Data

To operationalize the variables at hand, this study makes use of an original cross- national dataset covering the late nineteenth and early twentieth centuries. Building on the pioneering data-gathering efforts of Flora et al. (1983; 1987), this dataset contains a range of new and updated indicators relevant for comparative work on political economy and social policy. For this analysis, a subset of this dataset is used, with the selected data encompassing 17 relatively advanced countries: Austria, Australia, Belgium, Canada, Den- mark, Finland, France, Germany, Ireland, Italy, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States. For most of these cases, the data span from 1880 to 1939, with the three main exceptions being Australia (1901– 1939), Finland (1919–1939), and Ireland (1922–1939). Although Austria was technically in a monarchic union with Hungary before 1919, it is treated as a separate entity in the data due to the pronounced weakness of this political tie-up. A list of the specific measures used and their summary statistics can be found in Table 2.3, and more specific details about the construction of these measures and their sources can be found in Appendix A (outcome variables) and Appendix B (explanatory variables).

As presented earlier, a pair of composite indexes is used to gauge the institutional devel- opment of firm- and school-based systems, which are two of the three outcome variables in this inquiry. Each index is comprised of three equally weighted components representing

the levels of coverage, formality, and intensity found in each educational arrangement. The coverage component captures the extent to which a system is present in all geographical areas and is linked to multiple economic sectors. A system is not deemed to have high coverage if it is confined to a few regions or urban areas or if it is limited to a small set of economic activities, such as the construction trades. Next, the formality component as- sesses the degree to which the system has incorporated common standards and oversight mechanisms to uphold these standards. A set technical-oriented curriculum, a universal certification process, and a regulated teaching corps are all elements of the ideal type used in coding this dimension. Lastly, the intensity component measures the level of involve- ment (e.g., administration and resources) that the principal player has in the system. For a firm-based system, businesses are considered to be the main player, while the state is assigned this role in a school-based system. Adding these components together produces an index of institutional development for each form of vocational training that ranges from 0 to 6 with half-point increments.

To measure the institutional scope of general education, the third outcome variable, the secondary enrollment rate is used as a proxy. As mentioned earlier, this indicator is calculated by dividing the number of student enrolled in schools at the secondary level by the total number of persons aged 10 through 19. As the definition of secondary schools varies from country to country, efforts have been made to harmonize these data to make them reasonably comparable between countries.

Turning to the explanatory variables, the hypothesized economic predictors of education- training regimes are operationalized using a set of conventional measures. Following stan- dard practice, real gross domestic product (GDP) per capita (in 1990 US dollars, logged) is used to capture the level of affluence. To gauge the relative prevalence of industrial activity, the percentage share of nominal GDP originating in industrial sectors is used. Similarly,

the trade openness predictor is measured using the sum of exports and imports as a percent- age of GDP, all at current prices. The land area of a given country (in square kilometers, logged) is employed as a proxy for internal economic diversity.

For the hypothesized determinants tied to political engagement, another collection of familiar measures is put to use. The indicator for democracy is a slightly modified version of the oft-used Polity index for regime type, with the scale ranging between firm autocracy and strong democracy. As the measure for left-liberal government, a binary variable classi- fying the head of government is used, with one category indicating the presence of a liberal or socialist in the chief executive position. Labor mobilization is operationalized as union density, which is defined as the percentage share of a labor force belonging to trade unions. To capture the dispersion of public authority in general education – a key explanatory variable – a three-point ordinal scale for the level of federalism is used; the possible values are none (unitary state), weak, and strong. In all countries with strong federalism included in this study, the power to craft education policy was reserved for subnational units (e.g., states, provinces, etc.). In Austria, which had weak federalism from 1919 onward, the cen- tral government could alter education policy, but any such action required the approval of a two-thirds majority in the lower house of parliament (Nationalsrat). As a consequence, the Austrian states enjoyed significant influence over the setting of education policy (Schratz 2012, 97). For countries with unitary systems, the ultimate authority over education sys- tems rested with the central government. Even if some of this authority was delegated to lower levels of the state in practice, there was always the potential for direct intervention by the central government.

The final explanatory variable of interest, coordination legacies, is quantified using a four-point ordinal index. The index is comprised of two elements: the first represents the degree to which coordination systems established by guilds continued to persist up until 1900 and the second captures the degree to which central governments had been involved

in the establishment and support of vocational training at post-secondary levels of education prior to 1870. The guild component consists of a three-item scale, with the possible values being none, weak, and strong, whereas the state component is a simple binary measure indicating the presence or absence of a meaningful history of state involvement in other areas of training.

Given that demographic change can also influence education policy, a control variable is included to capture this effect. In particular, the share of the population aged 10 to 19 is used as a basic measure of the school-aged population for secondary education. This measure should capture any demand effects produced by rises and falls in the population of potential students for secondary education.

Despite the great efforts made to assemble complete data series for the explanatory vari- ables employed in this analysis, missingness remains an issue for some variables. As Table ??indicates, five variables have incomplete series, with the level of missingness for these variables ranging from 0.22 percent to 12.84 percent. To avoid dropping observations with missing data, which can severely bias regression estimates, multiple imputation is applied to the working dataset. Drawing on practices and techniques first devised by Rubin (1987) and later extended by Honaker and King (2010), 10 sets of imputed data are generated prior to each individual analysis. Each estimation procedure described in the next section is then applied to these datasets, and the 10 sets of results are subsequently pooled together using formulas developed by Rubin (1987).