rafmacalaba/datause-encoder-data huggingface dataset:
To control for the possible effect of weather shocks, we use data from the Global Precipitation Climatology Project (GPCP) (Miguel et al. 2004; see also Huffman et al. 1995, 1997).<sup>11</sup> These series are not complete—we have rainfall data for 797 of the 907 country-year observations for which we can calculate infant mortality, although there does not appear to be a common pattern of countries and years with missing rainfall data. We take two approaches to assessing whether our basic results are sensitive to the inclusion of data on rainfall. Our first approach identifies as “dry” periods those years in which rainfall was below the 10<sup>th</sup> percentile of the annual rainfall data for a particular country, and as “wet” periods years in which rainfall was above the 90<sup>th</sup> percentile of observed rainfall.<sup>12</sup> We create indicator variables for births in wet and dry years and then include them in a specification similar to Equation (7) along with interaction terms between these indicators and log per capita GDP. In our second approach we simply include the continuous precipitation measure as a regressor along with an interaction term with log per capita GDP. The coefficients on log per capita GDP (analogous to the coefficients in Table 2) are presented in the second panel of Table 3. These results make clear that adjusting for rainfall has a negligible effect on the main results.<sup>13</sup> In the next panel of Table 3, we explore the possible effect of conflict. We consider two definitions of conflict. The first of these is based on Sambanis (2000). We define as a conflict any war event (internal or external) that, over the course of the conflict, killed 0.5 percent or

> 11 The GPCP data rely on a combination of actual weather station rainfall gauge measures, as well as satellite information on the density of cold cloud cover (which is closely related to actual precipitation), to derive rainfall estimates, at 2.5 latitude and longitude degree intervals. The units of measurement are in millimeters of rainfall per day and are the average per month. We multiply each monthly average by the number of days in a given month, which gives us an estimate of total monthly rainfall. We then sum all of the total monthly estimates in a given year to generate an estimate of total yearly rainfall. Next, each yearly rainfall estimate per 2.5 latitude / longitude degree node is averaged over all nodes in a given country to produce an estimate of total yearly rainfall per country. 12 For example, in Bangladesh we have rainfall and infant mortality data between 1983 and 1999. Mean annual rainfall over this period was 1559 milliliters per year. In 1983 and 1984, rainfall was below the 10<sup>th</sup> percentile for the Bangladesh series (1180 and 1260 milliliters, respectively), and in 1993 and 1998 rainfall was above the 90<sup>th</sup> percentile for the series (1821 and 1914 milliliters, respectively).

13 Infant mortality tends to be higher in “wet” years. However, the interaction between the rainfall terms and GDP is small and insignificant.

15
**Table 3 Economic growth and economic freedom, civil and political rights and economic and entitlement rights – panel estimates.**

||**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|**Coefficient**|
|---|---|---|---|---|---|---|---|---|
|**Column**|1||2|3|4|5|6|7|
|**initial EF**|1.415***|||1.152***||1.358***|1.116***|2.671***|
|**dEF(-1)**|1.032**|||1.039**||1.069**|1.072**|-0.193|
|**initial CPR**||1.087***||0.737***|0.979***||0.691***|0.941**|
|**dCPR(-1)**||0.519*||0.700**|0.548*||0.699**|0.548*|
|**initial ER**|||-0.711***||-0.546***|-0.428**|-0.353*|-1.469***|
|**dER(-1)**|||0.008||-0.071|0.312|0.188|0.213|
|**initial GDP per**|-1.072***|-1.436***|-0.659***|-1.181***|-1.014***|-1.048***|-1.149***|-8.474***|
|**capita**|||||||||
|**Tropics**|-2.752***|-3.647***|-3.407***|-2.418***|-3.325***|-3.109***|-2.733***||
|**Resources**|1.471*|3.925***|0.196|2.623***|2.195***|1.613**|2.659***||
|**Remoteness**|-0.241***|-0.445***|-0.336***|-0.243***|-0.328***|-0.245***|-0.246***||
|**Constant**|-4.021**|-0.863|6.591***|-6.283***|1.585|-1.922|-4.406**||
|**No of obs.**|511|624|573|508|564|511|508|508|
|**No of countries**|121|136|121|121|121|121|121|121|
|**R-sq:**|||||||||
|**within**|0.087|0.146|0.162|0.118|0.170|0.102|0.131|0.237|
|**between**|0.435|0.308|0.280|0.425|0.340|0.426|0.418|0.075|
|**overall**|0.138|0.146|0.102|0.173|0.159|0.147|0.178|0.004|
|<br>***** significant at 1%, **|<br>**** significant**|**at 5%, * signifi**|**cant at 10***||||||
|**Dependent variable:**|**average annu**|**al growth of G**|**DP per capita i**|**n constant USD**|**over 5 year in**|**tervals from 1**|**975 to 2005.**||

