rafmacalaba/datause-encoder-data huggingface dataset:
Table 1. Sample composition

||All firms<br> <br>||
|---|---|---|
||2008<br>2014|Total|
||Panel A. Sample composition by <br> <br>|size<br>|
|Small (0-19)|3,811<br>1,806|5,617|
|Medium(20-99)|1,110<br>665|1,775|
|Large (100 and above)|126<br>145|239|
||Panel B. Sample composition by|sector|
|Manufacturing|2,621<br>1,427|3,837|
|Services|2,426<br>1,196|3,419|
|Total|5,047<br>2,623|7,670|

Source: Enterprise Surveys.

# **Output**

We associate firm output with sales, as the surveys have no information on inventory management. Hence, we use the terms sales and output interchangeably. Total sales increased between both periods, but the behavior was different between exporting firms and those that produce for the domestic market (Figure 1). Sales go up between 2008 and 2014 driven by the increase in domestic markets while sales of exporting firms decreased in the same period. The fall in the exporter firm sales may be explained by the resource curse of the oil boom registered during the period of analysis; these micro-level data coincide with falling non-oil and gas exports registered at the macro level.<sup>2</sup> Sales increased across all the regions except in the East regions (North East and South East) where sales go down between 2008 and 2014.

Production became more concentrated at the sector level between 2008 and 2014, as implied by the Herfindahl_Hirschman Index (HHI): the increase in concentration is more notable in the services sector (Figure 2), which as will be shown in the next section, is the one with highest productivity. The concentration of production at the sector level may give rise to economies of scale, which in turn explains productivity growth vis a vis other drivers of productivity growth such as resource reallocation towards more productive sectors (World Bank, 2017).

> 2 See the CID-Harvard Atlas data for Nigeria exports at the product level in 2008 and 2014.

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0.7 hours more a week. The difference in enrollment rates is statistically significant at the five percent level. Looking at the subsamples, the signs of the point estimates are consistent with the overall averages, but the magnitude of the effect is stronger in San Cristobal as well as for girls. In fact, the differences in attendance and enrollment rates are statistically significant for both subgroups at the five percent level. These differences may reflect that families have reallocated resources between registered children (and away from girls in particular) due to the treatment.

Estimating the effects on non-registered siblings is more difficult because families can vary both in the number of students they register and the number of children in the household. It is possible, for example, that receiving a subsidy may generate a larger change in a smaller household because educational resources are divided over fewer children. At the same time, it would be incorrect to simply estimate the relationship between the outcomes of unregistered siblings on the number of treated children because the differences between registered and unregistered children suggest that the selection of which children families register is endogenous. To account for both of these possibilities, we estimate a variation of the instrumental variables model provided by Equation 3 in which we model the outcomes of unregistered children as a function of the fraction of treated children in the household and use the fraction of registered children receiving a treatment as an instrument.<sup>18</sup> In total, we include 24,552 school aged siblings in our data set between the ages of 7 and 22.<sup>19</sup> On average, each family has 2.5 school aged children and receives 1.15 subsidies. Thus, the average percentage of treatment in a family is 46 percent, and since the vast majority of families receive only 1 treatment, the variation in treatment density is driven primarily by differences in family size.

The results of these estimates are presented in Table 10. Given the large sample, we use the same format as Tables 5, 6, and 7, estimating the effects of each treatment individually. We also provide the average outcomes for unregistered siblings in families receiving no treatment under the heading “control average” for reference. To facilitate interpretation, the point estimates are also rescaled based on the average treatment density in each sub-sample so the presented results provide the effect experienced by the average household.

> 18 We have also estimated the simple treatment effect on siblings by comparing families that received and did not receive a treatment while registering only one child.  The estimates are similar to those presented in Table 10. 19 We chose the age range for school-aged children by using the range of ages for children who report being enrolled in school in the SISBEN survey.  The results are consistent for other choices of the age range.

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# **Figure 11 Relative profitability of SOEs**

(percentage points relative to private firms)

# **Figure 12 Return on assets of industrial firms, aggregate data**

(profit as a share of total assets, percent) <!-- Start of picture text --> ROA Leverage<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> <!-- Start of picture text --> State owned State holding<br>Private and foreign<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2001 2004 2007 2010 2013 2016<br><!-- End of picture text --> Source: Authors’ regression estimates based on NBS’s Source: NBS; authors’ calculations. Industrial Enterprise Survey. Note: The figure plots the coefficient on the ownership dummy by year.

