minimum wages and public health a literature review

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Minimum wages and public health: A literature review

Affiliations.

1 Center for Healthcare Policy and Research, University of California Davis School of Medicine, Sacramento, CA, USA; Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; Center for Poverty Research, University of California, Davis, USA. Electronic address: [email protected].
2 Department of Biology, University of Nevada, Reno, USA.
3 Department of Economics, Old Dominion University, Norfolk, VA, USA.
PMID: 30316876
DOI: 10.1016/j.ypmed.2018.10.005

We evaluate evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe. We search four scientific websites from the inception of the research through May 20, 2018. We find great variety (20+) in measured outcomes among the 33 studies that pass our initial screening. We establish quality standards in a second screening resulting in 15 studies in which we create outcome-based groups. Outcomes include four broad measures (general overall health, behavior, mental health, and birth weight) and eight narrow measures (self-reported health, "bad" health days, unmet medical need, smoking, problem-drinking, obesity, eating vegetables, and exercise). We establish criteria for "stronger" findings for outcomes and methods. Stronger findings include: $1 increases in minimum wages are associated with 1.4 percentage point (4% evaluated at mean) decreases in smoking prevalence; failure to reject null hypotheses that minimum wages have no effects for most outcomes; and no consistent evidence that minimum wages harm health. One "suggestive" finding is that the best-designed studies have well-defined treatment (or likely affected) and control (unaffected) groups and contain longitudinal data. The major methodological weaknesses afflicting many studies are the lack of focus on persons likely affected by minimum wages and omission of "falsification tests" on persons likely unaffected. An additional weakness is lack of attention to how findings might differ across populations such as teenagers, adults, men, women, continuously employed and unemployed persons. Research into health effects of minimum wages is in its infancy and growing rapidly. We present a list of "better practices" for future research.

Keywords: Social determinants; Social epidemiology.

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Minimum Wages and Public Health: A Literature Review

30 Pages Posted: 23 May 2018 Last revised: 14 Oct 2018

J. Paul Leigh

University of California, Davis - Department of Public Health Sciences

Wesley Leigh

University of Nevada, Reno - Department of Biology

Old Dominion University - Strome College of Business

Date Written: February 27, 2018

We evaluate evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe. We search four scientific websites from the inception of the research through May 20, 2018. We find great variety (20 ) in measured outcomes among the 33 studies that pass our initial screening. We establish quality standards in a second screening resulting in 15 studies in which we create outcome-based groups. Outcomes include four broad measures (general overall health, behavior, mental health, and birth weight) and eight narrow measures (self-reported health, “bad” health days, unmet medical need, smoking, problem-drinking, obesity, eating vegetables, and exercise). We establish criteria for “stronger” findings for outcomes and methods. Stronger findings include: $1 increases in minimum wages are associated with 1.4 percentage point (4% evaluated at mean) decreases in smoking prevalence; failure to reject null hypotheses that minimum wages have no effects for most outcomes; and no consistent evidence that minimum wages harm health. One “suggestive” finding is that the best-designed studies have well-defined treatment (or likely affected) and control (unaffected) groups and contain longitudinal data. The major methodological weaknesses afflicting many studies are the lack of focus on persons likely affected by minimum wages and omission of “falsification tests” on persons likely unaffected. An additional weakness is lack of attention to how findings might differ across populations such as teenagers, adults, men, women, continuously employed and unemployed persons. Research into health effects of minimum wages is in its infancy and growing rapidly. We present a list of “better practices” for future research.

Keywords: low income, social determinants of health

JEL Classification: I1, J3

Suggested Citation: Suggested Citation

J. Paul Leigh (Contact Author)

University of california, davis - department of public health sciences ( email ).

Davis, CA United States 530-754-8605 (Phone) 530-752-3239 (Fax)

University of Nevada, Reno - Department of Biology ( email )

Reno, NV 89503 United States

Old Dominion University - Strome College of Business ( email )

Norfolk, VA 23529 United States

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Minimum Wages and Public Health: A Literature Review

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Raising the Minimum Wage and Public Health

1 School of Public Health, Boston University, Boston, Massachusetts
2 Partnered Evidence-Based Policy Resource Center, Veterans Health Administration, Boston, Massachusetts
Original Investigation History of Low Hourly Wage and All-Cause Mortality Katrina L. Kezios, PhD; Peiyi Lu, PhD; Sebastian Calonico, PhD; Adina Zeki Al Hazzouri, PhD JAMA

Some states, and perhaps soon the federal government, are considering increasing the minimum wage. President Joe Biden voiced his support for raising the federal minimum wage to $15 per hour while on the campaign trail, and also included it in his recent economic stimulus package. A ballot initiative in Florida to raise the statewide minimum by 2026 passed in November 2020. While discussions around raising the minimum wage typically center on the economic benefits and potential labor force impacts that would affect low-wage workers, there are also population health benefits to consider. These range from better physical and mental health outcomes to indirect influences on individual behaviors that affect health—but the existing research is limited at best. However, what we do know suggests that raising the minimum wage may not be uniformly helpful. Should policy makers pursue raising the minimum wage, it will be key to do so in a way that does not exacerbate long-standing inequities in both income and health.

Effects of a $15 Minimum Wage

The federal minimum wage has held steady at $7.25 since 2009, although 29 states have set a higher rate. Many states that have not are in the southern United States, where the highest proportion of minimum-wage workers is found. These are also the states that report some of the worst health outcomes, including high rates of obesity and low birth weights. Some of the strongest existing research on health outcomes associated with wage increases hint at what could happen if the federal minimum wage were increased to $15 per hour. Researchers have observed associations between increased wages and decreases in both suicide mortality 1 and hypertension, 2 better birth outcomes, 3 and lower rates of sexually transmitted infections among women. 4 Some research suggests that wage increases can improve health by influencing the individual behaviors that affect health, such as increased fruit and vegetable consumption, or even better mental health as a result of increased leisure time or job satisfaction.

The Minimum Wage and Existing Disparities

As state and federal policies regarding minimum wages have been enacted over the years, researchers have been able to observe associations with health outcomes, but the reported outcomes vary widely, as so do the methods used and populations studied. 5 This makes it difficult to fully understand exactly how an increase in the federal minimum wage would affect different subpopulations, and it would be unwise to assume that all populations will experience the effects the same way.

Despite the weaknesses of the evidence base, a broad look at the impact of increasing the minimum wage on health suggests that it can positively improve health outcomes for the entire population—but not without trade-offs. Depending on the state, increasing incomes could make people ineligible for public benefits assistance, including Medicaid, and ultimately harm instead of improve health. Some of the same research that suggests increased wages are associated with overall increases in fruit and vegetable consumption in the entire population shows that this does not hold true for all subpopulations, specifically Black men. 2

A recurring argument against raising the minimum wage is that it could decrease work hours or increase unemployment among low-skilled workers. It is also important to understand who these individuals are. Black and Hispanic men are particularly at risk of this type of job loss because they are disproportionately represented in low-pay occupations, such as construction, landscaping, or agricultural work.

Enhancing Public Health, Improving Health Equity

Significant race-based and gender-based wealth disparities are prevalent in the United States, and raising the minimum wage could make a small contribution in reducing or widening them. The potential for health benefits further enhances the value of a minimum wage increase. In assessing the social value of minimum wage increases, all potential benefits to health and well-being should be considered.

But so too should the potential harms. To strengthen our understanding of the connection, future research should focus on how minimum wage increases would particularly affect the health of marginalized and underrepresented populations. If research indicates that certain subpopulations might be more at risk of economic displacement and thus poorer health outcomes, there is a public health imperative to find other avenues of assistance that are not only helpful but also culturally appropriate.

It is natural for policy makers to consider raising the minimum wage, especially when more individuals are struggling to make ends meet, a situation the coronavirus disease 2019 pandemic has only exacerbated. More and more US residents report delayed care due to cost or having to choose between paying bills or buying food for their families. However, to use economic policy to improve lives, we need better and more nuanced data. Researchers have the opportunity to fill a gap in the literature, asking specifically: who will benefit from a wage increase, and who will be harmed? What strategies exist that can be used to protect workers who may be harmed?

In turn, policy makers need to support such evaluation and heed its findings to craft inclusive policies that do not inadvertently exacerbate racial and economic inequity. Members of racial and ethnic minority groups have significantly different experiences simply existing in this country, ranging from discriminatory hiring practices to White-centered medical care, and the assumption that a blanket policy will have universal impacts is not substantiated by evidence. While a federal minimum wage increase is overdue, to maximize benefits and minimize harms, it has to be done with a degree of sensitivity to disparate impacts across subpopulations not often present in broad legislative agendas. Alongside considering increasing the minimum wage, policy makers should consider alternative policies to advance protections for frequently marginalized populations. This could include supporting job training for displaced workers, increasing earned income tax credits, or establishing a universal basic income.

Our experience with the minimum wage reveals general truisms often overlooked—economic policy is also health policy. Furthermore, the benefits of economic policies often do not accrue to those most in need.

Correction: This article was corrected on January 28, 2021, to fix an error in the first paragraph.

Corresponding Author: Cecille Joan Avila, MPH, School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 ( [email protected] ).

Conflict of Interest Disclosures: None reported.

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Avila CJ , Frakt AB. Raising the Minimum Wage and Public Health. JAMA Health Forum. 2021;2(1):e201587. doi:10.1001/jamahealthforum.2020.1587

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Minimum wages and health: evidence from European countries

Research article
Published: 22 November 2022
Volume 23 , pages 85–107, ( 2023 )

Cite this article

minimum wages and public health a literature review

Laetitia Lebihan ORCID: orcid.org/0000-0003-4878-8020 1

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This study investigates the effects of minimum wage on health, well-being, and income security in European countries. The empirical strategy consists of exploiting variations in the minimum wage across European countries over time. We show that minimum wage increases improve individuals’ self-reported health and income security. Minimum wage increases also improve life satisfaction and happiness. The effects are largest among women, employed individuals, married individuals, and those with less than a secondary education. Our results are robust to several robustness checks and consistent with existing evidence on the relationship between minimum wage and health.

The Impact of Minimum Wages on Well-Being: Evidence from a Quasi-experiment in Germany

The impact of minimum wages on population health: evidence from 24 OECD countries

Do minimum wages improve self-rated health? Evidence from a natural experiment

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Introduction

To improve the standard of living for low-skilled workers, one solution policymakers have proposed is to increase the minimum wage. Advocates of a higher minimum wage often point to a decrease in income inequalities (via higher earnings) and an increase in the well-being of lower-income individuals (by increasing consumption and investing in health) (Kuroki, 2018 ). Meanwhile, opponents of a higher minimum wage argue that it will increase lay-offs and prices (Andreyeva and Ukert, 2018 ). The empirical evidence of minimum wage’s effect on employment is mixed and inconclusive, with some studies reporting a negative relationship between minimum wage increases and employment (Neumark et al., 2014 ; Neumark & Wascher, 1992 ) while others report no significant relationship (Card & Krueger, 1994 ). Regarding prices, in general, studies have shown a modest rise (Card & Krueger, 1995 ).

The existing literature on the effects of minimum wage changes on health is rarer and also mixed. On the one hand, several studies have shown that higher minimum wages improve individuals’ physical and mental health (Reeves et al., 2017 ; Lenhart, 2017a ) and birth outcomes (Wehby et al., 2020 ). On the other hand, some studies have found negative or no effects of minimum wage increases on health outcomes (Horn et al., 2017 ; Averett et al., 2017 ). Footnote 1

In this paper, we investigate the effect of minimum wage increases on the health and well-being of individuals in European countries. Minimum wages can affect health through several pathways. First, minimum wages can impact health outcomes through changes in income. In the Grossman ( 1972 ) model, individuals inherit an initial stock of health which depreciates over time, but which can be positively affected through investments like exercise and a healthier diet. Assuming health as a normal good, workers will increase health inputs and see their health improve when minimum wage increases. However, increased income could also increase risky behaviors by enabling individuals to purchase unhealthy goods (e.g., junk food, tobacco, alcohol, and illicit drugs). Footnote 2 Second, minimum wages could affect health by impacting workers’ financial stress and income security. The medical literature reports the existence of physiological reactions to stress in the form of complications with the circulatory system and heart diseases (Henry, 1982 ). Existing evidence also shows that minimum wage increases have a beneficial effect on mental health while reducing financial stress (Horn et al., 2017 ; Reeves et al., 2017 ; Lenhart, 2017a ). Third, rises in minimum wage increase opportunity costs of leisure time and may not allow workers to invest in health-enhancing activities (Horn et al., 2017 ). In other words, an increase in hourly wages could induce individuals to work more hours and reduce the number of hours allocated to activities improving their health, such as exercise and healthier diet. Given the lack of consensus in economic theory on the relationship between minimum wage increases and health, there is a need for further research in this field.

We contribute to the existing literature on the relationship between minimum wage and health/well-being in four ways. First, to the best of our knowledge, this is the first study to provide an empirical analysis of the impact of minimum wage on health/well-being in European countries. Previous studies have focused on a single country, such as the United States (the majority of previous work) (Horn et al., 2017 ; Andreyeva and Ukert, 2018 ) or the United Kingdom (Reeves et al., 2017 ). Lenhart ( 2017b ) examined the relationship between minimum wages and population health for 24 OECD countries. However, the study used countries as the units of analysis, and the sample sizes varied between 63 and 381. Using individual-level data is crucial when investigating the relationship between minimum wage and health, as is the case in our paper, because the effects of minimum wage are unlikely to be uniform. For example, the health effects may be different depending on whether the individual remains employed or experiences a decrease in employment outcomes. Moreover, Lenhart’s study did not focus on those directly or most likely affected by minimum wages (i.e., low-wage/low-skilled workers), but rather combined low- and high-wage workers. However, it is unlikely that minimum wage affects high-wage workers (Leigh et al., 2019 ). Moreover, European countries are different from the United States in several ways, including fewer social inequalities, public health insurance, a more redistributive tax/transfer system, labor markets, and the healthcare system.

Second, we contribute to a small but growing collection of literature that seeks to investigate the effects of minimum wage changes on non-employment outcomes. More generally, we investigate the causal impact of increased income on health and well-being outcomes. Footnote 3

Third, we examine heterogeneous effects of minimum wage on a variety of characteristics (gender, employment status, age, education level, marital status, minority, and country characteristics). Indeed, following an increase in minimum wage, improvements in health outcomes could be more plausible for some sub-populations, like women or employed individuals.

Finally, this paper has important implications for policymakers and could contribute to the ongoing debate regarding the introduction of a common framework on minimum wage in Europe (Forbes, 2020 ). This is particularly crucial following the Covid-19 pandemic, which may cause health inequalities to increase.

In this study, we use the European Social Survey (ESS) data. Our empirical strategy consists of exploiting variations in the minimum wage across countries and over time using individual-level data. Our estimates suggest that minimum wage increases improve individuals’ self-reported health and income security. Minimum wage increases also improve life satisfaction and happiness. These positive effects are largest among women, employed individuals, married individuals, minorities, those with less than a secondary education, and those living in the poorest countries. Our results are robust to several robustness checks.

The rest of the paper is organized as follows. In the sections Data and Empirical strategy present, respectively, the sample data and the empirical strategy. In Section Results summarizes the empirical results, and section Conclusion concludes the paper.

We use data from the European Social Survey (ESS), a cross-sectional survey of more than 30 European countries. Since 2001/2002, ESS interviews have been conducted biennially and include questions on the attitudes, beliefs, and behaviors of European residents over 15 years old. Footnote 4 In this study, we use the 2001/2002 to 2016/2017 cycles of ESS on 17 European countries. Footnote 5

Our sample includes individuals 18 to 64 years old with no more than a high school degree. This approach is consistent with existing evidence on minimum wages and health for several reasons (Andreyeva and Ukert, 2018 ; Horn et al., 2017 ). First, we focus on individuals 18 to 64 years old because we would like to know how minimum wages affect the health of individuals likely to be persistently impacted by low wages throughout their careers. Second, we want to focus on individuals likely affected by minimum wages (i.e., lesser-skilled workers). Existing evidence uses education as an hourly wage proxy and classifies individuals with high school education or less as the group most commonly affected by minimum wage (Leigh et al., 2019 ; Andreyeva and Ukert, 2018 ; Horn et al., 2017 ; Hoynes et al., 2015 ; Sabia & Nielsen, 2015 ; Evans & Garthwaite, 2014 ). Thus, we follow the approach adopted by previous studies and focus on low-educated individuals–a group most likely to be affected by minimum wages.

We also excluded respondents not in the labor force or who were self-employed. Footnote 6 These sample restrictions allow us to focus on those individuals likely to be affected by changes in the minimum wage and whom policymakers target when considering raising the minimum wage: low-skilled workers with low salaries.

Next, we match individuals surveyed in a particular country, month, and year with annual data on the real hourly minimum wages, which are collected from the OECD database (OECD Database). Footnote 7

We use self-reported health status as an outcome variable to measure an individual’s health with the question: “How is your health in general?” Responses are coded on a 5-point Likert scale: 1 (“Very good”), 2 (“Good”), 3 (“Fair”), 4 (“Bad”), and 5 (“Very bad”). We also construct three indicator variables: a dummy that equals 1 if the individual is in “very good” health and 0 otherwise; a dummy that equals 1 if the individual reports “very good” or “good” health and 0 otherwise; and finally a dummy that equals 1 if the individual reports “bad” or “very bad” health. All of these indicators are very common in the health economics literature and are, in particular, used to investigate the relationship between minimum wages and health (Lebihan & Takongmo, 2018 ; Horn et al., 2017 ; Barbaresco et al.., 2015 ; Humphreys et al., 2014 ). Existing evidence shows that self-assessed health variables are associated with objective measures of health (DeSalvo et al., 2006 ; Idler & Benyamini, 1997 ).

We also use two variables related to well-being: life satisfaction and happiness. Evidence shows that life satisfaction and happiness are associated with overall health and, specifically, with mental health (Lombardo et al., 2018 ; Siahpush et al., 2008 ; Bray & Gunnell, 2006 ). We measure life satisfaction using the following question: “All things considered, how satisfied are you with your life as a whole nowadays?” Responses are coded on a scale from 0 (extremely bad) to 10 (extremely good). Happiness is measured using the following question: “Taking all things together, how happy would you say you are?” Responses are coded on a scale from 0 (extremely unhappy) to 10 (extremely happy).

