first systematic analysis of exporting firm turnover and survival by sectors across a large number of countries.

The rest of the paper is organized as follows. Section 2 describes the data and Section 3 describes the methodology. The results on export entrepreneurship at the country level focusing on the firm margin are presented in Section 4, the results focusing on the product margin are presented in Section 5, and the results focusing on the destination margin are presented in Section 6. Section 7 discusses the results on export entrepreneurship across sectoral typologies. Section 8 concludes.

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<!-- Start of picture text --> Figure 1a. Global Imbalances<br>(Current Account as % GDP)<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>‐5.0<br>‐10.0<br>Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006 Y2007 Y2008<br>Euro_Area_CAD_%gdp 0.5 ‐0.6 0.1 0.7 0.6 1.2 0.5 0.3 0.2 ‐0.5<br>Middle_East_CAD_%gdp 2.7 11.3 6.3 4.7 8.3 11.7 20.0 21.1 18.4 22.9<br>China_CAD_%gdp 1.4 1.7 1.3 2.4 2.8 3.6 7.2 9.4 11.3 9.5<br>Japan_CAD_%gdp 2.6 2.6 2.1 2.9 3.2 3.7 3.6 3.9 4.8 4.0<br>US_CAD_%gdp ‐3.2 ‐4.3 ‐3.8 ‐4.4 ‐4.8 ‐5.3 ‐5.9 ‐6.0 ‐5.3 ‐4.6<br>Axis Title<br><!-- End of picture text --> Source: Authors' calculations WDI‐Global Develpoment Finance of the World Bank and the April 2009 WEO, IMF.

<!-- Start of picture text --> Figure 1b. Global Imbalances<br>(Current Accounts as % of World GDP)<br>1.00<br>0.50<br>0.00<br>‐0.50<br>‐1.00<br>‐1.50<br>‐2.00<br>Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006 Y2007 Y2008<br>Euro area _CA in % worldGDP 0.10 ‐0.11 0.03 0.15 0.13 0.29 0.10 0.07 0.05 ‐0.11<br>Middle East _CA in % worldGDP 0.05 0.22 0.13 0.09 0.16 0.23 0.45 0.52 0.47 0.71<br>China _CA in % worldGDP 0.05 0.06 0.05 0.11 0.12 0.16 0.36 0.51 0.68 0.64<br>Japan _CA in % worldGDP 0.37 0.37 0.28 0.34 0.37 0.41 0.37 0.35 0.39 0.31<br>United States _CA in % worldGDP ‐0.97 ‐1.31 ‐1.21 ‐1.40 ‐1.41 ‐1.50 ‐1.62 ‐1.62 ‐1.34 ‐1.07<br>Current Account Balance (% World GDP)<br><!-- End of picture text --> Source: Authors' calculations WDI‐Global Develpoment Finance of the World Bank and the April 2009 WEO, IMF.

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Policy Research Working Paper 5679

# **Abstract**

This paper uses detailed data on bound and applied tariffs to assess the consequences of the World Trade Organization’s December 2008 Modalities for tariffs levied and faced by developing countries, and the welfare implications of these reforms. The authors find that the tiered formula for agriculture would halve tariffs in industrial countries and lower them more modestly in developing countries. In non-agriculture, the formulas would reduce the tariff peaks facing developing countries and cut average industrial country tariffs by more than a third. The authors use a political-economy framework to assess the implications of flexibilities for the size of the tariff cuts and find they are likely to substantially reduce the outcome. However, despite the flexibilities, there are likely to be worthwhile gains, with applied tariffs facing developing countries cut by about 20 percent in agriculture and 27 percent in non-agriculture, and sizeable cuts in tariffs facing industrial countries. The welfare impacts of reform are evaluated using a new approach to aggregation that improves on the traditional, flawed approach of weighted-average tariffs. This substantially increases the estimated benefits of an agreement along the lines of these modalities, with estimated global income gains of up to $160 billion per year from market access reform.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group; and Development Prospects 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 author may be contacted at wmartin1@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
rates by mosquitoes and is not due to the reduction in other insect-borne diseases such as sleeping sickness and kala azar which in The Gambia are now quite rare. There is a possibility that elimination of some flies might have reduced enteric infections but the reduction in acute respiratory infections is greater than the reductions in the diarrhoeas and the severe gastro-enteritic infections.

