rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Average values of selected water and soil quality parameters in Punjab _By period and region, 1971-94_

||||_Soil_|||_Wat_|_er_|
|---|---|---|---|---|---|---|---|
|_System_|_Period'_|_Organic_<br>_matter_<br>_(°te)_|_Available_<br>_phosphorus_<br>_(ppm)_|_Soil pH_|_Soluble_<br>_salts_<br>_(No)_|_Residual_<br>_carbonate_<br>_(me/J)_|_Electrocon_<br>_-ductivity_|
||1|0.85|5.94|8.01|0.28|1.35|848|
|Wheat-mixed|2|0.68|5.00|8.33|0.34|1.83|943|
||3|0.57|5.01|8.39|0.45|2.66|1172|
||1|1.02|7.42|7.74|0.12|1.20|743|
|Wheat-rice|2|0.72|6.23|8.26|0.33|1.72|800|
||3|0.59|4.60|8.50|0.42|2.78|874|
||1|0.86|5.94|8.30|0.20|2.16|1010|
|Wheat-cotton|2|0.62|5.00|8.63|0.44|2.41|1099|
||3|0.54|5.01|8.41|0.41|3.03|1214|
||1|0.90|7.53|8.08|0.27|1.18|939|
|Wheat-mungbean|2|0.62|5.17|8.61|0.42|1.37|1006|
||3|0.53|4.83|8.50|0.38|2.00|1141|
||1|0.89|7.04|8.06|0.22|1.54|896|
|Punjab|2|0.66|5.46|8.46|0.39|1.90|993|
||3|0.56|4.83|8.44|0.42|2.67|1123|

= I = Green Revolution 1966-74 (In this table, however, the mean is only for 1971-74 as data for earlier years are not available) , 2 Intensification 1975-84, 3 = Post Green Revolution, 1985-94 _Source:_ Authors' own calculation 12
description of the firms that access international markets, estimations of the probability of firms becoming international, using cross-sectional and panel estimations. These estimations help to identify whether international firms have some common, distinguishable features relative to firms listed only in the domestic market. Finally, we compare the evolution of firm characteristics over time. These comparisons are useful to understand to what degree differences between international and domestic firms exist even before internationalization and how firm characteristics change over time, along the internationalization process.

The analysis shows that only a relatively small fraction of countries and firms use international markets. The firms more likely to go abroad are located in certain countries, specifically in those with bigger economies, higher income levels, and better macroeconomic, but worse institutional environments. International firms themselves tend to be larger, grow faster, have higher rates of return and have more foreign sales. In other words, the firms that internationalize tend to be drawn from a particular group of countries and seem different from other firms. The results suggest that certain characteristics related to countries and firms are important factors in the internationalization process, suggesting that it might be difficult for many firms to participate directly in international capital markets.

The analysis in this paper improves over related previous work. The dataset of firms, countries, and observations is very comprehensive and allows for a relatively complete study of the integration of capital markets at the firm level. By employing a large and diverse number of countries and firms over various years, the analysis characterizes well both country- and firm-level factors related to the likelihood of using international equity markets. Therefore, relative to the papers that use only country-level information or microeconomic data for a small set of countries, our analysis sheds light on which (including how many) countries and firms are able to capture the gains from internationalization. Moreover, the paper tests the importance of both macroeconomic and microeconomic variables in the process of going abroad. Additionally, the analysis captures a broader process of internationalization than before by including access to more than one international stock exchange and different forms of internationalization (capital raising, issuing, and trading). The period under study is also interesting because it 5
randomly assigned given workers’ baseline characteristics. In our specification all workers are ‘treated’ but they differ in the intensity of treatment, that is, their firm’s exposure to the GFC impact on GDP in destination markets. It is not possible to consider a control group of nontradable firms since for them the firm GFC shock is not defined. The inclusion of sector-year and region-year fixed effects allows to isolate the effect of the GFC firm shocks from other domestic or international factors, namely those related to the credit crisis ensuing from the GFC. We estimate Equation (2) by ordinary least squares (OLS), obtaining robust standard errors clustered by firm. These standard errors allow for a correlation in the unobserved components in the outcomes of workers that were employed by the same firm in 2007.

