Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study

Rachel M Thomson; Daniel Kopasker; Patryk Bronka; Matteo Richiardi; Vladimir Khodygo; Andrew J Baxter; Erik Igelström; Anna Pearce; Alastair H Leyland; S Vittal Katikireddi

doi:10.1371/journal.pmed.1004358

. 2024 Mar 4;21(3):e1004358. doi: 10.1371/journal.pmed.1004358

Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study

Rachel M Thomson ^1,^*, Daniel Kopasker ¹, Patryk Bronka ², Matteo Richiardi ², Vladimir Khodygo ¹, Andrew J Baxter ¹, Erik Igelström ¹, Anna Pearce ¹, Alastair H Leyland ¹, S Vittal Katikireddi ¹

Editor: Charlotte Hanlon³

PMCID: PMC10947674 PMID: 38437214

Abstract

Background

Population mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population.

Methods and findings

Adults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: “Partial” (value equivalent to existing benefits), “Full” (equivalent to the UK Minimum Income Standard), and “Full+” (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI.

Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI −0.49, −0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (−0.32% [95% UI −0.65, 0.00]), those without children (−0.40% [95% UI −0.68, −0.15]), and those with least education (−0.42% [95% UI −0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026.

The main limitations of our methods are the model’s short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations.

Conclusions

UBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial.

Rachel M Thomson and colleagues used a policy model to simulate how a Universal Basic Income might influence mental health for working-age adults in the UK.

Author summary

Why was this study done?

Universal Basic Income (UBI) is a radical social security policy proposal where everyone in a society would receive a regular, unconditional cash payment.
It has been suggested that UBI might be beneficial for mental health.
However, there has never been a trial of a true UBI in a high-income country to know what its potential mental health impacts might be.

What did the researchers do and find?

We used a policy model to simulate how a UBI might influence mental health for working-age adults in the United Kingdom.
We found that the effects of UBI were very sensitive to assumptions we made about how people’s employment choices might respond to receiving the money—if people choose to stay in work, UBI may have small benefits for population mental health, but if people are more likely to leave work, population mental health may actually worsen.
The groups most likely to experience positive mental health effects of UBI were women and those with the lowest educational qualifications.

What do these findings mean?

UBI may have potential to improve population mental health in high-income countries, but only if people do not choose to leave work in response to the policy.
More real-world research is needed to know how people are likely to respond to receiving a UBI in reality.
The main limitation of our modelling study is that it looks at how UBI would influence mental health only through income and employment, and other pathways might also be important to include in future research.

Introduction

Many high-income countries are experiencing economic pressures in the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic and subsequent global energy crisis [1]. There is considerable evidence recessions are linked to deteriorations in mental health, particularly for those of working age [2,3]. There have been growing calls for a radical policy shift to counter the social consequences of such economic fluctuations, as well as the increasing likelihood of wide-scale job losses and insecurity secondary to increasing automation [4–6]. One such proposal is the replacement of existing social security systems with a Universal Basic Income (UBI), which guarantees each individual a regular, unconditional payment designed to meet basic needs, regardless of household income or personal circumstances [7]. Political arguments advocating for UBI originate from both sides of the political spectrum, with the left noting its potential to lift people out of poverty and reduce stigma associated with benefits [8], and the right highlighting reduced bureaucracy and increased personal autonomy [9]. UBI has also been suggested as a potential approach to reducing health inequalities [8], and recent trials of UBI or UBI-like policies have been undertaken or planned in several high-income countries [10,11].

In the United Kingdom (UK), population mental health in working-age adults has deteriorated over the last 15 years against a background of economic crises, austerity policies, and COVID-19 [12–15]. Mental health inequalities have also increased during this period, particularly for younger working-age adults and women [16,17]. Current economic circumstances have potential to worsen this mental health crisis further [1,18,19]. There has been developing interest in the use of UBI-type approaches within the UK’s devolved administrations, with the Scottish Government developing detailed plans for a potential UBI pilot [20] (though this did not move forward to implementation) and the Welsh Government recently introducing a small trial of a basic income for individuals leaving the care system [21].

While historically much evaluative research on UBI has tended to focus on its potential impacts on workforce participation, over the last 2 decades there has been a shift to include a broader range of outcomes, including health [22]. Two recent reviews of interventions similar to UBI suggest they may be beneficial for mental health and well-being [23,24]. However, no studies in these reviews evaluated an actual UBI: most were delivered to restricted population groups, some offered one-off rather than regular payments, and most fell considerably short of providing a long-term liveable income [23]. This is perhaps not surprising, as the very high costs and complexities inherent in delivering UBI alongside existing taxation and welfare systems make it challenging to implement meaningful trials, requiring considerable political buy-in over many years [11]. As a result, to our knowledge, no fully universal UBI policy has ever been trialled in a high-income country. In such situations, policy modelling studies can provide a useful complement to smaller-scale pilots in informing the evidence base for policymakers [25]. We therefore aimed to use microsimulation to model the implementation of a population-wide UBI in the UK population and to examine its potential impacts on mental health and inequalities in the working-age population.

Methods

Microsimulation is a modelling approach that simulates individuals with defined characteristics and evolves them over time according to a predetermined set of rules, allowing for exploration of counterfactual scenarios and inequalities between population subgroups [26]. For this analysis, we used 2 models: a static tax-benefit microsimulation model (UKMOD [27]), which we used to calculate the immediate/overnight effects of policies on individual and household incomes; and SimPaths [28], a dynamic, stochastic, discrete-time microsimulation model of economic outcomes, recently modified to estimate the impact of these policy changes on mental health outcomes over time as part of the Health Equity and its Economic Determinants (HEED) project [29]. We summarise the key elements of the model(s) within the paper, with additional technical specifications available in previous publications [27,28] and in S1 Appendix.

Intervention design

The 3 UBI scenarios modelled in UKMOD (Table 1) were based on recommendations produced for the Scottish Government outlining a UBI trial suitable for the UK context [20]. Two levels of UBI were proposed: 1 low-level (or “Partial”) UBI, set at approximately the level of existing welfare benefits, and 1 high-level (or “Full”) UBI, set at the Minimum Income Standard (MIS) developed by the Joseph Rowntree Foundation [30]. The former would test the effect of universality and allocation of payments directly to individuals (rather than households), and the latter would test the additional effect of making this payment sufficient to live on. In both cases, it was recommended some current means-tested UK benefits were retained to avoid disadvantaging those with higher needs (Table 1). However, this removes 1 key advantage of UBI purported by advocates—the removal of means-testing and reduction in administrative burden [9]. Therefore, we modelled 2 “Full” UBI scenarios (Table 1): without (“Full,” Scenario 3) and with (“Full+,” Scenario 4) these means-tested benefits. Full details of individual benefits retained or suspended in each scenario are in the Appendix (Table C in S1 Appendix).

Table 1. Baseline and UBI scenarios modelled.

	1—Baseline	2—Partial UBI	3—Full UBI	4—Full+ UBI
UBI payment per week	Nil	0–15 years: £98.84 16+ years: £84.89 Pensioners^*: £196.53	0–15 years: £149.69 16+ years: £291.62 Pensioners: £246.16	0–15 years: £149.69 16+ years: £291.62 Pensioners: £246.16
Income tax^**	20% from £12.6k 40% from £37.7k 45% from £125.1k	20% from £12.6k 45% from £30k 60% from £50k	59% from £15.2k 70% from £30k 85% from £50k	59% from £15.2k 70% from £30k 85% from £50k
	Benefit categories retained (✓) or suspended (✗)
Benefit cap^***	✓	✗	✗	✗
Child benefit; income support; pension; unemployment benefits	✓	✗	✗	✗
Means-tested benefits for caring, childcare, disability, housing, and limited capability for work	✓	✓	✗	✓
Maternity pay, statutory sick pay, student benefits	✓	✓	✓	✓

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Baseline = planned tax/benefit policies for UK. Partial UBI = UBI set at the level of existing benefits. Full UBI = UBI set at the level of MIS. Full+ UBI = MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work.

*The pension-age benefit is applied when an individual reaches pensionable age; this differs by gender and with other circumstances in the UK, but is typically 66.

**While there is some minor variation in the lower/upper income tax rates and bands in Scotland, for simplicity, the rates applied in the majority of the UK are shown in the table. In the UBI scenario, the same tax rates were applied to all 4 devolved nations.

***The “benefit cap” (which limits the amount of total benefits that can be delivered to a single household/benefit unit in the UK) was suspended in all UBI scenarios to avoid interference with the UBI delivery.

MIS, Minimum Income Standard; UBI, Universal Basic Income; UK, United Kingdom.

