rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7. Star graphs of household asset portfolios**

(Pre-earthquake and June conditions) <!-- Start of picture text --> Housing Index Housing Index<br>80 80<br>70 70<br>60 60<br>50 50<br>Durable Goods Index 40 Agricultural Assets Index Durable Goods Index 40 Agricultural Assets Index<br>30 Overall 30 Overall<br>20 Quintile 1 20 Quintile 1<br>100 Quintile 2 100 Quintile 2<br>Quintile 3 Quintile 3<br>Quintile 4 Quintile 4<br>Quintile 5 Quintile 5<br>Productive Assets Index Social Index Productive Assets Index Social Index<br>Education Education<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

Figure 7 first shows that physical capital, productive capital and agricultural capital have decreased after the quake in the directly affected areas. Indeed, physical capital has decreased after the earthquake in February, and increased slightly between February and June, though not yet to the pre-earthquake level. Productive capital has decreased during both time intervals, while agricultural capital has also decreased after the earthquake and between February and June. Second, Figure 7 shows that the correlation between the asset index and other indexes is generally positive, except with the social capital index (no correlation appears). The correlation is negative with the agricultural asset index. Finally, we note that the correlation between the asset index and the durable goods index has decreased after the earthquake: the between quintiles gaps are lower in June than they were before the earthquake.

# _(d) Directly affected areas_

Table 4 presents descriptive statistics for households living in directly affected areas. The food consumption score is calculated based on the number of different food groups consumed by the household, to represent diversity, and the number of times a week they are consumed. Notably, we observe that the average food consumption score is 55.79, with a standard deviation of 19.75. A majority of households is above the limit food consumption thresholds (the limit consumption threshold being 42 and the critical threshold being 26).

Table 4 also shows that, in June, 44% of the households in directly affected areas had received assistance and that 32% had received food assistance. 37% of the respondents’ houses 14
**Figure 4. The density of the district poverty rate**

A. The kernel density of the MOLISA poverty rate B. RDD manipulation test using local polynomial of districts density estimation <!-- Start of picture text --> 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>The 2006 MOLISA poverty rate (%) The 2006 MOLISA poverty rate (%)<br>kernel = epanechnikov, bandwidth = 4.5118<br>point estimate 95% C.I.<br>.03 .04<br>.03<br>.02<br>.02<br>Density<br>.01<br>.01<br>0 0<br><!-- End of picture text --> Note: This graph shows density of districts by the 2006 poverty rate around the threshold of 50%.

36
21

**Table 8: Panel Data Estimates: Bond Flows, 1988.01-1992.09** - **Latin America (6 countries)  Asia (7 countries).**

||**Lati**|**n America**||**Asia**|
|---|---|---|---|---|
||Nominal interest<br>rates|Real interest<br>rates|Nominal interest<br>rates|<br>Real interest<br>rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating<br>PCS|7.374*|12.708**|1.643**<br>(0.318)|1.599**<br>(0.315)|
||(3.534)|(3.328)|||
|Return|-0.046|-0.067|0.003|0.003|
||(0.137)|(0.139)|0.009|0.009|
|Price-Earning Ratio|-0.860*|**_-0.5010_**|-1.**171****|**-1. 101****|
||(0.412)|(0.406)|(0.426)|(0.428)|
|**PH**|-20.316**<br>(3.567)|-13.406<br>(3.052)|-10.001**<br>(3.205)|-0.494**<br>(3.218)|
|Adjusted R`|0.984|0.983|0.075|0.078|
|AI|-0.016|-0.037|0.035<br>(0.851)|0.040<br>(0.842)|
|NOBS|342|342|399|399|

Note: See Table 7. Two dummies are used for Latin America to carture the Brady Plan.

The impact of country-specific factors varies by type of flow and by region. For bond flows, the importance of the credit rating variable (in Asian countries) or the first principal component of credit rating and secondary market price (in Latin American countries) appears to be clearly established (see Table 6 and 8 as well as Table AS and A7 in Annex 2), but not for equity flows. Indeed, the "t" statistics of the first principal component of credit rating and secondary market prices are not significantly different from zero (at the 15 % level of significance) for equity flows to Latin America. In fact, the big standard errors for this countryspecific factor emphasize that multicollinearity is still a problem here.'" Among other countryspecific factors, the price-earning ratios are consistently significant across models, but the domestic stock market return variable behaves differently for Asia and Latin America. The 17 This is confirmed, for example by using Klein's rule (R<sup>2</sup> **<** R2i: 0.309 < 0.440) in Table 8 (first regression). Note that multicollinearity does not affect the index of global factors because the latter has big coefficients.
**Table 1** . Overview of the variables used to construct the MDP used in this study.

