1 Supervised by: Prof. W. van der Brug (Department of Political Science, UvA)
When does trust matter? An experimental inquiry into the conditions of political trust as heuristic Abstract
This study examined the conditions under which political trust acts as a heuristic for policy support. The trust-as-heuristic literature is integrated with psychological heuristics literature to see whether trust acts as an affective heuristic. The trust-as-heuristic thesis supposes risk and sacrifice to enable the heuristic function of trust. Political trust was hypothesized to have a stronger effect on policy support when risk and sacrifice increase. Additionally, the trust-support association is expected to be stronger for politically less sophisticated citizens. However, most empirical evidence was obtained via observational methods. For this study risk (health risks) and sacrifice (phone costs) were manipulated in a two-by-two factorial survey experiment using a representative sample from the Netherlands (N=859; May 2019). Linear regression models were used to analyse the results. I found that the effect of trust varies according to the level of sacrifice, in accordance with the trust-as-heuristic approach, but that risk does not condition the effect of trust on support. Political sophistication was not found to condition the trust-support relationship. I suggest that trust negates the negative effects of increased risk and sacrifice on support, but only when the initial effect stays below a certain threshold. I urge scholars to further investigate whether this threshold exists. Additionally, future studies are encouraged to explore the conditions under which political trust acts as a heuristic as well as who are more likely to be affected by them.
Introduction
“[I]f, other things equal, people perceive the architect of policies untrustworthy, they will reject its policies; if they consider it trustworthy, they will accept them”
(Hetherington and Globetti, 2002, p. 254).
Political trust is one of the most influential concepts in political science, since it is often considered as an indicator for democratic legitimacy and vitality (Almond and Verba, 1963; Dalton, 2004, Easton, 1965; Easton, 1975; but also see Thomassen, Andeweg and Van Ham, 2017). The consistent decline of political trust in the United States and elsewhere accelerated academic research on the antecedents of political trust (e.g. Bovens and Wille, 2008; Citrin, 1975; Chanley, Rudolph and Rahn, 2001; Hetherington and Rudolph, 2008; Miller, 1974; Miller and Listhaug, 1990; Mishler and Rose, 2001; Newton, 2001; Putnam, Leonardi and Nanetti, 1994; Van der Meer and Hakhverdian, 2017). For a time the motivation to study political trust was restricted to its formation and changes, but some scholars also turned to its consequences (Levi and Stoker, 2000; Zmerli and Van der Meer, 2017). Among others, these consequences are (non-)voting behavior (Bélanger and Nadeau, 2005; Citrin and Green, 1986; Hetherington, 1998; Hooghe, Marien and Pauwels, 2011; Peterson and Wrighton, 1998; Van der Brug, 2003), law compliance (Fairbrother, 2017; Marien and Hooghe, 2011; Scholz and Lubell, 1998), political participation (Bäck and Christensen, 2016; Hooghe and Marien, 2013) and policy support (Fairbrother, 2017; Gabriel and Trüdinger, 2011; Haugsgjerd and Kumlin,
2 2017; Hetherington, 2005; Hetherington and Globetti, 2002; Rudolph, 2009; Rudolph and Evans, 2005; Trüdinger and Bollow, 2017; for an overview see Rudolph, 2017). With regard to the latter, trust is argued to function as a heuristic when evaluating government policy. This is based upon the premise that citizens long for cognitive shortcuts that enable them to evaluate complex issues. Heuristics are basically decision rules that allow people to make sensible judgements under conditions of limited information (Hetherington, 2005; Sniderman, Brody and Tetlock, 1991; Tversky and Kahneman, 1974). Among the most influential heuristics in citizens’ political decision making is the affect heuristic (Sniderman et al., 1991). Arguably, heuristics based on emotive affections are more relevant to politically less sophisticated citizens than for the sophisticated citizens. Politically sophisticated citizens can use affective heuristics among many other ways to come to judgment, whereas politically less sophisticated citizens have a limited amount of cues available. Interestingly, political trust is recognized as an emotional attachment towards political actors and institutions (Hetherington, 2005; Rudolph and Popp, 2009; but also see Putnam et al., 1994).
Marc Hetherington is considered the first to identify the heuristic function of political trust. In his 2005 book Why Trust Matters he presents the trust-as-heuristic approach, later to be further developed with Thomas Rudolph. Central to the approach is the idea that political trust encompasses a ‘willingness to accept government promises about the future consequences of a policy’ (Rudolph, 2017, p. 200). Therefore citizens that hold trust in politics, politicians and in government specifically are more likely to support government policies.
Moreover, the trust heuristic supposedly becomes increasingly important under two conditions: risk and sacrifice. First, when policies are deemed to be risky, the importance of trust increases ‘because citizens are more reliant upon government assurances about the future’ (Rudolph and Popp, 2009, p. 336). Analogous to this, high risk investments are more difficult to make because of the trust the investor needs to have in the broker. Low risk investments, on the other hand, are more easily made. Second, the association between political trust and policy support is greatest among those who ‘incur costs with little expectation of future return in the form of government benefits’ (Rudolph and Evans, 2005, p. 660). For example, trust is of key importance for benefactors to a charity, whereas for beneficiaries trust is supposed to play a marginal role. Even though these conditions are theoretically intuitive, empirical evidence for these conditions is limited and inconclusive. While some studies find support for – one of – these conditions (e.g. Hetherington, 2005; Hetherington and Globetti, 2002), others show no or little effects (e.g. Gabriel and Trüdinger, 2011). A possible problem with these papers, however, is the use of observational survey designs. Instead of measuring risk directly Hetherington uses attitudes towards blacks as an indirect measurement of the risk perception of racial policies (2005, Ch. 5). In turn, Gabriel and Trüdinger (2011) examine different types of welfare policies and assume varying risk levels among them.
