Austerity and young people’s political attitudes in the UK

Abstract

This article studies the impact of the 2012 British austerity policies on youth political attitudes using a difference-in-differences. The study achieves this by combining longitudinal survey data from “Understanding Society” with a district-level estimate of the austerity shock that each individual faced between the years 2013 and 2015. The findings indicate that the welfare cuts had a negative impact on the political efficacy of young people, as they were more inclined to believe that public officials did not care about them and that they had no say in the actions of the government. Additionally, their satisfaction with politics, as determined by their perceived level of political influence, also decreased. Overall, the results suggest that the implementation of austerity measures increased the likelihood of political disenfranchisement among young people in Britain.

“The age of irresponsibility is giving way to the age of austerity,” declared David Cameron in his speech to the 2009 Conservative Party forum in Cheltenham. ¹ A year before the British general elections, the Conservative Party set up a campaign that denounced the “Labor’s Debt Crisis” and aimed to restore the country’s finances. The Great Recession had triggered a considerable increase in public spending, preceded by some years of deficit. By 2010, British public debt to GDP reached 75 per cent and peaked at 87 per cent in 2015. As Europe started to grapple with a sovereign debt crisis, a Conservative-led coalition was elected in 2010 in the UK. Then-Prime Minister David Cameron established the Office for Budget Responsibility and began to implement severe austerity measures to deliver on its promise to provide “more for less.”

While aiming to enhance work incentives and simplify the welfare system, the cuts were sizeable. Data from the OECD Economic Outlook 106 show that the cyclically adjusted primary balance as a percentage of potential GDP improved from −6.4 in 2009 to 0.2 in 2017. This represents a fiscal consolidation of 6.6 per cent of potential GDP over eight years. Innes and Tetlow (2015) estimate that between 2009–10 and 2014–15, English local authorities cut net service spending by 23.4 per cent per person on average (in real terms). As a consequence, Fetzer (2019) argues that support for UKIP rose and that austerity played a role in the subsequent Brexit vote. Those results echo similar research on the link between austerity and social unrest in Europe (Ponticelli and Voth 2020) or the rise of the Nazi party in Germany (Galofré-Vilà et al. 2017). Additional research by Stuckler et al. (2017)shows the social costs of these policies by highlighting that austerity in the UK correlated with an increase in suicides.

This article aims to study how the 2012 Welfare Reform Act impacted youth political attitudes and their sense of political representation. Rather than focusing on the political cost and turnover austerity can cause, I shed light on whether and how austerity can influence individual political attitudes, particularly young people. I show that the welfare cuts that started to be implemented in 2013 in the UK had a negative effect on young people’s opinion of politicians and made them more prone to political disengagement.

The article has three main contributions. First, it adds the literature on the political consequences of austerity policies and shows that welfare cuts can have political repercussions. Second, it provides further evidence that negative income shocks interact with the life cycle and that young people are more prone to updating their political views in the face of economic hardship. Finally, it highlights trade-offs that government should take into account when facing budget imbalances. In particular, it emphasises the need to evaluate potential unintended consequences of fiscal policies on certain parts of the population.

My analysis relies on longitudinal survey data from Understanding Society (UnderSoc), which follows a representative sample of 40,000 households from 2009 to 2019 (University of Essex 2019). I focus on five outcome variables: (i) whether one is interested in politics, (ii) whether one agrees that public officials don’t care, (iii) whether one agrees to have no say in what the government does, (iv) whether one feels a sensation of satisfaction when they vote, and (v) whether they believe their vote will make a difference. The survey includes each respondent’s local area district of residence, a unit of analysis that is smaller than counties. This allows me to merge survey responses with a district-level estimate of each respondent’s austerity shock. Similarly to Fetzer (2019), I use a difference-in-differences (DiD) with Beatty and Fothergill (2016)’s estimates of regional welfare cuts. They are a proxy of the intensity of the austerity shock in each district. With this, I can compare over time the answers of young individuals from districts exposed to different austerity shocks while controlling for individual and regional characteristics.

On average, my results suggest that austerity significantly affected political attitudes among the British youth. Young people’s political efficacy diminished as they were more likely to believe public officials do not care about them and that they have no say in what the government does. In parallel, their sense of satisfaction with politics, measured, for example, by their perceived political influence, also decreased. These results provide grounds to see the welfare cuts as a factor in the lower engagement of young people in the Brexit referendum.

My article is structured as follows: Section 2 provides details on the context of my empirical setting, its policy relevance, and previous literature, Section 3 describes the data used, section 4 presents my empirical strategy, Section 5 displays the results, finally Section 6 concl concludes.

