rafmacalaba/datause-encoder-data huggingface dataset:
(irrespective of when), and (iii) those that received support only in Round-2, and not in Round-1. In the context of fixed effects regression, the third group – i.e., those that switched government support status across rounds – serves as the treatment group.

While firm characteristics are captured by the firm fixed effects, the endogeneity and selection concerns remain, as discussed above, which we try to address by adding a wide range of time-varying controls from the COV-ES data (see Section 4.5). To further address the selection issue, we estimate the same model for restricted samples. As discussed in Section 4.6, this is done differently for El Salvador and for Georgia. In particular, for El Salvador, we use firms’ responses to the question on reasons that they did not receive government support and drop firms saying that either that (i) they are not eligible for the support, or (ii) they do not need it, or (iii) they applied but did not get it, or (iv) they were not aware of any such support. The firms that provided one of these responses are unlikely to be a good comparison for the firms that received government support because of the likely discrepancy in the way the pandemic shaped firms’ demand and supply. The question on reasons for not receiving support was not asked in Georgia. There, the firms that did not receive government support but also do not expect to receive it in the next three months are excluded from the sample.

Table 11 and Table 12 report results from our main panel fixed effects regressions for El Salvador and Georgia, respectively. We use linear estimation when the outcome variable is the percentage change of permanent full-time workers and logistic regressions for the remaining three outcome variables, which are binary. For each outcome variable, we estimate fixed effect regressions first using the full sample and next using the restricted sample, hence two columns for each outcome variable. Similar to matching estimation results, the effect of government support on the percentage change of permanent full-time workers is positive but not statistically significantly different from zero in both countries. Most of the other coefficients of interest are also not statistically significantly different from zero, apart from the incidence of decreased total hours of operation per week in El Salvador, where firms that received government support were less likely to have done so.

It is important to note the direction of changes in coefficients of interest from full sample to restricted sample. Recall that the samples are restricted to exclude firms that perhaps were less negatively hit by the pandemic, making the control group of firms look better than a purely random 23
reported that of those women who are working, most are likely to be in informal employment. In terms of business ownership and management, only 35 percent of firms are reported as having female participation in ownership, and 41 percent as having a female top manager, despite women having higher average education levels than men (10.3 years of schooling compared to 9.8 for men). Moreover, a recent World Bank study (2020)<sup>3</sup> highlights how nationally women are widely excluded from paid labor, with men being twice as likely as women to be engaged in paid work. According to the report, women’s exclusion from paid labor did not stem from unwillingness to participate but from time consumed on unpaid labor, lack of job opportunities, limited access to credit and lack of skills. Husbands and family members surveyed as part of this report appeared supportive of women accessing paid labor as long as _activities outside the household would not interfere with their full responsibility of housework and child and elderly care._ The Equal Measures 2030: 2019 Report, which examines progress against gender equality in the Sustainable Development Goals (SDGs) across 129 countries, placed Myanmar at 98th. The country’s most significant gender gaps were found in the economic participation of women, equal representation in government/powerful positions, and gender-based violence. In the Asia and the Pacific region, Myanmar scores on the lower end of the gender index positioning itself only slightly above Nepal, Lao PDR, Bangladesh, and Pakistan. Table 1 contains some select Equal Measures indicators showing the gender gap in Myanmar.

**Table 1. Select indicators from Equal Measures 2019 Report**

|**Indicator**|**Score out of 100**|
|---|---|
|Proportion of ministerial/senior government positions held by women|10|
|Proportion of seats held by women in national parliament|20.3|
|Extent to which the country has laws mandating women’s workplace equality|20.0|
|Proportion of women who hold a bank account at a financial institution|26.0|
|Proportion of women who have made or received digital payments in the past year|7.4|
|Proportion of women with access to internet services|27.6|
|Level of personal autonomy, individual rights and freedom from discrimination|31.3|
|Openness of gender statistics|45.0|

**The participation of women in Myanmar’s labor force is less than that of men, as is their representation in senior or decision-making roles across the public and private sectors.** Despite women accounting for 51.8 percent of Myanmar’s population, a United Nations Capital Development Fund Assessment of women and girls’ financial inclusion estimated that women owned only around 25 percent of all MSMEs in Myanmar (28 percent of small enterprises and 20 percent of medium enterprises), predominantly in services, but also in the manufacture of textiles

> 3 Women’s agency in Mon and Kayin States, World Bank, June 2020.

4
- 31 -

31. Law 12 of 1986 is designed to bring<sup>about improvements in the</sup> finances of smaller cities and brought<sup>major changes in the sales tax</sup> transfer.<sup>20</sup> Law 12 has three important objectives: o To strengthen the smaller municipalities,<sup>both by giving them more</sup> resources and by encouraging their<sup>own fiscal efforts.</sup> O To transfer to municipalities certain<sup>national responsibilities</sup> equal in amount to the amolnt of<sup>additional revenues transferred.</sup> O To increase the efficiency of the<sup>local sector; by reducing the</sup> number of agencies working at<sup>the level and by increasing the capacity</sup> of local institutions.