Starting in 1979, SOEs were given more autonomy and non-SOEs were allowed to enter the market, creating competition. After 1992, China prioritized ownership reforms, including passing the 1992 Company Law and 1993 Competition Law and developing an institutional framework for a business sector with diversified ownership. The authorities also allowed SOEs to shed redundant workers in the process of ownership change. In 1995, the policy of “grasping the large, letting go of the small” deepened SOE reform. In 2003, the State-owned Assets Supervision and Administration Commission (SASAC) was created with the goal to preserve and increase state-owned assets through restructuring and consolidation.<sup>17</sup> In line with other studies (e.g., Brandt and Zhu, 2010; Hsieh and Song, 2015), our results show that labor productivity and efficiency at manufacturing SOEs improved after 1998.

However, our findings also show that industrial SOE performance—both in terms of efficiency and financially—worsened after 2007. According to the multitask theory of SOE reform of Bai et al. (2006), SOEs are assigned a mix of commercial and policy objectives. As a result, SOEs play an important role in macroeconomic stabilization, maintaining social stability, investing in public infrastructure and in underdeveloped regions, which has come at the expense of financial performance. To be able to provide these public goods, SOEs have benefited from soft budget constraints, i.e. preferential access to government subsidies, tax exemptions, credit, and land (Kornai et al., 2003). The deterioration in SOE performance could be related to the fact that SOEs were called upon to fulfill their public policy objectives in the aftermath of the global financial crisis.

> 17 Refer to Song (2018) for an overview of SOE reform in China.

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with 77 percent of its population living on less than US$1.25 a day per person. By 2008, this figure had dropped to only 14 percent.<sup>1</sup> These achievements in poverty stand in stark contrast to the global trend in hunger. According to recent figures from the Food and Agriculture Organization (FAO 2012d), the percentage of hungry people in the world—unable to consume 1,800 kcal per person per day—has declined only slightly, from 16 percent in 1990 to 13 percent in 2008. This modest improvement was not enough to offset the absolute number of people facing starvation, which rose from 848 million in 1990 to 850 million in 2008. This number has been rising due to successive crises in the cost of food, the international financial crisis, and the recent famine in the Horn of Africa. Estimates of the nutritional effects of crises since 2007 (Tiwari and Zaman 2011; Brinkman et al. 2010) indicate that between 63 and 91 million more people have been added to the total number of people facing starvation.<sup>2</sup> In its latest report on fulfillment of the Millennium Development Goals, the United Nations warns that if the historic trends in hunger reduction and the high prices of food (not to mention their volatility) continue, it will be very difficult to meet the goal of reducing hunger in many regions of the world (UN 2012). But the regions in which this target _will_ be met by 2015 are East Asia and Latin America. The enormous progress in China since 1990, as well as in Indonesia and the Philippines, supports these predictions. In China alone, the starving population declined from 210 million in 1990 to 129 million in 2008 (FAO 2012c). At the same time, major progress has been made in average caloric intake in China, which increased from 2,580 kcal a day per person in 1990 to 2,990 in 2008 (FAO 2012c). In East Asia, the rates of malnutrition in children under five years of age have been cut in half, from 15 percent in 1990 to 6 percent in 2009.

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# Table 3: Brazil - Distribution of Non-Reported Income Heads By Kinship to Reported Head, Metropolitan Areas, 1984 (percent)

<!-- Start of picture text --> Kinship of Income Head to Reported Head<br>Sex of Other<br>Reported Head Spouse Offspring Relative Total<br>Male 69 25 6 100<br>Female 3 74 22 100<br><!-- End of picture text --> Source: author's tabulations Note: Joint-headship cases are excluded.
# **1. Introduction**

This paper examines how investment in port facilities affects trade costs and facilitation. We focus on developing countries in East Asia where international trade has played a major role over the past several decades in economic development. Port infrastructure has played a key role in facilitating trade in the region. However, serious congestion in seaports is evident from data on maritime shipping and trade. The scope of the study includes the costs, as well as benefits, of the construction of port infrastructure to address traffic congestion in East Asia. The paper also discusses the important role of port investment to stimulate demand and productivity, especially in a time of economic crisis.<sup>2</sup>

# _International Trade in East Asia_

International trade in East Asia has grown more rapidly than other regions in the world over the past two decades. At the same time, rapid economic growth fostered development in the region (Table 1). In particular, ASEAN countries in the early 1990s and China after the late 1990s recorded remarkable growth in trade even as the Asian economic crisis had a negative effect on the economic growth and trade in the region for the late 1990s.