UBI implementation in UKMOD followed the approach of De Henau and colleagues [31]. In Scenario 2 (Partial UBI), income tax rates were increased to around the highest acceptable levels reported in UK surveys of public attitudes to taxation [32,33]. In Scenarios 3 and 4 (Full/Full+ UBI), rates were set at those suggested by Kumar and colleagues when designing a Scottish UBI to meet the 2019 MIS [34]. Income tax thresholds were lowered in all intervention scenarios to reduce the government deficit caused by the policy, though based on patient/public involvement (PPI; see below), we did not try to achieve full fiscal neutrality. The UBI was treated as taxable income, with the Personal Allowance (the amount of tax-free income individuals are allowed in the UK, currently £12,570) set to the level of the UBI payment in Scenarios 3 and 4.

Model structure

SimPaths evolves a representative sample of the UK population in 1-year increments through 6 modules: Demography, Education, Health, Household composition, Nonlabour market income, and Labour supply (Fig 1). It was developed using JAS-mine, an open-source, object-oriented Java-based platform specifically designed for discrete-event simulations [35]. An open-source version of the model is available on GitHub (https://github.com/centreformicrosimulation/SimPaths). Each module includes several subprocesses estimated on longitudinal data external to the simulation [28]. The input data are described below. Alignment within the ageing and fertility processes ensures simulated population demographics do not deviate substantially from official projections from the Office for National Statistics.

In the Labour supply module, individuals within households (which may be made up of multiple benefit units) select the number of hours they wish to work based on a structural random utility model, with potential wages calculated using a Heckman-corrected wage equation. This is a nonforward-looking model of labour supply, in which employment decisions are made in order to maximise within-period utility of benefit units, given exogenous hourly wage rates predicted by the wage model. The model is unitary, which means that decision-making is at the level of the benefit unit—for example, one single choice involving labour supply decisions for each partner is made by couples, in order to maximise their joint utility.

A proof-of-principle causally informed mental health module was added for HEED (Fig 1, lower panel), estimating an individual’s likelihood of experiencing a common mental disorder (CMD) based on their demographic characteristics (Step 1) and on empirical epidemiological estimates of the effects of economic transitions on mental health (Step 2). These economic determinants of health are thought to be important in explaining the development and persistence of health inequalities over time [36,37], but considerable potential for bias, confounding, and reverse causation means application of cross-sectional relationships to predict the effect of counterfactual scenarios may be problematic [29,38]. We therefore estimated the short-term (1-year) effects of income changes and transitions in or out of poverty/employment on mental health in working-age adults, and the short-term effect of spending 2 consecutive years in poverty/unemployment on mental health [38,39].

The key structural and theoretical assumptions of SimPaths are illustrated in Table A in S1 Appendix. For this analysis, the strongest assumptions were that any macroeconomic impacts of the policy intervention (which are not modelled within SimPaths) would not influence outcomes and that individuals would respond to UBI income in the same way as any other additional income when deciding whether to work. As it has been suggested that employment impacts of radical UBI-like interventions may be smaller than expected [23,40], we explored the potential influence of this second assumption on our primary analysis (where the model endogenously produces labour supply responses) in 2 structural sensitivity analyses (see below).

Models were subjected to both internal and external validation, as described in S1 Appendix and elsewhere [27,28]. Simulations were run from 2022 to 2026, with UBI introduced from 2023 in intervention scenarios.

Model input data

UKMOD analyses income data from the Family Resources Survey, an annual representative cross-sectional survey of UK private households [41]. The model generates multiple output files simulating the effect of each annual tax-benefit policy scenario on incomes for every individual within the dataset.

SimPaths’ simulated UK population and the parameter estimates for the modules are drawn from the UK Household Longitudinal Study (also called “Understanding Society”), a representative panel-based study that includes income, health, and sociodemographic measures [42]. The labour supply module uses utility functions estimated on the data from the Family Resources Survey [41]—these estimates inform the decisions individuals and benefit units take about how to prioritise labour time over leisure time.

UKMOD output files are integrated into SimPaths’ simulation processes in the Labour Supply module (Fig 1). Households in SimPaths are probabilistically matched with “donor” households from the relevant UKMOD output file with similar characteristics, with this matched unit then used to convert the SimPaths household’s simulated gross income (including earnings, capital income, and pensions) to disposable income (i.e., subtracting taxes and adding benefits) according to the selected tax-benefit regime for that year.

The mental health module also uses data from Understanding Society (Table B in S1 Appendix). The Step 2 causal estimates for transitions between poverty and employment states in the working-age population aged 25 to 64 years were calculated using double-robust marginal structural modelling; we draw on our previous causal epidemiological analyses (with full methodological details including our causal framework and estimation approach published previously [38,39]). As effects differed by gender (but not markedly by education level or age group), estimates in the mental health module were calculated separately for men and women.

Model outputs

Output produced by the model consists of an SQL database tables and/or CSV files at the individual, benefit unit, and household levels, which can be linked through the unique identifiers. The output files contain the values of simulated variables for each individual unit in each year of the simulation, effectively producing a “synthetic” panel dataset. The initial UKMOD output also included a calculation of the Gini coefficient for each policy scenario, a commonly used measure of income inequality from 0 (perfect equality) to 1 (perfect inequality), which expresses the expected absolute gap between people’s incomes relative to the mean population income [43].

We calculated all outcomes in the working-age population aged 25 to 64 years, comparing trends in median income, poverty, employment, and mental health for baseline versus intervention scenarios. Employment was defined as being in any form of paid work, including self-employment. Poverty was defined as an annual income less than 60% of the median in the baseline policy scenario, before housing costs.

Our primary health outcome was the binary version of the General Health Questionnaire (GHQ-12), which identifies symptoms of psychological distress [44]. Scoring 4 or more indicates a likely CMD. CMD prevalence was calculated for the whole working-age population and for population subgroups of interest: gender (male versus female), education (high versus medium versus low), age (25 to 44 years versus 45 to 64 years), and household structure (has children versus no children; if lone parent). Number of additional/reduced CMD cases was calculated using Organisation for Economic Co-operation and Development (OECD) estimates of the size of the UK working-age population in Quarter 4 of 2022 (41.6 million) [45]. Our secondary outcome was the 36-point GHQ Likert score, a continuous measure scored from the same questionnaire, with higher scores indicating higher levels of psychological distress.

Relative and slope indices of inequality (RII/SII) in CMD (by education) were calculated for the whole working-age population, and for men and women separately. These inequality measures regress the outcome for those in a particular socioeconomic group on the proportion of the population that has a higher position in the hierarchy, in this case, those with higher education [46]. They can be interpreted as the ratio (RII) or absolute difference (SII) in CMD prevalence between those with the hypothetically least and the most education, taking into account population size.

Sensitivity analyses

To investigate assumptions about individuals’ decision-making and experiences of employment following receipt of a liveable UBI, we conducted 2 structural sensitivity analyses for Scenarios 3 and 4 (Full and Full+ UBI). Firstly, to simulate the possibility there would be no marked fall in employment in response to UBI delivery, we modified utility values in the SimPaths Labour Supply module so employment rates remained constant between the baseline and intervention scenarios. Secondly, to simulate the possibility that moving out of employment following UBI receipt may be more akin to the effect of voluntarily exiting the labour force, we substituted the causal effect estimates for employment transitions used in Step 2 of the mental health module with those for economic inactivity for our primary outcome (Table D in S1 Appendix).

Finally, an additional analytical sensitivity analysis investigated whether the patterning of findings differed when using effect estimates from systematic reviews for our primary outcome (Table D in S1 Appendix).

Uncertainty analysis

SimPaths accounts for parameter uncertainty by including routines that facilitate bootstrapping parameter estimates, based on estimated point values and covariance matrices. This involves repeated simulations, each based on a different random draw for model parameters. Similarly, Montecarlo variation can be explored by conducting repeated simulations each based on fresh set of random draws or by arbitrarily scaling-up the simulated population size. These methods can be used to generate a distribution of model outcomes, around central projections.

To account for stochastic and parameter uncertainty, we ran 1,000 simulations for each analysis, with parameter estimates for all processes randomly drawn from a distribution based on their variance. Results are presented as the median of the outcomes from all 1,000 simulations, and the difference in medians between the intervention scenario and baseline, with 95% uncertainty intervals (UIs) generated from the 2.5th and 97.5th percentiles. Data were analysed in RStudio version 2022.12.0+353.

