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Social Justice Research

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05.07.2019

Risk Exposure, Humanitarianism and Willingness to Pay for Universal Healthcare: A Cross-National Analysis of 28 Countries

verfasst von: Luis Maldonado, Francisco Olivos, Juan Carlos Castillo, Jorge Atria, Ariel Azar

Erschienen in: Social Justice Research | Ausgabe 3/2019

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Abstract

In this article, we explore the associations of people’s valuations of universal healthcare with risk exposure and humanitarianism across diverse institutional contexts. We argue that both micro-level factors increase the valuations. Furthermore, interactions between material interests and humanitarians are expected. This work also hypothesizes that institutional contexts with employment-independent healthcare systems should modify the effect of risk exposure. Following a comparative framework, we test the expectations by using the International Social Survey Programme 2011 health module for 28 developed and developing countries. Results suggest opposite effects for the factors under analysis. While risk exposure decreases the willingness to pay taxes for the provision of universal healthcare, humanitarianism strongly fosters the valuation. Furthermore, we find statistical significant interactions between material interests and humanitarianism. Results also suggest substantive cross-level interactions between risk exposure and healthcare systems. Findings are robust to different modeling strategies that control for standard micro-level variables (income and egalitarianism), individual factors and observed and unobserved country characteristics. The article lays out implications of these findings.

Vorheriger Artikel Self-Objectification, System Justifying Beliefs, and the Rise of Labiaplasty

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See Rehm (2016) for an updated review of the literature about micro-sources of redistributive preferences.

For implications of the insurance mechanism at the macro-level, see Rehm et al. (2012).

We thank a reviewer of a previous version of our article for this insightful idea.

Hacker (2004) classified employment-independent healthcare systems as hierarchical institutional models. These systems depend on a single-payer model in which the state is the central purchaser or provider of healthcare. For example, Canada presents centralized financing coupled with decentralized decision-making. Notice that the core feature of Gingrich and Ansell’s typology is the kind of employment-benefits association, not the number of payers.

The low-cost hypothesis appeared originally in the context of contemporary German sociology and has inspired a large quantity of empirical studies and theoretical discussion in this academic community. For an overview, see Best and Kroneberg (2012).

We analyze only data for Belgium-Flanders.

To evaluate problems of sample selections, we estimate the probability of being in our analytical sample by using a logistic regression with an indicator for people with information on occupation and several socio-demographic predictors. Most of the predictors present significant associations with the dependent variable. To correct for these differences, we conducted further sensitivity analysis by estimating our core model (model 4 in Table 2) with inverse probability weights. These estimates are consistent with the findings presented in the paper.

The effective sample presents 21,652 cases with complete information for all variables used in the analysis.

Because the middle value is neutral, the dependent variable perhaps produces some imprecisions in regression models. We evaluated alternative codifications (e.g., binary dependent variable with a value of 1 for the alternatives “very willing” and “fairly willing”), and the findings are consistent with estimations that use the outcome with the five-point scale. Furthermore, this binary variable is a solution for measurement problem due to variation of reference points across persons and countries. Following Cavaille and Marshall (2018), we anchor the binary variable around a reference point that is likely to be interpreted similarly by respondents across nations. In doing so, we assume that individuals who offer response 1 express support to improve the level of healthcare for all people.

The unemployment rate for an occupation j was computed as [(number of people unemployed in j)/(number of people unemployed in j + number of people employed in j)]*100.

For some countries, we used data from different years. Data from 2011 were used for Chile and Israel; for the Philippines, we used information from 2011; for Russia and South Africa, averages for 2010–2011 were computed. Due to lack of information, occupational unemployment rates of France and Japan were computed for 2012. Sensitivity analysis that does not consider these countries does not substantively modify our results. Estimations of Australia and the USA do not use ILO information: Labour Force Survey (Australian Bureau of Statistics) for Australia and Labor Force Statistics from the Current Population Survey for the United States.