3 Sacrifice, on the other hand, is frequently operationalized by looking at different social groups who are (non-)beneficiary, such as whites and blacks (Hetherington and Globetti, 2002), having a child (ibid.) or the (non-)poor (Hetherington, 2005). The question remains whether the relevance of political trust increases for a policy when people perceive more risk or sacrifice. By manipulating various policy elements in an experimental design, Fairbrother (2017) shows that the effect of political trust – more specifically cynicism – varies with different forms of policy. The main aim of this paper is to test the conditions under which trust affects policy preferences. Subsequently, I use a survey experiment to risk and sacrifice perceptions for government proposed 5G (wireless internet network) policy. More specifically, I manipulate sacrifice and policy risks for the implementation of the 5G wireless network in the Netherlands by pointing out either the high or low risk on health issues and a potential in- or decrease of phone subscription fees. Following the trust-as-heuristic approach, the effect of trust on policy support should be greatest for those with conditions of increasing health risks and subscription fees. Earlier research suggests that the conditional effects of risk and sacrifice could not be found as a result of risk and sacrifice perception invariance (Gabriel and Trüdinger, 2011, p.287). By explicitly manipulating the levels of risk and sacrifice associated with the policy, I ensure risk and sacrifice perceptions to vary between the experimental groups. Do changes in the (perceived) risk and sacrifice associated with the 5G policy enable the heuristic function of political trust in the Netherlands?
Most of the empirical research on heuristics-based political judgment focuses on the U.S. context. This paper uses empirical data from the Netherlands, a country with a different political system compared to the US: a founding member of the EU with a history of multiparty elections and coalition governments. However, (partial) support of the trust-as-heuristic approach was found in other European contexts, such as in Sweden (Svallfors, 2002), Norway (Haugsgjerd and Kumlin, 2017), United Kingdom (Fairbrother, 2017) and Germany (Gabriel and Trüdinger, 2011; Trüdinger and Steckermeier, 2017).
I find that the effect of trust varies according to the level of sacrifice, in accordance with the trust-as-heuristic approach, but that risk does not condition the trust-support linkage. The negative effect of increased financial costs is negated by political trust, whereas health risks lead to lower levels of support for trusting and distrusting citizens alike. The exact nature of the trust-support linkage could not be determined in this paper and caution is advised when treating political trust as a heuristic. Finally, future studies could build upon the results from this paper by further exploring the conditions under which political trust acts as a heuristic as well as who are more likely to be affected by them.
4 Heuristics in political decision-making
The heuristics literature originates from psychology and was originally developed to explain how people can make reasoned and reliable choices with limited information (Tversky and Kahneman, 1974). Other scholars have applied this line of reasoning to political science – notably Sniderman et al. (1991) and Popkin (1994). Political choices are highly complex decisions which generally need simplification in order to be handled effectively. This relates to the notion of bounded rationality, developed by Herbert Simon (1955); humans are rational, yet bound to cognitive and temporal limits (Keren and Wu, 2015). Since we cannot decide on the basis of full information – with regard to political judgment, who has full information on any political issue? – people ‘use heuristical principles which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations’ (Tversky and Kahneman, 1974, p. 1124).
Sniderman et al. (1991) argue that ordinary citizens use cognitive heuristics as tools to manipulate the (little) information they possess to form evaluative judgments (cf. Tversky and Kahneman, 1974). They identify the affect heuristic to be of particular importance. The authors conceptualize the role of affect by ‘concentrat[ing] on people’s feelings toward politically salient groups [..] in an effort to understand how people’s thinking about politics shapes [..] their likes and dislikes’ (1991, p. 8). In theory, people can make sensible political decisions based purely on how they feel towards certain groups. The idea is that affective attachments to, for instance, conservatives or ethnic minorities matter when people are asked to evaluate policies. Since everyone is considered to have affective attachments to societal groups – at least to some extent – everyone should be capable of using affect as a heuristic in their evaluative process. This way ‘people can be knowledgeable without having much knowledge of politics’ (Sniderman et al., 1991, p. 22). In short, the way people feel about social, cultural and political groups is linked to their evaluation of social and political events.
Political trust as a heuristic
The affect heuristic argument can be considered as the foundation for Hetherington’s trust-as-heuristic thesis. If one considers political trust to be a feeling, rather than a behavior the affect heuristic argument should apply to political trust as well – for trust as a behavior, see: Midden and Huijts (2009) and Putnam et al. (1994). Hetherington argues that ‘trust is more affective (feeling) than cognitive (thinking)’ (2005, p. 51), while other advocates of the approach define political trust more narrowly as ‘a global affective orientation towards government’ (Rudolph and Popp, 2009, p. 335). Hetherington’s argument is that if people do not trust the government – or politics generally – this should have an effect on their stance towards the role of the government in society. More
5 specifically, ‘if people distrust the government, other things being equal, they will likely distrust the policies it formulates’ (Hetherington and Globetti, 2002, p. 253).
Hetherington postulates two criteria for trust to be a heuristic: possession and accessibility (2005, pp. 50-51). The former simply means that political trust is an actual attitude and not a so-called non-attitude: a measurement artifact emanating from social desirable answering patterns (Converse, 2006; Krosnick, 1991). The latter criterion holds that ‘people should be able to find the attitude in their brain [before they] use it’ (Hetherington, 2005, pp. 51-52). According to Hetherington, the constant focus on political (in)effectiveness and the (un)trustworthiness of parties and politicians in the media also keeps political trust and distrust easily accessible to all citizens – including those who are not particularly interested in politics (ibid.)1. In accordance with the criteria outlined by Hetherington (2005), feelings about politically salient groups are both real and easily accessible for ordinary citizens. Following this line of reasoning, I postulate the first hypothesis:
Political trust has a direct and positive effect on policy support (H1). Under what conditions does trust function as a heuristic?