Financial distress is known to have a detrimental effect on well-being (Berrill et al., 2021 ). Increasing minimum wage may reduce financial stress on vulnerable individuals because studies show that minimum wage increases raise income for low-income groups (Gertner et al., 2019 ). We measure economic insecurity using the following question: “Which of the descriptions on this card comes closest to how you feel about your household’s income nowadays?” Responses were coded as: 1 (“Living comfortably on present income”), 2 (“Coping on present income”), 3 (“Difficult on present income”), and 4 (“Very difficult on present income”). We construct an indicator variable on economic insecurity: a dummy that equals 1 if it is “difficult on present income” or “very difficult” and 0 otherwise.

We include several covariates to control for individual- and country-level characteristics that might correlate with both minimum wage and our dependent variables. The individual-level controls are gender, age and age squared, immigrant status (whether the respondent was not born in the country of residence), partnership status (whether the respondent is married/cohabiting), minorities (whether the respondent is a visible minority), education categories (less than secondary education, secondary schooling), religion (whether the respondent belongs to particular religion or denomination), and living in an urban area. We also include the natural logarithm of household size.

The country-level characteristics are the natural logarithm of real GDP per capita (in 2018 US dollars), government health expenditures and family expenditures (as a share of total GDP), annual unemployment rate, and the number of hospital beds and physicians per 1000 people. We also include net replacement rate in unemployment, tax wedge, trade union density, and collective bargaining coverage. Net replacement rate in unemployment and tax wedge are measured for a single person without children earning an average wage. Net replacement rates in unemployment measure the proportion of income that is maintained after two months of unemployment. Tax wedge is used as a control for labor taxation. Trade union density is defined as the number of net union members (i.e., excluding those who are not in the labor force, unemployed, and self-employed) as a proportion of the number of employees. The collective bargaining coverage rate represents the share of workers covered by valid collective agreements in force. These variables are available in the OECD database and are similar to those used in studies of minimum wage (Andreyeva and Ukert, 2018 ; Horn et al., 2017 ). Footnote 8

Appendix Table 5 provides an overview of the OECD countries studied in this paper. The year when minimum wage was introduced as well as summary statistics for the minimum wage and the Kaitz index is presented for each country throughout the study period. In our sample, the first country to introduce a minimum wage was Spain (1963); the last country to do so was Germany (2015). The three countries with the most generous minimum wages are Belgium (11.00 USD PPP), France (11.77 USD PPP), and Germany (11.33 USD PPP). The three countries with the less generous minimum wages are Estonia (3.48 USD PPP), Latvia (3.04 USD PPP), and Slovak Republic (2.44 USD PPP). We note large variations in minimum wages and the Kaitz index between countries and within countries during the period of this study. Poland and Slovenia experienced the largest jump in their minimum wages and the Kaitz index. In our sample, all the countries have a national minimum wage system, meaning that, according to the law, the minimum wage is geographically homogeneous in the country. There are no geographically heterogeneous minimum wage policies within the country. Footnote 9

Table 1 presents the summary statistics for our study sample. We show statistics for the dependent variables. The average self-reported general health is 2.12, with 20.9% reporting their health as very good health and 71.3% reporting their health as very good or good. About 3.5% of respondents report that their health as bad or very bad, and 30.1% find that it is difficult or very difficult to live on their present income. The average life satisfaction and happiness are, respectively, 6.59 and 7.09. We also present statistics for country-level and individual characteristics. For example, in our sample of low-educated individuals, about 27% of respondents have less than a secondary education and 73% have completed secondary schooling. The average age is 40.9 years, and the minority share is roughly 6%. On average, the unemployment rate is 9.14%, and the GDP per capita is around US $35,681. The average net replacement rate in unemployment is 60.4%.

Empirical strategy

Our empirical strategy consists of exploiting the variation in the minimum wages across countries and over time. We estimate the following model:

where $Y_{icmt}$ is an outcome variable for individual i in country c in month m and in year t . The $MW_{ct}$ variable is the current minimum wage in country c in year t . $Z_{ct}$ and $X_{icmt}$ are, respectively, country and individual control variables. $\theta _{c}$ , $\tau _{t}$ , and $M_{m}$ are, respectively, country, year, and month fixed effects. Country fixed effects control for time-invariant country-level characteristics that influence individuals, and year fixed effects control for changes in health over time common to all countries. Month fixed effects control for seasonality in health outcomes (Christodoulou et al., 2012 ). We also include country-specific linear time trends $\varOmega _{ct}$ to control for time-varying country-level factors. Finally, $\varepsilon _{ictm}$ is the error term.

In the Grossman ( 1972 ) model, there could be a time delay between minimum wage variations and health. In order to take this into account, we also estimate the following model with the lagged minimum wage:

where $MW_{ct-1}$ is the one-year lagged minimum wage for each country and year.

Following the minimum wage literature, we consider the natural logarithm of the minimum wage, and coefficient estimates can be interpreted as semi-elasticities. Footnote 10 For continuous dependent variables, we use ordinary least squares (OLS); for binary dependent variables, we use linear probability models. Footnote 11 We also use the weights available in the ESS data. Standard errors are clustered at the country level to account for shocks correlated within country over time.

This section is arranged as follows. First, we report the main estimates of minimum wage on health. Second, we explore heterogeneous effects. Finally, we present results from a series of robustness checks.

Main estimates

In Table 2 , four specifications are presented for the main estimates: (i) only countries, year, and month dummies; (ii) the addition of individual control variables; (iii) the addition of country-specific control variables; and (iv) the addition of linear country-specific time trends. In the first three specifications, the results are consistent. Indeed, Panel A (Column 3) shows that minimum wage increases have a beneficial and significant effect on the health status of individuals. The results also suggest that a 10% increase in minimum wage is associated with a 1.25 percentage point increase in the likelihood of being in very good health. Relative to the baseline proportion (0.209), this coefficient estimate implies a 6% increase in this probability. Similarly, we report that a 10% increase in minimum wage is associated with a 1.74 percentage point increase in the likelihood of being in very good/good health and a 0.40 percentage point decrease in the likelihood of being in bad/very bad health. The results also show that an increase in minimum wage significantly raises life satisfaction and happiness. In addition, we find that the minimum wage increases are associated with a decrease in the likelihood of finding it difficult to live on the present income.

Panel B shows that these findings are similar when using the one-year lagged minimum wage. In the last specification, we include state-specific time trends and show that our results remain similar. Footnote 12

Overall, the results indicate that minimum wage improves individuals’ self-reported health, well-being, and income security. These findings are in line with Lenhart ( 2017a ), who found that the introduction of the National Minimum Wage (NMW) in the United Kingdom improved the self-reported health status of individuals and reduced their financial stress. The author also shows that the NMW improved overall job satisfaction and satisfaction with the pay. Our results are consistent with evidence from the United States. Indeed, Andreyeva and Ukert ( 2018 ) reported that minimum wage increases are associated with a decrease in the number of days in poor health. Similarly, Kuroki ( 2018 ) found a positive and significant relationship between life satisfaction of low-skilled workers and higher minimum wages. Finally, Lenhart ( 2017b ) showed that higher minimum wage levels are associated with significant improvements in population health (mortality, life expectancy, doctor consultations, etc.) and poverty.

Heterogeneous effects

Following an increase in minimum wage, improvements in outcomes could be more plausible for some sub-populations. For example, women are more likely to be paid minimum wage than men, suggesting that the impact of minimum wage increases can be more important for women. Similarly, the effects may be different depending on whether the individual remains employed or experiences a decrease in employment outcomes. Individuals with less than secondary education account for a larger proportion of low-income individuals, suggesting the effects of increases in the minimum wage may be larger for this group.

Thus, in Table 3 , we evaluate the heterogeneous effects across gender, employment status, age group, education level, marital status, minorities, and country characteristics. The results show that the minimum wage increases significantly improve individuals’ health, well-being, and income insecurity, regardless of gender; however, the effects are larger for women.

Minimum wage increases are also expected to have different effects depending on employment status. Indeed, existing evidence reports negative effects on employment, particularly in European countries. For example, Caliendo et al. ( 2018 ) find that overall employment was reduced by around 140,000 jobs, or 0.4%, after the implementation of a minimum wage in Germany. Similar results are obtained by Holtemöller and Pohle ( 2020 ). Consistent with these findings in Germany, studies of the UK’s minimum wage show small negative effects on employment (Dolton et al., 2015 ) Workers who remain employed following a minimum wage increase will experience income gains (all else being equal) whereas those who lose their jobs because of the minimum wage will experience income losses. Workers with higher incomes should invest more in market goods and see their health improve when minimum wage increases (all else being equal) (Grossman, 1972 ). However, job losers earn less and, therefore, their health could decline as a result of this decrease in income. Footnote 13 According to the relative income hypothesis and as wage changes are common knowledge, individuals who lose their jobs could also experience worse health outcomes because they are not benefiting from the higher salaries and they compare themselves to their employed peers who are benefiting from them. Job loss due to minimum wage can also have a negative effect on financial security and therefore on mental health. Our findings here are consistent with all of these hypotheses. Indeed, the beneficial effects of minimum wage on health, well-being, and income security are driven by employed individuals. The coefficient on the interaction term $MW*Employed$ is significant for all outcomes, suggesting that individuals who remain employed with an increase in the minimum wage experience fewer financial difficulties, which is beneficial for their health and well-being. Thus, low-wage workers who are still employed are better off in terms of health and income security at the expense of those who lost their jobs due to minimum wage. Our results are consistent with those of Horn et al. ( 2017 ), who document heterogeneous effects by employment status. Clearly, to the extent minimum wages cause unemployment, there is also a negative effect on health that needs to be accounted for. Although the causality between minimum wage and unemployment is beyond the scope of this paper, these results could be important for policymakers and choosing to put these policies in place could improve the outcomes of low-wage workers. Indeed, if these minimum wage increases lead to unintended consequences, such as reduced health outcomes in addition to job losses, these effects should not be ignored.

In Table 3 , we also report the heterogeneous effects across age groups. We show that minimum wage increases have a significant and beneficial impact on health and income insecurity, regardless of individuals’ age; however, the effects are more important for older individuals. Footnote 14 We also find significant beneficial effects on individuals’ health, well-being and income insecurity, regardless of education, but the effects are significantly larger for respondents with less than secondary education. For example, the results show that a 10% increase in minimum wages is associated with a 0.56 percentage point decrease in the likelihood of being in bad/very bad health for all individuals, but this effect is 0.76 percentage point (0.056 + 0.020 = 0.076) for lower educated. Similarly, we report a larger impact of minimum wage increases on married individuals and minorities. Finally, we explore if there are differential effects between rich and poor countries because existing evidence has reported that income inequality and poverty are important drivers of adverse health outcomes in poor countries (Deaton, 2003 ). Estimates show that the minimum wage increases have a significant effect on health, well-being, and income insecurity, regardless of the richness of country; however, the effects are larger for poor countries. Footnote 15

Clearly, the positive effects on health are largest among women, employed individuals, married individuals, minorities, those with less than secondary education, and those living in the poorest countries. Indeed, these subgroups are more vulnerable to financial constraints and economic insecurity and have a higher marginal utility of income. These findings are similar to those reported by Andreyeva and Ukert ( 2018 ) and Lenhart ( 2017b ).

Robustness checks

In Table 4 , we present estimates from a number of robustness checks. In each panel, a different regression is shown. In Panel A, we exclude individuals 55 years old or older because they may be marginally attached to the labor force. Panel B excludes respondents younger than 20 years old. Teens could move into higher-wage jobs and earn more than the minimum wage once they get additional education or gain more work experience (Horn et al., 2017 ). In Panel C, we exclude the years of the recession (2008 and 2009) from the sample. These first three specifications allow us to test whether the sample composition and sample period affect our findings. Regardless of the specification, our results remain similar and indicate that minimum wage increases have a beneficial effect on health, well-being and income security.

In Panel D, we replace state-specific linear time trends with state-specific quadratic time trends. This allows us to be less restrictive concerning specific forms on unobservable differences, and we show that results remain similar.

In existing evidence on minimum wage, standard errors are clustered at the country level (or state level). However, several concerns about inference exist (Cameron et al., 2008 ; Donald & Lang, 2007 ; Bertrand et al., 2004 ). Indeed, the conventional methods underestimate cluster-adjusted standard errors when they are limited in number, like in our study with 17 clusters. To overcome this, in Panel E, we use the wild cluster bootstrap suggested by Cameron et al. ( 2008 ) and Webb ( 2014 ). Again, no matter the specification, our estimates remain consistent.

We also estimate the impact of minimum wage using the Kaitz index. This index, available in the OECD database, measures the ratio between a country’s minimum wage and the mean wages of full-time workers. The Kaitz index has been widely used in studies examining minimum wage effects and has the advantage of including information on the relative level of minimum wages (Lenhart, 2017b ; Neumark et al., 2014 ; Brown et al., 1982 ). Estimates are shown in Appendix Table 6 and confirm the findings presented earlier.

In Appendix Table 7 , we report additional robustness tests. In Panel H, we replace the one-year lagged minimum wage, in equation (2), with the two-year lagged minimum wage. Similarly, we replace the one-year lagged minimum wage with the three-year lagged minimum wage. We found no significant effect of minimum wage increases on the outcomes studied, suggesting that the effects are essentially contemporaneous or with a time lag of up to one year.

In Panel I (Appendix Table 7 ), the minimum wage is divided into five quantiles in order to study the potential effects of non-linearities (the first quantile is the reference category). The estimates show that the effects are significant across the entire minimum wage distribution (significant effects across all five quantiles), although the effects are larger for the highest quintiles.

In Panel J (Appendix Table 7 ), as a falsification test, we replace the one-year lagged minimum wage in equation (2) with the one-year lead minimum wage for each country and year. The underlying intuition is that current health outcomes are unlikely to be affected by future minimum wages. We found that the lead minimum wage has no significant effect on outcomes at the 5% level, except for financial security. This finding may simply reflect that minimum wage changes are correlated with some unobservable variables that affect financial security. However, we argue that it is reassuring that this concerns only one outcome and that our results are robust to all robustness tests.

Finally, similar to Huang et al. ( 2021 ), we conduct a falsification test using one placebo group: university-educated adults aged 18–64 years old. The DD estimates show no significant effect of the minimum wage on this group (Table 4 , Panel F). The only exceptions are life satisfaction and income insecurity with lagged minimum wage, but the coefficient estimates instead show a decrease of life satisfaction and an increase of income insecurity for this group. Footnote 16 In Panel G, we use the university group as the placebo group to estimate a difference-in-differences-in-differences (DDD) model. We add an indicator for whether an individual belongs to the low-educated group. We find that the observed effects are mostly significant for low-educated individuals. The estimates are consistent with those estimated in the DD model. Clearly, these results give us confidence that our results are not spurious.

This paper presents new empirical evidence of the effects of minimum wage on self-reported health, well-being, and income insecurity in European countries. To the best of our knowledge, this study is the first to investigate the impact of minimum wage on health/well-being in European countries using individual-level data. We show that minimum wage increases improve individuals’ self-reported health and income security. Minimum wage increases also improve life satisfaction and happiness. The effects are larger for women, employed individuals, married individuals, minorities, those with less than secondary education, and those living in the poorest countries. Our results are also robust to several robustness checks and consistent with existing evidence.

These findings have important implications for policymakers and contribute to the ongoing debate on the introduction of a common framework on minimum wage in Europe, especially in the context of the Covid-19 pandemic. In general, countries wishing to introduce or increase the minimum wage are motivated to reduce poverty and improve social equity. Our results show that this type of reform can also have unexpected impacts on health outcomes and reduce existing health disparities. Due to data limitations, this study is viewed as an important first step in exploring the relationship between health and minimum wages, and future studies should examine the effects of minimum wage increases on other health outcomes and its potential mechanisms, such as health care access and health care utilization.

See Leigh et al. ( 2019 ) for a review of evidence on the effect of minimum wages on health outcomes.

For example, Huang et al. ( 2021 ) showed that a $1 increase in the minimum wage in the United States raises the prevalence of smoking by about 2.3% and diminishes cessation by about 13.7% among the low-skilled employees. They also report an income effect as a potential mechanism for increased smoking.

Several studies have exploited income shocks, such as changes in the Earned Income Tax Credit (Evans & Garthwaite, 2014 ) or inheritances and lottery winnings (Gardner & Oswald, 2007 ), to estimate the causal effect of increased income on health. The use of minimum wages as natural experiments is a very recent approach adopted in the literature (Leigh et al., 2019 )

The ESS questionnaire includes a combination of repeated key items (the core section), which remains relatively similar from round to round, as well as several rotating modules, repeated at intervals.

Only countries that had effective minimum wages in place during our study period are included in the analysis. These countries are Belgium, the Czech Republic, Estonia, France, Germany, Hungary, Ireland, Israel, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, the Slovak Republic, Slovenia, Spain, and the United Kingdom.

Respondents who are not in the labor force include those who are retired, are students, or reported being a homemaker.

“Real hourly minimum wages are calculated first by deflating the series using the consumer price index taking 2020 as the base year. The series are then converted into a common currency unit (USD) using Purchasing Power Parities (PPPs) for private consumption expenditures in 2020.” (OECD Database).

In Europe, and particularly in our sample, between 90% and 99% of the total population is covered by public health insurance. Many European countries benefit from universal access to health care. This contrasts sharply with the United States, where only about 30% of the total population is covered by public health insurance such as Medicare and Medicaid. (OECD, 2021 ).

Some OECD countries such as the United States or Canada have a regional minimum wage, meaning the minimum wage varies by region. In these countries, there is a federal wage minimum wage, but states may set a minimum wage above the federal level. In our sample, all countries have geographically homogeneous minimum wage policies within the country (Adema et al., 2019 ).

Results using unlogged minimum wages are similar and available from the authors.

Linear models are commonly used in the literature for ease of interpretation. Results are similar if we use ordered probit models (available on request). Moreover, existing evidence shows that results are similar when well-being and health are treated as an ordinal or cardinal concept (Kuroki, 2018 ; Haeck et al., 2018 ; Horn et al., 2017 ).

In general, existing evidence includes state-specific linear time trends to remove bias due to unobservable state-specific time trends when studying the impact of minimum wages on labor and health outcomes. However, Sabia and Nielsen ( 2015 ) reported that the using such trends substantially reduces the ability to identify variations (decrease of more than 60%). Thus, some studies have not included these trends in their models (Averett et al., 2018 , 2017 ). Here, we present the results both with and without these trends, showing that, although the magnitude of the effects decreases, minimum wage increases still improve individuals’ health, well-being, and personal income security.

