rafmacalaba/datause-encoder-data huggingface dataset:
are provided. These profiles add relevant scope for the relatively new—and still lackluster—capital markets in Southeast Europe. These markets are an inept form of strong capital market efficiency because of their apparent market transaction and information costs. Without liquid daily trading data, the capital markets in Southeast Europe are also likely to be considered inept when using the semi-strong form of capital market efficiency.

Finally, the empirically confirmed weak form of capital market inefficiency in Southeast Europe would bring up considerations that the capital markets may fail to play a fully functional role in the economies of those countries. Such a failure would be apparent in the lack of price discovery, improved transparency, and, ultimately, in an increase in the liquidity and cost effectiveness of those capital markets to be able to provide financing to the most productive economic sectors. In contrast, developed markets are efficient capital markets. This is clearly evident in the absorption of publicly available macroeconomic information in stock prices.

The results in frontier and emerging markets are not so clear, and that is often because of the specific ineptitudes inherent in the business environment. Recent research on the development of capital markets in Southeast Europe using capitalization and turnover ratios shows that there is a relationship between structural and macroeconomic indicators and the fluctuations in gross domestic product (GDP) (Fink, Haiss, & Vuksic, 2005; Lazarov, Miteva-Kacarski, & Nikoloski, 2016; Drazenovic & Kusanovic, 2016). A number of comparative studies also consider capital market efficiency by using bivariate tests on the relationship between macroeconomic indicators and the performance of stock market indexes in Slovenia and Croatia (Barbic & Condic-Jurkic, 2011). This research complements that study with extensive and approximated full-economic cycle data, as well as with more reliable multivariate and panel study tests. A number of researchers have proven that the relationship between macroeconomic indicators and capital markets increases with the length of time that it is under study (Fama, 1990; Binswanger, 1999).

Price variations in capital markets over time are common and related, in plausible ways, to overarching market conditions that incorporate research samples in a specific economic environment. In recognizing the existence of anomalies in the markets of Southeast Europe, this paper investigates the surrounding conditions under which the implicit empirical statistical tests are conducted.

4 | P a g e
on the right-hand tail of the return distribution and more on default probabilities, collateral, and cash flows. Consequently, firms might use bank or bond financing to fund tangible investments, such as PPE, and equity financing to fund intangible investments. The distinction between equity and bond financing is thus particularly important when analyzing the performance of high-R&D firms around capital market issuances. Because of the innovative nature of these firms and their higher dependence on intangible assets, we hypothesize that high-R&D firms would observe relatively higher growth rates around equity issuances than other issuing firms.

To study how high-R&D firms perform around episodes of capital market financing, we extend our baseline specification (Equation 1) and include a high-R&D dummy interacted with the pre-, during, and post-issuance dummies. The estimation results are reported in Table 7. Specifically, for each of the five measures of firm growth, we estimate: G G i i 0 1 P i i 2P I i i 3 i i 4H ii GGh = γγ + γγ5 PPGGPP i i ∗H+ γγ PPI PP 6(P+ γγ PPGGPPGGPI i i PP∗H + γγ HHhRR&DD) ii ii + γγ7(PPGGP i i ∗HHHhRR&DD ) + γγ PPIi PP i i , (3)HHhRR&DD ii ii where H + γγ is a dummy that equals one if the firm is in a high-R&D industry and zero (PPGGPPGGP PP HHhRR&DD ) + δδ + ωω + εε ii otherwise. HHhRR&DD To compute the industry-level R&D intensity, we follow the literature and use the United States as a benchmark.<sup>16</sup> This approach assumes that publicly listed U.S. firms face relatively frictionless capital markets and, thus, their R&D investment is primarily driven by technological demand (Rajan and Zingales, 1998). Specifically, we obtain the ratio of R&D expenditures to total investments for the median firm in the United States within each 2-digit SIC industry over the period

> 16 We conduct several robustness tests using a number of alternative samples and measures of R&D intensity. Notably, we obtain similar results to those reported in Table 7 if we: (a) use the continuous version of the high-R&D dummy (based on U.S. data), (b) exclude the U.S. from the sample, and (c) restrict the sample to the manufacturing industry, defining high-R&D industries as those with a SIC code in the groups 28, 35, 36, and 38.

