The Impact of Universal Credit on Mental, Physical, and Financial Well-Being: Longitudinal Evidence from the UK Household Longitudinal Survey

The Impact of Universal Credit on Mental, Physical, and Financial Well-Being: Longitudinal Evidence from the UK Household Longitudinal Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Universal Credit on Mental, Physical, and Financial Well-Being: Longitudinal Evidence from the UK Household Longitudinal Survey Yihong Bai, Chungah Kim, Kristine Ienciu, Peiya Cao, Michel Grignon, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7882810/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Universal Credit (UC) is a major UK welfare reform that consolidates six means-tested benefits into a single monthly payment, aiming to simplify benefits delivery and incentivize labour market participation. However, concerns have emerged regarding its potential adverse consequences on recipients’ mental and physical well-being. Existing evidence is limited by methodological weaknesses, short follow-up time, and a narrow focus on psychological distress. Methods Applying the heterogeneous difference-in-differences approach developed by Callaway and Sant’Anna, we used waves 6–14 of the UK Household Longitudinal Survey (UKHLS), focusing on working-age individuals receiving social benefits to evaluate the short and long-term effects of that welfare reform on psychological distress (GHQ-12), mental functioning (SF-12 MCS), physical functioning (SF-12 PCS), but also employment, perceived financial outlook, benefits income, and total income. Results Transitioning to UC significantly increased GHQ-12 scores by 1.20 points (95% CI: 0.33 to 2.07) and decreased SF-12 MCS scores by 2.19 points (95% CI: − 3.79 to − 0.59), indicating deteriorating mental health. No significant effect was observed for SF-12 PCS. UC was also associated with a £93.05 reduction in monthly benefit income, a borderline significant decrease in total income of £222 and an 8 percentage-point decrease in perceived financial optimism. No significant effect on employment status was detected. Conclusions Our findings suggest that the transition to UC adversely affected mental health and financial well-being, while yielding limited employment benefits. These adverse impacts appear to reflect both implementation challenges, such as payment delays and benefit deductions, and structural design flaws, including rigid conditionality and reduced income security for vulnerable groups. The results underscore the need for welfare reforms that integrate health considerations and provide more flexible, targeted support to mitigate unintended harms. Universal Credit UK difference-in-differences mental health distress physical health income Figures Figure 1 Introduction Universal Credit (UC) is a means-tested benefit from the United Kingdom (UK), first announced at the 2010 Conservative Party annual conference, designed to replace and combine six benefits into a single monthly benefit geared towards low-income households. With a phased introduction beginning in 2013, the six benefits to be replaced include the Employment and Support Allowance, Jobseeker’s Allowance, Income Support, Child Tax Credit, Working Tax Credit, and Housing Benefit. Unlike these previous benefits schemes, UC was designed to encourage claimants to transition to paid employment by ensuring that payments would taper off gradually rather than abruptly stop if they engaged in paid employment [1]. Although UC was intended to be a modern and flexible benefit plan, it faced many criticisms following implementation. It was criticized for its complex online application process, which poses a significant barrier for those without safe computer access or adequate digital literacy, exacerbating digital exclusion [1]. Additionally, the system's reliance on helplines with long wait times and a cumbersome online identity verification process, notably the problematic "Verify" system, has further complicated access to benefits [2]. The built-in five-week waiting period for payments, often extending to an average of 7.5 weeks, coupled with the unrealistic expectation of claimants having adequate savings, has led to severe financial hardships, including rent arrears and food insecurity [3]. Figures from the Department for Work and Pensions released under Freedom of Information Act show that, as of mid-2018, one in three UC claimants had their benefits reduced [4], where up to 40% of the total benefits can be deducted (a higher proportion compared to the older benefits), which has led to increased reliance on food banks, often described as a degrading experience [1]. A qualitative study based in the North East of England found that claimants experienced difficulties with the online claims process, delays in payment, and sanctions associated with failing to meet additional conditionality requirements, which led to increased levels of debt, housing insecurity, and food insecurity, and subsequently, feelings of distress [5]. Another study highlighted the impact of UC on food insecurity, using aggregate data from 309 local authorities in England and a first-difference method. They provided evidence that an increase in the proportion of UC recipients was associated with increased use of food banks [6]. Three quantitative studies have investigated the impact of UC on mental health outcomes. One study examined the effects of UC on children in households where parents were unemployed [7]. The authors found that UC was associated with increased psychological distress among children from larger families, highlighting potential intergenerational consequences of welfare reform. Another study evaluated the mental health impact of UC among 52,187 working-age individuals [8]. The authors used a difference-in-differences (DID) design, identifying individuals as “treated” if they were unemployed during a given wave, and using the staggered rollout of UC across local authorities as a proxy for treatment timing. The study reported a 6.57% increase in psychological distress following UC introduction, offering important early evidence of potential harm. However, certain design choices introduce complexities in interpreting the results. For instance, using unemployment as a proxy for UC eligibility may conflate the policy effect with distress related to job loss, while area-level rollout as a timing variable may not fully capture individual-level transitions. A third study used the Household Longitudinal Survey (UKHLS) waves 1 to 8 to explore how the mental health effects of unemployment differed under UC compared to the legacy system [9]. This study advances the literature by estimating UC eligibility based on multiple household characteristics, including housing tenure, employment status, and income. The authors found that mental health deteriorated among single adults and lone parents—groups with fewer alternative supports—while couples appeared to benefit slightly from UC. These findings offer valuable insight into heterogeneity in policy impacts. However, the study was limited by a relatively short post-treatment window and relied on inferred, rather than observed, transitions to UC, which may have introduced some uncertainty in treatment classification. Our review of the existing literature highlights important contributions toward understanding the effects of UC, particularly in identifying early signals of adverse mental health outcomes and uncovering potential subgroup differences. However, several gaps remain. Prior studies have often relied on indirect proxies for UC eligibility or short follow-up windows, limiting their ability to assess longer-term and dynamic effects. To address these limitations, we build on this growing body of work by using a quasi-experimental approach with ten years of longitudinal data from the UK Household Longitudinal Survey (UKHLS). Specifically, we apply the heterogeneous difference-in-differences method developed by Callaway and Sant’Anna [10], which provides a flexible framework for accounting for staggered treatment timing and variation in effects across time and groups. This approach enables a more comprehensive assessment of the consequences of transitioning to UC. By focusing specifically on individuals receiving social benefits, our analysis directly evaluates the population most affected by the reform, thereby strengthening the policy relevance of our findings. In addition to capturing mental and physical health outcomes, we also examine financial pathways—such as benefit income, household net income, and perceived financial expectations—as potential mechanisms linking UC to well-being. Taken together, these innovations allow our study to provide new insights into the multifaceted effects of welfare reform on both health and socioeconomic security. Methods Data This study used data from waves 6–14 of the UK Longitudinal Household Survey (UKHLS), a nationally representative survey. Data were collected through structured interviews conducted by professionally trained interviewers via face-to-face, telephone, or online interviews at the interviewee’s convenience. Detailed information regarding the UKHLS is accessible through its official website [11]. We restricted our sample to working-aged individuals (18–64) who were benefit recipients. The Institute for Social and Economic Research at the University of Essex approved the data collection, and the data are publicly available ( https://www.understandingsociety.ac.uk/ ). All data collection for the ULHLS was approved by the University of Essex Ethics Committee (ETH1920-0123). Treatment and control groups At baseline, all participants received benefits from the original six programs. The treatment group is comprised of individuals from households that transitioned from these legacy programs to UC during the study period. Importantly, the transition to UC was not determined by individual household choice but by eligibility rules and policy roll-out schedules set by the Department for Work