rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 1. Occupation and skill categorizations using machine learning**

By processing text information in job posts and jobseekers’ CVs, we identify their occupations and associated skill categories. Our text processing method basically follows the one developed by Atalay et al. (2018). By estimating a continuous bag of words (CBOW) model, which uses machine learning, job titles and descriptions in job posts are mapped to, respectively, ISCO08 occupations and skill categories. The main output of a CBOW model is to find synonyms for input words and phrases. That is, once a model is estimated, it tells how similar two words are to each other. The model judges the similarity between two words based upon whether these two words are used in “similar” contexts. For example, the model concludes that _realtor_ and _real estate agent_ are synonyms of one another if they tend to be used near similar words, like _sales_ , _purchase_ , _buildings_ , and _land_ .

In our text processing, each job title in the online job data is mapped to the ICSO08 occupation title that our CBOW model finds is the most similar to the job title. A jobseeker’s ISCO08 occupation is identified by finding the ISCO08 occupation title that is most similar to the jobseeker’s previous job title. Skill requirements in job posts are identified in a similar manner. We first adopt a set of keywords for each skill category from previous studies, namely Deming and Kahn (2018) and Spitz-Oener (2006), then modify these keywords in the context of the Pakistan online job portal by applying a CBOW model, and lastly look at whether a job <u>description includes any of these keywords.</u> We also identify required skill categories by applying a machine learning method that captures skill-related words in job descriptions. Table 3 presents what percentage of jobs require each skill category. Nonroutine analytic skills are much more frequently required than routine cognitive skills. Nonroutine interactive skills and soft skills—namely social, character, and customer service skills—are frequently required in all occupations. Computer skills are required in about one-third of the jobs of professionals and clerical support workers. Table A1.2 presents, for each skill category, the most skill-intensive occupations at the ISCO08 two-digit level. The occupation of science and engineering professionals is the most intensive occupation in nonroutine analytical skills; teaching professionals in nonroutine interactive skills; assemblers in routine cognitive skills; legal, social and cultural professionals in writing skills; and hospitality, retail and other services managers in people management.

16
Table S.24: Impacts on children’s educational achievement - General science: complementarity <u>(OLS)</u>

|Dependent variable:||G|eneral science:|GPA z-sco|res||
|---|---|---|---|---|---|---|
|_X_:|No. of||Schoo|l-level ratio|of||
||students<br>in each|no. of<br>teachers|no. of<br>blackboards|no. of<br>benches|no. of<br>fans|no. of<br>toilets|
||school-|to|to|to|to|to|
||grade|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|0.122|-0.316|0.036|0.183|0.012|-0.054|
||(0.214)|(0.298)|(0.365)|(0.391)|(0.086)|(0.098)|
|One product|0.412**|0.062|0.269|0.553*|0.065|-0.134|
||(0.167)|(0.302)|(0.270)|(0.281)|(0.077)|(0.117)|
|All products _× X_|-0.001|15.216|0.768|-0.300|9.201**|27.496|
||(0.003)|(11.079)|(16.472)|(0.808)|(4.047)|(26.193)|
|One product _× X_|-0.007**|-0.934|-12.144|-1.173*|-5.425|45.031*|
||(0.003)|(12.333)|(13.003)|(0.599)|(7.647)|(26.201)|
|No. of students|-0.005|-|-|-|-|-|
|in each school-grade|(0.006)||||||
|R-squared|0.303|0.299|0.298|0.300|0.301|0.300|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.281|0.110|0.126|-0.319|-0.101|-0.178|
||(0.209)|(0.302)|(0.226)|(0.223)|(0.091)|(0.142)|
|One product|-0.239|0.203|0.176|-0.168|-0.053|-0.296**|
||(0.268)|(0.397)|(0.318)|(0.367)|(0.098)|(0.139)|
|All products _× X_|0.004|-8.855|-11.964|0.496|-0.099|19.661|
||(0.003)|(12.302)|(11.357)|(0.450)|(3.506)|(27.584)|
|One product _× X_|0.003|-11.066|-12.549|0.241|-2.046|61.741**|
||(0.005)|(14.569)|(14.982)|(0.797)|(8.351)|(30.417)|
|No. of students|-0.002|-|-|-|-|-|
|in each school-grade|(0.004)||||||
|R-squared|0.150|0.149|0.150|0.149|0.148|0.153|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