A potential concern with Equation (2) is that the estimated _βt_ could reflect differential preGFC trends across firms with different shocks even in the absence of the GFC shock and even after controlling for firm controls pre-GFC in _Xj_ 2007 (e.g., a downward trend in employment of severely shocked firms). The data however shows similar trends in firm employment and wages between 2004 and 2008 across firms experiencing shocks of different sizes (see Appendix Figure C2). This evidence suggests that we can interpret the _βt_ as causal effects of the GFC shock.<sup>22</sup> As dependent variable in Equation (2) our first worker outcome is a measure of average months worked per year from 2009 until the current year _t_ defined exactly as in Dix-Carneiro & Kovak (2019): where _monthsis_ is the number of months worker _i_ is formally employed in year _s_ .<sup>23</sup> Our second worker outcome is a measure of average real wages from 2009 until the current year _t_ as multiples of pre-GFC wages, also defined exactly as in Dix-Carneiro & Kovak (2019): where _wageis_ is the monthly real wage of worker _i_ in year _s_ obtained as the monthly nominal wage deflated by a consumer price index and _avgwageipre_ is the average monthly real wage of worker _i_ in 2004-2007 before the GFC.<sup>24</sup> Table 1 shows that in the post-2009 period, workers are employed on average 8.5 months per year and average real wages are 1.9 times the average real wages before the GFC.

> 22Appendix Figure C2 plots the evolution of pre-GFC firm employment and total wages averaged for firms in different quartiles of the distribution of the firm GFC foreign shock.

> 23The firm subscript _j_ is not included in the worker outcomes since their construction does not rely on information pertaining to the employing firm as of 2007.

> 24Consumer price indexes (with base year 2015) from Brazil’s statistical office are used as deflators.

12
“ _Nobody sits beside me in school. They do not like to talk with me, and do not share books. Nobody likes to play with me in school. When I play, some children shout ‘‘don’t touch her, don’t play with her, she’s got arsenic. I will not go to school_ .” Taslima, aged 10, a girl who developed black spots on her palms and soles on schooling (Quoted in Hassan et al. / _Social Science & Medicine_ , 2005).

# **1 Introduction**

In the parlance of the World Health Organization, arsenic has been responsible for “the largest mass poisoning of a population in history”. The victims are as many as 20 million Bangladeshis who have been drinking groundwater water contaminated with naturally occurring inorganic arsenic for nearly two decades (Rohde, 2005). Continuous drinking from arsenic-laced wells is highly cancerous and causes various other health disorders including birth complications. Lower doses of arsenic, consumed over years, can ultimately cause death. A low-cost solution to the problem <mark>is not <?> yet available</mark> in developing countries. No affordable technology exists that can either remove arsenic from drinking water or at least reduce it to a level at which it has no adverse effect on human well-being. Therefore, majority of the affected population continues to remain exposed to the crisis.

While the adverse effect of arseniosis on adult health is well-documented, little is known on the consequences of the catastrophic health crisis caused by drinking metal-contaminated groundwater for skill formation. For instance, children growing up in affected households are likely to be vulnerable to health shocks and hence have a poorer prospect for developing cognitive skills. Arsenic poisoning is also alleged to have created problems of exclusion for females at school and within the community and hence, has implications for their educational participation. This study therefore aims to test for the relationship between arsenicosis and child learning outcomes in Bangladesh. Most children are exposed to arsenic largely through fluid intake at home and in school. Therefore, we use primary data on test score for a large sample of secondary school students in rural Bangladesh and jointly investigate the effects of exposure to arsenicosis at home and school.

