Technical High School and Vocational Training in Latin America Preliminary draft. Only to be submitted to the LACEA conference. Please do not cite.

Abstract

This paper surveys earnings differences for workers who followed different types of secondary and tertiary education. We perform two comparisons: (a) for those whose schooling attainment does not surpass high school, we compare those who followed the technical path or specialty vis-à-vis those who followed a humanistic or general one; and (b) for those whose schooling attainment reached the tertiary level, we compare those who attended a (three years or more) vocational program vis-à-vis those who pursued a university degree. The comparisons are performed following a matching approach as in Ñopo (2008) and represent 13 Latin American countries for the period comprised between 1995 and 2009. The results indicate that: (a) at the secondary level, workers who followed the technical path earn between 5% and 10% more than their peers who followed the humanistic path, the gaps are homogeneous along the earnings distribution and this did not changed much during the period of analysis; (b) at the tertiary level, workers who attended college earn between 40% and 50% more than their peers who attended technical studies, this gap is increasing along the earnings distribution (that is, there are higher earning gaps for higher earnings workers) and such gap increased between 10 and 20 percentage points during the period.

Keywords: Latin America, returns to schooling, technical education, vocational training JEL codes: J31, I24

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1. Introduction Balance is key for long-run sustained growth, especially in economics. On that regard, fostering an appropriate balance between the technical and the humanistic formation of an economy’s human capital, appropriately connected to the needs of their labor markets, should be on the list of policy-makers’ priorities. This is particularly relevant for Latin America as the statistics show an imbalance. According to UNESCO’s data for 2008-2009, out of nine young Latin Americans only three are pursuing tertiary level studies and only one of those attends a technical program. This situation contrasts with what is seen in countries with higher productivity and better income distributions. Out of nine youngsters in (just to name a few countries) New Zealand, Sweden or the UK, six are enrolled in tertiary level studies; and among them three do it so in technical programs (UNESCO, 20XX). Additionally, in Latin America dropout rates in technical programs is very high. Out of every five students who enroll in a technical tertiary program only one ends up graduating from it (Table 1). At the secondary level the imbalance is even more pronounced. Among workers with high school and no further studies, for every individual who attended a technical program there are ten other workers who attended a humanistic one (Figure 1). Why so few young Latin Americans pursue the technical paths of human capital formation? An answer to it would involve individuals’ (and families’) aspirations, social prestige, supply and costs of quality programs, connectedness of such programs to actual needs of labor markets (pertinence), and of course, expected income profiles. This paper is devoted to explore such latter explanation. To what extent is there a penalty or premium for those who followed technical paths of human capital formation instead of the traditional ones? We explore such question for workers from secondary and tertiary levels in thirteen Latin American countries using harmonized household surveys of living standards with national representativeness. We do so following a

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matching comparison approach such that the premium or penalty is assessed comparing workers with the same productive characteristics. Since the development of human capital theory (Becker 1964), countless estimates of the economic benefits of investing in education for the individual have been published. There are indeed thousands of estimates from a wide variety of countries, obtained with a diverse range of methodological approaches. Yet, all reaffirm the importance of investing in education for individual progress and economic development. A number of observations on the pattern of returns across countries have been highlighted in the literature. The highest returns, on average, are found in countries of the Sub-Saharan Africa (over 13%) and the lowest in the OECD countries (just under 7%), with Latin America, Asia and Europe/Middle East covering the middle ground. High returns are associated with countries in an earlier stage of development and low average educational levels, while lower returns are associated with advancements in the development process and educational levels (Psacharopoulos and Patrinos 2004; Denny et al. 2003; Card 2001; Flabbi et al. 2008; Harmon et al. 2003). Returns to investment in technical or vocational education, however, have been less studied. The empirical evidence on the merits of technical education worldwide is mixed. Psacharopoulos shows that general secondary education offered higher social rates of return, largely because the higher costs of providing technical education more than compensated the gains in the labor market (Psacharopoulos, 1986, 1987, 1994). However, other studies stress that returns to vocational education substantially depend on the level of economic development, the availability of private sector jobs and whether or not people are employed in a field related to their training. Bennell (1996), for example, shows that social rates of return of vocational education are at least equal to those for general secondary education in a number of countries. Other papers that document higher returns from technical education include Neuman and

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Ziderman (1989, 1991, 1999) for Israel; Moenjak and Worswick (2003) for Thailand; El-Hamidi (2006) for Egypt and, more recently, Bucarey and Urzua (2012) find that secondary vocational students earn more than their “academic” counterparts during adulthood in Chile. Other studies report higher returns to technical education using comparable data for different countries. Bishop and Mane (2004) analyze 12 years of international longitudinal data and find that those who spent about one-sixth of their time in high-school to occupation-specific technical courses earned at least 12% more than workers without vocational training one year after graduating and about 8% more seven years later (holding other variables, such as attitudes and ability in 8th grade, family background and college attendance, constant). In addition, a recent study by Pema and Mehay (2012) use longitudinal data on the careers of military recruits who completed high school Junior Reserve Officers’ Training Corps (JROTC), a military science program that has features of a vocational training and school-to-work program. They find that the occupation-specific training received via JROTC reduces early turnover and improves long-run job stability for those who choose military jobs, suggesting that an important effect of vocational training is to improve job match quality. On the other hand, some studies find that returns to general education exceed returns to technical or vocational education, such as Horowitz and Schenzler (1999) in Suriname and Kahyarara and Teal (2008) in Tanzania. Other researches find no significant differences in labor market outcomes between the two educational tracks. Hotchkiss (1993) finds no short-run wage gains, regardless of whether vocational education matches future occupations. Chung (1995) reviews the literature published between the early 1970s and the early 1990s and finds that twelve studies on returns to vocational education in developing countries reported higher returns to vocational training, ten studies reported lower returns to vocational education or not different from other forms of learning, and five studies concluded that there is no basis to compare the

