rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7223

# **Abstract**

This study examines trends in school dropout at the upper secondary education level across Latin America over the past two decades, and attempts to identify factors influencing these rates. The methodology contributes to the existing literature by employing repeated cross sections of data to track the life cycle path of representative groups of individuals belonging to a birth cohort, by constructing and analyzing a synthetic data base of household survey data from 18 countries. A key finding is that while upper secondary enrollment rates increased in the region, the proportion of upper secondary age youth dropping out of school has remained persistently high, despite relatively favorable macroeconomic conditions. Furthermore, the study traces the moment in the life cycle at which the majority of dropout takes place to reveal differences between countries. Finally, to explain the trends in upper secondary dropout rates, the study examines the impact of three groups of factors: (i) shifts in the cohort size and socioeconomic composition of the population eligible for entering upper secondary education; (b) the macroeconomic environment and labor market opportunities; and (c) the returns to schooling. A series of regressions shows that an important factor that may be driving higher dropout levels has been the higher numbers of students from poor socioeconomic backgrounds reaching the upper secondary level. In addition, high returns to education have been a pull factor into the schooling system, while, especially in countries where the majority of youth dropout early (prior to upper secondary education), the data confirm an apparent substitution effect due to the opportunity cost of forgoing employment opportunities. Overall, the findings confirm the importance of policy makers’ focus on upper secondary education across Latin America and suggest implications for focusing the policy agenda.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Rbkattan@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
developing countries falls about as often as it rises (Ravallion 2001; Ferreira and Ravallion 2009). Moreover, a number of high-inequality, economically growing, developing countries have succeeded in attenuating and even reducing inequality. The available evidence leads one to doubt that higher inequality is a necessary “price” of higher economic growth and lower absolute poverty (Ravallion 2005).

However, as is evident from figure 1, inequality between countries matters more to the evolution of total inequality. Recent economic growth in India and (especially) China has played an important role in the evolution of the between-country component. Given their initially low average incomes and high population weights, economic growth in China and India has been a strong force for global inequality reduction. This is likely to change when these countries reach the overall mean for the developing world. That has not yet happened, but will soon do so in the case of China, where mean consumption in 2010 was 95 percent of the mean for the developing world as a whole (of $5.03 per person per day). Continuing improvement in Sub-Saharan Africa’s economic growth performance would work in the opposite direction, putting downward pressure on overall inequality.

We have seen that substantial economic growth in the developing world as a whole over the period 1980-2010 has come with falling inequality, though with signs of rising inequality since 2005. What then has been the record for poverty reduction?

Following past practice, the incidence of poverty is measured here by the standards used to define poverty in the poorest countries of the world. On this basis, the World Bank’s current international poverty line is $1.25 per person per day at 2005 purchasing power parity (Ravallion et al. 2009). The latest (survey-based) estimate of the proportion of the population of the developing world living below $1.25 a day is 21 percent for 2010 (representing 1.2 billion people), down from 33 percent in 2000 and 43 percent in 1990. Figure 2 plots the full series over time.<sup>7</sup> The overall poverty rate (“headcount index”) has been falling at a robust 1 percentage point per year over 1981–2010, and this was maintained after 2005. The regression coefficient of the poverty rate on the year in figure 2 is −1.04 (standard error = 0.05; n = 11). The “$1.25-aday” poverty rate for the developing world as a whole in the year of writing (2012) is estimated to be 19 percent (standard error = 0.6 percent), representing 1.1 billion people.

> 7 The estimates of absolute poverty measures used here are from Chen and Ravallion (2012), who discuss data and methods. (The update for 2010 is 20.8 percent below $1.25 a day, representing 1.227 billion people.) A fuller discussion of the absolute measures (with various tests of robustness) can be found in Chen and Ravallion (2010).