Bangladesh along with West Bengal (India) today constitutes the largest population in the world exposed to arsenic from drinking water. In both regions, children constitute nearly 2
# _HOUSEHOLD_

<!-- Start of picture text --> TRAV\<br>_ ONEV LOS CREDIT EUITIES<br><!-- End of picture text -->

# _BUSINESS_

## Fig. 9 Finaincial Instruments in lioustIhold atn Business Flows (Based on experience of six developinig counitries)

The tigtare shtuws tle relative size of annual flows into and out of each of the six types of financial instruments to and from the household and business sectors. The width of the nrrows indicates the size of the flows, thus households acquired money in an amount equivalent to 6.1% of GNP, while business obtained loans in an amount equivalent to 10.3% of _GNP_
(measured in either _GDP,_ or _ENR_ ), ii) the energy sources status ( _REN_ ), iii) climate vulnerabilities of the countries ( _IVUL_ ), iv) governance level of the countries ( _GOVR_ ), and v) Ease of Doing Business in the countries ( _EDB_ ).

While we use only six groups of right hand side variables, we still need to calculate some of them, and the dependent variable, using additional variables. Therefore, we report here all data we have used and which variables have used which variables in their construction. Annual GDP (both in current and 2000 constant US$ absolute and per capita terms), energy and electricity production, import, and consumption (both in absolute and per capita terms), and the volume of CO2 emissions (in terms of total kiloton of oil equivalent, per capita CO2, and CO2 per dollar of 2000 PPP GDP) data for all countries of the world during 1960-2003 are obtained from the World Development Indicators (WDI, World Bank, 2007).<sup>6</sup> In addition, country level estimates of total energy available from nonrenewable sources (e.g., coal, oil, gas, oil shale, and bitumen) and annual energy available from renewable (e.g., solar, onshore and offshore wind, hydro, geothermal, and biofuels) sources are obtained from Buys et al (2007).

These data are used to construct variables reflecting economic development, available energy resources, and emission intensity of the host and investor countries. Economic development variables include _Total GDP_ ( _GDP_TOT_ ), _Average GDP_ ( _GDP_AVG_ ), and _GDP per Capita_ ( _GDP_PC_ ). Energy resources of each country are constructed by adding up potential non-renewable and renewable energy resources and are represented by two variables: _Nonrenewable Energy_ ( _NREN_ ) and _Renewable Energy_ ( _REN_ ). In addition, two separate variables are constructed to reflect intensity of energy use in each country: _Avr Annual Energy Use_ ( _ENR_AVG_ ) and _Annual per Capita Energy Use_ ( _ENR_PC_ ). See Appendix 3 for further detailed description of the variables considered for this study and their specification.

Buys et al (2007) provide measures for countries’ vulnerability to climate change and emissions reduction mandates. They first construct individual indices for alternative sources of vulnerability and then calculate composite index values by categorizing the sources of vulnerability into two major groups: impact and source vulnerabilities. We use the first one in our empirical analysis. Impact vulnerability refers to the country’s ability to sustain climate change impacts such as weather damage and sea level rise. The quantitative score (scale of 1 to

> 6 Data on CO2 per dollar of 2000 PPP GDP were not available prior to 1975.

10
The most closely related paper to ours is by Ayalew (2005) who uses Raven’s CPM test scores for school-age children in one village in Ethiopia to measure child ability and using a household fixed effects model finds that parents consider child ability when making school enrollment decisions. There are several key differences between our papers. First, we focus on absolute and relative direct measures of the ability of a child’s siblings to generate inferences about the role of sibling rivalry in influencing schooling decisions. Second, we explore both alternative ability measures by using different cognitive tests and alternative outcomes such as school expenditures and grade progression, in addition to current enrollment, which is the focus of the Ayalew paper. Third, we exploit the panel data structure as a robustness check to address potential reverse causality concerns about schooling influencing measures of child ability.