Occupational unemployment rates can be computed at different levels of ISCO-88 classification and differentiating by gender or not. We used nine different values (ISCO88-1d, without differences by gender) because ILO data only provide this classification. In spite of these limitations, Rehm (2016) indicated that more detailed typologies yield similar results.

Due to lack of data, we used only a single item to measure humanitarianism. Future research should consider multidimensional measures.

We also considered the ordinal scale of the variables by using polychoric correlation, being 0.121 in the pooled data.

ISSP health module contains variables that show low correlations with WTP taxes but may affect humanitarianism. They are items about government involvement into specific areas (preventive medical checkups and organ transplants). Following an instrumental variable procedure, we estimated predicted humanitarianism on the basis of these items and we used these predicted values as independent variable in our WTP equations. In doing so, we use the variance of humanitarianism that is low correlated with WTP taxes to identify the effect of this value. The findings confirm our results. However, they must be considered with caution because we cannot argue that items of government involvement are good instruments, insofar as exogeneity of them is hardly credible.

In ISSP’s information about household income differs across countries, for example, whether the survey asked for income before or after tax and in what currency. However, we fix this kind of problem for cross-national comparison by including country dummies in our regression models. Following studies of redistributive preferences (Alt and Iversen, 2017; Bady and Bostic, 2015; Barnes, 2015; Rehm, 2016; Cusack et al., 2006; Dimick et al., 2018), our measure of income does not adjust for equivalence scales.

Australia, Spain and Portugal present significant private insurance sectors. However, Gingrich and Ansell classified these countries as employment-independent because the public system provides uniform benefits at the national or state level in these nations. We test the sensitivity of our results to the exclusion of these countries. Though efficiency decreases, empirical findings are consistent with the main results of the present research.

The following countries of our effective sample are not considered in the typology: Bulgaria, Chile, Croatia, Lithuania, the Philippines, Poland, Russia, Slovak Republic, Slovenia, South Africa and Turkey. These countries are not classified by Gingrich and Ansell (2012).

The rest of developing countries of our effective sample also present mixtures of diverse healthcare models, and, thus, it is difficult to isolate the influence of particular institutional models (Haggard & Kaufman, 2008).

Polychoric correlation between our measures of egalitarianism and humanitarianism was only 0.10, suggesting that these indicators measure different constructs.

Pearson correlations among welfare state’s measures are lower than 0.40, especially for expenditures (ρ = − 0.12).

In regression models, we specified humanitarianism and egalitarianism as continuous variables. We also estimated alternative models with both constructs included with sets of dummy variables. The findings are consistent with results of our main specifications.

Country fixed effects were estimated but not shown in Table 2.

In comparison with fixed effects regressions, models of Table 4 do not control for unobserved country level characteristics.

Results of regressions used for full interacted models are available upon request with authors.

We estimated separate models for risk exposure and humanitarianism to avoid problems of multicollinearity. However, a single model with both interaction terms does not substantively modify the results.

Findings of fully interacted models confirm patterns illustrated by Fig. 3.

These additional models are also robust to two additional topics. First, fixed effect estimates in ordered logit models can be inconsistent, but we confirmed with OLS fixed effects regressions our results. Second, the middle category of WTP taxes is neutral. In binary logistic regressions, we avoid this problem by modeling the propensity to support the alternatives of very willing/fairly willing (value of 1 vs. the rest of the alternatives). All robustness analyses are available under request with authors.

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Titel: Risk Exposure, Humanitarianism and Willingness to Pay for Universal Healthcare: A Cross-National Analysis of 28 Countries
verfasst von: Luis Maldonado
Francisco Olivos
Juan Carlos Castillo
Jorge Atria
Ariel Azar
Publikationsdatum: 05.07.2019
Verlag: Springer US
Erschienen in: Social Justice Research / Ausgabe 3/2019
Print ISSN: 0885-7466
Elektronische ISSN: 1573-6725
DOI: https://doi.org/10.1007/s11211-019-00336-6

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