24
**Figure 12. Continuous Treatment Effects of Exports and Related Exports on Employment**

<!-- Start of picture text --> Coefficients on Direct Exports and Related Exports<br>Dependent variable: change in log employment<br>(Shock: October -November 2008)<br>0.08 1<br>0.06 0.8<br>0.6<br>0.04<br>0.4<br>0.02<br>0.2<br>0<br>0<br>-0.02<br>-0.2<br>-0.04 -0.4<br>-0.06 -0.6<br>coef: direct export shock coef: related export shock<br>Stayers<br>Coefficients on Direct Exports and Related Exports<br>Dependent variable: change in log wages of stayers<br>(Shock: October -November 2008)<br>0.03 0.3<br>0.25<br>0.02<br>0.2<br>0.15<br>0.01<br>0.1<br>0 0.05<br>0<br>-0.01<br>-0.05<br>-0.1<br>-0.02<br>-0.15<br>-0.03 -0.2<br>coef: direct export shock coef: related export shock<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text -->

**Figure 13. Continuous Treatment Effects of Exports and Related Exports on Wages of Stayers**

45
job, including farming, non-farm self-employment, casual work and non-farm regular wage employment, to reflect the potentially different suitability of alternative job opportunities from women’s point of view.

Table 2: Description of variables used in the empirical analysis

|**Variables**<br>**Definition**|
|---|
|**Dependent Variable**|
|Labor Force Participation (Usual Status) for persons aged 15 years and above|
|**Independent Variables**|
|**Individual Variables**|
|Age<br>Age (Years)|
|age_sq<br>Age Squared (Years)|
|schooling<br>Number of years of schooling (Lahiri et. al. 2013)|
|schooling_sq<br>Schooling Years Squared|
|marital_dummy<br>Whether currently married or not|
|**Household Variables**|
|log_landown<br>Log of Land Owned by Household (in hectares)|
|log_hhsize<br>Log of Household Size|
|children_under_6<br>Share of Children below 6 years in the household|
|children_above_6<br>Share of Children 6 years or more in the household|
|female_adult<br>Share of female adults in the household aged 15-59|
|female_dependent<br>Share of female dependents (aged 60+) in the household|
|male_dependent<br>Share of male dependents (aged 60+) in the household|
|female_hh_dummy<br>Household head is a female or not|
|max_schooling<br>Maximum schooling of household|
|st_dummy<br>Household belong to Scheduled Tribes or not|
|sc_dummy<br>Household belong to Scheduled Castes or not|
|obc_dummy<br>Household belong to Other Backward Castes or not|
|hindu_dummy<br>Household belongs to Hindu religion|
|muslim_dummy<br>Household belongs to Muslim regilion|
|**Time Variable**|
|survey<br>NSS61 =1, NSS68=2|
|**Indicator Variable for Location**<br>Average size of village in substratum for rural, Type of stratum for urban|
|rank<br>Population (0-999=1, 1000-4999=2, 5000-9999=3, 10000>=4) for rural|
|Below million=5, Million plus=6 for urban|
|**Misclassification of urban areas**|
|gap<br>Urban share in Census (based on 5000 population)-Urban share in NSS, district level|
|**Employment Variables (Substratum /district level for rural , district level**|
|**for urban)**<br>**all=Usual Status, Working Age Population =Population 15 years and above**|
|all_farmers_share<br>Share of Agricultural Self Empoyed and Regular workers aged 15+ in working age population|
|all_non_farm_self_share<br>Share of Non Farm Self Empoyed workers aged 15+ in working age population|
|all_casual_share<br>Share of All Casual workers aged 15+ in working age population|
|all_non_farm_regular_share<br>Share of Non Farm Regular Wage workers aged 15+ in workingagepopulation|

