rafmacalaba/datause-encoder-data huggingface dataset:
We combine administrative records at the student level with information on higher education supply from the National System of Information on Higher Education (SNIES, in Spanish), which contains HEI-level information such as HEI type, geographic location, and number and types of programs offered. Using SNIES, we classify HEIs into those that can only offer SC programs (SC HEIs) and those than can offer all programs (other HEIs). We compute binary indicators for the existence of these HEI types in our students’ high school municipalities by using HEIs’ geographic locations reported in SNIES. We link our dataset to the Municipal Panel of the Center of Studies on Economic Development (CEDE, in Spanish), which contains municipality-level information such as population (urban, rural, and total), GDP, and area. We match the municipal- and student-level data using the municipality identifier of students’ high school. Last, we restrict our estimating sample to students in high schools with more than 20 students. The final dataset consists of 328 _,_ 358 students, or 81% of all _Saber 11_ test-takers in 2005.

# **3.1 Descriptive statistics**

As Table A1 shows, 53 _._ 8% of high school graduates in the sample are women, 27 _._ 2% come from low-income households (with income lower than the minimum wage), and 42 _._ 5% have mothers with at most primary education. More than half of high school graduates (61 _._ 2%) did not enroll in higher education ("NE students").<sup>8</sup> Meanwhile, 9 _._ 4% enrolled in SC programs ("SC students") and 29 _._ 4% in a bachelor’s program ("BP students"). Further, 3 _._ 6% and 14 _._ 8% of high school graduates obtained a SC degree and bachelor’s degree, respectively, and half (49 _._ 3%) participated in the formal labor market in 2013.

Table 1 compares SC, BP, and NE students. Among the three student groups, NE students come from the most disadvantaged backgrounds and have the lowest _Saber 11_ average performance. Relative to BP students, on average SC students are less likely to be female. They are also slightly older; belong to larger, lower-income households; have less educated mothers; and perform worse in _Saber 11_ . The share of students who complete any degree is higher among BP than SC students by almost 10 percentage points, while the share of BP students transferring out of their initial enrollment choice is lower than that of SC students (1 _._ 2% and 16 _._ 2%, respectively). Moreover, less than half of SC students who transfer to a bachelor’s program earn a bachelor’s degree. This is consistent with the fact that SC programs in Colombia are usually viewed as terminal degrees and rarely offer a pathway towards a bachelor’s degree. In terms of labor market outcomes, SC and BP students participate in the formal market at similar rates (51 _._ 7%

> 8These students do not match to SPADIES (the dataset of higher education enrollment) or OLE (the dataset of labor market earnings). SPADIES does not contain information on SENA students; therefore, we classify them as NE. Since they account for a negligible share of higher education enrollment in 2005, this does not pose a problem for our analysis.

8
<!-- Start of picture text --> 10.00<br>9.00<br>8.00<br>7.00<br>LOW INCOME<br>6.00 LOWER MIDDLE INCOME<br>UPPER MIDDLE INCOME<br>HIGH INCOME<br>5.00<br>Z-Score<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> 32
somewhat less front-loaded). In addition to the speed of liberalization, another important difference is that initial tariffs in Tunisia and the Mediterranean more generally are much higher than in the CEECs (see Appendix Table 1). Average protection levels in the CEECs are in the 6% range, as compared to over 15 percent for many Mediterranean countries. For manufacturing, the differences are even greater, given that the average protection rate is in the 20-30 percent range.

**Table 4: Tariff Liberalization Commitments by Tunisia (Industrial Products)**

||**Shar**<br>**tra**<br>**with**|**e of**<br>**de**<br> EU|**Share in**<br>**domestic**<br>**output**<br>_________|**Share**<br>**in total**<br>**tariff**<br>**revenue**<br>________|**Average**<br>**tariff**<br>**weighted**<br>**by EU**<br>**imports**|**Num**<br>**6-digi**<br>**Total=**|**ber of**<br>**t lines**<br>**5019**|**S**<br>**Mac**<br>**Inte**|**hare of**<br>**hinery and**<br>**rmediates**|
|---|---|---|---|---|---|---|---|---|---|
||**Exp**|**Imp**|||||**% of**<br>**total**|**by**<br>**line**<br>**(%)**|**by**<br>**import**<br>**value (%)**|
|ANNEX 3:**5**year<br>transition|16|24|20|12.5|26.7|1810|41|93|87|
|ANNEX 4: 12 year<br>transition|7|29|22|9.2|30.4|1127|26|94|89|
|ANNEX 5: 8 year<br>transition starting in<br>year 5|75|36|43|32.9|33.8|944|22|8|4|
|ANNEX 6: Exempted|1|I|I|n.a.|n.a.|37|1|0|0|
|Industrial goods not<br>listed in an Annex:<br>Immediate<br>liberalization|1|10|14|3.6|21.6|470|10|100|100|

Source: Own calculations based on COMEXT and World Bank data. All data are for 1994.