Patient and public involvement

Our Advisory Group included representation from third sector organisations including the Mental Health Foundation, Joseph Rowntree Foundation, and Basic Income Conversation, and policymakers from Public Health Scotland and Public Health Wales. The group informed all preparatory work on causal modelling [38,39] and shaped the selection and development of UBI scenarios (see S1 Appendix). As there is considerable debate on the optimal way to fund a UBI, and many proposals include the use of novel tax levers that are difficult to model (e.g., wealth or carbon taxes; [47]), the group felt strongly that we should focus explicitly on health impacts rather than aiming for fiscal neutrality.

Results

Model performance

Internal validation of SimPaths, including parameter sweeps and stress-testing with extreme scenarios, suggested the model performed as expected, albeit with small overestimates of employment rates and earnings for those with low education (Figs A and B in S1 Appendix). On external validation, our predicted CMD prevalence for 2012 to 2018 was broadly comparable with observed data from the Health Survey for England [48], though trends were flatter (Fig C in S1 Appendix).

Fiscal and distributive impacts of UBI

All 3 UBI scenarios modelled in UKMOD increased disposable incomes more for those with lower starting incomes, reducing income inequality: the Gini coefficient reduced by 0.04 for the Partial UBI scenario, 0.15 for Full UBI, and 0.16 for Full+ UBI (Table E and Figs D-F in S1 Appendix). Partial UBI resulted in a fiscal deficit of £95.6 billion (bn); deficits for the Full/Full+ UBI scenarios were £30.9bn and £65.2bn, respectively.

Primary analysis

In the primary analysis, levels of poverty were reduced in all UBI scenarios compared with Baseline from the year of policy implementation, with this reduction being sustained throughout the rest of the simulation (Fig 2). The reduction was considerably more marked for the Full UBI scenarios, where poverty reduced to 0.01% (95% UI 0.00, 0.03) for Full+ UBI in 2023, compared with 7.13% (95% UI 6.62, 7.66) in the Partial UBI scenario, and 9.10% (95% UI 8.47, 9.71) at Baseline (Table F in S1 Appendix). Median annual incomes in 2023 were higher in all UBI scenarios compared with the Baseline of £22,578 (95% UI 22,111, 23,015): £27,173 (95% UI 26,599, 27,653) with Partial UBI, £27,421 (95% UI 27,191, 27,630) with Full UBI, and £27,719 (95% UI 27,474, 27,922) with Full+ UBI (Table F in S1 Appendix).

For employment rates, there was a small increase from Baseline in the Partial UBI scenario (Fig 2), from 78.93% (95% UI 77.94, 79.86) to 79.38% (95% UI 78.41, 80.24), though this waned by 2026. Employment fell in Full UBI scenarios, to 75.69% (95% UI 74.44, 76.92) with Full and 74.10% (95% UI 72.62, 75.43) with Full+ UBI (Table F in S1 Appendix). Mean weekly hours worked reduced by 1.76 hours (95% UI −2.10, −1.42) with Full and 2.31 hours (95% UI −2.72, −1.91) with Full+ UBI (Table F in S1 Appendix). As with poverty, the differential trajectories of lower employment for the Full UBI policies compared with Baseline were sustained throughout the study period following implementation.

In the Partial UBI scenario, resultant changes in income, poverty, and employment led to a CMD prevalence which did not differ markedly from Baseline throughout the study period (Fig 3). In the Full UBI scenarios, CMD prevalence was slightly higher than Baseline in the first 2 years of implementation, though this effect was small and waned over time: the absolute difference in prevalence for Full+ UBI was 0.38% (95% UI 0.13, 0.69) in 2023 and 0.08% (95% UI -0.78, 0.94) in 2026 (Table G in S1 Appendix). In 2023, this would equate to approximately 157,951 additional CMD cases (95% UI 54,036, 286,805). There was no notable influence of any UBI scenario on inequalities in CMD prevalence by education using either the RII or SII; for example, with Full+ UBI, the RII in 2023 reduced by 0.03 points (95% UI −0.09, 0.02) from 1.33 (95% UI 1.13, 1.56) at Baseline (Fig G and Table G in S1 Appendix).

Fig 3 — Baseline = planned tax/benefit policies for UK. Partial UBI = UBI set at the level of existing benefits. Full UBI = UBI set at the level of MIS. Full+ UBI = MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work. Whiskers = 95% UIs. CMD, common mental disorder; MIS, Minimum Income Standard; UBI, Universal Basic Income; UI, uncertainty interval; UK, United Kingdom.

On stratification by gender, education, age, and household structure (Fig 4), the short-term worsening of mental health with Full+ UBI remained small, albeit slightly more pronounced for men (0.63% [95% UI 0.31, 1.01]), those with high education (0.48% [95% UI 0.13, 0.91]), and those with children (0.64% [95% UI 0.18, 1.14]) (Table H in S1 Appendix). For men only, this worsening was potentially sustained over time with a difference of 0.29% (95% UI −0.90, 1.41) remaining in 2026 compared to Baseline, equivalent to an additional 60,018 men with CMD (95% UI −186,264, 291,814); however, UIs were very wide. The only population subgroups who potentially benefited from the implementation of Full UBI by 2026 were women (−0.15% [95% UI −1.53, 1.24]) and lone parents (−0.23% [95% UI −6.82, 5.91]), and, again, UIs were wide.

Sensitivity analyses

The results of all sensitivity analyses are summarised in Table 2 (shown in full in Tables I-M and Figs H-P in S1 Appendix). Our first structural sensitivity analysis modelled the possibility people would be less likely to exit employment in response to the most generous UBI scenarios. Here, poverty reductions associated with both Full and Full+ UBI interventions remained the same, but employment changes were minimised to approximate the Baseline scenario (Fig H and Table I in S1 Appendix). Under these conditions, in a reversal of the primary analysis findings, there was a small short-term reduction in prevalence of CMD in the UBI scenarios: −0.27% (95% UI −0.49, −0.05) in 2023 for Full+ UBI, equivalent to a reduction of 112,228 CMD cases (95% UI 20,783, 203,673) (Table 2). As in the primary analysis, these differences waned over time, and there was little impact on inequality measures. On stratification, the groups for whom the Full+ UBI brought most short-term improvement in CMD prevalence were women (−0.32% [95% UI −0.65, 0.00] in 2023), those with low education (−0.42% [95% UI −0.97, 0.15]), and those without children (−0.40% [95% UI −0.68, −0.15]), though these improvements were not sustained past 2023 (Fig M and Table K in S1 Appendix).

Table 2. Difference in prevalence of CMD and CMD inequality measures for Full+ UBI policies versus Baseline policies from 2023 to 2026.

ANALYSIS	2023	2024	2025	2026
	ABSOLUTE % DIFFERENCE IN CMD PREVALENCE
1. Main analysis	0.38% (0.13, 0.69)	0.16% (−0.78, 0.94)	0.08% (−0.73, 0.89)	0.08% (−0.78, 0.94)
N. of CMD cases	+157,951 cases (54,036, 286,805)	+66,506 cases (−324,215, 390,720)	+33,253 cases (−303,432, 369,937)	+33,253 cases (−324,215, 390,720)
2. Reduced unemployment	−0.27% (−0.49, −0.05)	−0.11% (−0.88, 0.69)	−0.02% (−0.88, 0.72)	−0.06% (−0.95, 0.76)
N. of CMD cases	−112,228 cases (−203,673, −20,783)	−45,723 cases (−365,781, 286,805)	−8,313 cases (−365,781, 299,275)	−24,940 cases (−394,877, 315,902)
3. Economic inactivity effects	−0.31% (−0.93, 0.35)	−0.16% (−1.23, 0.82)	−0.13% (−1.23, 1.03)	−0.11% (−1.33, 1.11)
N. of CMD cases	−128,855 cases (−386,564, 145,481)	−66,506 cases (−511,262, 340,841)	−54,035 cases (−511,262, 428,130)	−45,723 cases (−552,828, 461,383)
4. Alternative SR causal estimates	0.05% (−0.14, 0.22)	0.04% (−0.74, 0.84)	0.03% (−0.82, 0.88)	0.04% (−0.80, 0.87)
N. of CMD cases	+20,783 cases (−58,192, 91,445)	+16,626 cases (−307,588, 349,154)	+12,470 cases (−340,841, 365,781)	+16,626 cases (−332,528, 361,624)
	DIFFERENCE IN RELATIVE INDEX OF INEQUALITY (RII)
1. Main analysis	−0.03 (−0.09, 0.02)	−0.01 (−0.26, 0.22)	−0.01 (−0.27, 0.24)	−0.01 (−0.25, 0.24)
2. Reduced unemployment	−0.02 (−0.06, 0.03)	−0.01 (−0.25, 0.23)	−0.00 (−0.26, 0.25)	−0.01 (−0.24, 0.23)
3. Economic inactivity effects	−0.01 (−0.09, 0.07)	−0.00 (−0.24, 0.24)	−0.00 (−0.25, 0.25)	0.00 (−0.24, 0.24)
4. Alternative SR causal estimates	−0.02 (−0.06, 0.02)	−0.01 (−0.27, 0.24)	−0.00 (−0.25, 0.25)	−0.01 (−0.25, 0.25)
	DIFFERENCE IN SLOPE INDEX OF INEQUALITY (SII)
1. Main analysis	−0.00 (−0.01, 0.00)	−0.00 (−0.04, 0.03)	−0.00 (−0.04, 0.04)	−0.00 (−0.04, 0.04)
2. Reduced unemployment	−0.00 (−0.01, 0.00)	−0.00 (−0.04, 0.03)	−0.00 (−0.04, 0.04)	−0.00 (−0.04, 0.03)
3. Economic inactivity effects	−0.00 (−0.01, 0.01)	−0.00 (−0.03, 0.03)	−0.00 (−0.04, 0.04)	−0.00 (−0.04, 0.04)
4. Alternative SR causal estimates	−0.00 (−0.01, 0.00)	−0.00 (−0.04, 0.03)	−0.00 (−0.04, 0.03)	−0.00 (−0.04, 0.04)

