rafmacalaba/datause-encoder-data huggingface dataset:
# **1. INTRODUCTION**

The Indian economy grew at unprecedented rates in the late eighties and nineties with manufacturing growth exceeding that of the rest of the economy. Rapid economic growth raises questions about what the Indian labor market will look like in the near future. If the experience of developed countries over the past few decades is any guide, demand for education will increase substantially. Predicting this demand is important, both because of the practical need to guide investments by individuals and institutions and because accelerated demand for education not matched by a matching surge in supply will lead to increased income inequality.

A growing consensus among labor economists is that a major factor leading to increased demand for education in the U.S. is the bias of technological change toward more educated labor, so called “skill-biased” technological change (SBTC). Autor and Katz (1999) provide a survey of this literature. Several authors have extended this conclusion to other OECD countries, including Katz, Loveman and Blanchflower (1995) and Berman, Bound and Machin (1998). Berman and Machin (2000) provide evidence of SBTC in manufacturing industries of middle-income economies as well, suggesting that SBTC could be a global phenomenon. Trade liberalization may be associated with SBTC in developing countries. For example, Pavcnik (2002) and Attanasio et al (2004) provide some evidence that imported technology adoption and trade are related to skill upgrading in Chile and Columbia respectively.<sup>3</sup> Motivated by evidence of skill-biased technological change (SBTC) in middle income and OECD countries in the 1980s, we look for SBTC in Indian manufacturing in the 1980s and 1990s. In the Berman and Machin (2000) sample it appeared to be conspicuously absent in the 1980s in India. Yet, India's traditionally controlled economy underwent a sharp reform in 1991, suggesting the possibility that technology absorption may have accelerated during the past 2
formal salaried jobs. On the other hand, this asymmetry also exists with the contract sector so it is not limited to entering formal employment. Further, should deductions for training costs be a substantial fraction of the wage, such an asym;metry would be expected since returning workers may not work as apprentices. More fundamentally, the discount with respect to the other informal sectors suggests that the composition of the premium in the absence of distortions is distinct for this sector. This might be the case, for example, if the premium for risk is lower, if the 30% of the informal salaried who are related to the owner receive a large fraction of their income in kind, or if training costs are substantial.

_ii. Are the differentials consistent with an integrated labor market?_ Though we cannot credibly prove or disprove segmentation based on these differentials, we can ask whether they seem plausibly consistent with an absence of segrnentation: Ifthe responses of those reporting voluntary moves are reliable, is the 15-20% differential plausibly large enough to cover the value of benefits (which, on paper constitute between 31-60% of the wage), some return to risk, and whatever value is placed on one styl[e of work vs another? The breakdown by motivation in table 3 could, in theory, offer some measure of the value of the latter but the differential for "independence" does not vary significantly from that for "higher pay" and, depending on the question, implies contradictory signs on the premium."3 If'we interpret this ambiguity as implying a small effect, and the risk premium is positive, these leave the value of benefits even smaller-- a half or even a quarter of those on paper.

Three factors make this plausible and suggest a reason why the attraction of formal sector

