Exploring different methods to evaluate the impact of basic income interventions: a systematic review

Effects of guaranteed basic income interventions on poverty-related outcomes in high-income countries: A systematic review and meta-analysis

Background

High-income countries offer social assistance (welfare) programs to help alleviate poverty for people with little or no income. These programs have become increasingly conditional and stringent in recent decades based on the premise that transitioning people from government support to paid work will improve their circumstances. However, many people end up with low-paying and precarious jobs that may cause more poverty because they lose benefits such as housing subsidies and health and dental insurance, while incurring job-related expenses. Conditional assistance programs are also expensive to administer and cause stigma. A guaranteed basic income (GBI) has been proposed as a more effective approach for alleviating poverty, and several experiments have been conducted in high-income countries to investigate whether GBI leads to improved outcomes compared to existing social programs.

Objectives

The aim of this review was to conduct a synthesis of quantitative evidence on GBI interventions in high-income countries, to compare the effectiveness of various types of GBI versus "usual care" (including existing social assistance programs) in improving poverty-related outcomes.

Search Methods

Searches of 16 academic databases were conducted in May 2022, using both keywords and database-specific controlled vocabulary, without limits or restrictions on language or date. Sources of gray literature (conference, governmental, and institutional websites) were searched in September 2022. We also searched reference lists of review articles, citations of included articles, and tables of contents of relevant journals in September 2022. Hand searching for recent publications was conducted until December 2022.

Selection Criteria

We included all quantitative study designs except cross-sectional (at one timepoint), with or without control groups. We included studies in high income countries with any population and with interventions meeting our criteria for GBI: unconditional, with regular payments in cash (not in-kind) that were fixed or predictable in amount. Although two primary outcomes of interest were selected a priori (food insecurity, and poverty level assessed using official, national, or international measures), we did not screen studies on the basis of reported outcomes because it was not possible to define all potentially relevant poverty-related outcomes in advance.

Data Collection and Analysis

We followed the Campbell Collaboration conduct and reporting guidelines to ensure a rigorous methodology. The risk of bias was assessed across seven domains: confounding, selection, attrition, motivation, implementation, measurement, and analysis/reporting. We conducted meta-analyses where results could be combined; otherwise, we presented the results in tables. We reported effect estimates as standard mean differences (SMDs) if the included studies reported them or provided sufficient data for us to calculate them. To compare the effects of different types of interventions, we developed a GBI typology based on the characteristics of experimental interventions as well as theoretical conceptualizations of GBI. Eligible poverty-related outcomes were classified into categories and sub-categories, to facilitate the synthesis of the individual findings. Because most of the included studies analyzed experiments conducted by other researchers, it was necessary to divide our analysis according to the "experiment" stage (i.e., design, recruitment, intervention, data collection) and the "study" stage (data analysis and reporting of results).

Main Results

Our searches yielded 24,476 records from databases and 80 from other sources. After screening by title and abstract, the full texts of 294 potentially eligible articles were retrieved and screened, resulting in 27 included studies on 10 experiments. Eight of the experiments were RCTs, one included both an RCT site and a "saturation" site, and one used a repeated cross-sectional design. The duration ranged from one to 5 years. The control groups in all 10 experiments received "usual care" (i.e., no GBI intervention). The total number of participants was unknown because some of the studies did not report exact sample sizes. Of the studies that did, the smallest had 138 participants and the largest had 8019. The risk of bias assessments found "some concerns" for at least one domain in all 27 studies and "high risk" for at least one domain in 25 studies. The risk of bias was assessed as high in 21 studies due to attrition and in 22 studies due to analysis and reporting bias. To compare the interventions, we developed a classification framework of five GBI types, four of which were implemented in the experiments, and one that is used in new experiments now underway. The included studies reported 176 poverty-related outcomes, including one pre-defined primary outcome: food insecurity. The second primary outcome (poverty level assessed using official, national, or international measures) was not reported in any of the included studies. We classified the reported outcomes into seven categories: food insecurity (as a category), economic/material, physical health, psychological/mental health, social, educational, and individual choice/agency. Food insecurity was reported in two studies, both showing improvements (SMD = -0.57, 95% CI: -0.65 to -0.49, and SMD = -0.41, 95% CI: -0.57 to -0.26) which were not pooled because of different study designs. We conducted meta-analyses on four secondary outcomes that were reported in more than one study: subjective financial well-being, self-rated overall physical health, self-rated life satisfaction, and self-rated mental distress. Improvements were reported, except for overall physical health or if the intervention was similar to existing social assistance. The results for the remaining 170 outcomes, each reported in only one study, were summarized in tables by category and subcategory. Adverse effects were reported in some studies, but only for specific subgroups of participants, and not consistently, so these results may have been due to chance.

Authors' Conclusions

The results of the included studies were difficult to synthesize because of the heterogeneity in the reported outcomes. This was due in part to poverty being multidimensional, so outcomes covered various aspects of life (economic, social, psychological, educational, agency, mental and physical health). Evidence from future studies would be easier to assess if outcomes were measured using more common, validated instruments. Based on our analysis of the included studies, a supplemental type of GBI (provided along with existing programs) may be effective in alleviating poverty-related outcomes. This approach may also be safer than a wholesale reform of existing social assistance approaches, which could have unintended consequences.