69
Table 8 Exploration of FDI Productivity Effects Prior to and Following WTO Entry at the end of 2001

||A|ll|Foreign-inv|ested firms|Domes|tic firms|
|---|---|---|---|---|---|---|
||Non-SOEs|SOEs|Non-SOEs|SOEs|Non-SOEs|SOEs|
|Foreign share (by|-0.00444|0.0389|0.00610|-0.0477|||
|HK-Taiwan-Macau)|(0.00287)|(0.0564)|(0.00483)|(0.0671)|||
|Foreign share (by other countries)|0.00418|0.107***|0.0159***|0.0844|||
||(0.00300)|(0.0376)|(0.00499)|(0.0713)|||
|State share|-0.00351|-0.0252***|0.00191|0.0155|-0.00320|-0.0255***|
||(0.00289)|(0.00440)|(0.00827)|(0.0307)|(0.00331)|(0.00439)|
|Hor_1998|0.199**|0.0630|0.296***|-0.731|0.119|0.0676|
||(0.0815)|(0.112)|(0.0787)|(0.528)|(0.0895)|(0.112)|
|Hor_1999|0.158**|0.0463|0.186**|-0.719|0.126|0.0479|
||(0.0781)|(0.0948)|(0.0772)|(0.526)|(0.0832)|(0.0946)|
|Hor_2000|<br>0.216***|<br>0.123|<br>0.170**|<br>-0.561|<br>0.217***|<br>0.121|
||(0.0699)|(0.0779)|(0.0656)|(0.550)|(0.0721)|(0.0767)|
|Hor_2001|0.158**|0.0866|0.0992|-0.820|0.157**|0.0889|
||(0.0744)|(0.0708)|(0.0735)|(0.542)|(0.0750)|(0.0699)|
|Hor_2002|0.0835|0.0422|0.0217|-0.344|0.0862|0.0480|
||(0.0782)|(0.0974)|(0.0730)|(0.480)|(0.0789)|(0.0955)|
|Hor_2003|0.0598|0.0217|-0.0145|-0.424|0.0884|0.0238|
||(0.0801)|(0.0887)|(0.0753)|(0.496)|(0.0821)|(0.0890)|
|Hor_2004|0.188*|0.0596|0.142|0.386|0.200*|0.0476|
||(0.104)|(0.134)|(0.0969)|(0.422)|(0.113)|(0.134)|
|Hor_2005|0.196|0.0872|0.171|0.148|0.216|0.0741|
||(0.123)|(0.143)|(0.118)|(0.359)|(0.130)|(0.142)|
|Hor_2006|0.244*|0.118|0.263**|0.423|0.253*|0.106|
||(0.129)|(0.164)|(0.131)|(0.456)|(0.133)|(0.163)|
|Hor_2007|0.289*|0.227|0.322**|0.176|0.309*|0.207|
||(0.163)|(0.186)|(0.151)|(0.491)|(0.175)|(0.184)|

46
that a significant fraction of the growth in these countries might have come from more efficient private firms that displaced the SOEs (Hsieh and Klenow, 2009).

# **4. Firm dynamics and the use of capital markets**

We now investigate the link between firm dynamics and their use of equity and bond markets. It is well-known that the larger firms within an economy have greater access to capital markets, due at least in part to cost and liquidity considerations. In practice, these considerations render the minimum issue size rather large for smaller firms (Beck et al., 2006). But even among the publicly listed companies, not all firms actually raise capital in capital markets on a recurrent basis, as shown above. Therefore, we analyze which firm characteristics are related to the probability of raising capital in bonds or equity markets. We also study the firm dynamics around the capital raising activity and the implications of our findings for the distribution of firm size.