**13** World Bank estimates. See also Davila-Capalleja(1996)

9
Table 16 C6te d'lvoire: Selected Basic Needs Indicators by Poverty Status, 198546

|||1985|||1986||
|---|---|---|---|---|---|---|
||Without<br>H-S weight|With<br>H-S Weight|Difference<br>_(%)_|Without<br>H-S Weight|With<br>H-S Weight|Difference<br> <br>(%)|
|Literacy: Percent able to read<br>|||||||
|Very Poor<br>|17.1<br>|15.0|-12.3|14.9|13.4|-10.1|
|Mid-Poor<br>|21.8<br>|19.3<br>|-11.5|23.9|23.1|-3.3|
|Non-Poor<br>|37.2|36.3|-2.4|36.7|36.2|-1.4|
|All|31.8|30.9|-2.8|31.8|31.9|+0.0|
|Net Primary Enrollment Rates(24)<br>|||||||
|Very Poor<br>|27.3<br>|24.6<br>|-9.9<br>|24.5|23.2|_-5.3_|
|Mid-Poor<br>|39.8|39.4|-1.0|37.8|36.8|-2.6|
|Non-Poor<br>|51.8<br>|51.9<br>|+0.2|53.2|52.5|-1.3|
|All|46.4|46.2|-0.4|46.6|46.3|-0.6|
|Percent of households w/access to tag water<br>|||||||
|Very Poor<br>|1.3<br>|I.5|+15.4|2.3|4.4|+91.3|
|Mid-Poor<br>|9.9<br>|7.9<br>|-20.2|8.6|7.7|-10.5|
|Non-Poor<br>|29.2|28.7|-1.7|31.2|29.4|-5.8|
|All|22.0|21.8|-0.9|22.9|22.7|-0.9|
|Percent of ill people who consult doctor or nurse<br>|_(%)_<br>||||||
|Very Poor<br>|32.8<br>|30.7<br>|-6.4|30.9|29.2|-5.5|
|Mid-Poor<br>|38.2<br>|36.5<br>|-4.5|32.4|30.3|-6.5|
|Non-Poor<br>|51.5<br>|49.7<br>|-3.5|51.5|49.7|-2.0|
|All|47.2|45.8|-3.0|44.8|43.9|-2.0|

33
where η , η = 1.017 , is the 2003 per-capita gross rate of growth, which is consistent with the observed rate of growth of GDP of 3.84% in 2003, using an annual growth rate of the population aged 15-64 of 2.1% (source: DANE). This implies β = 0.94 . Note that the model is calibrated so that the length of a model period is one year.

Ostry and Reinhart (1992) estimate the elasticity of substitution between nontradables and importables in consumption to be 1.28 for a sample of developing countries. I take this value as a proxy for the elasticity of substitution between C<sup>ˆ</sup> ht and C<sup>ˆ</sup> mt . This implies a value for υ equal to -0.22. From the equilibrium versions of the household’s first order conditions for<sup>cˆ and</sup> ht<sup>cˆ</sup> mt , evaluated at the steady state, and setting relative prices equal to unity in the steady state, an expression for α can be obtained: where C mva and C hva are defined as the average shares of imported consumption goods and home consumption goods in total value added, respectively. Given the assumed structure of the economy, all the required information on the right-hand side is known. Then set α = 0.883 . Lacking specific evidence on the elasticity of substitution between home and imported goods in investment, I use the same estimate as for the consumption elasticity. This implies ν<sup>i</sup> = -0.22 .

The first order conditions for<sup>iˆ</sup> thk and<sup>iˆ</sup> tm<sup>, evaluated at the steady state, yield an</sup> expression for ζ in terms of known magnitudes: i Set ζ<sup>i</sup> = 0.337 . Exploiting the specification of the Armington aggregator calibrate λ<sup>i</sup> = 1.868 from the following expression 13
using a neighbor’s well, or a community well. This mutes the potentially adverse effect of arsenicosis that operates via distorting time-allocation of school-going children between study and household chores.

# **3 Data and methodology**

# **3.1 Sample and survey description**

The data used in this paper have been collected in 2005 as part of a larger study to assess school quality in rural Bangladesh. The primary sampling unit of the survey was chosen to be unions<sup>6</sup> . To account for regional variation in school participation rate and so on, 60 unions were selected with proportional allocation from 6 divisions in the country. In the second step, for each sample union, a complete list of secondary schools in our sample unions was compiled. Using this list, all secondary educational institutes in each of the sample union were selected for data collection. In sum, a simple clustered sampling procedure was followed to select schools for the survey. All recognized schools and madrasas in each selected union were surveyed. In total, 321 schools and madrasas could be identified in the 60 unions (or school catchment areas). A detailed description of survey methodology and sampling is available in Asadullah, Caudhury and Dar (2007).