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**<u>Table 3. Effects of foreign ownership: SADC sample</u>**

||sales growth|TFP|TFPtran|
|---|---|---|---|
|foreign ownership|0.200**|0.184***|0.141***|
||(0.081)|(0.050)|(0.048)|
|largest owner|0.174**|-0.022|0.029|
||(0.075)|(0.035)|(0.026)|
|ln(firm age)|-0.168***|0.000|-0.018|
||(0.049)|(0.012)|(0.012)|
|loss_crime|0.039***|-0.015***|-0.022***|
||(0.003)|(0.002)|(0.001)|
|ln(GDPpc)|-0.218***|0.065|0.075**|
||(0.053)|(0.041)|(0.036)|
|firing_costs|-0.001*|-0.002***|-0.002***|
||(0.001)|(0.000)|(0.000)|
|interest spread|-0.015***|-0.004**|-0.004***|
||(0.003)|(0.002)|(0.001)|
|government effectiveness|0.170*|-0.081|-0.123*|
||(0.101)|(0.067)|(0.065)|
|ln(sales)|0.041<br>(0.025)|||
|industry dummies|yes|no|no|
|Number of observations|3,842|2,938|2,929|
|Adjusted R2|0.094|0.083|0.066|

standard errors in parentheses, clustered at the country level. *, ** and ***: statistical significance at the 10, 5 and 1 percent levels.

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85 percent of the control group has an occupation (in most cases working in informal jobs or self-employment). Even though the effect is not statistically significant in Lao PDR, the point estimate is non-negligible, suggesting a 15.5 percentage points increase, or 46 percent, relative to the control group.<sup>17</sup> Next, we examine the effects measured after individuals exited the public works program. These post-program effects remain substantial in Sierra Leone at 1 month (11.2 percentage points, or 34 percent relative to the control mean), Egypt Community at 1 month (21 percentage points, or 65 percent relative to the control mean), though not significant in Egypt Infrastructure at 4 months (1 percentage points; 1 percent relative to the control mean). Three studies continue to document positive employment effects at 12 months (Rural DRC and Tunisia) and at 25 months (Urban DRC). Four studies, however, find effects that are not statistically significant (Djibouti at 9 months, Côte d’Ivoire at 12 months, Ethiopia at 12 months, and Egypt Community at 27 months) and one study finds negative impacts (Tunisia at 66 months). Overall, the medium-run treatment effects are not significantly different from zero at 3.4 percentage points (95 percent CI: -1.2, 7.9) and decline with length of time since program completion. A fading-out of effects over time is observed across studies and is also observed within studies that include two follow-up surveys (Côte d’Ivoire, Djibouti, Egypt Community, and Tunisia).

The positive effects on employment are primarily obtained in studies with low levels of baseline employment. The largest relative effects are observed in Comoros (263 percent), Djibouti (256 percent) and Tunisia (87 percent), where employment in the control group is relatively low (11.9 percent, 21.3 percent, and 9 percent, respectively). These large relative effects can be driven by the specific groups that are targeted in these cases, such as youth or women who are more likely to be unemployed or outside the labor force. They might also be explained by differences in the structure of the labor market across settings. For instance, underemployment is more widespread in less formalized economies, where many individuals are engaged in low-paying occupations and few are formally unemployed.

The average effect on the likelihood of employment also hides substitution between occupations. In Côte d’Ivoire, a substantial share of treated youth substituted out of informal wage jobs and self-employment to take public works jobs, limiting the net effects on employment. In Ethiopia, the program increased employment in public works by 4.6 percentage points but also decreased employment in the private sector by 4.7 percentage points, resulting in an overall null result.

> 17 The authors also note that in an alternative specification there is a 16 percentage points increase in the likelihood of working in paid work. There is also an increase in being a ‘regular earner’.

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Does Central Bank Independence Increase Inequality?<sup>_⇤_</sup> Micha ̈el Aklin<sup>_†_</sup> Andreas Kern<sup>_‡_</sup> Mario Negre<sup>_§_</sup> _Keywords_ : inequality, central bank independence, labor market policy _JEL Code_ : D63, D72, E02, E42, E44, E50, E52, J08

> _⇤_ We are grateful to Benjamin Braun, David Hou, Marco Schneebalg, David Singer, Jakob Schwab, and seminar participants at Georgetown University, the German Development Institute, the Board of Governors of the Federal Reserve System, and audience members at APSA for helpful comments. All errors are ours.

> _†_ University of Pittsburgh. Contact: aklin@pitt.edu.

> _‡_ Georgetown University. Contact: ak679@georgetown.edu.