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returns to vocational education with the returns from other forms of learning. Sakellariou (2003) found for Singapore that, while average returns to technical education are only slightly higher than returns to general academic education, there are sharp differences between male and female workers. Returns to general academic education are consistently higher for men (although barely so for post- secondary education), while returns to technical/vocational education are clearly higher for women (at least for lower-secondary and post-secondary). Meer (2007) uses the National Educational Longitudinal Survey of 1988 to examine the returns to secondary vocational education in the United States and finds that long-run wage gains for vocational education graduates are due to self-selection into tracks (vocational or academic). Malamud and Pop-Eleches (2008) analyze en educational reform in Romania in 1973, which moved a large percentage of students from vocational training to general education, keeping total years of schooling unchanged. They show that, in contrast to cross-sectional findings, there is no difference in labor market participation or earnings between cohorts that were affected and those that were not affected by the policy. Thus, they conclude that differences in labor market returns between graduates of vocational and general schools are largely driven by selection. Chae and Chung (2009) find that vocational secondary education in South Korea is not associated with better labor market outcomes in terms of employment rate, wage levels, employment stability and transition to the first job, when compared to general secondary education. Two recent papers examine the outcomes of vocational secondary school graduates in Indonesia. (Chen, 2009) follows a single cohort of students three years after graduation and finds that vocational school graduates, experience similar wage and employment outcomes compared with general school graduates. Newhouse and Suryadarma (2009) examine the association between the type of senior high school attended by Indonesian youth and their subsequent labor market outcomes. They find that

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private vocational school graduates do at least as well as private general graduates in the labor market, despite coming from more disadvantaged socioeconomic backgrounds. Hanushek and Woessmann (2011) have taken a broader perspective on vocational education programs, and instead of focusing on the school-to-work transition of youth they have studied the difference in life-cycle work experience – employment, wages, and career-related training – between individuals receiving vocational and general education. To test their main hypothesis that any relative labor-market advantage of vocational education decreases with age, they employ a difference-in-differences approach that compares employment rates across different ages for people with general and vocational education. They use micro data for 18 countries from the International Adult Literacy Survey, and find strong support for the existence of such a trade-off, which is most pronounced in countries emphasizing apprenticeship programs. In sum, general academic vs. vocational-technical education debate is not conclusive about an advantage of technical graduates in the labor market, even during the transition from school to work. The evidence is mixed and probably depends on several factors, both of the labor market and economic structure of the country, and also on the quality and pertinence of the technical education in each education system. For Latin America, there are no studies analyzing comparable data for different countries in the region. The rest of the paper is simply organized: the next section shows our data sources, then in Section 3 we show the earnings gaps that remain after matching individuals on the basis of their observable characteristics and we conclude in Section 4.

2. The Data The data comes from nationally representative household and labor surveys of thirteen LatinAmerican countries in two moments in time, circa 1995 and circa 2009. Table A1 reports the

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specifics of each data source: country, survey name, years, and the number of observations (original and expanded, after using sample weights) for: (i) workers who attended a Technical Secondary education program and did not advanced towards a tertiary degree, (ii) workers who attended an Academic/Humanistic secondary education program and did not advanced towards a tertiary degree, (iii) workers who attended a Technical Tertiary program, regardless of their type of high school, and (iv) workers who attended a University Tertiary program, regardless of their type of high school. In all those cases we considered only workers aged between 16 and 65, not enrolled in any education program at the time of the survey. Table A2 in the Appendix shows the survey questions we used in each country to identify each of the above-mentioned subpopulations, specifically how we identified the different education profiles. Data from all countries is pooled into a single data set but we use their corresponding expansion factors from each country such that the relative size of each sample proportionally corresponds to the working population their corresponding country. Figure 1 shows the proportion held by each group within the full sample of workers. The figure shows that the subgroups under analysis expanded during the 14-year span of our data, especially the Academics/Humanist branch of high school graduates (from 20% to 27% of the working population) and the University branch of tertiary graduates (from 10% to 13%). All in all, the sum of the four subgroups under analysis moved from 39% to 50% of the working population. This reflects the expansion of schooling opportunities that Latin America faced at the turn of the century.

Figure 1 Proportion held by each sub-population group in the full sample of workers a. Circa 1995

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b. Circa 2009

Source: Authors’ calculations from household surveys.

Table 1 shows descriptive statistics for observable characteristics in all countries’ data sets for the two periods of analysis, split by each subpopulation of interest. As expected, the group with technical secondary education reports more years of schooling that the one with humanistic secondary; and among those with tertiary education the university graduates show more years of schooling than their technical counterparts. The latter group is the one that, by far, exhibits the highest dropout rate among the four groups. This is also the group that shows the lowest male participation. During the 14-year span of our analysis the sample of workers aged. The proportion of workers 54 and older increased in all four groups while the proportion of workers 24 and under dropped in three of them.