Context, research question, and literature review

The Brexit vote was followed by commentaries on young people’s low turnout at the poll. Even though the link between age and lower vote turnout has been established in the literature, the low turnout surprised as young people’s future was more likely to be affected by the outcome of the referendum and as a majority of them supported the Remain side. ² Some Op-Eds emerged, with titles in the vein of the FT’s “Young people feel betrayed by Brexit but gave up their voice” (2016).

In parallel, estimates of youth turnout varied and were revised over time, as no exit poll was conducted. Sky Data, Sky News polling branch, initially evaluated the 18–24 turnout at 36%, but polls conducted later by IpsosMori and Opinium estimated, respectively, that 60% and 64% of registered voters aged 18–24 voted. While not as striking as earlier figures, these numbers are still well below the average turnout of 72% for all age groups. The BBC further documented that counties with a higher share of young people had lower voting turnout during the referendum (BBC 2021).These numbers are disconcerting given the expected repercussions of Brexit for young people: those aged 18 to 24 will have to live with the consequences of the Brexit vote for an average of 69 years, compared with 16 years for those over 65 (Generation Citizen 2016).In light of this evidence, one could wonder whether some economic factors played a role in the lower than anticipated turnout for young people. In particular, substantial welfare cuts took place before the Brexit vote that seems to have played a role in the referendum’s results (Fetzer 2019).

A wealth of research examines how political attitudes are developed. A number of studies focus on childhood factors and in particular the effect of early forces in the political socialisation of individuals. They highlight the role played by parents’ political orientation on a child’s political attitude formation (Jennings 1996; Jennings and Niemi 1968; Jennings et al. 2009; Maccoby et al. 1954) as well as the educational system and peers’ (Campbell 1980; Jennings and Niemi 1974; Tedin 1980), or even genetic influences (Alford et al. 2005). Another area of research focuses on the influence of recent or contemporary events on political attitudes. Specifically, several articles have demonstrated how economic circumstances can affect political attitudes and electoral outcomes (Durr 1993; Fiorina 1978; Kinder and Kiewiet 1981; Kramer 1983; Lewis-Beck and Stegmaier 2000)

Margalit (2019) lays out four possible outcomes of the impact of economic shocks on political behaviour:

• they can lead to an increase in support for left parties and redistributive policies

• the losers of the economic shock can embrace anti-establishment contenders and far-right parties

• they can trigger a vote against the incumbent

• they can lead to a reduced interest in politics and lower the voting turnout

The rise of populism that followed the Great Recession led to considerable research on the second point (see, e.g., Algan et al. (2017), Dustmann et al. (2017), and Rhodes-Purdy et al. (2020)). However, recent articles also highlight the role of abstention in the rise of populist and radical right parties (Guiso et al. 2017). Yet, out of the four outcomes Margalit (2019) lists, abstention and a decrease in the interest in politics have been the least developed topic. In fact, evidence on the effect of economic shocks on turnout appears to be still limited and so far points to an interaction with the life cycle.

Indeed, two articles, Finseraas (2017) and Emmenegger et al. (2017), highlight that economic shocks that take place when young affect political interest and participation. Exploiting the discovery of oil outside the Norwegian county of Rogaland, Finseraas (2017) finds that cohorts that experienced a positive shock in family income during their childhood are more likely to vote. Emmenegger et al. (2017) find a symmetrical effect when analysing whether being unemployed interacts with the life cycle stage in depressing political interest. They use German panel data to show that unemployment spells among young adults trigger a drop in political interest, which is not visible later in life. They explain this finding by young people being more malleable by the economic conditions they live in while forming their opinion. This is consistent with the “impressionable years” or “formative years” theory from the psychology literature, according to which adolescence and early adulthood are more sensitive to major life events and can lead someone to revise their preferences (Krosnick and Alwin 1989; Sears 1983). That susceptibility decreases thereafter and remains low during the rest of the life cycle.

Hence, this article adds to a small but growing literature on how the experience of economic shock during formative years increases young people’s detachment from politics. In particular, I test the following hypotheses:

• H1: The welfare cuts led to decreased political efficacy/sense of political representation among young people.

• H2: Such effect was not visible in other age groups.

I treat the welfare cuts as a negative income shock and interpret my results in line with the cited literature. However, I should note that the decrease in political efficacy I observe among young people could also reflect a political disappointment following the government’s decision to cut benefits. I am not able to disentangle these two effects.