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SR = systematic review; N. = number. Baseline = planned tax/benefit policies for UK. Partial UBI = UBI set at the level of existing benefits. Full UBI = UBI set at the level of MIS. Full+ UBI = MIS plus means-tested benefits for caring, childcare, disability, housing, and limited capability for work. 95% UIs in brackets. Analysis 1 is the primary analysis indicating maximal employment effects of UBI. Analysis 2 altered participant decision-making processes to approximate a scenario with minimal employment effects of UBI, i.e., where fewer people gave up work and/or opted to stay out of employment as a result of the income increase. Analysis 3 maintained the employment changes from Analysis 1, but treated moves out of employment as representing moves into economic inactivity (which are less detrimental to mental health). Analysis 4 used causal effect estimates for poverty and employment transitions sourced from systematic reviews, rather than our epidemiological analyses. Absolute number of CMD cases calculated using OECD estimates of the size of the UK working age population in Quarter 4 of 2022 (41,566,000).

CMD, common mental disorder; MIS, Minimum Income Standard; OECD, Organisation for Economic Co-operation and Development; UBI, Universal Basic Income; UI, uncertainty interval; UK, United Kingdom.

A second structural sensitivity analysis modelled the possibility that voluntary transitions out of employment under a Full/Full+ UBI would have a less negative impact on mental health than standard unemployment transitions. Under these conditions, CMD prevalence with Full+ UBI was again lower than Baseline in 2023 by −0.31% (95% UI −0.93, 0.35) and waned slightly less over the study period, but there were very wide UIs for all estimates (Table 2). On stratification, in contrast with other analyses, this was driven mostly by improvements for men, with a −0.58% (95% UI −1.52, 0.37) reduction in male CMD prevalence with Full+ UBI in 2023, and a −0.61% (95% UI −2.32, 1.06) reduction in 2026 (Fig N and Table K in S1 Appendix). Other stratified results were patterned similarly to the primary analysis.

In our analytical sensitivity analysis using alternative causal estimates from systematic reviews, effect magnitudes were smaller, but we found no marked divergence from our primary findings, i.e., Partial UBI generated small improvements to poverty but no clear mental health improvement, and Full/Full+ UBI scenarios generated large improvements to poverty, reductions in employment, and slightly higher CMD prevalence for short periods of time (Figs O and P and Tables L and M in S1 Appendix).

Results for our secondary outcome (GHQ Likert score, which represents a continuous measure of psychological distress rather than a binary measure of likelihood of CMD) are shown in Tables N and O in S1 Appendix. These were in keeping with the findings for our primary outcome, with results from the main analysis showing higher GHQ Likert scores with Full+ UBI compared to the baseline scenario (0.03 [95% UI 0.00, 0.06]) and the structural sensitivity analysis with reduced employment assumptions showing instead a reduced GHQ score (−0.06 [95% UI −0.08, −0.04]). On stratification, as with the primary outcome, the positive effects of UBI in this sensitivity analysis were larger for women (−0.08 [95% UI −0.12, −0.05]) and those with least education (−0.09 [95% UI −0.13, −0.06]), and there were also larger effects for lone parents (−0.08 [95% UI −0.15, −0.01]).

Discussion

Our findings suggest a low UBI set at the level of existing welfare benefits is unlikely to markedly affect population mental health in the UK, despite considerable financial cost. For a UBI set at the level of a liveable income, impacts are less clear and highly dependent on associated employment effects. If people are less likely to work, mental health may deteriorate in the short term (particularly for men), whereas if employment levels remain unchanged, a small improvement in mental health is anticipated, with women and those with low education seeing most improvement. For a comprehensive Full+ UBI, our analyses suggest a worst-case scenario of 157,951 additional cases of common mental disorders (95% UI 54,036, 286,805) if employment rates fall, and a best-case scenario of 112,228 fewer cases (95% UI 20,783, 203,673) if they do not. In both situations, impacts appear relatively short-lived, with no meaningful impact on mental health inequalities by education. There is also considerable uncertainty around many of our estimates, particularly after the first year of policy implementation.

These findings are driven by the relative importance of income, poverty, and employment for mental health [49]. While there is a well-evidenced and large cross-sectional association between income and health, evidence for a longitudinal or causal effect of income has been less clear [50]. Our recent systematic review found that, while income changes do impact future mental health and well-being, the effect size is perhaps smaller than anticipated—though moves across a poverty threshold exert the largest of these small effects [51]. Contrastingly, several systematic reviews report large effects of employment transitions on mental health [52,53]. In keeping with this review evidence, the causal effects we estimated for poverty transitions were around 11% of the size of those for employment transitions (Table B in S1 Appendix). This explains why the modelled mental health effects of a policy with such considerable impacts on poverty levels are smaller than might be anticipated, and why findings are so sensitive to assumptions regarding employment.

It is not unreasonable to assume labour supply effects of UBI may be minimal, making our primary analysis a reasonable “worst-case scenario” for mental health. There is little evidence UBI-like interventions are associated with large increases in unemployment [23], with existing reviews finding few and surprisingly small adverse labour supply effects [40,54]. This is in keeping with more recent real-world evidence from the introduction of COVID-related benefits, which were found to result in minimal work disincentives in the United States despite considerable expansion compared with the prepandemic provision of welfare [55,56]. On UBI’s potential health effects, 2 recent reviews report consistent evidence of improved mental health and well-being for UBI-like policies in trials, more so than other adult health outcomes [23,24]. In contrast, we report here very modest and uncertain findings even for our “best-case scenario.”

This could be because we estimate only material pathways from UBI to mental health via income, poverty, and employment, rather than psychosocial pathways of perceived social/economic status or security. There is some evidence UBI partially improves well-being through increased confidence, financial independence, and stability [57,58]. Our model will not capture these mechanisms if they do not act solely through income levels or employment status, potentially underestimating some positive effects of UBI on mental health [59]. In our future work, we intend to expand the included causal pathways to reduce this limitation. A recent microsimulation study modelling UBI and mental health in children and young people also reported larger effects than we do [60,61], though this is not necessarily surprising as it is known income interventions are more likely to improve health in children than adults [62,63], and employment transitions are less relevant to this population group. Future modelling work combining these 2 population groups would be of interest and beneficial in determining to what extent our estimates may be conservative.

As the first microsimulation study, to our knowledge, considering the adult health impacts of a population-wide UBI, our work has many important strengths. We modelled a range of policy scenarios and sensitivity analyses developed through third sector and policymaker engagement. We use 2 established microsimulation models and draw on advanced epidemiological methods to estimate effects for parameterisation, with our causal framework and estimation process clearly described and previously published [38,39]. We present a range of validation results, demonstrating that this model performs satisfactorily. Finally, we report results across a broad range of population subgroups to interrogate differential effects, as well as assessing effects on measures of mental health inequalities. There are, however, some limitations to our work. We model only short-term (1-year) causal effects of economic transitions and deal with a relatively short time horizon (4 years from implementation), potentially underestimating longer-term impacts of the policies and introducing the more marked uncertainty seen around the estimates in the years after the intervention. We cannot model interactions between our simulated population and the macroeconomy, and in reality, changes to macroeconomic factors such as economic growth, public service provision, and income inequality are highly likely under UBI [20]. Adding these interactions via integration with macroeconomic models is complex [64], but we intend to explore this possibility in future work to overcome this limitation. Finally, by including only the causal effects of income, poverty, and employment transitions, we assume these are the only meaningful causal pathways through which a UBI will influence population mental health, which may not be the case [23,24].