Sniderman, Brody and Tetlock specifically warn that ‘heuristics become a buzzword, with every correlation between independent and dependent variables being taken as evidence of a new judgmental shortcut’ (1991, p. 70). Taking this warning seriously, special attention is paid to the conditions under which heuristics are enabled. Sniderman et al. (1991) point out that it is highly unlikely that all people share a single chain of reasoning in order to reach political judgment. They emphasize that in contrast to the evaluations and judgments we make on a daily basis, political judgments ask people to rearrange what information they possess. Information and knowledge about policy areas and politics is not equally dispersed among the population of the US (Easton, 1975; Sniderman et al., 1991) and of other countries as well (Fortunato, Stevenson and Vonnahme, 2016). It should be no surprise that citizens’ political knowledge is highly relevant for their political decision making. Pointing out that citizens may derive their political information from a limited number sources, Sotirovic and McLeod argue that
‘more diverse perspectives could help citizens to make connections between various aspects of reality, to evaluate and revise their preexisting knowledge, and thus to reach a more complete understanding of what is happening and why’ (2004, p. 385, emphasis added).
1 Additionally, Tversky and Kahneman (1974) argue ‘accessibility’ to be an element of heuristics: for people to be able to use something as a heuristic, they need to be able to cognitively access that information.
6 Hence, additional knowledge of political affairs should allow citizens to reflect on earlier held beliefs and engage political problems as a rational actor. Vice versa, those who do not possess the additional knowledge are necessarily bound to employ another type of reasoning.
Sniderman et al. (1991) argue that this is the case because the politically sophisticated – who by definition possess more political information – use more and different arguments than the politically less sophisticated do. This does not mean that the sophisticated do not incorporate feelings into their reasoning at all, but they are less dependent on it when deconstructing a complex issue. If political trust is used as a heuristic and is ‘more affective than cognitive’ (Hetherington, 2005, p. 51), then political trust should be more relevant for the policy preference of the politically less sophisticated. The relationship between affect and political decision-making is therefore expected to be conditional on individual level political sophistication. The second hypothesis thus states:
The effect of political trust on policy support should be smaller for the politically sophisticated than for the politically less sophisticated (H2)
Whereas political sophistication conditions the effect of affective heuristics in general, I continue with two conditions that apply specifically to trust-support relationship: sacrifice and risk. I first return to the seminal question: why does political trust matter for policy support? The first part of an answer lies in the concept of trust itself, as Easton puts it: ‘the presence of trust would mean that members would feel that their own interests would be attended to even if the authorities were exposed to little supervision or scrutiny’ (1975, p.447). Having trust in the government explicitly means that people feel that their own interests (either ego- or sociotropically defined) are being taken into account and that their propositions are the best possible. Bear in mind that policy proposals frequently favor either the one or the other group of citizens: which group would support that policy most? Those who are or are perceived to be favored are more likely to support the policy. Among those who are or are perceived to be neglected, the level of trust can really make a difference; political trust can nudge someone who is the disfavored towards a positive evaluation of the policy. For those who discern immediate costs trust becomes more relevant, as Midden and Huijts (2009) show with respect to the attitudes towards CO2 storage. When these storages are placed in a nearby area, trust and other affective reasons determine the attitude; for the attitude towards CO2 storage in general cognitive attitudes are most influential. The relevance of trust is thus related to the perceptions of costs and benefits.
In the trust-as-heuristic approach this conditional mechanism is coined sacrifice, since the individuals who bear the costs but not receive the benefits have to sacrifice their personal interests for the common interest. Sacrifice in material terms is defined by having ’less personal incentive to support [the policy] and little to no personal experience with which to evaluate it’ (Hetherington and
7 Globetti, 2002, p. 254). Hetherington points out that ‘Americans support big government when they benefit from it, but they want limited government when they are asked to make sacrifices’ (Hetherington, 2005, p.3-4). Moreover, ‘when someone perceives that self-interest or group interest is at stake, he or she will need to trust the government to support a government-sponsored policy’ (2005, p. 48). For instance, the relevance of trust is higher for Whites than for Blacks when it comes to racial policy support (Hetherington and Globetti, 2002), higher for the non-poor than for the poor for anti-poverty policy (Hetherington, 2005). Similarly, sacrifice may also be conceptualized in ideological terms (Rudolph, 2009; Rudolph and Evans, 2005; Rudolph and Popp, 2009). Government policy is often an indication of the administration’s political ideology. Similar to material sacrifice, the relevance of trust for policy support increases for those who perceive ideological costs but no ideological benefits. The effect of trust on support is found to be greater for liberals than conservatives with respect to policies on government spending (Rudolph and Evans, 2005), tax cuts (Rudolph, 2009), and social security privatization (Rudolph and Popp, 2009). Summarizing the above, I hypothesize that sacrifice conditions the relationship between trust and policy support.
The effect of political trust on policy support should be greater when the sacrifice associated with that policy is larger (H3a).
From a psychological perspective, risk perceptions are considered of great importance when discussing heuristics, and policy acceptance especially (Mercer, 2005; Quattrone and Tversky, 1988; Siegrist, 1999; Slovic, 1987). Since people tend to be risk averse, perceptions of increased risk lead to lower levels of acceptance; people generally prefer a status quo over risky alternatives (Mercer, 2005; Quattrone and Tversky, 1988). Similar to the mechanism for sacrifice, negative effects of risk may thus be countered by the level of trust; with regard to the acceptance of gene technology, Siegrist puts it the following way:
“Personal experience cannot be used for the evaluation of the benefits and risks associated with new technologies. For information about the effects of [..] new technologies on society, we must rely on experts. Therefore, we hypothesize that trust in experts and institutions influences perceived risk and perceived benefit. People who show confidence in scientists and gene technological enterprises should also show trust in the information given by those experts. They should thus perceive gene technology as less risky and more beneficial than should people who do not trust these sources of information.” (Siegrist, 1999, p. 2095)
With increasing levels of risk trusting and distrusting individuals supposedly diverge in their perception of risk, which in turn affects their judgment. Eiser et al. (2002) show that trust in institutions and risk directly affect the acceptance of gene technology, though Midden and Huijts
8 (2009) find no direct effect of risk on attitudes towards CO2 storage. Both studies, however, use attitudinal measures towards a general principle instead of a concrete policy (i.e. questions about the desirability of CO2 storage and the genetic engineering). From a heuristics perspective, there are few other cues than what ‘the experts’ tell. Public policies, however, are implemented by people who are (often) affiliated with political groups that people may use as cues (Hetherington, 2005).