|**Group**|**Deprivation**|**Criteria**|**Weight**|**SDG**|**Source**|
|---|---|---|---|---|---|
|Education|Years of<br>Schooling|No eligible household member<br>has completed six years of<br>schooling.|2/12|4|SHSD|
|Education|School<br>attendance|Any child under 12 is not<br>attending school.|2/12|4|SHSD|
|Living standards|Sanitation|The household has unimproved<br>or no sanitation facility.|1/12|6|SHSD|
|Living standards|<sup>Drinking</sup><br>Water|The household does not have<br>access to tube well or tap water.|1/12|6|SHSD|
|Living standards|Electricity|The household does not have<br>access to electricity.|1/12|7|SHSD|
|Living standards|Housing|The household lives in jhupri or<br>kutcha housing.|1/12|11|SHSD|
|Health|Hospital|Hospital further than 20km away.|1/12|3|Own<br>analysis|
|Health|Health|Health center further than 20km<br>away.|1/12|3|Own<br>analysis|
|Health|Stunted|Child under 5 died in the past 5-<br>years.|2/12|3|MICS|

# **3.2 Risk analysis and quantification**

We perform a risk analysis, evaluating damages to housing, infrastructure assets and agricultural land. A risk analysis combines data on the spatial extent of the hazard with the location of exposed assets and land-use, after which the resulting damages are evaluated using specific vulnerability curves and reconstruction costs (Meyer et al., 2013). For the vulnerability curves and reconstruction costs, we have used country-specific data where possible, supplemented by data from countries with comparable characteristics as Bangladesh in terms of asset vulnerability ( _e.g._ engineering standards, building types). A brief overview with more details is included in Appendix D.

# **_3.2.1 Hazard data_**

We consider two types of hazards; cyclone-induced coastal flooding and extreme wind. For coastal flooding, we utilize modeled flood water levels from the Institute of Water Modelling (IWM) in Bangladesh (IWM, 2018). They developed a hydrodynamic model to simulate how extreme storm surges of different magnitudes propagate through the tidal channels in the coastal zone. The modeled flood water levels are then overlayed with a digital elevation model, assuming a complete inundation of unprotected areas and inundation of embanked areas (see below) above their design standards. This approach is known to be simplistic, in particular in terms of the inundation dynamics of the polders and the probability of polders breaching before design conditions are reached. However, given a lack of more refined data to include this failure 13
# **Latin America’s Growth: Looking through the Demand Glass**

Augusto de la Torre and Alain Ize<sup>⊗</sup>

**JEL classification codes:** 040, 054, F10.

**Keywords:** growth, convergence, Latin America, export-led growth, import substituting industrialization, commodity dependence, natural resource curse, export diversification.

> ⊗ Augusto de la Torre (adelatorre@worldbank.org and <u>apd2151@columbia.edu) is adjunct professor at Columbia</u> University’s School of International and Public Affairs (SIPA), Director of the Economics Research Center (CIEE) at the Universidad de las Américas (UDLA) in Quito, Ecuador, and consultant to the World Bank. Alain Ize (ai2372@columbia.edu) is adjunct professor at SIPA. This paper, which builds on De la Torre and Ize (2019, 2020a, and 2020b), was prepared for a book project sponsored by the UNDP and the Yale Center for the Study of Globalization. The authors gratefully acknowledge comments from Bernardo Acosta, Alejandro Castañeda, Santiago Levy, William Maloney, Samuel Pienknagura, Jorge Thompson Araujo, Andrés Velasco, and participants in seminars at El Colegio de Mexico and Tulane University.
In fact, in several countries in the region, judicial effectiveness has been made worse following the constitutional and other legislative changes towards independence of judges. In those countries, the new institution of Judicial Councils typically halted further reforms of the judiciary and soon negated the values in the name of which they had been put in place. The authors conclude that independence may have come too early and served to incapsulate existing inefficiencies.

Independence may allow judges’ biases and beliefs to influence judicial decisions. Copp et al (2022) explore the extent to which the implementation of a pretrial risk assessment instrument in a US first-instance court corresponded to changes in the pretrial processing of defendants. They find little evidence of reductions in detention lengths or increases in the use of nonfinancial forms of release following the tool’s adoption. This was largely attributable to the exercise of judicial discretion, as judges frequently departed from the tool’s recommendation using alternatives that were more punitive and often included financial conditions—particularly for Black and Latino defendants. Angelova et al (2022) similarly find that judges override default procedural decisions incorrectly in over 90 percent of cases. Discretion is, in other words, costly to effectiveness.