Two mathematics tests were administered on all students (both boys and girls) enrolled in grade 8 and present on the day of the survey. The first test was based on secondary-standard mathematics knowledge<sup>7</sup> . If there were two classrooms in grade 8, both were selected for participation in the math test. However, if any of the schools had more than two classrooms, only two were randomly selected. Once again, all students present on the day of the survey were interviewed. Given the cluster-based sampling, the survey led to a near-census of all secondary school going children (currently enrolled in grade 8 and present on the day of the survey) in the sample unions. The dataset contains a total of 321 schools and 8,475 students. Data on 5 schools were discarded, however, for poor quality and missing data problems.

> 6 Union is an administrative unit bigger than a village but smaller than thana/upazila.

> 7 The first test instrument was constructed by adopting 20 items previously used in the Trends in International Mathematics and Science Study (TIMSS), 1999. The second test instrument is based on primary school mathematics syllabus.

8
Longitudinal Study (CHARLS) pilot conducted in 2008, we note 45 percent of urban men aged 60 to 64 were still employed at least one hour per week (Panel A), but in rural areas nearly 86 percent of men in this age range were still working. The difference in employment of urban and rural women aged 60 to 64 was even wider, with only 16 percent working in urban areas and nearly 57 percent still employed in rural areas.

If anything the CHARLS pilot, with relatively small sample sizes in urban Zhejiang and Gansu Provinces and representative of only two provinces, may overstate the employment rates of older men and women. Also presented in Panel A of Figure 1 are the 1991 and 2009 estimates from the China Health Nutrition Survey (CHNS), which show a substantially lower employment rate of 31 percent for urban men in the 60 to 64 age range. When comparing employment rates across the age distribution over time using the CHNS, one observes declines for both men and women in urban areas, but less pronounced declines in employment rates in rural areas.<sup>8</sup> Rural women between age 45 and 65 are somewhat less likely to be working in 2009 than in 1991, but this does not appear to be true for older women or for rural men.<sup>9</sup> One of the sharper changes from 1991 to 2009, as viewed from the CHNS, lay in the decline in employment rates of women over age 45 in urban China. Reviewing representative evidence from census data (Figure A.1) this effect appears strongest among women from age 45 to 55, as the population census and one-percent sample suggest modest increases in the employment of women over 55. Examining further evidence from three waves of the China Urban Labor Survey (CULS), which allows one to carefully distinguish employment (under our definition) from labor force participation, it is clear that China’s booming economy and a tight labor market has increased women’s employment across the age distribution for women under 45, but that after 45 employment rates drop sharply (Figure A.2, Panel A). Moreover, when we turn to labor force participation (Figure A.2, Panel B) it is evident that in 2001 and 2005 many of the urban women

> 8We review evidence from the CHNS as it is the publicly available data source that researchers used (pre-CHARLS) to study labor supply, health status and retirement of older workers  in China (e.g., Benjamin et al, 2003; Dong, 2011). As it is based on a panel of households which does not enumerate complete information on family members who have split off of households, one should be concerned that later waves of the CHNS over-represent those who remain in the households. As we are interested in the over-45 population, this is less of a problem than if we were considering the employment decisions of individuals who were children in 1991 and likely to have moved out of households.

> 9In Appendix Figure A.1, we present statistics from the 1990 and 2000 population census and the 2005 one percent population sample. Employment rates are somewhat lower in 2005 for older men and somewhat higher than one observes in the CHNS.