A number of studies have attributed various factors to the growth in trade; including rapid industrialization of developing countries in the region and an active trade liberalization and facilitation agenda tied to development plans. In turn, many studies have pointed out that expansion in international trade stimulated the economic growth. This mechanism also functioned in the early 2000s when most of the ASEAN member economies took the path of recovery from the crisis, and China emerged as a dominant exporter in the region. China’s accession to the World Trade Organization in 2001 contributed to extremely high rates of growth in trade and direct investment. The regional trade liberalization in ASEAN, such as a free trade

> 2 The issue of the impact of port infrastructure improvement on economic welfare and growth (through trade) in different countries is also an important empirical and policy question.  This is beyond the scope of our analysis here; however, future work on welfare and growth impacts of improved port infrastructure would be helpful in a possible extension of this analysis.

2
In the sections that follow, I present the first evidence on the long-term effects of reducing helminth infection in _early_ childhood by exploiting externalities from a randomized deworming intervention in Kenya.<sup>2</sup> Though it was aimed only at school-age children, this kind of community-based deworming has large epidemiological spillovers both on other children (Miguel and Kremer 2004) and on others in the community (Bundy, _et al._ 1990). Taking advantage of these spillovers, I gathered new data in 2009 and 2010 in order to compare children who were in their first years of life at the time that treatment started in their communities to children from the same cohorts in as-yet untreated communities.

I find large effects on cognitive performance equivalent to half a year of schooling, robust to a variety of specifications, more than ten years after the original intervention. Effects are strongest among those who were likely to have an older sibling in school at the time of the original intervention—and particularly so if that sibling was female—as one might expect from an epidemiological perspective. I do not find any statistically significant long-term effect on measures of stunting or height, suggesting that—consistent with the nature of the intervention and the nutritional status of the population—extreme caloric deprivation was neither a central issue for this population, nor (even if, for some, it is a central issue) was it the condition this intervention chiefly addressed. My results support the theories that sensitive periods in early childhood are essential for cognitive development, and provide evidence that inexpensive actions are available that could produce lasting improvements in the lives of millions. The main result, along with the identification strategy, is summarized in Figure 1, and is discussed in greated detail in the sections that follow.

The remainder of this paper is organized as follows: In Section 2, I discuss the nature of the disease and the original intervention in Kenya; in Section 3, I provide details on the new data collection undertaken in 2009 and 2010; Sections 4 and 5 present the identification, estimation, and results of my analysis in light of the existing literature; Section 6 discusses relative costs and benefits; and Section 7 concludes.

> 2Recently, Croke (2014) has documented similar effects using literacy and numeracy tests in Uganda. In his manuscript, he conducts a long-term follow-up of the Alderman, et al. (2006) study by matching study areas to recently collected Uwezo data. Croke demonstrates higher mathematics and English scores for children given deworming medication early in life; effect sizes are comparable to those here, though the mechanics of the intervention and the nature of the outcome variables differ somewhat.

3
plant. They are instead represented in **_INDEX2_** which is obtained by summing firm responses to the two activities, _Discontinued at least one product line_ and _Closed at least one existing plant or outlet_ . **_CORE_** is an aggregate index obtained by summing firm responses to two activities, _Developed a major new product line,_ and _Introduced new technology that has substantially changed the way that the main product is produced,_ to reflect the narrow definition of core innovation as used in existing literature.<sup>12</sup> In addition to the rich detail on the innovative activities undertaken by firms, the survey has information on firm size, age, legal organization, number of establishments and capacity utilization, all of which are used as firm level controls in our study. The survey defines firms of different sizes, small, medium and large firms, on the basis of the number of full time workers.<sup>13</sup> Small firms are defined as those with less than 20 employees, medium firms employ 20 to 99 employees, and the large firms employ 100 or more employees. The sample is largely dominated by small and medium sized firms. Small firms make up 37% of the sample, medium firms constitute 35%, and large firms constitute 28% of the overall sample. Thus, the survey provides data across a much broader cross-section of firm sizes than is available in commercial data bases, such as Worldscope.

Table 1 summarizes the proportion of firms in each country that undertook each of the ten innovative activities over the year prior to the survey. All the countries in the sample are developing economies with GDP per capita in 2000 ranging from 192 U.S. dollars in the case of Tanzania to 11,646 U.S. dollars in the case of Slovenia.

The countries in the sample show a great deal of variation across the ten different categories of firm innovation. Firms in any country are more likely to undertake only certain types of innovative activities than others and hence in no single country are firms uniformly less or more innovative across the 10 different categories. Nevertheless, the aggregate indices, INDEX8 and CORE2, suggest that firms from Cambodia, Brazil, and South Africa are the most innovative, while firms from Egypt, Oman, and Turkey are the

> 12 In unreported regressions we also analyzed a core innovation index which includes _upgrading an existing product line_ .  While the results were unchanged in majority of the specifications, we prefer to leave this index out of the core innovation index as it may overestimate the extent of innovation, since even very minor changes may qualify as upgrades.

