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Poverty and Psychology pp 87–101 Cite as

Poverty and Unemployment

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In The World Employment Report 2001 the World Health Organisation ( WHO, 2001 ) estimated that globally “at the end of 2000 some 160 million workers are unemployed, most of them first-time job-seekers,” about two-thirds of these in the so-called “developing” world. In addition, the WHO estimated that “about 500 million workers are unable to earn enough to keep their families above the US– 1 -a-day poverty line. These are almost entirely in the developing world. And of the workers who are not among the poor, many lack basic job and income security.” The situation appears to be deteriorating.

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  • Unemployed People

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Fryer, D., Fagan, R. (2003). Poverty and Unemployment. In: Carr, S.C., Sloan, T.S. (eds) Poverty and Psychology. International and Cultural Psychology Series. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0029-2_5

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Introduction, acknowledgements.

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Connections between unemployment insurance, poverty and health: a systematic review

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Emilie Renahy, Christiane Mitchell, Agnes Molnar, Carles Muntaner, Edwin Ng, Farihah Ali, Patricia O’Campo, Connections between unemployment insurance, poverty and health: a systematic review, European Journal of Public Health , Volume 28, Issue 2, April 2018, Pages 269–275, https://doi.org/10.1093/eurpub/ckx235

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Since the global economic crisis in 2007, unemployment rates have escalated in most European and North American countries. Unemployment protection policies, particularly the unemployment insurance (UI) system, have become a weighty issue for many modern welfare states. Decades of research have established concrete findings on the adverse impacts of unemployment on poverty- and health-related outcomes. This provided a foundation for further exploration into the potential protective effects of UI in offsetting these adverse outcomes.

We developed a systematic review protocol in four stages (literature search, study selection, data extraction and quality appraisal) to ensure a rigorous data collection and inter-rated reliability. We examined the full body of empirical research published between 2000 and 2013 on the pathways by which UI impacts poverty and health.

Out of 2233 primary studies identified, a total of 12 met our inclusion criteria. The selected studies assessed poverty-related outcomes (absolute/relative poverty and material hardship) or one or more health-related outcomes (health behaviors, self-rated health, well-being and mental health). Across various UI systems, jurisdictions from high income countries, and study designs, we found good support for our conceptual framework, by which UI attenuates the effect of unemployment on both poverty and health, with a few exceptions.

Whether UI impacts differ by age and region might be explored further in future research. The complex mediating relationship between unemployment, UI, poverty and health should further be assessed in light of economic and historical contexts. This could inform decision-making processes during future periods of economic recession.

Since the global economic crisis in 2007, unemployment rates have escalated in most European and North American countries. 1 As a result, unemployment protection policies, particularly the unemployment insurance (UI) system, have become a weighty issue for many modern welfare states. 2 The wealth of primary and secondary research that examines the social and public health impacts of job loss 3–5 has provided a foundation for further exploration into the potential protective effects of UI in offsetting the adverse outcomes of unemployment. In this review, we systematically synthesize the evidence on the effect of UI on poverty- and health-related outcomes.

Unemployment, poverty and health

The major consequence of unemployment in a labor market is loss of income, an important resource necessary for life in market economies 6 and lack of money (poverty) is a major social determinant of health. 7 Social epidemiologic evidence supports the hypothesis that poverty, loss of income or financial strain mediates the relationship between unemployment and health. Poverty, in particular, leads to poor physical and mental health via multiple material and psychosocial pathways. 8 , 9

Decades of research have amassed abundant evidence on the adverse impacts of unemployment on poverty-related outcomes, as well as individual health status. 3–5 Studies examining poverty-related outcomes report that the unemployed have a higher risk of experiencing poverty and material deprivation due to the loss of income and work-related benefits. 3 This financial strain experienced by the unemployed consequently manifests itself as a mechanism that can lead to damaging health outcomes. 10 Evidence shows that unemployment affects health status in myriad ways. For instance, unemployment has been associated with an increase in unhealthy coping behaviours (e.g. smoking and poor diet), 11 lower self-esteem and increase psychological distress. 12 The stress experienced during spells of unemployment is due to a number factors, including, the loss of income (also referred to as an economic need for employment), 13 the stigmatization (i.e. being out of work is considered an ‘undesirable’ social position), 3 , 13 , 14 the psychosocial need for employment (e.g. structured days and consistent schedules), 13 , 15 a social norm to work, 16 the loss of one’s role and social network, 17 and/or, the search for a new job. 18

Unemployment insurance

UI is a public benefit administered by the state. The whole UI system is comprised of several components, which varies in terms of generosity across countries and jurisdictions. 19 UI components include: eligibility criteria (e.g. unemployed through no fault of the worker, worked for a minimum amount of hours over a specific period of time), replacement rates (i.e. the percentage of income replaced), replacement duration (i.e. the time UI is available during unemployment) and waiting period (i.e. the time for which no benefit is paid even after granted eligibility). 14 , 20

We developed a conceptual framework to situate our research around the associations between UI and poverty, and, UI and health (see figure 1 ). Within this conceptual framework, we focus on two pathways by which UI impacts poverty and health among the working population.