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In line with the demographic transitions experienced in Latin America, the presence of children (12 years old or younger) in the workers’ households slightly dropped during the period. It is still the case that the households of workers with university tertiary studies show the lowest presence of children. No much change can be seen regarding presence of elderly at home. These two variables, presence of children and elderly at home, will be later used as proxies for household responsibilities of the workers that limit their possibilities for full labor market participation. Another household variable we later use in the analysis is the presence of other labor income generator at home. This is highest among workers with technical tertiary studies and is consistent with the fact that they are less likely to be head of household than those in the other groups, although the data for the period show some changes towards equalization among the four groups. Finally, workers with tertiary studies (both technical and university) tend to work parttime at their main job (30 hours per week or less) in higher proportion than those with only secondary studies. Consistently, they tend to have more than one job in a higher proportion as well. This latter is also observed among workers with university studies. In terms of regional representativeness of the data, it should be noted that Argentina, Mexico and, to a smaller extent, Chile, are the predominant countries in the pooled sample of workers.

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Table 1 Descriptive Statistics, by Group Secondary Education Technical

Average years of education

Tertiary Education

Academic/Humanist

Technical

University

Circa 1995 Circa 2009 Circa 1995 Circa 2009 Circa 1995 Circa 2009 Circa 1995 Circa 2009 11.03 11.40 9.96 10.02 12.90 13.05 15.99 16.15

Personal Characteristics Drop out (the individual did not finished his studies)

48.9%

42.1%

67.4%

68.1%

80.8%

77.9%

34.4%

37.9%

Men (gender)

75.1%

66.9%

68.7%

67.2%

47.0%

48.5%

64.0%

57.3%

25.0% 37.6% 28.7% 13.7% 2.5%

17.6% 32.5% 26.5% 16.3% 7.1%

32.9% 37.6% 28.7% 13.7% 2.5%

25.5% 31.4% 24.7% 13.1% 5.3%

24.9% 37.6% 28.7% 13.7% 2.5%

14.8% 36.5% 29.0% 14.6% 5.0%

6.2% 37.6% 28.7% 13.7% 2.5%

7.0% 32.4% 30.6% 21.6% 8.4%

Presence of children (≤12 years) in the household

62.5%

57.5%

67.0%

63.3%

61.7%

60.4%

59.0%

50.5%

Presence of elder (≥65 years) in the household

15.5%

13.7%

12.1%

12.6%

14.1%

14.9%

15.7%

15.9%

Head of the Household

51.0%

49.7%

47.4%

47.7%

36.8%

42.3%

54.4%

49.3%

Presence of other household member with labor income

64.4%

64.0%

65.8%

66.4%

71.4%

70.8%

66.4%

69.0%

Part time workers (≤30 hours)

12.3%

16.0%

13.5%

14.0%

18.0%

20.5%

21.6%

19.3%

More than one job

5.5%

6.7%

5.5%

5.9%

8.8%

12.5%

14.5%

12.1%

4.8% 18.5% 76.7%

3.3% 18.7% 78.0%

4.1% 16.7% 79.2%

3.5% 19.1% 77.4%

3.3% 13.1% 83.6%

4.6% 12.5% 82.8%

7.3% 14.9% 77.9%

6.3% 12.5% 81.3%

2.7% 1.2% 23.9% 1.3% 9.2% 24.3% 7.9% 6.2% 23.3%

3.9% 1.9% 19.4% 1.4% 8.3% 24.6% 10.2% 5.9% 24.3%

6.4% 0.9% 22.4% 0.7% 7.0% 23.2% 8.0% 3.1% 28.3%

5.6% 1.0% 20.3% 0.9% 8.1% 24.6% 9.1% 2.8% 27.7%

1.9% 1.1% 15.1% 1.2% 3.1% 20.9% 5.6% 6.3% 44.9%

2.7% 2.5% 15.1% 1.6% 3.2% 16.8% 4.6% 4.7% 48.9%

1.6% 0.9% 10.6% 1.1% 3.9% 14.7% 4.7% 11.5% 51.1%

1.6% 1.0% 9.9% 0.9% 3.6% 12.3% 4.4% 8.7% 57.6%

34.1% 3.0% 33.2% 1.5%

24.7% 6.3% 42.5% 2.4%

12.9% 4.2% 16.8% 2.0%

9.7% 5.1% 12.7% 3.0%

7.6% 0.7% 8.1%

10.4% 0.7% 11.3%

13.1% 2.4% 9.8%

11.0% 4.1% 9.3%

1.1% 0.9% 0.1% 62.3% 0.3%

0.9% 1.1% 0.1% 54.8% 0.3%

9.2% 1.8% 0.7% 41.0% 0.9%

10.3% 1.4% 1.5% 49.1% 2.2%

15.5% 3.5%

16.9% 3.5%

19.6% 1.6%

9.9% 1.1%

Age groups 24 and under 25 to 34 35 to 44 45 to 54 54 and over

Labor Characteristics

Type of Employment Employer Self-employed Employee Economic Sector Agriculture, hunting, forestry and fishing Mining and quarrying Manufacturing Electricity, gas and water supply Construction Wholesale and retail trade and hotels and restaurants Transport, storage Financing insurance, real estate and business services Community, social and personal services By Country

Argentina Bolivia Chile Costa Rica Ecuador El Salvador Honduras Mexico Nicaragua Paraguay Peru Uruguay Source: Authors’ calculations from household surveys.

