rafmacalaba/datause-encoder-data huggingface dataset:
of more effective labor market policies in India and elsewhere. Much of the current literature on big data analytics in economic research is limited to methodological reviews and analyses of data sources, and does not examine real-world applications. This report not only reviews the prospective research applications of big data analytics, but also demonstrates their utility through an analysis of actual Babajob data. By using data from India, the study provides a unique case study in a developing country context. Unlike data from online job portals in the United States or Europe, labor market data in India is not regularly updated, making it difficult to assess changes in the labor market. This paper can help shed new light on labor market dynamics in India and expand the possibilities for using big data analytics to enhance labor market policies and promote demand-driven skills development.

The paper discusses five potential applications of big data in formulating and refining labor market policies: (i) conducting labor market monitoring and analysis; (ii) assessing demand for workforce skills; (iii) observing job-search behavior and improving skills matching; (iv) predictive analysis of skills demand; and (v) experimental studies. The paper applies the following analytical methodologies: descriptive-data analysis, time-series analysis with spatial information, text analysis, predictive analysis and transactionaldata analysis.

# **3. Literature on Economic Research Using Big Data**

The term “big data” first appeared in the early 2000s, as a surge in data processing capacity, internet use and detailed data collection created an unprecedented wealth of granular, real-time information on the behavior of individuals and organizations. “Volume, variety and velocity” are the three defining properties of big data (Laney 2001). Big data analytics was initially leveraged by firms seeking to adapt their business models and day-to-day decision making to reflect emerging data on consumer behavior. Since then, big data analytics has been increasingly utilized as a critical management tool and knowledge source (McAfee and Brynjolfsson, 2012), occupying a space between academic and applied knowledge (Savage and Burrows 2007, 2009; Taylor, Schroeder, and Meyer 2014).

More recently, the use of big data has expanded rapidly in economics and other social sciences. The advent of big data has already prompted novel research designs across a range of topics and refined the measurement of economic effects and outcomes. Over time, these data are likely to affect the types of questions economists pose, allowing for greater attention to variation across populations and facilitating analyses across a broader range of economic activities and interactions. Einav and Levin (2013) point to three unique characteristics of big data that can help contribute to the economic literature: (i) the real-time 4
those for the benchmark group of countries but does not seem to be correlated with linkage rates of resource rich countries.

Results of comparison of GVC linkage rates and changes in these rates by country groups are described in Figures 1 through 9, of which we highlight four main items.<sup>6</sup> First, linkage rates of Africa’s manufacturers to global value chains (GVCs) are reasonably high by the standards of the group of external comparators. This applies to natural resource rich countries of the region as well as to non-resource rich ones. However, it is also true that linkages are significantly higher in the non-oil resource rich group than they are for non-resource rich. This pattern is depicted in Figure 2, which shows an aggregate GVC linkage of rate of 45% for the non-oil resource rich groups in 2015 as compared with 37% for the non-resource rich group and 33% for the benchmark group.

Secondly, linkage rates have declined steeply in recent years in non-resource rich countries of the region while increasing as sharply in natural resource rich countries. This is evident in Figure 2, which compares changes in upstream and downstream linkage rates between 1995 and 2015 across groups and shows that the overall rate of linkage fell by 7% for the nonresource rich group, while rising by 5 percentage points for non-oil resource rich, by 10% for oil exporters and by 1 % for the external comparators. As a result, on aggregate non-resource rich countries had lower linkage rates to manufacturing GVCs than they had in the 1990s and yet there were some that were integrating more by 2015. On other hand, the non-oil resource rich countries were integrating to manufacturing GVCs even more than the external comparators were by 2015.

Third, while significant differences exist between the resource groups of countries in linkage rates and changes in these rates, both sets of indicators vary substantially across countries within each group _._ This is observed in Figures 3, 4 and 5. The first of these presents four country cases within the non-resource rich group and shows, for example, that Rwandan

> 6 As a rule, extreme outliers observed in all three indicators are dealt with by winsorizing at the (1, 99) percentiles. The exception is indicators in the country-sector data, in which case the proportionate share of DVX in gross exports and GVC participation rate are each winsorized at the (1, 90) percentiles.