Turning to the relevance of risk for the relationship between political trust and government policy, Hetherington does not provide us with a detailed definition of what kind of risk is relevant in the trust-as-heuristic approach. He argues that the relevance of political trust increases ‘when a person perceives that a measure of risk is involved and that only the government is in a position to mitigate this risk’ (2005, p. 49). It is the government’s task to judge risks on behalf of their citizens and, if necessary, mitigate them by changing a status quo. Especially when people have only limited knowledge of the topic and the associated risks, they need to trust that the government has taken all things into consideration. In contrast to Hetherington, Rudolph and Popp define policies as risky when:
‘(i) the probable outcomes of the policies are uncertain – e.g. there is considerable doubt about the intended effects of the policy outcomes – and when (ii) the policy outcomes can be inherently evaluated positively or negatively’ (Rudolph and Popp, 2009, p. 335).
Under the condition of risk, trust in government is supposed to act as a cue for how well the government will implement the policy. Hence the acknowledgement of the prospective nature of trust: ‘A trusts B to do X’ (Hardin, 1999, p. 26, emphasis added) or ‘citizen A trusts government B to effectively tackle problem X’. Rudolph holds trust to be ‘an expression of citizens’ willingness to accept government promises about the future consequences of a policy’ (2017, p. 200). In line with this conceptualization, Hetherington (2005) considers the endorsement of anti-Black stereotyping as an indication of the risks associated with the government promise about future consequences of racial policy:
‘(i)f people believe that the recipients of government spending are undeserving and thus likely to waste whatever government money they receive, then they will perceive a substantial risk to spending in these areas’ (2005, p. 83).
Policy risks make confidence in the abilities of the government essential for breaking through the status quo, especially when people cannot decide on the basis of earlier experiences (cf. Hetherington, 2005; Midden and Huijts, 2009). The condition of risk implies that one needs to trust the government – or: to trust in their abilities to ensure positive outcomes – in order to support the risky policy. Therefore, the final hypothesis holds that:
9 The effect of political trust on policy support should be greater when the risk associated with that
policy is higher (H3b). Experimental design
In order to assess whether the relationship between trust and policy support varies with the degree of risk and sacrifice, the trust-support association should be examined under different circumstances of risk and sacrifice. Even though the empirical findings for the relationship between trust and support are promising (Hetherington and Globetti, 2002; Hetherington and Rudolph, 2017; Haugsgjerd and Kumlin, 2017; Popp and Rudolph, 2011; Rudolph and Evans, 2005; Rudolph and Popp, 2009) the evidence for the risk and sacrifice conditions are less convincing. The few studies that have taken into account the conditional effect of risk come up with inconclusive results. Hetherington finds evidence for the conditional effect of risk (2005, Ch. 5) while another study finds no evidence for the conditioning role of sacrifice and risk (Gabriel and Trüdinger, 2011). However, the operationalization of risk differs between both studies. In the former personal risk assessments are measured while in the latter different risk levels are merely assumed between the policies. Additionally, these studies have used observational designs which allows to observe risk and sacrifice as they are actually distributed (i.e. Gabriel and Trüdinger, 2011; Hetherington, 2005, Ch. 5). In order to assess the effect of changes in risk and sacrifice, we need to manipulate the actual distribution so that we can observe the relevance of trust under alternative conditions of risk and sacrifice. Survey experiments allow the researcher to compare these alternatives by randomly assigning participants to a predefined condition (Morgan and Winship, 2014; Shadish, Cook and Campbell, 2002). Fairbrother (2017) uses a survey experiment to assess the conditionality of the relationship between political trust and support for environmental taxes by manipulating elements of the policy and the wording2. His experiment ‘investigates the impact of political distrust, by randomly assigning subjects to conditions under which political trust explicitly is (or is not) relevant’ (2017, p. 7).
Although I use a survey experiment as well, the substantive topic as well as the conditions differ with that of Fairbrother (2017). I examine the support for the government policy on the instalment of the latest generation of wireless Internet (5G). This policy is chosen for the following reasons. First, during the fieldwork period (May 2019) the government’s policy on the 5G network was not heavily politicized. Whereas the government did put out a statement after some public
2 When the item stated that the taxes would be spent on unspecified programs for environmental protection, the difference in willingness between cynics and non-cynics is largest. When the item stated that the taxes would be offset by other tax cuts (low sacrifice), the difference in willingness between cynics and non-cynics is smallest. When the statements above are combined with a government promise instead of policy fact (high risk), the difference between the cynics and non-cynics falls in between. Hence, low sacrifice yields the lowest effect of cynicism on willingness. Adding high risks subsequently increases the effect.
10 concern with the health risks associated with the policy3, the political debate was only marginal at the time – although on the rise, see figure 14. Second, the policy is very suitable for plausible manipulations of risk and sacrifice. Neither the risks nor the costs or benefits are easily explained due to the advanced technology involved. The inherent uncertainty of future costs allow to manipulate participants towards a plausible viewpoint, while the discussion among scientists and with government do give reason to question the potential health effects. Because these manipulations may occur in real life via the news, social media or peers, I have tried to approach real life treatments as best as possible (cf. Shadish et al., 2002).
