rafmacalaba/datause-encoder-data huggingface dataset:
China was the largest contributor to growth in these U.S. varieties; however, most of this growth was in the earlier period from 1972 to 1988. Other papers highlight a strong positive correlation between the number of export varieties a country produces and its living standard (see Funke and Ruhwedel, 2001). Hummels and Klenow (2005) fi nd that larger and richer countries export more varieties of goods, using data for 1995. This fi nding is suggestive that a large portion of China’s export growth would be associated with exports of new varieties. However, our analysis of China’s export growth patterns between 1997 and 2005 shows that most of its export growth was actually in existing varieties (the intensive margin). This large growth in the intensive margin is also supportive of predictions consistent with traditional theories with an important role for terms of trade e ff ects, where the welfare gains for importing countries arise through lower import prices. As China increases its supply of existing varieties on world markets, this is likely to exert downward pressure on world prices of these goods. Indeed, between 1997 and 2005, average prices of goods exported from China to the US fell by an average of 1.5 percent per year whereas the average prices of these products from the rest of the world to the US increased on average by 0.4 percent per year.<sup>1</sup> The rest of the paper is organized as follows. Section 2 describes the data. Section 3 examines the reallocation of exports across industries. Section 4 looks at the skill intensity of exports. Section 5 examines whether there has been increased diversi fi cation or specialization as exports have grown. Section 6 decomposes export growth into the intensive and extensive margins. Section 7 compares China’s export prices to the United States to those from the rest of the world. Section 8 concludes.

# 2. Data

The most disaggregated export data available for China is at the HS 8-digit level, from China Customs Beijing, which includes 8,900 product codes. The trade data are in current

> 1 This is a Tornqvist chain weighted price index using HS 10-digit goods that China exported during this period.

3
# Table 5. Simulating the Characteristics of Other Household Members

|||Bangla|desh|||Pe|ru|||Thai|land||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2|000|2|010|2|004|2|010|2|000||2009|
||Actual|<br>Simulated|Actual|<br>Simulated|Actual|<br>Simulated|Actual|<br>Simulated|Actual|<br>Simulated|Actua|l<br>Simulated|
||(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|
|**Education Structure**|||||||||||||
|Illiterate & Incomplete primary|54.4|54.5|42.1|42.3|50.3|50.8|45.4|44.6|36.6|42.3|27.3|22.1|
|Primary & Low Secondary|36.2|36.0|48.6|48.2|36.5|35.6|39.7|40.7|37.1|32.9|44.4|49.0|
|High Secondary & Terciary|9.5|9.6|9.3|9.4|13.3|13.6|15.0|14.7|26.3|24.8|28.3|28.9|
|_P-value of Pearson chi-square_||_100%_||_100%_||_98%_||_98%_||_49%_||_48%_|
|**Occupation**|||||||||||||
|Non-employed|79.8|83.0|78.0|80.1|57.9|58.7|49.5|50.4|49.4|47.0|47.3|50.6|
|Self-employed - Non Agriculture|4.4|4.8|4.1|4.4|26.2|25.7|32.2|31.6|9.5|10.3|10.8|9.9|
|Salaried|7.2|7.5|9.7|9.9|15.9|15.6|18.3|18.0|41.0|42.8|41.9|39.5|
|Daily workers|8.6|4.7|8.2|5.6|||||||||
|_P-value of Pearson chi-square_||_59%_||_81%_||_98%_||_98%_||_88%_||_81%_|
|**Economic Sectors for salaried wor**<br>|**kers**<br>||||||||||||
|-Manufacturing|42.6|42.4|47.3|47.5|||||||||
|-Agriculture|4.4|3.4|3.0|3.7|17.0|17.7|14.8|13.9|20.7|18.9|9.1|11.0|
|-Industry|2.5|2.0|2.6|2.7|13.2|12.8|13.2|13.9|36.0|32.2|38.8|41.2|
|-Services|50.5|52.2|47.2|46.0|52.8|51.4|56.7|58.3|27.2|25.6|34.7|35.9|
|-Public Sector|||||16.9|18.1|15.3|14.0|16.1|23.3|17.4|11.9|
|_P-value of Pearson chi-square_||_94%_||_97%_||_98%_||_97%_||_27%_||_52%_|

Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.

32
# 3.2 Randomization

The GSO conducted the random assignment of 480 households to each arm. Households within an EA were randomly assigned to the interviewers working in the EA. Each interviewer was assigned an equal number of households from each treatment arm within the EA. Randomization at the interviewer-level was undertaken to reduce the potential for interviewer error that could bias FAFH and other responses. Randomization within the EA also reduces the potential for any bias stemming from variation across EAs. An additional sample of replacement households were likewise randomly assigned to each arm, and only surveyed after households from the main sample could not be reached or refused to participate.