22
To understand the sub-regional movements further, Table 4 shows the three countries with the largest absolute changes in vulnerability. The decrease observed in the Andean region is driven by Colombia, where the vulnerable population falls by 6 pp (equivalent to 3 million fewer vulnerable people). About 31 percent of the change in Central America is driven by Guatemala, where vulnerability decreases by 6.8 pp (equivalent to 1 million fewer vulnerable).

Table 4. Countries with the largest absolute changes in vulnerability. Millions of people

||**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|
|---|---|---|---|---|---|---|
|_LAC_|_-29_|_-30_|_-31_|_-30_|_-30_|_-30_|
|**Brazil**|-8|-9|-9|-9|-9|-7|
|**Mexico***|-8|-8|-8|-9|-9|-9|
|**Andean Region**|||||||
|Colombia|-3|-3|-3|-3|-3|-3|
|Peru|-2|-1|-2|-2|-2|-2|
|Ecuador|-1|-1|-1|-1|-1|-1|
|**Central America**|||||||
|Guatemala*|-1|-1|-1|-1|-1|-1|
|Dominican Republic|-1|-1|-1|-1|0|-1|
|El Salvador|-1|-1|-1|-1|-1|-1|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

*For Mex: Projections for 2015, 2017, and 2019. For Guatemala: Projections from 2015 onwards

The middle class has slightly increased in the region and remains the largest socioeconomic group in the LAC region since 2010. This group is measured as the share of the total population between $13 and $70 a day or $14 and $81 a day expressed in 2011 PPP or 2017 PPP, respectively. Between 2010 and 2020, changes in the middle class at the LAC level are relatively small as increases in the three subregions (i.e., Andean, Central America, and Southern Cone) and Mexico are offset by Brazil. In 2017, the middle class in the region grew by less than 1 pp (0.9 pp), or 4.8 million more middle-class people, with the 2017 PPPs. The largest changes in millions of middle-class people in 2017 are observed in the Southern Cone (2.2 million more people) and Brazil (2 million fewer people). The change in the Southern Cone is mainly driven by Argentina.

# Regional and country-level profiles

This section focuses on assessing regional profiles of the poor at the upper-middle-income line ($6.85 in 2017 PPP), the vulnerable, and middle-class populations. Even though a significant shift in poverty impacts poverty profiles, these movements are not big enough for inducing changes in policy choices (see Table 5). For instance, the poor are still more concentrated in urban areas, belong to the cohort between 15 and 64 years old, are slightly more educated, work in services, and are less self-employed. Similarly, vulnerability decreases markedly with the new thresholds, but the characteristics of vulnerable people remain the same.<sup>16</sup> The majority of the vulnerable are settled in urban areas, a little bit older by

> 16 Variations are not statistically significant.

19
7 segment A, B1 is likely to be steep, and redistributive policies will not be undertaken because of the need to re!y on highly distortionary tax policies to effect the current transfer.<sup>6</sup> Consider now the intertempora' tradeoff. The no-intervention path leads to A2, whi!e B2 represents the outcome of having redistributed current income during period 1. Here it is assumed that the redistribution of current income leaves the primary distribution of income in future periods unaffected. As depictcd here, the dynamic cost of current transfers is quite large as B2 is well below A2 .' (Because capital transfers involve investments with a long gestation lag, they were generally not among the strategies considered during the 1980s, when the key issue was to determine whether protecting the current income of the poor was feasible in light of the overriding concern with maintaining -- or establishing -- efficiency during the crisis.<sup>8</sup> ) The main objective of the OECD project was to determine to what extent the process of adjustment, which in itself included non-neutral policias, affected the distribution of income. In terms of figure 1, the issue is how much did A2 deviate from ON, that is, how much did strategies that included a transfer redistribution component (B2) deviate from ON and what was the cost of redistributive policies (how much belo X LA2 is LB2). In applying the framework in figure 1 to individual countries, initial conditions are important. For example, as the discussion below indicates, Indonesian and Malaysian authorities placed a relatively high weight on equity issues in their policy choices. In terms of figure 1, this implies that the intertemporal base run scenarios in the modeling exercises would be less steep than the distribution-neutral path A,N in figure 1. Also, to take a specific example, in the Indonesian case, dift._ient combinations of government current and capital expenditures yield significantly different short- and long-run effects on growth and income distribution.
28 six provide a rough estimate of the per unit government subsidy."<sup>7</sup> At higher levels fees paid by students must be subtracted from recurrent expenditures."' The total numbers of students attending each schooling category are derived directly from the SUSENAS data tapes. But, not all students go to public schools. The proportion of all students who are public students (students attending schools run by the MOEC and other government ministries) for 1986/87 is derived from MOEC and BPS statistics. How these compare to 1978 percentages (quoted in Meesook, 1984) can be seen in Table 12.