In terms of policy implications, even with changes to income tax rates far beyond what is considered publicly acceptable [32,33], the fiscal deficit associated with our most comprehensive UBI is £65.2bn. In our best-case scenario for mental health outcomes, this equates to a cost of just under £600,000 per case of CMD avoided, substantially higher than the approximately £350/month individuals state they would be willing to pay to avoid depression [65], which might suggest such a policy approach should not be pursued based on its mental health benefits alone. However, we do note that this does not take into account potential improvements in child and adolescent mental health, which may be larger and longer-lasting [61]. Given our principal finding that mental health effects of UBI may be contingent on changes in employment rates, policymakers considering this approach should proactively consider how to mitigate or prevent such changes and may wish to pilot their chosen policy with close monitoring of employment outcomes prior to broader implementation. Trial data could be usefully compared to outputs from this modelling to determine which labour market scenario is most likely, informing future policy modelling and implementation decisions. Future research addressing the limitations of our modelling would be useful, in particular to expand the causal pathways considered, integrate the microsimulation with macroeconomic simulations, and include longer-term economic exposures over a longer time period [29]. Finally, using “real-world” data from ongoing UBI trials (such as the Welsh care leavers study [21]) to further refine the labour supply elements of the model or generate improved causal effect estimates would be highly valuable.

In conclusion, while it comes with considerable financial cost, our exploratory modelling analyses suggest a liveable UBI may reduce the number of UK working-age adults diagnosed with a common mental health problem by around 112,000 cases on introduction, if recipients choose to remain in work. However, in the worst-case scenario for employment effects, our simulations suggest the same policy could instead lead to an increase of 157,951 cases. Our work highlights how modelling approaches can be a useful complementary method where trials can only be small scale, or where interventions are likely to exert complex effects on wider systems. Future work considering additional causal pathways (including psychosocial pathways) would improve confidence in our findings.

Declarations

Ethics approval

Model input data are from Understanding Society. The University of Essex Ethics Committee has approved all data collection on the Understanding Society main study and innovation panel waves, including asking consent for all data linkages except to health records. Requesting consent for health record linkage was approved at Wave 1 by the National Research Ethics Service (NRES) Oxfordshire REC A (08/H0604/124), at BHPS Wave 18 by the NRES Royal Free Hospital & Medical School (08/H0720/60) and at Wave 4 by NRES Southampton REC A (11/SC/0274). Approval for the collection of biosocial data by trained nurses in Waves 2 and 3 of the main survey was obtained from the National Research Ethics Service (Understanding Society—UK Household Longitudinal Study: A Biosocial Component, Oxfordshire A REC, Reference: 10/H0604/2). For data used in external validation, ethical approval for each year of the survey was obtained by the Health Survey for England team. No further approval was required for the current analysis of the existing data.

Supporting information

S1 Appendix. Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study.

Table A: Key model assumptions of UKMOD and SimPaths. Table B: Effect estimates for use in Step 2 of SimPaths causal mental health module. Table C: All individual benefits retained and/or suspended in each UBI scenario. Table D: Alternative effect estimates for use in Step 2 of SimPaths causal mental health module during sensitivity analyses. Figure A: Internal validation graphs from the SimPaths GUI contrasting predicted outcomes with observed Understanding Society data from 2011–2017 (yo = years old). Figure B: Cumulative mean prevalence of common mental disorder and poverty by number of model iteratio. Figure C: Prevalence of common mental disorder (CMD) in SimPaths versus the Health Survey for England from 2012–2018. Table E: Population-level economic impacts of Universal Basic Income (UBI) policies modelled in UKMOD. Figure D: Gainers and losers by household income decile (before housing costs) ranging from low to high, with Partial UBI compared with baseline tax/benefit policies in 2023 (Scenario 2). Figure E: Gainers and losers by household income decile (before housing costs) ranging from low to high, with Full UBI compared with baseline tax/benefit policies in 2023 (Scenario 3). Table F: Median income, prevalence of poverty, employment rate, and mean hours worked in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Table G: Estimated prevalence of common mental disorders (CMD) and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure G: Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Table H: Estimated prevalence of common mental disorders (%) in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, and household structure (95% uncertainty intervals. Table I: Structural Sensitivity Analyses—Median income, prevalence of poverty, employment rate, and mean hours worked in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure I: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Figure J: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Table J: Structural Sensitivity Analyses—Estimated prevalence of common mental disorders and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure K: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Figure L: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Figure M: Structural Sensitivity Analysis 1, relaxing employment assumptions—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022 to 2026 with 95% uncertainty intervals, stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification. Figure N: Structural Sensitivity Analysis 2, using economic inactivity effects—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022 to 2026 stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification. Table K: Structural Sensitivity Analyses—Estimated prevalence of common mental disorders in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, previous poverty/employment status, and household structure (95% uncertainty intervals). Table L: Analytical Sensitivity Analyses—Median income, prevalence of poverty, and prevalence of unemployment in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure O: Analytical Sensitivity Analysis, using alternative estimates from systematic reviews—Estimated prevalence of common mental disorder (CMD) for modelled Universal Basic Income (UBI) policies from 2022–2026. Table M: Analytical Sensitivity Analyses—Prevalence of common mental disorders and mental health inequalities in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Figure P: Analytical Sensitivity Analysis, using alternative estimates from systematic reviews—Estimated relative (left panel) and slope (right panel) indices of inequality by education for common mental disorder (CMD) in modelled Universal Basic Income (UBI) policies from 2022–2026. Table N: Estimated GHQ Likert score in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 (95% uncertainty intervals). Table O: Estimated GHQ Likert score in baseline scenario and three simulated Universal Basic Income (UBI) scenarios from 2022–2026 stratified by gender, education, age, previous poverty/employment status, and household structure (95% uncertainty intervals).

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pmed.1004358.s001.pdf^{(2.1MB, pdf)}

Acknowledgments

We thank Justin van de Ven for his advice and his contributions to the SimPaths codebase, which assisted with our analysis. We also extend our thanks to the project’s Advisory Group members for their guidance in shaping this work, and to the broader HEED team for their contributions to the model’s development.

This paper is dedicated to the memory of William Old Thomson MBChB FRCS, 1947–2024.

Abbreviations

CMD: common mental disorder
COVID-19: Coronavirus Disease 2019
HEED: Health Equity and its Economic Determinants
MIS: Minimum Income Standard
OECD: Organisation for Economic Co-operation and Development
PPI: patient/public involvement
UBI: Universal Basic Income
UI: uncertainty interval
UK: United Kingdom

Data Availability

UKMOD is freely available for download on the Centre for Microsimulation and Policy Analysis (CeMPA) website: https://www.microsimulation.ac.uk/ukmod/access/. Prospective users must first show their eligibility to utilise the source input data, which is freely available from the UK Data Service provided the user accepts the Terms and Conditions of the End User License (https://www.understandingsociety.ac.uk/documentation/access-data). SimPaths is available open-access on GitHub: https://github.com/centreformicrosimulation/SimPaths. The analytical code in R is available open-access on GitHub: https://github.com/rachelmthomson/thomson-microsim-analysis.

Funding Statement

This work was supported by the Wellcome Trust (218105/Z/19/Z [RT] and 205412/Z/16/Z [AP]), European Research Council (949582 [SVK]), Health Foundation (2135162 [SVK]), Medical Research Council (MC_UU_00022/2 [AL]) and Chief Scientist Office (SPHSU17 [AL]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004358.r001

Decision Letter 0

Alexandra Schaefer

15 Sep 2023

Dear Dr Thomson,

Thank you for submitting your manuscript entitled "Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

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Please re-submit your manuscript within two working days, i.e. by Sep 19 2023 11:59PM.

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Kind regards,

Alexandra Schaefer, PhD

Associate Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004358.r002

Decision Letter 1

Alexandra Schaefer

27 Nov 2023

Dear Dr. Thomson,

Thank you very much for submitting your manuscript "Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study" (PMEDICINE-D-23-02607R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to four independent reviewers, including two statistical reviewers. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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We look forward to receiving your revised manuscript.

Sincerely,

Alexandra Schaefer, PhD

PLOS Medicine

plosmedicine.org

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Requests from the editors:

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ACADEMIC EDITOR COMMENTS

A very interesting and relevant study. The authors have done a great job at conveying complex methods in an understandable form, although I support reviewer requests for some further explanations to come into the main paper. One reviewer raised an important concern about using a cut-off on the GHQ, which I agree with. All other review comments seemed pertinent and important.

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ABSTRACT

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2) Throughout, suggest reporting statistical information as follows to improve clarity for the reader “22% (95% CI [13%,28%]; p</=)”. Please amend throughout the abstract and main manuscript. Please note the use of commas to separate upper and lower bounds, as opposed to hyphens as these can be confused with reporting of negative values.

3) Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

4) Please include any important dependent variables that are adjusted for in the analyses.

5) Please define ‘UK’ at first use.

6) Please add a unit when discussing age (years, months etc.).

7) Abstract Methods and Findings:

*Please include the number of participants and the length/time frame of follow up/microsimulation model (i.e. what means short-term effect?).

*In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

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At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

The summary should include 2-3 single sentence, individual bullet points under each of the questions. The last bullet under ‘What Do These Findings Mean?’ point should describe the main limitation of the study's methodology.

It may be helpful to review currently published articles for examples which can be found on our website here https://journals.plos.org/plosmedicine/

INTRODUCTION

1) Please define ‘UK’ at first use.

2) Please expand your introduction about previous research (e.g., what did recent trials show?) and explain the need for and potential importance of your study.

METHODS AND RESULTS

1) Please change “(41.6m)” to “(41.6 million)” or define ‘m’ at first use.

2) Please provide a complete list of model parameters, including clear and precise descriptions of the meaning of each parameter, together with the values or ranges for each, with justification or the primary source cited, and important caveats about the use of these values noted.

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5) Please provide sensitivity analyses to identify which parameter values are most important in the model. Uncertainty estimates seek to derive a range of credible results on the basis of an exploration of the range of reasonable parameter values. The choice of method should be presented and justified.

6) Please discuss the scientific rationale for this choice of model structure and identify points where this choice could influence conclusions drawn. Please also describe the strength of the scientific basis underlying the key model assumptions.

7) If you use the points above (2-6), indicate that these are derived from Geoffrey P Garnett, Simon Cousens, Timothy B Hallett, Richard Steketee, Neff Walker. Mathematical models in the evaluation of health programmes. (2011) Lancet DOI:10.1016/S0140-6736(10)61505-X.

8) Under the subheading ‚Sensitivity analysis‘ you write “…two structural sensitivity analyses for Scenarios 3 and 4 (Full UBI).”. Shouldn’t Scenario 4 be ‘Full+ UBI’? Please revise throughout the entire main manuscript. Also, please be consistent/clear in your description of the different scenarios, e.g., Scenario 3, Scenario 3 (Full UBI) or Full UBI.

9) Please define ‘bn’ at first use.

10) Please define ‘RII’ and ‘SII’ at first use.

11) “Under these conditions, in a reversal of the primary analysis findings, there was a small short-term reduction in prevalence of CMD in Full UBI scenarios: -0.27% (-0.49, -0.05) in 2023, equivalent to a reduction of 112,228 CMD cases (20,783-203,673) (Table 2).” – In this sentence and the following paragraphs, you switch between "Full UBI" and "Full+ UBI", while Table 2 only shows results for the Full+ UBI scenario, please revise.

DISCUSSION

Please discuss what the study adds to existing research and where and why the results may differ from previous research. Please remove the ‘Conclusion’ heading as the one-paragraph conclusion should be part of the discussion.

TABLES

1) Please define abbreviations used in each table (including those in Supporting Information files).

2) Table 1: Please define ‘pensioners’ (i.e. starting age). Please add definitions for the four different scenarios.

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FIGURES

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3) Please in the figure legend/description, define abbreviations used in each figure (including those in Supporting Information files).

4) Please provide titles, legends and descriptions for all figures (including those in Supporting Information files).

5) Figure 2/Figure 3: Please add definitions for the four different scenarios. Please indicate in the figure caption the meaning of the whiskers. Please show the axis beginning at zero. If this is not possible, please show a break in the axis. Please add x-axis labels. The red line which denotes the reform implementation point should be described as dashed red line (also see the comment #2). For Figure 2, please introduce the abbreviation ‘UBI’ in the figure title (as done e.g., in Figure 3).

6) Figure 3: The color scheme for the different scenarios does not match the one used in Figure 2. Please use a consistent color scheme throughout the manuscript (including the figures in the supplementary materials).

7) Figure 4: Please add definitions for the two different scenarios. Please show the axis beginning at zero. If this is not possible, please show a break in the axis. Please add x-axis labels. The red line which denotes the reform implementation point should be described as dashed red line (also see the comment #2). Please define ‘y’ or write ‘years’ in full. Please mention that the shaded areas show the 95% uncertainty intervals (e.g., “…95% uncertainty intervals (shaded areas)…”).

SUPPLEMENTARY MATERIAL

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2) For references in the supplementary material, please see REFERENCES.

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4) Figure S7-S16: Please see comments for Figure 2/3/4 and amend accordingly.

5) For supplementary tables: Please ensure to state the unit of the numbers presented and the meaning of the numbers in brackets/define all numerical values (e.g., for Table S8 and S11 change to “Estimated prevalence of common mental disorders (%, 95% Uncertainty intervals)…”).

REFERENCES

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Comments from the reviewers:

Reviewer #1: This is a well-written paper on an interesting issue of public health and policy salience - UBI is currently of great political interest and the paper makes a contribution to this discourse. As the authors point out, in the absence of rigorous trials of UBI, microsimulation is a valuable tool for exploring the range of potential outcomes that might follow the introduction of a UBI. But, of course, any microsimulation rests heavily on the assumptions being made and this study, although articulating and discussing these limitations, is perhaps less forthright about the resultant very conservative nature of their findings than it might be.

The study is well situated in the context of existing literature, although - perhaps because of the timing of submission - recent UK based microsimulation studies of basic income are not referenced:

* Parra-Mujica, F., Johnson, E., Reed, H., Cookson, R. & Johnson, M. (2023) Understanding the relationship between income and mental health among 16- to 24-year-olds: Analysis of 10 waves (2009-2020) of Understanding Society to enable modelling of income interventions A. Moretti (ed.). PLOS ONE. 18(2): e0279845. DOI: 10.1371/journal.pone.0279845

* Reed, H.R., Johnson, M.T., Lansley, S., Johnson, E.A., Stark, G. & Pickett, K.E. (2023) Universal Basic Income is affordable and feasible: evidence from UK economic microsimulation modelling. Journal of Poverty and Social Justice. 31(1): 146-162. DOI: 10.1332/175982721X16702368352393.).

More attention could also be given to the different pathways through which a UBI might be expected to influence mental health (see, for example, the models of UBI impact in Johnson, M.T., Johnson, E.A., Nettle, D. & Pickett, K.E. (2022) Designing trials of Universal Basic Income for health impact: identifying interdisciplinary questions to address. Journal of Public Health. 44(2): 408-416. DOI: 10.1093/pubmed/fdaa255 and Huss R. Can universal basic income reduce poverty and improve children's health?Archives of Disease in Childhood Published Online First: 30 March 2023. doi: 10.1136/archdischild-2022-324799). These models theorize that the ways in which UBI affects health go far beyond the material effects of income alone and need to be viewed within a broader social determinants of health framework.

The methods of the study are well-described. This is a useful case study application of the causally informed mental health module within HEED. The sensitivity analyses related to potential impacts on employment are a strength of the modelling in light of the findings related to employment in the Finnish UBI pilot.

The Discussion section does include some discussion of the two major assumptions of the modelling - that macroeconomic impacts of UBI have no effect on mental health and that income changes due to UBI will have the same mental health impact as other changes in income. These are clearly quite important assumptions, given relationships between income inequality and mental health and indeed the modelled effect on the Gini coefficient (see, for example, Ribeiro, Wagner Silva, et al. "Income inequality and mental illness-related morbidity and resilience: a systematic review and meta-analysis." The Lancet Psychiatry 4.7 (2017): 554-562 and Patel, V., Burns, J.K., Dhingra, M., Tarver, L., Kohrt, B.A. and Lund, C. (2018), Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry, 17: 76-89. https://doi.org/10.1002/wps.20492) and the likely importance of income stability, increased levels of societal trust and other psycho-social benefits of a true UBI (as per models referenced above). The authors do recognise that their estimates are likely to be conservative, but perhaps don't give the weight to these assumptions in their inference that they might merit. A useful next step would be to incorporate at least the impact on income inequality into future modelling.

Reviewer #2: This paper is a microsimulation model of the effect of a universal basic income (UBI) on mental health. This is a relevant topic for PLOS Medicine as there is strong evidence for impact of income on mental health, but limited evidence for the effect of structural interventions that target income on mental health (or other population health outcomes). The methodological approach is appropriate given feasibility issues of implementing a UBI at the population-level, and this paper will complement results from smaller pilots globally that are targeted based on income or other population characteristics. Potential issues with the extent to which these scenarios may be grounded in practical application are addressed through the input of UBI leaders and health and government decision-makers in selecting the parameters of the four UBI scenarios. The assumptions of the model are important for microsimulation to make contributions in this field of research, and while the authors raise the issue of macroeconomic impacts, these were not included in sensitivity analysis. This is the paper's major weakness and requires either inclusion in the microsimulation or more justification as to why this was not included. I recommend major revisions before this paper can be published with PLOS Medicine.