A possible factor underlying the back-loaded nature of the tariff reduction process is that the government may have been concerned with the revenue implications of a more uniform move to free trade with the EU. The dependence on trade taxes in Tunisia--as in the other countries of the region-is relatively high. Some 28 percent of government revenues are derived from trade taxes. The EU accounts for 68 percent of total imports, and generates 58 percent of total tariff revenue. Most of the tariff revenue generated from trade with the EU is currently collected on the imports of consumer 17
Table 5: Enrollment and Percent of Female Students in Universities and Equivalent Institutions in Selected Arab Countries

||196|5|197|5|1980|'s||
|---|---|---|---|---|---|---|---|
|Country|||||||Year|
||Total|aFem|<br>Total|%Fem|Total|%Fem||
|Algeria|8,177|20.1|41,847|-|108,181|-|1984|
|Egypt|64,506|19.9|411,097|30.1|613,570|33.6|1983a|
|Jordan|1,169|40.2|4,805b|29.2|25,929|38.8|1984|
|Morocco|<br>7,979|11.3|35,081|19.2|126,481|32.5|1984|
|Syria|32,653|16.8|65,348|22.4|123,735|29.7|1983|
|Tunisia|<br>5,629|18.1|20,505|25.7|38,829|36.0|1984c|
|Yemen, A.|R.<br>-|-|2,408|10.2|3,403d|14.5|1988/89|
|Yemen, P.|D.R.<br>-|-|934b|18.2|3,645|52.3|1983c|

a 1983 excluding Al-Azhar UJniversity b Figures for 1974 c Third set of figures for all levels of higher education Source: George I. Za'rour I Universities in Arab Countries n PPR Working Paper No.62 51. However, in spite of the Yemen Government's promotion of female participation in higher education and the country's development, in 1988/89 female students comprised only 14.5% of the total student population of 23,447 -- by comparison with 52% share of female students in the former Peoples Democratic Republic of Yemen's tertiary institutions as a whole as early as 1983. In certain faculties, Arts, Medicine, Education and Science, female student enrollment was much larger than the University-wide average, namely 44.6%, 44.3%, 23.3% and 27.2% respectively. The Law Faculty had the lowest female enrollment, 1.9%. Because of the low enrollment of female students, the female graduate output over the years has continued to be small vis a vis male graduate output (see Graph III). It was not possible to ascertain from which socioeconomic background the majority of the female students and graditates came from.

- 19 -
investment outcomes are created or might be reduced. _What is the effect of investment organizations’ evaluation practices on gender disparities in funding innovation?_ <mark>Examining this question requires access to investment organizations’ evaluation templates, investors’ evaluation practices, and investment outcomes by gender. This research is d</mark> ifficult to conduct at <mark>scale because most investment organizations prefer not to share their evaluation processes and outcomes publicly (e.g., Da Rin et al. 2013). To examine this question, we employ unique data from Village Capital (</mark> Vilcap <mark>), a global investment organization that</mark> selects qualified early-stage startups for consideration by its own investors, and introducing startups to a broader and more diverse set of potential follow-on investors.

Typically, investment organizations evaluate startups using organization-level criteria over three months (Tyebjee and Bruno 1984, Fried and Hisrich 1994, Cohen et al. 2019b, Gompers et al. 2020). Investors receive information – i.e., a pitch deck or overview, meet founders, and then make a decision on whether to continue to diligence (or inquire about) the startup. This process repeats as investors progress through deeper stages of diligence before making a decision to invest in a startup. Working with Vilcap, we <mark>designed a two-part intervention to reduce gender disparities in their investment outcomes by changing their evaluation templates that structured inquiry, specifically how Vilcap prompted individual investors to inquire about risk in stage one, and startup progress in stage two. We examined the effects on investors’ evaluation, analyzing over 3</mark> 1,000 scores investors allotted to startups during the period of study.