The relative importance of the private sector has declined for the lower two education levels and increased for the two higher, particularly the tertiary, levels. Unfortunately, the SUSENAS utilization data does not specify which type of facility children attend. This poses a problem in distributing public subsidies across households. The paper follows Meesook, 1984, in assuming that 100 percent of the students in the lowest 40 percent of the per capita expenditure distribution go to public schools, while the rest of the public places are distributed evenly between the remaining expenditure groups.'9 All the amounts relevant to calculating per student subsidies and their sources are presented in Table 13. The methodology described above is essentially identical to that detailed in Meesook (1984). In the next section, the paper combines the per student subsidy estimates with the

**'7** Recurrent expenditures denote expenditures from both the development and routine budgets which are devoted to operations and maintenance of schools.

1s In principle, student fees are meant to cover the non-salary operational needs of schools.

**19** Percentages assumed to be in public school are as follows:

||Primary|Junior<br>Secondary|Senior<br>Secondary|University|
|---|---|---|---|---|
|Lower 40%|100|100|100|100|
|Middle 30%|89.0909|38.70968|27.8619|30.9524|
|Upper 30%|89.0909|38.70968|27.8619|30.9524|
|Total|94|62|47|42|
|First stage<br>Table 8: Export participation and share of workers trained by occupational group,|(1)<br>IV estimates|(2)<br>for switchers|
|---|---|---|
|Dependent variable: Export (=1 if yes)|||
|Industry-specific trade-weighted log real exchange rate|0.004**|0.004**|
||(0.002)|(0.002)|
|F-statistic|10.673|10.771|
|Second stage|||
|A: Dependent variable: share of workers in skill level 4 receiving technical upgrading|||
|Export (=1 if yes)|0.052|0.049|
||(0.067)|(0.065)|
|B: Dependent variable: share of workers in skill level 3 receiving technical upgrading|||
|Export (=1 if yes)|0.086|0.097|
||(0.229)|(0.241)|
|C: Dependent variable: share of workers in skill level 2 receiving technical upgrading|||
|Export (=1 if yes)|0.061*|0.063*|
||(0.034)|(0.035)|
|D: Dependent variable: share of workers in skill level 1 receiving technical upgrading|||
|Export (=1 if yes)|0.104**|0.106**|
||(0.042)|(0.042)|
|Firm effects|Yes|Yes|
|Firm controls|No|Yes|
|Observations|18170|18170|
|Notes: Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05, * <br>year effects. Firms controls are: log employment; average age of workers; average tenure; <br>share of workers with less than high school completed; share of workers with exactly hi<br>workers with more than high school completed; average log wage; share of white workers; <br>of skill levels 1 to 4.|p<0.1. All regre<br> male to female <br>gh school comp<br> and share of w|ssions include<br> workers ratio;<br>leted; share of<br>orkers in each|
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Bruner, Jerome S. and Cecile C. Goodman (1947) “Value and Need as Organizing Factors in Perception”, _Journal of Abnormal and Social Psychology_ , _42_ : 33-44 Burchfield, Marcy, Henry Overman, Diego Puga and Matthew Turner (2006) “The determinants of sprawl: A portrait from space”, _Quarterly Journal of Economics_ 121(2): 587-633.