Conceptual framework of the relationship between unemployment, unemployment insurance, poverty- and health-related outcomes

Conceptual framework of the relationship between unemployment, unemployment insurance, poverty- and health-related outcomes

This review is part of the broader SOPHIE project ( http://www.sophie-project.eu/project.htm ) with the pursuit in evaluating the impact of structural policies on health inequalities and their social determinants and aiming to foster change.

We developed a systematic protocol in four stages (in line with the PRISMA statement) 21 based on team work and triangulation to ensure a rigorous data collection and inter-rated reliability. 22

Literature search

We employed a systematic review method to examine the full body of empirical research by pooling, assessing and synthesizing evidence on a specific topic. For our review, we undertook a systematic search for primary studies. Electronic searches were performed for the period 2000–2013 using the following data bases: ‘Ovid Medline’, ‘Social Sciences Citation Index by Web of Science’, ‘Ovid EMBASE’, ‘ProQuest’, ‘International Bibliography of the Social Sciences’, ‘Worldwide Political Science Abstracts’, ‘Political and International Studies International’, ‘EBSCO’, ‘FRANCIS’, ‘Sociological Abstracts’, ‘Applied Social Science Index and Abstracts’, ‘PsycINFO’, ‘EconLit’ and ‘International Political Science Abstracts’. Search terms included: ‘unemployment insurance’, ‘employment insurance’, ‘unemployment assistance’, ‘employment assistance’, ‘unemployment protection’, ‘employment protection’, ‘unemployment benefit*’, ‘jobseeker's allowance’, ‘jobseeker’s benefit*’, ‘workseeker’s allowance’, ‘workseeker’s benefit*’ and ‘unemployment compensation’.

Study selection

Figure 2 shows a flow chart diagram of our search strategy. We first systematically screened all titles and abstracts of the initial set of 2233 articles. We applied the following questions to guide inclusion: Is the focus of the study UI policies? Is outcome either poverty or health? Does the study report empirical findings? Second, we read the full text of 66 articles and applied detailed inclusion/exclusion criteria (as described in Supplementary table A ). Reference lists of included articles were also searched to identify possible candidates, adding 26 articles to fully screen. Each selection stage was independently performed by two team members and discrepancies were resolved during bi-weekly team meetings.

Flow diagram of the systematic review and selection process

Flow diagram of the systematic review and selection process

It should also be noted that some papers were excluded because the analyses actually did not distinguish between UI and other welfare allowances or insurance types. However, we included studies that investigated UI characteristics at the country level, in addition to those using individual level data. The aim of some included articles was not necessarily to assess the impact of UI on poverty or health, but only descriptively addressed the issue in the course of their analyses. Moreover, we relied more on results tables than on the text because we found some discrepancies or unjustified interpretations in some papers.

Data extraction

A total of 92 articles was full-text investigated. We recorded the reason for exclusion. For each of the 12 articles that met inclusion criteria, we extracted key information in a standardized form to characterize the study, the key variables used and the main results including information such as study aim, research design, data source and sample size, type of analysis, unit of analysis, UI-, poverty- and health-related variables and their measurement, comparison group if any, controls if any, main findings.

A group analysis activity using a single paper allowed us to test the data extraction tool and ensure inter-rater reliability. When a high level of agreement was achieved across analysts, each team member was given a different set of papers from which to extract information. Extraction results were regularly shared and discussed with the larger research team to increase transparency, ensure consistency and enable reflexive feedback. Uncertainties were addressed during team meetings and emails were sent to authors to clarify some published results (only one did not answer back). One team member finally reviewed all extraction sheets for completeness and accuracy. Because of the heterogeneity of study designs, populations and outcomes, we could not do a meta-analysis.

Quality appraisal

We used a modified version of the quality assessment tool for quantitative studies. 23 As this critical appraisal tool was mainly designed for clinical studies, we adapted it to be more reflective of the type of study designs used in the field of public health or social sciences. For instance, as none of the included study was a randomized control trial, none would have been eligible for the highest quality score based on the initial rating system and definitions. We rated studies according to five criteria: selection bias, study design, adjustment for confounders, data collection methods and withdrawals/dropouts (for longitudinal studies). Each of the five criteria was rated as strong, moderate or weak. A final quality rating (strong, moderate or weak) was created based on the number of weak ratings (respectively no, one or two and more weak criteria). We used this quality assessment to qualitatively weight studies when summarizing evidence and drawing conclusions, allocating more importance to ‘strong’ studies and less to ‘weak’ studies. 24

Outcome used and population of reference in the 12 included studies

Note: Unemployment insurance (UI).