Another limitation of my article is its focus on attitudes towards politicians and voting, which I measure with survey data. Therefore, I can only see how declared political beliefs evolve and do not evaluate any attitudinal feedback on real-life political outcomes. Nevertheless, although political interest and attitudes towards politicians are conceptually different from political behaviour, they remain important determinants of political participation and views of the political system (Brady et al. 1995; Powell 1986).

Finally, this article contributes to two additional strands of literature. The first one is in labour economics and suggests economic shocks have heterogeneous effects depending on when they occur in a person’s life. Negative labour shock when young have been shown to have long-term consequences in terms of earnings (Oreopoulos et al. 2012; Schwandt and Von Wachter 2019), the likelihood of facing poverty (Schwandt and Von Wachter 2019), many social outcomes ranging from fertility decisions, marriage, and divorce to criminal activities, risky alcohol consumption, and lower health outcome (Von Wachter 2020), as well as the choice of major and line of job (Cotofan et al. 2023). Secondly, it adds to the debate on the political cost of austerity. While some articles find no link between austerity and a fall in popularity of governments and/or electoral defeat (Alesina and Ardagna 2010; Alesina et al. 1998; Arias and Stasavage 2019), others observe a relation between fiscal consolidations and social instability (Ponticelli and Voth 2020; Vegh and Vuletin 2014). Finally, and as cited above, Fetzer (2019) shows that austerity in the UK caused a rise in the popularity of UKIP, the pro-Brexit political party. My results support the latter side of the debate.

Data

The post-recession period saw the implementation of severe austerity measures in the UK, triggered by the election of a Conservative-led coalition government in 2010. Fetzer (2019)describes three implementation phases. The first one consisted of budget cuts across most Westminster departments in 2010. Local governments experienced a decrease in funding to conduct their daily activities. Nominal wage freezes were then implemented for public sector employees from 2011 to 2013, with a public sector wage growth cap set at 1 per cent in 2014. Finally, a reform of the welfare state took place, with the 2012 Welfare Reform Act. This reform is the bulk of the austerity plan advocated by the ruling government and consisted of substantial welfare cuts among several dimensions.

Beatty and Fothergill (2016) ³ provide a quantitative estimate of these welfare cuts, focusing on ten measures. These measures encompass changes in housing benefits, non-dependent deductions, household benefit caps, council tax benefits, disability living allowances, incapacity benefits, child benefits, tax credits, and the reduction in annual up-rating of working-age benefits. While the figures are estimates, they are “deeply rooted in official statistics” and come, for example, from “the Treasury’s own estimates of the financial savings, the government’s Impact Assessments, and benefit claimant data” (beatty2016, beatty2016, p.6). Overall, they calculate that these measures yielded savings of almost £14 billion a year by 2016. Taken as a share of the benefit claimants, this amounts to an average welfare loss of £345 per working-age adult, per annum. ⁴

This average hides considerable variation between districts. Indeed, the estimated annual financial losses per working-age adult range from £100 in the City of London to £720 in Blackpool, with a standard deviation of £84. Figure 1 displays the geographical variation of the austerity shock, per annum, across districts in the UK. I use this estimate as a proxy for the regional intensity of the austerity measures voted in 2012. In particular, I compare individuals across districts subject to different austerity shocks.

Figure 1. Distribution of the austerity shock in the UK - Source: Beatty and Fothergill (2016).

There are two important caveats to my analysis. First, the austerity shock’s regional variation is driven by the heterogeneity in the distribution of the benefits claimants over the country. This could imply a bias in my estimation as the districts most affected by the welfare cuts are mainly populated by vulnerable households. To address this issue, I include individual and district-level fixed effects in my estimation.

Second, the austerity shock provided by Beatty and Fothergill (2016) contains a set of measures implemented before the election of the Conservative-led government. In particular, the Labour party introduced the Employment and Support Allowance (ESA). The Conservatives-led coalition then added new elements to it, such as the time-limiting and non-means-tested ESA. Therefore, my austerity shock is partly “contaminated” by additional measures that pre-date the 2012 Welfare Reform Act. Nevertheless, the vast majority of the austerity cuts took place as part of the Welfare Reform Act. In addition, I expect the austerity cuts to increase negative attitudes towards politicians and politics in general. Therefore, an overestimation of the fiscal shock will likely lead to underestimating the elasticity of political attitudes to austerity. Hence, my estimates should be interpreted as lower bounds.