32. The major changes introduced by Law 12<sup>were: (a) the increase in</sup> the proportion shared of the value-added<sup>tax from 30Z to 50% by 1992 and</sup> especially the share going to small<sup>cities (see Table 8);21 (b) the</sup> introduction of a "tax effort" formula<sup>for small municipalities; (c) the</sup> use of population as reflected in the<sup>1985 census as the sole basis for</sup> sharing (modified by "effort" in small<sup>municipalities)--i.e. the</sup> elimination of the 30% share that<sup>formerly was divided equally among</sup> departments; (d) the additional funds<sup>for municipalities in excess of</sup> 25.8Z (1986 share) to be spent on<sup>investments (including maintenance, debt</sup> service and investment in FFDU bonds);<sup>(e) new shares for the Public</sup>

- 20/ The discussion of Law 12 (1986)--its<sup>content and effects and the issues</sup> it raises--closely follows IBRD Colombia<sup>-A Review of Recent</sup> Decentralization Measures (Washington,<sup>D.C.; April 6, 1987), pp. 33-46.</sup>

- 21/ Note that Luw 12 maintains the<sup>retention feature of the previous</sup> allocation system in that for cities with<sup>population of</sup> 100,000-500,000, 302 of their potential<sup>transfers will be redirected</sup> to department FERs while for cities over<sup>500,000, the amount would be</sup> 50%.
# CONTENTS

|1.|VOCATIONAL SCHOOLING IN ISRAEL||
|---|---|---|
||Introduction<br>.... . . . . . . . . . . . . . . . . . . .<br>.....<br>Size, growth and structure of the vocational schooling system.<br>.<br>.|1<br>4|
|2.|VOCATIONAL AND ACADEMIC SCHOOLS: COMPARISON OF LABOR MARKET OUTCOUES||
||Data<br>....<br>. . . . . .<br>. . . . . .<br>. . . . . .<br>. . . . . .<br>. . .<br>.|9|
||Methodology<br>.... . . .<br>. . . . . . . . .<br>. . . . . . . . . . .<br>.|13|
||Regression<br>results<br>.... . . . . . . . . . . . . . . . . . . . .<br>.|14|
||Age cohort<br>analysis<br>. . . . . . . . . . . . . . . . . . . . . . . .<br>|19<br>|
||Training<br>costs<br>.... . . . . . . . . . . . . . . . . . . . . . .<br>.|21|
|3.|VOCATIONAL EDUCATION, OCCUPATIONAL CHOICE AND EARNINGS||
||Education-occupation matchings ... . . . . . . . . . . . . . . .<br>.|26|
||Earnings functions . . . . . . . . . . . . . . . . . . . . . . . . .|29|
||Costs and benefits of vocational schooling . . . . . . . . . . . . .|38|
|4.|VOCATIONAL EDUCATION AND NON-FORMAL TRAINING ALTERNATIVES . . . . .|41|
|5.<br>REF|SUMMARY AND CONCLUSIONS .... . . . . . . . . . . . .<br>. . . . .<br>.<br>ERENCES|46|

APPENDIX: Matching of Vocational Education Course with Occupation

- iv -
35 Appendix Table A2: Dependence of cotton-producing developing countries on cotton export earnings, average 2000-02