With respect to the experimental treatments, a 2 x 2 factorial design is used; there are four experimental groups for which both risk and sacrifice are manipulated to be either low or high. The risk treatment consists of a sentence in which the chance on health risks as a result of radiation emanating from the 5G network is manipulated. In the high risk condition, this is presented as a viable risk; in the low risk condition the risk is minimalized. The high risk treatment should increase the risk perceptions of the participants, especially since the risks are unknown and dread – ‘the extent of perceived lack of control and catastrophic potential’ (Siegrist, 1999, p. 2093). According to Slovic (1987), these kind of risk perceptions are most influential for preference formation. The sacrifice treatment, in turn, consists of a sentence in which the subscription fees for (mobile) phones either stays equal (low sacrifice) or increases (high sacrifice).
Participants are randomly assigned to one of the experimental groups – see table 1 for the experimental treatments per group5. The randomization of the experimental treatments is of vital importance, since it ensures that statistical equivalence between the experimental groups is achieved (Morgan and Winship, 2014). No statistically significant differences between the groups should occur besides the dependent variable of support. Any significant differences between the groups are therefore attributable to the experimental treatment of risk and sacrifice.
3
https://www.rijksoverheid.nl/binaries/rijksoverheid/documenten/kamerstukken/2019/04/17/kamerbrief-over-5g-en-gezondheid/kamerbrief-over-5g-en-gezondheid.pdf
4 Note that this figure includes all articles from the four newspapers that include “5G”; the number of articles that mention health risks or actively discuss party positions is considerably lower still.
11 Figure 1: Number of articles in four large Dutch daily newspapers with “5G” mentioned
Data and methods
In this paper data of the LISS (Longitudinal Internet Studies for the Social sciences) panel administered by CentERdata (Tilburg University, The Netherlands) is used. The LISS panel is a representative sample of Dutch individuals who participate in monthly Internet surveys. The panel is based on a true probability sample of households drawn from the population register. Households that could not otherwise participate are provided with a computer and Internet connection. The data is collected in May 2019 and 859 panel members participated in the survey experiment. The survey experiment was placed at the end of the questionnaire, ensuring that the experiment could not affect other variables.
Operationalizations
After the experimental treatment, participants are asked whether they favour or oppose the government policy. A four-point answering scale is used, so participants are forced to choose to support or oppose the policy; this follows the literature on heuristics that argues that heuristics are used when people more or less have to pass judgement (Sniderman et al., 1991). The scale ranks from ‘I am a strong opponent to ‘I am a strong supporter.
Political trust is measured by using the mean of three separate trust indicators: trust in parliament, government and political parties. These are measured by asking participants to what extent they trust each on a four-point scale. The answer categories rank from ‘no trust at all’ to ‘a lot of trust’, which resembles trust in government item from the ANES questionnaire6. A reliability analysis of the three items confirms their scalability (α = .863).
6 See: Hetherington (2005) [CATEGORIENAAM] [CATEGORIENAAM] 0 2 4 6 8 10
Number of articles between January 1st 2018 and April 30th 2019 From newspapers: de Telegraaf, de Volkskrant, NRC Handelsblad, Trouw
12 The variables for risk and sacrifice are dichotomous, with the reference category set at ‘low’. Additionally, educational level serves as a proxy variable for political sophistication (cf. Sniderman et al., 1991). It is measured in six categories, but is transformed into two categories; participants with a higher vocational or university degree are coded as higher educated and those without such a degree as having a lower level of education.
Results
First, statistical equivalence among groups is established using Kruskal-Wallis tests. The distribution of all non-experimental variables are statistically equal among the treatment groups7. The distribution of policy support, however, is significantly different between these groups. This suggests that manipulating the risk and sacrifice manipulations have led to significant changes in support. In particular, the proportion of supporters is greatest among the low risk, low sacrifice group (75 percent) and smallest among the high risk, high sacrifice group (39.1 percent). Descriptive statistics and test results are shown in the appendix.
Multivariate analyses
In table 2 the results from the regression analyses are presented. In the null model we observe that the level of risk and sacrifice both have a negative effect on support of the 5G policy. The effect of risk is much stronger than that of sacrifice; the b-coefficients are -.519 and -.163 respectively. This means that the risk manipulation has led to a 0.52 decrease of support (on a four-point scale), whereas the sacrifice manipulation only decreases support by 0.16. The adjusted R2 value of .094 indicates that nearly ten percent of the variance in policy support is explained by the treatments.
In model 1 individual-level characteristics – trust and educational level – are added. The
7 Sex, age and each of the trust measures separately are also taken into account and Kruskal-Wallis tests show no significant differences.
Table 1: Experimental treatments in English
Low sacrifice High sacrifice
Low risk ‘A group of scientists recently concluded that there is almost no chance of health risks of
the radiation from the 5G network’ +
‘The government hopes to receive billions of euros from telecom companies by auctioning the licenses. The phone subscription fees will
most likely stay equal’
‘A group of scientists recently concluded that there is almost no chance of health risks of
the radiation from the 5G network’ +
‘The government hopes to receive billions of euros from telecom companies by auctioning
the licenses. Because of this, the phone subscription fees will most likely increase’. High risk ‘a group of scientists recently warned that
that there is a viable chance of health risks of the radiation from the 5G network’
+
‘The government hopes to receive billions of euros from telecom companies by auctioning the licenses. The phone subscription fees will
most likely stay equal’
‘a group of scientists recently warned that that there is a viable chance of health risks of
the radiation from the 5G network’ +
‘The government hopes to receive billions of euros from telecom companies by auctioning
the licenses. Because of this, the phone subscription fees will most likely increase’.