I combine this regional austerity shock with survey data from the Understanding Society (UnderSoc) database (University of Essex 2019). The UnderSoc is a longitudinal survey run by the Institute for Social and Economic Research at the University of Essex, which replaced the British Household Panel Survey (BHPS) in 2009. It follows a representative sample of 40,000 British households across nine waves, from 2009 till 2019, and interviews them on socio-economic issues. One particularity, compared to BHPS, is that the interviews are conducted over 2.5 years and that the waves overlap. Table 1 maps each wave to its corresponding years. In general, each individual is interviewed around the same time every year, yet I structure my dataset as a wave-individual panel rather than a year-individual panel. The UnderSoc database provides the local authority district of residence of each respondent. Therefore, I can map each individual to its corresponding regional austerity shock, my main explanatory variable.

Table 1. UnderSoc waves and corresponding years

I use five dependent variables of political attitudes from this survey:

1. 1. Public officials don’t care stems from the question “How far do you agree or disagree with the following statements? Public officials don’t care much about what people like me think.” Respondents can strongly agree, agree, neither agree/disagree, disagree, or strongly disagree. It is coded 1 if the respondent answered strongly agree or agree.

2. 2. No say in what government does follows the same format as above: people are asked whether they agree with the statement “People like me don’t have any say in what the government does.” It is coded 1 if the respondent answered strongly agree or agree.

3. 3. Interest in Politics is created from the question “How interested would you say you are in politics? Very interested, fairly interested, not very interested, or not at all interested.” It is coded 1 if the respondent answered very interested or fairly interested.

4. 4. Personal benefit from voting asks respondents whether they strongly agree, agree, neither agree nor disagree, disagree, or strongly disagree with the following statement “I feel a sense of satisfaction when I vote.” It is coded 1 if the respondent answered strongly agree or agree.

5. 5. Perceived political influence asks respondents “On a scale from 0 to 10, where 0 means very unlikely and 10 means very likely, how likely is it that your vote will make a difference in terms of which party wins the election in this constituency at the next general election?” and was entered as is in the regression.

These variables correspond to standard measures of political efficacy and sense of political representation. In particular, question 1 has been used as a measure of external political efficacy in the past, while questions 2 and 5 are usually used to measure internal political efficacy (Craig and Maggiotto 1982; Craig et al. 1990; Miller and Traugott 1989; Niemi et al. 1991). ⁵ Question 1 and 4 are additional measures of political attitudes that correlate with voting.

While the first question is asked at all waves, the second and third ones are asked at waves 3, 6, and 9, and the latter two at waves 2, 3, 6, and 9 ⁶ I choose to exclude the waves that occurred after wave 6 for two reasons. First, the Conservatives voted for new austerity measures in 2015, and hence, the austerity shock is different starting in 2016. In addition, the Brexit vote took place in June 2016 and likely led to significant adjustments in political attitudes. Indeed, intense political turmoil took place following the Brexit vote, and polls highlighted that some Brexiters regretted their voting choice. ⁷ Therefore, I choose to focus on the period pre-Brexit and post-Welfare Reform Act to estimate how austerity affected political attitudes in the UK. ⁸

I use the longitudinal nature of the survey and run a DiD where 2013 is the start of my treatment. ⁹ I include the longitudinal weights provided by UnderSoc to ensure that my analysis remains representative of the British population over the different waves. Further details about my empirical strategy are provided in section 4.

Table 2 provides summary statistics of my five dependent variables for waves 2, 3, and 6, for the whole sample and young people. On average, half of the respondents declare they are interested in politics, agree that public officials don’t care and that they have no say in what the government does, and feel a sense of satisfaction from voting. The mean perceived political influence is 3.22 (out of 10). Those numbers are lower for younger people, implying that their sense of political representation is lower than the population’s average. This is consistent with lower turnout rates and lower electoral registration among young people. The standard deviations in Table 2 are large as the variables are dummies or categorical.

Table 2. Dependent variables - summary statistics

Estimated with longitudinal weights.

I construct indices following a principal component analysis to reduce the risk of multiple hypothesis testing and because my dependent variables are correlated (see Table 3). I group the variables according to their level of correlation and create a first index that measures the level of low political efficacy in my sample with the variables Public officials don’t care and No say in what government does, and a second one that measures the level of satisfaction with politics with the variables Interest in Politics, Personal benefit from voting, and Perceived political influence. Grouping the variables this way will also make the interpretation easier as the first and second indices correlate negatively.

Table 3. Correlations between the outcome variables

Empirical strategy

DiD

I first run a standard DiD specification (equation 1) for young people and “non-young” people separately, using the austerity shock as treatment.