Quality assessment details for the 12 included articles a

All criteria are rated using a 3-point Likert, whereby, 1 = strong , 2 = moderate and 3 = weak.

Poverty-related outcomes

We found three studies that assessed the effect of UI on poverty-related outcomes, 25–27 one of which considering youth only. 25 In 1995, a comparative study of Scandinavian countries (quality: weak) investigated the relationship between UI and economic hardship among unemployed youth (ages 18–24) 25 and found that Finland had the highest level of economic hardship, and the lowest coverage rate (i.e. the percentage of unemployed receiving UI) and the second lowest replacement rates (i.e. the ratio of previous earning). In contrast, authors found that Denmark had the fewest economic hardships and the highest coverage and replacement rates. This suggests a possible trend among unemployed youth, where the higher the UI coverage and replacement rate is, the lower the economic hardship, thus providing a protective effect. The Zuberi study explained the divergence in relative household poverty rates in Canada and the United States using a cross-sectional time series from 1974 to 1994 (quality: moderate). 26 The authors reported a slight protective effect of UI on relative poverty. Finally, Scruggs investigated the relationship between welfare state generosity of three social insurance programs, including unemployment, sickness and pensions, on poverty in advanced democracies by undertaking a cross-sectional time series study for high-income countries (quality: strong). 27 The results showed high correlations between the three indicators of social insurance programs and UI when UI was the only program included in the models. However, when all three programs were included in the regression models, UI generosity was not significantly related to reductions in poverty (relative or absolute) anymore.

Despite different quality rating, time periods, locations and populations, all three studies tend to support a protective effect of UI on poverty when UI is the only program considered.

Healthy behaviors

Two studies that met our inclusion criteria assessed the impact of UI on health behaviors outcomes among adults using longitudinal data in the United States 28 and Japan 29 in the early 2000’s. In a longitudinal US study that investigated change in smoking, drinking and body weight (quality: moderate), 28 the authors found that unemployed individuals who received UI experienced a slight protective effect for increases in alcohol consumption over time and weight loss, compared with the unemployed who did not receive UI. However, there was no significant effect regarding changes in smoking consumption. Matoba’s longitudinal study of Japanese laid-off workers 29 (quality: weak) analysed the patterns of health behaviurs with prolonged UI benefits. Healthy behaviors remained unchanged whether laid-off workers were receiving UI or UI ceased (approximately 1 year or less).

Results for healthy behaviors show slightly protective effect to no effect, we, therefore, cannot draw strong conclusions.

We identified three papers that assessed the association between UI and subjective well-being among adults. 30–32 The Sjoberg study of 21 European countries examined the cross-national relationship between UI generosity and subjective well-being among unemployed, as well as employed individuals (quality: moderate). 31 In this study, UI generosity is an index defined by replacement rate, benefit duration and country expenditure per unemployed and is used as an ecological variable to characterize the level of social protection in each country, but does not inform, at the individual level, whether unemployed people effectively receive UI or not. The results demonstrated a significant correlation between UI generosity and absolute levels of subjective well-being for the unemployed and employed alike. Drawing from similar data with a focus on Eastern and Central Europe and transitioning countries, Ferrarini and colleagues (quality: moderate) 30 found that country level UI generosity (same index than in the Sjoberg study including an additional variable: the recipient rate) had a significant protective effect on subjective well-being both among the employed, and an even stronger effect among the unemployed. The Krueger study (quality: weak) compared unemployed people in New Jersey (US) before and after UI lapsed or was exhausted. 32 In this state, endowed with a remarkable generous UI system compared with other US states, results showed that while life satisfaction decreased and mood worsened with unemployment duration, no effect was reported on life satisfaction or mood from UI lapse, or exhaustion on the study population.

Results based on moderate quality studies of European countries show a protective effect of UI generosity on subjective well-being for both the unemployed and employed.