||Share of<br>total<br>merchandise<br>export<br>revenue<br>from cotton<br>(%)|Value of<br>cotton<br>exports<br>(US$m)|National<br>share of<br>global<br>value of<br>cotton<br>exports<br>(%)|Cumulative<br>share of<br>global<br>value of<br>cotton<br>exports<br>(%)|Per<br>capita<br>income<br>(US$)|
|---|---|---|---|---|---|
|Benin|46.8|131|1.9|1.9|380|
|Burkina Faso|37.2|94|1.4|3.3|250|
|Chad|32.9|59|0.9|4.2|210|
|Uzbekistan|23.8|747|11.1|15.3|310|
|Mali|22.4|161|2.4|17.7|240|
|Tajikistan|12.3|89|1.3|19.0|180|
|Togo|9.1|35|0.5|19.5|270|
|<br>Turkmenistan|7.6|201|3.0|22.5|1120|
|Kyrgyzstan|6.7|33|0.5|23.0|290|
|Zimbabwe|6.3|133|2.0|25.0|<730|
|Guinea-Bissau|5.0|3|0.0|25.0|130|
|Paraguay|4.9|58|0.9|25.9|1170|
|<br>Cameroon|4.6|87|1.3|27.2|550|
|Tanzania|4.0|31|0.5|27.6|290|
|Syria|3.9|221|3.3|30.9|1130|
|<br>Central African Rep.|3.8|6|0.1|31.0|250|
|<br>Egypt|3.2|216|3.2|34.2|1470|
|Côte d'Ivoire|3.2|137|2.0|36.2|620|
|Sudan|2.9|50|0.7|37.0|370|
|Greece|2.6|276|4.1|41.0|11660|
|Mozambique|2.1|14|0.2|41.3|200|
|Uganda|1.7|8|0.1|41.4|240|
|Australia|1.5|943|14.0|55.4|19530|
|Malawi|1.2|5|0.1|55.4|160|
|Senegal|1.1|9|0.1|55.6|470|
|Kazakhstan|0.9|85|1.3|56.8|1520|
|Pakistan|0.9|85|1.3|58.1|420|
|Zambia|0.8|9|0.1|58.2|340|
|Ethiopia|0.8|4|0.1|58.3|100|
|<br>Guinea|0.5|3|0.0|58.3|410|
|Bolivia|0.4|5|0.1|58.4|900|
|Gambia|0.4|0|0.0|58.4|270|
|Ghana|0.3|5|0.1|58.5|270|
|United States|0.3|2121|31.5|90.1|35400|
|Azerbaijan|0.3|6|0.1|90.2|710|
|**WORLD**<br>Source: FAOSTAT|**0.1**<br>database at|**6656**<br>www.fao.o|**100.0**<br>rg<br>,except f|**100.0**<br>or final col|**5510**<br>umn whic|

Source: FAOSTAT database at <u>www.fao.org, except for final column which is from the</u> World Bank’s _World Development Indicators 2004_ for the year 2002.
way that the investment climate surveys address these issues is by asking enterprises how great a burden trade and customs regulations are to enterprise operations and growth. In most of the countries covered by this study, enterprises involved in exporting were significantly more likely to say that trade and customs regulations were a serious obstacle than exporters in the three Asian economies (see Table 5). Whereas about 40 percent of enterprises involved in exporting claimed that customs and trade regulations were a serious obstacle in the eight African countries, only 28 percent of exporters did the same in Asia.

Enterprises involved in exporting were more likely to complain about trade and customs regulations than non-exporters. Whereas about 40 percent of exporters said that trade and customs regulations were a serious obstacle, only 33 percent of non-exporters said the same. This pattern held for most of the individual country samples and also held in the Asian economies. This is not surprising—exporters would seem to be more likely to have to interact with customs and trade officials and to comply with regulations than enterprises not involved in exporting.

In addition to the qualitative questions, enterprises were also asked several quantitative questions about trade and customs regulations. One question that was asked on all of the surveys was the longest and average time that it takes imports and exports to clear customs after arriving at the point of entry or exit in their country.<sup>8</sup> Although some countries, such as Zambia and Uganda, appear to perform relatively well with respect to delays, the average delay for both imports and exports is significantly longer in many of the African countries than in China or the Philippines (see Table 5). Enterprises were more likely to report that customs and trade regulations were a serious obstacle in countries where processing delays were longer. The crosscountry correlation between the percent of exporters that rated trade and customs regulations as a major obstacle and days to clear customs for exports and imports are 0.52 and 0.49 respectively.

> 8 Consequently, it can include delays other than delays simply due to customs procedures (e.g., port delays).  It is important to note, however, that most exporters in the sample countries export to neighboring countries.  The reason that the question is asked in this way is that entrepreneurs typically know only the total delay, not who is responsible for it.  This is especially likely to be true when they use agents to process imports or exports.

8
humans to be complementary to machines. Our assumption implies, empirically, that individuals employed in occupations that are complementary to automation should have better levels of educational attainment and skills; however, an unresolved question is what types of skills complement automation and, subsequently, what types of educational attainment provide these skills? For example, Frey and Osborne (2013) and Brynjolfsson and McAfee (2011, 2014) find that non-cognitive skills including creativity, social-intelligence as well as advanced perception will be humans’ comparative advantage over machines given current technological trends.

We test several hypotheses about which types of skills and educational attainment are positively associated with being in a non-automation-prone occupation. First, we test the hypothesis, more general than the automation literature, that cognitive skills and non-cognitive skills are positively associated with non-automation-prone occupations. In the PIAAC and STEP we use literacy achievement as a measure of cognitive skills. In the STEP data, we use the “Big 5” personality traits. Second, we test whether general education versus vocational or technical education is positively associated with non-automation-prone occupations. Finally, we test whether postsecondary general education is better than general secondary programs. In doing so we validate the underlying assumptions of the model but also better define what types of skills associate with employment that is complementary to automation and which types of education programs best promote these skills.