12
|Soil erosion<br>0-100%|0|1,783|
|---|---|---|
|Water recharge<br>0-100%|0|765|
|Total indirect|0|2,548|
|Average % loss|0|100|
|Total indirect , bil Rs|0.0|63.4|
|Total degradation losses,<br>Bill Rs|60.5|170.2|
|Total deforestation losses<br>(20% carbon losses only)<br>Bill Rs|9.14|25.47|
|Total|69.7|195.6|
|%GDP|0.11%|0.30%|
|% GDP for the poor|0.60%|1.68%|

_Source:_ Staff estimates applying secondary data from GAISP (2005-2006), Gundimeda (2005), Gundimeda (2001).

If related to the GDP of the poor that was about 17% of the total GDP in India in 2010, then losses in the forestry sector are at about 0.6-1.7%.

28
# **3 The Sources of Misallocation**

What are the sources of dispersion in the _arpk_ ? David and Venkateswaran (2017) use a rich model of investment dynamics and show that combining the information contained in the moments reported in Table 1 can shed light on this question. In particular, it is possible to _simultaneously_ measure the contributions of (i) adjustment frictions (ii) information frictions and (iii) a broad class of other firm-specific factors influencing investment decisions.

**The model.** The environment is an extension of the canonical Hsieh and Klenow (2009) framework to include dynamic considerations in firms’ investment decisions. We defer a full description to Appendix A and only discuss the key elements here. First, adjustment frictions are modeled using a standard quadratic adjustment cost function, along the lines of, e.g., Cooper and Haltiwanger (2006), Bloom (2009) and Asker, Collard-Wexler, and De Loecker (2014).

Next, informational considerations are introduced along the lines of David, Hopenhayn, and Venkateswaran (2016) by assuming that the firm, at the time of making its period- _t_ investment choice, has a noisy signal of its future productivity, _ait_ +1. In a Gaussian setting, a sufficient statistic for this uncertainty is the posterior variance of the firm, denoted V . Given the AR(1) assumption on productivity, V takes on values between zero and _σμ_ 2: if V = 0 , the firm is perfectly informed about _ait_ +1 at the end of period _t_ . If V = _σ_ 2 the firm has no information _μ_ about the following period’s innovation (but obviously knows its current productivity).<sup>13</sup> Finally, all other factors influencing investment choices are assumed to take the form of a wedge (an implicit proportional ‘tax’ on the cost of capital) in the firm’s optimality condition. The model yields the following log-linearized law of motion for capital: where _ξ_ is a composite parameter that summarizes the severity of adjustment costs and _β_ the discount rate, the expectation operator E _it_ reflects the possibility that the firm has imperfect information about future productivity and _τit_ is the effective wedge that acts on the firm’s investment decisions. We assume that _τit_ has 3 components: 13 <u>V</u> In what follows, we report values fo ~~r~~ _σ_ , which denotes the posterior uncertainty as a percent of the prior<sup>2</sup> _μ_ (values of V are easily calculated by multiplying by _σ_<sup>2</sup> as reported in Table 1). This statistic takes on values between _μ_ zero and one and is increasing in the extent of uncertainty.

7
the resolution numbers by the Ministry, which does not describe whether these are the dates of decision or of publication. Furthermore, termination dates and resolution numbers were only available for a select number of cases. Lastly, specific revocation resolutions were listed for only two cases. Other duties are known to have been revoked, but no details were available. Therefore, these revocations dates are simply listed as the standard five-year expiration date. Finally, the “DUMP” and “INJ” columns are identical, since Colombia uses a single track antidumping process. Firm-specific information is also currently unavailable.

# **3.6 European Union (EUN)**

Antidumping investigations in the European Union are handled by the Trade Directorate of the European Commission. Investigations follow a single-track i.e. the system assigns the responsibility for dumping and injury assessment only to the Commission. The initiations and outcomes of each investigation are published in the European Union’s _Official Journal_ . Electronic versions of antidumping-related documents are available online in the form of Information and Notices (prefix C) and Legislation (prefix L). The source of the data was primarily the European Union’s _Official Journal_ website. In cases where the online documents lacked certain data items, the Office for Official Publications provided the complete versions of these documents upon request by email. There is a fairly extensive research literature on various aspects of EU antidumping, e.g., for procedural descriptions and analyses see Messerlin (2001) and Vermulst (2005).

