rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>17</sup>

no other groups cluster on those same parties. Finally, some ethnic groups may in fact have common preferences.<sup>9</sup> The third intermediate cluster variable, “one party-many groups”, therefore classifies ethnic groups as clustered if they disproportionately support one and only one party, whether or not other groups also cluster on that party.

Small ethnic groups and small parties can introduce distortions. If a tiny ethnic group clusters on a large party that is also the favored party of a large ethnic group, neither group is classified as strictly clustered. In contrast, if a small fraction of a large ethnic group provides disproportionate support for a very small party, it could be classified as strictly clustered. To minimize such distortions, respondents from ethnic groups that comprise less than five percent of the sample are simply excluded from the analysis. In addition, groups are not recorded as clustering if they disproportionately support parties with less than 5 percent support in the sample.

The clustering variables are based on answers to two questions in the Afrobarometer survey. Question 86 of the Afrobarometer survey asks, conditional on respondents expressing a partisan preference, which party they prefer. Respondent ethnicity is based on question 79, asking respondents to self-identify their ethnic group with the open-ended question “What is your tribe?” and the prompt “You know, your ethnic or cultural group.” Ethnic classifications are therefore generated by the self-perceptions of respondents and are 9 For example, the salient ethnic groups that emerge from the self-identification question in Afrobarometer are sometimes more disaggregated than those identified by outside observers (e.g., Alesina, et al. 2002). The “many groups-one party” clustering rule then captures whether a party makes a credible commitment to pursue the common interests of these ethnic groups.
similar political consensus. In any case, countries currently seem unwilling to countenance rules targeting measures which do not discriminate in any way but are deemed to be too stringent.

Finally, if such disciplines were incorporated into the GATS, the result could be an additional “hold-back” problem. In the goods trade world, where national treatment is a general obligation, quotas are prohibited, and tariffs are bound, strict disciplines on technical barriers targeted the main remaining source of impediments to trade without creating an incentive to retain protection. In services, where specific commitments do not yet cover all sectors or measures (as is evident from the discussion in Section I), premature stringency of regulatory disciplines that kick in when a country makes specific commitments, could inhibit the willingness to make binding commitments even to provide market access and national treatment. Reaching for the first best of deep goods-like disciplines could thus make even the second best of across-the-board basic disciplines hard to attain.

# **IV. Regulatory cooperation as a precondition for liberalization**

It is convenient to begin by describing the more conventional producer-centric case for regulatory cooperation, as a complement to liberalization. That will help set the stage for the alternative consumer-centric case for cooperation, as a precondition for liberalization.

# **IV.1** **_Producer-centric regulatory cooperation to address regulatory heterogeneity_**

Regulatory heterogeneity arises when requirements differ across countries because of either differences in institutions (leading typically to “horizontal” differentiation e.g. in legal services) or differences in social preferences (leading to “vertical differentiation” e.g. stringency of financial or privacy regulation). The traditional case for regulatory cooperation arises from the fact that regulatory heterogeneity segments international markets in a way that prevents the exploitation of economies of scale in production. This is illustrated in Figure 5 drawn from De Bruijn et al. (2008). For example, since each East African country has its own regulatory requirements for services professionals, compliance costs cannot be spread out over provision of professional services in other East African countries but must be incurred separately in each market. De Bruijn et al. (2008) estimate that EU stock of FDI could increase by 20-35 percent if regulatory heterogeneity was reduced as a result of a common services regulation directive.

Such regulatory heterogeneity cannot be addressed by imposing traditional trade disciplines because the problem is not due to protectionism or anti-competitive intent. But there is nevertheless an economic cost of such heterogeneity because each country is independently choosing its regulations without considering the negative impact on foreign producers and hence on competition. Therefore, there are potential gains from international cooperation where each 13
tended to have higher Buenos Aires loan ratios (statistically insignificant coefficient of 0.134). However, most of the deviations from the static-based predictions likely reflect dynamic changes in the goals of the organizations, i.e., decisions to change lending directions.

Banks that were acquired by foreign organizations appear to have increased loans relative to other types of assets, and have more of their loan portfolios allocated to consumer loans and less to manufacturing loans. Again, the dynamic results are not very consistent with predictions based on static differences. The coefficients on _Foreign – No Governance Change_ in Table 4 would have predicted significant dynamic shifts into manufacturing loans and into Buenos Aires loans that did not materialize after foreign acquisitions. The selection effects do not appear to explain much of the dynamic results, again suggesting substantial dynamic changes in organizational goals that overwhelm the predicted effects.