Estimates of logit model (8) applied to 21 PIAAC country data sets are presented in Table 1, and 7 STEP country data sets are presented in Table 2. In this model, the dependent variable is being in an automation-prone occupation; positive coefficients imply that a variable is positively associated with being in an automation-prone occupation. First, in all countries, there is a positive and statistically significant difference in the likelihood of being in an automation-prone occupation between those with general upper secondary education and those with general postsecondary education. Second, there is a negative and statistically significant difference between those in general upper secondary and those in vocational upper secondary in 9 countries and a positive association in 1 country, but in the remaining 15 countries (which have a vocational upper secondary track), there is no statistically significant association. Third, in half of the countries, literacy achievement has a statistically significant and negative association with being 25
2007), but this variable is left out in the regression for the enrollment/tutoring index since it applies only to children currently enrolled in school.

For easier interpretation of results, we estimate equation (1) for all the outcomes above (except for expenditure and hours spent on tutoring) using an OLS model, where results can be readily read off of the estimated coefficients. For expenditure and hours spent on tutoring,<sup>27</sup> we use a Tobit model instead since a large number of children have zero values for these variables and subsequently provide separate estimates for the marginal effects. Let Eij∗ be the latent variable that represents the household’s potential spending (or hours) on tutoring; we observe tutoring spending Eij only when this potential spending is larger than 0, and observe zero spending otherwise. The Tobit model has the form It should be noted that there are two main ways to interpret the marginal impacts of the explanatory variables in a Tobit model, depending on the outcomes of interest. If we are interested in looking at the marginal effects of family size (or other explanatory variables) on household propensity to spend on tutoring classes, we can just look at the estimated coefficients in Table 6. However, if we want to know the marginal effects on households’ actual (observed) spending, we should look at the marginal effects provided in Table 1.2.<sup>28</sup> While the former interpretation may be more relevant for forecasting the future, the latter is

> 27 While the number of years of tutoring can also be fitted in a Tobit model, we prefer to use the OLS model for better interpretation. Estimation results using an IV-Tobit provide very similar results.

> 28 For a survey on the Tobit model, see Amemiya (1984). The marginal impacts for household propensity to spend can be calculated as ∂E(Ei∂FamSize∗j|FamSizeii,Zij) = β, and the marginal impacts for household actual spending can ∂E(Eij|FamSizei,Zij) α+βFamSizei+γZij be calculated as ∂FamSizei = βΦ( σ ~~)~~ where we also assume εij~N(0, σ<sup>2</sup> ) as in the OLS models. See, for example, Greene (2012) for more discussion on the marginal effects with the Tobit model.

16
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the same information as the potential employer at the time of her decision whether to further screen a job seeker. I find that after controlling for other covariates, stating that one is an immigrant is associated with receiving about 10-20% fewer employer visits to the profile page than job seekers who state they are South African.

This unique data set also allows me to at least partly address previous studies’ limitation that the pool of applicants from which employer can hire (in addition to the fictitious applicant) is not observed. This is an important concern as the size, qualification and racial composition of the applicant pool affect the cost of discrimination. Intuitively, the fewer and relatively less qualified applicants of the preferred race, the more costly are discriminatory hiring practices.<sup>4</sup> For the classified ad market I investigate, we observe the full set of available candidates presented to employers in her local labor market. What is more, the nationality composition presented to employers is quasi-random as the order is determined by the time of the day that job seekers post their profile. Consistent with theoretical predictions from a tournament model in which employers discriminate against foreigners, I find that being pooled with a larger share of immigrants benefits _both_ foreign and South African job seekers. Employing an estimator that soley exploits variation in the composition of the choice set is a methodological contribution that may be applied to estimate preferences in otthe scenarios.

Exisiting audit studies have been criticized on the ground that minority workers may be able to identify discriminating employers, which would alleviate the effects of employer discrimination (Heckman (1998). In an extension of Becker (1957), Arrow (1973) shows that the wage differential between groups can disappear if there are a sufficient number of non-discriminating employers. In equilibrium, this may result is a (partially) segregated employment without (wage) discrimination. The extent to which (wage) differentials disappear depends on how well minorities can target non-discriminating employers in their job search (Black (1995); Lang and Lehmann (2012)). On the website, I observe both where job seekers live and the location where they search for work. These data allow me to shed some light on whether minority applicants adjust their search behavior in response to discrimination. Employing a residential location fixed effect strategy, I find that immigrants search 21% further away and suggestive evidence that higher levels of discrimination faced in the area of residence induces immigrants to search for work in a different suburb. Geographic sorting in response to discrimination

> 4A related challenge to interpreting audit studies is that results are consistent with a world in which employers’ racial preferences are proportionate to the population’s racial composition but in which the share of minority applicants is disproprtionately high.

2