> _§_ The World Bank. Contact: mnegre@worldbank.org.
reduced stamp duty rates in recent years. As can be seen from the trends in collections in these States, there is a strong positive trend over time, suggesting that revenues may in fact increase with rate reductions. In addition, the stamp duty rate in Rajasthan was lowered from 12 percent to 7 percent in 1996-1997, and the World Bank (2000) documents that revenues increased by 36 percent between 1996-1997 and 1998-1999.<sup>30</sup> Further evidence that a reduction in stamp duty rates may actually lead to an increase in revenues is found by Das-Gupta (2002), who calculates for Karnataka a negative correlation between the growth rates of the effective tax rate and the average transaction value per document; that is, he finds that a 1 percent increase in the growth rate of the effective tax rate is associated with a -0.6 percent decrease in the average transaction value.<sup>31</sup> Although any conclusions must remain tentative at this time, it is certainly possible that stamp duties are so restrictive that States may not have much revenues to lose – and may in fact have much to gain – from a significant reduction in stamp duty rates. This issue is examined in more detail later.

# **B. Stamp Duty Revenue Losses from Undervaluation**

As discussed earlier, it is widely believed that the high rates of stamp duties lead to significant undervaluations of the transactions. Such undervaluations lead directly to stamp duty revenue losses. To illustrate the magnitudes of these losses, consider a “representative” transaction, in which a property with true value of Rs. 1 million is exchanged. Suppose also that the individual understates the true value by various percentages: 10 percent, 25 percent, and 50 percent. The stamp duty revenue loss then depends upon the duty rate. The simple calculations in Table 11 indicate the magnitude of the revenue loss for different duty rates.

**Table 11. Stamp Duty Revenue Loss for “Representative” Rs. 1 Million Transaction** <u>(in Rs.)</u>

||**Percent Under**<br>|**valuation**<br>||
|---|---|---|---|
|**Duty Rate**|**10%**|**25%**|**50%**|
|5%|5000|12,500|25,000|
|10%|10,000|25,000|50,000|
|15%|15,000|37,500|75,000|
|20%|20,000|50,000|100,000|
|25%|25,000|62,500|125,000|

The potential impact on stamp duties in selected States can also be calculated. Assume that undervaluation is, say, 25 percent for all transactions. Table 12 shows the stamp duty

> 30  See the World Bank (2000), "Rajasthan: Averting Fiscal Crisis and Accelerating Growth", Draft Report, Poverty Reduction and Economic Management Unit, South Asia Region (Washington, D.C.).

> 31  See Arindam Das-Gupta (2002), “The Stamps and Registration Department in Karnataka: A Review of Institutions and Administration”, Indira Gandhi Institute of Development Research (Mumbai, India).

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education, while the few people who indicated that the completion of an adult literacy program, other literacy program, or church or mosque schooling constituted their highest educational attainment were imputed to have spent 6 years in school.

Information on the educational attainment of the manager is missing for small urban firms. However, we have information on the educational attainment of the workforce and use this to impute the educational attainment of the manager.

If the typical worker has between

- 0-3 years of schooling: the manager is assumed to have 3 years of education.

- 4-6 years of schooling: the manager is assumed to have 6 years of education.

- 7-12 years of schooling: the manager is assumed to have 12 years of education.

- More than 13 years of schooling: the manager is assumed to have 15 years of education.

_Gender of the manager:_ A dummy variable which takes the value 1 if the manager is a woman for rural firms and for small urban firms. For large urban firms, it is coded 1 if at least one of the managers is female.<sup>26</sup> For urban firms, the share of female managers is thus likely to be overestimated.