Banks that were privatized tend to have decreased loans relative to other assets, and have less of their loan portfolios allocated to mortgages, Peso loans, and agricultural loans. This is consistent with the possibility that banks lent more prudently after privatization, similar to findings in other studies of developing countries (e.g., Haber 2005). Some of these changes are also consistent with predictions based on static and selection effects. For example, the reduction in Peso loans when these banks become privatized might be predicted because state-owned banks with no change in governance tend to have high Peso loan ratios and those selected for privatization tend to have even higher Peso loan ratios. However, other dynamic changes, such as the steep reduction in the total loan ratio, would not be predicted based on static and selection effects. It is difficult to determine the extent to which these changes reflect changes in organizational goals and abilities versus the transferring of nonperforming loans to residual entities. Presumably, a significant portion of the short-term decline in _Total Loans/Assets_ reflects this removal.

Finally, state-owned banks that were restructured tend to have decreased loans relative to other assets, similar to privatized banks, and also have less of their loan portfolios allocated to manufacturing loans and more to agricultural loans. Again, these dynamic effects may be difficult to interpret because of the stripping off of nonperforming loans at the time of restructuring.

# **6.  Conclusions**

We test the effects of corporate governance on bank performance using data from Argentina in the 1990s, although our principal contribution may be methodological. Our method represents an expansion of the existing research literature that might be usefully applied elsewhere. In particular, we 24
Our paper relates closely to a large literature that quantifies the impact on the labor market – looking at employability and wages - of students who complete technical-vocational education compared with general education students. The evidence from Brazil is quite small, but some studies have shown a positive wage gap for students who have completed a technical-vocational education course in relation to those who pursued only general education. Neri (2010) explores OLS and PNAD data and finds a wage premium of 15.1% for individuals who have completed a technical education course at the secondary level and 23.3% for individuals who completed a technical education course at the tertiary level. He finds different returns for short term training courses across sectors (11.5% in "commerce and management", 8.4% in "manufacturing and maintenance", and 7.7% in "health and welfare"). Vasconcellos et al. (2010) explore a methodology similar to ours but use fewer control variables. They find wage gains of 12.5% due to completion of a technical education course at the secondary level.

This topic is also of great relevance in many other developiong countries. Tan and Nam (2012) in Almeida, Behrman, and Robalino (2012) summarize the most recent studies in developing countries. Table 1 summarizes this literature and shows that the evidence is not as robust as the main findings for Brazil.

Table 1: Returns to TVET in selected countries.

|Country and Year|Impact on Earnings|Methodology|Author(s)|
|---|---|---|---|
|<br>Studies showing mor|<br>e favorable results for TVET than for general education<br>|<br>||
|Egypt (1998)|Returns to vocational secondary education are 35.4%,<br>while returns to general secondary education are 6.1%<br>for men.|Mincerian earnings<br>regression (1<sup>st</sup><br>stage: ordered logit)|El-Hamidi<br>(2006)|
|Singapore (1998)|Returns to vocational/technical education are 10.3% at<br>the secondary level and 12.7% at the post-secondary<br>level, compared with the corresponding figures of 9.4%<br>and 11.3% for general education.|Extrapolation of<br>Mincer equation<br>results from 1980s<br>Singapore data|Salkeriou<br>(2003)|
|Sri Lanka (2002)|Returns to formal vocational training are 17 % compared<br>with7.9% for general education.|Mincerian earnings<br>regression (not<br>controlling for<br>selectivitybias)|Riboud,<br>Savchenko,<br>Tan (2007)|
|Thailand (1989-1995)|Returns to vocational education at the upper secondary<br>level exceed those to general education by 63.9 percent<br>for men and 49.4 percent for women.|Mincerian earnings<br>regression (1<sup>st</sup><br>stage: probit)|Moenjak and<br>Worswick<br>(2003)|
|Israel (1983)|Vocational school graduates employed in occupations<br>related to their field of study earn between 8.1 percent<br>(widely related) and 9.6 percent (directly related) more<br>than peers that graduated from general education or<br>that are workingin fields unrelated to their studies.|Mincerianearnings<br>regression (OLS,<br>without first stage)<sup>1</sup>|Neuman and<br>Ziderman<br>(1991)|