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

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.

PROTOCOL: Effects of guaranteed basic income interventions on poverty-related outcomes in high-income countries: A systematic review

This is the protocol for a Campbell systematic review. The objectives are as follows: to appraise and synthesize the available quantitative evidence on GBI interventions in high-income countries, for the purpose of comparing the relative effectiveness of specific forms of GBI for alleviating poverty.

Health inequity in pandemic anxiety about COVID-19 infection and socioeconomic consequences in Japan: A structural equation modeling approach

Background

Health inequity in relation to COVID-19 infection and socioeconomic consequences is a major global concern. Mental health issues in vulnerable populations have received special attention in research and practice during the COVID-19 pandemic. However, there is limited evidence on the nature of the anxieties experienced as a result of COVID-19, and how such concerns vary across demographic groups.

Aim

This study examines anxiety among the working population of Japan (aged 18-59), in terms of both COVID-19 infection and socioeconomic consequences, using an internationally validated tool, the Pandemic Anxiety Scale (PAS).

Methods

Data were collected using an online survey (n = 2,764). The analyses included an exploratory factor analysis (EFA), a confirmatory factor analysis (CFA), and structural equation modeling (SEM), followed by validation of the Japanese version of the PAS.

Results

A two-factor latent variable model shows the multidimensionality of anxiety in regard to the COVID-19 pandemic and the disparity across population groups in predicting the two defined anxiety dimensions. Several path coefficients showed somewhat unexpected and/or unique results from Japan compared with previous European studies. Specifically, self-reported health status was not significantly related to disease anxiety, and those who were not in paid employment reported lower consequence anxiety. The SEM results showed a greater number of significant exogenous variables for consequence anxiety compared to disease anxiety, highlighting disparities in pandemic anxiety by socioeconomic status in regard to socioeconomic consequences of the pandemic.

Conclusion

In contrast to existing European studies, evidence from the current study suggests contextual patterns of health inequity. Due to the prolonged socioeconomic consequences of the pandemic, multidisciplinary research on mental health issues and the quality of life remains an important research agenda in exploring socioeconomic measures in context, towards addressing inequity concerns.

Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania

The current world crisis caused by the COVID-19 pandemic has transformed into an economic crisis, becoming a problem and a challenge not only for individual national economies but also for the world economy as a whole. The first global lockdown, which started in mid-March of 2020 and lasted for three months in Lithuania, affected the movement and behavior of the population, and had an impact on the economy. This research presents results on the impact of lockdown measures on the economy using nonparametric methods in combination with parametric ones. The impact on unemployment and salary inequality was estimated. To assess the impact of lockdown on the labor market, the analysis of the dynamics of the unemployment rate was performed using the results of the cluster analysis. The Lithuanian data were analyzed in the context of other countries, where the dynamics of the spread of the virus were similar. The salary inequality was measured by the Gini coefficient and analyzed using change point analysis, functional data analysis and linear regression. The study found that the greatest impact of the closure restrictions on socio-economic indicators was recorded in 2020, with a lower impact in 2021. The proposed multi-step approach could be applied to other countries and to various types of shocks and interventions, not only the COVID-19 crisis, in order to avoid adverse economic and social outcomes.

Exploring different methods to evaluate the impact of basic income interventions: a systematic review

BACKGROUND

Persistent income inequality, the increase in precarious employment, the inadequacy of many welfare systems, and economic impact of the COVID-19 pandemic have increased interest in Basic Income (BI) interventions. Ensuring that social interventions, such as BI, are evaluated appropriately is key to ensuring their overall effectiveness. This systematic review therefore aims to report on available methods and domains of assessment, which have been used to evaluate BI interventions. These findings will assist in informing future program and research development and implementation.

METHODS

Studies were identified through systematic searches of the indexed and grey literature (Databases included: Scopus, Embase, Medline, CINAHL, Web of Science, ProQuest databases, EBSCOhost Research Databases, and PsycINFO), hand-searching reference lists of included studies, and recommendations from experts. Citations were independently reviewed by two study team members. We included studies that reported on methods used to evaluate the impact of BI, incorporated primary data from an observational or experimental study, or were a protocol for a future BI study. We extracted information on the BI intervention, context and evaluation method.

RESULTS

86 eligible articles reported on 10 distinct BI interventions from the last six decades. Workforce participation was the most common outcome of interest among BI evaluations in the 1960-1980 era. During the 2000s, studies of BI expanded to include outcomes related to health, educational attainment, housing and other key facets of life impacted by individuals' income. Many BI interventions were tested in randomized controlled trials with data collected through surveys at multiple time points.

CONCLUSIONS

Over the last two decades, the assessment of the impact of BI interventions has evolved to include a wide array of outcomes. This shift in evaluation outcomes reflects the current hypothesis that investing in BI can result in lower spending on health and social care. Methods of evaluation ranged but emphasized the use of randomization, surveys, and existing data sources (i.e., administrative data). Our findings can inform future BI intervention studies and interventions by providing an overview of how previous BI interventions have been evaluated and commenting on the effectiveness of these methods.

REGISTRATION

This systematic review was registered with PROSPERO (CRD 42016051218).