and Pensions, rendering the transition plausibly exogenous and suitable for quasi-experimental analysis. The national roll-out of UC occurred gradually between 2013 and 2018. However, residing in a UC rollout area did not automatically make individuals eligible to claim UC. Eligibility was conditional on satisfying a series of criteria that included experiencing a change in housing or employment circumstances, being single with no partner or children, aged 18–60, with low or no income (below £270 per month for those under 25 or £330 for those 25 and older), not being self-employed, not in education or homeless, possessing less than £16,000 in savings, and agreeing to a claimant commitment to seek employment. These conditions were progressively relaxed over time to expand eligibility—couples without children became eligible from July 2014, and households with children from January 2016. In contrast, the control group (never treated) consisted of those from households that remained on the original six programs throughout. It is noted that a full “managed migration” plan—intended to move all remaining legacy benefit recipients onto UC—was introduced by the UK government in 2019, with a pilot launched in Harrogate, Yorkshire. Although postponed in March 2020, the program resumed in June 2022 and is scheduled for full implementation by 2024, after which UC will become the sole welfare system in the UK. Outcomes Psychological distress was measured using the short-form General Health Questionnaire (GHQ), a validated instrument widely recognized for its effectiveness, reliability, and sensitivity [12, 13]. The GHQ consists of twelve self-reported items designed to assess psychological distress within the general population. In the context of the UKHLS, responses to these items were combined into a single scale ranging from 0, indicating minimal distress, to 36, representing maximal distress [14, 15]. Additionally, mental and physical health were assessed using the Mental Component Summary (MCS) and Physical Component Summary (PCS) scores from the SF-12 instrument. The SF-12 is a validated generic, non-preference-based measure comprising eight dimensions, including aspects such as depression and physical functioning commonly found in general health assessments. For the UK population, SF-12 scores are standardized on a 0–100 scale [16], facilitating health comparisons across different groups. The SF-12 meets the essential criteria for practicality, reliability, and validity, thereby establishing its widespread use globally [17, 18]. In addition to health outcomes, we investigated whether the introduction of UC was associated with changes in several socioeconomic indicators. First, we examined changes in unemployment status based on responses to the question: “Which of these best describes your current employment situation?” A binary unemployment indicator was constructed, taking a value of 1 if the respondent selected “Unemployed” and 0 otherwise. Second, we assessed perceived future financial expectations by coding responses as 1 if participants reported expecting to be financially better off in the future compared to their current situation, and 0 if they expected their financial situation to remain the same or worsen. Third, we included two measures of economic well-being: monthly total household income from social benefits and total household net income. Statistical Analysis To estimate the short and long-term effects of transitioning to UC, we employed the difference-in-differences methodology developed by Callaway and Sant’Anna (CSDID) [10]. Specifically, we estimated the average treatment effect on the treated (ATT) over time—referred to as the dynamic treatment effect—accounting for the staggered rollout of UC. This approach was selected to address the methodological limitations of the two-way fixed effects estimator, which may yield biased estimates in the presence of treatment effect heterogeneity and variation in treatment timing [10, 19–22]. The CSDID estimator offers a more robust framework than standard DID, as it allows treatment effects to vary across groups and over time. It is grounded in the assumption of conditional parallel trends, which permits differential trends between treated and control groups, as long as these differences are appropriately modelled through observed covariates. $$\:ATT\:(g,\:t)=E[{Y}_{t}-{Y}_{g-1}|\:{G}_{g}=1]-E[{Y}_{t}-{Y}_{g-1}|\:{C}_{g}=1]\:\:\:\:\left(1\right)$$ Under this framework, the ATT for a group first treated in period g is defined as the average difference between post-treatment and pre-treatment outcomes for the treated group relative to a suitable control group. The treatment group is identified by an indicator variable \(\:{G}_{g}\) , which equals 1 if an individual is treated from period g onward and 0 otherwise (the timing of their initial enrollment in UC). The control group C consists of individuals who are either never treated (in the main analysis) or not yet treated (in the sensitivity test) at time g . $$\:{ATT}_{D}^{}=\frac{1}{\gamma\:-1}{\sum\:}_{e=1}^{\gamma\:-1}ATT(g,\:g+e)\:\:\:\left(2\right)$$ Then, we computed the aggregated average treatment effect on the treated over time ( \(\:{ATT}_{D}^{}\) ), which represents the simple average of ATT estimates across all post-treatment exposure periods, denoted as ATT( g, g + e ), where γ represents the total number of post-treatment periods analyzed. This approach permits the use of doubly robust inverse probability weighting estimators, which help mitigate potential confounding differences between treatment and control groups. The covariates included in the analysis are age, sex, race, educational attainment, rurality, marital status, parental status, and region of residence. The analysis is conducted using the CSDID package in STATA. To ensure generalizability to the UK population, longitudinal sampling weights provided by the UKHLS are applied. Sensitivity test To assess the robustness of our main findings, we conducted a series of sensitivity analyses. First, we re-estimated the models using individuals who had not yet transitioned to UC as the control group. Second, we excluded observations of individuals who initially transitioned to UC but subsequently discontinued participation in order to ensure treatment consistency. Third, we employed alternative outcome definitions. Specifically, psychological distress was operationalized using a binary indicator based on the GHQ-12 threshold of 20/21 [23]; depression was defined using a cut-off score of 42/43 on the SF-12 MCS [24, 25]; and physical health was assessed using a 50/51 threshold on the SF-12 PCS [26]. Finally, we conducted subgroup analyses by sex to explore potential heterogeneity in the effect of UC on men and women. Results Table 1 : Sample characteristics of treatment and control groups at baseline Table 1 Sample characteristics of treatment and control groups at baseline Control Treatment N 3,448 (78.7%) 931 (21.3%) age 38.936 (12.827) 37.488 (12.225) Household size 3.430 (1.475) 3.488 (1.655) Sex Women 2,017 (54.4%) 599 (64.2%) Men 1,692 (45.6%) 334 (35.8%) Rurality Rural 815 (22.0%) 162 (17.4%) Urban 2,893 (78.0%) 770 (82.6%) Education No degree 2,496 (67.4%) 738 (79.6%) Post-secondary degree 1,206 (32.6%) 189 (20.4%) Race Non-White 679 (18.3%) 177 (19.0%) White 3,030 (81.7%) 756 (81.0%) Children No 1,608 (43.3%) 416 (44.6%) Yes 2,101 (56.7%) 517 (55.4%) Marital Status No partner 2,041 (55.1%) 628 (67.6%) Has partner 1,662 (44.9%) 301 (32.4%) Country England 3,074 (82.9%) 793 (85.0%) Wales 166 (4.5%) 34 (3.7%) Scotland 324 (8.7%) 78 (8.4%) Northern Ireland 143 (3.9%) 27 (2.9%) For continuous variables (age, household size), the value in brackets is the standard deviation; for qualitative variables, the value in brackets is the percentage of the category (e.g., Women) in the group (control or treatment – for instance, 54.4% means that 54.4% of individuals in the control group are Women). The exception is the first row (N), where the value in brackets is the proportion of the total sample in the control (78.7%) or treatment (21.3%) group. Table 1 presents the baseline characteristics of study participants by treatment status. Overall, there were no substantial differences between the groups in terms of age, household size, racial composition, the presence of children in the household or country of residence. The only differences were that individuals in the treatment group had less education and were more likely to be female or live in urban areas than those in the control group. Figure 1 : Event study graph showing dynamic effects for average treatment effects on the treated (ATT) over time (with time = 0 at the time of transition to universal credit) Figure 1 (and Supplementary Table S1 ) presents the estimated dynamic treatment effects of UC on health outcomes, capturing differences between the treatment and control groups across time, from seven periods before treatment (T–7) to seven periods following treatment (T + 7). The event study estimates support the validity of the DID approach. Table S1 shows that pre-treatment coefficients (T–7 to T–1) were not statistically different, providing evidence that the parallel trends assumption holds. Post-treatment estimates (T0 to T + 7), in contrast, reveal a consistent pattern of increasing effect magnitude over time, indicating a cumulative adverse impact of UC exposure on mental health outcomes. Table 2 reports the aggregated ATT across the entire study period. Transitioning to UC was associated with a statistically significant increase of 1.20 points in the GHQ-12 score (95% CI: 0.33 to 2.07; p = 0.007), or around 0.25 standard deviation of the distribution of GHQ-12; this suggests that those who transited to UC experienced elevated psychological distress relative to those who did not transition. Similarly, a significant decrease of 2.19 points was observed in the SF-12 MCS score (95% CI: − 3.79 to − 0.59; p = 0.007), representing approximately 0.22 standard