Colman and Dave ( 2013 ) report that individuals who lose their jobs experience a decrease in overall physical activity and an increase in sedentary activity (e.g., television watching).

Older individuals include those 30 years old or older.

The division of countries is based on GDP per capita. Poor countries include countries whose GDP per capita is lower than the median GDP per capita in the sample (i.e., the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia). Rich countries include countries whose GDP per capita is higher than the median GDP per capita of the sample (i.e., Belgium, France, Germany, Ireland, Israel, Luxembourg, the Netherlands, Spain, and the United Kingdom).

In Appendix Table 7 , we report DD results using a different placebo group: retired adults who are 65 years old and older (Panel H). Despite a few exceptions, the results generally show a non-significant effect of minimum wage increases on the outcomes studied.

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Acknowledgement

We wish to thank Professors Catherine Haeck, Pierre Lefebvre and Philip Merrigan. All computations were prepared by the authors, who assume responsibility for the use and interpretation of these data.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Lebihan, L. Minimum wages and health: evidence from European countries. Int J Health Econ Manag. 23 , 85–107 (2023). https://doi.org/10.1007/s10754-022-09340-x

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DOI: 10.1001/JAMAHEALTHFORUM.2020.1587
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Raising the Minimum Wage and Public Health.

Cecille Joan Avila , A. Frakt
Published in JAMA Health Forum 4 January 2021
Economics, Political Science, Medicine

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Beyond minimum wage: broader employment policies can significantly affect food insecurity., association of increasing the minimum wage in the us with experiences of maternal stressful life events, 5 references, effects of the minimum wage on infant health, minimum wages and public health: a literature review, examining the association of changes in minimum wage with health across race/ethnicity and gender in the united states, states with higher minimum wages have lower sti rates among women: results of an ecological study of 66 us metropolitan areas, 2003-2015, association between state minimum wages and suicide rates in the u.s., related papers.

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Minimum wages and health: evidence from European countries

Laetitia lebihan.

Department of Economics, University of Reunion Island, 15 avenue René Cassin - CS 92003, 97744 Saint-Denis Cedex 9, Reunion Island France

Introduction

In this paper, we investigate the effect of minimum wage increases on the health and well-being of individuals in European countries. Minimum wages can affect health through several pathways. First, minimum wages can impact health outcomes through changes in income. In the Grossman ( 1972 ) model, individuals inherit an initial stock of health which depreciates over time, but which can be positively affected through investments like exercise and a healthier diet. Assuming health as a normal good, workers will increase health inputs and see their health improve when minimum wage increases. However, increased income could also increase risky behaviors by enabling individuals to purchase unhealthy goods (e.g., junk food, tobacco, alcohol, and illicit drugs). 2 Second, minimum wages could affect health by impacting workers’ financial stress and income security. The medical literature reports the existence of physiological reactions to stress in the form of complications with the circulatory system and heart diseases (Henry, 1982 ). Existing evidence also shows that minimum wage increases have a beneficial effect on mental health while reducing financial stress (Horn et al., 2017 ; Reeves et al., 2017 ; Lenhart, 2017a ). Third, rises in minimum wage increase opportunity costs of leisure time and may not allow workers to invest in health-enhancing activities (Horn et al., 2017 ). In other words, an increase in hourly wages could induce individuals to work more hours and reduce the number of hours allocated to activities improving their health, such as exercise and healthier diet. Given the lack of consensus in economic theory on the relationship between minimum wage increases and health, there is a need for further research in this field.

We use data from the European Social Survey (ESS), a cross-sectional survey of more than 30 European countries. Since 2001/2002, ESS interviews have been conducted biennially and include questions on the attitudes, beliefs, and behaviors of European residents over 15 years old. 4 In this study, we use the 2001/2002 to 2016/2017 cycles of ESS on 17 European countries. 5

We also excluded respondents not in the labor force or who were self-employed. 6 These sample restrictions allow us to focus on those individuals likely to be affected by changes in the minimum wage and whom policymakers target when considering raising the minimum wage: low-skilled workers with low salaries.

Next, we match individuals surveyed in a particular country, month, and year with annual data on the real hourly minimum wages, which are collected from the OECD database (OECD Database). 7

Appendix Table Table5 5 provides an overview of the OECD countries studied in this paper. The year when minimum wage was introduced as well as summary statistics for the minimum wage and the Kaitz index is presented for each country throughout the study period. In our sample, the first country to introduce a minimum wage was Spain (1963); the last country to do so was Germany (2015). The three countries with the most generous minimum wages are Belgium (11.00 USD PPP), France (11.77 USD PPP), and Germany (11.33 USD PPP). The three countries with the less generous minimum wages are Estonia (3.48 USD PPP), Latvia (3.04 USD PPP), and Slovak Republic (2.44 USD PPP). We note large variations in minimum wages and the Kaitz index between countries and within countries during the period of this study. Poland and Slovenia experienced the largest jump in their minimum wages and the Kaitz index. In our sample, all the countries have a national minimum wage system, meaning that, according to the law, the minimum wage is geographically homogeneous in the country. There are no geographically heterogeneous minimum wage policies within the country. 9

Minimum wage (details), 2001−2017 (Appendix)

Country (Year of Introduction	Variable	Mean	SD	Min.	Max.	System
of the Minimum Wage)	Variable	Mean	SD	Min.	Max.	System
Belgium (1975)	Minimum Wage	11.00	0.16	10.76	11.40	National
Belgium (1975)	Kaitz index	0.41	0.01	0.39	0.43	National
Czech Republic (1991)	Minimum Wage	3.95	0.28	3.34	4.44	National
Czech Republic (1991)	Kaitz index	0.33	0.01	0.31	0.35	National
Estonia (1991)	Minimum Wage	3.48	0.64	2.54	4.78	National
Estonia (1991)	Kaitz index	0.33	0.02	0.30	0.37	National
France (1970)	Minimum Wage	11.77	0.22	11.43	12.10	National
France (1970)	Kaitz index	0.51	0.00	0.50	0.51	National
Germany (2015)	Minimum Wage	11.33	0.09	11.29	11.57	National
Germany (2015)	Kaitz index	0.42	0.00	0.42	0.43	National
Hungary (1991)	Minimum Wage	3.68	0.54	3.22	4.80	National
Hungary (1991)	Kaitz index	0.38	0.03	0.34	0.43	National
Ireland (2000)	Minimum Wage	8.97	0.22	8.78	9.38	National
Ireland (2000)	Kaitz index	0.36	0.01	0.33	0.38	National
Israel (1987)	Minimum Wage	5.78	0.40	5.31	6.61	National
Israel (1987)	Kaitz index	0.42	0.01	0.39	0.44	National
Latvia (1991)	Minimum Wage	3.04	0.00	3.04	3.04	National
Latvia (1991)	Kaitz index	0.37	0.00	0.37	0.37	National
Lithuania (1990)	Minimum Wage	3.95	0.71	3.19	4.88	National
Lithuania (1990)	Kaitz index	0.42	0.02	0.39	0.44	National
Luxembourg (1973)	Minimum Wage	10.87	0.03	10.86	11.07	National
Luxembourg (1973)	Kaitz index	0.45	0.00	0.45	0.46	National
Netherlands (1969)	Minimum Wage	10.96	0.11	10.80	11.17	National
Netherlands (1969)	Kaitz index	0.41	0.01	0.39	0.42	National
Poland (1970)	Minimum Wage	4.42	0.99	3.26	6.41	National
Poland (1970)	Kaitz index	0.37	0.03	0.34	0.44	National
Slovak Republic (1991)	Minimum Wage	2.44	0.39	1.82	2.90	National
Slovak Republic (1991)	Kaitz index	0.35	0.01	0.34	0.37	National
Slovenia (1995)	Minimum Wage	6.49	0.88	5.30	7.55	National
Slovenia (1995)	Kaitz index	0.46	0.04	0.41	0.50	National
Spain (1963)	Minimum Wage	7.42	0.39	6.64	8.07	National
Spain (1963)	Kaitz index	0.31	0.02	0.28	0.34	National
United Kingdom (1999)	Minimum Wage	9.10	0.40	8.79	9.95	National
United Kingdom (1999)	Kaitz index	0.40	0.02	0.38	0.44	National

Hourly minimum wages are measured in USD PPP

Descriptive statistics

	Mean	SD	Min	Max

Health	2.116	0.777	1	5
Very good health	0.209	0.407	0	1
Very good or good health	0.713	0.452	0	1
Bad or very bad health	0.035	0.184	0	1
Life satisfaction	6.594	2.251	0	10
Happiness	7.089	1.934	0	10
Difficult or very difficult to live on present income	0.301	0.459	0	1

Minimum wage	6.769	3.271	1.820	12.100
Government health expenditures (% of GDP)	6.075	1.512	3.608	9.576
Family expenditures (% of GDP)	2.169	0.738	0.905	3.941
GDP per capita	35,680.51	13,493.21	16,163.75	97,605.62
Hospital beds (per 1000)	5.538	1.661	2.540	8.130
Physicians (per 1000)	3.152	0.504	2.160	4.560
Unemployment rate	9.137	4.470	3.100	26.100
Tax wedge	41.319	7.529	20.351	56.331
Net replacement rate in unemployment	60.418	11.591	35.000	85.000
Trade union density	20.943	12.066	4.700	56.900
Collective bargaining coverage	52.396	29.866	8.300	100.000

Female	0.458	0.498	0	1
Age	40.949	11.811	18	64
Household size	3.246	1.405	1	20
Less than secondary education	0.273	0.445	0	1
Secondary schooling	0.727	0.445	0	1
Immigrant	0.087	0.282	0	1
Partnered	0.674	0.469	0	1
Minority	0.063	0.243	0	1
Any religion	0.518	0.500	0	1
Urban	0.281	0.449	0	1

This table displays the weighted summary statistics for outcome variables, country, and personal characteristics

Empirical strategy

Our empirical strategy consists of exploiting the variation in the minimum wages across countries and over time. We estimate the following model:

where Y icmt is an outcome variable for individual i in country c in month m and in year t . The M W ct variable is the current minimum wage in country c in year t . Z ct and X icmt are, respectively, country and individual control variables. θ c , τ t , and M m are, respectively, country, year, and month fixed effects. Country fixed effects control for time-invariant country-level characteristics that influence individuals, and year fixed effects control for changes in health over time common to all countries. Month fixed effects control for seasonality in health outcomes (Christodoulou et al., 2012 ). We also include country-specific linear time trends Ω ct to control for time-varying country-level factors. Finally, ε ictm is the error term.

where M W c t - 1 is the one-year lagged minimum wage for each country and year.

Following the minimum wage literature, we consider the natural logarithm of the minimum wage, and coefficient estimates can be interpreted as semi-elasticities. 10 For continuous dependent variables, we use ordinary least squares (OLS); for binary dependent variables, we use linear probability models. 11 We also use the weights available in the ESS data. Standard errors are clustered at the country level to account for shocks correlated within country over time.

Main estimates

	MW	MW	MW	MW	N
	(1)	(2)	(3)	(4)

Health	−0.358***	−0.420***	−0.347***	−0.167*	45,464
Health	(0.095)	(0.091)	(0.105)	(0.094)
Very good health	0.087	0.114**	0.125**	0.115***	45,464
Very good health	(0.051)	(0.043)	(0.049)	(0.039)
Very good or good health	0.205***	0.233***	0.174***	−0.014	45,464
Very good or good health	(0.051)	(0.051)	(0.057)	(0.065)
Bad or very bad health	−0.054***	−0.062**	−0.040*	−0.055*	45,464
Bad or very bad health	(0.017)	(0.021)	(0.020)	(0.028)
Life satisfaction	1.600***	1.705***	0.885***	0.493	45,313
Life satisfaction	(0.279)	(0.288)	(0.237)	(0.422)
Happiness	1.213***	1.306***	0.770***	0.774**	45,272
Happiness	(0.200)	(0.227)	(0.222)	(0.298)
Difficult or very difficult to live	−0.386***	−0.410***	−0.227***	−0.201**	45,140
on present income	(0.060)	(0.078)	(0.040)	(0.072)

Health	−0.331***	−0.387***	−0.323**	−0.029	45,309
Health	(0.104)	(0.101)	(0.121)	(0.129)
Very good health	0.072	0.097*	0.077	0.031	45,309
Very good health	(0.054)	(0.047)	(0.058)	(0.072)
Very good or good health	0.202***	0.227***	0.181**	−0.061	45,309
Very good or good health	(0.057)	(0.057)	(0.065)	(0.075)
Bad or very bad health	−0.049***	−0.055**	−0.058***	−0.049*	45,309
Bad or very bad health	(0.016)	(0.020)	(0.016)	(0.028)
Life satisfaction	1.250***	1.325***	1.177***	−0.361	45,158
Life satisfaction	(0.327)	(0.315)	(0.363)	(0.507)
Happiness	0.958***	1.025***	0.541**	−0.026	45,117
Happiness	(0.227)	(0.228)	(0.222)	(0.466)
Difficult or very difficult to live	−0.316***	−0.333***	−0.111	−0.031	44,985
on present income	(0.061)	(0.076)	(0.081)	(0.081)
Countries dummies	Yes	Yes	Yes	Yes
Year dummies	Yes	Yes	Yes	Yes
Month dummies	Yes	Yes	Yes	Yes
Individual X’s	No	Yes	Yes	Yes
Country-specific X’s	No	No	Yes	Yes
Linear country-specific time trends	No	No	No	Yes

For each dependent variable, we report the estimated effects under different specifications ( β 1 shown). Standard errors (in parentheses) are clustered at the country level. ***significant at 1%; **significant at 5%; *significant at 10%

Heterogeneous effects

	By sex		By employment status		By age		By education
	MW	MW*Female	MW	MW*Employed	MW	MW*Noyoung	MW	MW*LessHS

Health	−0.149	−0.043**	−0.095	−0.098***	−0.099	−0.077***	−0.168*	−0.067*
Health	(0.093)	(0.018)	(0.105)	(0.012)	(0.096)	(0.013)	(0.092)	(0.032)
Very good health	0.112***	0.007	0.098**	0.023***	0.099**	0.018**	0.115**	−0.010
Very good health	(0.038)	(0.010)	(0.041)	(0.007)	(0.040)	(0.008)	(0.040)	(0.013)
Very good or good health	−0.028	0.033***	−0.055	0.056***	−0.060	0.053***	−0.012	0.057***
Very good or good health	(0.064)	(0.011)	(0.069)	(0.006)	(0.065)	(0.008)	(0.064)	(0.018)
Bad or very bad health	−0.054*	−0.003	−0.043	−0.017***	−0.051*	−0.004*	−0.056*	−0.020*
Bad or very bad health	(0.028)	(0.004)	(0.029)	(0.003)	(0.028)	(0.002)	(0.027)	(0.009)
Life satisfaction	0.485	0.020	0.042	0.604***	0.374	0.135***	0.500	0.253***
Life satisfaction	(0.430)	(0.048)	(0.391)	(0.043)	(0.427)	(0.043)	(0.430)	(0.075)
Happiness	0.782**	−0.019	0.478	0.401***	0.669**	0.120***	0.780**	0.261***
Happiness	(0.302)	(0.046)	(0.277)	(0.038)	(0.301)	(0.030)	(0.305)	(0.062)
Difficult or very difficult to live	−0.193**	−0.021**	−0.073	−0.174***	−0.199**	−0.003	−0.204**	−0.095***
on present income	(0.072)	(0.009)	(0.065)	(0.015)	(0.076)	(0.009)	(0.075)	(0.029)

Health	−0.011	−0.040**	0.042	−0.098***	0.033	−0.078***	−0.034	−0.063*
Health	(0.129)	(0.017)	(0.140)	(0.012)	(0.134)	(0.014)	(0.130)	(0.030)
Very good health	0.028	0.006	0.015	0.023***	0.017	0.018**	0.030	−0.011
Very good health	(0.072)	(0.010)	(0.074)	(0.007)	(0.071)	(0.008)	(0.072)	(0.013)
Very good or good health	−0.074	0.031***	−0.101	0.056***	−0.104	0.054***	−0.056	0.055***
Very good or good health	(0.074)	(0.010)	(0.081)	(0.006)	(0.079)	(0.008)	(0.076)	(0.017)
Bad or very bad health	−0.048	−0.003	−0.037	−0.017***	−0.046	−0.004	−0.051*	−0.019*
Bad or very bad health	(0.027)	(0.004)	(0.028)	(0.003)	(0.028)	(0.002)	(0.027)	(0.009)
Life satisfaction	−0.369	0.018	−0.800	0.604***	−0.469	0.136***	−0.339	0.251***
Life satisfaction	(0.516)	(0.047)	(0.497)	(0.044)	(0.507)	(0.045)	(0.498)	(0.072)
Happiness	−0.017	−0.021	−0.317	0.402***	−0.123	0.120***	−0.002	0.255***
Happiness	(0.474)	(0.044)	(0.462)	(0.039)	(0.463)	(0.031)	(0.461)	(0.057)
Difficult or very difficult to live	−0.021	−0.021**	0.097	−0.174***	−0.027	−0.004	−0.040	−0.093***
on present income	(0.082)	(0.009)	(0.094)	(0.015)	(0.084)	(0.009)	(0.079)	(0.028)

	By marital status		By minority		By country characteristics
	MW	MW*Married	MW	MW*Minority	MW	MW*Developing

Health	−0.079	−0.118***	−0.149	−0.043**	−0.736	0.598
Health	(0.096)	(0.016)	(0.093)	(0.018)	(0.464)	(0.477)
Very good health	0.074*	0.055***	0.112***	0.007	0.185	−0.074
Very good health	(0.037)	(0.010)	(0.038)	(0.010)	(0.226)	(0.214)
Very good or good health	−0.058	0.059***	−0.028	0.033***	0.349	−0.381
Very good or good health	(0.067)	(0.009)	(0.064)	(0.011)	(0.245)	(0.260)
Bad or very bad health	−0.053*	−0.003	−0.054*	−0.003	−0.199**	0.151**
Bad or very bad health	(0.028)	(0.004)	(0.028)	(0.004)	(0.069)	(0.064)
Life satisfaction	0.302	0.256***	0.485	0.020	0.333	0.169
Life satisfaction	(0.430)	(0.073)	(0.430)	(0.048)	(1.321)	(1.265)
Happiness	0.697**	0.104**	0.782**	−0.019	0.605	0.178
Happiness	(0.292)	(0.045)	(0.302)	(0.046)	(0.976)	(0.823)
Difficult or very difficult to live	−0.176**	−0.035**	−0.193**	−0.021**	0.267	−0.492**
on present income	(0.076)	(0.015)	(0.072)	(0.009)	(0.193)	(0.185)