22.6% 2.2% 3.4%

19.3% 2.0% 2.8%

60.4% 2.2% 1.5%

64.3% 3.1% 2.0%

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Table 2 shows relative hourly earnings at the main job for the groups being compared, by observable characteristics. Earnings are computed as hourly earnings, measured in terms of purchasing power parity (PPP, US$, 2005). Hourly earnings for each individual are computed dividing the monthly income by 4.3 times the number of hours worked in a week.1 Average school technicians’ earnings circa 1995 have been set equal to 100 for each group (i.e., people with secondary education and people with tertiary education). The typical earning patterns arise as the individuals with higher earnings are those with university studies, who did not dropout, males, older, with no children or elderly at home, household heads, with no other income generator at home, part-timers and employers.

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The monthly income corresponds to the monthly earnings received from the main occupation in the month previous to the survey. The job schedule is captured with survey questions of the type, for example “¿Cuántas horas trabaja efectivamente en su empleo o actividad principal? Señale horas semanales, ¿cuántas horas efectivas al día trabajó la semana pasada? ¿Cuántas horas trabajó la semana pasada en la ocupación principal? El mes pasado, ¿cuántas horas a la semana trabajó en este negocio o empresa? ¿Cuántas horas por semana trabaja regularmente como...? ¿Cuántas horas, días y en qué jornada trabajo efectivamente la semana anterior?”.

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Table 2 Relative Hourly Earnings at the Main Job, by Group Relative Hourly Earnings (Base: Average Technicians earnings circa 1995 in each group=100) Secondary Education Technical

Average Hourly Earninngs

Tertiary Education

Academic/Humanist

Technical

University

Circa 1995 Circa 2009 Circa 1995 Circa 2009 Circa 1995 Circa 2009 Circa 1995 Circa 2009 100.0 77.3 82.9 65.0 100.0 90.2 152.8 133.7

Personal Characteristics Drop out (the individual did not finished his studies) No Yes