5
|Service|-0.02|0.00***|-0.10||0.01***|
|---|---|---|---|---|---|
|BPO|0.17|0.01***|0.17||0.01***|
|Elementary|-0.13|0.00***|-0.19||0.01***|
|Machine-related|0.17|0.01**|0.03||0.01*|
|Clerical|0.00|0.00***|-0.29||0.01***|
|Sales|0.27|0.00***|0.14||0.01***|
|Other|0.28|0.01***|0.11||0.02***|
|Female|-0.10|0.00***|-0.24||0.01***|
|Interaction between occupation cat|egories and female|||||
|Professional*female|||0.05||0.01***|
|Service*female|||0.04||0.01***|
|BPO*female|||-0.02||0.01*|
|Elementary*female|||-0.06||0.01***|
|Machine-related*female|||0.14||0.02***|
|Clerical*female|||0.43||0.01***|
|Sales*female|||0.19||0.01***|
|Other*female|||0.24||0.02***|
|Constant|8.58|0.01***|8.715||0.01***|
|Number of observations|301,380|||301,380||
|Adjusted R-squared|0.2419|||0.2705||

*** p<0.01, ** p<0.05, * p<0.1

Figure 6: Predictive Margins - Ad-specified salary gap by gender <!-- Start of picture text --> Clerical<br>19%<br>0% Other<br>-5% Sales<br>-11% Machine-related<br>-19% Professional<br>-21% Service<br>-24% Teaching<br>-27% BPO<br>-31% Elementary<br>‐35% ‐25% ‐15% ‐5% 5% 15%<br><!-- End of picture text --> _Note:_ Bars on the right-side (left-side) indicate higher (lower) salary for female-specified job compared to salary offered in male-specified jobs.

Other than clerical positions, there exists high discrimination against women in specified-salary in job ads in all other occupational categories. Ads for clerical positions not only show female-bias, an ad targeting women also specifies 19 percent higher salary than the ones targeting men. Among all occupations, ads for elementary occupations have the highest odds of being gender-targeted, they also have the highest gender gap in specified-salary. In general, a job ad in an elementary occupation offers 31 rupees less in a female-targeted ad for each 100 rupees offered in a maletargeted one. However, this is not unexpected as there exists high segregation by job category and men are targeted in relatively high-intensive outdoor jobs. Interestingly, though teaching and BPO jobs are the ones with the least gender-targeted ads and the ones that come with gender preference are more likely to target women – are associated with high salary gap favoring men. In gender14
migrant, and forced migrant students’ in terms of the aforementioned variables. The table reveals large gaps in college admission test registration and applications for government financial aid between native-born and migrant students. While 83 percent of native students registered for the test, only 49 percent of migrants and 31 percent of forced migrants did so despite having equal access to aid. These gaps generate differences in both access to benefits and enrollment in higher education: the number of forced migrants is almost half the percentage of natives who access these services. It is important to highlight that the largest difference in Table 5 relates to registering for the college selection test.

Table 5: Graduating High School Students: Access to Higher Education (2017)

||Ty|pe of Stude|nt|
|---|---|---|---|
|Variable|Chilean Students|Migrants|Forced Migrants|
|Financial Aid Application|65%|40%|27%|
|Benefts|32%|16%|8%|
|College Selection Test|83%|49%|31%|
|Enrollment Higher Education|49%|33%|19%|

**Note:** The table shows the percentage of high school graduates that are natives, migrants and forced migrants, and refugees that applied for financial aid, got financial aid, took the college selection test, and enrolled in higher education. The base category is the total number of students for each type of migrant status.

These patterns in our data are consistent with both national and international evidence indicating that migrant status is associated with fewer applications to admission processes or benefits programs (UNHCR, 2019). Survey evidence from Chile demonstrates that migrant students are not only less likely to enroll in tertiary education; they also tend to receive less government financial aid to fund their studies. In fact, 71 percent of surveyed migrants pay for their tuition themselves; 21 percent receive free tuition and 9 percent receive a loan or scholarship (Servicio Jesuita a Migrantes, 2019).

The data presented thus far seems to indicate a bottleneck in access to higher education for migrant and forced migrant students in Chile. However, when looking only at enrollment in tertiary education conditional on applying for financial aid (Table 6), we see that migrant students enroll 3 percent more than native students, and forced migrants’ enrollment is only 4 percent lower than that of natives. Likewise, enrollment conditional on taking the college selection test is very similar across groups. These results suggest a strong self-selection component of both migrants and forced migrants into higher education. Moreover, Table 6 illustrates that access to financial aid is a 16
nomics, 95 (1), pages 30-41.