deviations of the SF-12 MCS score distribution, suggesting a deterioration in mental health. The effect on the SF-12 PCS score was negative (–0.79 or 0.8 standard deviation; 95% CI: − 1.95 to 0.36) but did not reach statistical significance (p = 0.179). Table 2 Aggregate average treatment effects on the treated (ATT) from CSDID, estimating the treatment effect of transitioning to Universal Credit Aggregate ATT 95% CIs p-value (a) GHQ 1.196 0.325 2.067 0.007 (b) SF-12 MCS -2.188 -3.790 -0.586 0.007 (c) SF-12 PCS -0.793 -1.948 0.362 0.179 (d) Unemployment 0.008 -0.041 0.056 0.747 (e) Better future financial situation -0.081 -0.146 -0.015 0.015 (f) Monthly benefit income -93.052 -177.448 -8.657 0.031 (g) Monthly total net income -222.095 -486.998 42.807 0.100 With regards to the dynamic effects (Supplementary Table S1 ), the GHQ-12 score rose by 2.87 points at T + 4 (95% CI: 0.64 to 5.11), 3.30 points at T + 5 (95% CI: 0.94 to 5.66), and peaked at 5.02 points at T + 6 (95% CI: 1.29 to 8.75), indicating a cumulative deterioration in psychological well-being following UC transition. Similarly, the SF-12 MCS score displayed some early fluctuations but showed a consistent and statistically significant decline beginning at T0, when the UC transition occurred. At T0, the MCS score dropped by 1.47 points (95% CI: − 2.80 to − 0.15), followed by a further decline at T + 4 (–4.71; 95% CI: − 8.39 to − 1.02) and T + 5 (–6.38; 95% CI: − 10.48 to − 2.29). These findings suggest that the adverse effects of UC on mental health were not only immediate but also amplified over time, particularly for mental functioning, highlighting the long-term psychological toll associated with welfare reform. Table 2 : Aggregate average treatment effects on the treated (ATT) from CSDID, estimating the treatment effect of transitioning to Universal Credit To explore potential mechanisms through which UC may influence mental health, we examined its impact on employment status, perceived future financial expectations, monthly household social benefit income, and total household net income. The aggregate ATT, presented in Table 2 (with dynamic effects shown in Fig. 1 and Supplementary Table S1 ), suggests that the transition to UC was associated with a significant reduction of £93.05 in monthly social benefit income (95% CI: –£ 177.4 to –£ 8.7). In addition, UC reduced the likelihood of reporting an improved financial outlook by 8 percentage points (95% CI: − 14.6% to − 1.5%). Although UC was associated with a reduction in total household net income (–£222.10), the effect was only marginally significant (p = 0.10) over the entire study period. However, in the longer run, the reductions in total net income were significant and consistently observed in years 6 and 7 after the transition to UC (Fig. 1 ). Results from multiple sensitivity analyses support the robustness of the main findings. First, re-estimating the models using individuals not yet treated with UC as the control group (Supplementary Table S2), and second, excluding observations from individuals who subsequently discontinued participation in UC (Supplementary Table S3), yielded results consistent with those of the primary specification. Analyses using binary outcome measures (Supplementary Table S4) further confirmed that UC significantly increased the probability of psychological distress by 8.6 percentage points (95% CI: 3.6% to 13.5%). However, the estimated effects on binary indicators for depression and poor physical health were negative but did not reach statistical significance. Gender-stratified models (Supplementary Table S5) revealed the UC transition had a negative impact on mental health for both men and women but presented in different measurements (i.e. GHQ was significantly increased for women; SF-12 MCS was significantly reduced for men). For physical health, a weakly significant negative effect of UC on the SF-12 PCS was found among women (–1.25; p = 0.05), but no significant effect was observed among men. Employment-stratified models (Supplementary Table S6) indicated that the adverse effects of UC transition were concentrated among the unemployed. Among individuals who remained unemployed throughout the study period, UC transition was associated with a 2.9-point increase in GHQ scores (p = 0.01) and a 5.6-point decline in SF-12 MCS scores (p = 0.04), effects more than twice as large as those estimated in the full sample. Discussion This study investigated the short- and long-term impacts of transitioning to UC on psychological distress, mental health functioning, and physical health functioning among UK social benefit recipients. Using longitudinal data from the UKHLS and a heterogeneous DID framework, we found that UC transition was associated with significantly worse mental health outcomes. Specifically, individuals who moved to UC experienced a statistically significant increase in GHQ-12 scores (by 1.2 points) —indicating higher psychological distress—and a significant decline in SF-12 MCS scores (by 2.2 points), reflecting deteriorating mental health functioning. To put our findings into context, a previous study found that losing a job increased GHQ-12 by around 2.1 [27], and reduced SF-12 MCS by around 1.6 [28]. Although the effect on physical health functioning, as measured by SF-12 PCS, was negative, it did not reach statistical significance over the full study period. Additionally, UC transition was associated with a reduction in monthly social benefit income and a decrease in perceived financial optimism, both of which may contribute to adverse mental health outcomes. While a marginally significant reduction in total household net income was observed, this effect became more pronounced in later years, suggesting cumulative financial strain over time. No significant impact was found on unemployment status, indicating that employment outcomes may not be the primary pathway through which UC affects health. Sensitivity analyses confirmed the robustness of these findings across alternative control group definitions and binary outcome specifications. Rather than reiterating prior evidence [8, 9], it is important to consider what our findings reveal about the mechanisms underlying UC’s impacts and the broader policy lessons they imply. Our stratified analyses provide insights into the mechanisms through which UC may affect health. The observed gender differences suggest that women experienced greater increases in psychological distress, while men exhibited more pronounced declines in mental health functioning. These patterns may reflect distinct pathways of vulnerability: women, particularly those with caregiving responsibilities, may have been more exposed to financial strain and the stress of sanctions, whereas men may have been more affected by conditionality tied to work expectations. Moreover, the stronger negative effects observed among unemployed recipients indicate that UC did not provide the intended security during periods of labour market inactivity, but instead compounded stress through reduced benefit generosity and heightened conditionality. These findings underscore that UC’s adverse health impacts were not uniform, but concentrated among groups already facing greater socioeconomic precarity. In addition, the sharp decline in household net income observed around year 5 likely reflects both policy dynamics and sample composition. As eligibility expanded to more complex households and long-term UC recipients accumulated deductions or sanctions, the remaining treatment sample increasingly consisted of the most disadvantaged claimants, amplifying the cumulative financial strain of UC over time. Beyond subgroup mechanisms, our findings also raise broader questions about the policy itself. If future analyses confirm that adverse effects were stronger in the early years of implementation, this would suggest that technical glitches and administrative challenges—such as payment delays and difficulties with digital systems—amplified harm. However, the persistence of negative effects over time would imply that the design of UC itself, by consolidating multiple tailored programs into a single, rigid system with strict conditionality, is structurally ill-suited to support claimant well-being. In this light, our results suggest that UC’s challenges may arise not only from implementation difficulties but also from features of its underlying design, indicating the importance of welfare reforms that better balance employment incentives with adequate protection of health and financial security. This study has several limitations. First, our study relies on self-reported measures of mental health, which are the standard in population-based research and widely validated for reliability and sensitivity [12, 13]. While no fully “objective” measure of mental health exists, we strengthened confidence in our findings by applying alternative thresholds and operationalizations of each outcome in sensitivity analyses, all of which yielded consistent results. Second, we accounted for observed covariate differences between treatment and control groups by applying inverse probability weighting within the CSDID framework. While this adjustment improved balance on measured characteristics, unobserved confounders may still remain, and as with all quasi-experimental designs, results should be interpreted with appropriate caution. Third, although our identification strategy targets the treatment and control groups to precisely attribute observed effects to UC, it is still possible that other policy changes and contextual factors occurring during the study period could have affected the outcomes. Nevertheless, this study provides evidence on the impact of UC, highlighting its real-world implications for people’s lives. Conclusion This study provides evidence on the impact of UC, showing that transition to the program was associated with worsening mental health, reduced benefit income, and greater financial pessimism, with no significant employment gains. The sharp