After dropping households with missing information in critical areas for this study (mainly, FAFH consumption), there are over 475 households in each treatment arm, except for the 7-day individual diary, which has 473 households. About 20 percent of the final sample in each treatment arm consists of “replacement” households, with only T3 having a higher share (25.8%) than the other arms (see Table 1). In Section 5.1, we test for the effects of non-response by running the models only with the original sample and find that the results hold.

_Table 1 Samples of original and replacement households for each treatment arm._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||T1: 7-day<br>one-line<br>recall|T2: 7-day<br>individual<br>diary|T3: 7-day<br>individual<br>recall|T4: 7-day<br>HH<br>informant|
|Original|381|378|354|378|
|Replacement|97|95|123|98|
|Total|478|473|477|476|

By design, random assignment should result in groups that are statistically equivalent in both observable and unobservable characteristics, and therefore allow for differences across groups to be attributed to the experiment. In practice, however, some deviations may occur. Table 2 shows a balance table comparing differences in means of various household and survey characteristics across treatment groups. We focused on the demographic and socioeconomic characteristics of households.

In our sample, the average household head<sup>11</sup> is slightly under 54 years old and is male in 63 percent of households. He/she has attained university-level education in 26 percent of the households. The average

> 11 The respondent ID was unfortunately not collected and thus we cannot test differences across respondent characteristics.

12
O.A.M.D.G.

data assembled by the Medina et al. (2001) from National Police records and other municipal data allows us to measure guerrilla attacks directly. Through a ‘public order problem’ dummy in the _Familias_ survey, we also pick up qualitatively whether paramilitaries or guerrillas are at all present in a municipality. To our knowledge, there is currently no source that quantifies the degree of paramilitary actions reliably. But also the official statistics can be challenged, as the National Police records only actions they have been notified of, which might not always be the case.<sup>14</sup> Regarding common delinquency, our data sources pick up the homicide rate. There are obviously further activities by the perpetrators of homicides, such as muggings, and some of these will be committed in conjunction with a homicide. But of the available statistics, homicides are the most reliable, as they are certified and tracked by the Forensic Medecine (Medicina Legal)<sup>15</sup> . All other indicators of common delinquency are likely to be strongly under-reported. From the above, we can detect two issues that will affect the interpretation of our empirical results. First, the uncertain measurement of some violence variables may entail an attenuation effect on the corresponding covariates. Second, as we possibly do not pick up all relevant variables, such as muggings, there will likely be some Omitted Variable Bias in the coefficient of the homicide rate. - In the light of this, let us examine the variation and correlation of the observed violence variables.

According to our data, the incidence of the different types of violence is high. All municipalities reporting data experience arrests under the Drug Act, varying from 29 per million to over 1,500 per million. About 75% of the households live in municipalities

> 14 For example, National Police statistics on paramilitaries appear less complete than for guerrillas; and the overwhelming majority of empirical studies on Colombia relies on guerrilla and homicide figures alone. Also, as the paramilitaries avoid combat situations, they have fewer encounters with the police, and are less likely to be tracked, compare Vargas et al (2004).

> 15 Levitt and Rubio (2000) for example consider only the homicide rate a reliable violence indicator in Colombia.

13
I; 1(Y RESFARCH WORKING PAPER 2089

# Summary findings

Using data for more than 2,000 companies from nine East Asian economies, Claessens, Djankov, Fan, and Lang examine the interactions between ultimate ownership, group affiliation, and corporate diversification.

They find evidence that allocating resources within business groups is associated with higher market valuation when external markets are less developed.

They also find that group affiliation and firm-level diversification are used complementarily to exploit the relative cost-effectiveness of internal markets.

They reject the hypothesis that diversification patterns can be explained by large blockholders' incentive to reduce risk. But they find support for the hypothesis that controlling owners use diversification to expropriate other shareholders.

Group affiliation is widespread among publicly traded corporations in East Asia. Group-affiliated firms are on average associated with diversification discounts. Further analysis reveals that the discounts are attributable to diversified firms in the more developed East Asian economies. By contrast, group affiliation positively contributes to diversification performance in less developed economies.

The authors find that group-affiliated firms are more likely to diversify in developing economies but are equally likely to diversify in developed economies.<sup>When</sup> diversifying in more developed economies, groupaffiliated firms destroy more value than do independent firms. In developilng economies, group-affiliated firms are more likely than independent firms to benefit<sup>from</sup> diversification in developing economies.