> 13 Employment is typically the most reliable figure in developing countries. Hence, number of full time workers is used as a measure of firm size by the World Bank Group and other international survey teams including RPED and the Oxford Centre for the Study of African Economies.

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In 1991, the UPCI tried to develop a methodology that would draw a clearer line between the effect of dumping and the effect of other factors on the industry so as to be able to apply antidumping duties for less than the margin of dumping found. The European Community applied lesser duties in its investigations on a regular basis, but its methodology did not suit the UPCI’s authorities because its extrapolation to Mexico at that time would have been protectionist in some Mexican sectors that still faced high tariffs, since it was based on the price of domestic manufacturers. That is why the UPCI developed a methodology of its own, which was first applied in 1991 in the Di-iodohydroxy-quinoline case.

**Table 9 - Cases in which the Lesser Duty Rule was Applied, 1991-1996**

|**Date of the final**<br>**determination**|**Product**|**Country**|**Final dumping**<br>**margin**|**Lesser Duty**<br>**(less than the**<br>**margin found)**|
|---|---|---|---|---|
|17-Oct-91|Di-iodohydroxy-quinoline|India|15.56<br>USD/kg*/|4.09 USD/kg|
|27-Nov-92|Telephone connectors|United States|35.34 USD/kg|4.59 USD/kg|
|8-Dec-92|Tri-poly-phosphate|Spain|50%|36%|
|23-Dec-93|Hydrogen peroxide|United States|72%|34.5%|
|27-May-96|Sulfuric acid|Japan|2,484%|91.10%**/|

*/ In the provisional determination, a 12.34 USD/kg (equal to the dumping margin) was applied. **/In the provisional determination, a lesser duty than the margin of 166.67% was applied. Source: Based on data taken from determinations published in the DOF.

# **(a) The Di-Iodohydroxy-Quinoline Case**

The Di-Iodohydroxy-Quinoline case features three interesting aspects: (i) it was the first case in which the Mexican authority applied a lesser duty than the dumping margin; (ii) its determination explicitly revealed the new authorities’ concerns about and conception of the administration of contingent trade protection mechanisms in a context in which the Mexican trade liberalization process was being consolidated and reinforced; and (iii) it meant the use, for the first time, of a method different from any other employed until 1991 by the few antidumping systems in force that applied lesser duties in the world.

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**Table 4: Codes of Conduct**

|**Country**|**Banking**|**Insurance**|**Non-Bank Credit**<br>**Institutions**|**Securities**|**Pensions**|
|---|---|---|---|---|---|
|Azerbaijan|No|No|No|No|No|
|Bulgaria|Yes|In process|Leasing<br>Credit cooperatives|Management companies|Yes|
|Croatia|Yes|Yes|No|Management companies|No|
|Latvia|Yes|Only brokers|Leasing|No|Yes|
|Lithuania|No|Yes|No|Yes|No|
|Romania|In process|Yes|No|Management companies|Yes|
|Russia|Consumer loans &<br>mortgages|Only motor<br>insurance|No|Management companies|No|
|Slovakia|In process|No|Installment sales<br>companies|Yes|No|

_Source:_ Diagnostic Reviews Note: Codes of conduct are under preparation for brokers in Bulgaria, Romania and Russia

**Financial intermediaries were seen in all the country studies.** As noted in Table 5, in all nine country studies financial intermediaries are used, either as agents tied to one financial institution, multi-agents working for several financial institutions, brokers representing consumers, or through multi-level sales distribution systems.<sup>33</sup> In the country studies, financial intermediaries were most often seen in the insurance sector. However intermediaries were also present for securities, pensions and consumer credits, including residential mortgages.

**Improved regulation of financial intermediaries is often needed** . At a minimum, clear liability and responsibilities should be set for agents and brokers. For agents, the financial institutions that they represent should be liable for the agents’ advertising and statements made to customers. Brokers should have a clear legal fiduciary duty to the consumer. Non-bank payment systems should also be regulated. Non-bank payment providers (for example, those that in Russia operate ATM-like terminals that collect payments rather than disburse cash) should be registered with a financial supervisory agency, which should check that the owners and key managers are free of criminal records and bankruptcy.

> 33 Terminology for financial intermediaries is confusing. Securities are sold primarily through intermediaries known as brokerage firms (brokers), which also directly sell investment funds. The individual officers working for brokers are known as agents. By contrast, insurance is sold either through agents (which may be companies or individuals) or through brokers (which also may be companies or individuals). This Working Paper’s recommendations on intermediaries refer only to companies acting as agents or brokers.

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