5
Policy ReseaRch WoRking PaPeR 4894

# **Abstract**

Over the past two decades, globalization, and more specifically the increased exposure to competition from low-price producers in China and India, has created a new economic environment for other emerging economies. The most advantageous way for manufacturing firms in those economies to position themselves in domestic and international markets is to offer upgraded and differentiated rather than “mundane” labor-intensive products. This paper investigates whether increased competitive pressure from imports forces firms to improve the quality of their products. The econometric analysis relies on a rich dataset of Chilean manufacturing plants and their products. Product quality is measured with unit values (average prices) and industry-level transport costs are used as an exogenous measure of import competition. The authors find a positive and robust effect of import competition on product quality. This effect is found to be particularly strong for non-exporting plants. The results also show that increased import competition from less advanced economies is the major cause for the positive impact on quality upgrading. The overall evidence points to the benefits of trade openness for product innovation but demonstrates at the same time that competitive pressure alone will not enable local plants to catch up with leading world producers.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the links between trade costs, competition, and innovation. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at afernandes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work 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 less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<u>Table C.7. Descriptive statistics of estimation sample, Arab Republic of Egypt</u>**

|**Variables**||**2012-2015**|||**2015-2017**|||**2017-2020**||
|---|---|---|---|---|---|---|---|---|---|
||**2012**|**2015**|**diff**|**2015**|**2017**|**diff**|**2017**|**2020**|**diff**|
|L f r it nmtin|8.58|8.97|0.4***|8.95|9.32|0.4***|9.30|9.48|0.2***|
|og o pe capa cosupo|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd`|41.32|43.97|2.7***|42.23|44.20|2.0***|43.14|42.89|-0.3*|
|eas age|(0.12)|(0.09)|(0.1)|(0.09)|(0.09)|(0.1)|(0.09)|(0.10)|(0.1)|
|Hd i fml|0.13|0.13|0.0|0.12|0.13|0.0|0.12|0.13|0.0|
|ea s eae|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i i|0.13|0.15|0.0***|0.15|0.15|0.0|0.15|0.14|-0.0***|
|eas ges eucaon eve s prmary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i d|0.31|0.34|0.0**|0.35|0.36|0.0|0.37|0.37|0.0|
|eas ges eucaon eve s seconary|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd' hiht dti ll i tti|0.20|0.19|-0.0|0.19|0.18|-0.0|0.18|0.21|0.0***|
|eas ges eucaon eve s erary|(0.01)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Ub|0.42|0.42|-0.0|0.41|0.42|0.0|0.41|0.43|0.0**|
|ran area|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

85
# **2.2 Data Collection**

The sample frame for the Chaldal survey was all small and medium sized suppliers (“vendors”) who had supplied goods through Chaldal in the last 3 months. The survey was implemented by computer-assisted telephone interviews (CATI), fielded in June 2021 by the Chaldal call center. The vendor list provided by Chaldal included 346 firms. However, some firms could not be reached or had closed, and 35 refused to participate. The final sample consists of 127 firms with a completed interview. Some 42 percent of interviews were conducted with the owner of the firm, 34 percent with a manager, and 51 percent with someone in the sales department.

Data on Daraz vendors was collected through an online survey, sent to vendors who had filled at least 50 orders on Daraz during the last 6 months by email. The target sample included 14,660 firms in Bangladesh, Pakistan, Nepal, Sri Lanka, of which only owners were interviewed. Data collection took place in July 2021. A total of 1,842 firms responded to at least some questions, with 1,549 continuing to the end of the questionnaire. Interviews were conducted only with firm owner/directors.

The full questionnaires used to collect data from firms supplying Daraz and Chaldal are available in Appendix A4. Given the different structures of these two e-commerce platforms, the two questionnaire are similar but not identical.

# **2.3 Descriptive Statistics: Chaldal Survey**

Firms in the Chaldal sample are mostly established manufacturing or trading firms, and they range widely in size (table 1). The average firm has 749 employees, although this number is driven by a few very large firms, with 14 surveyed firms having a workforce of more than 1,000. In the sample, 36.6 percent of the firms have 1-19 employees (including the owner), 27.7 percent have 20-99 employees, and 35.7 percent have 100 or more employees (figure 1). About half of the firms in the sample (50.5 percent) have monthly sales of at least 10 million Bangladesh Taka (115,800 USD in 2021) and 11 percent have monthly sales below 500,000 Taka.