Second, there is sparse evidence that increasing judicial pay – another indirect measure of independence - improves court effectiveness. Choi et al (2009) argue that there is a general sentiment that judges are underpaid. The primary argument being made in favor of a pay increase is that it will raise the quality of judging. Theory suggests that increasing judicial salaries will improve judicial performance only if judges can be sanctioned for performing inadequately or if the appointments process reliably screens out low-ability candidates. However, federal judges and many state judges cannot be sanctioned, and the reliability of screening processes is open to question. Choi et al’s empirical study of the high court judges of the 50 US states provides little evidence that raising salaries would improve judicial performance.

More generally, there is mixed evidence to suggest that court budgets affect judicial effectiveness. Voigt and El Bialy (2013) analyze the determinants of judicial efficiency based on data of the 47 member states of the Council of Europe. They found three variables that had a negative correlation with judicial efficiency: (i) the increase in the budget of the judiciary, (ii) the introduction of court specialization, and (iii) the establishment of judicial councils, a body that 23
irregular components of compensation. In addition, the information on schooling and detailed occupation makes it possible to examine heterogeneity in the wage effects of revenue shocks across workers in different positions of the wage distribution, and how these effects are shaped by the skills of top executives.

In the empirical analysis, we find that unexpected demand shocks have a significant impact on sales, employment, investment and average wages. Using employer-employee panel data, we find that firm revenue windfalls, in the form of unexpected demand shocks, are partly transmitted to workers in the form of higher average wages, but in a highly unequal way, with most of the wage increases occurring close to the top of the within-firm wage distribution. Interestingly, firm adjustments are much larger for negative demand shocks. More specifically, a demand increase leads to higher sales, exports, investment, employment and wages when actual demand falls short of expectations, while we do not find similar statistically significant responses to demand increases above the expected level. This suggests the presence of short-run capacity constraints that make output harder to adjust upwards than downwards, and our results are consistent with firms setting short-run capacity according to expected demand.

Although parts of the aforementioned wage increases consist of increased overtime payments in exchange for more hours worked, we also find that unexpected demand shocks lead to significant increases in base wages and other wage components. This suggests that that firms tend to share the unexpected changes in revenue with their workers, which is inconsistent with perfectly competitive labor markets, and also suggests that firms have some degree of product market power. Our analysis reveals indications of several different channels of rent sharing. First, we find that increases in base wages are much stronger for firms with firm-level (as opposed to more centralized) wage agreements, which we take as an indication of collective bargaining power. Second, we also find somewhat stronger and more significant wage effects for longer-tenured workers, which might be caused by hiring or training costs that give individual bargaining power to this type of workers. Finally, we also find evidence of explicit profit-sharing arrangements whereby revenue windfalls are transmitted to workers through increases in performance-based payments. However, this seems to occur only in firms with high-skilled managers. This category of firms is also found to be the main driver of the aforementioned unequal rent distribution across workers within the firm, thus highlighting the importance of managerial skill.

In addition to the literature mentioned above, this paper relates to several strands of existing research. A large body of literature seeks to quantify and explain wage differentials across firms.<sup>2</sup>

> 2Influential contributions include, among many others, Abowd, Kramarz, and Margolis (1999) (henceforth AKM), Card, Heining, and Kline (2013), Card, Cardoso, and Kline (2015) and Song, Price, Guvenen, Bloom, and von Wachter (2019).

2
**Figure 10a: Prevalence of Adequacies, Resource‐poor households**

<!-- Start of picture text --> Adequacy status - Resource-poor<br>Recent year<br>Bangladesh (HK, 2010) Bangladesh (IFPRI, 2011) Bolivia (2008) Cambodia (2010)<br>3%11% 3%11% 9% 19% 22%<br>42% 34% 9% 36%<br>38%<br>41% 3% 52% 5% 20% 32% 10%<br>Ethiopia (2011) Indonesia (RKD, 2010) Nepal (2011) Peru (2012)<br>6% 3% 25% 1%12% 1% 21%<br>33% 55% 33% 24% 49% 40% 7%<br>6% 1%6% 31% 14% 32%<br>Zimbabwe (2010)<br>None<br>11%<br>Food only<br>29% 53% Care only<br>Environmental health only<br>7% Two of three adequacies<br>All three adequacies<br>Source: Author estimates. See Table 5 for the specific components used in each country for the adequacy measures.<br><!-- End of picture text -->