104.3 95.6

83.7 68.6

109.6 70.0

76.4 59.7

83.2 104.0

79.6 93.2

172.1 116.1

153.1 102.0

77.6 107.4

68.7 81.6

79.3 84.5

58.5 68.2

95.2 105.5

87.1 93.4

133.4 163.7

124.5 140.6

64.3 89.0 124.5 139.4 140.5

52.9 69.7 87.9 92.5 98.7

54.4 85.6 101.3 119.9 110.3

45.4 62.7 74.3 83.1 85.2

66.6 94.3 120.1 143.9 163.0

54.1 88.4 100.4 102.0 116.2

107.9 140.0 161.5 175.1 183.1

79.1 120.1 141.2 150.7 160.4

106.5 96.1

79.3 75.9

86.4 81.2

66.0 64.4

106.0 96.3

91.9 89.1

162.3 146.2

135.2 132.2

103.0 83.7

77.8 74.6

83.7 76.9

65.6 60.9

101.7 89.9

92.6 76.5

156.4 133.8

137.7 112.5

75.1 123.9

66.5 88.3

68.1 99.3

54.2 76.9

86.2 123.7

76.7 108.7

127.9 173.7

112.6 155.4

113.5 92.5

79.8 76.0

93.3 77.5

74.9 60.0

118.6 92.5

109.9 82.1

168.7 144.8

144.3 129.0

94.8 137.4

71.7 107.1

74.4 137.1

60.9 90.3

94.0 127.4

84.0 114.3

150.8 160.1

129.4 151.6

100.1 98.0

77.0 82.6

82.1 96.0

64.6 71.3

99.5 105.0

89.5 95.3

154.0 145.8

133.2 137.5

194.1 106.9 92.5

125.0 89.2 72.5

148.4 93.6 77.2

138.0 70.9 60.2

125.1 93.6 100.0

155.8 85.9 87.2

176.3 123.3 156.3

200.5 101.7 133.5

60.0 131.5 100.2 112.9 93.3 89.8 94.8 139.7 106.5

64.9 111.4 86.2 107.9 76.9 65.2 80.4 86.0 77.0

52.1 100.6 74.1 105.6 71.2 80.9 91.9 112.0 94.5

43.4 102.4 61.2 81.8 63.4 62.9 66.6 80.4 70.7

67.2 130.3 87.1 141.3 113.3 81.6 109.2 120.6 107.5

59.3 73.1 90.4 84.5 79.2 81.5 89.7 112.7 94.4

125.7 192.8 166.9 164.5 170.6 113.8 140.1 163.4 158.4

102.0 144.2 131.2 147.8 166.6 100.4 90.3 128.3 143.8

99.0 69.8 96.1 81.3

69.4 72.3 80.4 96.5

92.1 55.83 85.03 78.21

62.6 45.8 75.6 74.4

119.47 98.31 115.57

99.9 67.0 107.6

169.03 120.95 195.67

137.2 98.2 179.4

4.23 137.38 122.69 109.77 52.91

2.6 106.1 128.9 96.9 60.0

5.79 157.34 122.38 208.05 75.75

3.5 119.5 145.7 165.8 74.6

55.77 66.58

57.1 80.9

80.87 164.24

87.6 159.9

Men No Yes Age groups 24 and under 25 to 34 35 to 44 45 to 54 54 and over Presence of children (≤12 years) in the household No Yes Presence of elder (≥65 years) in the household No Yes Head of the household No Yes Presence of other household member with labor income No Yes Labor Characteristics Part time No Yes More than one job No Yes Type of Employment Employer Self-employed Employee Economic Sector Agriculture, hunting, forestry and fishing Mining and quarrying Manufacturing Electricity, gas and water supply Construction Wholesale and retail trade and hotels and restaurants Transport, storage Financing insurance, real estate and business services Community, social and personal services By Country

Argentina Bolivia Chile Costa Rica Ecuador El Salvador Honduras Mexico Nicaragua Paraguay Peru Uruguay Source: Authors’ calculations from household surveys.

115.7 42.8 115.7

83.0 45.8 79.8

83.5 37.28 105.58

65.2 38.2 77.6

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3. Earnings gap decompositions The extent to which the earnings differentials can be attributed to differences in observable characteristics is explored next. This is done using matching comparisons such that each individual with technical studies is paired with a worker with non-technical studies but with the same observable characteristics (for methodological details see Ñopo, 2008). That is, we compare individuals’ hourly earnings from the main job for workers in group (i) vis-à-vis those in group (ii); and workers in group (iii) vis-à-vis those in group (iv). The characteristics considered for this matching exercise are (in this particular order): whether the individual dropped out (i.e., did not complete the last level of education initiated), gender, age, education, presence of kids (12 or younger) in the household, presence of elders (65 or older) in the household, whether the workers is or not household head, presence of other wage earners in the household, whether the individual has a part-time work, whether the individual holds a secondary job, the job type (employed, self-employed, employee), and the economic sector. All together the complete set of observable characteristics just listed will be referred as the “full set”. These variables are sequentially added as matching criteria such that the distributions of observable characteristics for the individuals in the groups under comparison are sequentially more similar. After the inclusion of each new matching variable the sample size of the individuals under comparison drops but at the same time the comparison gets cleaner (in the sense of having a comparison group that gets sequentially more precise). Confidence intervals for the earnings gaps measured among matched individuals are reported in Figure 2. The earnings comparisons (after matching) of workers with technical and humanistic secondary studies reflect that: (i) there are no statistically significant changes after adding the control variables as the confidence intervals for the unexplained gaps remain almost unaltered,

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and (ii) there are also no statistically significant changes between circa 1995 and circa 2009. If any, the gap slightly increased (but again, the change seems to be not statistically significant). The fact that the gaps are negative reflects that the earnings of those with technical secondary studies are higher than those of the workers with humanistic secondary studies. The comparison between workers with technical and university tertiary studies shows more action. While the original earnings gap (the one that does not take into account differences in observable characteristics) dropped from almost to 70% to close to 60% during the 14-year span, the gap after controlling for the full set of characteristics (the one at the right extreme of the panel b of the figure) actually increased from around 30% to almost 50%. The inclusion of observable characteristics helped to reduce the unexplained earnings gap circa 1995 but it did not change but the gap circa 2009. Among all observable characteristics it seems that the dropout condition is the one that helps the most towards a reduction on the unexplained gap (the gap that remains after matching individuals on the basis of their observable characteristics). This is the case in both periods.

Figure 2 Confidence Intervals for the Unexplained Earnings Gap Controlling by Observable Characteristics a. Secondary Education Technicians vis-à-vis Secondary Education Academic/Humanists

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0 -5 -10 -15 -20

% of Average Teachnicians' Earnings

Original Drop gap out

Gender +Age +Children+Elders +Hh. head

+Oth. earner

+Part time

+More than 1 job

CI(90%) Circa 1995

CI(90%) Circa 2009

CI(99%) Circa 1995

CI(99%) Circa 2009

+Job +Economic Type Sector

60 50 40 30 20 10 0

% of Average Teachnicians' Earnings

70

b. Tertiary Education Technicians vis-à-vis Tertiary Education Universitaries

Original Drop gap out

Gender +Age +Children+Elders +Hh. head

+Oth. earner

+Part time

+More than 1 job

CI(90%) Circa 1995

CI(90%) Circa 2009

CI(99%) Circa 1995

CI(99%) Circa 2009

+Job +Economic Type Sector

Source: Authors’ calculations based on household surveys Note: Boxes show 90 percent confidence intervals for unexplained earnings; whiskers show 99 percent confidence intervals.