The 'frailty protection' effect resulting from t:he avoidance of malarial infections is more difficult to explain. As with measles infections, there may be considerable and lasting gains to be had from the avoidance of infection by the malarial parasite and avoidance of the consequences of this infection such as anaemia.

The demographic impact of the bed-nets There are a variety of ways of estimating what might be referred to as the 'gross' and 'net' effects of the bed-net intervention on childhood mortality. Here we compare the mortality outcomes we might anticipate by re-analyzing the life tables constructed from the surveillance data using the new information on cause of death. Three different comparisons are made. One is to compare the mortality before the intervention with the mortality which would ensue if all the deaths whose main cause was judged to be malaria were eliminated. A second comparison is between this mortality in the absence of malaria with the observed mortality rates in the post-intervention period in the PHC villages. Finally, we can check our calculations by comparing these results with the data from the non-PHC villages.

Table 5 contains the basic data for these comparisons. Using the data from the PHC villages first, we can see that the elimination of all malaria deaths would reduce infant mortality to 83% of its pre-intervention levels but the reduction in the mortality of 1-4 year-olds would be much greater: 56% of preintervention levels. Moving to the last line of the table to consider the same comparison for the non-PHC villages, similar 15
Differences by socioeconomic status (i.e., consumption quintiles) in community coping mechanisms are not statistically significant except for evacuation routes by the end of 2021 and early 2023. The proportion of households with access to evacuation routes in the poorest quintiles was 6 percentage points lower than those in the middle- and high-consumption quintiles in 2023 (Figure 7c). By the end of 2022, a higher proportion of households had access to evacuation routes regardless of the consumption quintiles of households (Figure 7b). Following evacuation routes, shelters are the mechanism most available to households in the community. By early 2023, 45 percent of households reported an identified shelter to go to in the event of a natural disaster. This proportion was higher for households in the two bottom consumption quintiles than for those in the middle or higher quintiles (47 percent vs. 43 percent). These gaps were not statistically significant.

**Figure 7: Community disaster mechanisms by consumption quintiles (%) – (2021-2023)** a) By consumption quintiles (2021)                               b) By consumption quintiles (2022)

<!-- Start of picture text --> 63.460.2 63.967.766<br>56.3<br>45.347.546.5 42.444.443.5<br>24.922.823.8 21.718.920.1 28.624.8 26.5 24.221.422.6<br>9.18.9 9 9.49.59.5<br>Evacuation Shelter Trained Committee Emergency Evacuation Shelter Trained Committee Emergency<br>routes community plan routes community plan<br>Low consumption quintiles (Bottom 40 percent) Low consumption quintiles (Bottom 40 percent)<br>Middle & High consumption quintiles (Top 60 percent) Middle & High consumption quintiles (Top 60 percent)<br>Total Total<br>c) By consumption quintiles (2023)<br>65.963.1<br>59.5<br>47.343.5 45.1<br>26.524.9 25.6 2423.1 2...<br>10.812.111.5<br>Evacuation Shelter Trained Committee Emergency<br>routes community plan<br>Low consumption quintiles (Bottom 40 percent)<br>Middle & High consumption quintiles (Top 60 percent)<br>Total<br><!-- End of picture text --> Note a): Results from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Note b): Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Note c): Results for 2023 are from entire sample of wave 4.