(1) $${y_{i,d,w,t}} = {\alpha _0} + {\alpha _1} \times {\bf{1}}(Year \ge 2013) \times Austerit{y_d} + {\lambda _i} + {\gamma _t} + {\nu _d} + {\varepsilon _{i,d,w,t}}$$

where ${y_{i,d,w,t}}$ corresponds to one of my outcome variables of political attitudes for individual i, living in district d, at wave w and time t; ${\alpha _0}$ is a constant intercept across all individuals, districts, wave, and periods; ${\lambda _i}$ is an individual fixed effect; ${\gamma _t}$ is a year fixed effect; ${\nu _d}$ is a district-level fixed effect. I cluster the standard errors at the year and district levels.

My main explanatory variable, $Austerit{y_d}$ , takes the value of the austerity shock in district d (described in Figure 1) when the year is superior or equal to 2013. This is equivalent to having it take the value of the austerity shock for wave 6 only. One particularity of my estimation is that the treatment is a continuous variable that applies to all individuals after 2013. My results come from the variation in the intensity of the treatment and imply linearity in reaction to the treatment.

Equation 1 is similar to Fetzer (2019)’s, with the exception that my explanatory variable is at the district level while my measure of political attitude is at the individual level. Hence, I aim to capture how macroeconomic conditions influence one’s views on government rather than individual conditions. Nevertheless, my measure of austerity stems from the heterogeneous distribution of benefit claimants over the UK. This implies that districts with more welfare beneficiaries will encounter a higher austerity shock and might be more prone to adverse consequences on political attitudes as their inhabitants rely more on the welfare state. To address this issue, I use fixed effects at the district and the individual level. The former controls for regional specificities that might explain why some regions react differently than others to the austerity shock, while the latter controls for the same characteristics for individuals. In particular, my district fixed effects allow me to control for the impact of secular structural shocks at the regional level, such as import competition or automation, that could impact political attitudes. The use of individual fixed effects enables me to compare the evolution of an individual’s reaction to the austerity shock, controlling for all the individual fixed characteristics that determine her/his preferences.

One should note that the fixed effects control for individual and community characteristics that may influence political engagement and may be correlated with the austerity shock, but not for the heterogeneous impacts of the austerity shock. Indeed, the impact on political attitudes could come from (i) districts with larger cuts having more people affected by the cuts, (ii) districts with larger cuts experiencing larger cuts in benefits, or (iii) people living in districts with larger cuts reacting more intensely to the cuts. I cannot distinguish between the three channels here.

I define young people as individuals that are 25 years old or younger. ¹⁰ Running equation 1 on the sample of young people will give us an estimate of how young people updated their political attitudes following the austerity shock. I can then compare it to the estimate I obtain when running the same equation on the sample of individuals that are older than 25.

For my results to be credible estimates, I need to assume that the parallel assumption holds between people living in different districts. This means assuming that the expected variation in political attitudes of people living the most affected districts and those living in the least affected ones would have been the same. I cannot test for the parallel trend assumption here and return to this issue in section 4.3. Comparing the estimates I get for young and non-young enables me to relax this assumption, providing that the bias is the same for the two groups living in the same district.

DiD with interaction terms

Another way to look at the effect of austerity on young people is to add interaction terms. I run a new specification, similar to equation equation 1, where I interact the continuous treatment variable interacted with a dummy for young people. In practice, I add the terms ${\beta _2} \times {\bf{1}}(Year \ge 2013) \times Youn{g_i} \times Austerit{y_d}$ and $Youn{g_i} \times {\gamma _t}$ to equation 1. It takes the following form:

(2) $$\matrix{ {{y_{i,d,w,t}} = {\beta _0} + {\beta _1} \times {\bf{1}}(Year \ge 2013) \times Austerit{y_d}} \hfill \cr { + {\beta _2} \times {\bf{1}}(Year \ge 2013) \times Youn{g_i} \times Austerit{y_d}} \hfill \cr { + {\lambda _i} + {\gamma _t} + Youn{g_i} \times {\gamma _t} + {\nu _d} + {\varepsilon _{i,d,w,t}}} \hfill \cr } $$

where $Youn{g_i}$ is a dummy for individuals younger than 25. I also cluster the standard errors at the year and district levels.

This second equation enables me to check whether young people have been more affected by the welfare cuts than their older peers. If significant, my coefficient ${\beta _2}$ would provide additional evidence of an heterogeneous effect of austerity on the British youth. This would be while controlling for the impact of the austerity shock in general with the term ${\beta _1} \times {\bf{1}}(Year \ge 2013) \times Austerit{y_d}$ and for the “usual” evolution of young people’s political attitudes over time with $Youn{g_i} \times {\gamma _t}$ .

