Policy feedback and economic risk: the influence of privatization on social policy preferences

ABSTRACT

Through policy feedback mechanisms, public policies can shape individuals’ preferences for those policies. While research has focused on the direct link between policies and preferences, how policies alter individuals’ preferences through indirect means remains less explored. Broadly, we argue that how micro-level factors influence policy preferences is contingent on the policy context, and specifically we contend that how economic risk influences preferences is contingent on the policy institutions that privatize social protection responsibilities. Using healthcare policy as the empirical context, we show that the level of privatization in national healthcare systems will colour how the risk of unemployment affects preferences for government healthcare.

KEY WORDS:

• Economic risk
• policy feedback
• policy institutions
• social policy preferences

ACKNOWLEDGEMENTS

We would like to thank Philipp Rehm for generously sharing his replication dataset with us, as well as the reviewers and editors for their insightful comments. Earlier, we presented this paper at the 106th American Political Science Association Annual Meeting (Washington, DC, 2010).

SUPPLEMENTAL DATA AND RESEARCH MATERIALS

Supplemental data for this article can be accessed on the Taylor & Francis website, http://dx.doi.org/10.1080/13501763.2015.1031159

Notes

1 E.g., Cusack et al. 2006; Iversen 2005; Iversen and Soskice 2001; Korpi 1983; Lipsmeyer and Nordstrom 2003; Margalit 2013; Meltzer and Richard 1981; Moene and Wallerstein 2001; Rehm 2011; Rehm et al. 2012.

2 Previous work focused on broad social policy regime classifications (Jæger 2009; Larsen 2008; Linos and West 2003; Svallfors 1997) rather than acknowledging that preferences can vary across policies.

3 This policy is traditionally characterized by a high level of public support regardless of labour market situations and cross-country differences in policy institutions (Carpenter 2012).

4 While we focus on the economic factors, we acknowledge that others emphasize additional individual-level factors – class, interpersonal relationships, race, ethnicity, altruism (see Rehm et al. [2012] for discussions of the existing approaches).

5 In a comparative study of pensions, Lynch and Myrskyla (2009) ask why they do not create a feedback effect.

6 Others have noted that welfare states produce norms and values that correspond with varying levels of support for social assistance (e.g., Brooks and Manza 2007; Esping-Anderson 1990).

7 Jacobs explains that the relationship between politicians and the public evolved into representation in the UK and the US: ‘In attempting to manipulate public opinion, government officials became more sensitive to popular preferences' (1992: 212).

8 We include representative samples from 19 countries: Australia; Canada; Czech Republic; Denmark; Finland; Germany; Hungary; Ireland; Japan; Netherlands; New Zealand; Norway; Poland; Portugal; South Korea; Spain; Sweden; Switzerland; and UK. The survey was fielded from 2006 to 2008.

9 Although most prior empirical studies use spending preferences to measure individuals’ attitudes toward the role of government in social policy (for example, see Gingrich and Ansell [2012]; Iversen [2005]; Rehm [2009]), citizens could have high expectations for government responsibility in healthcare, while simultaneously supporting controlling spending. To examine both aspects of citizens’ preferences, we include a second dependent variable in the Online Appendix: a question on the ISSP that asks respondents’ opinions on whether the government should be responsible for providing care to the sick.

10 In 2008, the OECD launched a survey to collect information on member states’ national healthcare systems. Twenty-nine OECD countries participated in this survey and provided information based on healthcare system characteristics in the previous three years. We use the OECD's original coding for each institutional indicator. The United States did not participate in the 2008 OECD survey of national health systems. Owing to the lack of comparable US data for estimating a national-level measure of healthcare privatization, we did not include the US as a country case in our analysis.

11 See the Online Appendix for details regarding the privatization scale.

12 We assess occupation-based unemployment rates from the ILO online database of labour statistics, LABORSTA (http://laborsta.ilo.org/). For the data on unemployment, we use the variable 3E (unemployment by occupation), and for the data on employment, we use the variable 2C (employment by occupation). We calculate the total civilian labour force as the sum of the employed and the unemployed.

13 Iversen's s1 measure is based on individuals’ ISCO-88 occupation classifications and takes into account both the share of ISCO-88 level 4 groups and the share of the labour force. We access data for the skill variable online at http://www.people.fas.harvard.edu/ iversen/SkillSpecificity.htm. We assign Iversen's measurement score to ISSP respondents based on their ISCO-88 occupation codes.

14 These controls are from the ISSP survey data. We code Male as Male = 1 and Female = 0, and Married as Married = 1 and Otherwise = 0. For Education, we use the year of education. The Left–Right measure is a 1–5 scale, where 1 refers to the strongest support for left-wing parties. Union is 1 = currently a union member and 0 otherwise. We recode the variable for people's work status (WRKST) into a dummy variable with Nonemployed = 1 and Employed = 0. Nonemployed includes the following groups: helping families; home duties; students; retired; disabled; and other people who are not in the labour force.

15 Data are from the OECD Health Data: Healthcare Financing and Expenditure, measured in 2005. One can also address the cross-country heterogeneity by including country fixed effects. The fixed effects specification, however, limits making meaningful inferences about the privatization variable, when it is measured at the country level and invariant within countries (Franzese 2005).

16 The correlation between occupation-based Unemployment Risk and country-level Healthcare Privatization is negative and significant, but near zero (corr. = –0.087, p = 0.000).

17 When using cross-country public opinion data to test the effects of macro-level policy institutions on micro-level preferences, it is important to consider that different cultures, historical backgrounds, or some other unobserved political factors may affect both healthcare privatization and economic protection. In the Online Appendix, we show alternative model specifications that consider country-level heterogeneity – a hierarchical linear model (HLM) and an alternative model with clustering standard errors by country. These alternative models highlight the robustness of our findings regarding the impact of privatization.

18 In Section 1 of the Online Appendix, we test for the potential for reverse causality between privatization and preferences (Brooks and Manza 2007; Campbell 2012; Gingrich and Ansell 2012) and find no evidence for it. In an ordinary least squares (OLS) model using aggregated healthcare preferences to predict the level of healthcare privatization, the coefficient on the variable for aggregated healthcare preferences is near zero (b = –0.037) and statistically insignificant.

19 Given the symmetrical nature of multiplicative interactions between two independent variables, this specification means that the effect of privatization is contingent on risk (Berry et al. 2012). Because our main theoretical interest is about the effect of risk contingent on privatization, we have relegated the figures for the effect of privatization contingent on risk to the Online Appendix.

20 Our analysis relies on cross-sectional data to compare how healthcare privatization affects individuals’ social policy preferences. This is just a starting point for unpacking the role of social policy institutions in preference formation. A natural extension to our research is to adopt a cross-sectional time-series design (when longitudinal cross-country surveys are available) to generalize evidence based on both cross-country differences in policy institutions and within-country institutional changes.

Additional information

Biographical notes

Ling Zhu is Assistant Professor in the Department of Political Science, University of Houston, USA.

Christine S. Lipsmeyer is Associate Professor in the Department of Political Science, Texas A&M University, USA.