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Before turning to an application of the vulnerability line to India based on synthetic panel data, we provide a validation example for synthetic panel data using survey data from Vietnam. Since we have true panel data for Vietnam, we construct synthetic panel data from the two panel halves of the VHLSS in 2006 and 2008, pretending that they are two different cross sections. Using the synthetic panels, we estimate the vulnerability line corresponding to a vulnerability index of 10 percent as D5,500,000, which is very close to that of D5,480,000 estimated based on the true panels. Estimation results are provided in Table 7, where estimates based on these synthetic panel data (Panel B) can then be compared against those based on the true panel data (Panel A). To keep household composition stable over the two periods for the synthetic panel data, we restrict heads’ age to the range 25 to 55 in the first cross section in 2006 and adjust accordingly for the second cross section (i.e., keep heads’ age in the range 27-57); we also do the same for the true panel data for better comparison.

While estimates are not perfect, they appear quite encouraging with the majority of the different consumption categories (i.e., two-thirds of the inner transitions and one half of the row and column totals) falling within the 95 percent confidence intervals the true estimates, which are presented in bold. Two-thirds of these bold-faced categories are in fact within one standard error of the true estimates. The two categories where the synthetic panels do not accurately predict are those who remain vulnerable or middle class over time. Still, for these two categories, the predictions are not far off with the differences between the estimates and true rates falling within 2 to 4 percentage points (which are roughly 7 to 14 percent in relative terms).<sup>25</sup> Coupled with the theoretical and extensive validation results for survey data from several different

> 25 Our validation is predicated on the assumption that the true panel data for Vietnam have good quality. If the mobility in the true panel data is partly caused by spurious changes due to measurement errors (or attrition bias) in household consumption, our estimates based on the synthetic panel data would be more accurate since cross sections are free of such data issues.

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marginal value of external funds (Morck, Wolfenzon and Yeung, 2005). This index ranges from 1 to 10, with larger values implying stronger protection of minority shareholders.<sup>10</sup> The final measure of property rights institutions concerning the financial markets is “WEF Access to Stock Markets,” which captures the ease of raising money by issuing shares on the local stock market. This indicator measures the strength of property right institutions because stock markets tend to be strongly regulated by the government, with entry being approved by government regulators. Easier access to stock market listing implies that non-elite investors have better access to the equity market and that the government is more non-discriminatory toward non-elite investors.

Table 2 lists our key measures of financial and institutional development and their definitions. These variables have different scales, so we standardize each of them into a new variable with a mean of zero and a standard deviation of one, to aid interpretation and comparison of relative magnitudes,. Coefficients for these standardized variables are directly comparable (Rodrik et al., 2004), and their summary statistics are not necessary to show.<sup>11</sup> In addition to these key financial and institutional variables, we sometimes also control for the following macro variables: gross domestic product (GDP) per capita, the growth rate of GDP per capita, population, and the inflation rate, all once-lagged. All of these variables are from the World Bank’s World Development Indicators database.

Table 3 provides definitions and summary statistics for the remaining variables. Overall, we can see that the external financing variables and other control variables all exhibit substantial variation as shown by their standard errors (relative to the means). On average, about 30% of firms’ working capital and 33% of their investment in fixed assets is financed by external sources. Of our sample firms, 28.8 percent are micro, 41.8 percent are small, 16.7 percent are medium, and 12.7 percent are large. Micro and small firms thus account for the vast majority (over 70%) of the sample. Foreign ownership of firms averages 8.6 percent, and state ownership only 0.7 percent.

> 10 This strength of investor protection index includes numerous sub-components measuring various aspects of conflict of interest regulation and shareholder rights.  “Creditor rights” and “Investor protections” are based on data from questionnaires administered to corporate and securities lawyers and verified through analysis of laws and regulations. For more details see http://www.doingbusiness.org/methodology/getting-credit and http://www.doingbusiness.org/methodology/protecting-minority-investors.

> 11 These variables do exhibit substantial variations. Before standardization, for instance, the Heritage Property Rights index has a standard deviation that is almost half of the mean.  Variations in financial development are even larger, with their standard deviations sometimes larger than their means.

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