While the specification is close to a triple DiD, it displays several important distinctions. First, a triple DiD would also include the interaction of each of the three components with each other and each component as a fixed effect. In this case, it means adding the interaction $Young \times Austerit{y_d}$ and a fixed effect for young people. Yet, including these items risks saturating my model as the individual fixed effects already control for these two items. Second, the treatment here affects everyone, with a different intensity. Ideally, with a triple DiD, the causal effect estimate will come from changes in the treatment units, and not changes in the control units. For this identification strategy to be a perfect triple DiD, I would need to have an austerity treatment that only affects young people, and not older cohorts.

Hence, specification 1 gives me an estimate of how people updated their attitudes following the austerity shock by comparing them to individuals from the same age category that lived in a different location. On the other hand, specification 2 provides an estimate of how young people reacted differently than elder peers subsequently to the welfare cuts, while controlling for a number of time variant specificities.

Potential threats to validity

There are several threats to the validity of my results. First, respondents might move between the second, third, and sixth waves of the survey. This could bias my results as the evolution of one’s political attitudes could change following their move to a different district. This is particularly relevant in this setting as young people are more prone to moving than other age cohorts. To control for it, I re-run my analysis, restricting my sample to non-movers only. These results, presented in section 5.3, show that there is no significant difference whether I use my sample of non-movers or all survey respondents. This is because few people moved between wave 2 or 3 and wave 6.

Second, as I compare three or two waves, I only have two to three data points per individual. In addition to the fact that I use a continuous treatment, this means I cannot test for the parallel trend assumption. A potential alternative would be to perform a falsification test with an alternative dependent variable. In my case, finding an appropriate alternative variable is complicated as it is unlikely that austerity influences political attitudes without affecting other social variables. This is in combination with another drawback of my empirical strategy: I cannot use district × year fixed effects to control for district-specific trends. Indeed, they would be collinear to my explanatory variable. Therefore, my identification relies on using the correct functional form and sufficient controls with individual, time, and district fixed effects. This means that I postulate that no other shock at the district level is collinear or dependent on the austerity shock I use. Hence, I cannot claim to identify any clear causal effect, especially as the effects of the Great Recession were still lingering.

Thirdly, I consider that the austerity shock started in 2013, which raises two issues. First, some respondents in wave 3 were interviewed in 2013, which can lead to a downward bias in my coefficients. I drop these respondents in a robustness check and show that it does not change my results (section 5.3). In fact, most of the interviews take place during the first two years of each wave, and respondents from wave 3 interviewed in 2013 account for only 6 per cent of the wave’s sample. Secondly, the Conservatives’ austerity plan included three phases and started in 2010, as described earlier. The 2012 Welfare Reform Act is the latest component, which means that political attitudes might have been affected by austerity measures earlier to waves 2 and 3. As a result, my coefficient should be seen as a lower bound of the effect of these austerity measures on political attitudes.

Overall, equations 1 and 2 are a very demanding specification that include about 20,000 fixed effects. As discussed above, a more accurate identification strategy should control for district-specific trends, this is not possible in this context. Therefore, while I control for many factors, my results can only be seen as suggestive and not causal.

Results

Main specification

Table 4 presents the standardised coefficients of the DiD using the principal component indices All variables are normalised before constructing the principal component indices. ¹¹ We see that, on average, the index of low political efficacy increases by 0.44 for a one standard deviation increase in the welfare cuts. In contrast, the index of satisfaction with politics decreases by 0.35. It shows that young people affected by the welfare cuts were significantly more likely to update their political attitudes. We compare these results with those for “non-young” people, i.e. individuals older than 25. Table 5 shows no significant impact, even though the sample is much bigger. Hence, the association between the welfare cuts and lower political efficacy and satisfaction with politics is only visible among young adults.

Table 4. Impact of the regional welfare cuts on young people’s political attitudes - indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

Table 5. Impact of the regional welfare cuts on individuals older than 25 - indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

One drawback of principal component indices is that they make interpreting the results more difficult. Hence, we re-run our analysis on the sub-components that comprise the indices. Table A1 and table A2 display the results for young people and older generations, respectively. We see that an increase in welfare cuts increases the probability that a young person feels public officials do not care and that they have no say in what the government does by 0.13 of an s.d. and 0.18 of an s.d., respectively. In parallel, it decreases their perceived level of political influence by 0.76 of an s.d. The coefficients for the variables Interest in Politics and Personal benefit from voting are much smaller and not significant. Nevertheless, they exhibit negative signs and are almost significant at the 15% level and 10% level, respectively. It indicates that the results obtained on the index of satisfaction with politics in Table 4 are not just driven by the variable measuring one’s perceived political influence. ¹²

The results are also quite sizeable, especially compared to the dependent variables’ means for young people before austerity, which are 0.37 for Interest in politics, 0.41 for Public officials don’t care, 0.45 for No say in what the government does, 0.34 for Personal benefit from voting, and 2.87 for Perceived political influence.