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- 27 -
62 complete answer to this question requires an analysis of how labor supply to wage employment responds to a change in market wage offers (after taxes). Most empirical labor supply studies in high- and lo-income countries indicate that women respond positively to an increase in their own market wage, and negatively to a wage increase of their husband. Consequently, the taxable supply of women's labor increases with an investmnt in women's capabilities **and** decreases with a conparable investment in sen's capabilities. Moreuver, male labor supply estimates often reveal a tendency for men to reduce their labor supply as their own wages rise.<sup>25</sup> Thus, to the extent that governments recoup public expenditures on human capital investments, the social return to investing in women should increase relative to those from investing in men. 26 A more generally accepted rationale for public expenditures on education of both mon and women is that there **are** social benefits to an educated population that increase welfare in a manner that cannot be readily captured by the more educated individual or their families. This argument for public support of education suggests that a more educated society is more capable of managing a political system that protects individuals' rights and facilitates efficient and equitable growth.. Though these claims are difficult to substantiate, there are more concrete examples that have been empirically confirmed. Most of them favor the schooling of females.
stage, where we plug estimated expected values into a Bellman equation to construct a linear regression and retrieve structural parameters of the model.

# **Step 1: PPML Regression**

In this step, our goal is to estimate expected values _V_<sup> ̃</sup> _t_<sup>_i,s_</sup> and bilateral resistance parameters _Ct_<sup>_ij,s_</sup> . We construct a simple expression for flows between options, similar to the gravity equation, which is essentially a Poisson pseudo maximum likelihood regression available in many different types of statistical software.

The Stage 1 regression equation is where _yt_<sup>_ij,s_</sup> is total number of agents with state ( _i, s_ ) who choose _j_ (hence _yt_<sup>_ij,s_</sup> = _L_<sup>_i,s_</sup> _t_<sup>_mij,s_</sup> _t_ ), Λ<sup>_j,s_</sup> _t_ is the destination fixed effect, Γ<sup>_i,s_</sup> _t_ is the origin fixed effect, and Ψ<sup>_ij,s_</sup> _t_ is the bilateral resistance term. The equation above can be interpreted as a Poisson pseudo-maximum likelihood regression<sup>7</sup> .

_Derivation of the Stage 1 regression equation:_ If we multiply (6) with _L_<sup>_i_</sup> _t_<sup>,weget</sup> then we can arrange the terms as _i_ -specific terms, _j_ -specific terms and bilateral terms. (Note that, we need to drop either destination or fixed effect for one choice. Otherwise the regression matrix becomes singular. Assume that we drop the destination fixed effect for the choice _i_ = 1).

> 7See Gourieroux, Monfort and Trognon (1984) and Cameron and Trivedi (1998) for properties of the PPML regression.

9
Finally, model (5)’s coefficient for _some tracking_ is the difference between males in tracking schools versus those not in tracking schools. As can be seen, there is no significant difference. In other words, we observe a measurable difference in achievement between tracking and non-tracking schools only for females. Model (6) differs from model (5) with the inclusion of controls for other school, student, and family background variables. The conclusions drawn from this model are the same as drawn from (5), except, now they have taken into account differences in the other background variables. The chief conclusion of these results is that _some tracking_ is positively associated with mathematics achievement for females; no evidence of this exists for males. Additionally, the association is different for males than females.

# 7. Low Achievers and Tracking

Low achieving students may benefit from tracking due to a homogenous learning environment or may be inhibited by tracking due to a lack of exposure to higher achieving peers. In the preceding estimations, a significant difference in math achievement associated with tracking is found only among females, and this difference is positive. While this result tends to be consistent with the connection between learning and ability-homogeneous peers, the preceding estimations do not relate this difference to achievement level; it is possible, for example, that tracking associates negatively for low achievers while positively for middle and higher achievers.

Previous studies which sought to measure peer effects among low achieving students typically have baseline data on the achievement of students in their sample (Duflo et al. 2008). For the PISA 2003 dataset, this is not the case. In order to use the PISA dataset to examine differences in tracking’s association across the distribution of achievement, the multinomial logit model for proficiency levels of (4) is estimated. As shown in (4<sup>/</sup> ), this model allows us to estimate the odds ratio associated with _some tracking_ for different consecutive pairs of proficiency levels. This reflects whether this association is stronger or weaker for lower achieving students versus higher achieving students. Table 7 presents estimates of the proportion of general high school, grade 10 students at the various levels of mathematics proficiency. Table 7 reveals that most students are concentrated in levels 3 through 5.

14