20
Table S2.5 Relative Supply of Education

|||||Re|lative s|upply||||
|---|---|---|---|---|---|---|---|---|---|
||HS/p|rimary|or less|Colleg|e/prima|ry or less|Co|llege/|HS|
|Country|1995|2002|2015|1995|2002|2015|1995|2002|2015|
|Argentina|0.16|0.22|0.41|0.50|0.62|0.97|0.31|0.36|0.42|
|Bolivia|0.15|0.11|0.34|0.37|0.36|0.96|0.41|0.29|0.35|
|Brazil|0.06|0.09|0.24|0.21|0.36|0.78|0.30|0.24|0.30|
|Chile|0.19|0.27|0.56|0.61|0.85|1.39|0.32|0.32|0.41|
|Colombia|n.a.|0.17|0.30|n.a.|0.49|0.96|n.a.|0.36|0.31|
|Costa Rica|0.10|0.13|0.17|0.27|0.30|0.53|0.35|0.44|0.32|
|Ecuador|n.a.|0.07|0.24|n.a.|0.51|0.72|n.a.|0.13|0.33|
|Guatemala|n.a.|0.04|0.04|n.a.|0.16|0.24|n.a.|0.24|0.16|
|Honduras|0.02|0.03|0.06|0.16|0.18|0.24|0.14|0.16|0.24|
|Mexico|0.08|0.10|0.20|0.24|0.30|0.38|0.34|0.35|0.53|
|Nicaragua|0.04|0.08|0.13|0.15|0.22|0.34|0.31|0.34|0.39|
|Panama|0.10|0.12|0.28|0.57|0.59|0.84|0.18|0.20|0.33|
|Peru|0.25|0.31|0.52|0.66|0.73|1.16|0.38|0.43|0.45|
|Paraguay|0.05|0.05|0.23|0.21|0.27|0.65|0.23|0.18|0.35|
|El Salvador|0.03|0.07|0.09|0.22|0.29|0.44|0.16|0.25|0.20|
|Uruguay|0.09|0.11|0.19|0.13|0.35|0.38|0.65|0.31|0.52|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Relative supply of individuals in the working age population (16-65). All education categories (college, high school, and primary education) follow country-specific classifications for university degrees, secondary education, and primary education, defined in each household survey. The college educated labor force comprises workers who completed a university degree or higher. “High school” includes complete secondary education and incomplete college education. “Primary or less” includes no formal education, incomplete primary, complete primary, and incomplete secondary education.

56
However, even though this form of data collection solves the problem of underreporting or misreporting, it raises a problem for recovering the full wage (income) distribution, which is key to understanding and analyzing inequality.

To better use the information from these types of surveys, we propose an imputation approach to simulate the distribution of the data that is only available in brackets. The method is an extension on the imputation approach described in Royston (2007), which considers heteroskedastic errors to model the conditional distribution of the censored data. The estimated conditional distribution is then used impute the data using draws from the estimated conditional distribution. Once the imputed data is obtained, standard aggregation methods (Rubin, 1987) can be used to analyze the censored data as if it were fully observed. For example, it can be used calculate poverty or inequality measures, as well as perform regression analysis. To demonstrate the flexibility of this approach, we use a Monte Carlo simulation to analyze the sensitivity of our method. As an empirical example, we use the approach to analyze wage inequality in Grenada utilizing the country’s Labor Force Survey.

Other approaches exist in the literature, and have been used for analyzing this kind of data. To measure income inequality with right-censored (top-coded) data, Jenkins et al. (2011) propose multiple-imputation methods for estimation and inference where censored observations are imputed using draws from a flexible parametric model fitted to the censored distribution, such as Generalized Beta of the second kind (GB2), Sigh-Maddala or Dagum distributions. Chen (2017) provides a generalized approach for the estimation of parametric income distributions using grouped data, showing its consistency through complementary simulation results. More recently, Walter and Weimer (2018) propose an iterative kernel density algorithm that generates pseudo samples from the interval-censored income variable to estimate poverty and inequality indicators. While the interval regression approach we propose fits with the models described in Chen (2017), Jenkins et al (2011), and Walter and Weimer (2018), these papers focus on recovering the unconditional distribution of income, without considering the relationship with explanatory variables.