fall in household net income after five years highlights the cumulative strain of long-term UC exposure, suggesting that disadvantage deepens over time. Gender and employment-stratified analyses further show that adverse effects were concentrated among vulnerable groups, including women and the unemployed, underscoring the uneven burden of UC. These findings point to the need for reforms that address both implementation challenges—such as delays and deductions—and structural design flaws, including the rigidity of a consolidated benefits system. Welfare reforms must integrate health and financial security alongside work incentives to avoid unintended harms. Without such changes, UC risks entrenching rather than alleviating inequalities among low-income households. Declarations Funding: The study is supported by the principal investigator, Antony Chum, through the Canada Research Chair program (CRC-2021-00269). The project was also partially funded by the Social Sciences and Humanities Research Council (SSHRC) Grant, File No.: 435-2023-1102. The funding source had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, the preparation, review, or approval of the manuscript, or the decision to submit the manuscript for publication. Competing Interests: The authors declare no relevant financial or non-financial interests. Author Contributions: Yihong Bai: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing - Original Draft, Writing - review & editing Chungah Kim: Conceptualization, Investigation, Methodology, Resources, Software, Validation, Writing - Original Draft, Writing - review & editing Peiya Cao : Writing - Original Draft, Writing - review & editing Kristine Ienciu: Writing - Original Draft, Writing - review & editing Michel Grignon: Writing - Original Draft, Writing - review & editing Olivia Ramraj: Writing - Original Draft, Writing - review & editing Jasmin Tiwana: Writing - Original Draft, Writing - review & editing Antony Chum: Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - Original Draft, Writing - review & editing Ethics approval: This study was exempt from ethics review, as it posed no foreseeable risk of harm and utilized existing data collections that contained only non-identifiable information about human beings. Data availability: Data are available in a public, open-access repository. This data can be accessed through the UKHLS website: https://www.understandingsociety.ac.uk/ References Cheetham M, Moffatt S, Addison M (2018) “It’s hitting people that can least afford it the hardest” the impact of the roll out of Universal Credit in two North East England localities: a qualitative study’. Gatesh Counc BBC News UK (2019) Universal credit claimants “struggling to cope” Reeves A, Loopstra R (2021) The continuing effects of welfare reform on food bank use in the UK: the roll-out of universal credit. J Soc Policy 50:788–808 Savage M, Jayanetti C (2018) Third of UK’s universal credit claimants hit by deductions from payments. The Observer Mandy Cheetham, Suzanne Moffatt, Michelle Addison, Alice Wiseman (2019) Impact of Universal Credit in North East England: a qualitative study of claimants and support staff. 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Themed Issue Treat Eff 1 225:175–199. https://doi.org/10.1016/j.jeconom.2020.09.006 Cornelius BL, Groothoff JW, van der Klink JJ, Brouwer S (2013) The performance of the K10, K6 and GHQ-12 to screen for present state DSM-IV disorders among disability claimants. BMC Public Health 13:128. https://doi.org/10.1186/1471-2458-13-128 Vilagut G, Forero CG, Pinto-Meza A, et al (2013) The mental component of the short-form 12 health survey (SF-12) as a measure of depressive disorders in the general population: results with three alternative scoring methods. Value Health 16:564–573 Yu DSF, Yan ECW, Chow CK (2015) Interpreting SF-12 mental component score: an investigation of its convergent validity with CESD-10. Qual Life Res 24:2209–2217. https://doi.org/10.1007/s11136-015-0959-x Soh S-E, Morello R, Ayton D, et al (2021) Measurement properties of the 12-item Short Form Health Survey version 2 in Australians with lung cancer: a Rasch analysis. Health Qual Life Outcomes 19:157. https://doi.org/10.1186/s12955-021-01794-w Farré L, Fasani F, Mueller H (2018) Feeling useless: the effect of unemployment on mental health in the Great Recession. IZA J Labor Econ 7:1–34 Stauder J (2019) Unemployment, unemployment duration, and health: selection or causation? Eur J Health Econ 20:59–73 Additional Declarations No competing interests reported. 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17:15:15","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76163,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7882810/v1/fc5fa9d6fd02b71a61afcc33.html"},{"id":99309703,"identity":"12504b86-174d-452b-935e-478b237fc1a7","added_by":"auto","created_at":"2025-12-31 16:11:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60512,"visible":true,"origin":"","legend":"\u003cp\u003eEvent study graph showing dynamic effects for average treatment effects on the treated (ATT) over time (with time=0 at the time of transition to universal credit)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7882810/v1/63a8b2bebce7697dbd9dadbe.png"},{"id":99322652,"identity":"4a62a050-b61a-4532-a996-d782d0e15bb4","added_by":"auto","created_at":"2025-12-31 16:43:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":648302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7882810/v1/b5c53f25-cf85-48e7-9f99-2af239d6ed68.pdf"},{"id":99309723,"identity":"8bb984db-3e86-4bcc-be5d-e979aa543501","added_by":"auto","created_at":"2025-12-31 16:11:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23816,"visible":true,"origin":"","legend":"","description":"","filename":"UCSPPEsup.docx","url":"https://assets-eu.researchsquare.com/files/rs-7882810/v1/d5be94b39583be77b02b0d85.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Universal Credit on Mental, Physical, and Financial Well-Being: Longitudinal Evidence from the UK Household Longitudinal Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUniversal Credit (UC) is a means-tested benefit from the United Kingdom (UK), first announced at the 2010 Conservative Party annual conference, designed to replace and combine six benefits into a single monthly benefit geared towards low-income households. With a phased introduction beginning in 2013, the six benefits to be replaced include the Employment and Support Allowance, Jobseeker\u0026rsquo;s Allowance, Income Support, Child Tax Credit, Working Tax Credit, and Housing Benefit. Unlike these previous benefits schemes, UC was designed to encourage claimants to transition to paid employment by ensuring that payments would taper off gradually rather than abruptly stop if they engaged in paid employment [1]. Although UC was intended to be a modern and flexible benefit plan, it faced many criticisms following implementation. It was criticized for its complex online application process, which poses a significant barrier for those without safe computer access or adequate digital literacy, exacerbating digital exclusion [1]. Additionally, the system's reliance on helplines with long wait times and a cumbersome online identity verification process, notably the problematic \"Verify\" system, has further complicated access to benefits [2]. The built-in five-week waiting period for payments, often extending to an average of 7.5 weeks, coupled with the unrealistic expectation of claimants having adequate savings, has led to severe financial hardships, including rent arrears and food insecurity [3]. Figures from the Department for Work and Pensions released under Freedom of Information Act show that, as of mid-2018, one in three UC claimants had their benefits reduced [4], where up to 40% of the total benefits can be deducted (a higher proportion compared to the older benefits), which has led to increased reliance on food banks, often described as a degrading experience [1].\u003c/p\u003e \u003cp\u003eA qualitative study based in the North East of England found that claimants experienced difficulties with the online claims process, delays in payment, and sanctions associated with failing to meet additional conditionality requirements, which led to increased levels of debt, housing insecurity, and food insecurity, and subsequently, feelings of distress [5]. Another study highlighted the impact of UC on food insecurity, using aggregate data from 309 local authorities in England and a first-difference method. They provided evidence that an increase in the proportion of UC recipients was associated with increased use of food banks [6].\u003c/p\u003e \u003cp\u003eThree quantitative studies have investigated the impact of UC on mental health outcomes. One study examined the effects of UC on children in households where parents were unemployed [7]. The authors found that UC was associated with increased psychological distress among children from larger families, highlighting potential intergenerational consequences of welfare reform. Another study evaluated the mental health impact of UC among 52,187 working-age individuals [8]. The authors used a difference-in-differences (DID) design, identifying individuals as \u0026ldquo;treated\u0026rdquo; if they were unemployed during a given wave, and using the staggered rollout of UC across local authorities as a proxy for treatment timing. The study reported a 6.57% increase in psychological distress following UC introduction, offering important early evidence of potential harm. However, certain design choices introduce complexities in interpreting the results. For instance, using unemployment as a proxy for UC eligibility may conflate the policy effect with distress related to job loss, while area-level rollout as a timing variable may not fully capture individual-level transitions. A third study used the Household Longitudinal Survey (UKHLS) waves 1 to 8 to explore how the mental health effects of unemployment differed under UC compared to the legacy system [9]. This study advances the literature by estimating UC eligibility based on multiple household characteristics, including housing tenure, employment status, and income. The authors found that mental health deteriorated among single adults and lone parents\u0026mdash;groups with fewer alternative supports\u0026mdash;while couples appeared to benefit slightly from UC. These findings offer valuable insight into heterogeneity in policy impacts. However, the study was limited by a relatively short post-treatment window and relied on inferred, rather than observed, transitions to UC, which may have introduced some uncertainty in treatment classification.