In addition to the standard antidumping information described in section 2, the AD-EUN workbook contains much additional information that may be of use to researchers, as we describe in the table below.

23
- Kochhar, Kalpana, Utsav Kumar, Arvind Subramanian, Raghuram Rajan, and Ioannis Tokatlidis, 2005, “India’s Pattern of Development: What Happens, What Follows.” IMF Working Paper No. 06/22.

- Kletzer, Kenneth, 2005, “Liberalizing Capital Flows in India: Financial Repression, Macroeconomic Policy and Gradual Reforms.” In Bery, Suman, Barry Bosworth, and Arvind Panagariya (eds.) India Policy Forum 2004. _Brookings Institution Press_ .

- Kuijs, Louis, 2005, “Investment and Savings in China.” World Bank Policy Research Working Paper No. 3633.

- Kuijs, Louis, 2006, “China in the Future: A Large Net Saver or Net Borrower?” Mimeo, World Bank.

- Lane, Philip, 2006, “The International Balance Sheets of China and India.” Mimeo, Trinity College Dublin.

- Lane, Philip and Gian Maria Milesi-Ferretti, 2002, “Long-Term Capital Movements.” _NBER Macroeconomics Annual_ 16, 73-116.

- Lane, Philip and Gian Maria Milesi-Ferretti, 2006, “The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 19702004.” IMF Working Paper No. 06/69.

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- Li, Chunxiang, Andong Zhu, and Gerald Epstein, 2005, “Capital Flight from China, 1982-2001.” In Epstein, Gerald (ed.), <u>Capital Controls and Capital Flight from Developing Countries.</u> _Edward Elgar_ .

- Li, David, 2006, “Large Domestic Non-Intermediated Investments and Government Liabilities: Challenges Facing China’s Financial Sector Reform.” Mimeo, Tsinghua University.

- Lim, Edwin, Michael Spence, and Ricardo Hausmann 2005, “China and the Global Economy: Medium-Term Issues and Opinions.” Center for International Development Working Paper No. 126.

- Ma, Guonan and Robert McCauley, 2002, “Rising Foreign Currency Liquidity of Banks in China.” _BIS Quarterly Review_ , September, 67-74.

- Ma, Guonan and Robert McCauley, 2006, “Are China’s Capital Controls Still Binding?” Mimeo, Bank of International Settlements.

- McKinnon, Ronald, 2005, “China’s New Exchange Rate Policy: Will China Follow Japan into a Liquidity Trap?” _The Weekly Economist_ .

- Mei, Jianping, Jose Sheinkman, and Wei Xiong, 2005, “Speculative Trading and Stock Prices: Evidence from the Chinese A-B Share Premia.” Mimeo, Princeton University.

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- Mishra, Deepak, 2006, “Financing India’s Rapid Growth and its Implications for the Global Economy.” Mimeo, World Bank.

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- Naughton, Barry, 2006, “Top-Down Control: SASAC and the Persistence of State Ownership in China.” Mimeo, University of California, San Diego.

- OECD, 2005, OECD Economic Surveys, Vol. 2005/13, Paris.