Health	0.047	−0.113***	−0.025	−0.036	−0.902*	0.912*
Health	(0.135)	(0.016)	(0.130)	(0.024)	(0.460)	(0.451)
Very good health	−0.003	0.051***	0.030	0.016	0.217	−0.194
Very good health	(0.072)	(0.010)	(0.073)	(0.018)	(0.215)	(0.194)
Very good or good health	−0.099	0.057***	−0.063	0.029*	0.518*	−0.604**
Very good or good health	(0.078)	(0.009)	(0.075)	(0.015)	(0.274)	(0.264)
Bad or very bad health	−0.047	−0.003	−0.049*	0.003	−0.158**	0.114*
Bad or very bad health	(0.028)	(0.004)	(0.027)	(0.010)	(0.067)	(0.058)
Life satisfaction	−0.529	0.251***	−0.385	0.256*	−0.503	0.149
Life satisfaction	(0.502)	(0.073)	(0.496)	(0.129)	(1.020)	(1.333)
Happiness	−0.092	0.099**	−0.042	0.173	0.373	−0.417
Happiness	(0.476)	(0.044)	(0.462)	(0.133)	(0.874)	(1.078)
Difficult or very difficult to live	−0.008	−0.034**	−0.028	−0.026	0.489	−0.543*
On present income	(0.083)	(0.015)	(0.081)	(0.026)	(0.299)	(0.300)

Minimum wage increases are also expected to have different effects depending on employment status. Indeed, existing evidence reports negative effects on employment, particularly in European countries. For example, Caliendo et al. ( 2018 ) find that overall employment was reduced by around 140,000 jobs, or 0.4%, after the implementation of a minimum wage in Germany. Similar results are obtained by Holtemöller and Pohle ( 2020 ). Consistent with these findings in Germany, studies of the UK’s minimum wage show small negative effects on employment (Dolton et al., 2015 ) Workers who remain employed following a minimum wage increase will experience income gains (all else being equal) whereas those who lose their jobs because of the minimum wage will experience income losses. Workers with higher incomes should invest more in market goods and see their health improve when minimum wage increases (all else being equal) (Grossman, 1972 ). However, job losers earn less and, therefore, their health could decline as a result of this decrease in income. 13 According to the relative income hypothesis and as wage changes are common knowledge, individuals who lose their jobs could also experience worse health outcomes because they are not benefiting from the higher salaries and they compare themselves to their employed peers who are benefiting from them. Job loss due to minimum wage can also have a negative effect on financial security and therefore on mental health. Our findings here are consistent with all of these hypotheses. Indeed, the beneficial effects of minimum wage on health, well-being, and income security are driven by employed individuals. The coefficient on the interaction term M W ∗ E m p l o y e d is significant for all outcomes, suggesting that individuals who remain employed with an increase in the minimum wage experience fewer financial difficulties, which is beneficial for their health and well-being. Thus, low-wage workers who are still employed are better off in terms of health and income security at the expense of those who lost their jobs due to minimum wage. Our results are consistent with those of Horn et al. ( 2017 ), who document heterogeneous effects by employment status. Clearly, to the extent minimum wages cause unemployment, there is also a negative effect on health that needs to be accounted for. Although the causality between minimum wage and unemployment is beyond the scope of this paper, these results could be important for policymakers and choosing to put these policies in place could improve the outcomes of low-wage workers. Indeed, if these minimum wage increases lead to unintended consequences, such as reduced health outcomes in addition to job losses, these effects should not be ignored.

In Table 3 , we also report the heterogeneous effects across age groups. We show that minimum wage increases have a significant and beneficial impact on health and income insecurity, regardless of individuals’ age; however, the effects are more important for older individuals. 14 We also find significant beneficial effects on individuals’ health, well-being and income insecurity, regardless of education, but the effects are significantly larger for respondents with less than secondary education. For example, the results show that a 10% increase in minimum wages is associated with a 0.56 percentage point decrease in the likelihood of being in bad/very bad health for all individuals, but this effect is 0.76 percentage point (0.056 + 0.020 = 0.076) for lower educated. Similarly, we report a larger impact of minimum wage increases on married individuals and minorities. Finally, we explore if there are differential effects between rich and poor countries because existing evidence has reported that income inequality and poverty are important drivers of adverse health outcomes in poor countries (Deaton, 2003 ). Estimates show that the minimum wage increases have a significant effect on health, well-being, and income insecurity, regardless of the richness of country; however, the effects are larger for poor countries. 15

Robustness checks

		Health	Very good	Very good or	Bad or very	Life satisfaction	Happiness	Difficult or very difficult
			health	good health	bad health			to live on present income
Cur. min.wage	MW	−0.091	0.106**	−0.091	−0.066**	0.250	0.724*	−0.156*
Cur. min.wage	MW	(0.094)	(0.045)	(0.070)	(0.023)	(0.429)	(0.395)	(0.075)
Lag. min.wage	MW	−0.069	0.028	−0.037	−0.069**	−0.545	0.048	−0.007
Lag. min.wage	MW	(0.129)	(0.076)	(0.074)	(0.028)	(0.486)	(0.494)	(0.082)
Cur. min.wage	MW	−0.169*	0.118***	−0.015	−0.056*	0.473	0.791**	−0.210**
Cur. min.wage	MW	(0.088)	(0.037)	(0.064)	(0.027)	(0.450)	(0.307)	(0.072)
Lag. min.wage	MW	−0.020	0.025	−0.067	−0.053*	−0.344	−0.033	−0.042
Lag. min.wage	MW	(0.129)	(0.072)	(0.076)	(0.027)	(0.525)	(0.480)	(0.085)
Cur. min.wage	MW	−0.082	0.055	−0.055	−0.062*	−0.088	0.487	−0.090
Cur. min.wage	MW	(0.081)	(0.061)	(0.062)	(0.031)	(0.402)	(0.344)	(0.073)
Lag. min.wage	MW	0.044	−0.047	−0.059	−0.050	−0.897**	−0.179	−0.027
Lag. min.wage	MW	(0.139)	(0.083)	(0.072)	(0.035)	(0.379)	(0.418)	(0.087)

Cur. min.wage	MW	−0.227*	0.146**	0.017	−0.056**	0.739	0.919**	−0.194**
Cur. min.wage	MW	(0.115)	(0.057)	(0.061)	(0.024)	(0.441)	(0.337)	(0.078)
Lag. min.wage	MW	−0.141	0.081	−0.009	−0.060*	0.122	0.384	−0.098
Lag. min.wage	MW	(0.126)	(0.070)	(0.071)	(0.030)	(0.409)	(0.364)	(0.068)

Cur. min.wage	MW	0.1692	0.016	0.8639	0.0681	0.4224	0.0831	0.1431
Lag. min.wage	MW	0.8579	0.7157	0.4995	0.1111	0.6146	0.972	0.7648

Cur. min.wage	MW	−0.164	0.118	0.012	−0.027	0.371	0.601	0.140
Cur. min.wage	MW	(0.122)	(0.070)	(0.091)	(0.038)	(0.252)	(0.357)	(0.132)
Lag. min.wage	MW	−0.093	0.077	−0.003	−0.005	−1.077**	−0.117	0.207**
Lag. min.wage	MW	(0.174)	(0.099)	(0.128)	(0.036)	(0.376)	(0.324)	(0.079)

Cur. min.wage	MW	−0.146	0.130**	−0.030	−0.042	0.287	0.681***	−0.060
	MW	(0.086)	(0.046)	(0.055)	(0.024)	(0.260)	(0.218)	(0.102)
	MW*LowEduc	−0.005	−0.031***	0.030*	−0.005	0.327***	0.310***	−0.094***
	MW*LowEduc	(0.023)	(0.010)	(0.015)	(0.004)	(0.110)	(0.070)	(0.026)
Lag. min.wage	MW	0.022	0.051	−0.103	−0.023	−0.836*	−0.262	0.081
	MW	(0.122)	(0.072)	(0.062)	(0.022)	(0.424)	(0.326)	(0.085)
	MW*LowEduc	−0.007	−0.030***	0.031**	−0.005	0.323***	0.304***	−0.090***
	MW*LowEduc	(0.022)	(0.010)	(0.014)	(0.004)	(0.107)	(0.070)	(0.026)

All estimates include country dummies, year dummies, month dummies, individual characteristics, country characteristics, and linear country−specific time trends. Standard errors (in parentheses) are clustered at the country level. ***significant at 1% ; **significant at 5% ; *significant at 10%

Kaitz index (Appendix)

	MW	MW	MW	MW
	(1)	(2)	(3)	(4)
Health	−0.798	−1.055*	−1.054**	−0.099
Health	(0.485)	(0.553)	(0.422)	(0.397)
Very good health	0.166	0.268	0.417	−0.129
Very good health	(0.254)	(0.264)	(0.251)	(0.237)
Very good or good health	0.481*	0.600*	0.537**	0.205
Very good or good health	(0.266)	(0.293)	(0.226)	(0.213)
Bad or very bad health	−0.123*	−0.157	−0.083	−0.027
Bad or very bad health	(0.069)	(0.090)	(0.078)	(0.115)
Life satisfaction	2.607	3.202	1.161	−1.524
Life satisfaction	(1.816)	(2.059)	(0.926)	(1.497)
Happiness	2.952*	3.421*	0.744	−0.766
Happiness	(1.406)	(1.619)	(0.720)	(0.993)
Difficult or very difficult to live	−0.452	−0.637	−0.047	0.419
on present income	(0.476)	(0.567)	(0.265)	(0.345)

Health	−1.308***	−1.475***	−1.008***	−0.048
Health	(0.335)	(0.442)	(0.316)	(0.406)
Very good health	0.406**	0.476**	0.390*	0.055
Very good health	(0.164)	(0.172)	(0.200)	(0.281)
Very good or good health	0.750***	0.823***	0.459**	−0.106
Very good or good health	(0.187)	(0.237)	(0.161)	(0.234)
Bad or very bad health	−0.127*	−0.150	−0.137**	−0.096
Bad or very bad health	(0.069)	(0.093)	(0.060)	(0.095)
Life satisfaction	2.231	2.551	1.768*	−2.072*
Life satisfaction	(1.662)	(1.903)	(0.843)	(1.151)
Happiness	2.784**	3.025**	1.063**	−0.323
Happiness	(1.191)	(1.411)	(0.465)	(0.656)
Difficult or very difficult to live	−0.480	−0.607	−0.034	0.536
on present income	(0.416)	(0.517)	(0.248)	(0.381)
Countries dummies	Yes	Yes	Yes	Yes
Year dummies	Yes	Yes	Yes	Yes
Month dummies	Yes	Yes	Yes	Yes
Individual X’s	No	Yes	Yes	Yes
Country-specific X’s	No	No	Yes	Yes
Linear country-specific time trends	No	No	No	Yes

In Appendix Table Table7, 7 , we report additional robustness tests. In Panel H, we replace the one-year lagged minimum wage, in equation (2), with the two-year lagged minimum wage. Similarly, we replace the one-year lagged minimum wage with the three-year lagged minimum wage. We found no significant effect of minimum wage increases on the outcomes studied, suggesting that the effects are essentially contemporaneous or with a time lag of up to one year.

Robustness checks (Appendix)

		Health	Very good health	Very good or	Bad or very	Life satisfaction	Happiness	Difficult or very difficult
				good health	bad health			to live on present income

Two-year lags	MW	−0.069	0.067	−0.023	−0.023	−0.588	−0.236	−0.085
Two-year lags	MW	(0.121)	(0.077)	(0.047)	(0.024)	(0.579)	(0.438)	(0.069)
Three-year lags	MW	−0.113	0.056	0.038	−0.020	−0.552	−0.089	−0.063
Three-year lags	MW	(0.136)	(0.069)	(0.061)	(0.017)	(0.365)	(0.299)	(0.084)

Cur. min.wage	Q2	−0.026*	0.016**	0.007	−0.002	0.125*	0.122***	−0.019
	Q2	(0.014)	(0.007)	(0.009)	(0.003)	(0.062)	(0.038)	(0.014)
	Q3	−0.058	0.054**	−0.008	−0.005	0.247*	0.110	−0.047
	Q3	(0.054)	(0.023)	(0.025)	(0.011)	(0.121)	(0.135)	(0.034)
	Q4	−0.220***	0.098***	0.093***	−0.022*	0.310**	0.064	−0.057*
	Q4	(0.042)	(0.020)	(0.022)	(0.011)	(0.124)	(0.104)	(0.029)
	Q5	−0.221***	0.093***	0.090***	−0.032***	0.469***	0.201*	−0.093**
	Q5	(0.051)	(0.028)	(0.022)	(0.011)	(0.137)	(0.103)	(0.035)
Lag. min.wage	Q2	0.002	0.009	−0.020*	−0.008**	0.042	0.134*	0.009
	Q2	(0.018)	(0.010)	(0.010)	(0.004)	(0.096)	(0.069)	(0.022)
	Q3	−0.021	0.022	−0.008	−0.007	0.059	0.017	−0.028
	Q3	(0.049)	(0.018)	(0.032)	(0.010)	(0.200)	(0.193)	(0.054)
	Q4	−0.005	0.023	−0.015	0.002	0.250	0.237	−0.024
	Q4	(0.061)	(0.024)	(0.038)	(0.018)	(0.229)	(0.198)	(0.056)
	Q5	0.038	−0.008	−0.028	0.001	0.414	0.391	−0.071
	Q5	(0.065)	(0.027)	(0.044)	(0.018)	(0.257)	(0.234)	(0.056)

Lead min.wage	MW	−0.127	0.099	−0.001	−0.019	0.635	0.842*	−0.211***
Lead min.wage	MW	(0.137)	(0.088)	(0.056)	(0.018)	(0.451)	(0.413)	(0.068)


Cur. min.wage	MW	−0.134	0.068**	0.053	−0.063	0.874	1.401**	−0.116
Cur. min.wage	MW	(0.217)	(0.030)	(0.106)	(0.109)	(0.542)	(0.506)	(0.101)
Lag. min.wage	MW	0.251**	0.005	−0.154*	0.046	−0.489	−0.032	0.114
Lag. min.wage	MW	(0.110)	(0.052)	(0.080)	(0.067)	(0.375)	(0.309)	(0.109)

In Panel I (Appendix Table Table7), 7 ), the minimum wage is divided into five quantiles in order to study the potential effects of non-linearities (the first quantile is the reference category). The estimates show that the effects are significant across the entire minimum wage distribution (significant effects across all five quantiles), although the effects are larger for the highest quintiles.

In Panel J (Appendix Table Table7), 7 ), as a falsification test, we replace the one-year lagged minimum wage in equation (2) with the one-year lead minimum wage for each country and year. The underlying intuition is that current health outcomes are unlikely to be affected by future minimum wages. We found that the lead minimum wage has no significant effect on outcomes at the 5% level, except for financial security. This finding may simply reflect that minimum wage changes are correlated with some unobservable variables that affect financial security. However, we argue that it is reassuring that this concerns only one outcome and that our results are robust to all robustness tests.

Finally, similar to Huang et al. ( 2021 ), we conduct a falsification test using one placebo group: university-educated adults aged 18–64 years old. The DD estimates show no significant effect of the minimum wage on this group (Table 4 , Panel F). The only exceptions are life satisfaction and income insecurity with lagged minimum wage, but the coefficient estimates instead show a decrease of life satisfaction and an increase of income insecurity for this group. 16 In Panel G, we use the university group as the placebo group to estimate a difference-in-differences-in-differences (DDD) model. We add an indicator for whether an individual belongs to the low-educated group. We find that the observed effects are mostly significant for low-educated individuals. The estimates are consistent with those estimated in the DD model. Clearly, these results give us confidence that our results are not spurious.

Acknowledgement

See Tables Tables5, 5 , ,6 6 and and7 7 .

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declarations

Author declares that they have no conflict of interest.

1 See Leigh et al. ( 2019 ) for a review of evidence on the effect of minimum wages on health outcomes.

2 For example, Huang et al. ( 2021 ) showed that a $1 increase in the minimum wage in the United States raises the prevalence of smoking by about 2.3% and diminishes cessation by about 13.7% among the low-skilled employees. They also report an income effect as a potential mechanism for increased smoking.

3 Several studies have exploited income shocks, such as changes in the Earned Income Tax Credit (Evans & Garthwaite, 2014 ) or inheritances and lottery winnings (Gardner & Oswald, 2007 ), to estimate the causal effect of increased income on health. The use of minimum wages as natural experiments is a very recent approach adopted in the literature (Leigh et al., 2019 )

4 The ESS questionnaire includes a combination of repeated key items (the core section), which remains relatively similar from round to round, as well as several rotating modules, repeated at intervals.

5 Only countries that had effective minimum wages in place during our study period are included in the analysis. These countries are Belgium, the Czech Republic, Estonia, France, Germany, Hungary, Ireland, Israel, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, the Slovak Republic, Slovenia, Spain, and the United Kingdom.

6 Respondents who are not in the labor force include those who are retired, are students, or reported being a homemaker.

7 “Real hourly minimum wages are calculated first by deflating the series using the consumer price index taking 2020 as the base year. The series are then converted into a common currency unit (USD) using Purchasing Power Parities (PPPs) for private consumption expenditures in 2020.” (OECD Database).

8 In Europe, and particularly in our sample, between 90% and 99% of the total population is covered by public health insurance. Many European countries benefit from universal access to health care. This contrasts sharply with the United States, where only about 30% of the total population is covered by public health insurance such as Medicare and Medicaid. (OECD, 2021 ).

9 Some OECD countries such as the United States or Canada have a regional minimum wage, meaning the minimum wage varies by region. In these countries, there is a federal wage minimum wage, but states may set a minimum wage above the federal level. In our sample, all countries have geographically homogeneous minimum wage policies within the country (Adema et al., 2019 ).

10 Results using unlogged minimum wages are similar and available from the authors.

11 Linear models are commonly used in the literature for ease of interpretation. Results are similar if we use ordered probit models (available on request). Moreover, existing evidence shows that results are similar when well-being and health are treated as an ordinal or cardinal concept (Kuroki, 2018 ; Haeck et al., 2018 ; Horn et al., 2017 ).