# **4.1.  Which firms use capital markets?**

To conduct the analysis, we rely on our merged data set that combines the SDC Platinum database on the use of capital markets with firm-level balance sheet information on the post-IPO period from Orbis and Worldscope.<sup>22</sup> We split firms into users and non-users of capital market financing, according to whether firms issue equity or bonds within our sample period. Because the firm-level balance sheet information is only available for the 2003-2011 period, we classify a firm as a user of equity or bond markets if it had at least one non-IPO capital raising between 2003 and 2011. Non-user firms are those that do not issue any equity or bond after their IPO. Based on the matched SDC-Orbis data set, in China, 425 firms are equity users, 195 are bond users, and 1,915 are non-users; in India, those numbers are 727, 291, and 3,428, respectively.

> 22 Because we work with listed firms, all firms had an IPO during or before our sample period. Moreover, because we do not have accurate information before their IPO, we cannot compute growth rates of variables of interest between the IPO and the pre-IPO year. Therefore, we exclude from the analysis the balance sheet information for the IPO year.

19
Grossman, G. and E. L. C. Lai, 2002. International Protection of Intellectual Property. NBER Working Paper 8704.

Grubert, H., 1998. Taxes and the Division of Foreign Operating Income among Royalties, Interest, Dividends, and Retained Earnings. _Journal of Public Economics_ 68: 269-290.

- Hakanson, Lars, 1990.  International Decentralization of R&D – the Organizational Challenges.  In. C. Bartlett, Y. Doz, and G. Hedlund, Eds., _Managing the Global Firm_ .  Routledge: London.

- Hall, B., A. Jaffe, and M. Trajtenberg, 2001.  The NBER Patent Citation Data File: Lessons, Insights, and Methodological Tools.  NBER Working Paper 8498.

- Hayes, R. and S. Wheelwright, 1988. _Dynamic Manufacturing:  Creating the Learning Organization_ .  The Free Press: New York, NY.

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Heston, A., R. Summers, and B. Aten, 2002. Penn World Table Version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP).

- Hines, J., 1995.  Taxes, Technology Transfer, and R&D by Multinational Firms.  In M. Feldstein, J. Hines, and R. G. Hubbard, eds., _Taxing Multinational Corporations_ . University of Chicago Press.

Keller, W., 2002. Geographic Localization of International Technology Diffusion. _American Economic Review_ 92: 120-142.

Kuemmerle, W., 1999. The Drivers of Foreign Direct Investment into Research and Development: An Empirical Investigation. _Journal of International Business Studies_ , 30 (1), pp. 1-24.

- Lai, E.  1998.  International Intellectual Property Rights Protection and the Rate of Product Innovation. _Journal of Development Economics_ 55:  pp. 133-153.

- Lanjouw, J., 1997.  The Introduction of Pharmaceutical Product Patents in India: “Heartless Exploitation of the Poor and Suffering?”  NBER Working Paper 6366.

- Lee, J. Y. and E. Mansfield, 1996.  Intellectual Property Protection and U.S. Foreign Direct Investment.  The _Review of Economics and Statistics_ 78:  181-186.

Lerner, J., 2001. 150 Years of Patent Protection. NBER Working Paper No. 7478. Lerner, J., 2002. Patent Protection and Innovation over 150 Years. NBER Working 40
and average of 2005-2007. The only country where shadow production share increased (by negligible 0.4 percentage points) was Portugal. In contrast, extensive shadow economies (the Baltics, Bulgaria, Romania, Greece etc.) shrank the most, weakening their leading positions in the countries’ ranking. Faster reduction of shadow economy was generally recorded in all the new member states; Finland and Sweden also decreased the share of their respective shadow economies considerably.

**_Figure 2: Increase/decrease (+/-) of the shadow economy in Europe, average 1999-2001 compared to average 2005-2007, difference in percentage points_**

<!-- Start of picture text -->
1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>-3.5<br>-4.0<br>Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Norway Poland Portugal Romania Slovak Republic Slovenia Spain Sweden United Kingdom<br><!-- End of picture text -->

_Source: Schneider et al. (2010), own calculations_

# **2.** **<u>Factors influencing the shadow economy</u>**

Previous part showed that the overall trend in size of shadow economy in Europe has been towards further growth in recent years. That means that the relevance of this issue increases in time. What are the main factors driving economic subjects to informality? This and the next sections offer a short literature overview. Generally, there exist no general and universal factors determining the existence, size and development of a shadow economy. Instead, it is a result of a complex interplay of various factors varying between countries. Moreover, economic factors can only partly explain the development of shadow economies; interdisciplinary approach to this issue is necessary (see Enste, 2003).