Table 1: Characteristics of sellers on Chaldal

||Mean|Standard Deviation|
|---|---|---|
|How many workers work at this business?|749.2|2098.38|
|Firm age (years)|16.5|15.13|
|Time on Chaldal (years)|3.2|2.58|
|Trader/wholesellers|0.4|0.49|
|Manufacturer|0.5|0.50|
|Farm/dairy|0.1|0.23|
|BA or higher education|0.8|0.36|
|Owns two-wheeler|0.6|0.49|
|Owns car|0.3|0.45|
|Owns phone|1.0|0.09|
|Owns refrigerator|1.0|0.13|
|Owns land|0.9|0.34|
|VAT reg.|.92|.27|
|Observations|127||

6
The above two tables reveal that most of the indices are above "one", indicating that these countries trade with each other above the "normal" level of trade based on their trade with the rest of the world. Singapore and Thailand have an intense trading relationship with the largest number of countries in the region and some of these are at a very high level, particularly with the newer ASEAN members.

Table 4.6 summarizes the data in the previous two tables indicating the top five trading partners (with an index of 1 or above) for each East Asian country.

Table 4.6

|||Top|FiveT<br>(<br>|rading<br>Based<br>|Partn<br> on Tr<br>|ersforEast<br>adeIntensity|Asian Ec<br> Index)|onom|ies<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||_19_|_95-19_|_97_||||1998-|2000||
|Brunei|N.A.||||||THA|JPN|KOR|SGP<br>3||
||||||||-<br>11.9|8..|59|38<br>36||
|Cambodia|THA|SGP|MYS|ICHN|HK||VNM|SGP|THA|USA||
||17.11|<br>**5.5**|2.7|<br>2.0|14||68.5|5.0|30|18||
|China|HK|JPN|VNM|LAO|KOR||HK|MYN|JPN|VNM<br>**CAM**||
||**5.9**|**2.8**|**2.8**|<br>**2.5**|1.8||6.2|5.0|28|20<br>1||
|Hong|CHN|PHL|SGP|TWN|VNM||CHN|CAM|PHL|USA<br>SGP||
|Indonesia|CAM|JPN||SGP|VNM|KOR||MYN|SGP|CAM|JPN<br>KOR||
||61|39|37|<br>3|2.4||74|57|40|388<br>30||
|Japan|THA|TWN|PHL|IDN|**KOR**||PHL|TWN|THA|KOR<br>**ION_**||
||32|<br>3.1|28|<br>27|2.41||36|35|301|<br>25<br>23||
|South|VNM|CHN|IDN|I|**HK**||VPHL|<br>**HL**|CHN|IDN<br>MYN||
||62|331|<br>31|24|<br>|||9|<br>32|3<br>31||
|Korea|.|.|<br>||<br>2||||<br>.|<br>||
|LaoPDR|THA|TWN|JPN|CHN|EU||THA|**JPN**||||
||142|19|1.6|11|11||96|79||||
|Malaysia|**SGP**|CAM|THA|VNM|TWN||BRU|SGP|MYN|THA<br>PHL|IDN|
||81|42~,|314|<br>211|2C0||93|89|76|37<br>291|291|
|Myarnar|N.A.||||||SGP|MYS|**CHN**|BRU<br>JPN||
||||||||35|32|20|1.6<br>1.2||
|Philippines|THA|JPN|SGP|USA|TWN||TWN|SGP|MYS|THA<br>JPN||
||1<br>2.8|2.7|2.4|2.3|201||4.0|36|3.5|2.8<br>2.||
|Singapore|CAM|MYS|VNM|THA|LAO||BRU|MYS|**CAM**|MYN<br>VNM||
||1<br>16.7|12.1|7.2|4.2|3.9||15.1|14.0|12.1|9.1<br>5.9||
|Taiwan|**HK**<br>4.91|VNMI<br>3.11|PHL<br>2.4|THA<br>2.0|CHN<br>1|IDN<br>MYS<br>1|HK<br>6.6|VNMI<br>4.71|PHL<br>3.6|IDN<br>THA<br>2.0<br>2.||

14
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20