13 effect of trust on support is positive and significant (.217, sig.<.001), meaning that political trust is associated with more support for the government’s 5G policy. This effect is in line with hypothesis 1, which states that there is a direct effect of political trust on policy support. The explained proportion of variance increases to .116, which indicates that the trust and education together explain roughly two percent of the variance in policy support.
Table 2: Regression coefficients on 5G policy support
Null model Model 1 Model 2 Model 3 Model 4
Trust in politics .217*** .251*** .154~ .185* Risk -.519*** -.516*** -.514*** -.506~ -.413~ Sacrifice -.163** -.173** -.174** -.187** -.589** Education (ref. = Low) .032 .249 .035 .200 Two-way interactions: Trust x risk -.042 -.044 Trust x sacrifice .193* .185~ Trust x education -.093 -.071 Adjusted R2 .094 .116 .115 .118 .118
Unstandardized b-coefficients presented, intercept not shown; *** p < .001; ** p < .01; * p < .05; ~p < .10
In model 2 the interaction variable of trust and education is added. The expectation formulated in hypothesis 2 – the effect of trust on policy support is stronger for politically less sophisticated – implies that the interaction variable holds a negative sign: an increase in educational level and sophistication should lead to a decrease in the effect of trust on policy. The results in fact show a negative coefficient, but this effect is not statistically significant. This means the effect of trust on policy is not significantly different for participants with a higher vocational degree or higher. Since education is used as a proxy for political sophistication the second hypothesis is refuted.
In model 3 I have included the interaction variables of sacrifice and risk with political trust. As formulated in hypotheses 3a and 3b, increases of sacrifice and risk are expected to result in larger effects of trust on policy. Hence, both interaction variables’ coefficients should hold a positive sign and be statistically significant. The coefficient for trust and sacrifice is both positive and significant (.193; p<.05), meaning that the effect of trust on support is significantly stronger under the condition of sacrifice. In contrast, the coefficient for trust and risk holds a negative sign and is not significant(-.042; p>.10). In other words, the relationship between trust and policy support does not differ between the conditions of low and high risk. In figure 1 these effects are presented graphically; the predicted policy support increases with the amount of political trust, but it increases even more when sacrifice increased. The conditions of sacrifice and risk have negative effects on the policy support, but the predicted policy support of trusting individuals (‘a lot’) under the condition of sacrifice is roughly equal to that of the baseline (low sacrifice, low risk). This indicates that the
14 negative effects of the experimental treatment of sacrifice (i.e. higher costs) can be overcome with political trust. Likewise, it seems that the negative effect of experimental treatment of risk (i.e. health risks) are not countered by trust.
Figure 2 – Predicted values for policy support for risk, sacrifice and no treatment (Model 3)
Discussion
This paper is an attempt to test the mechanisms of the trust-as-heuristics approach. This approach intends to explain why citizens, who generally have limited information about political or government affairs, are able to judge government policy in accordance with their other policy preferences (Hetherington, 2005; Rudolph, 2017). The central claim of the approach is that people use their political trust to assess whether they support or oppose policy. Underlying this train of thought is the intuitive idea that people are more likely to accept proposals coming from those they trust than from those they distrust; this claim is supported by the results. More specifically, the focus of this paper lays on the conditions under which trust functions as a heuristic, notably risk and sacrifice. In contrast to earlier studies with cross-sectional survey designs (e.g. Hetherington, 2005), this paper uses experimental methods to assess the mechanism for trust-as-heuristics. The experiment revolves around the support for a new wireless Internet network (5G) in the Netherlands for which a 2 x 2 factorial design is used; participants would either receive a low or high health risk treatment and a de- or increase in personal costs (phone subscription fees).
First, the results show that increasing health-related risks and the costs of mobile phone subscriptions both diminish the support for the 5G network. However, increasing health risks has a much greater impact on the support than the increasing fees. Taking into account the impact of deteriorating health vis-à-vis a (small) increase in telephone bills, this is unsurprising. Second, the results show that trust becomes increasingly important for policy support when the policy asks for
1 2 3 4
'None at all' 'A bit' 'Somewhat' 'A lot'
Po lic y s uppo rt ; 1 = S tr ong o ppo ne nt / 4 = st ro ng suppo rt er
Trust in overall politics
Risk Sacrifice None
15 sacrifices. It confirms the findings by Hetherington (2005, Ch. 7) and Hetherington and Rudolph (2011) that political trust matters more for individuals who need to sacrifice their material needs for the policy. Third, increasing the perceived health risks associated to the policy does not increase the relevance of trust support of the 5G policy. In contrast to Hetherington’s claim that risk reinforces the relevance of trust in policy evaluation (2005, Ch. 5), I do not find risk to alter the trust-support linkage. Finally, this paper (re)connects the consequences of political trust to heuristical decision making by taking political sophistication into account. Although the book by Sniderman et al. (1991) is considered an important work, the ideas regarding the conditionality of heuristics in political decision making seem forgotten. In contrast to their findings, however, political sophistication does not moderate the relationship between trust and support. Since political trust is conceptualized as a feeling or emotional attachment (Hetherington, 2005, p. 51; Rudolph and Popp, 2009, p. 335) I expected political trust to behave in accordance with the affect heuristic. However, the highly complex topic of wireless networks may have obfuscated the importance of political sophistication. Even though sophisticated citizens may be more likely to retrieve information about this area, the relative obscurity of information surrounding this topic may hold back the differences in information between the more and less sophisticated citizens.