12 In general, existing evidence includes state-specific linear time trends to remove bias due to unobservable state-specific time trends when studying the impact of minimum wages on labor and health outcomes. However, Sabia and Nielsen ( 2015 ) reported that the using such trends substantially reduces the ability to identify variations (decrease of more than 60%). Thus, some studies have not included these trends in their models (Averett et al., 2018 , 2017 ). Here, we present the results both with and without these trends, showing that, although the magnitude of the effects decreases, minimum wage increases still improve individuals’ health, well-being, and personal income security.

13 Colman and Dave ( 2013 ) report that individuals who lose their jobs experience a decrease in overall physical activity and an increase in sedentary activity (e.g., television watching).

14 Older individuals include those 30 years old or older.

15 The division of countries is based on GDP per capita. Poor countries include countries whose GDP per capita is lower than the median GDP per capita in the sample (i.e., the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia). Rich countries include countries whose GDP per capita is higher than the median GDP per capita of the sample (i.e., Belgium, France, Germany, Ireland, Israel, Luxembourg, the Netherlands, Spain, and the United Kingdom).

16 In Appendix Table Table7, 7 , we report DD results using a different placebo group: retired adults who are 65 years old and older (Panel H). Despite a few exceptions, the results generally show a non-significant effect of minimum wage increases on the outcomes studied.

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Own-Wage Elasticity: Quantifying the Impact of Minimum Wages on Employment

The own-wage elasticity (OWE) of employment estimated using minimum wage increases provides an economically meaningful measure of the policy on jobs. We discuss how to interpret the magnitude of the OWE, including in terms of welfare and under alternative models of the labor market. We present a comprehensive set of OWE estimates from 88 studies and introduce an regularly updated repository of the estimates---https://economic.github.io/owe---an up-to-date snapshot of the existing literature for scholars and policymakers. We find that most studies to date suggest a fairly modest impact of minimum wages on jobs: the median OWE estimate of 72 studies published in academic journals is -0.13, which suggests that only around 13 percent of the potential earnings gains from minimum wage increases are offset due to associated job losses. Estimates published since 2010 tend to be closer to zero.

We thank Attila Lindner and Michael Reich for valuable comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Published: 09 September 2024

Exploring the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom: a qualitative systematic review

Emma S. Hock 1 ,
Lindsay Blank 1 ,
Hannah Fairbrother 1 ,
Mark Clowes 1 ,
Diana Castelblanco Cuevas 1 ,
Andrew Booth 1 ,
Amy Clair 2 &
Elizabeth Goyder 1

BMC Public Health volume 24 , Article number: 2453 ( 2024 ) Cite this article

Metrics details

Housing insecurity can be understood as experiencing or being at risk of multiple house moves that are not through choice and related to poverty. Many aspects of housing have all been shown to impact children/young people’s health and wellbeing. However, the pathways linking housing and childhood health and wellbeing are complex and poorly understood.

We undertook a systematic review synthesising qualitative data on the perspectives of children/young people and those close to them, from the United Kingdom (UK). We searched databases, reference lists, and UK grey literature. We extracted and tabulated key data from the included papers, and appraised study quality. We used best fit framework synthesis combined with thematic synthesis, and generated diagrams to illustrate hypothesised causal pathways.

We included 59 studies and identified four populations: those experiencing housing insecurity in general (40 papers); associated with domestic violence (nine papers); associated with migration status (13 papers); and due to demolition-related forced relocation (two papers). Housing insecurity took many forms and resulted from several interrelated situations, including eviction or a forced move, temporary accommodation, exposure to problematic behaviour, overcrowded/poor-condition/unsuitable property, and making multiple moves. Impacts included school-related, psychological, financial and family wellbeing impacts, daily long-distance travel, and poor living conditions, all of which could further exacerbate housing insecurity. People perceived that these experiences led to mental and physical health problems, tiredness and delayed development. The impact of housing insecurity was lessened by friendship and support, staying at the same school, having hope for the future, and parenting practices. The negative impacts of housing insecurity on child/adolescent health and wellbeing may be compounded by specific life circumstances, such as escaping domestic violence, migration status, or demolition-related relocation.

Housing insecurity has a profound impact on children and young people. Policies should focus on reducing housing insecurity among families, particularly in relation to reducing eviction; improving, and reducing the need for, temporary accommodation; minimum requirements for property condition; and support to reduce multiple and long-distance moves. Those working with children/young people and families experiencing housing insecurity should prioritise giving them optimal choice and control over situations that affect them.

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Introduction

The impacts of socioeconomic position in childhood on adult health outcomes and mortality are well documented in quantitative analyses (e.g., [ 1 ]). Housing is a key mechanism through which social and structural inequalities can impact health [ 2 ]. The impact of housing conditions on child health are well established [ 3 ]. Examining the wellbeing of children and young people within public health overall is of utmost importance [ 4 ]. Children and young people (and their families) who are homeless are a vulnerable group with particular difficulty in accessing health care and other services, and as such, meeting their needs should be a priority [ 5 ].

An extensive and diverse evidence base captures relationships between housing and health, including both physical and mental health outcomes. Much of the evidence relates to the quality of housing and specific aspects of poor housing including cold and damp homes, poorly maintained housing stock or inadequate housing leading to overcrowded accommodation [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. The health impacts of housing insecurity, together with the particular vulnerability of children and young people to the effects of not having a secure and stable home environment, continue to present a cause for increased concern [ 7 , 8 , 11 , 14 ]. The National Institute for Health and Care Research (NIHR) Public Health Reviews (PHR) Programme commissioned the current review in response to concerns about rising levels of housing insecurity and the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom (UK).

Terminology and definitions related to housing insecurity

Numerous diverse terms are available to define housing insecurity, with no standard definition or validated instrument. For the purpose of our review, we use the terminology and definitions used by the Children’s Society, which are comprehensive and based directly on research with children that explores the relationship between housing and wellbeing [ 15 ]. They use the term “housing insecurity” for those experiencing and at risk of multiple moves that are (i) not through choice and (ii) related to poverty [ 15 ]. This reflects their observation that multiple moves may be a positive experience if they are by choice and for positive reasons (e.g., employment opportunities; moves to better housing or areas with better amenities). This definition also acknowledges that the wider health and wellbeing impacts of housing insecurity may be experienced by families that may not have experienced frequent moves but for whom a forced move is a very real possibility. The Children’s Society definition of housing insecurity encompasses various elements (see Table 1 ).

Housing insecurity in the UK today – the extent of the problem

Recent policy and research reports from multiple organisations in the UK highlight a rise in housing insecurity among families with children [ 19 , 22 , 23 ]. Housing insecurity has grown following current trends in the cost and availability of housing, reflecting in particular the rapid increase in the number of low-income families with children in the private rental sector [ 19 , 22 , 24 ], where housing tenures are typically less secure. The ending of a tenancy in the private rental sector was the main cause of homelessness given in 15,500 (27% of claims) of applications for homelessness assistance in 2017/18, up from 6,630 (15% of claims) in 2010/11 for example [ 25 ]. The increased reliance on the private rented sector for housing is partly due to a lack of social housing and unaffordability of home ownership [ 23 ]. The nature of tenure in the private rental sector and gap between available benefits and housing costs means even low-income families that have not experienced frequent moves may experience the negative impacts of being at persistent risk of having to move [ 26 ]. Beyond housing benefit changes, other changes to the social security system have been linked with increased housing insecurity. The roll-out of Universal Credit Footnote 1 , with its built-in waits for payments, has been linked with increased rent arrears [ 27 , 28 ]. The introduction of the benefit cap, which limits the amount of social security payments a household can receive, disproportionately affects housing support and particularly affecting lone parents [ 29 , 30 , 31 ].

The increase in families experiencing housing insecurity, including those living with relatives or friends (the ‘hidden homeless’) and those in temporary accommodation provided by local authorities, are a related consequence of the lack of suitable or affordable rental properties, which is particularly acute for lone parents and larger families. The numbers of children and young people entering the social care system or being referred to social services because of family housing insecurity contributes further evidence on the scale and severity of the problem [ 32 ].

The COVID-19 pandemic exacerbated housing insecurity in the UK [ 24 ], with the impacts continuing to be felt. In particular, the pandemic increased financial pressures on families (due to loss of income and increased costs for families with children/young people at home). These financial pressures were compounded by a reduction in informal temporary accommodation being offered by friends and family due to social isolation precautions [ 24 ]. Further, the COVID-19 pandemic underscored the risks to health posed by poor housing quality (including overcrowding) and housing insecurity [ 24 , 33 ]. Recent research with young people in underserved communities across the country also highlighted their experience of the uneven impact of COVID-19 for people in contrasting housing situations [ 34 ].

While the temporary ban on bailiff-enforced evictions, initiated due to the pandemic, went some way towards acknowledging the pandemic’s impact on housing insecurity, housing organisations are lobbying for more long-term strategies to support people with pandemic-induced debt and rent-arrears [ 33 ]. The Joseph Rowntree Foundation has warned of the very real risk of a ‘two-tier recovery’ from the pandemic, highlighting the ‘disproportionate risks facing people who rent their homes’ ([ 35 ], para. 1). Their recent large-scale survey found that one million renting households worry about being evicted in the next three months, and half of these were families with children [ 35 ]. The survey also found that households with children, renters from ethnic minority backgrounds and households on low incomes are disproportionately affected by pandemic-induced debt and rent arrears [ 35 ].

The cost-of-living crisis is exacerbating the impact of the COVID-19 pandemic, with many households experiencing or set to experience housing insecurity due to relative reductions in income accompanying increases in rent and mortgage repayments [ 36 ]. People experiencing or at risk of housing insecurity are disproportionately affected, due to higher food and utility costs [ 37 ].

Research evidence on relationships between housing in childhood and health

Housing is a key social determinant of health, and a substantive evidence base of longitudinal cohort studies and intervention studies supports a causal relationship between the quality, affordability and stability of housing and child health [ 38 ]. Evidence includes immediate impacts on mental and physical health outcomes and longer-term life course effects on wider determinants of health including education, employment and income as well as health outcomes [ 39 ].

The negative health impact of poor physical housing conditions has been well documented [ 40 , 41 ]. Housing instability and low housing quality are associated with worse psychological health among young people and parents [ 42 , 43 ]. The UK National Children’s Bureau [ 22 ] draws attention to US-based research showing that policies that reduced housing insecurity for young children can help to improve their emotional health [ 44 ], and that successful strategies for reducing housing insecurity have the potential to reduce negative outcomes for children with lived experience of housing insecurity, including emotional and behavioural problems, lower academic attainment and poor adult health and wellbeing [ 45 ]. A variety of pathways have been implicated in the relationship between housing insecurity and child health and wellbeing, including depression and psychological distress in parents, material hardships and difficulties in maintaining a good bedtime routine [ 38 ]. Frequent moves are also associated with poorer access to preventive health services, reflected, for example, in lower vaccination rates [ 46 , 47 ].

Housing tenure, unstable housing situations and the quality or suitability of homes are inter-related [ 48 ]. For example, if families are concerned that if they lost their home they would not be able to afford alternative accommodation, they may be more likely to stay in smaller or poor-quality accommodation or in a neighbourhood where they are further from work, school or family support. In this way, housing insecurity can lead to diverse negative health and wellbeing impacts relating to housing and the neighbourhoods, even if in the family does not experience frequent moves or homelessness [ 49 ]. Thus, the relationship between housing insecurity and child health is likely to be complicated by the frequent coexistence of poor housing conditions or unsuitable housing with housing insecurity. The relationship between unstable housing situations and health outcomes is further confounded by other major stressors, such as poverty and changes in employment and family structure, which may lead to frequent moves.

The evidence from cohort studies that show a relationship between housing insecurity, homelessness or frequent moves in childhood and health related outcomes can usefully quantify the proportion of children/young people and families at risk of poorer health associated with housing instability. It can, however, only suggest plausible causal associations. Further, the ‘less tangible aspects of housing’ such as instability are poorly understood [ 40 ]. Additional (and arguably stronger) evidence documenting the relationship between housing insecurity and health/wellbeing comes from the case studies and qualitative interviews with children and young people and families that explore the direct and indirect impacts of housing insecurity on their everyday lives and wellbeing. Thus, the current review aimed to identify, appraise and synthesise research evidence that explores the relationship between housing insecurity and the health and wellbeing among children and young people. We aimed to highlight the relevant factors and causal mechanisms to make evidence-based recommendations for policy, practice and future research priorities.

We undertook a systematic review synthesising qualitative data, employing elements of rapid review methodology in recognition that the review was time-constrained. This involved two steps: (1) a single screening by one reviewer of titles and abstracts, with a sample checked by another reviewer; and (2) a single data extraction and quality assessment, with a sample checked by another reviewer) [ 50 , 51 , 52 ]. The protocol is registered on the PROSPERO registry, registration number CRD42022327506.

Search strategy

Searches of the following databases were conducted on 8th April 2022 (from 2000 to April 2022): MEDLINE, EMBASE and PsycINFO (via Ovid); ASSIA and IBSS (via ProQuest) and Social Sciences Citation Index (via Web of Science). Due to the short timescales for this project, searches aimed to balance sensitivity with specificity, and were conceptualised around the following concepts: (housing insecurity) and (children or families) and (experiences); including synonyms, and with the addition of a filter to limit results to the UK where available [ 53 ]. To expedite translation of search strings across different databases, searches prioritised free text search strings (including proximity operators), in order to retrieve relevant terms where they occurred in titles, abstracts or any other indexing field (including subject headings). The searches of ASSIA and IBSS (via ProQuest) and Social Sciences Citation Index (via Web of Science) used a simplified strategy adapted from those reproduced in Additional File 1. Database searching was accompanied by scrutiny of reference lists of included papers and relevant systematic reviews (within search dates), and grey literature searching (see Supplementary Table 1, Additional File 2), which was conducted and documented using processes outlined by Stansfield et al . [ 54 ].

Inclusion criteria

We included qualitative studies, including qualitative elements of mixed methods studies from published and grey literature (excluding dissertations and non-searchable books), that explored the impact of housing insecurity, defined according to the Children’s Society [ 15 ] definition (which includes actual or perceived insecurity related to housing situations), on immediate and short-term outcomes related to childhood mental and physical health and wellbeing (up to the age of 16), among families experiencing / at risk of housing insecurity in the UK (including low-income families, lone-parent families, and ethnic minority group families including migrants, refugees and asylum seekers). Informants could include children and young people themselves, parents / close family members, or other informants with insight into the children and young people’s experiences. Children and young people outside a family unit (i.e., who had left home or were being looked after by the local authority) and families from Roma and Irish Traveller communities were excluded, as their circumstances are likely to differ substantially from the target population.

Study selection

Search results from electronic databases were downloaded to a reference management application (EndNote). The titles and abstracts of all records were screened against the inclusion criteria by one of three reviewers and checked for agreement by a further reviewer. Full texts of articles identified at abstract screening were screened against the inclusion criteria by one reviewer. A proportion (10%) of papers excluded at the full paper screening stage were checked by a second reviewer. Any disagreements were resolved through discussion.

Grey literature searches and screening were documented in a series of tables [ 54 ]. One reviewer (of two) screened titles of relevant web pages and reports against the inclusion criteria for each web platform searched, and downloaded and screened the full texts of potentially eligible titles. Queries relating to selection were checked by another reviewer, with decisions discussed among the review team until a consensus was reached.

One reviewer (of two) screened reference lists of included studies and relevant reviews for potentially relevant papers. One reviewer downloaded the abstracts and full texts of relevant references and assessed them for relevance.

Data extraction

We devised a data extraction form based on forms that the team has previously tested for similar reviews of public health topics. Three reviewers piloted the extraction form and suggested revisions were agreed before commencing further extraction. Three reviewers extracted and tabulated key data from the included papers and grey literature sources, with one reviewer completing data extraction of each study and a second reviewer formally checking a 10% sample for accuracy and consistency. The following data items were extracted: author and year, location, aims, whether housing insecurity was an aim, study design, analysis, who the informants were, the housing situation of the family, reasons for homelessness or housing insecurity, conclusion, relevant policy/practice implications and limitations. Any qualitative data relating to housing insecurity together with some aspect of health or wellbeing in children and young people aged 0–16 years were extracted, including authors’ themes (to provide context), authors’ interpretations, and verbatim quotations from participants. We sought to maintain fidelity to author and participant terminologies and phrasing throughout.

Quality appraisal

Peer-reviewed academic literature was appraised using the Critical Appraisal Skills Programme (CASP) checklist for qualitative studies [ 55 ] and the quality of grey literature sources (webpages and reports) was appraised using the Authority, Accuracy, Coverage, Objectivity, Date, Significance (AACODS) checklist [ 56 ]. Because of concerns about the lack of peer review and/or the absence of a stated methodology, it was decided to use the AACODS tool that extends beyond simple assessment of study design. A formal quality assessment checklist was preferred for journal articles that passed these two entry criteria. One reviewer performed quality assessment, with a second reviewer formally checking a 10% sample for accuracy and consistency.

Development of the conceptual framework

Prior to undertaking the current review, we undertook preliminary literature searches to identify an appropriate conceptual framework or logic model to guide the review and data synthesis process. However, we were unable to identify a framework that specifically focused on housing insecurity among children and young people and that was sufficiently broad to capture relevant contexts, exposures and impacts. We therefore developed an a priori conceptual framework based on consultation with key policy and practice stakeholders and topic experts and examination of key policy documents (see Fig. 1 ).

A priori conceptual framework for the relationship between housing insecurity and the health and wellbeing of children and young people

We initially consulted policy experts who identified relevant organisations including research centres, charities and other third sector organisations. We obtained relevant policy reports from organisational contacts and websites, including Child Poverty Action Group (CPAG), Crisis, Joseph Rowntree Foundation (JRF) and HACT (Housing Association Charitable Trust), NatCen (People Living in Bad Housing, 2013), the UK Collaborative Centre for Housing Evidence (CaCHE), and the Centre on Household Assets and Savings Management (CHASM) (Homes and Wellbeing, 2018). We also identified a key report on family homelessness from the Children’s Commissioner (Bleak Houses. 2019) and a joint report from 11 charities and advocacy organisations published by Shelter (Post-Covid Policy: Child Poverty, Social Security and Housing, 2022). We also consulted local authority officers with responsibility for housing and their teams in two local councils and third sector providers of housing-related support to young people and families (Centrepoint). Stakeholders and topic experts were invited to comment on the potential focus of the review and the appropriate definitions and scope for the ‘exposure’ (unstable housing), the population (children and young people) and outcomes (health and wellbeing). Exposures relate to how children and families experience housing insecurity, impacts are intermediate outcomes that may mediate the effects of housing insecurity on health and wellbeing (e.g., the psychological, social, and environmental consequences of experiencing housing insecurity), and outcomes are childhood health and wellbeing effects of housing insecurity (including the effects of the impacts/intermediate outcomes).