Major revisions

Methods:

-The authors identify several structural and theoretical assumptions of the model, including that any macroeconomic impacts of the policy intervention would not affect the outcomes. This is a significant weakness of the model that requires further justification and discussion, or to be addressed through an additional sensitivity analysis. It is reasonable to expect that mental health services and supports would be less available with the introduction of a UBI either owing to cut-backs related to UBI financing, or labour markets impacts of people leaving lower-paid jobs social care roles and making care less available. While this may not have impacts on people with more transitory or situational mental health concerns related to work or income stress that may be addressed through the UBI, it could worsen mental health for those who require more fulsome supports.

Minor revisions

Introduction

-The authors indicate that that mental health has been worsening over the past 15 years "against a background of economic crises, austerity policies, and COVID-19", but only one citation shows some empirical evidence for this relationship. Suggest providing more explanation of other trends that are attributable to the increase in mental health disorders (i.e., generational shift in mental health disorders among adolescents and young people, increased use of digital media, etc). It may otherwise be interpreted that greater economic security should result in a more significant decrease in mental disorders than was found through the microsimulation model.

-The authors use the definition of a UBI (versus a guaranteed income or basic income) which includes universality, however the examples provided (Finland, Ontario, and individuals leaving the care system) do not meet that criteria. Revise to indicate that these pilots are not a UBI, which would be consistent with the authors statement that "no fully universal UBI has been trialed".

-The statement that no fully universal UBI has been trialed is accurate for high-income countries, but some low-income country trials do meet the criteria of universality in smaller geographic areas (e.g., GiveDirectly in rural Kenya). Revise to indicate that no fully universal UBI has been trialed in a high-income context.

Methods

-The authors indicate that the mental health module was developed using empirical epidemiological estimates of the effects of economic transitions on mental health - where were estimates derived from?

Results:

-The authors stratify results based on several sociodemographic factors (gender, education, age, and household structure) but there is no stratification based on level of taxation. Under the partial, full and full+ UBI scenarios, there is a significant change in taxation for those who are currently 'middle income', e.g., 70% from 30K and 85% from 50K for the full and full+ UBI (unless I have misunderstood the income tax rates in Table 1, in which case more explanation is required). I would expect some increase in CMDs is from people who were previously in a higher income bracket but whose income is now lower post-taxation, and so it would be interesting to see if the increases are higher in that group.

Discussion:

-The authors state that "there is little evidence UBI-like interventions are associated with large increases in unemployment", however the evidence for minimal impacts is thin. Hum (1993) was published before current labour market trends (e.g., precarity, frictional employment) that could impact the behavioural response to a UBI, and de Paz-Banez (2020) indicates significant methodological weaknesses in what is used as proxies for a UBI While there is still relatively good consensus that UBI would not impact unemployment, suggest citing more robust literature given how important increases in unemployment were to the sensitivity analysis. I suggest the authors draw from more recent literature ( see Marinescu, Skandalis and Zhao; Scott, Altonji et al.) which concluded that COVID-19 benefits resulted in minimal work disincentives from the expansion of benefits during COVID-19. Although these benefits were temporary, they may be of more relevance to the behavioural response to a UBI than the older literature that is cited.

Reviewer #3: Thank you to the authors for submitting this paper, I very much enjoyed reading it. I definitely think it has the potential for publication in this journal, but I have a number of comments that I think should be addressed or considered before publication. I have listed them in roughly the order they appear in the text and have indicated which comments I think are minor and which are major. I hope the comments are useful and potentially improve the paper.

Major comment - I think a significant amount of further justification needs to given regarding the choice to dichotomise the GHQ-12 and use this as your primary outcome measure. I completely understand that it is easier to interpret (i.e. you can estimate an approximate cost per common mental disorder avoided), but to me it feels as though you are just throwing away potentially useful information. Although the interpretation of the results would clearly be a little bit more difficult, I think looking of the relationship in a little bit more granular detail would be useful rather than a dummy variable. At the very least I think these results should be presented as part of the online appendix.

Major comment - I fully appreciate that it is a very complicated (and impressive) set of methods, but I really think that further detail in the main text is needed regarding the Simpaths model, especially the transitions between the different states (for example the probabilities and distributions used). I know some of this information is included in the online appendices, but I think it is really needed in the main text in order for the paper to flow. The model comes across as a bit of a black box at the moment.

Major comment - I would also like some discussion of the 'causal' methods used in the main text, including the g-computation methods used. They are briefly referenced in the appendix (and weirdly the acknowledgements) but for me this isn't enough. They are an important aspect of the paper so should be in the main text somewhere. This is contributing to the 'black box' feel of the model as it currently reads. I would move Table S2 (and potentially Table S4) from the appendix into the main text.

Major comment - The measures of inequality used in certain parts of your analyses (relative index of inequality, slope index of inequality, gini coefficient) need to be explained in the methods. A reader with no background quantitative measures of inequality such as these would have no idea what they mean.

Major comment - I think you need to further emphasise the significant level of uncertainty present in the vast majority of the results you present. Looking at Table 2, it looks like your "Main Analysis" in 2023 is the only estimate where the confidence intervals do not contain 0. Can you comment further on the possible reasons for this significant level of uncertainty? Where in the model do you think it is coming from?

Minor comment - Is the Full UBI (orange) line missing from the left hand panel of Figure 2?

Minor comment - I would use the same scale for each of the four panels in Figure 4 so it is easier for the reader to compare the different sub-groups. I also personally find the colour scheme difficult to read - that might just be me though!

Minor Comment - In the sensitivity analysis section you note that you "modified the utility values... so employment rates remained constant". I think you need to explain this comment further, what utility values are you talking about? It isn't clear

Minor Comment - Is there any particular reason you used the median values in the stochastic uncertainty analysis? I personally have only ever seen the mean values used in these instances. One sentence explaining why you use the median values will probably do.

Minor comment - Similar to a previous comment, but in the discussion can you comment on the possible reasons why the impacts on mental health are relatively short lived and don't sustain over the long term?

Minor comment - Does "your recent systematic review" reference much of the economics literature which has used IV/RDD methods to identify a causal impact of income on health? If so I think it would be briefly worth mentioning here the difficulties of using these methods and the drawbacks in terms of interpretation of the results from such studies.

Minor comment - Are you aware of any applied economic literature which has looked at the willingness to pay of avoiding mental illness/disorders? If there are any papers regarding this it would be good to bring into your discussion to put your "£600,000 per case of CMD avoided" into some context.

Reviewer #4: This is a really interesting and well written paper on the potential impact of implementing a universal basic income on mental health in the UK.

I would like to commend the authors on their inclusion of PPIE in this study and the role they played in shaping the research and also on moving the focus away from fiscal neutrality. However, I would have liked to have seen fiscal neutrality as a scenario as they is likely needed from a political perspective.

I thought the choice of timeframe (5 years) and the introduction on UBI one year into the cycle will help with how the implementation of such a policy would work in reality in the UK. However, would it have been worthwhile looking at the implementation of the partial UBI and then perhaps a transition to a full UBI within the same model run but with a longer time horizon to allow this progression? I believe that any government would more likely follow this route rather than introduce full or full+ straight off. Furthermore, from a policy perspective, I would have also liked to have seen the reverse i.e. to model the successor government post-five years removing the policy and then seeing how quickly the impact is reversed.

Minor point - I would like to see some justification of the use of 1000 simulation runs for each analysis in your uncertainty analysis.

Minor point - table 2: I am not sure anyone finds values of -0.00 helpful - would it be possible to change to > unless the value is actually 0.

I would also like to see reference to the review by Pinto et al on Exploring different methods to evaluate the impact of basic income interventions: a systematic review in your background prior to introducing the microsimulation approach employed in your paper.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2024 Mar 4;21(3):e1004358. doi: 10.1371/journal.pmed.1004358.r003

Author response to Decision Letter 1

18 Dec 2023

Attachment

Submitted filename: PM UBI microsim revision - response letter v2.docx

pmed.1004358.s002.docx^{(60.4KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004358.r004

Decision Letter 2

Alexandra Schaefer

19 Jan 2024

Dear Dr. Thomson,

Thank you very much for re-submitting your manuscript "Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study" (PMEDICINE-D-23-02607R2) for review by PLOS Medicine.