\u003c/p\u003e \u003cp\u003eOur review of the existing literature highlights important contributions toward understanding the effects of UC, particularly in identifying early signals of adverse mental health outcomes and uncovering potential subgroup differences. However, several gaps remain. Prior studies have often relied on indirect proxies for UC eligibility or short follow-up windows, limiting their ability to assess longer-term and dynamic effects. To address these limitations, we build on this growing body of work by using a quasi-experimental approach with ten years of longitudinal data from the UK Household Longitudinal Survey (UKHLS). Specifically, we apply the heterogeneous difference-in-differences method developed by Callaway and Sant\u0026rsquo;Anna [10], which provides a flexible framework for accounting for staggered treatment timing and variation in effects across time and groups. This approach enables a more comprehensive assessment of the consequences of transitioning to UC. By focusing specifically on individuals receiving social benefits, our analysis directly evaluates the population most affected by the reform, thereby strengthening the policy relevance of our findings. In addition to capturing mental and physical health outcomes, we also examine financial pathways\u0026mdash;such as benefit income, household net income, and perceived financial expectations\u0026mdash;as potential mechanisms linking UC to well-being. Taken together, these innovations allow our study to provide new insights into the multifaceted effects of welfare reform on both health and socioeconomic security.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThis study used data from waves 6\u0026ndash;14 of the UK Longitudinal Household Survey (UKHLS), a nationally representative survey. Data were collected through structured interviews conducted by professionally trained interviewers via face-to-face, telephone, or online interviews at the interviewee\u0026rsquo;s convenience. Detailed information regarding the UKHLS is accessible through its official website [11]. We restricted our sample to working-aged individuals (18\u0026ndash;64) who were benefit recipients. The Institute for Social and Economic Research at the University of Essex approved the data collection, and the data are publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.understandingsociety.ac.uk/\u003c/span\u003e\u003cspan address=\"https://www.understandingsociety.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e All data collection for the ULHLS was approved by the University of Essex Ethics Committee (ETH1920-0123).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTreatment and control groups\u003c/h3\u003e\n\u003cp\u003eAt baseline, all participants received benefits from the original six programs. The treatment group is comprised of individuals from households that transitioned from these legacy programs to UC during the study period. Importantly, the transition to UC was not determined by individual household choice but by eligibility rules and policy roll-out schedules set by the Department for Work and Pensions, rendering the transition plausibly exogenous and suitable for quasi-experimental analysis. The national roll-out of UC occurred gradually between 2013 and 2018. However, residing in a UC rollout area did not automatically make individuals eligible to claim UC. Eligibility was conditional on satisfying a series of criteria that included experiencing a change in housing or employment circumstances, being single with no partner or children, aged 18\u0026ndash;60, with low or no income (below \u0026pound;270 per month for those under 25 or \u0026pound;330 for those 25 and older), not being self-employed, not in education or homeless, possessing less than \u0026pound;16,000 in savings, and agreeing to a claimant commitment to seek employment. These conditions were progressively relaxed over time to expand eligibility\u0026mdash;couples without children became eligible from July 2014, and households with children from January 2016. In contrast, the control group (never treated) consisted of those from households that remained on the original six programs throughout. It is noted that a full \u0026ldquo;managed migration\u0026rdquo; plan\u0026mdash;intended to move all remaining legacy benefit recipients onto UC\u0026mdash;was introduced by the UK government in 2019, with a pilot launched in Harrogate, Yorkshire. Although postponed in March 2020, the program resumed in June 2022 and is scheduled for full implementation by 2024, after which UC will become the sole welfare system in the UK.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003ePsychological distress was measured using the short-form General Health Questionnaire (GHQ), a validated instrument widely recognized for its effectiveness, reliability, and sensitivity [12, 13]. The GHQ consists of twelve self-reported items designed to assess psychological distress within the general population. In the context of the UKHLS, responses to these items were combined into a single scale ranging from 0, indicating minimal distress, to 36, representing maximal distress [14, 15]. Additionally, mental and physical health were assessed using the Mental Component Summary (MCS) and Physical Component Summary (PCS) scores from the SF-12 instrument. The SF-12 is a validated generic, non-preference-based measure comprising eight dimensions, including aspects such as depression and physical functioning commonly found in general health assessments. For the UK population, SF-12 scores are standardized on a 0\u0026ndash;100 scale [16], facilitating health comparisons across different groups. The SF-12 meets the essential criteria for practicality, reliability, and validity, thereby establishing its widespread use globally [17, 18].\u003c/p\u003e \u003cp\u003eIn addition to health outcomes, we investigated whether the introduction of UC was associated with changes in several socioeconomic indicators. First, we examined changes in unemployment status based on responses to the question: \u0026ldquo;Which of these best describes your current employment situation?\u0026rdquo; A binary unemployment indicator was constructed, taking a value of 1 if the respondent selected \u0026ldquo;Unemployed\u0026rdquo; and 0 otherwise. Second, we assessed perceived future financial expectations by coding responses as 1 if participants reported expecting to be financially better off in the future compared to their current situation, and 0 if they expected their financial situation to remain the same or worsen. Third, we included two measures of economic well-being: monthly total household income from social benefits and total household net income.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo estimate the short and long-term effects of transitioning to UC, we employed the difference-in-differences methodology developed by Callaway and Sant\u0026rsquo;Anna (CSDID) [10]. Specifically, we estimated the average treatment effect on the treated (ATT) over time\u0026mdash;referred to as the dynamic treatment effect\u0026mdash;accounting for the staggered rollout of UC. This approach was selected to address the methodological limitations of the two-way fixed effects estimator, which may yield biased estimates in the presence of treatment effect heterogeneity and variation in treatment timing [10, 19\u0026ndash;22]. The CSDID estimator offers a more robust framework than standard DID, as it allows treatment effects to vary across groups and over time. It is grounded in the assumption of conditional parallel trends, which permits differential trends between treated and control groups, as long as these differences are appropriately modelled through observed covariates.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ATT\\:(g,\\:t)=E[{Y}_{t}-{Y}_{g-1}|\\:{G}_{g}=1]-E[{Y}_{t}-{Y}_{g-1}|\\:{C}_{g}=1]\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eUnder this framework, the ATT for a group first treated in period \u003cem\u003eg\u003c/em\u003e is defined as the average difference between post-treatment and pre-treatment outcomes for the treated group relative to a suitable control group. The treatment group is identified by an indicator variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{g}\\)\u003c/span\u003e\u003c/span\u003e, which equals 1 if an individual is treated from period \u003cem\u003eg\u003c/em\u003e onward and 0 otherwise (the timing of their initial enrollment in UC). The control group \u003cem\u003eC\u003c/em\u003e consists of individuals who are either never treated (in the main analysis) or not yet treated (in the sensitivity test) at time \u003cem\u003eg\u003c/em\u003e.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{ATT}_{D}^{}=\\frac{1}{\\gamma\\:-1}{\\sum\\:}_{e=1}^{\\gamma\\:-1}ATT(g,\\:g+e)\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThen, we computed the aggregated average treatment effect on the treated over time (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ATT}_{D}^{}\\)\u003c/span\u003e\u003c/span\u003e), which represents the simple average of ATT estimates across all post-treatment exposure periods, denoted as ATT(\u003cem\u003eg, g\u0026thinsp;+\u0026thinsp;e\u003c/em\u003e), where \u003cem\u003eγ\u003c/em\u003e represents the total number of post-treatment periods analyzed. This approach permits the use of doubly robust inverse probability weighting estimators, which help mitigate potential confounding differences between treatment and control groups. The covariates included in the analysis are age, sex, race, educational attainment, rurality, marital status, parental status, and region of residence. The analysis is conducted using the CSDID package in STATA. To ensure generalizability to the UK population, longitudinal sampling weights provided by the UKHLS are applied.