The conditions outlined by the trust-as-heuristics theory thus are only partially supported. Although risk was not found to moderate the effect of trust on support in this experiment, other circumstances may allow it to do so. The operationalization of risk and sacrifice in this experiment is of the utmost importance, not only in the sense of scientific good practice, but more profoundly because there is only one manipulation of risk and sacrifice. We do not know what would have happen if the manipulation was different. In this experiment the degree to which sacrifice and risk are manipulated vary; the sacrifice treatment signals the (limited) increase of subscription fees, which is relatively low impact and has only a small effect on policy support. In contrast, the risk treatment holds high impact content: health effects8. The smaller negative impact of sacrifice is found to be compensated by higher levels of trust, whereas the larger negative impact of risk is not. This supports the idea of trust as a “lubricant” that lowers transaction costs and subsequently lowers the threshold for ‘acceptable’ policy (i.e. Wilson and Eckel, 2017). This would mean that policies that are unpopular at first can become popular when overall trust increases, and vice versa. But imagine, what arguments would suffice to counter the negative feelings about a high impact policy (e.g. a 20 percent raise income tax raise)? Obviously, a politician needs very strong arguments to defend such a policy – the government would probably need some extraordinary circumstance to justify it. In
8 Notably, the high risk treatment did not speak of the exact health risks that would come about. The uncertain character of the risk and its potential life threatening character would make the high risk treatment be
16 contrast, what arguments would suffice for a low impact policy (e.g. a one percent income tax raise)? It is clear that the argument ‘I trust the government to do what is right’ is more likely to act as a lubricant to support the latter policy. The trust lubricant might work only when the policy preference is not too strong: firm negative attitudes towards policy seem unlikely to be compensated by political trust.
However, this conjecture needs empirical evidence that this paper cannot provide. Future research may shed light upon the conditions under which risk and sacrifice are relevant themselves. Experimental designs may prove essential in disentangling the interdependent relations of political attitudes and preferences. More complex survey experiments would allow to manipulate more aspects of policy and include more gradual treatments as well instead of low versus high (cf. Sniderman, 2011; also see Fairbrother, 2017). A remaining question is whether or not political trust can be rightfully called a heuristic. The conditions under which heuristics are enabled in political decision making are less well explained. I therefore consider the work by Sniderman et al. (1991) a leading publication still. One could argue about whether political trust is an affective attitude outside the United States – or whether political trust should be considered a ‘likability’ heuristic (cf. Rudolph, 2017)9. The political history of the United States is rife with cues and narratives on the (un)trustworthiness of the – federal – government, as illustrated in the campaign advertisement by the later US president George W. Bush: “He [Al Gore] trusts government. I trust you.” (in: Hetherington, 2005, p. 2). Scholars of political trust may want to examine to what extent political trust holds similar substantive meanings in and outside of the Unites States. Taking these plausible differences into account is particularly important considering the vast number of influential political science papers from the other side of the Atlantic. In conclusion, there is still a world to win when it comes to the political trust as a heuristic; when, how, where and for whom does trust act like a heuristic?
9 The likability heuristic is developed by Sniderman et al. (1991) and is supposed to affect the reasoning of political sophisticated citizens more than politically less sophisticated citizens. However, the results in this study show no significant effects whatsoever.
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22 Appendix
Table A.1 – Experiment wording in Dutch
“Deze herfst begint de Nederlandse overheid met het verkopen van licenties voor het toekomstig
gebruik van het 5G-netwerk. 5G is de nieuwste technologie voor draadloze dataverbindingen
(zoals mobiel internet). 5G heeft een veel grotere capaciteit en veel hogere snelheid dan het huidige 4G. Men verwacht dat de 5G technologie in 2020 beschikbaar komt.”
De overheid hoopt vele miljarden te ontvangen van telecombedrijven door de licenties te veilen.
De prijzen van telefoonabonnementen zullen hoogstwaarschijnlijk gelijk blijven / hoger worden .
Een grote groep wetenschappers concludeerde / waarschuwde recent dat er bijna geen / een
reële kans is op gezondheidsrisico’s door de straling van het 5G netwerk
Table A.2 – Experiment debriefing in Dutch
Respondent debriefing:
Als onderdeel van deze vragenlijst heeft u meegedaan aan een experiment waarbij we deelnemers verschillende stukjes tekst hebben voorgelegd over het 5G-netwerk. U heeft daarbij eenzijdige informatie voorgelegd gekregen. Hieronder vindt u welke informatie wij u hebben gegeven of onthouden. De aanpassingen staan hieronder dikgedrukt weergegeven en worden gescheiden door een / .
“Deze herfst begint de Nederlandse overheid met het verkopen van licenties voor het toekomstig
gebruik van het 5G-netwerk. 5G is de nieuwste technologie voor draadloze dataverbindingen
(zoals mobiel internet). 5G heeft een veel grotere capaciteit en veel hogere snelheid dan het huidige 4G. Men verwacht dat de 5G technologie in 2020 beschikbaar komt.”
De overheid hoopt vele miljarden te ontvangen van telecombedrijven door de licenties te veilen.
De prijzen van telefoonabonnementen zullen hoogstwaarschijnlijk gelijk blijven / hoger worden .
Een grote groep wetenschappers concludeerde / waarschuwde recent dat er bijna geen / een
reële kans is op gezondheidsrisico’s door de straling van het 5G netwerk.