In the first stage of the field experiment, we used a cross-sectional design to assess the first stage of investment outcomes – an investor’s decision to conduct further diligence on a startup, or to exclude it from further consideration. We randomized a diverse set of 278 investors into a treatment group, which Vilcap prompted to systematically inquire about risk and reward, and a control group 4
stage F statistics for the excluded instruments and Shea‘s adjusted partial R-squared statistics indicate that the instruments are strong predictors of financial development in the poor subsample. They also perform reasonably well for the full sample and for the rich sub-sample when private credit/GDP is the endogenous variable. They do not perform as well when stock market capitalization is treated as endogenous. This is not surprising because, as shown above, trust levels and net petroleum exports are not as strongly correlated with stock market capitalization.

The main finding from the GMM regressions in Table 6 is that private credit/GDP remains positively associated with labor growth in the sample of low-income countries, though the coefficient is smaller than in the OLS regressions. We view this as evidence consistent with predictions from new structural economics and Gerschenkron (1962): in the early stages of economic development, banks are better able to foster firm growth than are stock markets. The p-values for Hansen‘s J test of over-identifying restrictions are far larger than critical values, providing additional support for the validity of our instruments.

Stock market capitalization/GDP is not significantly linked to labor growth in the sample of high-income countries, in contrast to the OLS regressions. The first-stage F statistics for the excluded instruments are small (around 2) whenever stock market capitalization is the endogenous financial structure variable in the GMM regressions. This reflects the low degree of correlation between stock market capitalization and our instruments. The instruments also do not pass Hansen‘s J test of over-identifying restrictions when stock market capitalization appears in the GMM regressions, indicating that trust and net petroleum exports likely do not affect labor growth only through their effects on market capitalization. In all, the GMM regressions do not provide support for a causal link between stock market capitalization and labor growth among firms in high-income countries.

Since weak instruments could be the cause of the non-results for stock market capitalization in the GMM regressions, we also present Limited Information Maximum Likelihood (LIML) estimates (Appendix C). Weak instruments cause less serious problems when estimation is conducted by LIML than by GMM. However, qualitative results are similar for stock market capitalization in the LIML and GMM regressions, again providing no support for a causal link between that variable and labor growth in our sample. Indeed, the coefficients 20
the CVI is more informative for future socio-economic outcomes compared to the commonly used Debt at Risk approach based on ICR (IMF, 2017; FSB, 2015).

The CVI is related to a large literature on corporate default modeling based on accounting data (e.g. Altman, 1968) and distance-to-default or contingent claim models based on market prices inspired by Merton (1974). Unlike the CVI, these models however require default and price data which are not readily available for a wide range of countries and/or firms. Another important advantage of our balance-sheet-based approach is that it can easily be extended to cover nonlisted firms.

We also add to the “early warning” literature which suggests corporate debt overhangs can be a leading indicator of crises and growth slowdowns as well as having the effect of amplifying shocks (for Europe: Goretti and Souto, 2013; Aiyar et al., 2015; Bergthaler et al. 2015; for emerging markets: IMF, 2015; Lindner and Jung, 2014). Finally, unlike many previous studies that are based on small samples or individual countries, our paper is based on a large sample of 14,207 listed firms, spanning 11 years (2006 to 2016) and 69 EMDEs; this should make our findings more representative.

At the global level, the CVI suggests that vulnerabilities have risen sharply since 2013, but have stabilized in 2016. This global trend is caused by an increase in leverage and a deterioration of both profitability and debt service capacity. However, regional trends are heterogeneous as corporate vulnerabilities in Eastern Europe and Central Asia (ECA) have been elevated since 2007, while Latin America (LAC) shows a rapid increase in vulnerability since 2013. In 2016, most regions have stabilized except the Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA), whose corporate sector’s vulnerabilities have risen. A key finding is that energy-linked sectors have shown an increase in financial vulnerabilities, especially since 2014, in conjunction with the decline in oil prices.

This paper is organized as follows. Section 2 describes recent trends in non-financial corporate sector debt in EMDEs and discusses the methodology to construct the CVI. Section 3 describes the data used for the construction of the index. Section 4 presents the main results. Section 5 describes robustness tests and extensions. Section 6 concludes.