Beine, M. and S. Salomone (2013): Network Effects in International Migration: Education versus Gender. Scandinavian Journal of Economics, 115 (2), pages 354-380.

Bertoli, S. and J. Fernandez-Huertas Moraga (2013): Multilateral resistance to migration. Journal of Development Economics, 102 (C), pages 79-100.

Blackden, M., Canagarajah, S., Klasen, S., Lawson, D., (2006), ”Gender and growth in Sub-Saharan Africa: Issues and evidence”, UNU-WIDER Research Paper no. 2006/37, UNUWIDER: Helsinki.

Borjas, G. J. (1987): Self-Selection and the Earnings of Immigrants. American Economic Review, 77 (4), pages 531-53.

Clair, G., G. Gaullier, Th. Mayer and S. Zignago (2004): A note on CEPII’s distances measures. Explanatory note, CEPII, Paris.

Clemens, M.A. and L. Pritchett (2008): Income per Natural: Measuring Development for People Rather Than Places. Population and Development Review, 34 (3), pages 395-434.

Cohen, D. and M., Soto (2007): Growth and human capital: good data, good results. Journal of Economic Growth, 12 (1), pages 51-76.

Coulombe, S. and J-F. Tremblay (2006): Literacy and Growth. B.E. Journal of Macroeconomics, 6 (2), pages 1-34.

Docquier, F. and A. Marfouk (2004): Measuring the International Mobility of Skilled Workers - Release 1.0. Policy Research Working Paper no. 3382,.World Bank, Washington, DC.

43
# **7. Figures and Tables**

**Figure 1. Density Plots for Maturity and Sector Threshold**

<!-- Start of picture text --> (a) Maturity CDF (b) Maturity Density Plot<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>0 .5 1 1.5 0 .5 1 1.5<br>Maturity Threshold Maturity Threshold<br>(a) Purpose of Credit CDF (b) Purpose of Credit Density Plot<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>0 .5 1 1.5 0 .5 1 1.5<br>Purpose Threshold Purpose Threshold<br>1.5<br>1<br>Density<br>.5<br>Cumulative Probability<br>0<br>1.5<br>1<br>Density<br>.5<br>Cumulative Probability<br>0<br><!-- End of picture text --> 22
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

**Table 9. Demographics, household size, and composition**

|||Nairo<br>|bi<br>||||Dakar<br>||
|---|---|---|---|---|---|---|---|---|
||P|oor|N|onpoor|Po|or|No|npoor|
|||||||Percent|||
||N|Percent of total|N|Percent of total|N|of total|N|Percent of total|
|Households|1,282|72.5|473|27.5|1615|82.4|345|17.6|
|Household size_a,_ _d_|3.38||1.88||10.06||7.25||
|Single-person households a|199|15.2|283|60.1|13|0.8|21|6.1|
|Female-headed households c|238|18.5|66|14.0|333|22.0|67|22.0|
|Mean age of household head (yrs) d|34.8||34.6||53.2||48.1||
|Median age: household head (yrs)|32.0||32.0||53||47||
|Age profile|N|Percent|N|Percent|N|Percent|N|Percent|
|Age 0–4|717|16.5|108|11.9|1,968|12.0|347|13.8|
|Age 5–14 (school age children)|877|20.2|99|10.9|3,799|23.2|562|22.3|
|Age 15+ (“adults”)|2,751|63.3|704|77.3|10,601|64.8|1,612|63.9|
|Total no. of individuals|4,345|100.0|911|100.0|16,368|100.0|2,521|100.0|
|Gender ratio, male: female|Ratio (N)|Ratio (%)|Ratio (N)|Ratio (%)|Ratio<br>(N)|Ratio<br>(%)|Ratio (N)|Ratio (%)|
|All individuals|2,320: 2,025|53: 47|579: 332|64: 36|8,203:<br>8,165|50:50|1,267: 1,254|50:50|

_Note:_ Statistical significance for difference in the corresponding indicator value between “poor” and “nonpoor” households: (1) In Nairobi: “a”— 1% level; “b”—5% level; “c”—10% level; (2) In Dakar: “d”—1% level; “e”—5% level; “f”—10% level.