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity test\u003c/h3\u003e\n\u003cp\u003eTo assess the robustness of our main findings, we conducted a series of sensitivity analyses. First, we re-estimated the models using individuals who had not yet transitioned to UC as the control group. Second, we excluded observations of individuals who initially transitioned to UC but subsequently discontinued participation in order to ensure treatment consistency. Third, we employed alternative outcome definitions. Specifically, psychological distress was operationalized using a binary indicator based on the GHQ-12 threshold of 20/21 [23]; depression was defined using a cut-off score of 42/43 on the SF-12 MCS [24, 25]; and physical health was assessed using a 50/51 threshold on the SF-12 PCS [26]. Finally, we conducted subgroup analyses by sex to explore potential heterogeneity in the effect of UC on men and women.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Sample characteristics of treatment and control groups at baseline\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics of treatment and control groups at baseline\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,448 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e931 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.936 (12.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.488 (12.225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.430 (1.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.488 (1.655)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,017 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e599 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,692 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e815 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,893 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e770 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,496 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738 (79.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-secondary degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,206 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,030 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e756 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,608 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,101 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e517 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,041 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e628 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,662 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e301 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,074 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e793 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Ireland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFor continuous variables (age, household size), the value in brackets is the standard deviation; for qualitative variables, the value in brackets is the percentage of the category (e.g., Women) in the group (control or treatment \u0026ndash; for instance, 54.4% means that 54.4% of individuals in the control group are Women). The exception is the first row (N), where the value in brackets is the proportion of the total sample in the control (78.7%) or treatment (21.3%) group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;insert here\u0026gt;\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of study participants by treatment status. Overall, there were no substantial differences between the groups in terms of age, household size, racial composition, the presence of children in the household or country of residence. The only differences were that individuals in the treatment group had less education and were more likely to be female or live in urban areas than those in the control group.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Event study graph showing dynamic effects for average treatment effects on the treated (ATT) over time (with time\u0026thinsp;=\u0026thinsp;0 at the time of transition to universal credit)\u003c/p\u003e \u003cp\u003e\u0026lt;insert here\u0026gt;\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) presents the estimated dynamic treatment effects of UC on health outcomes, capturing differences between the treatment and control groups across time, from seven periods before treatment (T\u0026ndash;7) to seven periods following treatment (T\u0026thinsp;+\u0026thinsp;7). The event study estimates support the validity of the DID approach. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows that pre-treatment coefficients (T\u0026ndash;7 to T\u0026ndash;1) were not statistically different, providing evidence that the parallel trends assumption holds. Post-treatment estimates (T0 to T\u0026thinsp;+\u0026thinsp;7), in contrast, reveal a consistent pattern of increasing effect magnitude over time, indicating a cumulative adverse impact of UC exposure on mental health outcomes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the aggregated ATT across the entire study period. Transitioning to UC was associated with a statistically significant increase of 1.20 points in the GHQ-12 score (95% CI: 0.33 to 2.07; p\u0026thinsp;=\u0026thinsp;0.007), or around 0.25 standard deviation of the distribution of GHQ-12; this suggests that those who transited to UC experienced elevated psychological distress relative to those who did not transition. Similarly, a significant decrease of 2.19 points was observed in the SF-12 MCS score (95% CI: \u0026minus;\u0026thinsp;3.79 to \u0026minus;\u0026thinsp;0.59; p\u0026thinsp;=\u0026thinsp;0.007), representing approximately 0.22 standard deviations of the SF-12 MCS score distribution, suggesting a deterioration in mental health. The effect on the SF-12 PCS score was negative (\u0026ndash;0.79 or 0.8 standard deviation; 95% CI: \u0026minus;\u0026thinsp;1.95 to 0.36) but did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.179).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAggregate average treatment effects on the treated (ATT) from CSDID, estimating the treatment effect of transitioning to Universal Credit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAggregate ATT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CIs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(a) GHQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(b) SF-12 MCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(c) SF-12 PCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(d) Unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(e) Better future financial situation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(f) Monthly benefit income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-93.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-177.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(g) Monthly total net income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-222.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-486.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith regards to the dynamic effects (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), the GHQ-12 score rose by 2.87 points at T\u0026thinsp;+\u0026thinsp;4 (95% CI: 0.64 to 5.11), 3.30 points at T\u0026thinsp;+\u0026thinsp;5 (95% CI: 0.94 to 5.66), and peaked at 5.02 points at T\u0026thinsp;+\u0026thinsp;6 (95% CI: 1.29 to 8.75), indicating a cumulative deterioration in psychological well-being following UC transition. Similarly, the SF-12 MCS score displayed some early fluctuations but showed a consistent and statistically significant decline beginning at T0, when the UC transition occurred. At T0, the MCS score dropped by 1.47 points (95% CI: \u0026minus;\u0026thinsp;2.80 to \u0026minus;\u0026thinsp;0.15), followed by a further decline at T\u0026thinsp;+\u0026thinsp;4 (\u0026ndash;4.71; 95% CI: \u0026minus;\u0026thinsp;8.39 to \u0026minus;\u0026thinsp;1.02) and T\u0026thinsp;+\u0026thinsp;5 (\u0026ndash;6.38; 95% CI: \u0026minus;\u0026thinsp;10.48 to \u0026minus;\u0026thinsp;2.29). These findings suggest that the adverse effects of UC on mental health were not only immediate but also amplified over time, particularly for mental functioning, highlighting the long-term psychological toll associated with welfare reform.