# **3. Burkina Faso Social Protection Evaluation Survey**

The panel survey was conducted in June 2008 (Year 1) and June 2009 (Year 2) in Nahouri province in southern Burkina Faso, located approximately 100 miles from the capital and bordering Ghana. Households were randomly selected from a village-level census conducted by our project team immediately prior to the Round 1 survey in the 75 rural villages of Nahouri province that each has a primary school. The survey is part of an ongoing project evaluating social protection strategies in Burkina Faso. Households in this region are predominantly subsistence farmers growing sorghum and groundnuts and have mean annual per capita expenditures of approximately $90.

Our analysis focuses on primary school-aged children ages 5 to 15 in households with multiple biological children. There are 4,635 children in this age range in 1,507 different households. Poor households were oversampled for our survey; 89 percent of the sample is considered poor, an assignment based on a series of ten questions in the village census conducted 6
20 plausibility. The basic credibility of these results are enhanced by the stylized facts of four regions: SubSaharan Africa, East Europe, the "good" guys, and the LDCs as a whole. I give these examples not because the statistical results are driven by these countries (as the I showed above, the result is robust to the choice of sample) but because these examples enhance the credibility of the regressions.

The educational attainment of Africa's labor force actually grew at a faster percentage rate than any other region, including East Asia. This is partly because of the initial low base, but even its absolute growth in years of schooling is nearly as high as other regions. Yet the statistics on performance in SSA are well known (Easterly and Levine, 1995). Growth of GDP per worker in Sub-Saharan Africa was half that of Latin America and only a quarter that of the more rapidly growing regions. Again, this is not to say the empirical result is driven by SSA, as excluding it from the sample does not change the results. Rather SSA, contrary perhaps to intuition, did in fact accumulate a great deal of **educational capital over the last** three decades. But that this increased education appears not to have paid off in aggregate growth.

|Table 5: Regional<br>co|mparisons<br>of educa|tion<br>and growth||||||
|---|---|---|---|---|---|
||Educat|ional growth|||Growth ofoutput<br>k|
||||||per worer|
||Barro-Lee,|1960-1985|N-S-D, 1960-19|87||
|Region:<br>Regio:<br>*capital|Educational<br>growth|Absolute<br>increase in<br>**years**of<br>schooliniz|Educational<br>**capital**growth|Absolute<br>increase in<br>**years**of<br>schooling||
|Sub-Saharan Africa|4.16|1.11|4.56|1.97|.753|
|South Asia|3.73|1.44|2.54|1.66|1.05|
|Latin America|2.46|1.77|2.74|2.44|1.58|
|EastAsia and Pacific|12.81||2.57|4.00|12.83|3.66|
|North Africa,<br>Mediterranean|3.98|**2.38**<br>**I**|4.74<br>**I**|3.19|3.99|
|OECD|1.78|2.22|.603|**.973**|2.45|
term financing and have less access to long-term financing (e.g. Demirguc-Kunt and Maksimovic,1999; Fan, Titman, and Twite, 2010).

Next we use data on US listed firms from Compustat to study recovery from the 2008 financial crisis in the US. We then compare the US experience with that of the 3S episodes. The comparison with emerging markets is particularly relevant since Calvo and Kung (2010) draw parallels between the recovery process in the US crisis and past emerging market episodes and suggest that the US recovery is a new Phoenix miracle. Boone and Johnson (2008) commenting on the 2008 US financial crisis argue that “the evolution of the current crisis seems remarkably similar to emerging market crises of a decade ago.” Others (e.g. Claessens, Kose, and Terrones, 2009) have made the case for Phoenix Miracles in developed markets as well, with Krugman (2009) noting the role of self-financing in the partial recovery of Japanese firms after the turn of the century.<sup>5</sup> We have the following main findings: First, a closer look at the macro evidence suggests that there is heterogeneity across countries even amongst the Phoenix Miracles identified by the macro literature. In some countries like Thailand, there is no evidence of credit recovery even two periods after GDP recovery, suggesting a true Phoenix Miracle. However, some of the other episodes identified as Phoenix Miracles do not appear to be such even on the basis of macro evidence alone. For instance, while private credit lags GDP recovery by one period in most cases, in the instance of Korea and two crisis episodes in Turkey, private credit recovers at the same time as GDP calling into question whether they are truly Phoenix Miracles. In both Korea and the first crisis episode in Turkey in 1992-1996 (as well as in Russia though credit lags GDP recovery there), private credit far surpasses original credit levels before the crisis. Overall, of the 9 crisis episodes in our sample that were identified as 3S episodes in Calvo et al. (2006a), we are able to identify only 6 as potential miracles and 3 as non-miracles (Korea and the two Turkey crisis episodes) on the basis of macro statistics alone.