3
in operational margins for Hong Kong, Indonesia and Singapore, possibly reflecting their higher wage growth while at the same time they were facing increased competition.

**Table 3: Operational Margin for Nine Asian Countries, US and Germany**

|||||(%,<br>|medians<br>|)|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Country|1988|1989|1990|1991|1992|1993|1 1994|1995|1 1996|1988-96|
|Hong Kong|23.5|19.5|22.2|19.6|17.4|16.6|17.3|14.6|14.2|18.7|
|Indonesia|n.a.|n.a.|n.a.|35.7|33.3|34.4|32.8|31.2|30.6|32.9|
|Japan|22.2|22.7|22.9|22.4|21.9|21.8|21.8|23.1|23.3|22.1|
|Korea|13.7|16.8|17.3|16.9|19.2|18.7|19.6|21.4|22.1|19.6|
|Malaysia|16.4|16.3|17.1|17.3|17.6|17.4|18.4|19.5|25.5|18.1|
|Philippines|n.a.|n.a.|n.a.|36.1|26.4|26.4|27.5|30.8|33.3|27.7|
|_Singapore_|17.3|16.7|16.8|_15.5_|_15.5_|15.2|14.1|13.6|13.1|14.9|
|Taiwan|n.a.|n.a.|n.a.|25.4|21.4|22.7|22.6|22.3|21.9|22.6|
|Thailand|21.9|24.3|25.7|27.3|25.9|25.1|24.9|24.7|22.7|25.2|
|US|14.1|13.9|14.1|14.3|15.5|14.0|14.7|14.8|14.6|14.4|
|Gennany|13.2|13.4|13.7|13.5|13.8|14.1|15.6|16.7|17.1|14.6|

Note: Table A2 reports means, standard deviations, and sample sizes.

The cross-country differences in returns on assets do not reflect themselves directly in differences in sales growth, which are also more variable over time (Table 4). Most East Asian corporates recorded on average high, real sales growth over the period. Malaysia, Indonesia and Thailand stand out, with 11.9%, 10.6% and 9.7% on average, followed by Taiwan with 9.3%. Other countries also had high sales growth rates, which are about double those of Germany (2.6%) and the US (3.7%). The country with the lowest corporate sales growth in East Asia is Japan, averaging 7.7%. These high sales growth rates mirror the high growth in export and domestic demand that has characterized this region over the last decade. We do observe some slowdown, however, in 1996 in sales growth for Indonesia, Japan, Singapore, Taiwan, and Thailand, possibly reflecting lower exports growth rates.

**Table 4: Real Sales Growth (Year-on-Year) for Nine Asian Countries, Germany and the United States**

||||<br>|(%, me<br>|dians)<br>|||||
|---|---|---|---|---|---|---|---|---|---|
|Country|1989|J1990|1991|[1992|1993|]1994|1995|[1996|11988-96|
|Hong Kong|10.1|11.6|10.2|12.4|9.8|9.4|9.7|11.8|9.2|
|Indonesia|n.a.|n.a.|n.a.|10.7|12.1|12.4|9.4|8.3|10.6|
|Japan|7.4|8.2|8.4|8.3|8.8|8.5|7.2|4.3|7.7|
|Korea|8.4|8.7|8.2|8.3|7.6|7.3|7.2|8.6|8.2|
|Malaysia|9.7|12.3|11.8|12.7|13.1|12.6|11.7|11.9|11.9|
|Philippines|n.a.|n.a.|n.a.|8.4|6.7|7.6|10.6|12.2|8.2|
|Singapore|8.4|8.6|8.1|9.4|11.6|11.8|10.2|7.7|8.7|
|Taiwan|n.a.|n.a.|n.a.|7.1|11.3|10.3|9.7|8.4|9.3|
|Thailand|11.6|10.3|10.8|9.6|8.3|10.1|10.7|5.7|9.7|
|US|4.3|3.4|-1.8|4.3|2.8|6.9|4.1|4.3|3.7|
|Germany|5.0|4.4|_5.1_|1.1|-4.2|2.3|1.3|4.7|2.6|

Note: Table A3 reports means, standard deviations, and sample sizes.