When focusing on individuals older than 25 (table A2), the results disappear. I also run the same specification on different age cohorts separately. Tables A3, A4, A5, A6, and A7 display the results for the age categories 25–35, 35–45, 45–55, 55–65, and those 65 and over. The results are not significant, except for individuals aged 65 and over who exhibit a decrease in political efficacy following the cuts (at the 10% level), but no associated effect on their satisfaction with politics. Running the same equation on the sub-component of the index (see table A12) indicates it is likely driven by an increase in the view they have no say in what the government does (at the 10% level). In parallel, we can note that the perceived benefit from voting increases (also at the 10% level). This could imply that a political backlash from the austerity cuts was more likely to emanate from individuals older than 65.

Tables A8 to A11 display the breakdown results for the other categories. Unsurprisingly it paints a very similar picture. Two things stand out. First, individuals between 55 and 65 are less likely to declare that public officials don’t care following the welfare cuts. This could suggest some support for the welfare cuts in this age category. Second, the coefficient on Public officials don’t care for the 35–45 category is significant and positive (see table A9). It is the only significant coefficient for that age category, and the coefficient’s magnitude is about half that for young people. There is no clear interpretation of it. The result could mean this age category displayed lower levels of political efficacy following the cuts. The result could also come from multiple hypothesis testing. In fact, the index of low political efficacy remains non-significant for this age group.

Overall, no other age category exhibit the same patterns as young people. The significance and magnitude of the coefficients imply that the 2012 austerity policies impacted youth political attitudes. It suggests that the effects of the benefit cuts interact with the life-cycle stage, and in particular young people were more prone to feel politically marginalised following the austerity. These results echoes those of Emmenegger et al. (2017) and imply that (i) young people’s attitudes are more sensitive to economic shocks and that (ii) negative income shock lowers their feeling of political efficacy and satisfaction with politics.

Specification with interaction terms

I then look at the heterogeneous effect of the welfare cuts to estimate whether young people have been differentially affected by the austerity cuts compared to older cohorts. Table 6 presents the results using the index variables and table A13 for each sub-component. We can see from both tables that young people have been differentially affected. They were more likely to express feelings of low political efficacy and lower satisfaction with politics following the austerity cuts. The only variable that remains non-significant is Interest in politics from table A13, which is consistent with A1’s results.

Table 6. Differential impact of the regional welfare cuts on young people’s political attitudes – indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

The estimate of our coefficient of interest, ${\beta _2}$ from equation 2, is much smaller than the estimate of ${\alpha _1}$ in Tables 4 and A1. This is not surprising as ${\beta _2}$ measures the differential response to the austerity cut between young and “non-young” people from the same districts, where the austerity cuts impacted both control and treatment units.

I perform the same exercise on other age groups to see if the differential reaction observed among young people holds for older cohorts. Tables A14, A15, A16, A17, and A18 in the appendix, show that the austerity shock did not lead to a differential response in other age-groups compared to the average. The coefficient on ${\bf{1}}(Year \ge 2013) \times Austerit{y_d}$ becomes positive for the Low Political Efficacy Index once the sample of young people is added in. ¹³ This implies that young people drive the result.

We can also note that the 55–65 y.o. appear to be affected differently but in the opposite direction. They are less likely to express a low political efficacy following the welfare cuts compared to the population average, in line with the results from table A9.

Overall, these results convey that young people were more affected by the welfare cuts than the rest of the population. Austerity seems to have led to increased political disengagement among the British youth, a subpopulation already prone to lower political turnout and voting registration. ¹⁴ Other age cohorts do not appear to have been affected the same way by the welfare cuts.

Robustness checks

I perform several robustness checks to test the validity of my results.

I first re-run equation 2using different upper thresholds when I define who is young. In particular, I re-run my equation for individuals younger than 26, 27, 28, 29, and 30. Figure 2 and A1 display how stable the estimate of the coefficient ${\alpha _1}$ is for different samples of young people. Overall, the coefficients of the index variables (Figure 2) remain stable over time but tend to lose some significance as I expand the sample to older individuals. The same applies when I focus on each sub-component (figure A1) with less stability for the coefficients Interest in politics and Personal benefit from voting.