As discussed in Ñopo (2008) an extra advantage of the usage of matching for this type of analysis (instead of the traditional regression-based Blinder-Oaxaca approach) is the possibility of exploring not only average differences but also the distribution of earnings gaps between the groups in comparison. Figure 3 shows the unexplained earnings gaps along the individuals’

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earnings distribution for the two matched comparisons we analyze in this paper. For the comparison among high school graduates no clear pattern of differences arises. The unexplained earnings gaps between those with technical and those humanistic secondary education seems to be homogeneous along the earnings distribution. The most notorious reduction in earning gaps for the period analyzed are seen for those between 20th and 60th percentiles of the earnings distributions, but for individuals at other percentiles of the earnings distributions the change in earnings gaps seems to be statistically zero. For the comparison among those with tertiary education the situation is different. First, the gaps increase with earnings in both periods. The earnings premium for attending college (vis-àvis a vocational program) is close to zero for lower earnings individuals but it can surpass 50% for those at the other extreme of the earnings distribution, especially circa 2009. Second, as already reported in Figure 2 there is an increase in unexplained earnings gaps for this period, but what Figure 3 now reveals is that such increase homogeneously occurred along the earnings distribution

Figure 3 Unexplained Earnings Gaps along Percentiles of the Earnings Distribution (after Controlling by the Full set of Observable Characteristics) a. Secondary Education Technicians vis-à-vis Secondary Education Academic/Humanists

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Percentage of Technicians' Average Earnigns

10

5

0

-5

-10

-15

-20 0

10

20

30

40 50 60 Labor Earnings Percentile Circa 1995

70

80

90

100

Circa 2009

b. Tertiary Education Technicians vis-à-vis Tertiary Education Universitaries 80

Percentage of Technicians' Average Earnigns

70 60 50 40 30 20 10 0 -10 -20 0

10

20

30

40 50 60 Labor Earnings Percentile Circa 1995

70

80

90

100

Circa 2009

Source: Authors’ calculations based on household surveys

4. Conclusions While college graduates (and dropouts) earn more than their peers who attended technical programs (also, considering those who graduated and those who did not); those who attended technical high school earn more than who went to humanistic secondary programs. The technical high school premium, however, is substantially smaller than the college premium in tertiary. This

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leaves room for extra explorations to perform in this paper. (1) How do college dropouts compare to technical tertiary graduates? (2) How does the premium evolve with experience (the Hanushek hypothesis)? These analyses will be performed next (to be presented at the conference).

References XXXX XXXX XXXX

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Appendix Table A1 Data Sources and Sample Sizes, by Group Other Professionals and Technicians/Teachers (non tertiary) Working Populations* Technical Country

Name Of The Survey

Year

Academic/Humanist

(1) Number of

Secondary Education

(2) Expanded

Number of

Technical

(1 & 2) Expanded

Number of

Expanded

University

(3) Number of

Tertiary Education

(4) Expanded

Number of

(3 & 4) Expanded

Number of

Expanded

observations observations observations observations observations observations observations observations observations observations observations observations

Argentina Bolivia Chile Costa Rica Ecuador Honduras México Nicaragua Peru Paraguay El Salvador Uruguay

Encuesta Permanente de Hogares (EPH) Encuesta Nacional de Empleo (ENE) Encuesta Continua de Hogares (ECH) Encuesta de Caracterizacion Socioeconomica Nacional (CASEN) Encuesta de Hogares de Propósitos Múltiples (EHPM) Encuesta de Empleo, Desempleo y Subempleo (ENEMDU) Encuesta Permanente de Hogares de Propósitos Múltiples (EPHPM) Encuesta Nacional sobre Ingresos y Gastos de los Hogares (ENIGH) Encuesta Nacional de Hogares sobre medicion de Niveles de Vida (EMNV) Encuesta Nacional de Hogares (ENAHO)

1995 2002 1996 2007

2116 1388 198 189

471682 417664 41513 106096

1483 6137 2064 1470

1391220 1628425 451101 858893

3599 7525 2262 1659

1862902 2046089 492614 964989

1483 1653 126 58

290253 479066 25341 32045

2976 2929 648 532

729206 940535 136623 348620

4459 4582 774 590

1019459 1419601 161964 380665

1996

3876

459238

14543

1800798

18419

2260036

2174

311960

3411

544809

5585

856769

2009

9159

719023

28878

2148904

38037

2867927

5098

519067

6220

794499

11318

1313566

1994

269

20747

2506

219416

2775

240163

2009

391

40230

4875

512102

5266

552332

1998

117

41498

1194

511785

1311

553283

2010

234

39484

4107

873850

4341

913334

1995

31

4064

285

37431

316

41495

2009

56

4480

1679

125965

1735

130445

1994

195

312457

3840

6488083

4035

6800540

1397

2392681

1250

2291874

2647

4684555

2002

227

326543

7233

10839637

7460

11166180

1866

2518628

2437

4189298

4303

6707926

1998

157

30704

1144

236866

1301

267570

63

11055

222

50457

285

61512

2009

209

34402

3600

527134

3809

561536

78

11890

1304

190046

1382

201936

1997

631

594168

1152

1092705

1783

1686873

2009

2305

777236

2302

844772

4607

1622008

Encuesta de Hogares por Muestra (Mano de obra)

1996

172

46659

599

160468

771

207127

Encuesta Permanente de Hogares (EPH)

2009

114

48121

862

343232

976

391353

Encuesta de Hogares de Propositos Multiples (EHPM)

1998

353

35336

714

99565

1067

134901

2007

582

51960

1068

120549

1650

172509

1995

3037

134237

2028

90594

5065

224831

2010

6491

159277

3984

95460

10475

254737

Encuesta Continua de Hogares (ECH)

Source: Authors’ compilations from household surveys. Note: Working populations in each country are identified as those earning a salary in the main occupation, between 16 and 65 years of age and not enrolled in any education program.