Zhou et al. (2017) and Chih-Yuan et al. (2021) propose methodologies for the estimation of conditional quantile regressions using interval censored data, under different distributional
# **I. Introduction**

Nearly ninety countries around the world have reformed their bankruptcy codes since World War II and over half of them have done so during the last decade. The extent to which these reforms will succeed depends on their design and the context in which the new codes are binding (Claessens, S., et al., 2001; Franks and Loranth, 2005; Hart, 2000 and World Bank, 2004, 2005 and 2006). Yet, all countries seek to improve the efficiency of the bankruptcy system by encouraging the reorganization of viable firms and the liquidation of unviable ones. This requires a delicate balance (White, 1989). On the one hand, if the law is lenient towards failing firms, it will inevitably allow inefficient firms to continue operations. On the other, if the law favors liquidation, it will also liquidate viable firms.

Due to this trade-off, the design of bankruptcy laws is still a much debated topic (Claessens and Klapper, 2003; Smith and Strömberg, 2004 and references therein). The debate has focused on comparing two stylized bankruptcy proceedings: cash auctions or liquidations and structured bargainings or reorganizations. Critics of reorganizations argue that conflicting interests among claimholders and managers lead to excessively lengthy and costly negotiations (Baird, 1986; Aghion et al., 1992). Critics of liquidations claim that they contribute to the inefficient sale of assets due to market illiquidity and transaction costs (Shleifer and Vishny, 1992; Aghion et al., 1992).<sup>1</sup> While there is a growing literature estimating the costs of bankruptcy (Bris et al. 2005 for a review), there is little empirical evidence assessing how these costs affect the ability of laws to separate viable businesses from unviable ones, a key to ensuring efficiency. This paper attempts to fill this gap by using a bankruptcy reform that took place in Colombia.

In the midst of financial crisis, facing a backlog of failing businesses entering a very inefficient bankruptcy process, Colombia adopted a new reorganization code in late 1999. This law, known as Law 550, streamlined the reorganization process by

> 1 Using data from Sweden, Stromberg (2000) show that liquidations also suffer from conflicts of interest among claimholders.

2
domestic sales, firms also optimize their output decision between exports to the three possible export regions, based on relative prices the three regions and their constant elasticity of transformation production function for shifting output between the regions.

Tourism is one of the sectors in this category, and given its importance in the Tanzanian economy, it deserves its own discussion. All sales to tourists are considered exports, i.e., the sector does not produce for the domestic market. Based on a survey (discussed below), we have that the inputs of the sector are domestic transportation services, hotel and restaurant services, clothing, gold items, and certain food products, as well as foreign inputs such as international travel expenditures. Given that we have data only on tourist expenditure by sector and on tourism imports and exports, we assume that the output of this sector is produced without value added. But the tourism sector demands domestically produced services and goods that lead to valueadded in the Tanzanian economy. Moreover, the sector is intensive in services and this way we capture the impacts on services liberalization on services exports. We discuss the special data issues related to this sector in the data section below.

# **Goods Produced Subject to Increasing Returns to Scale**

These goods are differentiated at the firm level. We assume that manufactured goods may be produced domestically or imported for firms in any region in the model. Firms in these industries set prices such that marginal cost (which is constant) equals marginal revenue; and there is free entry, which drives profits to zero. For domestic firms, costs are defined by observed primary factor and intermediate inputs to that sector in the base year data. Foreigners produce the goods abroad at constant marginal cost but incur a fixed cost of operating in Tanzania. The cif import price of foreign goods is simply defined by the import price, and, by the zero profits assumption, in equilibrium the import price must cover fixed and marginal costs of foreign firms. Domestic firms set prices using the Chamberlinian large group monopolistic competition assumption within a Dixit-Stiglitz framework, which results in constant markups over marginal cost for both foreign firms and domestic firms.