# **_Sectoral Affiliation_**

In order to classify enterprises as belonging to a certain (sub-) industry, the following classification was adopted:

|**Table B.1: Sectoral Affiliation**<br>|<br>||<sup>**27**</sup>|
|---|---|---|---|
|**Industry/Group of Activities**|**Activities – “large”**<br>**manufacturing and**<br>**services datasets**|**Activities –**<br>**small/informal**<br>**dataset**|**Activities- rural data**|
|Food and Beverages|Food, beverages|Food|Food and beverages,<br>brewing/distilling, grain<br>milling **c**|
|Textiles and clothing|Polyester button, textiles,<br>garments|Textiles|Manufacture of textiles, of<br>wearing apparel; dressing<br>and dyingof fur|
|Woodwork & Metal|Woodwork, furniture and<br>metalwork, wire and nails||Manufacture of wood and<br>of products of wood and<br>cork, except furniture;<br>Manufacture of fabricated<br>metal products, except<br>machinery and equipment,<br>Manufacture of furniture|
|Leather|Leather||Tanning and dressing of<br>leather;manufacture of|

26 Since we do not have information on the number of managers per enterprise ,it was not possible to compute the proportion of female managers.

27 **c** indicates that this category contains a number of sub-activities – listings of which are available upon request.

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cycles as in government education) and a private health sector.<sup>8</sup> The database also includes twelve non-service (agricultural and industrial) sectors. The sectoral structure of Yemen as pictured in the SAM is described in Table 3.2.

Table 3.2: Economic sectoral structure in 2004

|Sector|VAshr|PRDshr|EMPshr|EXPshr|EXP-<br>OUTshr|IMPshr|IMP-<br>DEMshr|
|---|---|---|---|---|---|---|---|
|Agriculture|11.4|12.8|33.3|3.1|3.7|14.9|16.0|
|Oil|29.5|16.8|6.6|86.0|78.9|1.6|6.4|
|Manufactures|11.5|23.5|5.0|4.2|2.8|71.7|33.6|
|Services non-government|36.4|39.0|28.7|5.1|2.0|10.7|4.0|
|Servicesgovernment|11.3|7.8|26.4|1.6|3.2|1.1|2.2|
|TOTAL|100.0|100.0|100.0|100.0|13.5|100.0|15.5|

Vashr = value-added share (%) PRDshr = production share (%) EMPshr = share in total employment (%) EXPshr = sector share in total exports (%) EXP-OUTshr = exports as share in sector output (%) IMPshr = sector share in total imports (%) IMP-DEMshr = imports as share of domestic demand (%) Source: Yemen SAM 2004.

The factors of production include three types of labor, each of which is linked to a level of education (less than completed secondary; completed secondary; and completed tertiary). The growth in the labor force and changes in its composition depends on the education system and demography. The non-labor factors include public capital stocks (one for each government sector, a private capital stock, and a natural resource used in oil and gas extraction. The SAM also includes current and capital accounts for institutions (household, government, and rest of world), investment accounts (one par capital stock), and auxiliary accounts for taxes and interest payments.

Data related to the different MDGs, the labor market, and various elasticities were also used to calibrate the model. These data include levels of service delivery required to meet the different MDGs, number of students at different educational levels, student

> 8 According to official estimates, the share of students in private institutions is 2.3 percent, 2.0 percent and 14.9 percent for basic, secondary and high education, respectively.

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The estimation results of the gravity equations are reported in Appendix 3.<sup>3</sup> Specification 1 corresponds to equation (7) including country-pair and year fixed effects and Specification 2 corresponds to equation (8) including exporter-year fixed effects. In Appendix 3, a parameter with an upper index _a_ is significant at the 1% level, that with an upper index _b_ is significant at the 5% level and that with an upper index _c_ is significant at the 10% level.

The traditional gravity variables (distance, GDP and Population) depict the expected sign and magnitude in the two specifications. The estimated coefficients of the real exchange rate variable are negative, indicating a slight decreasing competitiveness among trading partners over the period 1960-1999 after controlling for the traditional gravity variables.

The time trend is not statistically significant in Specification 1. The bilateral geographical variables' coefficients are statistically significant with the expected signs and magnitudes.

In the second step, we extracted the estimated trade residuals from equations (7) and (8), and run a kernel regression as described in Section 2.2. We then used equation (13) to build the confidence interval as follows: for each grid point, we consider the standard deviation ( σ ) from equation (13) and use it to compute the 95% confidence interval of the trade effects defined as ± .196 ×σ , where σ = 1/ ( 12 _n_ ) , _n_ being the total number of years of existence of a given RTA and 1/12 being the variance of the uniform distribution.<sup>4</sup> The results are presented graphically in Appendix 4.