_<u>Level of economic development</u>_ is often considered one of the most important factors determining the size of shadow economy: less developed countries tend to have larger informal sectors (see Perry et al., 2007). In contrast, no consensus exists as regards the development of informality over the business cycle. Countercyclical development would be expected based on the view that informal sector mainly consists of employment excluded from formal sector as a result of labor market rigidities. This traditional view was supported by research of Loayza and Rigolini (2006). However, in a broader perspective taking into account also the voluntarily opt-out of formal sector, pro-cyclical development might be advocated. This is supported by the view that informal workers are not covered by employment protection and firms are free to dismiss them during downturns, enabling also more flexibility in hiring during expansions. People might also be more likely to decide on 8
Based on the investigation, the Ministry of Economy, Industry and Trade (MEIC, in Spanish) finally decided to apply a provisional measure that would be a tariff increase from 54% to 140% for a period no longer than 30 days. The implementation of the measure implied that the National Cabuya Board would commit itself to improving the use of the established capacity and production in order to have a better situation once the measure expired, an aspiration that does not seem quite reasonable because it entails long-term aspects that require much more than 30 days.

It is equally interesting to point out that the Investigating Authority also determined that, although it was true that there had been a substantial increase in cabuya sacks imports in 1995, to a great extent this was a response to the lack of supply in the domestic market, because since 1992, domestic production failed to meet domestic demand.

In 1997, the MEIC lifted the provisional measure that had been imposed based on the realization that its application had gone on for over six months, when it actually should have only been applied for 30 days. Therefore, and after verifying that the domestic industry had not made anyadjustments or improvements, the MEIC finally decided to close the investigation in April 1998, and not to impose any final measure.

This confirms that, although the imposition of the safeguard measure does not seem to have stemmed from protectionist intentions, its incorrect implementation did provide a margin of protection beyond what was contemplated.

# **4.c Summing Up**

The figures in Table 4.8 show that the applications for the imposition of safeguards and anti-dumping measures have mostly focused on manufactured goods, not surprising in view of the fact that Costa Rica still retains a high protection rate for basic consumption agricultural products.

**Table 4.8   Costa Rica: Summary of Cases Dealt With by the Investigating Authority**

|Measures|Manufactures<br>Secto|Agriculture<br>rs|Total|
|---|---|---|---|
|Safeguards|4|1|5|
|Antidumping|4|2|6|
|Total|8|3|11|

Source: Prepared by the authors based on data drawn from the resolutions on cases dealt with by the MEIC.

By the same token, Figure 4.1 shows that the applications for the imposition of measures not only have been rather balanced between safeguard and anti-dumping measures, but also have had a relative lag between the date of entry into force of the rules (1995) and the year when the last case was submitted (2000).

36
- 28 -

use of **health** facilities and schools. The resolution of this dispute depends on empirical evidence, and the LSMS data can be used to answer these questions.

In the area of health care, Certlcr and van der Caag (1989) hive used the Cote d'Ivoire and Peru data to answer the question: What will happen to the poor if higher fees are charged for publLcly provided health care services? Using a general model of household health care decisions, they esrimate price elasticities for different types of health care. These TABLE 71 **Effects of Increased User Fees on** Health Care Utilization in Cote d'Ivoire and **Peru**

||F|raction of|Ill Populat|ion Seeki|ng Health Car|e From||
|---|---|---|---|---|---|---|---|
|||Ho<br>Base|spital<br>Increased<br>Fees|Cl<br>Base|inic<br>Increased<br>Fees|Priv<br>Base|ate Doctor<br>Increased<br>Fees|
|C6te d'Ivoire|West Forest|22%|13%|23Z|22%|-|-|
||Savannah|15%|1%|18%|17%|-|-|
|Peru|Coastal|9%|8%|7%|7%|12%|_12%_|
||Sierra|11%|4Z|10%|9%|3%|6%|

Note: 1. Base case represents present fee structure. Increased fees represent substantial cost recovery. See Gertler and van der Caag (1989) for details.