# **Table 10. Education level across individual adults**

Individuals 15 <u>years of age or older</u>

|||Nairo<br>|bi<br>|||Daka<br>|r<br>||
|---|---|---|---|---|---|---|---|---|
||Poor||Nonpo|or|Poor||Nonpo|or|
|Education level|N|%|N|%|N|%|N|%|
|Less than primary education|669|22.6|117|13.6|6,814|65.2|907|56.7|
|Completed primary education|1,494|50.5|355|41.2|2,809|26.9|487|30.5|
|Some/completed secondary education|790|26.7|372|43.2|664|6.4|156|9.8|
|Postsecondary training|5|0.2|17|2.0|171|1.6|49|3.1|
|Total|2,958|100|861|100|10,458|100|1,599|100|

34
**23**

other hand, the estimated (slope) coefficients in (6) are (statistically) significantly different across the sectors, the composition of growth would also be important for poverty reduction. Note further that the intercept, which reflects the change in poverty in the absence of growth, could be interpreted as an estimate of the effect of the average change in income inequality during the time period under consideration.

To estimate (6) we bring together poverty measures derived from nationally representative household surveys with data on economic growth by sector. The poverty data here refer to spells of change in the poverty measures, the change being derived from two (comparable) household surveys in years _t_ −τ and _t_ . For comparability all poverty estimates are based on the US$1 a day poverty benchmark. The poverty data are part of the World Bank’s _Povcal_ database (World Bank, 2005d). Table 5 provides an overview of these.<sup>20</sup> Poverty spells are available for 82 countries and the total number of spells amounts to 289 (73 percent of the sample countries have more than one spell).<sup>21</sup> Exclusion of 7 urban-only surveys and systematic elimination of outliers reduced the sample to 75 countries and 222 observations.<sup>22</sup> We use three common poverty measures (the headcount index, H, the poverty gap index, PG, and the squared poverty gap index, SPG). Data on the GDP shares and per capita GDP growth for the agricultural and non-agricultural sectors are taken from World Bank (2005c).

> 20 Chen and Ravallion (2004) provide a descriptive analysis of the _Povcal_ data; the table in appendix A2 provides an overview of the data used.

> 21 While we did not weigh the spell observations by country population size, China and India, the most populous countries have the most spells in our sample, while other large countries such as Brazil and Indonesia tend to have more than one observation, indicating that our results are implicitly population weighted (albeit approximately). 22 Observations with poverty changes exceeding |50%| were eliminated. Observations dropped came largely from the richer eastern European countries with very low 1$ day poverty rates causing large relative (percentage) changes in poverty following small absolute (percentage point) changes in poverty.
Table 7: Child level school participation

|Sample restriction|2000<br>(1)<br>All|(2)<br>Not present<br>pre-program|(3)<br>Present<br>pre-program|2001<br>(4)<br>All|
|---|---|---|---|---|
|Treatment|0.159|0.203|0.141|0.111|
||(0.068)|(0.098)|(0.040)|(0.071)|
|Present pre-program|0.815<br>(0.029)||||
|Present pre-program * Treatment|0.008<br>(0.039)||||
|Girl|-0.018|-0.050|-0.001|0.006|
||(0.022)|(0.037)|(0.026)|(0.041)|
|Female head of household|0.057|0.088|0.010|-0.097|
||(0.028)|(0.046)|(0.040)|(0.059)|
|No of children <age 6 in household|-0.003|-0.006|0.007|-0.005|
||(0.008)|(0.014)|(0.010)|(0.009)|
|Age 5|0.257|0.381|0.073|0.492|
||(0.033)|(0.052)|(0.031)|(0.053)|
|Age 6|0.346|0.558|0.085|0.802|
||(0.029)|(0.051)|(0.035)|(0.043)|
|Age 7|0.709|1.251|0.140||
||(0.083)|(0.180)|(0.061)||
|Nobs|5110|3898|1213|4794|
|Number of panels|50|50|49|50|
|sigma v|0.595|0.830|0.360|0.795|
|sigma u|0.177|0.263|0.084|0.198|
|Predicted value if comparison|0.110|0.006|0.488|0.013|
|Predicted value if treatment|0.191|0.020|0.629|0.028|
|Mean if comparison|0.218|0.140|0.507|0.171|
|Mean if treatment|0.290|0.164|0.647|0.203|

The sample is the same as in Table 4a. School participation is averaged by child. The estimation model is two-sided tobit with random e¤ects at the school level. Predicted participation is at the mean of the dependent variables. Pre-program presence is based on one observation of school participation in term 1, 2000, prior to the implementation of the program. A constant was included in the regressions but the estimates are not reported. Standard errors are in parentheses.

23