**Figure 10b: Prevalence of Adequacies, Resource‐rich households**

<!-- Start of picture text --> Adequacy status - Resource-rich<br>Recent year<br>Bangladesh (HK, 2010) Bangladesh (IFPRI, 2011) Bolivia (2008) Cambodia (2010)<br>5% 3% 16% 15% 15%<br>24% 28% 37% 9% 26% 32%<br>6% 25% 12% 2% 41% 39% 11%10% 4% 25% 12%<br>Ethiopia (2011) Indonesia (RKD, 2010) Nepal (2011) Peru (2012)<br>1% 7% 4% 28% 15% 3% 30% 1% 30%<br>30% 33% 3% 42%<br>56%<br>23% 1%<br>6% 3%1% 32% 14% 22% 17%<br>Zimbabwe (2010)<br>None<br>4%<br>17% Food only<br>9% 44% Care only<br>Environmental health only<br>18% 8% Two of three adequacies<br>All three adequacies<br>Source: Author estimates. See Table 5 for the specific components used in each country for the adequacy measures.<br><!-- End of picture text --> 37
23

# Diesel Particulates

Uncontrolled diesel fueled engines emit approximately 30 to 70 times more particulates than gasoline-fueled engines equipped with catalytic converters and burning unleaded fuel. Virtually all of these particles are small and respirable (less than 2.5 microns) and consist of a solid carbonaceous core on which a myriad of compounds adsorb. These include:

- unburned hydrocarbons;

- oxygenated hydrocarbons;

- polynuclear aromatic hydrocarbons; and

- inorganic species such as sulfates, sulfur dioxide, nitrogen dioxide and sulfuric acid.

Most of the toxic trace metals, organics, or acidic materials emitted from automobiles or fossil fuel combustion are highly concentrated in the fine particle fraction [Ozkaynak et al. 1986]. These emissions may cause cancer and exacerbate mortality and morbidity from respiratory disease [Clavel and Walsh 1984]. Health experts in FRG consider occupational exposure to diesel particulates a cancer risk in the workplace. A review by the Swedish National Institute of Environmental Medicine showed a statistically significant association between exposure to diesel exhaust and lung cancer among forklift truck drivers. The study indicated an excess risk of lung cancer among such truck drivers in the order of 30 to 50% [Per Camner et al. 1988].

# Aldehydes

Formaldehyde and other aldehydes are emitted in the exhaust of both gasoline- and diesel-fueled vehicles, and are a major emission component in the exhaust of alcohol-fueled vehicles. Aldehyde exhaust emissions from motor vehicles correlate reasonably well with exhaust hydrocarbon (HC) emissions. Diesel vehicles generally produce aldehydes at a greater percentage rate of total HC emissions than gasoline vehicles. Formaldehyde is also generated by photochemical reactions involving other organic emissions. Formaldehyde is of particular interest both due to its high photochemical reactivity in ozone formation and its suspected carcinogenicity. Formaldehyde can also be a short-term respiratory and skin irritant, especially for sensitive individuals [Carhart and Walsh 1987].

# Benzene

Benzene is present in both exhaust and evaporative emissions. Mobile sources (including refueling emissions) dominate the benzene emission inventory in most countries. For example, according to the U.S. EPA, roughly 70.2% of the total benzene emissions come from vehicles. Of the mobile source contribution, 70% comes from exhaust and 14% from evaporative emissions [Carhart and Walsh 1987]. Several epidemiology studies on workers have identified benzene as a carcinogen causing leukemia in humans [Carhart and Walsh 1987].
11 can be little doubt that electronically delivered services, which account for the bulk of electronically delivered products, fall within the scope of the General Agreement on Trade in Services (GATS). This is straightforward because the Agreement covers all services irrespective of the means of delivery.

There may, however, be some doubt about how electronically-delivered _media_ products should be classified. Some argue that their intangible state implies that they are best treated as services subject to GATS rules. Others argue that since their physical counterparts are covered by the rules of the General Agreement on Tariffs and Trade (GATT 1994)10, they too should be covered by the same rules. An example cited in support of the latter argument is that of a book, a product which is clearly identified in the customs classification systems for goods. The argument would be that since a book in physical form is a good, it makes sense to treat the electronic transmission of a book's contents as trade in goods, just as if the book had entered through normal customs channels at the frontier.<sup>1</sup> I We do not seek to provide an answer to how electronically-delivered _media_ products should be classified. WTO members are even now deliberating on this issue. Rather our purpose is to demonstrate the implications of alternative classification choices. The next section will show that treating media products "as _if'_ they were goods would imply a more liberal regime for cross-border trade in such products than if they were treated as services. But in the case of electronically-delivered services, there is no choice, the GATS applies.

