Measuring public spending preferences

To measure spending preferences of citizens, I rely on survey data from the Role of Government modules by the ISSP. These modules include a question that asks whether the respondent would like to spend more, less, or the same on some area of government. The exact wording of the question can be found in figure 4.1.

As can be seen in the figure, the respondents can choose between a range of answers on 5-point Likert scales. Since the survey questions ask whether the respondent wants to spend more or less compared to the spending levels at that moment, the question is interpreted as a question for policy change. The surveys are presumed to include representative samples of all citizens in each country, thus making the survey answers representative of the spending demands of the whole citizenries in each country-issue-year when aggregated.

Listed below are various areas of government spending. Please show whether you would like to see more or less government spending in each area.

Remember that if you say “much more”, it might require a tax increase to pay for it.

Spend

c. The police and law enforcement d. Education

34 The ISSP Role of Government surveys were conducted in a varying number of countries in 1985, 1990, 1996, 2006, and 2016. The survey was conducted later for some cases and this has been taken into consideration. The literature on responsiveness is mainly concerned with investigating the topic in advanced democracies as these systems are recognized as closer to fulfilling the democratic criteria of responsiveness and political equality. I follow this tradition and limit the sample to encompassing these countries, for which there is also much greater availability of data. The 26 advanced democracies on which data is available for at least one point in time are: Australia, Canada, the Czech Republic, Denmark, Finland, France, Germany, Great Britain, Hungary, Iceland, Ireland, Israel, Italy, Japan, Latvia, Lithuania, New Zealand, Norway, Poland, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, and the United States.³ This gives me a broad coverage of advanced democracies, and since I have data for several issues, the number of country-issue-year units is further strengthened. A table overview of country-years can be found in table 4.1.

Similar to Wlezien and Soroka (2012) and Bartels (2015), I quantify spending preferences using a scale from “spend much less” (-100) to “spend much more” (+100). If the respondent responds with “spend the same as now”, a score of 0 is assigned, while “spend more” and “spend less”

are given scores of +50/-50. Statistical analysis with variables with 5-point Likert scales is often criticized as it implies analysing ordinal variables, in which the distance between the steps of the scale are not equal, as if they are continuous (Kellstedt and Whitten 2018, 114). On the other hand, the zero point of the scale (“spend the same as now”) is meaningful in that it corresponds to the perceived status quo spending level. Moreover, the distance between “spend much less”

and “spend less” could be considered equal to the distance between “spend much more” and

“spend more”, etc., meaning that it really is a mirrored scale in wording.

Furthermore, the spending demand of the respondents is averaged for each country-year-issue on the -100 to 100 scale. I also calculate average preferences for different income groups across each country-year-issue, thus allowing me to investigate the effect of the preferences of different income groups on policy outcomes. Scholars vary in using mean or median. J.D. Huber and Powell (1994, 296) argued that the median would be preferred because the mean gives greater weight to cases more distant from the centre. However, the mean should work equally well and is also often utilized (Soroka and Wlezien 2010; Peters and Ensink 2015; Bartels

3 Attitude data are available for a few other affluent democracies, but they are excluded because they did not collect household income data for the specific country-year or because of missing corresponding spending data.

35 2015). I also considered using the interpolated median (see also Rosset 2013), arguably a better suited measurement of central tendency than the median for ordered rating scales with a limited number of categories (van der Eijk 2001, 339).

Table 4.1: Country-years

36 When looking more closely at the results, I found that the choice between the average or the median (interpolated or not) is basically inconsequential when examining the link between overall preferences and spending, but when it comes to comparing the influence of different income groups, these two measures of central tendency lead to very different results. This is because the differences between groups appear considerably smaller for the median as more

“extreme” values are not considered even though I would argue that they provide important information in this case.⁴ The mean is better at taking the variation of the distributions into account. To disentangle the effects of different income groups, I therefore use the mean in line with a great share of the cross-national responsiveness literature concerned with inequality in representation (e.g. Bartels 2015, 2017; Schakel, Burgoon, and Hakhverdian 2020).

Income data

To consider the preferences of different income groups, I rely on household income data that comprises the same respondents. The income data from the ISSP Role of Government survey modules is split by country and module and expressed in different currencies, in addition to being measured variously in either monthly or annual income in different countries and modules. The fact that the measurement varies by country and module would generally constitute a big problem for comparability across time and country. However, it was unproblematic in this case as my only intention is to decide what income groups the respondents belong in within their respective countries. Relative instead of absolute income levels allow easy comparisons even though measurement of income varies across countries and time. This approach is also appropriate because of it avoids the problem with inflation and since it allows me to hold different proportions of the population constant over time.

A more problematic aspect with the income data from the Role of Government modules is that the wording of the survey questions varies. In addition, the survey modules differ when it comes to asking respondents about gross or net income. On the other hand, again this obstacle is modest since I compare the rich and poor within the same country in a standardized way. More general issues with income data also applies for this data. Usually, respondents are less likely to answer the income question, and some groups are less likely to respond than others

4 Although the interpolated is better than the median in this sense, when there are fewer units, which is the case for the five income quintiles since they each are limited to a fifth of the survey respondents in each survey module, the interpolated median tended to stay quite close to the median.

37 (Micklewright and Schnepf 2010, 418). ⁵ Since the analysis depends on income data from each respondent, respondents that did not report income are dropped. The less educated, who are less likely to respond, may be slightly worse represented as a result of this.

The respondents are placed into five equally sized income quintiles for each survey based on the income brackets they assigned themselves to in the surveys. This is done using the quantile function in R.⁶ The first quintile consists of the 20 % respondents with the lowest income, while the fifth quintile consists of the 20 % respondents with the highest income. I mainly focus on the lowest, middle and highest quintiles in this thesis. For alternative measures of the relationship between the preferences of different income groups and policy output, I compute

“rich minus poor” and “rich minus middle” variables (Schakel, Burgoon, and Hakhverdian 2020, 154-155). This is done in order to adapt to the multicollinearity problem that typically arises when the preferences of two different income groups are included simultaneously in the regression models. The variables are self-explanatory in that they simply reflect the preferences of the high-income group minus the preference of the middle-income/low-income group.

Positive values would indicate that the high-income group would like more spending than the other income group, while negative values mean that the high-income group would like to spend less compared to the other group.

Table 4.2 reports descriptive statistics of some of the central variables included in the analysis that are concerned with preferences of different groups as well as spending change in the year after the survey waves. Moreover, it shows the variation in preferences and spending change between different issues and the differences between income groups, which will be more thoroughly explored in the next chapter.

Table 4.2: Spending preferences and subsequent one-year spending change

Issue Group Mean

Standard

deviation Median Minimum Maximum

Pooled Average 23.70 27.24 28.23 -63.59 82.11

5 Based on my calculations, about 21 % respondents did not answer on the income question in the cumulated ISSP Role of Government survey modules.

6 https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/quantile

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