Gezondheidsrisico’s 5G
Namens het kabinet hebben staatssecretaris Keijzer en Minister Bruins op 17 april jl. de Tweede Kamer geïnformeerd over de mogelijke gezondheidsrisico’s van 5G. Het 5G-netwerk heeft veel meer zendmasten nodig dan de oudere netwerken. De vraag was of de toename aan
elektromagnetische velden van de (nieuwe) 5G zendmasten een gezondheidsgevaar is voor mensen. Op basis van verschillende wetenschappelijke onderzoeken concludeert het kabinet dat dat niet zo is, zolang de sterkte van de elektromagnetische velden binnen de daarvoor geldende limieten blijft. Ze schrijven dat:
“Uit alle inmiddels afgeronde onderzoeken en de Gezondheidsraadadviezen waarin alle [wetenschappelijke] literatuur is meegenomen, blijkt echter dat er geen aanwijzingen hiervoor [gezondheidsrisico’s] zijn, zolang de blootstelling beneden de
blootstellingslimieten blijft. Dat geldt ook na de introductie van het 5G-netwerk”.10 Opbrengsten 5G en telefoonabonnementen
De kosten voor de aanleg van de infrastructuur voor 5G zijn afkomstig van zowel overheden – via subsidies of leningen – als van het bedrijfsleven. Zo heeft de Europese Commissie al 700 miljoen euro al investeringen vrijgemaakt. Providers (KPN, T-Mobile, Vodafone etc.) moeten hun
vergunningen voor het 5G netwerk opkopen, wat waarschijnlijk voor miljarden euro’s aan
10
23 inkomsten zal zorgen (in Italië hebben de licenties €6,5 miljard opgebracht, terwijl de opbrengsten in België zijn geschat op €680 miljoen). In 2012 bracht de vorige veiling in totaal €3,8 miljard op. We hebben nog geen precies beeld van hoeveel geld de licenties ditmaal zullen gaan opleveren. Het is dan ook nog niet duidelijk of de prijzen van telefoonabonnementen gaan stijgen. Sommigen wijzen erop dat in de huidige prijzen al rekening wordt gehouden met de veiling van frequenties, dus dat de prijzen min of meer gelijk zullen blijven. Anderen stellen dat de toename va het energieverbruik door 5G kan zorgen voor extra kosten voor telecomproviders en dat ze die misschien doorberekenen aan de consument.
Table A.3: Descriptive statistics for full sample and per experimental group
Full sample LrLs LrHs HrLs HrHs
Support for 5G policy
Strong opponent 11.2% 2.8% 8.5% 16.1% 17.2% Somewhat opponent 31.9% 22.2% 27.2% 35.1% 43.8% Somewhat supporter 43.3% 51.4% 46.0% 42.6% 32.3% Strong supporter 13.6% 23.6% 18.3% 6.2% 6.8% Trust in government None at all 9.3% 9.4% 9.4% 10.7% 7.3% A little 45.8% 46.7% 42.7% 45.9% 47.9% Some 42.7% 41.5% 46.0% 40.9% 42.7% A lot 2.2% 2.4% 1.9% 2.5% 2.1%
Trust in political parties
None at all 15.1% 15.6% 13.6% 19.8% 10.4%
A little 60.8% 62.3% 60.1% 56.2% 65.6%
Some 23.4% 21.2% 25.8% 23.1% 23.4%
A lot 0.7% 0.9% 0.5% 0.8% 0.5%
Trust in parliament (Tweede Kamer) None at all 8.4% 10.8% 6.1% 9.9% 6.3% A little 45.5% 42.5% 43.7% 46.7% 49.5% Some 44.1% 44.8% 48.8% 40.5% 42.7% A lot 2.0% 1.9% 1.4% 2.9% 1.6% Sex Male (ref.) 52.7% 56.1% 55.4% 50.4% 49.0% Female 47.3% 43.9% 44.6% 49.6% 51.0% Educational level Low (ref.) 60.0% 59.0% 63.4% 55.4% 63.0% High 40.0% 41.0% 36.6% 44.6% 37.0% Age in years* 59.74 (15.77) (14.79) 60.93 (15.49) 60.53 (16.17) 58.30 (16.58) 59.36 Overall trust in politics* 2.29
(0.59) (0.60) 2.27 (0.58) 2.33 (0.62) 2.25 (0.54) 2.31
N 859 212 242 213 192
*Means shown with standard deviation in parentheses
Table A.4: Kruskal-Wallis tests of equal distribution over experimental groups Sig. level
Support for 5G policy 0*
24 Trust in political parties 379
Trust in parliament (Tweede Kamer) 512
Sex 302
Educational level 211
Age in years 374
Overall trust in politics 592
N 859
*Null hypothesis of equal distribution over groups rejected
Table A.5 Regression coefficients on 5G policy support with trust in government
Null model Model 1 Model 2 Model 3 Model 4
Trust in government .185*** .205*** .144* .165* Risk -.519*** -.517*** -.515*** -.494* -.486* Sacrifice -.163** -.168** -.169** -.410* -.404* Education (ref. = Low) .033 .168 .035 .159 Two-way interactions: Trust x risk -.010 -.012 Trust x sacrifice .102 .099 Trust x education -.056 -.051 Adjusted R2 .094 .115 .115 .115 .114
Unstandardized b-coefficients presented, intercept not shown; *** p < .001; ** p < .01; * p < .05; ~p < .10
Table A.6: Regression coefficients on 5G policy support with trust in political parties
Null model Model 1 Model 2 Model 3 Model 4 Trust in political parties .136** .189*** .096 .138
Risk -.519*** -.519*** -.518*** -.339~ -.347~ Sacrifice -.163** -.169** -.170** -.581** -.555** Education (ref. = Low) .059 .322 .059 .253 Two-way interactions: Trust x risk -.087 -.082 Trust x sacrifice .196* .183* Trust x education -.124 -.091 Adjusted R2 .094 .104 .105 .109 .109
Unstandardized b-coefficients presented, intercept not shown; *** p < .001; ** p < .01; * p < .05; ~p < .10
Table A.6: Regression coefficients on 5G policy support with trust in parliament
Null model Model 1 Model 2 Model 3 Model 4
Trust in parliament .183*** .209*** .128~ .151~ Risk -.519*** -.513*** -.512*** -.467* -.460* Sacrifice -.163** -.171** -.172** -.539* -.524** Education (ref. = Low) .033 .202 .035 .157 Two-way interactions: Trust x risk -.019 -.021
25
Trust x sacrifice .153~ .147~
Trust x education -.069 -.050
Adjusted R2 .094 .114 .114 .115 .115