The contextual factors and main pathways between housing-related factors and the health and wellbeing of children and young people identified were incorporated into the initial conceptual framework. We then used this conceptual framework to guide data synthesis.

Data synthesis

We adopted a dual approach whereby we synthesised data according to the a priori conceptual framework and sought additional themes, categories and nuance inductively from the data, in an approach consistent with the second stage of ‘best fit framework synthesis’ [ 57 , 58 ]. We analysed inductive themes using the Thomas and Harden [ 59 ] approach to thematic synthesis, but coded text extracts (complete sentences or clauses) instead of coding line by line [ 60 , 61 ].

First, one reviewer (of two) coded text extracts inductively and within the conceptual framework, simultaneously, linking each relevant text extract to both an inductive code based on the content of the text extract, and to an element of the conceptual framework. We assigned multiple codes to some extracts, and the codes could be linked to any single element or to multiple elements of the conceptual framework. During the process of data extraction, we identified four distinct populations, and coded (and synthesised) data discretely for each population. We initially coded data against the ‘exposure’, ‘impacts’ and ‘outcomes’ elements of the conceptual framework, however we subsequently added a further element within the data; ‘protective factors’. One reviewer then examined the codes relating to each element of the conceptual framework and grouped the codes according to conceptual similarity and broader meaning, reporting the thematic structure and relationships between concepts apparent from the text extracts both narratively and within a diagram to illustrate hypothesised causal pathways within the original conceptual framework, to highlight links between specific exposures, impacts and outcomes for each population. While we synthesised the findings by population initially, and present separate diagrams for each population, we present overall findings in this manuscript due to several similarities and then highlight any important differences for the domestic violence, migrant/refugee/asylum seeker, and relocation populations.

Study selection and included studies

Here we report the results of our three separate searchers. First, the database searches generated 3261 records after the removal of duplicates. We excluded 3025 records after title and abstract screening, examined 236 full texts, and included 16 peer-reviewed papers (reporting on 16 studies). The reasons for exclusion of each paper are provided in the Supplementary Table 2, Additional File 3. Second, we examined 726 grey literature sources (after an initial title screen) and included 37 papers. Third, we examined 85 papers that we identified as potentially relevant from the references lists of included papers and relevant reviews, and included six (two of which were peer-reviewed publications). Figure 2 summarises the process of study selection and Table 2 presents a summary of study characteristics. Of the included studies, 16 took place across the UK as a whole, one was conducted in England and Scotland, one in England and Wales and 17 in England. In terms of specific locations, where these were reported, 13 were reported to have been conducted in London (including specific boroughs or Greater London), two in Birmingham, one in Fife, two in Glasgow, one in Leicester, one in Rotherham and Doncaster, and one in Sheffield. The location of one study was not reported (Table 2 ).

Flow diagram of study selection

We identified four distinct populations for which research evidence was available during the process of study selection and data extraction:

General population (evidence relating to housing insecurity in general) (reported in 40 papers);

Domestic violence population (children and young people experiencing housing insecurity associated with domestic violence) (reported in nine papers);

Migrant, refugee and asylum seeker population (children and young people experiencing housing insecurity associated with migration status) (reported in 13 papers);

Relocation population (evidence relating to families forced to relocate due to planned demolition) (reported in two papers).

Evidence relating to each of these populations was synthesised separately as the specific housing circumstances may impact health and wellbeing differently and we anticipated that specific considerations would relate to each population. Some studies reported evidence for more than one population.

Quality of evidence

The quality of evidence varied across the studies, with published literature generally being of higher quality than grey literature and containing more transparent reporting of methods, although reporting of methods of data collection and analysis varied considerably within the grey literature. All 18 peer-reviewed studies reported an appropriate methodology, addressing the aim of the study with an adequate design. Eleven of the 18 peer-reviewed studies reported ethical considerations, and only two reported reflexivity. Most studies had an overall assessment of moderate-high quality (based on the endorsement of most checklist items) and no studies were excluded based on quality. Most of the grey literature originated from known and valued sources (e.g., high-profile charities specialising in poverty and housing, with the research conducted by university-based research teams). Although methodologies and methods were often poorly described (or not at all), primary data in the form of quotations was usually available and suitable to contribute to the development of themes within the evidence base as a whole. Quality appraisals of included studies are presented in Supplementary Tables 3 and 4, Additional File 4.

Housing insecurity and the health and wellbeing of children and young people

The updated conceptual framework for the impact of housing insecurity on the health and wellbeing of children aged 0–16 years in family units is presented in Fig. 3 for the general population, Fig. 4 for the domestic violence population, Fig. 5 for the refugee/migrant/asylum seeker population, and Fig. 6 for the relocation population (arrows represent links identified in the evidence and coloured arrows are used to distinguish links relating to each element of the model). Table 3 outlines the themes, framework components and studies reporting data for each theme.

Conceptual framework for the relationship between housing insecurity and health and wellbeing in the general population

Conceptual framework for the relationship between housing insecurity and health and wellbeing in the domestic violence population

Conceptual framework for the relationship between housing insecurity and health and wellbeing in the migrant, refugee and asylum seeker population

Conceptual framework for the relationship between housing insecurity and health and wellbeing in the relocation population

Exposures are conceptualised as the manifestations of housing insecurity – that is, how the children and young people experience it – and housing insecurity was experienced in multiple and various ways. These included trouble paying for housing, eviction or the prospect of eviction, making multiple moves, living in temporary accommodation, and the inaccessibility of suitable accommodation.

Fundamentally, a key driver of housing insecurity is poverty. Parents and, in some cases, young people cited the high cost of housing, in particular housing benefit not fully covering the rent amount [ 116 ], trouble making housing payments and falling into arrears [ 15 , 92 , 97 ]. Sometimes, families were evicted for non-payment [ 15 , 102 ], often linked to the rising cost of housing [ 109 ] or loss of income [ 102 ]. Some children and young people were not aware of reasons for eviction [ 90 ], and the prospect of facing eviction was also a source of housing insecurity [ 116 ].

The cost of housing could lead to families having to move multiple times [ 116 ], with lack of affordability and the use of short-term tenancies requiring multiple moves [ 109 , 116 ]. Children and young people were not always aware of the reasons for multiple moves [ 15 ]. Multiple moves could impact upon education and friendships [ 77 , 82 ].

Living in temporary housing was a common experience of housing insecurity [ 15 , 71 , 87 , 90 , 94 , 98 , 111 , 112 , 113 , 114 ]. Temporary housing caused worry at the thought of having to move away from school and friends [ 91 ] and acute distress, which manifested as bedwetting, night waking and emotional and behavioural issues at school [ 66 ]. Living in a hostel for a period of time could lead to friendship issues due to not being able to engage in sleepovers with friends [ 102 ].

The inaccessibility of suitable accommodation also contributed to insecurity. Sometimes, when a family needed to move, they had to fulfil certain requirements, for instance, to decorate their overcrowded 3-bedroom accommodation to be eligible for a more suitable property [ 15 ]. Further, some families encountered the barrier of landlords who would not accept people on benefits [ 15 , 85 , 117 ]. Waiting lists for social housing could be prohibitively long [ 97 , 98 , 116 ].

Dual exposures and impacts

Some phenomena were found to be both exposures and impacts of housing insecurity, in that some issues and experiences that were impacts of housing insecurity further exacerbated the living situation, causing further insecurity. These included not feeling safe, exposure to problematic behaviour, living far away from daily activities, overcrowding, and poor or unsuitable condition properties.

Not feeling safe was frequently reported by children and young people, and by parents in relation to the safety of children and young people. Parents and children and young people described being moved to neighbourhoods or localities [ 15 , 69 , 87 , 90 , 103 ] and accommodation [ 87 , 97 , 109 , 112 , 113 , 114 ] that did not feel safe. For one family, this was due to racial abuse experienced by a parent while walking to school [ 69 ]. In one case, a young person’s perception of safety improved over time, and they grew to like the neighbours and area [ 15 ], although this was a rare occurrence.

Often, this experience of being unsafe was due to exposure to problematic behaviour in or around their accommodation, including hearing other children being treated badly [ 112 ], being exposed to violence (including against their parents) [ 111 , 112 , 114 ], witnessing people drinking and taking drugs [ 69 , 83 , 90 , 111 , 112 , 114 ], finding drug paraphernalia in communal areas [ 112 , 114 ] or outside spaces [ 69 ], hearing threats of violence [ 111 ], hearing shouting and screaming in other rooms [ 114 ], witnessing people breaking into their room [ 83 ], and witnessing their parent/s receiving racist abuse and being sworn at [ 83 ].

‘There’s a lot [of] drugs and I don’t want my kids seeing that… One time he said ‘mummy I heard a woman on the phone saying ‘I’m going to set fire to your face’’ She was saying these things and my son was hearing it.’ ( [ 111 ] , p.15)

Another impact related to the family and children and young people being isolated and far away from family, friends, other support networks, work, shops, school and leisure pursuits due to the location of the new or temporary housing [ 15 , 83 , 87 , 97 , 104 , 109 ]. This affected education, friendships, finances and access to services (see ‘ Impacts ’).

Overcrowding was another issue that was both a source or feature of housing insecurity, as this created a need to move, as well as being an impact, in that families moved to unsuitable properties because they had little alternative. Overcrowding was largely a feature of temporary accommodation that was too small for the family [ 67 , 91 ], including hostels/shared houses where whole families inhabited one room and washing facilities were shared [ 100 , 102 ]. In turn, overcrowding could mean siblings sharing a room and/or bed [ 15 , 41 , 64 , 71 , 78 , 109 , 111 , 112 , 113 , 114 , 116 ] (which could lead to disturbed sleep [ 15 ]), children/young people or family members sleeping on the floor or sofa [ 15 , 71 , 102 , 110 ] (which caused aches and pains in children/young people; [ 100 ]), children/young people sharing a room with parents [ 64 , 71 , 94 , 109 , 111 , 112 , 113 , 114 ], a room being too small to carry out day to day tasks [ 112 , 113 , 114 ], a lack of privacy in general (e.g., having to change clothes in front of each other) [ 70 , 111 , 112 , 114 ], living in close proximity to other families [ 114 ], and cramped conditions with little room to move when too many people and possessions had to share a small space [ 15 , 64 , 90 , 97 , 103 , 109 , 114 ].

It’s all of us in one room, you can imagine the tension…. everyone’s snapping because they don’t have their own personal space …it’s just a room with two beds. My little brother has to do his homework on the floor.’ ( [ 97 ] , p..43)

It was thus difficult for children and young people to have their own space, even for a short time [ 98 ], including space to do schoolwork [ 102 , 103 ], play [ 91 ] or invite friends over [ 103 ]. Families sometimes ended up overcrowded due to cohabiting with extended family [ 110 ] or friends [ 91 , 102 ] (‘hidden homelessness’). Other families outgrew their property, or anticipated they would in future, when children grew older [ 70 , 116 ]. Overcrowding sometimes meant multiple families inhabiting a single building (e.g., a hostel or shelter), where single parents had difficulties using shared facilities, due to not wanting to leave young children alone [ 100 ]. Overcrowding could also lead to children feeling unsafe, including being scared of other people in shared accommodation [ 102 ], experiencing noise [ 102 ], and feeling different from peers (due to not having their own room or even bed) [ 102 ]. Living in overcrowded conditions could lead to, or exacerbate, boredom, aggressive behaviour, and mental health problems among children and young people (see ‘ Outcomes ’) [ 72 , 79 , 91 ]. Overcrowded conditions caused a ‘relentless daily struggle’ for families ([ 83 ], p.48).

Similarly, the need to take whatever property was on offer led to families living in properties in poor condition, which in turn could exacerbate housing insecurity, both because families needed to escape the poor condition housing and because they were reluctant to complain and ask for repairs on their current property in case the landlord increased the rent or evicted them [ 86 , 96 ]. Eviction was perceived as a real threat and families described being evicted after requesting environmental health issues [ 74 ] and health and safety issues [ 116 ] be addressed. Families experienced issues relating to poor condition properties, including accommodation being in a poor state of decoration [ 98 ], broken or barely useable fixtures and fittings [ 86 , 90 , 96 ], no laundry or cooking facilities [ 102 ], no electricity [ 67 ], no or little furniture [ 67 , 102 ], broken appliances [ 71 , 96 , 97 ], structural failings [ 97 ], unsafe gardens [ 90 ], mould [ 71 , 90 , 96 , 97 , 104 , 109 ], and bedbugs and/or vermin [ 67 , 76 , 77 ]. Even where the property condition was acceptable, accommodation could be unsuitable in other ways. Many families with young children found themselves living in upper floor flats, having to navigate stairs with pushchairs and small children [ 71 , 74 , 78 , 83 , 87 , 92 , 109 ]. One study reported how a family with a child who had cerebral palsy and asthma were refused essential central heating and so had to request a property transfer [ 75 ]. Lack of space to play was a particular issue in relation to temporary accommodation, often due to overly small accommodation or a vermin infestation [ 80 , 87 , 91 ]. In small children, the effects included health and safety risks [ 87 , 112 ] and challenges keeping them occupied [ 112 ]. In older children and young people, a lack of space meant a lack of privacy [ 63 , 112 ]. School holidays could be particularly challenging, particularly when outside play spaces were unsuitable due to safety concerns (e.g., people selling drugs, broken glass) [ 87 , 106 ], and some temporary accommodation restricted access during the daytime [ 112 ]. With shared temporary accommodation, such as a refuge or hostel, came the threat of possessions being removed by others [ 80 ].

Impacts are defined here as intermediate outcomes that may mediate the effects of housing insecurity on health and wellbeing, for instance, the psychological, social, and environmental consequences of experiencing housing insecurity. According to the evidence reviewed, these were overwhelmingly negative, with only a very small number of positive impacts, and, in many cases, these were offset by other negative impacts. Impacts on friendships, education, family relationships, diet, hygiene, access to services, feelings of being different, feelings of insecurity, parental wellbeing, the financial situation of the family, experiences of noise, leaving negative situations behind, and other impacts, such as leaving pets behind and time costs, were noted. Overlaying all of the above was a lack of choice and control experienced by the children/young people and their families.

A particularly large and disruptive impact of housing insecurity was the effect on friendships and social networks. Over multiple moves, children and young people faced the challenge of building new social networks and reputations each time [ 15 , 90 , 106 ], and worried about maintaining existing friendships [ 90 ]. The beneficial side to this was the potential to have friends all over town, although this was offset by difficulty in forming close friendships due to frequent moves [ 15 ]. Children and young people in temporary, overcrowded or poor condition accommodation often felt ashamed of their housing and concealed it from their friends [ 15 , 73 , 78 , 111 , 112 , 114 , 115 ], and in one case missing out on sleepovers with friends [ 102 ]. Moving far from friends presented difficulties in maintaining friendships and a social life, leading to boredom and isolation [ 102 , 114 ]. The threat of an impending long-distance move could cause sadness and worry [ 114 ] and young people missed the friends they had left behind [ 15 , 90 ]. Other associated social impacts of housing insecurity exacerbated by the wider experience of poverty included turning turn down invitations to go out with friends for financial reasons [ 115 ] or to avoid leaving a parent alone with younger sibling/s [ 114 ], and feeling different from peers, either because of looking unkempt or lacking in confidence [ 115 ].

Another key impact of housing insecurity was the effect on education, and this was closely intertwined with friendship impacts. Faced with moving, often multiple times, sometimes to uncertain locations, families were faced with the decision to keep the same school or to change schools. Multiple moves and/or an unfeasibly long journey to school, led to either a decision to, or anticipating the prospect of having to, change schools [ 15 , 66 , 90 , 91 , 102 , 106 , 108 , 111 , 116 ]. This could in turn impact on the child’s sense of stability, academic performance and friendships [ 90 , 105 , 106 , 111 , 115 , 116 ] and make them feel sad [ 102 ]. In the case of one family, staying at the same school during a move resulted in decreased educational attainment [ 69 ].

Staying at the same school created some stability and allowed for friendships and connections with teachers and the school to be maintained [ 15 , 102 ]. This was, however, quite often the only option, due to the family not knowing their next location, and thus which school they would be near [ 15 , 102 , 113 ], and was not without issues. Those who were unhappy with school were thus effectively prevented from changing schools due to housing insecurity [ 15 , 90 ]. Families were often re-housed at a considerable distance from the school [ 15 , 70 , 93 , 94 , 113 ]. This meant having to get up very early for a long journey by public transport [ 15 , 66 , 70 , 77 , 88 , 90 , 94 , 102 , 105 , 106 , 111 , 113 ], which also caused problems maintaining friendships [ 115 ], increased tiredness and stress [ 15 , 66 , 77 , 102 , 111 , 113 , 114 , 115 ] and left little time for homework and extra-curricular activities [ 113 , 114 , 115 ]. Some children and young people stayed with friends or relatives closer to school on school nights, although these arrangements were not sustainable longer-term [ 15 , 90 ].

Living in temporary housing was associated with practical challenges in relation to schooling, for instance, keeping track of uniform and other possessions, limited laundry facilities, and limited washing facilities [ 112 , 115 ]. Parents noted academic performance worsened following the onset of housing problems [ 111 , 113 , 116 ]. Limited space and time to do homework or revision [ 111 , 112 , 113 , 114 , 115 ], tiredness and poor sleep [ 111 , 113 ], travelling and disrupted routines [ 114 ], disruptions from other families (e.g. in a hostel) [ 114 ], a lack of internet connection [ 114 ], and the general impact of the housing disruption [ 111 , 113 , 116 ] made it challenging for those experiencing housing insecurity to do well at school. Families often had to wake up early to access shared facilities in emergency accommodation before school [ 113 , 114 ]. Some children and young people missed school altogether during periods of transience, due to multiple moves rendering attendance unviable [ 71 , 106 , 111 ], lack of a school place in the area [ 109 ], or not being able to afford transport and lunch money [ 81 ], which in turn affected academic performance [ 106 , 111 ].