27
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31
23

|**Table 4. R**|**esolving the Russian P**|**aradox**||
|---|---|---|---|
|Model|A|B|C|
|_Estimation Method_|_b_|_etween panel estimat_|_or _|
|_Dependent variable_|_de_|_gree of decentralizati_|_on_|
|Real GRP per capita<br>log,PPPadjusted|-0.192<br>(0.043)***||-0.166<br>(0.054)***|
|Real GRP per capita<br>log||-0.215<br>(0.062)***||
|Fuel industry share|-0.0022<br>(0.0008)**|-0.0020<br>(0.0011)*|-0.0026<br>(0.0009)***|
|Excess budget transfer revenue|-0.662|-0.118|-0.639|
||(0.242)***|(0.276)|(0.256)**|
|Social transfers|0.018|0.006|0.020|
|as share of incomein 1995|(0.013)|(0.018)|(0.014)|
|Infant mortality|0.008<br>(0.004)**||0.0057<br>(0.0040)|
|Infant mortality in 1990||-0.00007<br>(0.00065)||
|Gini coefficient||0.189|-0.007|
|(population in 2001)||(0.410)|(0.363)|
|Gini coefficient||0.083||
|(municipalities in 2001)||(0.085)||
|Ethno-linguistic|-0.021|0.043|-0.063|
|fractionalization|(0.081)|(0.148)|(0.135)|
|Privatization rate in 1996|0.0009<br>(0.0007)|0.0019<br>(0.0010)*|0.0009<br>(0.0008)|
|Ethnic republic dummy||-0.036|0.005|
|||(0.057)|(0.047)|
|Area|0.017|0.033|0.014|
|log|(0.010)*|(0.017)*|(0.014)|
|Population|0.045|0.007|0.044|
|log|(0.015)***|(0.025)|(0.025)*|
|Urbanization|0.0031<br>(0.0011)**|0.0057<br>(0.0019)***|0.0036<br>(0.0017)**|
|Population share||-0.032|0.040|
|of the largest city||(0.143)|(0.137)|
|Votes spread in 1st round of elections|0.00020|-0.00004|0.00013|
||(0.00043)|(0.00055)|(0.00045)|
|Students|-0.00024<br>(0.00013)*||-0.00031<br>(0.00021)|
|Education||-0.0048<br>(0.0034)||
|Share of population|||-0.428|
|ofworking age|||(0.511)|
|Distance from Moscow,|||0.019|
|<br>log|||(0.015)|
|constant|0.001|2.124|0.127|
||(0.172)|(0.540)|(0.377)|
|numberofobservations|450|374|449|
|numberofgroups (regions)|76|63|76|
|R<sup>2</sup>between|0.58|0.57|0.60|
|Standard errors in parentheses. Values signifi<br>***.|cant at 10% level are m|arked with *, at 5% w|ith **, at 1% with|
- 13 -

Table 4s Bank-supported VET Investments, By Component and Income Level*

# Income Level

||L|ow|Lower<br>Middle|Upper<br>Middle|All<br>Countries|
|---|---|---|---|---|---|
||Total|(Sub-Saharan<br>Africa)||||
|Secondary||||||
|1963-76|35.0|(38.9)|39.3|32.9|34.4|
|1977-86|17.3|<br>(28.1)|27.5|12.4|19.5|
|Post-secondary||||||
|1963-76|17.9|<br>(16.1)|10.6|36.7|27.5|
|1977-86|15.3|<br>(14.2)|26.0|29.9|26.0|
|Non-formal||||||
|1963-76|42.2|<br>(40.4)|46.1|27.8|34.9|
|1977-86|57.4|<br>(39.4)|43.2|55.2|50.5|
|Teacher Training||||||
|1963-76|4.9|(4.5)|4.0|2.6|3.2|
|1977-86|9.9|(18.3)|3.3|2.5|4.0|

* In percentage of Total VET investments (narrow definition) in each Income category.

Source: Schwartz (1988, forthcoming) Thus the broad pattern of declining proportional investments in secondary institutions, and a corresponding rise in non-formal systems, is differentiated by the income level of the country.

What factors have led to changing investment emphases between general education and VET, and between secondary, post-secondary and non-formal modes of training? Our review of industry VET projects, while not conclusive, offers several interesting hypotheses. Depressed economic conditions, particularly in low income countries, contributed to low productivity of training institutions.
Separately, a survey team of enumerators and health workers employed by the project collected information on worker and health characteristics from all workers. Worker information included employment history, demographic information, place of living, and household characteristics. The registered health worker first asked the worker a brief health history and then prepared a blood slide which was used to microscopically verify his malarial status. All workers who were parasitic positive according to the microscopy results from the collected slides were treated with the appropriate doses of Artemisinin Combination Therapy (ACT).<sup>15</sup> At the end of the fieldwork the activity information collected from the trackers was linked to plantation labor data, the worker and health information from the survey and the malaria test, producing a data set that enables the envisaged two-part analysis carried out below. In the first part we analyze the relationship of physical activity with both labor supply and productivity. The second part estimates the short-run effects of malaria testing and treatment on direct measures of physical activity, as well as labor supply and earnings.