We have made one significant modeling extension in the imperfectly competitive sectors compared to the Jensen, Rutherford and Tarr (2007) model. In the Jensen, Rutherford and Tarr model, domestic firms faced a perfectly elastic demand curve on export markets and they 27
# _2. Datasets_

_Overview of Existing Datasets_ Before the 1990s, standardized firm-level business surveys spanning multiple countries were practically non-existent. This began to change with an initial series of largely selfcontained projects which carried out business surveys for certain sets of countries and with various thematic scopes.

Four key projects of that period were sponsored by the World Bank: First, a first set of Africa-focused surveys carried out from 1992 to 1995 by the Africa Regional Program on Enterprise Development (RPED); second, the first round of the Business Environment and Enterprise Performance Survey (BEEPS) for 22 transition countries in 1999; third, the World Business Environment Surveys (WBES), implemented for 80 countries and the West Bank/Gaza territories from late 1998 to early 2000; fourth, a number of Firm Analysis and Competitiveness Surveys (FACS) in the Development Economics Research Group (DECRG). While these projects yielded unprecedented and highly useful data for the countries and issues they were designed for, they suffered from limited comparability amongst each other due to differing questionnaire designs and priorities.

The key development of the early 2000s was a push for greater standardization in order to build up a single, centralized database of comparable business climate surveys from around the world. For this purpose, a set of core questions was ‘pooled and consolidated’<sup>24</sup> from the earlier surveys. This set of core questions became the crucial component of the new, standardized business climate questionnaires known as Productivity and Business climate Surveys (PICS). In a specific country survey, around 50-60% will consist of the core modules (some 80 questions), the rest of nationally specific ones that can be added flexibly to the core instrument depending on each country’s data needs. The core instrument was also partly incorporated into the latest rounds of surveys that had started earlier, for instance BEEPS, the second and third round of which contain most of the core PICS questions.

Launched in 2001,<sup>25</sup> the new PICS surveys have been used to acquire detailed firm-level data in 15 to 20 countries a year. The results have been collected in a central database (www.enterprisesurveys.org) along with those of earlier, comparable projects such as BEEPS II and III. All surveys in this database are now commonly referred to as Enterprise Surveys (ES),<sup>26</sup> although the old terminology (PICS, BEEPS etc.) persists to some extent. The database currently holds information from almost 70,000 firms from

> 24 <u>http://iresearch.worldbank.org/InvestmentClimate</u> [‘About ICS’, 07/22/2008]

> 25 - Hallward-Driemeier, M. and R. Aterido. 2007. [http://www.businessenvironment.org/dyn/be/docs/158/Hallward <u>Driemeier.pdf, 07/21/2008]: 5.</u>

> 26 Not to be confused with the World Business Environment Surveys (WBES) mentioned above, which were a one-off project in 1999-2000.

8
<!-- Start of picture text --> °% 2 4 6 a 10<br>Decile<br><!-- End of picture text --> <!-- Start of picture text --> ° % 2 4 6 8 10<br>Decile<br><!-- End of picture text --> <!-- Start of picture text --> p=0.8<br>0 2 4 6 8 10<br>Decile<br>[—e— TrueDecies © — Deciles of F, |<br>QTT at Deciles<br><!-- End of picture text -->

Hower, the estimated variance of treatment effects increased slightly and as a result is this ca e, the estimated variance increases further and is significantly different from the true wider, but are qualitatively quite similar.

Figure 11 plots the true distribution of treatment effect and the deconvolution esti= 0 or 0.1, the estimated distribution is slig tly more dispersed but generally similar to the true distribution of treatment = 0.8, the estimated distribution is even more ver dispersed. The three gures in F gure 11 plot the deciles of the true distribution of treatment effects, the de onvolution esti ate of the distribution of treatment effects, and estimated , respectively. Similarly to Figure 10, the estimates are quite similar when . In both cases, the estimated deciles of the distribution of treatment effe tsa quite similar to the deciles from the true distribution. The correlation between the es ma ed and true deciles is about 0.92 in both cases. The deciles so that the deconvolution assumptions are violated, the deciles of the estimated di ribution of treatment effects are still quite similar to the true iles as the deconvolution estimates, whereas the QTTs were less correlated with the true