Self-rated health

Two studies assessed the association between UI and self-rated health (SRH) among working-age samples. 33 , 34 McLeod examined the relationship between unemployment and self-reported health status from countries in two archetypes of market economies [coordinated market economies (CMEs) i.e. Germany, and, liberal market economies (LMEs) i.e. US]. 33 This study (quality: strong) used two longitudinal cohorts of over 10 000 working-age German individuals (from 1994 to 2005), and over 9000 working-age US individuals (from 1984 to 2005). These two distinct labor market economies (CME and LME) are based on a capitalism typology and are presumed to cause or mediate employment-related health inequalities. For instance, the LME is based on a competitive market institution, including flexible labor markets, with few restrictions on hiring and firing and high levels of labor mobility, which motivates workers to develop general skills that can be transferred from one job to another. Incontrast, the CME is based on cooperation, this includes collaboration with trade unions and other employers in harmonized wage bargaining, vocational training schemes that provide workers with high level of industry-specific skills. Within the context of differing market economies, the authors found that the receipt of UI did not modify the association between unemployment and self-reported health status in Germany; however, in the United States, there was a significant association for the unemployed who did not receive UI, but then attenuated by the receipt of UI. Another large study undertaken in Finland in 1998 (quality: weak) 34 suggested that receiving generous benefits was more protective against the negative effect of unemployment on SRH than receiving basic allowances, although not always protective compared with permanent employees (significant gradient among women by type of unemployment allowances).

Based on results from only one strong study, there appears to be no effect of UI on SRH in Germany, a country where market economies are based on coordinated and cooperative values (more economic, social and affective support). A protective effect of UI on SRH was rather found in the United States, country with more liberal and competitive market economies. At a first glance, these results might seem counterintuitive. This is why knowing societal, political and economic orientation of countries, as well as basic statistics, is crucial. For instance, long-term unemployment was higher in Germany than in the United States, whereas 85% of unemployed Germans received UI only 12% of American did. 33 As economic/material support comes also from other programs/benefits and social transfer in Germany, the impact of household income is much less important than in the United States (the latter benefiting from few other resources of support).

Mental health

Regarding mental health outcomes, the results were mixed among four studies. 29 , 34–36 The Matoba study, mentioned earlier (quality: weak), found that mood disorders arose with length of unemployment episode and after the UI cessation. 29 A study based on a cross-sectional survey in Catalonia, Spain in 2006 (quality: moderate) found that UI had a significant protective effect on mental health for males and females alike. 35 In Finland, a study (quality: weak) suggests that all groups of unemployed have higher odds of suffering from mental health issues than permanent employees. 34 A gradient further shows that more generous benefits tend to be more protective against the negative effect of unemployment on mental health than receiving basic allowances compared with permanent employees. Finally, mixed findings were found in the Malmberg-Heimonen study 36 targeting unemployed youth (ages 18–24) in four European countries (quality: moderate). In Sweden and Finland, where UI is universal and generous, receiving UI was protective for mental health problems. In Spain, on the other hand, where youth unemployment is quite high and UI is less than ‘the minimum level of protection’, receiving UI was associated with an increased risk of mental health problems. And, lastly, no significant association was found in Germany: this might be explained by the fact that UI is an employment-based benefit or that the sample used showed both higher level of UI recipients and lower risk of mental health problems compared with the other three countries.

Receiving more generous UI tend to be protective against mental health among adults in different settings. However, we highlighted mixed results for the unemployed youth and a discrepancy with Spanish results (all based on studies whose quality was rated as moderate): Unemployed Spanish youth receiving UI are at higher risk of mental health issues 36 while unemployed adult in Catalonia (single region in Spain) seems rather protected by UI. 35 One explanation of the reverse effect in Spain could come from a social stigmatization of receiving financially assistance. 14 Conclusions are difficult to draw because of differences in populations, time-periods, geographic locations and political contexts within the country.

Across various UI systems, jurisdictions, study designs and study quality (not considering those rated as weak), we found good support for our conceptual framework, by which UI attenuate the effect of unemployment on both poverty and health, with a few exceptions. The wide-range of outcomes examined provides additional confirmation of our conceptual framework and the wide-ranging benefits of UI. Our findings highlight a mechanism involved in achieving a protective effect, as well as a potential societal impact of UI. Some studies found that the association between UI and health was further mediated by the generosity of the UI system: more generous UI systems tend to have a greater protective effect on the health of the unemployed, as opposed to jurisdictions with less generous UI systems 30 , 31 or less generous unemployment benefit programs. 34 Such findings are consistent with the literature on the impact of the overall welfare state generosity on health 37 and poverty reduction. 38 Some results suggest though that in more generous welfare regimes, the protective impact of UI might be lessened by other benefits or informal resources. 27 , 36 Another unanticipated finding is the protective effect of UI on the well-being of the employed, in addition of the unemployed. 30 , 31 This evidence suggests a more generalized impact of UI on psychological health wherein all citizens are beneficiaries and may be critical in the preservation of UI systems, particularly in jurisdictions with high concentrations of precarious work that can affect a large subset of a population. Evidence on the societal benefits can lead us to question the overall utility of UI within a society. For instance, in terms of the economic effectiveness of UI, some authors argue that UI can result in an adverse economic consequence, where UI is a disincentive and UI recipients lack motivation to find work. 29 Some economists refer to this unsolicited effect as a ‘moral hazard’, which may prolong unemployment spells. 39 Alternatively though, based on Keynesian economics, 40 UI is a means of stimulating the economy. Nevertheless, we believe that this argument more so relates to active labor market policies (e.g. employment placement services, training) that assists the unemployed to re-enter the workforce.