# _Worker sampling and order of treatment_

Two stages of worker selection were undertaken for this study, the second of which is most relevant to the analysis in this paper. The first concerns the offer of malaria testing and treatment, which was temporally randomized over an eight-week period for the entire worker population, stratified by work group. The second sampling activity determines the subsample of workers to receive an accelerometer to wear throughout the study period, forming the sample of interest for this paper.

Selected workers for the accelerometer sample totaled 83. Given a previous positivity rate (35%) from an earlier round of the study (Dillon et al. 2014), we oversampled malaria positive workers due to the ex-ante concern of low power to detect the effects of malaria infection.<sup>16</sup> Given

> 15 ACT treatment consists of a set of pills to be taken twice a day for three days in a row. Diagnosis was completed and reported to the worker within two days of the blood draw.

> 16 As a result of this sampling strategy, sample weights are calculated to reweight to the worker population based on the selection probabilities and the malaria positivity rate. Our regression results presented below do not change substantially if the sample weights are omitted.

13
_Figure 2. Number of SOEs in SOE that are misclassified as private in ORBIS in selected countries_ <!-- Start of picture text --> Cameroon<br>Kosovo<br>Colombia<br>Guyana<br>Cabo Verde<br>Botswana<br>Côte d'Ivoire<br>Kenya<br>Comoros<br>Eswatini<br>Namibia<br>Cambodia<br>Samoa<br>Costa Rica<br>Senegal<br>Angola<br>Benin<br>Philippines<br>Paraguay<br>Peru<br>Morocco<br>Rwanda<br>Nigeria<br>Bhutan<br>Dominican Republic<br>Bangladesh<br>Egypt<br>Nepal<br>Uruguay<br>Vietnam<br>Madagascar<br>North Macedonia<br>Mauritania<br>Afghanistan<br>Hungary<br>Slovenia<br>Sri Lanka<br>Montenegro<br>Poland<br>Indonesia<br>Chile<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br><!-- End of picture text --> <!-- Start of picture text --> Cameroon<br>Kosovo<br>Colombia<br>Guyana<br>Cabo Verde<br>Botswana<br>Côte d'Ivoire<br>Kenya<br>Comoros<br>Eswatini<br>Namibia<br>Cambodia<br>Samoa<br>Costa Rica<br>Senegal<br>Angola<br>Benin<br>Philippines<br>Paraguay<br>Peru<br>Morocco<br>Rwanda<br>Nigeria<br>Bhutan<br>Dominican Republic<br>Bangladesh<br>Egypt<br>Nepal<br>Uruguay<br>Vietnam<br>Madagascar<br>North Macedonia<br>Mauritania<br>Afghanistan<br>Hungary<br>Slovenia<br>Sri Lanka<br>Montenegro<br>Poland<br>Indonesia<br>Chile<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br><!-- End of picture text --> <!-- Start of picture text --> 10%<br><!-- End of picture text --> <!-- Start of picture text --> 20%<br><!-- End of picture text --> <!-- Start of picture text --> 30%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br><!-- End of picture text --> <!-- Start of picture text --> 50%<br><!-- End of picture text --> <!-- Start of picture text --> 60%<br><!-- End of picture text --> <!-- Start of picture text --> 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br><!-- End of picture text --> <!-- Start of picture text --> 90%<br><!-- End of picture text --> Source: Authors’ elaboration based on ORBIS data and BOS database We find that omission error varies significantly across regions and countries. For instance, in Bosnia and Pakistan, at least 40% of the final SOE registry corresponded to firms that were classified as private firms in ORBIS. Supplementary data sources to ORBIS also allowed to reduce the omission error from firms that were not covered in ORBIS even in ECA countries where ORBIS tends to have a better coverage according to the literature (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015).

15