19

Table A2 Questions and Answers Identifying Technicians and non Technicians at the Secondary and Tertiary Education Levels Questions and answers identifying each group Secondary Education Country

Year of the Survey

Technical or Technological

Academic/Humanist

Countries distinguishing between humanist/academic secondary education and technic secondary education Q. ¿Cuál es el último curso y nivel de Q. ¿Cuál es el último curso y nivel de instrucción aprobado? instrucción aprobado? 1996 A: Enseñanza técnica media A: Medio Bolivia Q. ¿Cuál fue el nivel y curso más alto Q. ¿Cuál fue el nivel y curso más alto de instrucción que aprobó? de instrucción que aprobó? 2007 A: Técnico de Instituto A: Secundaria Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado 1996 A: Educación media, técnico-profesional A: Educación media, humanista con/sin con/sin título título Chile Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado 2009 A: Educación media, técnico-profesional A: Educación media, humanista con/sin con/sin título título Q. ¿Cuál es el último grado o año Q. ¿Cuál es el último grado o año aprobado? aprobado? 1994 A: Secundaria técnica A: Secundaria académica Costa Rica Q. ¿Cuál es el último grado o año Q. ¿Cuál es el último grado o año aprobado? aprobado? 2009 A: Secundaria técnica A: Secundaria académica 1998 Nicaragua 2009

1996 Paraguay 2008

Q. Nivel de instrucción

Q. Nivel de instrucción

A: Técnico medio

A: Secundaria

Q. Nivel de instrucción

Q. Nivel de instrucción

A: Técnico medio

Tertiary Education Technical or Technological

Universitary

Q. ¿Cuál es el último curso y nivel de Q. ¿Cuál es el último curso y nivel de instrucción aprobado? instrucción aprobado? A: Enseñanza técnica superior A: Universidad Q. ¿Cuál fue el nivel y curso más Q. ¿Cuál fue el nivel y curso más alto alto de instrucción que aprobó? de instrucción que aprobó? A: Técnico Superior A: Universidad Pública/Privada Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado A: Centro de formación técnica e instituto A: Educación universitaria con/sin título profesional con/sin título Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado A: Centro de formación técnica/Instituto A: Educación universitaria con/sin título profesional con/sin título Q. ¿Cuál es el último grado o año Q. ¿Cuál es el último grado o año aprobado? aprobado? N/A A: Universitaria Q. ¿Cuál es el último grado o año Q. ¿Cuál es el último grado o año aprobado? aprobado? N/A A: Universitaria Q. Nivel de instrucción A: Técnico superior

Q. Nivel de instrucción Q. Nivel de instrucción

A: Secundaria

Q. Nivel de instrucción A: Técnico superior

Q. ¿Cuál es el último grado o curso aprobado de ....? /¿Cuál es el nivel más alto que cursó …?

Q. ¿Cuál es el último grado o curso aprobado de ....? /¿Cuál es el nivel más alto que cursó …?

Q. ¿Cuál es el último grado o curso aprobado de ....? /¿Cuál es el nivel más alto que cursó …?

Q. ¿Cuál es el último grado o curso aprobado de ....? /¿Cuál es el nivel más alto que cursó …?

A: Bachillerato Técnico Q. ¿A qué nivel corresponde la última etapa, grado, curso, ciclo o semestre más alto que aprobó? A: Bachillerato Técnico

A: Bachillerato Humanístico Q. ¿A qué nivel corresponde la última etapa, grado, curso, ciclo o semestre más alto que aprobó? A: Bachillerato-media Humanístico /Científico

N/A

A: Universitario Q. ¿A qué nivel corresponde la última etapa, grado, curso, ciclo o semestre más alto que aprobó?

Q. ¿A qué nivel corresponde la última etapa, grado, curso, ciclo o semestre más alto que aprobó? A: Técnica Superior

A: Universitario A: Universitario

A: Superior universitario

20

Countries distinguishing between universitary education and technic tertiary education Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado 1996 A: Educación media, técnico-profesional A: Educación media, humanista con/sin con/sin título título Chile Q. Indique el curso y tipo de estudio Q. Indique el curso y tipo de estudio actual o último curso aprobado actual o último curso aprobado 2009 A: Educación media, técnico-profesional A: Educación media, humanista con/sin con/sin título título Q. ¿Cuál es el nivel y año más alto de Q. ¿Cuál es el nivel y año más alto de educación que aprobó? educación que aprobó? 1995 N/A A: Ciclo diversificado y/o técnico Ecuador Q. Cuál es el nivel y año más alto de Q. Cuál es el nivel y año más alto de educación que aprobó educación que aprobó 2006 N/A A: Secundaria Q. ¿Cuál fue su último grado Q. ¿Cuál fue su último grado aprobado y título obtenido? aprobado y título obtenido? 1996 N/A A: Bachillerato El Salvador Q. ¿cuál fue el último nivel Q. ¿cuál fue el último nivel estudiado y grado que aprobó en ese estudiado y grado que aprobó en ese 2007 nivel? nivel? N/A A: Bachillerato Q. ¿Cuál es el nivel más alto de Q. ¿Cuál es el nivel más alto de estudio que está cursando o que estudio que está cursando o que cursó?/¿Cuál es el cursó?/¿Cuál es el 1995 último año aprobado en ese nivel? último año aprobado en ese nivel? A: Técnico formal A: Secundaria Honduras Q. Nivel educativo mas alto que Q. Nivel educativo mas alto que alcanzó alcanzó 2009