‘Their education was put on hold. My daughter was ahead on everything in her class and she just went behind during those two weeks.’ ([ 111 ] , p.15)

Children and young people also experienced an impact on immediate family relationships. Housing insecurity led to reduced family wellbeing [ 82 ], and family relationships becoming more strained, for instance, due to spending more time at friends’ houses that were far away [ 15 ]. In some cases, however, housing insecurity led to improved family relationships, for instance, in terms of a non-resident father becoming more involved [ 15 ], or children feeling closer to their parents [ 106 ].

Some impacts related to the child’s health and wellbeing. Impacts on diet were reported, including refusal of solid food (which affected growth) [ 113 ], stress and repeated moves leading to not eating properly (which resulted in underweight) [ 91 ], insufficient money to eat properly [ 15 , 99 , 106 ], a lack of food storage and preparation space [ 102 , 103 , 112 ], and a hazardous food preparation environment [ 112 ]. Unsuitable temporary accommodation, including converted shipping containers, hostels, B&Bs and poorly maintained houses were particularly likely to be associated with a wide range of other well-being related impacts. Unsuitable accommodation presented various problems, including excessive heat, dripping water, overcrowding, damp, dirt, electrical hazards, vermin, flooding and a lack of washing and laundry facilities [ 41 , 67 , 71 , 74 , 76 , 77 , 81 , 87 , 88 , 102 , 104 , 106 , 109 , 112 , 116 ]. Moving could also impact on access to services and continuity of care, including being unable to register with general practitioners [ 82 ], and difficulty in maintaining continuity of medical care [ 65 ].

Psychological impacts of housing insecurity included feeling different from peers [ 115 ], feeling disappointed in each new property after being initially hopeful [ 15 ], and having trouble fitting in, in a new area [ 15 ]. Feeling insecure (including uncertainty over when and where the next move will be, or if another move is happening) was a further impact of living in insecure housing situations (including temporary housing, making multiple moves, being evicted) [ 15 , 87 , 90 , 114 , 116 ], leading to stress and worry [ 15 , 114 ].

One of the major issues that [she] says affects her mental health is the uncertainty of their situation. She says it is hard to not know where they will be staying one night to the next. It is also difficult to adjust to living without her furniture and clothes ( [ 114 ] , p.17)

Multiple moves, or anticipating a move, disrupted children and young people’s sense of continuity and led to the experience of a loss of security and stability more generally [ 15 , 85 , 87 ]. This led children and young people to feel responsible for helping and providing support to their parents, including hiding their feelings [ 111 , 114 ], or not requesting things be bought [ 15 , 113 ]. Children and young people also felt a sense of displacement and a lack of belonging [ 15 , 115 ]. Loss of stability and security triggered a desire for stability, to be able to settle, have friends over, and not have to worry about moving [ 109 ].

Housing insecurity also had a negative effect on parent-wellbeing, and this impacted the wellbeing of young people both directly [ 15 , 65 , 102 , 106 ] and indirectly through increased arguments and family stress [ 15 , 93 ] and reduced parental ability to care for children with chronic conditions [ 41 ]. Parents also perceived their reduced wellbeing as negatively impacting their children's development [ 41 ]. The threat of sanctions for missed housing payment could lead to reduced well-being among the whole family, characterised by feelings of despair, failure and a loss of hope [ 93 ].

Moving also had a financial impact on families. Moving into much smaller temporary accommodation meant that possessions had to be left behind, with the family having to pay for decorating, carpets, curtains and furniture each time they moved [ 15 , 84 , 98 , 104 , 105 ], incurring considerable debt [ 98 ]. If the new location was far away from school, family, friends and, in some cases, shops, then the family incurred travel costs [ 15 , 87 , 94 , 112 , 114 ]. Because of all this, children and young people’s requests for possessions or experiences (e.g., trips out) were refused [ 113 ].

Excessive noise was another disruption that children and young people experienced in connection with their precarious housing situation. Sources of noise were traffic on a main road [ 15 ] a factory nearby [ 110 ], or from other people in a B&B, hotel, hostel, or neighbouring properties [ 15 , 91 , 102 , 106 , 112 ], and could disrupt sleep and daily activities.

If their current conditions were sufficiently bad, some children and young people felt positively about moving, to leave negative things behind. For instance, a move could take them close to friends [ 15 ] or they may have more space in the new property [ 15 ]. Quite often, however, negative impacts of moving seemed to offset any benefit [ 90 ].

Frequent moves could impact on children and young people’s health and wellbeing in other ways. Space might be even more squeezed by cardboard boxes in preparation for an impending move [ 15 ]. Some children reported having to leave beloved pets behind [ 90 ]. Time costs associated with moving meant less time for other activities [ 15 ]. Multiple moves, particularly across local authority boundaries, could impact the family’s access to services [ 41 , 71 ], including health services [ 90 ], specialist healthcare required to manage children’s health conditions [ 83 ], and social services [ 85 , 93 ].

One key impact that overlaid all of the above but was rarely mentioned was a lack of choice or control [ 109 ]. This was inherent in the families’ and children/young people’s accounts of their experiences of housing insecurity, through talk of not knowing where their next move would be or when, and having to move long distances away from the places they used frequently and the people who supported them. Even the journey into housing insecurity was often outside of families’ control, such as increases in rent, change in income, or eviction notices (see ‘ Exposure ’). Families often could not improve properties in poor condition because they could not afford repairs to properties in poor condition, so felt they had to live with these problems [ 90 ]. Some families avoided reporting problems to the landlord for fear of a rent increase or eviction (see ‘ Exposure ’). Children and young people in particular felt that they lacked control over their housing situation, and in some cases were not aware of reasons for moves [ 15 ].

Several childhood health and wellbeing outcomes have been documented in relation to, and they are overwhelmingly negative. These consisted of mental health problems, physical health problems, tiredness, and stunted child development. Living in temporary housing, making multiple moves, and the instability and insecurity associated with anticipating a move, or being uncertain whether a move would be needed, had an obvious negative impact on the mental health of children and young people [ 41 , 63 , 79 , 107 ], including in terms of self-harm [ 71 , 96 , 97 , 107 , 111 ], thoughts of suicide [ 71 ], anxiety [ 71 , 90 , 103 , 111 , 112 , 115 ], and depression [ 110 , 115 ]. Sometimes these problems manifested as physical pain [ 106 ], nightmares [ 84 ], night waking [ 107 ], or wetting the bed [ 63 , 107 , 111 ]. Stress, anger, isolation, fear, worry about the future (including about having to move again), worry about safety and acute distress were also reported [ 15 , 63 , 73 , 79 , 82 , 84 , 89 , 90 , 96 , 109 , 114 , 115 , 118 ]. One child with distress/mental health problems (as a result of having to make multiple moves) stopped eating properly (resulting in underweight and anaemia), and became socially withdrawn [ 79 ]. Another child reported weight loss and mental health problems due to worry about the future housing situation [ 95 ]. One study reported on stress and anxiety in children due to the trauma of losing their home and the emergency accommodation being unsuitable and temporary [ 111 ].

‘My six year old has been going to the doctors because he’s developed a nervous tick since we’ve been in that room. He was constantly nervous all the time. He’s so unsettled still and he knows that we’re still not settled. He’s really anxious. He’s become violent […]’ ( [ 111 ] , p.13)

Sometimes children and young people’s mental health issues would be displayed through problematic behaviour such as withdrawal, stealing, smoking, drug-taking, aggressive behaviour, and running away [ 68 , 71 , 79 , 84 , 97 , 107 , 114 , 115 ]. Teachers observed that younger children tended to get more withdrawn and older children and young people tended to get more angry and antagonistic, although the same child could cycle between these two states [ 115 ]. Separation anxiety was also reported [ 87 , 111 ].

Children and young people also experienced physical health problems as a result of living in temporary accommodation, poor condition housing, and making multiple moves, including the development or exacerbation of asthma [ 69 , 81 , 90 ] and eczema [ 41 , 81 , 90 , 111 ], stomach bugs [ 71 ], insect bites [ 112 ], infectious diseases [ 41 , 109 , 112 ], headaches [ 113 ], stomach aches [ 109 , 113 ], exacerbation of long-term conditions [ 41 , 75 , 109 ], rashes and asthma as a result of damp [ 100 ], a dermatological condition as a result of living in a hotel [ 91 ], other physical symptoms in young children, such as coughing and vomiting [ 100 ] and musculoskeletal pain from sleeping in unsuitable places [ 102 ]. One study reported illness in a baby following a difficult birth, attributed to housing-related stress in the mother [ 83 ]. Rarer outcomes included weight gain due to a lack of cooking facilities and thus reliance on fast food, weight loss due to stress [ 79 , 95 ] and head lice due to close contact with others [ 115 ]. Some properties presented risk of injury to babies and young children [ 41 ].

Tiredness was also reported, in relation to travelling a long distance to school and to visit family and friends [ 15 , 66 , 77 , 102 , 112 , 115 ]. Tiredness also resulted from poor quality sleep due to the unsuitable nature of the accommodation (e.g., poor state of repair, overcrowded), sleeping on a sofa [ 102 ], and worrying about the housing situation [ 15 , 41 , 87 , 109 , 112 , 114 ].

Impacts on the perceived development of young children were reported, in particular in relation to having no space to play, which impacted standing/walking and emotional development [ 87 , 111 ], and multiple moves, which impacted on potty training and speech development [ 87 , 111 ]. One study reported an impact on growth due refusal of solid food [ 113 ].

Protective factors

Protective factors were not presented in the original conceptual framework. However, we identified specific protective factors that were perceived to lessen the impact of housing insecurity on wellbeing among children and adolescents. These included friendship, keeping the same school, normalising housing insecurity, home-making, having a plan, hope, protective parenting, and some interventions.

Friendship was a key protective factor. Retaining connections with friends and peer networks following moves was important [ 15 , 90 ], and school facilitated this [ 114 ]. Indeed, another related strategy was to keep children and young people enrolled in the same school during and after moves, to retain some stability [ 15 , 70 , 90 , 108 ].

Some sources noted that children and young people tended to normalise and destigmatise their housing insecurity as something to be expected given that the family is poor or receives benefits [ 15 , 62 , 90 ]. This response could be a coping/defence mechanism to try to deal with the negative impacts of being insecurely housed.

Another, more positive, coping strategy was to make the property feel more like a home. For instance, decorating the property could lead to children and young people feeling more settled and ‘at home’, even if the ultimate intention was to move [ 15 ]. Further coping strategies included having a plan of how things could go to keep anxiety at bay and retain some control [ 15 ], seeing the advantages of a location [ 15 ], and hoping for a better house next time, and/or hoping that the family would settle in a permanent home [ 15 ].

Parents also acted to protect children and young people from the negative impacts of housing insecurity, by concealing the full extent of their financial and housing problems [ 113 ], including children and young people in decision-making [ 70 , 90 ] (for instance, allowing children and young people to influence their parents’ decisions on location, where there was a choice [ 70 ]), and presenting their situation as an adventure [ 114 ]. One study also documented parents taking their children out to parks to give them space to run around [ 91 ].

Lastly, some positive findings were reported by an evaluation of the Families Intervention Project (FIP), for families at risk of eviction due to anti-social behaviour [ 118 ]. Families that worked closely with a multi-agency team experienced increased housing security, reduced stress and anxiety, and fewer behavioural problems among the children [ 118 ]. Another study reported positive effects of a peer-led parenting programme on children’s behaviour, although it is unclear how this impacted on their health and wellbeing [ 64 ].

Key findings relating to other populations

Families that have experienced domestic violence.

Domestic violence could be a source of housing insecurity both for families who leave the family home to seek safety and for those who stay. Families that leave can end up moving multiple times (and frequently), perhaps initially to a refuge and then into other forms of temporary housing, with families experiencing uncertainty over when the next move would be [ 90 , 105 ]. One study reported that experience of living in different places was perceived to be beneficial, although little detail was given, and this was offset by difficulty building peer networks [ 90 ]. In one family, the alternative to housing insecurity was for the children to be placed in local authority care, which was avoided through the children and other parent leaving the perpetrator [ 90 ].

Among families who stay in the family home (with the perpetrator leaving), housing insecurity could be created by the perpetrator refusing to pay the mortgage, leaving the family worried and uncertain:

‘ I’ve lost two stone, my son has lost ten pounds – he is only 15 – he is having counselling at school. It has just been a nightmare…He hasn’t paid the mortgage for a year because he wants to get me out so he can have the money… ’ ([ 95 ], p. 68). Friendship was particularly impacted among this population. To prevent the perpetrator from finding them, children were not able to disclose personal information [ 63 ]. This made it difficult to form close friendships.

Parents reported a lack of support offered to children and young people, including services that they needed [ 80 ]. However, where support was offered to parents and children/young people who had moved to escape abuse in their previous home, this support could improve wellbeing [ 63 , 79 , 80 ], acting as a protective factor. Particular forms of useful support included a parenting course [ 79 ] and supportive staff and peers at hostels [ 80 ]. Hostels offered a feeling of safety due to closed-circuit television [ 80 ]. One study reported that refuge and hostel staff were perceived as helpful but powerless to keep families safe in some cases, although children and young people found it helpful to talk and open up to staff about their situation [ 63 ]. One intervention, the Sanctuary scheme, allowed people experiencing/at risk of domestic violence to remain in their own home, with additional security [ 95 ].

Migrant, refugee and asylum seeker families

Migrant, refugee and asylum seeker families experienced similar forms of housing insecurity and similar impacts on everyday life and childhood health/wellbeing as did the general population. However, migrant/refugee families reported having to move suddenly, with very little notice (e.g., 48 h) [ 77 , 82 ]. They also lacked support from services and assistance with housing from the local authority. Consequently, families would end up homeless and have to beg friends to let them sleep on their sofas [ 101 ].

Once homeless, families slept in unsuitable locations, such as on the night bus, in a church, and in the waiting room of the Accident and Emergency (A&E) department. This led to extreme tiredness; in some cases, children were too tired to attend school [ 102 ]. That type of homelessness was a particular feature of the experience of housing insecurity among this population.

‘We had to keep going to McDonalds every night and we would also go to A&E. I would have to wear my school clothes and sleep like that.… They would say we have to sleep where the people wait but it’s just like lights […] The chairs were hard.’ (child aged 9) ( [ 102 ] , p. 22)

Other considerations specific to migrant/refugee/asylum seeker families were language barriers, which compounded the challenge of adjusting to a new area [ 82 ], and pressure to cook British food rather than food from their home country in communal facilities [ 106 ].

Families forced to relocate due to demolition

Two papers identified from the database search examined experiences of relocation; families were living in local authority accommodation in Glasgow and experienced a forced move as the high-rise block of flats they lived in was due to be demolished [ 69 , 70 ]. This forced location creates housing insecurity.

Despite the common source, however, housing insecurity was experienced in different ways by different families. One family reported not wanting to move as the children liked the area and their school and nursery, and one family was offered a flat but needed outdoor space [ 70 ]. Many families experienced the pre-relocation area as unsafe due to problematic behaviour in outdoor shared areas [ 69 ]. Because of this and no access to a private garden children lacked space to play [ 70 ]. Families also reported feeling shame in relation to the local area and the poor condition of their pre-relocation housing (a high-rise block of flats), and were keen to move to a less stigmatising area with better condition housing [ 69 , 70 ].

Most families managed to relocate to areas close enough for their children and young people to attend the same schools. However two families changed schools [ 69 , 70 ]. Children and young people felt shame and stigma relating to the local area and the flats themselves, with many young people reluctant to invite friends over, or others socialising in the corridor without inviting friends inside [ 70 ]. Thus, relocation could have positive impacts on families and children/young people. For three families, moving was a positive experience, with children and young people enjoying having a garden and growing to like their new neighbours and the area [ 69 ].

Although we anticipated potentially different experiences, impacts and outcomes relating to housing insecurity across the four populations, the evidence reviewed suggests many similarities. Some exposures were common to multiple populations, for instance, being evicted or having a forced move, living in temporary accommodation, experiencing overcrowding, exposure to problematic behaviour, poor condition/unsuitable property, and making multiple moves. Common impacts included social, school-related, psychological, financial and family wellbeing impacts, having to travel long distances to attend school and see friends, having to live in a property that was unsuitable or in a poor state of repair, overcrowded and often noisy, all of which could then further exacerbate housing insecurity. Outcomes reported across multiple populations included mental health problems (which could manifest in physical ways, for example, trouble eating and sleeping, or wetting the bed) and physical health problems such as skin complaints and asthma related to poor housing conditions. Protective factors common to multiple populations included friendship and support, staying at the same school, having hope for the future, and parenting practices. Pervasive throughout all populations and accounts was an overall lack of choice or control over the housing situation and poverty as a driving force.

These findings support and build upon previous literature that has examined the impact of housing insecurity on the health and wellbeing of children and young people, in terms of reduced mental and psychological wellbeing [ 21 , 42 , 43 ], ill health relating to homelessness or poor housing conditions [ 40 , 41 ], and disrupted family processes [ 38 ]. Likewise, the findings build upon prior cohort studies that support a causal relationship with child health [ 38 ], by highlighting the details of the hardships faced by children and young people experiencing housing insecurity and exploring relationships between exposures, ‘less tangible’ impacts and health and wellbeing outcomes.

Many elements of the Children’s Society definition of housing insecurity were identifiable in our review findings. A key element of housing insecurity is financial insecurity [ 17 , 19 ]; this was borne out in our findings where families were frequently exposed to high/rising costs of housing or reduced income. Indeed, our review found that families incurred additional costs due to multiple and/or frequent moves and/or moving into temporary accommodation. This could potentially increase financial insecurity, thus creating a vicious circle of housing insecurity and poverty. Having ‘a home that does not provide a sense of safety and security’ ([ 18 ], paragraph 3) was evident when children and young people reported not feeling safe in their accommodation, and relational insecurity was evident in families’ accounts of being moved far from friends, school and support networks.

In addition, we identified certain population-specific considerations. Families experiencing domestic violence faced a difficult choice between choosing to remain in the property and leaving the property, both with insecurity attached. Housing insecurity negatively impacted on friendships for all populations, however this could be potentially more challenging for those escaping domestic violence, due to the need to keep personal information confidential in order to maintain family safety.