This paper - a product of the Financial Economics Unit, Financial<sup>Operations Vice Presidency-</sup> is part of a larger effort in the vice presidency to study corporate performance patterns<sup>in East Asia. Copies of the paper are available free from the</sup> World Bank, 1818 H Street NW, Washington, DC 20433.<sup>Please contact Rose Vo, room MCI0-628,</sup> telephone 202-4733722, fax 202-522-2031, Internet address hvolCiworldbank.org. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/home.html. The authors may be contacted at cclaessensCiworldbank.org or sdjankovCkworldbank.org. March 1999. (30 pages) _The Policy Research Workinig Paper Series disseminates the finidings of wvork 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_<sup>_less than fully polished. The_</sup> _papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and_<sup>_conclusions expressed in this_</sup> _paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World_<sup>_Bank, its Executive Directors, or the_</sup> _countries they represent._ Produced by the Policy Research Dissemination Center
especially for agricultural commodities, are collected systematically in these countries. While this provides a snapshot of costs for widely produced commodities, it is restrictive in quantifying the potential implications of transport cost reductions on trade flows and welfare. One way to bridge this gap and generate simulated trade flows is to start by surveying firms from various sectors of interest in order to estimate their freight demand, as Herrera Dappe et al. (2019) do for Bangladesh. In the absence of a comprehensive nationwide commodity flow survey, the authors sample four thousand economic establishments across the six largest freight-intensive sectors and across the country in order to understand where freight is likely to be originated. As a secondary data source informative about flows, they use truck traffic measurement from key arteries. Fitting observed traffic to the Freight Origin-Destination Synthesis (FODS) model of Holguin-Veras and Patil (2008), they estimate origin-destination flows. They complement this with links between ports and exporting or importing firms obtained from customs data, as discussed in Section 5.1. The fitted model facilitates the construction of a domestic trade flow matrix for Bangladesh.

A common limitation of all the approaches discussed so far is their inability to separate transport costs incurred in the first- and last-mile from those incurred in the main haul. The sizable share of first- and last-mile costs are well-known in transportation economics. However, unless explicitly asked, most surveys do not differentiate them from total shipment costs, thereby missing a potential source of nonlinearity in the transport cost function. A recent example overcoming this shortcoming by synthesizing multiple data sources and methodologies is Kebede (2019). Studying of the impact of the Universal Rural Road Access Program in Ethiopia, he uses surveybased information from the Ethiopian Socioeconomic Survey on costs incurred by farmers while transporting recorded agricultural commodities from the farm-gate to the nearest market town. For an average distance of 12 km traveled, farmers report ad-valorem transport costs of 11.4% on average, with the median at 6.5%. Kebede then uses responses at the village level to estimate the cost of transport by road types (paved, gravel, cobbled or earth roads). Additional information comes from standard price data collected from farms as well as main market towns, which is informative about the potential share of transportation in overall trade costs estimated from price differentials. These costs are then used to calculate least-cost itineraries and shipping cost measures throughout the entire road network, which are in turn used to impute a model-based market access measure for a quantitative welfare exercise.

In Section 2, I discussed the important question of whether transport costs are per-unit (additive) or ad-valorem (multiplicative). Both anecdotal evidence as well as direct measurement from surveys suggest that the former is a better characterization of transport costs. Imputation based methods are designed to deliver additive per19
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- 21 -

Table 2.4 Summary of Typical Joint Terminal Agreement <!-- Start of picture text --> Basic Concept<br>Railway A owns and operates a freight terminal at a specific location. Railway B obtains the right to use<br>this facility jointly with Railway A.<br>Contents of the Agreement<br>Identification of Facilities<br>Functional Responsibilities:<br>* Railway A to perform all business and operational functions at the terminal.<br>Costs Charged to Railway B (annual):<br>Fixed Amount:<br>* For existing facilities, 2.5 percent of the value stated in the agreement.<br>* For incremental improvements if made by Railway A, 3.5 percent of their value.<br>Variable Amount:<br>* Railway A's Costs to be AUlocated:<br>* Train movements within terminal (including salaries for switching forces).<br>* Taxes and assessments.<br>* Salaries of agents and clerks.<br>* Utilities for buildings.<br>* Maintenance of covered properties.<br>* Allocation Basis:<br>* Proportion of each railway's freigh t onnage to total freigh t onnage handled at terminal<br>in any month.<br>Accounting and Billing Procedures<br>Liabilities:<br>* As usual, provisions are complex.<br>* Each party is generally responsible for loss and damage which is the fault of its sole employees<br>or of defects in its own equipment.<br>* But: if loss or damage is caused by agents or employees involved in the joint operation, then<br>no-fault prevails.<br>Default and Termination; Arbitration<br>Note: Not only does Railway A fail to assure that it will handle B's traffic with the same dispatch as<br>its own, it makes itself non-liable for any deficiency or act done or omitted by A's agent with<br>respect to B's business.<br>Source: Pittsburgh, Chartiers & Youghiogheny Railway Co. and Pittsburgh & Lake Erie Railroad Co.,<br>joint terminal agreement for McKees Rocks, Pennsylvania, August 1, 1902.<br><!-- End of picture text -->
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24