1994 Mexico 2010

N/A

Ciclo común

Q. ¿Cuál es el último grado de estudios que terminó y aprobó en?

Q. ¿Cuál es el último grado de estudios que terminó y aprobó en?

N/A

A: Preparatoria o bachillerato

Q. Nivel de instrucción

Q. Nivel de instrucción

N/A

A: Preparatoria o bachillerato

Q. Indique el curso y tipo de estudio actual o último curso aprobado A: Centro de formación técnica/Instituto profesional con/sin título Q. Indique el curso y tipo de estudio actual o último curso aprobado A: Centro de formación técnica/Instituto profesional con/sin título Q. ¿Cuál es el nivel y año más alto de educación que aprobó? A: Superior no universitario

Q. Indique el curso y tipo de estudio actual o último curso aprobado A: Educación universitaria con/sin título Q. Indique el curso y tipo de estudio actual o último curso aprobado A: Educación universitaria con/sin título

Q. ¿Cuál es el nivel y año más alto de educación que aprobó? A: Superior universitario Q. Cuál es el nivel y año más alto de Q. Cuál es el nivel y año más alto de educación que aprobó educación que aprobó A: Superior no universitaria A: Superior universitaria Q. ¿Cuál fue su último grado Q. ¿Cuál fue su último grado aprobado y título obtenido? aprobado y título obtenido? A: Educación superior no universitaria A: Educación universitaria Q. ¿cuál fue el último nivel Q. ¿cuál fue el último nivel estudiado y grado que aprobó en ese estudiado y grado que aprobó en ese nivel? nivel? A: Educación superior no universitaria A: Educación universitaria Q. ¿Cuál es el nivel más alto de Q. ¿Cuál es el nivel más alto de estudio que está cursando o que estudio que está cursando o que cursó?/¿Cuál es el cursó?/¿Cuál es el último año aprobado en ese nivel? último año aprobado en ese nivel? A: Superior no universitaria A: Superior universitaria Q. Nivel educativo mas alto que Q. Nivel educativo mas alto que alcanzó alcanzó A: Técnico superior/Superior no universitaria Q. ¿Cuál es el último grado de estudios que terminó y aprobó en? A: Técnico con preparatoria, vocacional o normal Q. Nivel de instrucción A: Carrera técnica o comercial

A: Superior universitaria Q. ¿Cuál es el último grado de estudios que terminó y aprobó en? A: Superior Q. Nivel de instrucción A: Profesional

21

1998 Nicaragua 2009

1997 Peru 2009

1995 Uruguay 2010

Q. ¿Cuál es el nivel de estudio y el último grado o año que aprobó A: Técnico medio o básico Q. ¿Cuál es grado o año escolar más alto que aprobó? A: Técnico medio o básico Q. ¿Cuál es el último año y nivel de estudios que aprobó? N/A Q. ¿Cuál es el último año y nivel de estudios que aprobó? N/A Q. ¿Cuál es el nivel más alto que cursa o cursó? ¿cuál es el último año aprobado en ese nivel? N/A

Q. ¿Cuál es el nivel de estudio y el último grado o año que aprobó A: Secundaria Q. ¿Cuál es grado o año escolar más alto que aprobó? A: Secundaria Q. ¿Cuál es el último año y nivel de estudios que aprobó? A: Secundaria Q. ¿Cuál es el último año y nivel de estudios que aprobó? A: Secundaria Q. ¿Cuál es el nivel más alto que cursa o cursó? ¿cuál es el último año aprobado en ese nivel? A: Secundaria

Q. ¿Cuál es el nivel de estudio y el último grado o año que aprobó A: Técnico superior

Q. ¿Cuál es el nivel de estudio y el último grado o año que aprobó A: Universitario Q. ¿Cuál es grado o año escolar más Q. ¿Cuál es grado o año escolar más alto que aprobó? alto que aprobó? A: Técnico superior A: Universitario Q. ¿Cuál es el último año y nivel de Q. ¿Cuál es el último año y nivel de estudios que aprobó? estudios que aprobó? A: Superior no universitaria A: Universitaria Q. ¿Cuál es el último año y nivel de Q. ¿Cuál es el último año y nivel de estudios que aprobó? estudios que aprobó? A: Superior no universitaria A: Superior universitaria Q. ¿Cuál es el nivel más alto que Q. ¿Cuál es el nivel más alto que cursa o cursó? ¿cuál es el último año cursa o cursó? ¿cuál es el último año aprobado en ese nivel? aprobado en ese nivel? A: U.T.U. A: Universitario

Q. Nivel y año que cursa A: Bachillerato Tecnológico U.T.U (4to a 6to)

Q. Nivel y año que cursa A: Bachillerato Secundario (4to a 6to)

Q. Nivel y año que cursa A: Enseñanza técnica

Q. Nivel y año que cursa A: Universidad o similar

Source: Authors’ compilations from household surveys.

22