Parents and children/young people in migrant, refugee and asylum seeker populations spoke of having very little notice before having to move out of a property, sometimes only 48 h. This created a housing emergency, captured in accounts of families becoming homeless and having to sleep in unsuitable places, such as the Accident and Emergency department waiting room or on a night bus. In some families, parents had no recourse to public funds, so even when children and young people were born in the UK, the family still ended up destitute and homeless, leading to significant worry.

A key factor in relocation was that families were forced to move by a particular date, as the high-rise block they lived in was scheduled for demolition. Many families desired a move, due to a lack of space, overcrowding, and unsafe outdoor spaces. However many did not want to leave behind social networks and schools in the community, and even some who wanted to move had difficulty finding a suitable property (e.g., for their family size).

A key challenge to synthesising the evidence was the complexity of the data, in particular the relationships between exposures and impacts. Factors that families initially experienced as exposures could then become impacts, and particular impacts could then worsen housing insecurity, in a cyclical fashion. For instance, overcrowded conditions could precipitate a move, but then the only property available may be in a poor state of repair, with intolerable living conditions, thus prompting a further move. Another key challenge in synthesising the qualitative evidence was that many elements of the experience of housing insecurity that were experienced simultaneously by children and young people have been artificially separated within the updated conceptual frameworks, making analysis problematic. For instance, those living in poor-condition temporary accommodation may want to move due the poor state of a property, but be worried about where they may end up next and whether children/young people will have to change schools and move far from friends. Such complexity has proved challenging to our overall synthesis. Policymakers and practitioners should be aware that the diagrams illustrating the hypothesised causal pathways simplify the multiple inter-related factors related to housing insecurity that impact on the wellbeing of children and young people. Identifiable common stresses including poverty, financial difficulties and debt, immigration/refugee status and domestic abuse will also exert direct significant effects on family wellbeing that prove difficult to separate from those directly due to housing insecurity.

Limitations

Limitations of the evidence base.

We have identified numerous literature sources, many rich with data relating to the experiences of children and young people, and synthesised these data into diagrams that illustrate hypothesised causal pathways within the original conceptual framework, with accompanying descriptions of the experiences of housing insecurity in children and young people. However, we cannot establish claims for the comprehensiveness of our diagrams that map hypothesised causal pathways from housing insecurity to childhood health/wellbeing based on the original conceptual framework. We mapped associations where they were present in the accounts of children/young people and other informants. However, the evidence base may have missed other potential associations, particularly for populations covered by a small number of studies.

Within the evidence base, accounts from parents or other informants proved extremely useful in examining the impacts of housing insecurity on the health and wellbeing of children and young people, particularly for younger children who are not able to yet articulate their experiences and feelings. Nevertheless, such accounts proved an insufficient substitute for rich and nuanced data directly from the children and young people themselves. Our public involvement group have informed us that children and young people may find it difficult to talk about their housing situation, and noted that we did not identify any research that explicitly examined the perspectives of young people who provide care for a parent.

Likewise, little available information relates to the health and wellbeing of children and young people, and it is difficult to establish whether the evidence we have reviewed has captured all relevant health and wellbeing experiences. The majority of the accounts of young people focused on the impacts (or intermediate outcomes) of housing insecurity, which means that we have been able to present a rich picture of these ‘less tangible’ impacts, but also that the links from these impacts to health and wellbeing outcomes is less well understood. For instance, our public involvement group noted that we had not reported any evidence relating to bullying as a result of experiencing housing insecurity.

Strengths and limitations of the review

Strengths of our review method include the prior use of a conceptual model, developed in consultation with stakeholders and topic experts, and examination of key policy documents, which guided the process of synthesis. Synthesis was thus both deductive (i.e., informed by the a priori conceptual model) and inductive (i.e., conducted using established thematic synthesis methods), which allowed for an organised and yet rich and nuanced picture of the impacts of housing insecurity on health and wellbeing among children and young people in the UK. The review was conducted by an established team of experienced reviewers and a methodologist.

A key limitation is that literature sources were far more plentiful than anticipated, including numerous long and detailed reports identified through grey literature searching. While this enhanced the richness of the dataset, it also expanded the review workload, leading to additional time constraints. Limited time and resource could be allocated for double-checking full texts (in particular in the grey literature) and extractions, and thus only a sample were double-checked. Time constraints also prevented citation searches of key included studies. Nevertheless, such an approach remains consistent with established rapid review methods with minimal consequences for missing or mis-reported evidence [ 50 , 51 , 52 ]. Time and resource constraints also prohibited examination of how experiences may differ according to location within the UK.

Implications for policy

It is important that decisions made about housing at a national and local level reflect the impacts that insecure housing can have on children and young people, and ensures that housing insecurity is prevented in the first place. The current review findings suggest that policies should focus on reducing housing insecurity in its totality among families. One way to do this is to focus on eviction, which is a significant cause of instability and a leading cause of households seeking homelessness assistance [ 25 ]. This could include ending no-fault evictions, as has been done in Scotland for private renters since 2017 and as proposed, but yet to be introduced by the UK government in 2019. Scotland’s introduction of longer tenancy agreements with the removal of no-fault evictions may also facilitate families being able to settle and reduce the need for multiple moves. Similarly, legislating for minimum standards in the private rented sector, as currently being explored [ 119 ], will protect children and young people from being exposed to unhealthy and dangerous conditions.

Other changes could include (1) stipulating minimum requirements for space in family properties and minimum requirements for property conditions; (2) advocating for families living in the private rental sector to improve their housing situation; (3) reducing the use of short-term tenancies so families are not required to make multiple moves; (4) providing affordable housing options that give families more choice; and (5) engaging families in the design of systems and services that meet their housing needs. Addressing poverty more widely should also help to alleviate housing insecurity among families in the UK, as much of the evidence reported on how poverty initiated and/or exacerbated housing insecurity, for instance, by restricting choice and by increasing worry. However, any changes will need adequate support for enforcement, something made clear by the limited effectiveness of policy introduced to protect people from revenge/retaliatory eviction [ 97 , 120 , 121 , 122 ], improve the quality and suitability of temporary accommodation, and, where possible, reduce the need for temporary accommodation through preventative measures.

Among families escaping domestic violence, support systems are needed to avoid destitution caused by the perpetrator (e.g., not paying the mortgage). There should also be systems in place to ensure that families are housed in a permanent residence as soon as possible following the initial placement in emergency temporary accommodation after leaving the family home, with as few moves as possible. Appropriate support with housing should be made available to refugee/asylum seeker/migrant families, including those where the parents have no recourse to public funds, and short-notice and long-distance moves should be avoided, particularly where these take families away from their support systems and communities.

Implications for practice

Where possible, interventions to reduce or eliminate housing insecurity should be implemented. Where this is not possible, interventions should focus on reducing the impact of housing insecurity, for instance, by ensuring long journeys can be avoided, that accommodation is of a decent standard, and by providing adequate support to families and children young people. Practitioners who work to house families should prioritise stable, suitable and good quality housing. Practitioners who interact with children and young people experiencing housing insecurity and homelessness (e.g., clinicians, teachers, social workers) should recognise the complexity of the children and young people’s experiences, including how the situation and circumstances (e.g., escaping domestic violence, migration status) might impact on their health and wellbeing, and that impacts vary on an individual basis, particularly in assessments and family support plans. A multiagency approach should be utilised with families to mitigate the impacts of housing insecurity, poor housing conditions or unsuitable housing. Practitioners should consider the impacts of continuity of school, support and services, and the need for mental health support, parenting and counselling, for instance through providing support with transport to enable children and young people to stay at their current school, and support to maintain friendships. All those working with children/young people and families experiencing housing insecurity should consider ways to offer them optimal choice and control over situations that affect them.

All practitioners and professionals (e.g., teachers) who work with children and young people from families who have escaped domestic violence should ensure that the children and young people are receiving appropriate support from all relevant services, and that appropriate safety measures are in place to protect the family from the perpetrator.

Research recommendations

Future qualitative research could focus explicitly on the health and wellbeing of children and young people experiencing housing insecurity, and how they link with the impacts and outcomes identified in the current review. In particular, research could explore how the health and wellbeing of children and young people are affected by the impacts of housing insecurity on friendships, education, food and hygiene, financial impacts, long journeys, overcrowding, perceived safety, and access to services. Further qualitative research could examine the impact of interventions to address housing insecurity among families in the UK. Interventions with a participatory component that seek to ensure that the voices of children and young people remain central should be prioritised for further research. The voices of specific groups of young people who are likely to be marginalised (e.g., young carers) could be explored in future research. Future qualitative research should report methods of recruitment and data collection and analysis clearly and transparently, and should incorporate meaningful research reflexivity.

Conclusions

Housing insecurity has a profound impact on children and young people in families in the UK. Such housing insecurity can take many forms and result from often inter-related situations that are fundamentally connected to poverty. The resultant housing insecurity can have multiple (often simultaneous) impacts, including those that relate to educational, psychological, financial and family wellbeing impacts, having to travel long distances to attend school and see friends, and having to live in unsuitable, poorly repaired, overcrowded or noisy properties, any of which further exacerbate housing insecurity. Negative experiences can impact on health and wellbeing, in terms of mental health problems (which could manifest in physical ways) and physical health problems, as well as tiredness and developmental issues. Some experiences and situations can lessen the impact of housing insecurity on the health and wellbeing of children and young people. Negative impacts of housing insecurity on health and wellbeing may be further compounded by specific situations and life circumstances, such as escaping domestic violence, being a migrant, refugee or asylum seeker (or having a parent with that status), or experiencing a forced relocation due to housing demolition.

Availability of data and materials

All data presented in this review were already published, either in an academic journal, or a report that is publicly available. Search strings are available in Additional File 1. Data extracted from the published papers and reports included in the current study are available from the corresponding author on request.

the main social security payment in the UK; for more information see https://www.gov.uk/universal-credit

Abbreviations

Accident and Emergency (Department)

Authority, Accuracy, Coverage, Objectivity, Date, Significance

Applied Social Sciences Index and Abstracts

Bed and Breakfast (accommodation)

Critical Appraisal Skills Programme

Coronavirus Disease 2019

Families Intervention Project

United Kingdom

International Bibliography of the Social Sciences

National Institute for Health and Care Research

International prospective register of systematic reviews

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Acknowledgements

We wish to thank Katie Lewis and Liz Kitchin from the University of Sheffield for providing administrative support to the project, Karen Horrocks, from the UK Office for Health Improvement and Disparities, for revising the policy and practice implications, anonymous young people who provided PPI feedback on a lay summary and gave us an insight into key omissions from the evidence base, and Mary Crowder from the University of Sheffield for her support in accessing feedback from PPI members at a local youth organisation. We would also like to thank the policy and practice stakeholders and topic experts with whom we consulted to develop the initial conceptual framework.

This study is funded by the National Institute for Health Research (NIHR) Public Health programme (project reference 18/93 PHR Public Health Review Team). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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Emma S. Hock, Lindsay Blank, Hannah Fairbrother, Mark Clowes, Diana Castelblanco Cuevas, Andrew Booth & Elizabeth Goyder

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EH led the review, and undertook study selection, grey literature searching and selection, data extraction, quality assessment and coding, drafted the synthesis, and drafted and refined large parts of the manuscript. LB undertook study selection, data extraction, quality assessment and coding, compiled study characteristics, checked and refined the synthesis, and drafted and refined parts of the manuscript. HF undertook study selection, grey literature searching and selection, data extraction and quality assessment, co-ordinated patient and public involvement, provided topic expertise, checked and refined the synthesis, and drafted and refined parts of the manuscript. MC designed the search strategy, undertook database searches and drafted and refined parts of the manuscript. DCC undertook study selection and drafted and refined parts of the manuscript. AB provided methodological support and advice, checked and refined the synthesis, and drafted and refined parts of the manuscript. AC provided topic expertise and drafted and refined parts of the manuscript. EG undertook stakeholder consultation and protocol development, drafted and refined parts of the manuscript, and was the guarantor of the review. All authors reviewed the manuscript.

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AB is a Cochrane author and co-convenor of the Cochrane Qualitative and Implementation Methods Group, and was also previously a member of the NIHR Evidence Synthesis Advisory Group from 2018 to 2022 and a member of the NIHR HS&DR Funding Board from 2018 to 2022. No other authors have competing interests to declare.

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Hock, E.S., Blank, L., Fairbrother, H. et al. Exploring the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom: a qualitative systematic review. BMC Public Health 24 , 2453 (2024). https://doi.org/10.1186/s12889-024-19735-9

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Abstract. We evaluate evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe. We search four scientific websites from the inception of the research through May 20, 2018. We find great variety (20 ) in measured outcomes among the 33 studies that pass our initial ...
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Minimum Wages and Public Health: A Literature Review. J. P. Leigh, Wesley Leigh, Juan Du. Published in Preventive Medicine 27 February 2018. Economics. TLDR. Evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe is evaluated and a list of "better practices" for ...
Minimum wages and public health: A literature review
Public health research on minimum wages has found a range of likely state-level benefits from state-level increases on population wellbeing, including physical (Lenhart, 2017) and mental health ...
Minimum Wages and Public Health: A Literature Review
Request PDF | On Jan 1, 2018, J. Paul Leigh and others published Minimum Wages and Public Health: A Literature Review | Find, read and cite all the research you need on ResearchGate
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HEALTH AND HEALTH-RELATED BEHAVIOR 2.1 | Minimum wages, employment, earnings, and incomes There is a large literature on the effects of minimum wages on employment, wages, earnings, and incomes. Typically, elasticities of earnings with respect to the minimum wage, for lower-skilled workers affected by minimum wage increases, are in the range of ...
The effects of minimum wages on (almost) everything? A review of recent
I review and assess the evidence on minimum wage effects on health outcomes and health-related behaviors. The evidence on physical health points in conflicting directions, leaning toward adverse effects. ... Overall, policy conclusions that minimum wages improve health are unwarranted or at least premature. REFERENCES, & () . , , - ...
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Could Raising the Minimum Wage Improve the Public's ...
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have examined health-related effects of minimum wages, providing evidence that higher minimum wages can reduce mental illness [50], body mass index [41], and premature mortality [57], as well as improve birth outcomes [31, 59]. Overall, however, there is still much uncertainty about the. health effects of higher minimum wages as well as about ...
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This study contributes to recent work on the relationship between minimum wages and health by examining potential underlying mechanisms. Specifically, the roles of health insurance, health care access and utilization are explored. By analyzing Current Population Survey data for the years 1989-2009 and by estimating DD models, I find that higher minimum wages increase health insurance ...
Raising the Minimum Wage and Public Health
Should policy makers pursue raising the minimum wage, it will be key to do so in a way that does not exacerbate long-standing inequities in both income and health. Effects of a $15 Minimum Wage. The federal minimum wage has held steady at $7.25 since 2009, although 29 states have set a higher rate.
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Minimum Wages and Health: A Reassessment Cite as: Sylvia Allegretto and Carl Nadler (2020). "Minimum Wages and Health: A Reassessment". ... 3Based on a review of this literature, in 2014 the Congressional Budget Oﬃce (CBO) ... Fortin et al., 2018), even after accounting for possible reductions in public assistance for ...
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We evaluate evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe. We search four scientific websites from the inception of the research through May 20, 2018. We find great variety (20+) in measured outcomes among the 33 studies that pass our initial screening.
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First, increases in minimum wages can improve the health of low-wage workers and their families. 2 Second, with better ... Leigh JP, Leigh WA, Du J. Minimum wages and public health: a literature review. Prev Med. 2019; 118:122-134 ... Pickett KE, Wilkinson GR. Income inequality and health: a causal review. Soc Sci Med. 2015; 128:316-326 ...
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2023. TLDR. As state wage limits increase, the odds of hypertension significantly decreased among Black adults overall and the Black-White disparity in hypertension worsened as state minimum wage limits increased, and the magnitude of this disparity was larger among women. Expand.
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This study investigates the effects of minimum wage on health, well-being, and income security in European countries. ... W. A., &Du, J. (2019). Minimum wages and public health: a literature review. Preventive Medicine 118, 122-134. Lenhart, O. (2017). Do higher minimum wages benefit health? Evidence from the UK. Journal of Policy Analysis ...
Raising the Minimum Wage and Public Health.
Should policy makers pursue raising the minimum wage, it will be key to do so in a way that does not exacerbate long-standing inequities in both income and health. Some states, and perhaps soon the federal government, are considering increasing the minimum wage. President Joe Biden voiced his support for raising the federal minimum wage to $15 per hour while on the campaign trail, and also ...
Effects Of Minimum Wages On Population Health
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His wage, employment, and hours effects imply a negative effect on. incomes of workers whose wages are constrained by the minimum wage. Neumark, Schweitzer, and Wascher (2004) estimate wage, hours, employment, and total. earnings effects independently, using state variation in minimum wages to obtain treatment and.
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We evaluate evidence for the effectiveness of raising minimum wages on various measures of public health within the US, Canada, the UK, and Europe. We search f. Skip to main content ... Copy URL. Copy DOI. Minimum Wages and Public Health: A Literature Review. 30 Pages Posted: 23 May 2018 Last revised: 14 Oct 2018. See all articles by 17935 ...
Minimum wages and health: evidence from European countries
This study investigates the effects of minimum wage on health, well-being, and income security in European countries. ... Minimum wages and public health: a literature review. Preventive Medicine118, 122-134. Lenhart O. Do higher minimum wages benefit health? Evidence from the UK. Journal of Policy Analysis and Management. 2017; 36 (4):828 ...
Own-Wage Elasticity: Quantifying the Impact of Minimum Wages on
We find that most studies to date suggest a fairly modest impact of minimum wages on jobs: the median OWE estimate of 72 studies published in academic journals is -0.13, which suggests that only around 13 percent of the potential earnings gains from minimum wage increases are offset due to associated job losses.
Exploring the impact of housing insecurity on the ...
The impacts of socioeconomic position in childhood on adult health outcomes and mortality are well documented in quantitative analyses (e.g., []).Housing is a key mechanism through which social and structural inequalities can impact health [].The impact of housing conditions on child health are well established [].Examining the wellbeing of children and young people within public health ...
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Exploring the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom: a qualitative systematic review

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Key findings relating to other populations

Migrant, refugee and asylum seeker families

Families forced to relocate due to demolition

Limitations

Strengths and limitations of the review

Implications for policy

Implications for practice

Research recommendations

Conclusions

Availability of data and materials

Abbreviations

Acknowledgements

Author information