Individual-level: SES and Demographics

Having outlined the theoretical determinants of public opinion toward social policy, I construct a framework for measuring these determinants; in other words the independent variables (IVs). The remaining chapter discusses my measurements for these IVs based on what is preferable and available, and the work of others. The measurements I choose are restricted in their scope because they must be asked in a way that gives them cross-national comparability or are available in comparative statistical databases. For example, I would like to have a yearly measure of the strength of individualistic norms in all of the OECD countries spanning 30 years, but this simply does not exist. I attempt to use the best possible measurements within these constraints. Furthermore, I look for measures with the most detailed information on individuals, regions and countries. For example measures that are continuous as opposed to dichotomous are usually preferable.

education, income and occupational status. There are other ways to determine an individual's status such as wealth (material possessions) and social networks (for example social capital) but these are not often measured in cross-national surveys.

Education is a commonly measured individual attribute. It is often conceived of in years or in highest degree attained. Educational systems differ across advanced democracies making direct comparison somewhat challenging. Thus, highest degree completed, for example primary, secondary, or tertiary education is one way to standardize the measure. The problem with this practice is that it vastly reduces accuracy of measurement because it leads to an ordinal scale with 3 or 4 categories. Year of education solves this problem to some degree. A person who completed secondary and completed four years of tertiary education (but did not finish the tertiary degree) has a lot more time spent in formal education than a person who just finished secondary education and then left. Years of education is therefore preferable to highest degree completed because it is a more continuous measurement with years running from 0 to something around 24 and has categories that represent linear steps in time. Measurement in years may also be faulted for accuracy in the cases where an individual takes many more years than the average to complete a degree, but it is better than the other options.

Income is a relatively straightforward measure. Although income often suffers from lower measurement reliability, due to the fact that many people refuse to answer income questions and extremely high income individuals are a rare and difficult to sample population, income is regularly asked on surveys, usually as a weekly, monthly or yearly amount. I follow the lead of the surveys that I select for my analysis in measuring income and only utilize income when it has been standardized to an economic parity purchasing power (PPP) unit, such as international dollars or Euros. It is possible to improve upon this measurement of income by taking income in PPP and calculating it as a ratio to the

average blue-collar worker in a respondent's country (Kelley and Evans 1995). This helps to account for the fact that some countries are more prosperous than others. Data that allow for this exist in my first study in Chapter 4.1. With a small number of countries it is not practical to measure levels of development at the country-level, but by calculating this ratio, the individuals are standardized to their country's lowest common denominator of income. Furthermore, I take income always as family (i.e. household) income, so that individuals are placed within the structural location of their family regardless of their individual contribution to its overall income.

Occupational status is a highly contested measure. There are a plethora of schemes to calculate the relative value and social status that a society places on a given occupation. Similar research attempts to measure occupational prestige. Occupational status and prestige are both intended to measure class. Both concepts relate to the ranking of occupations in a hierarchy or into distinct groups. Prestige is focused on subjective perceptions of the worthiness of a given occupation while status relates to the opportunities afforded to an individual and the individual's offspring as a result of being in an occupation, but both have no widely agreed upon measurement schemes. Status is often measured based on income, whether the individual is a business owner or manager, the level of education necessary to attain the job, and even how much power the job affords an individual (see Leiulfsrud, Bison, and Jensberg 2005). Most of these rankings are non-linear for example the International Standard Classification of Occupations (ISCO) and the Erikson-Goldthorpe-Portocarero classifications (EGP) break occupations into categories such as unskilled worker, self-employed farmer, and service class. While it is self-evident that unskilled workers are lower status than the service class³, it is

3 As in managers and administrators, not to be confused with the 'service industry' in the United States which refers to restaurant or hospitality work

unclear how much higher a service class member is in status than an unskilled or self-employed farm worker. This means that the measurements must be taken as a pile of dummy variables in an analytical framework, one for each category of the selected measurement scheme (EGP has 11 for example).

Although there are strong arguments for why occupational status is a non-linear measurement (E. O. Wright 1985), class is theoretically something that exists on a continuum from the lowest proletarian to the highest bourgeoisie (Marx 1887). Thus, I elect to take a continuous measure from the work of Kelley (1990) who compiled a single continuous variable to delineate the relative status of occupations on a scale from 0 to 100. Although it may be interesting for other research to look at non-linear relationships of occupational status to public opinion, my study is only partly focused on this IV and having a single continuous measure provides the most parsimonious analytical models. In the end, as Kelley points out, the continuous measure is also preferable to a typology because, "...you would find that the bottom of Goldthorpe's 'service' class merges smoothly into the top of his routine white-collar class, and so forth down the hierarchy"

(1990:327). Thus, a continuous status scale reflective of the theoretical reality of class becomes truncated by lumping categories into typologies. Table 2 gives an overview of Kelley's Worldwide Status Scoring, which is based on the tradition of earlier continuous measures used by Blau and colleagues (e.g. the Socio-Economic Index) and is focused on the education and income associated with a job, but also the transmission of inherited privilege to offspring and cross-national comparability (Kelley 1990:344).

Table 2. Worldwide Status Scoring for Selected Occupations

Another SES variable I measure is those who are at risk. I create a dummy measurement which equals 1 when individuals have been sick, unemployed, or retired as their main activity in the past 7 days, and 0 otherwise. Although these three states are qualitatively different, they are all potentially states that increase the likelihood of needing welfare provisions and this might lead self-interest to increase opinions in favor of social policy (as discussed in Chapter 2.1).

I do not explicitly hypothesize about the impact of demographic variables. Thus I do not spend a great deal of time on their measurements. Female is an easy way to capture biological sex as a dummy variable, with females coded as a 1 and males 0. Age is conventional in years. Church attendance is taken as the natural log of days per year, with zero given a -0.5. This is done because days per year would give a similar sized gap in measurement between someone who attends church never or 1 day per year compared to someone who attends 51 compared to 52 days per year. Logically, I expect that someone attending never and someone attending 1 day per year is probably quite different in their religiosity than someone who attends 51 compared with 52 days. Thus, the log emphasizes the differences between those at the low values of days per year. Suburban refers to individuals living in suburban neighborhoods, and it is dichotomized so that

Higher Professionals 100

Administrators and Managers 75

Technical Employees 70

Higher Clerical Employees 60

Higher Sales Emloyees 51

Routine Clerical Workers 38

Skilled Manual Workers 37

Semi-Skilled Workers 24

Unskilled Manual Workers 14

Farm Laborers 0

Source: Kelley (1990)

suburban equals 1 while urban and rural equal 0. This is a data driven measurement decision, as I have observed that suburbanites tend to be less supportive of redistribution than both of the other groups (see Technical Appendix Two, section 10.2.1). Finally, I measure married as a dummy where those who are currently married or in a cohabitating partnership are coded 1 and all others 0.

I measure attitudes toward government effectiveness as a control variable in my first study. I do not hypothesize about this variable other than to worry that these attitudes might bias support of social policy, as those who do not think their governments are effective are not likely to support any government activities including social policy provision (more in section 4.1).