This review was a first step to understand a complex pathway involving UI and has some limitations. UI is often accompanied by other related programs such as labor activation programs where human capital capacity is built among participants to improve her/his position in the labor market. Thus, our findings may have been confounded by the effects of these co-occurring programs. We did include labor activation or other related programs in our study as the pathways by which those strategies are linked to poverty reduction or health differ from strategies that focus on income replacement. 3 , 10–18 , 22 It was also important to promote homogeneity in the intervention being studied. Another limitation is our inability to examine the European activation shift of policies that happened around the 2007–2012 recession, since our set of studies ends in 2009. An in-depth pre/post-recession analysis of the relationship of UI to poverty and health outcomes in a future study would complement this review.

Even though UI has the capability to prevent and/or reduce financial hardship, the stigmatization and stress-generating position of being unemployed might persist, and perhaps even exacerbated by the socially stigmatization of receiving financially assistance. 12 This is consistent with Strandh’s psychosocial and economic need for employment model: the economic need for employment may be resolved by the provision of UI, but not the psychosocial need for employment. 11 Unfortunately, none of our included studies took up this topic, and only one identified a reverse effect of UI. We therefore did not find good evidence to support such hypothesis.

Finally, we found, with a few exceptions, that UI attenuate the effect of unemployment on both poverty and health. Whether UI impacts differ by age and region might be explored further in future research. This complex mediating relationships should further be assessed in light of economic and historical contexts. This could inform policy makers in case of future periods of economic recession.

The wealth of primary and secondary research that examines the social and public health impacts of job loss has provided a foundation for further exploration into the potential protective effects of UI in offsetting the adverse outcomes of unemployment.

Across various UI systems, jurisdictions and study designs, we found good support for our conceptual framework, by which UI attenuates the effect of unemployment on both poverty and health, with a few exceptions.

Our findings also highlighted a potential societal impact of UI and a lessened impact in some generous welfare States.

We identified research gaps to address in order to better equip policy and decision makers in different global and economic contexts.

Special thanks to Carolyn Ziegler, medical librarian at St. Michael’s Hospital, Toronto, Canada for her assistance with selecting keyword search terms and electronic databases.

This research was supported by the European Community’s Seventh Framework Program (FP7/2007–2013) to the SOPHIE Project (Evaluating the Impact of Structural Policies on Health Inequalities and their Social Determinants and Fostering Change), grant no. 278173.

Supplementary data

Supplementary data are available at EURPUB online.

Conflicts of interest : None declared.

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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

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  • Published: 05 June 2020

Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates

  • Muhammad Khalid Anser 1 ,
  • Zahid Yousaf 2 ,
  • Abdelmohsen A. Nassani 3 ,
  • Saad M. Alotaibi 3 ,
  • Ahmad Kabbani 4 &
  • Khalid Zaman 5  

Journal of Economic Structures volume  9 , Article number:  43 ( 2020 ) Cite this article

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The study examines the relationship between growth–inequality–poverty (GIP) triangle and crime rate under the premises of inverted U-shaped Kuznets curve and pro-poor growth scenario in a panel of 16 diversified countries, over a period of 1990–2014. The study employed panel Generalized Method of Moments (GMM) estimator for robust inferences. The results show that there is (i) no/flat relationship between per capita income and crime rate; (ii) U-shaped relationship between poverty headcount and per capita income and (iii) inverted U-shaped relationship between income inequality and economic growth in a panel of selected countries. Income inequality and unemployment rate increases crime rate while trade openness supports to decrease crime rate. Crime rate substantially increases income inequality while health expenditures decrease poverty headcount ratio. Per capita income is influenced by high poverty incidence, whereas health expenditures and trade factor both amplify per capita income across countries. The results of pro-poor growth analysis show that though the crime rate decreases in the years 2000–2004 and 2010–2014, while the growth phase was anti-poor due to unequal distribution of income. Pro-poor education and health trickle down to the lower income strata group for the years 2010–2014, as education and health reforms considerably reduce crime rate during the time period.