Following Baldwin and Taglioni (2006), we choose the specification including the exporter-year fixed effects as our preferred estimation and comment the results in the next section.

# **3.2 The trade effect of some developing RTAs**

The ASEAN Free Trade Agreement (AFTA) was created in 1992 by six members of the Association of South East Asian Nations (Brunei Darussalam, Indonesia, Malaysia, Philippines, Singapore and Thailand), four other members joined subsequently (Vietnam in 1995, Laos and Myanmar in 1997, Cambodia in 1999). The AFTA members included in the estimation as exporter and importer are Indonesia, Malaysia, Philippines Singapore and Thailand, the remaining members being included only as importers. Figure 2 of

> 3 We do not report exporter-year, country-pair and year fixed effects to save space. Note also that since equation (8) introduces exporter-years fixed effects, the variables with the index _it_ are absorbed in this specification.

> 4See Bierens (1994) for a full explanation of this process.

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the three-year lagged average for the _Democracy_ variable is to avoid endogeneity (the contemporaneous effect of inequality on democracy) and more substantively, to allow for the fact that democracy is unlikely to affect inequality immediately. The results show that, with other factors being equal, countries in Africa would have a Gini coefficient about 10.3 points higher than WENAO countries (the omitted category). This comes on top of controlling for ethnic fractionalization which is the highest in Africa<sup>23</sup> and which also contributes to inequality. The Africa dummy is significant but smaller than the Latin America dummy by more than 2 Gini points. Test of equality of the two dummies however is not rejected. Thus while Africa seems to display greater inequality compared to the rest of the world, that “rest of the world” does not include Latin America and the Caribbean.

In formulation **2** , we introduce more controls for the political system: a dummy variable for the type of political system (presidential system, assembly-elected president, and parliamentary system), and an index of government cohesiveness. Both variables come from the Database of Political Institutions (DPI) developed by Beck et al. (2000). 24 Unlike _Democracy_ , the _System_ variable does not measure level of democracy but simply addresses the issue of where, in a given setting, the political power lies: in a president, in an assembly-elected president,<sup>25</sup> or in a prime minister that depends on parliamentary majority (regardless of how that majority is obtained—whether by variable, regional dummies were not included, ethnic fractionalization variable was insignificant. Once the regional dummies are included as controls, fractionalization becomes highly significant. This is explained by strong regional patterns of government consumption. The same seems a priori plausible here too as inequality does display strong regional features.

23 The unweighted average of the index is 0.68 in Africa (0.73 in sub-Saharan Africa), 0.52 in Asia, 0.45 in Latin America, and 0.26 in WENAO, and 0.2 in Eastern Europe/FSU where the index is available for only four countries (Hungary, Romania, Bulgaria and Poland).

24 We used the most recent (March 2003) version of DPI; it is available at <u>http://www.worldbank.org/research/bios/pkeefer.htm.</u> 25 These are systems where the chief executive is elected by parliaments but cannot be easily dismissed, that is the chief executive does not depend on parliamentary majority to remain in office. This is a difference between say, Egypt where the president is elected by the Parliament and can hardly be dismissed, and the UK where the Prime Minister cannot survive without Parliamentary majority. For the year 1997, countries with such a system are China, Egypt, Guyana, Indonesia, Laos, South Africa, Vietnam, Yemen. Before the transition, many Communist countries fell in the same category.

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# **Table 3: Transition Matrix**

||**T**<br>|**ransition Matrix**<br>**Status at E**<br>|**ndline**<br>||<br>|
|---|---|---|---|---|---|
|**Status at Baseline**|Self-employed|Wage-employed|Not working|Attrited|<br>Total|
|Self-employed|348|9|38|0|395|
||88.1%|2.3%|9.6%|0.0%|100.0%|
|Wage-employed|14|215|25|0|254|
||5.5%|84.7%|9.8%|0.0%|100.0%|
|Not Working|32|31|240|2|305|
||10.5%|10.16%|78.7%|0.7%|100.0%|
|Total|394|255|303|2|954|
||41.3%|26.7%|31.8%|0.2%|100.0%|

Note: Attrited is a category reserved for individuals that did not complete the endline survey. Not working is a composite category that comprises both the unemployed and those who are out of the labor force.

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