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Aggregate average treatment effects on the treated (ATT) from CSDID, estimating the treatment effect of transitioning to Universal Credit\u003c/p\u003e \u003cp\u003e\u0026lt;insert here\u0026gt;\u003c/p\u003e \u003cp\u003eTo explore potential mechanisms through which UC may influence mental health, we examined its impact on employment status, perceived future financial expectations, monthly household social benefit income, and total household net income. The aggregate ATT, presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (with dynamic effects shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), suggests that the transition to UC was associated with a significant reduction of \u0026pound;93.05 in monthly social benefit income (95% CI: \u0026ndash;\u0026pound; 177.4 to \u0026ndash;\u0026pound; 8.7). In addition, UC reduced the likelihood of reporting an improved financial outlook by 8 percentage points (95% CI: \u0026minus;\u0026thinsp;14.6% to \u0026minus;\u0026thinsp;1.5%). Although UC was associated with a reduction in total household net income (\u0026ndash;\u0026pound;222.10), the effect was only marginally significant (p\u0026thinsp;=\u0026thinsp;0.10) over the entire study period. However, in the longer run, the reductions in total net income were significant and consistently observed in years 6 and 7 after the transition to UC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResults from multiple sensitivity analyses support the robustness of the main findings. First, re-estimating the models using individuals not yet treated with UC as the control group (Supplementary Table S2), and second, excluding observations from individuals who subsequently discontinued participation in UC (Supplementary Table S3), yielded results consistent with those of the primary specification. Analyses using binary outcome measures (Supplementary Table S4) further confirmed that UC significantly increased the probability of psychological distress by 8.6 percentage points (95% CI: 3.6% to 13.5%). However, the estimated effects on binary indicators for depression and poor physical health were negative but did not reach statistical significance. Gender-stratified models (Supplementary Table S5) revealed the UC transition had a negative impact on mental health for both men and women but presented in different measurements (i.e. GHQ was significantly increased for women; SF-12 MCS was significantly reduced for men). For physical health, a weakly significant negative effect of UC on the SF-12 PCS was found among women (\u0026ndash;1.25; p\u0026thinsp;=\u0026thinsp;0.05), but no significant effect was observed among men. Employment-stratified models (Supplementary Table S6) indicated that the adverse effects of UC transition were concentrated among the unemployed. Among individuals who remained unemployed throughout the study period, UC transition was associated with a 2.9-point increase in GHQ scores (p\u0026thinsp;=\u0026thinsp;0.01) and a 5.6-point decline in SF-12 MCS scores (p\u0026thinsp;=\u0026thinsp;0.04), effects more than twice as large as those estimated in the full sample.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the short- and long-term impacts of transitioning to UC on psychological distress, mental health functioning, and physical health functioning among UK social benefit recipients. Using longitudinal data from the UKHLS and a heterogeneous DID framework, we found that UC transition was associated with significantly worse mental health outcomes. Specifically, individuals who moved to UC experienced a statistically significant increase in GHQ-12 scores (by 1.2 points) \u0026mdash;indicating higher psychological distress\u0026mdash;and a significant decline in SF-12 MCS scores (by 2.2 points), reflecting deteriorating mental health functioning. To put our findings into context, a previous study found that losing a job increased GHQ-12 by around 2.1 [27], and reduced SF-12 MCS by around 1.6 [28]. Although the effect on physical health functioning, as measured by SF-12 PCS, was negative, it did not reach statistical significance over the full study period.\u003c/p\u003e \u003cp\u003eAdditionally, UC transition was associated with a reduction in monthly social benefit income and a decrease in perceived financial optimism, both of which may contribute to adverse mental health outcomes. While a marginally significant reduction in total household net income was observed, this effect became more pronounced in later years, suggesting cumulative financial strain over time. No significant impact was found on unemployment status, indicating that employment outcomes may not be the primary pathway through which UC affects health. Sensitivity analyses confirmed the robustness of these findings across alternative control group definitions and binary outcome specifications.\u003c/p\u003e \u003cp\u003eRather than reiterating prior evidence [8, 9], it is important to consider what our findings reveal about the mechanisms underlying UC\u0026rsquo;s impacts and the broader policy lessons they imply. Our stratified analyses provide insights into the mechanisms through which UC may affect health. The observed gender differences suggest that women experienced greater increases in psychological distress, while men exhibited more pronounced declines in mental health functioning. These patterns may reflect distinct pathways of vulnerability: women, particularly those with caregiving responsibilities, may have been more exposed to financial strain and the stress of sanctions, whereas men may have been more affected by conditionality tied to work expectations. Moreover, the stronger negative effects observed among unemployed recipients indicate that UC did not provide the intended security during periods of labour market inactivity, but instead compounded stress through reduced benefit generosity and heightened conditionality. These findings underscore that UC\u0026rsquo;s adverse health impacts were not uniform, but concentrated among groups already facing greater socioeconomic precarity.\u003c/p\u003e \u003cp\u003eIn addition, the sharp decline in household net income observed around year 5 likely reflects both policy dynamics and sample composition. As eligibility expanded to more complex households and long-term UC recipients accumulated deductions or sanctions, the remaining treatment sample increasingly consisted of the most disadvantaged claimants, amplifying the cumulative financial strain of UC over time. Beyond subgroup mechanisms, our findings also raise broader questions about the policy itself. If future analyses confirm that adverse effects were stronger in the early years of implementation, this would suggest that technical glitches and administrative challenges\u0026mdash;such as payment delays and difficulties with digital systems\u0026mdash;amplified harm. However, the persistence of negative effects over time would imply that the design of UC itself, by consolidating multiple tailored programs into a single, rigid system with strict conditionality, is structurally ill-suited to support claimant well-being. In this light, our results suggest that UC\u0026rsquo;s challenges may arise not only from implementation difficulties but also from features of its underlying design, indicating the importance of welfare reforms that better balance employment incentives with adequate protection of health and financial security.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our study relies on self-reported measures of mental health, which are the standard in population-based research and widely validated for reliability and sensitivity [12, 13]. While no fully \u0026ldquo;objective\u0026rdquo; measure of mental health exists, we strengthened confidence in our findings by applying alternative thresholds and operationalizations of each outcome in sensitivity analyses, all of which yielded consistent results. Second, we accounted for observed covariate differences between treatment and control groups by applying inverse probability weighting within the CSDID framework. While this adjustment improved balance on measured characteristics, unobserved confounders may still remain, and as with all quasi-experimental designs, results should be interpreted with appropriate caution. Third, although our identification strategy targets the treatment and control groups to precisely attribute observed effects to UC, it is still possible that other policy changes and contextual factors occurring during the study period could have affected the outcomes. Nevertheless, this study provides evidence on the impact of UC, highlighting its real-world implications for people\u0026rsquo;s lives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides evidence on the impact of UC, showing that transition to the program was associated with worsening mental health, reduced benefit income, and greater financial pessimism, with no significant employment gains. The sharp fall in household net income after five years highlights the cumulative strain of long-term UC exposure, suggesting that disadvantage deepens over time. Gender and employment-stratified analyses further show that adverse effects were concentrated among vulnerable groups, including women and the unemployed, underscoring the uneven burden of UC. These findings point to the need for reforms that address both implementation challenges\u0026mdash;such as delays and deductions\u0026mdash;and structural design flaws, including the rigidity of a consolidated benefits system. Welfare reforms must integrate health and financial security alongside work incentives to avoid unintended harms. Without such changes, UC risks entrenching rather than alleviating inequalities among low-income households.