1 Introduction

The study evaluated different United Nation sustainable development goals (SDGs), i.e., goals 1 and 2 (poverty reduction and hunger), goals 3 and 4 (promotion of health and education), goal 10 (reduced inequalities), and goal 16 (reduction of violence, peace and justice) to access pro-poor growth and crime reduction in a panel of 16 heterogeneous countries. The discussion of crime rate in pro-poor growth (PPG) agenda remains absent in the economic development literature, though Bourguignon ( 2000 ) stressed to reduce crime and violence by judicious income distribution; however, a very limited literature is available to emphasize the need of social safety nets for vulnerable peoples that should be included in the pro-growth policy agenda for broad-based economic growth. Kelly ( 2000 ) investigated the relationship between income inequality (INC_INEQ) and urban crime, and found that INC_INEQ is the strong predictor to influence violent crime rather than property crime, while poverty (POV) and economic growth (EG) significantly affect on property crime rather than violent crime. The policies should be developed for equitable income and sound EG for reducing POV and crime across the globe. Drèze and Khera ( 2000 ) examined the inter-district variations of intentional homicides rate (IHR) in India for the period of 1981 and found that there is no significant relationship between urbanization/poverty and murder rates, while literacy rate has a strong impact to reduce criminal violence in India. The results further indicate the lower murder rate in those districts where female to male ratio is comparatively high. The study emphasized the need to reduce crime, violence and homicides by significant growth policies for sustained EG in India. Neumayer ( 2003 ) investigated the long-run relationship between political governance, economic policies and IHR using the panel of 117 selected countries for the period of 1980–1997 and concluded that IHR can be reduce by good economic and political policies. The results specified that higher income level, good civic sense, sound EG, and higher level of democracy all are connected with the lower homicides rate in a panel of countries. The study emphasized the need to improve governance indicators in order to lowering the IHR across the globe. Jacobs and Richardson ( 2008 ) examined the interrelationship between INC_INEQ and IHR in a panel of 14 developed democracies nation and found that intentional homicides is the mounting concerns in those nations where the inequitable income distribution exists, while results further provoke the presence of young males associated with the higher murder rates in a region. The policies should be formulated caution with care while devising for judicious income distribution with demographic variables in the pro-growth agenda. Sachsida et al. ( 2010 ) found inertial effect on criminality and confirmed the positive relationship between INC_INEQ, urbanization and IHR. The study emphasized the importance of public security spending to reduce IHR in Brazil. Pridemore ( 2011 ) re-assessed the relationship between POV, INC_INEQ and IHR in a cross-national panel of US states and found POV-homicides’ linkages rather than inequality-homicides’ association. The study argued that there is substantially desire to re-assess the inequality-homicides’ linkages as it might be the misspecification of the model. Ulriksen ( 2012 ) examined the relationship between PPG, POV reduction and social security policies in the context of Botswana and found that broad-based social security policies have a significant impact to reduce POV, thus there is a strong need to include social security protections in the pro-poor growth (PPG) agenda for lowering the POV rates across the globe. Ouimet ( 2012 ) investigated the impact of socio-economic factors on IHR in a panel of 165 countries for the period 2010 and found that GIP triangle are strongly connected with the IHR for all countries, while for sub-samples, the results only support the inequality-homicides association rather than POV and EG induced IHR. The results highlighted the importance of GIP triangle to reduce IHR in a panel of selected countries.

