2.2 Introducción a los algoritmos genéticos
2.2.4 Operador de cruce
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In order to test H3 econometrically, I estimate two specifications for each of the following models:
EDUit = 0 + 1ECIit + 2X’it + it (5)
EDUit = 0 + 1ECIit + 2ECI2it + 3X’it + it (6) where EDUit is a measure of educational expansion in country i in year t, while the other variables are the same as specified in the previous section. I specify educational expansion as the share of population with tertiary educa- tional attainment, and alternatively, as expenditure on education as a share of GDP.
I also use the alternative specification of the dependent variable – expendi- tures on education as a share of GDP – assuming that countries that pursue educational expansion have to invest in their educational systems. Neverthe- less, Castelló-Climent & Hidalgo-Cabrillana (2010) warn of the difficulty of drawing conclusions from data on educational expenditures, because such numbers suffer from substantial interpretational problems. For example, it is difficult to measure private investment in education. Countries also vary ac- cording to their demographics, so countries that educate more usually spend less on a per capita basis. Therefore, I do not include a measure of spending per student in my analysis and I take the results from the analysis of educa- tional expenditures as a share of GDP with a grain of salt.
When it comes to economic complexity, I include the squared form of ECI be- cause I want to check for the possibility of a U-shaped relationship between economic complexity and educational expansion. This is in line with the ex- pectations of the macroeconomic human capital literature (Castelló-Climent & Hidalgo-Cabrillana, 2010), which argues that expansion of tertiary education may be a ‘natural’ effect of economic development. In other words, I posit
28 that countries may reach a level of economic development via industrial up- grading at which: i) they can afford more investment in education than was the case in the earlier stages of re-industrialisation which was characterised by budgetary restraint; and/or ii) the growing demand for tertiary education as the economy becomes more complex would create a private supply of edu- cation. In fact, a recent PhD thesis by Tarlea (2015) indicates the presence of the latter mechanism in CEE. She shows that over the last few years, demand for tertiary education in CEE has risen because of growing MNC skill needs. This has led to the proliferation of private higher education in the region, alt- hough the rates of tertiary educational attainment still remain at significantly lower levels than in the Baltic.
Reflecting on these trends is an important step in our understanding of future trajectories of the knowledge economy in Eastern Europe. This is particularly the case since Ansell & Gingrich (2013) show that in the advanced capitalist economies partly private education systems lead to the generation of different types of knowledge-intensive service jobs (e.g. in finance and real estate) than the government-led educational expansion (e.g. public sector services such as health and education). While the data constraints in the empirical analysis conducted here do not allow us to distinguish between the different types of higher education financing structures, I will return to this matter in the sec- tion 3.5 where I assess how educational expansion affects the relative contri- bution of public vs private sector knowledge-intensive service employment to overall FLFP.
I include GDP per capita as the standard control variable that I include in all specifications, along with the same caveats from the previous section. Due to possible multicollinearity between ECI and GDP per capita, this control vari- able may be reducing the effect of ECI on educational expansion. Therefore, I show the results with and without GDP per capita. The other variables that
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might affect this relationship and are fiscal constraints, institutional and polit- ical constraints to reform and the extent of dependence on foreign capital. All of them will be covered in the FE estimations, because this estimation tech- nique controls for time invariant country level effects.16 Finally, because I propose a negative effect of industrial upgrading on educational expansion, reverse causality is not a concern, as it is difficult to theoretically posit why more tertiary educational expansion would lead to less industrial upgrading. Results of the econometric estimates of the impact of industrial upgrading on the share of population with tertiary education are shown in Table A-5 in the Appendix. The linear specification of the independent variable indicates a negative impact of economic complexity on tertiary educational attainment, significant at 1%, both for with and without FEs. The quadratic specification of ECI preserves the linear and negative PCSE OLS results, while the FE esti- mates show a U-shaped impact of economic complexity on tertiary education within the countries in my sample.
The differences between estimates with and without FEs using the LSDV es- timation technique are shown in Graph 2. While the graph shows a clear downward sloping relationship between economic complexity and tertiary educational attainment across the countries in my sample, the within country U-shaped relationship is the result of growth of tertiary education in the countries with the highest levels of economic complexity. This is, however, taking place from significantly lower levels of educational attainment than in the Baltic countries and Bulgaria.17
16 Fiscal constraints are not necessarily time invariant, but they have been persistent in many Eastern European countries throughout the 2000s. Therefore, for the purpose of this analysis, I consider them time invariant.
17 While Graph 2 appears to show that the level of tertiary education has not grown in the Baltic countries during transition, it in fact indicates predicted values of the variable over economic complexity. There has been substantial growth of tertiary education in the Baltic countries over the period of observation.
30 Graph 2. Predicted values of population with tertiary education: FE using LSDV with fitted values from the OLS regression
Graph 2 further indicates that the within country U-shaped relationship be- tween industrial upgrading and growth of tertiary education is driven by Slovenia and CEE. While Slovenia has been a persistent outlier,18 the case of CEE could be explained by findings from Tarlea (2015), who shows that MNCs have shaped the demand for higher education and its supply through private institutions in CEE during the later stages of transition. The fact that these countries did not see a substantial increase in female entry in their la- bour markets indicates that, following Ansell & Gingrich (2013), privately supplied education may not have as positive an impact on FLFP as public. While more research should be conducted in order for us to fully understand the mechanisms at play here, I find this empirical evidence compelling enough to support H3, with the caveat that industrial upgrading may impede
18 Because of its higher level of economic development than the rest of the countries in the sam- ple, as well as its low dependence on foreign capital and lower fiscal constraints than the rest of Eastern Europe.
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government-led educational expansion rather than both public and private educational expansion (as discussed in section 2.1).
Results from the estimates on the effect of industrial upgrading on education- al expenditures as a share of GDP are shown in Table A-6 in the Appendix. The PCSE OLS evidence also supports my hypothesis about the negative im- pact of industrial upgrading on educational expenditures. Furthermore, the inclusion of the quadratic term in the FE estimates indicates a U-shaped rela- tionship between the two variables. This U-shaped relationship is expected because the measure of educational expansion that I use does not distinguish between private and public provision of higher education, or between special- ised and general skills oriented higher education. Therefore, imprecise meas- urement of educational expansion and movement towards general skills is an important limitation of this econometric analysis.