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe study is supported by the principal investigator, Antony Chum, through the Canada Research Chair program (CRC-2021-00269). The project was also partially funded by the Social Sciences and Humanities Research Council (SSHRC) Grant, File No.: 435-2023-1102. The funding source had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, the preparation, review, or approval of the manuscript, or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no relevant financial or non-financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYihong Bai:\u003c/strong\u003e Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChungah Kim:\u003c/strong\u003e Conceptualization, Investigation, Methodology, Resources, Software, Validation, Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeiya Cao\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKristine Ienciu:\u003c/strong\u003e Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMichel Grignon:\u003c/strong\u003e Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOlivia Ramraj:\u003c/strong\u003e Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJasmin Tiwana:\u0026nbsp;\u003c/strong\u003eWriting - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntony Chum:\u003c/strong\u003e Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - Original Draft, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was exempt from ethics review, as it posed no foreseeable risk of harm and utilized existing data collections that contained only non-identifiable information about human beings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData are available in a public, open-access repository. This data can be accessed through the UKHLS website: https://www.understandingsociety.ac.uk/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCheetham M, Moffatt S, Addison M (2018) \u0026ldquo;It\u0026rsquo;s hitting people that can least afford it the hardest\u0026rdquo; the impact of the roll out of Universal Credit in two North East England localities: a qualitative study\u0026rsquo;. Gatesh Counc \u003c/li\u003e\n\u003cli\u003eBBC News UK (2019) Universal credit claimants \u0026ldquo;struggling to cope\u0026rdquo; \u003c/li\u003e\n\u003cli\u003eReeves A, Loopstra R (2021) The continuing effects of welfare reform on food bank use in the UK: the roll-out of universal credit. J Soc Policy 50:788\u0026ndash;808 \u003c/li\u003e\n\u003cli\u003eSavage M, Jayanetti C (2018) Third of UK\u0026rsquo;s universal credit claimants hit by deductions from payments. The Observer \u003c/li\u003e\n\u003cli\u003eMandy Cheetham, Suzanne Moffatt, Michelle Addison, Alice Wiseman (2019) Impact of Universal Credit in North East England: a qualitative study of claimants and support staff. BMJ Open 9:e029611. https://doi.org/10.1136/bmjopen-2019-029611 \u003c/li\u003e\n\u003cli\u003eSosenko F, Bramley G, Bhattacharjee A (2022) Understanding the post-2010 increase in food bank use in England: new quasi-experimental analysis of the role of welfare policy. BMC Public Health 22:1363 \u003c/li\u003e\n\u003cli\u003eSong H, Zhang A, Barr B, Wickham S (2024) Effect of Universal Credit on young children\u0026rsquo;s mental health: quasi-experimental evidence from Understanding Society. J Epidemiol Community Health 78:764\u0026ndash;771 \u003c/li\u003e\n\u003cli\u003eWickham S, Bentley L, Rose T, et al (2020) Effects on mental health of a UK welfare reform, Universal Credit: a longitudinal controlled study. Lancet Public Health 5:e157\u0026ndash;e164. https://doi.org/10.1016/S2468-2667(20)30026-8 \u003c/li\u003e\n\u003cli\u003eBrewer M, Dang T, Tominey E (2024) Universal Credit: Welfare reform and mental health. J Health Econ 98:102940 \u003c/li\u003e\n\u003cli\u003eCallaway B, Sant\u0026rsquo;Anna PH (2020) Difference-in-differences with multiple time periods. J Econom \u003c/li\u003e\n\u003cli\u003eUK Data Service (2021) Understanding Society: Waves 1-13, 2009-2022 and Harmonised BHPS: Waves 1-18, 1991-2009. https://beta.ukdataservice.ac.uk/datacatalogue/doi/?id=6614#!#13. Accessed 10 Apr 2024 \u003c/li\u003e\n\u003cli\u003eGilbody SM (2001) Routinely administered questionnaires for depression and anxiety: systematic review. BMJ 322:406\u0026ndash;409. https://doi.org/10.1136/bmj.322.7283.406 \u003c/li\u003e\n\u003cli\u003eHardy GE, Shapiro DA, Haynes CE, Rick JE (1999) Validation of the General Health Questionnaire-12: Using a sample of employees from England\u0026rsquo;s health care services. Psychol Assess 11:159 \u003c/li\u003e\n\u003cli\u003eGnambs T, Staufenbiel T (2018) The structure of the General Health Questionnaire (GHQ-12): two meta-analytic factor analyses. Health Psychol Rev 12:179\u0026ndash;194 \u003c/li\u003e\n\u003cli\u003eGoldberg DP, Hillier VF (1979) A scaled version of the General Health Questionnaire. Psychol Med 9:139\u0026ndash;145 \u003c/li\u003e\n\u003cli\u003eJenkinson C (1999) Comparison of UK and US methods for weighting and scoring the SF-36 summary measures. J Public Health 21:372\u0026ndash;376 \u003c/li\u003e\n\u003cli\u003eBrazier J, Ratcliffe J, Saloman J, Tsuchiya A (2017) Measuring and valuing health benefits for economic evaluation. OXFORD university press \u003c/li\u003e\n\u003cli\u003eWare JE, Kosinski M, Keller SD (1996) A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care 34:220\u0026ndash;233 \u003c/li\u003e\n\u003cli\u003eDe Chaisemartin C, d\u0026rsquo;Haultfoeuille X (2023) Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey. Econom J 26:C1\u0026ndash;C30 \u003c/li\u003e\n\u003cli\u003eDe Chaisemartin C, d\u0026rsquo;Haultfoeuille X (2020) Two-way fixed effects estimators with heterogeneous treatment effects. Am Econ Rev 110:2964\u0026ndash;2996 \u003c/li\u003e\n\u003cli\u003eGoodman-Bacon A (2021) Difference-in-differences with variation in treatment timing. Themed Issue Treat Eff 1 225:254\u0026ndash;277. https://doi.org/10.1016/j.jeconom.2021.03.014 \u003c/li\u003e\n\u003cli\u003eSun L, Abraham S (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Themed Issue Treat Eff 1 225:175\u0026ndash;199. https://doi.org/10.1016/j.jeconom.2020.09.006 \u003c/li\u003e\n\u003cli\u003eCornelius BL, Groothoff JW, van der Klink JJ, Brouwer S (2013) The performance of the K10, K6 and GHQ-12 to screen for present state DSM-IV disorders among disability claimants. BMC Public Health 13:128. https://doi.org/10.1186/1471-2458-13-128 \u003c/li\u003e\n\u003cli\u003eVilagut G, Forero CG, Pinto-Meza A, et al (2013) The mental component of the short-form 12 health survey (SF-12) as a measure of depressive disorders in the general population: results with three alternative scoring methods. Value Health 16:564\u0026ndash;573 \u003c/li\u003e\n\u003cli\u003eYu DSF, Yan ECW, Chow CK (2015) Interpreting SF-12 mental component score: an investigation of its convergent validity with CESD-10. Qual Life Res 24:2209\u0026ndash;2217. https://doi.org/10.1007/s11136-015-0959-x \u003c/li\u003e\n\u003cli\u003eSoh S-E, Morello R, Ayton D, et al (2021) Measurement properties of the 12-item Short Form Health Survey version 2 in Australians with lung cancer: a Rasch analysis. Health Qual Life Outcomes 19:157. https://doi.org/10.1186/s12955-021-01794-w \u003c/li\u003e\n\u003cli\u003eFarr\u0026eacute; L, Fasani F, Mueller H (2018) Feeling useless: the effect of unemployment on mental health in the Great Recession. IZA J Labor Econ 7:1\u0026ndash;34 \u003c/li\u003e\n\u003cli\u003eStauder J (2019) Unemployment, unemployment duration, and health: selection or causation? Eur J Health Econ 20:59\u0026ndash;73 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Universal Credit, UK, difference-in-differences, mental health, distress, physical health, income","lastPublishedDoi":"10.21203/rs.3.rs-7882810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7882810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUniversal Credit (UC) is a major UK welfare reform that consolidates six means-tested benefits into a single monthly payment, aiming to simplify benefits delivery and incentivize labour market participation. However, concerns have emerged regarding its potential adverse consequences on recipients\u0026rsquo; mental and physical well-being. Existing evidence is limited by methodological weaknesses, short follow-up time, and a narrow focus on psychological distress.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eApplying the heterogeneous difference-in-differences approach developed by Callaway and Sant\u0026rsquo;Anna, we used waves 6\u0026ndash;14 of the UK Household Longitudinal Survey (UKHLS), focusing on working-age individuals receiving social benefits to evaluate the short and long-term effects of that welfare reform on psychological distress (GHQ-12), mental functioning (SF-12 MCS), physical functioning (SF-12 PCS), but also employment, perceived financial outlook, benefits income, and total income.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTransitioning to UC significantly increased GHQ-12 scores by 1.20 points (95% CI: 0.33 to 2.07) and decreased SF-12 MCS scores by 2.19 points (95% CI: \u0026minus;\u0026thinsp;3.79 to \u0026minus;\u0026thinsp;0.59), indicating deteriorating mental health. No significant effect was observed for SF-12 PCS. UC was also associated with a \u0026pound;93.05 reduction in monthly benefit income, a borderline significant decrease in total income of \u0026pound;222 and an 8 percentage-point decrease in perceived financial optimism. No significant effect on employment status was detected.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggest that the transition to UC adversely affected mental health and financial well-being, while yielding limited employment benefits. These adverse impacts appear to reflect both implementation challenges, such as payment delays and benefit deductions, and structural design flaws, including rigid conditionality and reduced income security for vulnerable groups. The results underscore the need for welfare reforms that integrate health considerations and provide more flexible, targeted support to mitigate unintended harms.\u003c/p\u003e","manuscriptTitle":"The Impact of Universal Credit on Mental, Physical, and Financial Well-Being: Longitudinal Evidence from the UK Household Longitudinal Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 17:15:11","doi":"10.21203/rs.3.rs-7882810/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T10:18:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T15:06:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T02:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75273433748577921085102242047327258430","date":"2026-01-12T09:03:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295375357187357955319548303164555176630","date":"2026-01-11T21:57:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T18:17:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T07:54:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T11:07:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2025-10-17T05:08:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8d52977d-2f5c-4264-8e85-69dc2b8a0e42","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T14:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 17:15:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7882810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7882810","identity":"rs-7882810","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}