Liu et al. ( 2013 ) investigated the relationship between national scale indicators of socio-economic and demographic factors and crime rates in 32 Mexican states and found that EG, wages and unemployment negatively affect crime rates, while increase federal police force that is helpful to reduce crime rates; however, on the other way around, higher public security expenditures are linked with the higher crime rates in Mexican states. Chu and Tusalem ( 2013 ) investigated the role of state to reduce IHR in a panel of 183 nations and found that political instability increases IHR, while anocracies is the strong predictor to influence IHR in a panel of countries. The study concluded that IHR increases in those countries where there is high level of political instability and death penalty, while the amalgamation of democratic and autocratic features lead to increased IHR. The policies should be drawn to strengthen political governance across the globe. Adeleye ( 2014 ) evaluated the different determinants of INC_INEQ in a large panel of 137 countries using the time series data from 2000 to 2012 and found that per capita income (PCI), secondary education, rule of law index and unemployment rate are the strong predictors for INC_INEQ and IHR, while INC_INEQ considerably affected IHR rate in a region. Dalberis ( 2015 ) investigated the relationship between INC_INEQ, POV and crime rates in Latin American countries and found that INC_INEQ has no significant association with the crime rate in Colombia, Brazil, Uruguay and Salvador, while poverty is the strong predictor to influence crime in Brazil, Uruguay and Salvador. The results highlighted the need for pro-poorness of growth reforms that would be helpful to lowering the crime rates in Latin American countries. Harris and Vermaak ( 2015 ) considered the relationship between expenditures’ inequality and IHRe across 52 districts of South Africa and found that while keeping other district features constant, inequality does appear as a strong dominant player to induce IHR. The rational income distribution along with broad-based EG may play a vital role to reduce IHR in South Africa. Stamatel ( 2016 ) investigated the relationship between democratic cultural values and IHR in a panel of 33 democratic countries for the period 2010 and found that democratic cultural values have a positive and negative impact of IHR in the presence of strong democratic institutions and practices. Ahmed et al. ( 2016 ) identified the different predictors of economic and natural resources in the context of Iran using the time series data from 1965–2011 and found that labor productivity, exports, capital stock and natural resources are the main predictors of EG, which altogether are important for sustained long-term growth of the country. Enamorado et al. ( 2016 ) interlinked crime rates with higher INC_INEQ using a 20-year dataset of more than 2000 Mexican municipalities and confirmed the causal relationships between the two stated factors. The results confined that drug-related crime rates largely increase up to 36% if there is one-point increment in the INC_INEQ during the specified time period. The study concludes with the fact that drug-related violent crime rates are more severe due to high proliferation of large dispersion in the labor market in terms of negative job opportunities in illegal sector. Thus, the sound policies are imperative to seize drug trafficking organizations by force for pro-equality growth. Ling et al. ( 2017 ) analyzed the role of trade openness in Malaysian life expectancy using the data from 1960 to 2014. The results show that continued EG and trade openness substantially increase life expectancy during the study time period. Further, the results established the feedback relationship between income and life expectancy in a country. The study concludes that life expectancy may increase through imported healthcare goods, which improves the quality of life of the people, thus trade liberalization policies are imperative for healthy and wealthy wellbeing.

Zaman ( 2018 ) extensively surveyed the large weighted sample of intellectuals about crime–poverty nexus and explored the number of socio-economic factors that concerned with high crime rate and POV incidence in Pakistan, including INC_INEQ, injustice, unemployment, low spending on education and health, price hikes, etc. There is a high need to increase social spending on education and health infrastructure in order to combat POV and crime rates in a given country. Imran et al. ( 2018 ) considered a time series data of US for a period of 1965–2016 and concluded that incidence of POV increases the intensity of property crime in a given country, while other controlling factors including country’s PCI and unemployment rate are not significantly associated with property crime in a country. The study concludes that property crime should be restricted by strong legislative and regulatory measures, judicious income distribution, and increasing minimum wage rate, which altogether would be helpful for the poor to reap economic benefits from PPG reforms in a country. Zaman et al. ( 2019 ) evaluated the role of education in crime reduction in a panel of 21 countries for a period of 1990–2015 and found a parabola relationship between PCI and crime rates in the presence of quality education and equitable justice across countries. The study further confirmed few other causal conceptions among the variables for making sound policy implications in the context of criminal justice. Piatkowska ( 2020 ) examined the social cost of POV in terms of increasing suicides rates, crime rates, and total violent rates in the United States and across 15 European nations during the period of 1993–2000. The results show that suicides–crime–violent rates are substantially increasing due to increase in relative POV and infant mortality rates across countries. The study argued that relative POV is the strong predictor to increase social cost of nation that needs efficient economic policies to reduce crime rates. Mukherjee ( 2019 ) discussed the role of social sustainability in achieving economic sustainability by reducing different forms of violent/crime rates through state intervention in the context of Indian economy by utilizing the data for a period of 2005–2016. The results further highlighted the need of socio-economic infrastructure development that would be helpful to provide safety nets to the poor in order to reduce crime rates in a country. Duque and McKnight ( 2019 ) presented the channel through which crime rates and legal system provide a pathway to increase INC_INEQ and POV across countries. The study further discussed and highlighted the socio-economic vulnerability that escalates through unequal distribution of income and high POV incidence, which need effective legal system to reduce crime rates. Khan et al. ( 2019a ) surveyed the Bolivian economy to assess pro-poor environmental reforms that could improve the quality of life of the poor through judicious income distribution and sustainable environmental reforms. The results conclude that services’ sector and healthcare infrastructure would be helpful to reduce POV rate and achieve PPG process at country wide. Zaman et al. ( 2020 ) surveyed the large panel of countries (i.e., 124 countries) for a period of 2010–2013 to analyze the role of INC_INEQ and EG on POV incidence across countries. The results generally favor the strong linkages among the three stated factors to support GIP triangle, which forms PPG process. The study emphasized the need to adopt some re-corrective measures in order to provide social safety nets and income distribution in order to make a growth process more pro-poor. Kousar et al. ( 2019 ) confined its finding in favor of POV reduction through managing international remittances’ receipts and financial development that would be helpful to improve the mechanism of income distribution in a country like Pakistan. The study concluded that international remittances may play a vital role to reduce POV via the mediation of financial development in a country.