7
observations which requested preferences; in 30% of the cases when preferences were requested by traders the duty reduction was below 4%. The average utilization rate is 16% when the duty reduction is less than 4% and 43% when it is more.

Regression results are presented in Table 6. The variables GDP and population are highly correlated (Annex I shows the pairwise correlation coefficient between variables). Furthermore distance is highly correlated with GDP/POP. Therefore we proceeded by employing specifications where we included these variables separately. The first column includes only GDP of both countries, the second contains GDP divided by population, while the third contains GDP and population separately. Column 4 contains country specific dummy variables allowing one to capture country specific characteristics which might be omitted with the previous specifications. The final column uses country specific dummies and sectoral variables measuring the impact of preferences in each sector. While the results presented in Table 6 include sectoral dummies only for the selection equation, Table 7 presents results where sectoral dummies were included in both stages.

**Table 6 Results of Heckman estimation**

||(1|)|(2|)|(|3)|(4|)|(5|)|
|---|---|---|---|---|---|---|---|---|---|---|
||Utilization<br>rate|request|Utilization<br>rate|request|Utilization<br>rate|request|Utilization<br>rate|request|Utilization<br>rate|request|
|Ldistance|-0.041<br>(0.011)**||-0.048<br>(0.013)**||-0.049<br>(0.014)**||0.074<br>(0.053)||0.077<br>(0.053)||
|LGDP|0.004|0.019|||0.006|0.018|||||
||(0.003)|(0.002)**|||(0.003)*|(0.002)**|||||
|LGDP/POP|||0.007<br>(0.002)**|0.008<br>(0.001)**|||||||
|LPOP|||||-0.009<br>(0.003)**|0.004<br>(0.002)*|||||
|LPOPeu|||||0.040<br>(0.016)*|0.115<br>(0.011)**|||||
|LGDP/POPeu|||-0.065<br>(0.016)**|-0.101<br>(0.010)**|||||||
|LGDPeu|-0.031<br>(0.003)**|0.012<br>(0.002)**|||-0.070<br>(0.016)**|-0.098<br>(0.010)**|||||
|Freedom index|0.003<br>(0.002)|-0.003<br>(0.001)*|0.005<br>(0.002)**|0.004<br>(0.001)**|0.006<br>(0.002)**|-0.004<br>(0.002)**|||||
|French colony|0.020<br>(0.013)|0.060<br>(0.014)**|0.002<br>(0.013)|0.082<br>(0.015)**|0.023<br>(0.014)|0.065<br>(0.014)**|||||
|Non French<br>colony|0.016|-0.007|0.001|0.016|0.021|0.006|||||
||(0.008)*|(0.006)|(0.008)|(0.006)**|(0.008)**|(0.006)|||||
|SouthAfrica|-0.208<br>(0.015)**|-0.183<br>(0.009)**|-0.192<br>(0.009)**|-0.121<br>(0.006)**|-0.185<br>(0.017)**|-0.192<br>(0.010)**|||||
|preferences||0.037||0.037||0.037||0.029|||
|||(0.001)**||(0.001)**||(0.001)**||(0.001)**|||
|dutydifference|0.008||0.014||0.010||0.011||0.023||
||(0.026)||(0.026)||(0.026)||(0.026)||(0.026)||
|DUMagriculture||0.252||0.251||0.252||0.235|||
|||(0.010)**||(0.010)**||(0.010)**||(0.011)**|||
|DUMtextiles||0.362||0.365||0.358||0.302|||

23
# 5. Selected bibliography

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De la Torre, A., S. Claessens, A. Vivanco, M. Nakane, R. Rigobon, M. Kiguel, and Y. Seynabou Sakho (2006). “Brazil: Interest Rates and Intermediation Spreads”. World Bank Group. Brazil Country Management Unit.

Demirgüç-Kunt, A. and E. Detragiache (1998). “Financial Liberalization and Financial Fragility”. IMF Working Paper 98/83. International Monetary Fund.

Demirgüç-Kunt, A., L. Laeven, and R. Levine (2004). Regulations, market structure, institutions, and the cost of financial intermediation. _Journal of Money, Credit, and Banking,_ 36(3): 593-622.

Edwards, S. and M. Khan (1985). Interest Rate Determination in Developing Countries: A Conceptual Framework. Staff Papers (International Monetary Fund), Vol. 32, No. 3, pp. 377-403.

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33