I appreciate your detailed response to the editors' and reviewers' comments. I have discussed the paper with my colleagues and the academic editor, and it has also been seen again by all three original reviewers. The changes made to the paper were satisfactory to the reviewers. As such, we intend to accept the paper for publication, pending your attention to the editorial comments below in a further revision. When submitting your revised paper, please once again include a detailed point-by-point response to the editorial comments.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

We ask that you submit your revision within 1 week (Jan 26 2024). However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don’t hesitate to contact me directly with any questions (aschaefer@plos.org). If you reply directly to this message, please be sure to ‘Reply All’ so your message comes directly to my inbox.

We look forward to receiving the revised manuscript.

Sincerely,

Alexandra Schaefer, PhD

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

ABSTRACT

1) Please note that you can remove the units from the 95% UI values in parentheses. Since the corresponding unit is presented with the foregoing value, it is not necessary to repeat the units. Also, please ensure to add 95% UI before each set of parentheses and that the 95% UI values are presented in parentheses (please see comment 3) for an example).

2) ll.28-29: Please add "to our knowledge" or something similar.

3) ll.45-46: It seems the second pair of parentheses should directly follow ’74.1%’. Please revise. Editorial suggestion: “(for Full+ from 78.9% (95% UI [77.9,79.9]) to 74.1% (95% UI [72.6, 75.4]))”

4) In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

AUTHOR SUMMARY

We suggest changing the last bullet under ‘What Do These Findings Mean?’ point to: The main limitation of our modelling study is that it looks at how UBI would influence mental health only through income and employment, while other pathways were not considered in the analysis.

INTRODUCTION

ll. 110-111/115-116: Please add “to our knowledge” or similar.

METHODS AND RESULTS

1) l.248. Please define ‘OECD’ at first use.

2) l.329 and ongoing: Please see comment 1) under ABSTRACT and adjust the statistical reporting accordingly (for example, line 329: 0.01% (95% UI [0.00, 0.03])).

3) ll.331-333: Please provide reference to the relevant graph and/or table.

4) ll.336-337: Please check whether Table S6, Appendix is the (only) appropriate reference here. It seems the relevant data of employment rates described here are also visible in Figure 2 (right panel).

5) ll.337-338: Please provide reference to the relevant graph and/or table.

6) ll.338-340: You state that the employment effects of the Full UBI policies were sustained throughout the study period after implementation. However, when looking at Figure 2, one can see that while employment rates initially fell in both Full UBI scenarios after policy implementation, they rose steadily in the following years. You might consider modifying this conclusion accordingly.

7) ll.443-450: We suggest briefly reiterating what the GHQ Likert scores reflect, as it may be easier for readers who are not as familiar with the content to follow this section.

FIGURES

1) Please provide a figure description for Figure 1.

2) Figure 4: In the figure description, we suggest adding a definition of low, medium and high education.

REFERENCES

1) Please thoroughly revise all references and ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases (http://www.ncbi.nlm.nih.gov/nlmcatalog/journals), and are appropriately formatted and capitalised (e.g., for reference [1] The Lancet Regional Health – Europe should be Lancet Reg Health Eur)

2) When specifying the date of access, please write “accessed” instead of “cited”.

3) Please revise the format of reference [65].

SUPPLEMENTARY MATERIAL

1) p.7, please change to: “…considerable debate on the ‘best’ way to fund UBI.”

2) Figure S1/S13/S14 (and where applicable): Similar to Figure 4, we suggest adding a definition of low, medium and high education.

3) Table S4: Please change "Full years UBI only" to "Full UBI years only".

4) Table S8/S11/S15: We suggest adding a definition of low, medium and high education and changing “Younger 25-44y” and “Older 45-64y” to “Age group: 25-44 years” and “Age group: 45-64 years”.

5) Table S6/S9: We wondered why you chose to present only the differences and not the actual values for average weekly hours worked, and why you chose not to present the differences for median annual income, poverty, and employment.

6) Figure S12: Please note that in Figure S12 you use the term " economic inactivity causal effects" while in others you use the term " economic inactivity effects" - please try to be consistent.

7) Please revise the references according to the comments under REFERENCES.

SOCIAL MEDIA

To help us extend the reach of your research, please provide any X (formerly known as Twitter) handle(s) that would be appropriate to tag, including your own, your coauthors’, your institution, funder, or lab. Please respond to this email with any handles you wish to be included when we tweet this paper.

Comments from Reviewers:

Reviewer #1: The authors have responded in full to my points, and to the points raised by the other reviewers. The responses are detailed and thoughtful and I believe the paper will now make a significant and useful contribution to the literature.

Reviewer #2: Response to major comments: The authors have provided a detailed and comprehensive response to the major suggested revisions, specifically, the concerns raised by several reviewers on the macroeconomic impacts of the UBI on the outcomes of interest. The paper is now much clearer on the limitations posed by the key assumptions, and the justification of the considerable additional complexity of integrating a macroeconomic model at this stage are reasonable.

Response to minor comments:

R2.7: I appreciate the authors' response that stratification of results based on tax brackets is not possible due to the separation between the UBI modelling within UKMOD to the dynamic health modelling within SimPaths. While the UKMOD output includes a calculation of the Gini coefficient for each policy scenario, it may be interesting to include a comparison between current income distribution (by tax brackets) and income distribution under each of the scenarios as an appendix (in addition to Figure 2), with a very brief note in the discussion that references the current distribution of CMDs across the income distribution, and a note that the minimal change under the UBI scenarios may be due to changes in the proportions of people in each tax bracket.

Reviewer #3: Thank you to authors for their detailed response to each of my comments. I am now happy to approve this paper for publication.

Any attachments provided with reviews can be seen via the following link:

[LINK]

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General Editorial Requests

1) We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

2) Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

3) Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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To submit your revised manuscript:

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Your article can be found in the "Submissions Needing Revision" folder.

PLoS Med. 2024 Mar 4;21(3):e1004358. doi: 10.1371/journal.pmed.1004358.r005

Author response to Decision Letter 2

26 Jan 2024

Attachment

Submitted filename: PM UBI microsim revision - second response letter v1.docx

pmed.1004358.s003.docx^{(32KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004358.r006

Decision Letter 3

Alexandra Schaefer

5 Feb 2024

Dear Dr Thomson,

On behalf of my colleagues and the Academic Editor, Charlotte Hanlon, I am pleased to inform you that we have agreed to publish your manuscript "Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study" (PMEDICINE-D-23-02607R3) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript, and editorially there are only two remaining minor stylistic/presentation points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at aschaefer@plos.org.

Please see below the minor points that we request you respond to:

1) l.345: Please change to: "75.69% (95% UI 74.44, 76.92)"

2) In the references (including those in the Supporting Information), please note that the date of access to the website should include the day, month, and year. Please revise accordingly.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Alexandra Schaefer, PhD

Associate Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study.

(PDF)

pmed.1004358.s001.pdf^{(2.1MB, pdf)}

Attachment

Submitted filename: PM UBI microsim revision - response letter v2.docx

pmed.1004358.s002.docx^{(60.4KB, docx)}

Attachment

Submitted filename: PM UBI microsim revision - second response letter v1.docx

pmed.1004358.s003.docx^{(32KB, docx)}

Data Availability Statement

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PERMALINK

Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study

Rachel M Thomson

Daniel Kopasker

Patryk Bronka

Matteo Richiardi

Vladimir Khodygo

Andrew J Baxter

Erik Igelström

Anna Pearce

Alastair H Leyland

S Vittal Katikireddi

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Intervention design

Table 1. Baseline and UBI scenarios modelled.

Model structure

Fig 1. Structure of the SimPaths model.

Model input data

Model outputs

Sensitivity analyses

Uncertainty analysis

Patient and public involvement

Results

Model performance

Fiscal and distributive impacts of UBI

Primary analysis

Fig 2. Estimates of poverty (left panel) and employment (right panel) for modelled UBI scenarios from 2022 to 2026.

Fig 3. Estimated prevalence of CMD for modelled UBI policies from 2022 to 2026.

Fig 4. Estimated prevalence of CMD for modelled UBI policies from 2022 to 2026 with 95% UIs, stratified by gender (A), education (B), age (C), and household structure (D). Note different scales used for each stratification.

Sensitivity analyses

Table 2. Difference in prevalence of CMD and CMD inequality measures for Full+ UBI policies versus Baseline policies from 2023 to 2026.

Discussion

Declarations

Ethics approval

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Alexandra Schaefer

Roles

Decision Letter 1

Alexandra Schaefer

Roles

Author response to Decision Letter 1

Decision Letter 2

Alexandra Schaefer

Roles

Author response to Decision Letter 2

Decision Letter 3

Alexandra Schaefer

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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