# **IV.1 Implications of the differences in current WTO rules for goods and services trade**

There are many differences between the legal frameworks of the GATT, covering trade in goods, and the GATS, covering trade in services, but three in particular have a crucial bearing on the regime for electronic commerce (see the table below). The first difference is with respect to the national treatment rule, which obliges countries to treat foreign products no less favourably than domestic products. In the GATT, national treatment is a general obligation, but it applies only to internal measures, such as internal 9 See Panagariya (1999).

'° The original GATT 1947 as modified by the Marrakesh Agreement. "Guidance could also be received from the statistical convention for balance-of-payments purposes, which distinguishes between standardised and non-standardised products. A book or a mass consumption "over-thecounter" software package, for example, are standardized products and considered to be goods. But customised data on a CD, or customized software, would be treated as non-standardized products and classified as services. But this distinction may not be easy to make in practice. See Bacchetta, et al. (1997).
**<u>Table 1: Household characteristics, 1982, 1999, and 2008</u>**

||**1982**|**1999**|**2008**|
|---|---|---|---|
|**Household characteristic**||||
|Head’s age (years)|50.91|50.07|51.64|
|Head’s education (years)|2.20|4.29|4.97|
|Household size|7.35|6.53|5.68|
|# members >14 years old|4.65|4.38|4.01|
|Share of prime males|48.59|46.82|44.08|
|Share of prime female|43.37|43.10|42.76|
|<br>Share of elderly males|4.71|5.33|6.69|
|Share of elderly females|3.33|4.75|6.48|
|**Income and endowments**||||
|Area owned (acres)|8.64|5.60|4.46|
|Area cultivated (acres)|9.01|7.18|6.83|
|Owns machinery|0.07|0.20|0.32|
|... if yes, value (‘000 Rs./acre)|6.44|55.40|64.26|
|Total income (‘000 Rs)|30.71|65.65|79.96|
|Share from agriculture (%)|84.48|64.64|61.26|
|**Labor use**||||
|Using any labor for agric. production (%)|98.70|97.17|97.16|
|Using family labor for agric. production (%)|97.58|97.01|95.37|
|Using hired labor for agric. production (%)|70.01|72.13|77.86|
|Worked as agric. labor (%)|25.06|20.94|20.29|
|Worked as nonagricultural labor (%)|5.76|13.70|19.86|
|Engaged in nonagricultural self-employment (%)|6.41|7.86|17.55|
|**Cultivation details**||||
|Agricultural labor (days/acre)|80.81|52.58|51.39|
|Own labor (days/acre)|42.67|44.04|37.85|
|Male labor||28.25|26.49|
|Female labor||11.54|10.80|
|Hired labor (days/acre)|38.56|8.54|13.55|
|Male labor||5.33|8.72|
|Female labor||2.82|4.60|
|Rents or owns machinery|50.71|62.04|74.11|
|<br>... if yes, value (Rs./acre)|115.01|509.20|989.38|
|Share of who neither own nor hire tractors|0.49|0.38|0.26|
|**Village level variables**||||
|Agricultural wage (Rs/day )|15.00|45.35|47.59|
|Nonagricultural wage (Rs/day )|16.01|57.48|67.74|
|Number of observations|3,746|4,518|4,924|

_Source:_ Own computation from 1982, 1999, and 2008 ARIS-REDS surveys.

16
<!-- Start of picture text --> 200<br>fil<br>150<br>100<br>a<br>so<br>0<br>Jan, Jan, Jan, Jan, Jan,<br>_ 2009 2010 2011 2012 2013<br>6<br>~ —— agriculture @ minreal wage<br>=<br><!-- End of picture text --> <!-- Start of picture text --> 4000 60<br>3500 50<br>3000<br>ay 40<br>2500 ss<br>£5 2000 1 30 ba<br>Sea 20 *<br>1000<br>500 10<br>0 0<br>2009 2010, 2011-2012<br>Gum remittances recevied, min USD<br>—— remittances recevied, % GDP<br><!-- End of picture text -->