Figure 2. Coefficient stability of the austerity shock when varying the upper age threshold for young people, Indices.

I also re-run equation 2 excluding responses from 2013 to see if the downward bias on my coefficients is important. Table 7, with the indices, and table A24, with each variable, show my results remain stable. I note however that column 4 from table A24, Personal benefit from voting, becomes significant. Overall, these results suggest there is no significant downward bias from including data from the year 2013. This is in line with expectations as only 6 per cent of wave 6’s interviews took place in 2013.

Table 7. Impact of the regional welfare cuts on young people’s political attitudes - excluding responses from 2013 - indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

As stated previously, Conservatives voted for new austerity measures in 2015, which changes the austerity shock individuals are subject to from 2016 onwards. A few of the respondents in wave six were interviewed in 2016, which could bias my results. Hence, I conduct the same analysis, dropping respondents that answered in 2016. Table 8 shows it does not change my results, which is not surprising as respondents interviewed in 2016 account fo 3% of the sample. ¹⁵

Table 8. Impact of the regional welfare cuts on young people’s political attitudes - excluding responses from 2016 – indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

Another potential bias arises from people moving to another local area district between wave 2 or 3, and wave 6. For example, an individual who used to live in a community that suffered from substantial welfare cuts might update her political attitudes even though she might now live in a lightly affected area. The opposite could apply to a person moving from a lightly affected area to a heavily affected one. I address this issue by restricting my sample to non-movers and keeping respondents who reside in the same district in waves 2 and 6 and 3 and 6. Table 9 shows that my results hold when I restrict my sample to non-movers. ¹⁶ This is consistent with only 6.2 per cent of survey respondents moving to another district between wave 3 and wave 6, and 7.6 per cent between wave 2 and 6.

Table 9. Impact of the regional welfare cuts on young people’s political attitudes - sample of non-movers only – indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

Finally, I check whether some outliers are driving my results and drop observations for which the austerity shock is in the 99th ( $ \ge $ £714) or 1st ( $ \le $ £263) percentile of the distribution. In other words, I discard individuals that were subject to the strongest and and weakest intensity of my austerity shock to see if they are driving my estimates. Table 10 shows that my results do not change much when I discard the highest values of the austerity shock. ¹⁷ Table 11 show a similar picture when I discard the lowest values of the austerity shock. ¹⁸ Overall, this provides ground to believe outlier values of the austerity shock do not drive my results.

Table 10. Impact of the regional welfare cuts on young people’s political attitudes - discarding largest values of austerity shock – indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, ** p < 0.05, ***p < 0.01.

Table 11. Impact of the regional welfare cuts on young people’s political attitudes - discarding smallest values of austerity shock – indices

Standard errors in parentheses and clustered at the district and individual level.

Coefficients standardized by one s.d. of the austerity shock. *p < 0.1, **p < 0.05, ***p < 0.01.

Conclusion

These results provide further evidence of the social and political costs associated with the 2012 Welfare Reform Act. Specifically, it demonstrates the significant impact of the welfare cuts on young people’s political attitudes in the UK. The study reveals that austerity measures have adversely affected their views on politics and politicians, as well as their sense of political representation. These findings suggest that welfare cuts can contribute to the political marginalisation of certain groups in society and, in this specific context, of those who are already exhibiting lower levels of political participation and pronounced feelings of political disenfranchisement.

Given that political disaffection can translate into lower political participation, it is essential that governments consider the attitudinal and political costs associated with austerity policies. For instance, ex-ante assessments of the social impact of austerity policies could be conducted to better evaluate the trade-offs associated with welfare cuts. Such exercises could help prevent the further ostracization of parts of the population, which can undermine political legitimacy.

Furthermore, this article contributes to the literature on the political economy of austerity, providing additional evidence that welfare cuts affect political attitudes. It suggests that some of the previous studies that reported average null results may be hiding important variations in the impact of welfare cuts on different groups of people.

Lastly, this study adds to the research on economic shocks, political turnout, and their interaction with the life-cycle. It offers further evidence that young people react differently to some economic shocks, in this case welfare cuts, and sheds light on some of the mechanisms underlying the low levels of political engagement of young people. It implies that policies aimed at re-equilibrating public finances can have considerable political impacts on the younger segment of society. Overall, these findings underscore the need for an assessment of the heterogeneous effects of some economic policies and their consequences on youth political engagement.