2. For Peru assume private doctor prices remain unchanged.
Households: Excessive leverage and exposure to FX borrowing are relevant only for a minor share of households in the region. Debt overhang does not seem to pose a threat to aggregate investment and consumption in the household sector in any particular country.

Banking sector: Substantial levels of non-performing loans suggest that debt overhang may constrain future lending in several countries (e.g. Latvia, Hungary).

The above analysis suggests that the majority of policies taken by authorities in the region hardly address growth constraints arising from debt overhang: Authorities in most countries have focused on **policy measures to curb FX lending** and cushion the effects of exchange rate depreciations on existing FX borrowers, especially in the household sector. Our analysis suggests that these will have little effect on aggregate investment and consumption in the private sector. Indeed it is even questionable whether these measures are well targeted to deal with systemic risk in the banking sector. Recent data from the Hungarian Central Bank, for example, show that the share of non-performing loans is twice as high for local currency loans (12%) than for foreign currency loans (just over 6%). This suggests that the systemic risk to the banking sector in Hungary is currently arising from the real economy rather from foreign currency denominated lending.

Authorities in selected countries (e.g. Latvia and Hungary) have taken measures to **shift the burden of debt from households to banks** . These measures seem to be counterproductive in view of future economic growth: the marginal debt levels of households in the region suggests that over-indebtedness will hardly constrain economic activity in the household sector, while debt overhang in the banking sector seems to already pose a greater threat. From the viewpoint of social policy, the “relief” measures for the household sector also seem misguided as those households with mortgage debt are the more wealthy ones.

Should authorities in the region then take measures instead to shift the debt burden away from the banks instead? Should they follow the example of some industrialized countries in the recent crisis and East Asian countries at the end of the 1990s by using public funds to **take “toxic” assets off banks’ balance sheets** ? Our view is that such measures may not be necessary: those countries in the region that have experienced substantial losses in the banking sector are also characterized by a dominance of foreign-owned banks. As illustrated in section 4.3, these banks have shown the ability and willingness to support their subsidiaries in Emerging Europe even in times when they were facing substantial challenges in their home markets. It seems plausible that these international banks will provide continued support to their subsidiaries in terms of the necessary capital and debt funding. Finally, it seems both politically and economically infeasible that authorities in Emerging Europe could use taxpayer funds to subsidize the activities of foreign-owned banks.

47
In 1993 union members earned lower wages than non-members by 10, 30 and 29 percent less in small, medium, and large firms, respectively. In 1999 the effect of union membership on wages by firn size was increasingly negative.

# vi. Type of Contract

Both permanent and temporary worker contracts are examined in order to consider how these two types of contracts affect workers' wages and productivity. The main differences between the workers in these contracts are their employment benefits, the duration of these benefits, and their skills. Employers have been found to be more likely to invest in human capital if they were planning on retaining their employees over the long term.

||||Table17:<br>|Typeofco|ntract||||
|---|---|---|---|---|---|---|---|---|
|Variable||1|93|||1|99||
|Twork|Wage|P>jzl<br>|Productivity|**P>Izl**|Wage|P>Izl<br>|Productivity|**P>Izl**|
|ooled|-0.09|0.00|-0.09|0.00|0.03|0.04|<br>0.07|0.07|
|icro|-0.11|0.11|0.12|0.32|0.03|0.57|0.27|0.03|
|Small|_-0.15_|0.00|-0.03|0.61|0.04|0.33|0.17'|0.05|
|Medium|-0.06|0.02|-0.09|0.04|0.02|0.39|0.02|0.76|
|Large|-0.09|0.00|-0.03|0.46|0.04|0.09|<br>-0.11|0.11|

_Source:_ Authors' calculations based on ENTRAM-ENESTYC Table 17 shows the estimated wage differentials associated with a worker's type of contract - temporary or permanent. In 1993 permanent workers earned an average of 9 percent lower wages and were 9 percent less productive than temporarily-employed workers. However, the results shifted in 1999, whereby permanent workers earned 3 percent higher wages and were 7 percent more productive than temporary workers. Furthermore, we cannot reject the test of hypothesis that the wage and productivity coefficients are equal.

19