Second, we look at the existence of Phoenix Miracles at the micro level to examine if there is a recovery in aggregate sales without a recovery in aggregate short-term borrowing (according to the micro mechanism suggested by Calvo et al., 2006b). We analyze the financial

> 5 See also Calomaris, Klingebiel,and Laeven ( 2005), and Claessens, Klingebiel,and Laeven ( 2005) for  a discussion of the resolution of developing country crises.

4
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China imposes at least one NTM in 100% of the about 5,000 products at the HS 6-digit classification level, followed in incidence by the EU (90.1%), while Indonesia and Japan have less than 50% of the product lines affected by these types of measures. The coverage ratios show high levels of protection in China (100%) and the EU (86.2%).

To conduct the analysis at the sectoral level, we group NTMs into nine categories: A: SPS measures; B: TBTs; C: Pre-shipment inspection and other formalities; D: price control measures; E: licenses, quotas, prohibitions, and other quantity control measures; F: charges, taxes, and other para-tariff measures; H: anti-competitive measures; N: intellectual property; and P: export related measures. Tables 7-10 present frequency ratios.

The study reveals that China applies SPS measures, TBTs, price control measures, and quantitative controls to 100% of its products in sectors such as food; chemical; rubber, plastics, wood & paper; textile & footwear; base metal; and machine & equipment (Table 7).

Table 7. NTMs in China

|China|Food|Chemical|Rubber Plastics Wood Paper|Textile & Footwear|Base Metal|Machine &Equipment|
|---|---|---|---|---|---|---|
|A|100%|100%|100%|100%|100%|100%|
|B|100%|100%|100%|100%|100%|100%|
|C|0%|0%|2%|0%|2%|78%|
|D|100%|100%|100%|100%|100%|100%|
|E|100%|100%|100%|100%|100%|100%|

Source: Authors’ calculations using data from UNCTAD accessed through WITS (2012).

In the EU, most of the NTMs are SPS measures or TBTs (Table 8). In particular, 86.7% of the product lines associated to food are affected by SPS measures, while 85.4% of them have at least one TBT. For the rest of the sectors, TBTs are the most common measure. For example, more than 90% of the product lines corresponding to chemical, textile & footwear, and machine & equipment have at least one TBT. The rubber, plastics, wood & paper sector has the lowest percentage of products affected, 60.7%. Preshipment inspections are relatively important in the base metal and textile & footwear sectors, affecting 15.9% of the product lines in each of these categories.

Table 8. NTMs in the European Union Source: Gourdon and Nicita (2012).

Indonesia applies different categories of NTMs depending on the sector (Table 9). Contrary to what is observed in the case of the EU, there is not a particular type of NTM that predominates. Most of the NTMs in the food sector are SPS measures, TBTs, and pre-shipment inspections, which affect 67.9%, 33.5%, and 12.8% of product lines, respectively. The textile & footwear sector has 63.4% of its products affected by pre-shipment inspections and 65.8% by quantitative controls. More than half of the about 813 product lines in the chemical category are affected by TBTs. Machine & equipment and base metal are the sectors with lower barriers. In both cases, quantitative controls are the category of NTMs most commonly applied, 14.3% and 26.3%, respectively.

11