The real problem is how to make EG more equitable, which is helpful to reduce POV and crime rates, and make a growth more pro-poor. The SDGs largely provoked the need to sustained economic activities, which helpful to make growth policies more poor friendly. The previous studies are widely discussed crime rates and POV reduction (see Zaman 2018 ; Khan et al. 2015 ; Heinemann and Verner 2006 ; etc.); however, a very few studies interlinked POV–crime nexus under PPG and Kuznets curve (KC) hypothesis (see Saasa 2018 ; Berens and Gelepithis 2018 , etc.). Based on the interconnections between crime, POV, and PPG, the study formulated the following research questions, i.e.,

Does crime rate negatively influenced GIP triangle, which sabotages the process of PPG?

The recent study of Khan et al. ( 2019b ) provoked the need of PPG policies to ensure sustainability agenda by including socio-economic and environmental factors in policy formulation, which gives favor to the poor as compared to the non-poor. In the similar lines, the social spending on education and healthcare infrastructure, and reforms needed to reduce labor market uncertainty in the form of lessen unemployment rate is considered the viable option for crime and POV reduction across countries (Khan et al. 2017 ). Thus, the study evaluated the question, i.e.,

To what extent social spending on education, health, and labor market are helpful to reduce crime rate, poverty, and income inequality across countries?

This question would be equally benefited to the developmental economists and policy makers to devise a healthy and wealthy policy by increasing spending on social infrastructure for pro-equality growth (Wang 2017 ). The last question is based upon non-linear formulation of crime–POV nexus where it is evaluated as a second-order coefficient to check the parabola relationship between them, i.e.,

Does crime and poverty exhibit a parabola relationship between them?

The question is all about the second-order condition, which confirmed one out of three conditions, i.e., either it is accepted an inverted U-shaped or U-shaped or flat relationship between them. The second-order condition assessed the probability to reduce crime rates and incidence of POV in policy formulation.

In the light of SDGs, the study explored the impact of GIP triangle and crime rates on pro-growth and PPG policies, which is imperative for sustainable development across countries. The study added social expenditures in PPG dynamics to promote healthy and wealthy economic activities, which improves quality of life of the poor and helpful to reduce crime incidence across countries. The study is first in nature, as authors’ knowledge, which included GIP triangle and crime rate in PPG framework, while controlling different socio-economic factors, including education and health expenditures, unemployment rate, and trade openness. Further, an empirical contribution of the study is to include second-order coefficient of PCI for evaluating crime- and inequality-induced KC, while the study proceed to analyze forecast relationship between the crime and POV incidence over a next 10-year time period. Finally, the study estimated PPG index while including crime rate as a main predictor factor in GIP triangle for robust policy inferences. Thus, these objectives are achieved by different statistical techniques for robust analysis.

2 Data source and methodological framework

The study used number of promising socio-economic variables to determine the dynamic relationship between PPG factors and crime rate under the framework of an inverted U-shaped KC in a panel of 16 diversified countries, using system GMM estimator for the period of 1990–2014. The study used the following variables, i.e., crime rate (proxy by intentional homicides rate per 100,000 population), GINI index measures income inequality, poverty headcount ratio at $1.90 a day (2011 PPP) (% of total population), national estimates of unemployment in % of total labor force, education expenditures as % of GDP, per capita health expenditure in current US$, per capita income in constant 2005 US$, and trade openness as % of GDP. The samples of countries are presented in Table  7 in Appendix for ready reference. The data for the study are obtained from World Development Indicators published by World Bank ( 2015 ).

These countries are selected because of the devastating crime rate during the study time period. The recorded figures for Argentina crime rates about to 245% increase between the period of 1991 and 2007, while 2002 is considered the highest committed crime data recorded when the POV and INC_INEQ reached at their peak levels (Bouzat 2010 ). Brazil economy is working out for reduction of crime by focusing on three-point agenda, i.e., reduction in income disparity, to increase spending on education via an increase in enrollment of school dropout children, and to improve labor market conditionings. These three policies design to deter the crime rates in a given country (World Bank 2013 ). The robbery complaints largely increase since last two decades in Chile, which is being planned by controlling two action strategies, i.e., plan cuadrante and country security plan. Both the plan designed to restructured police force to reduce robbery and violence in a country (Vergara 2012 ). The rural China is suffered by high INC_INEQ that leads to higher crime rate (South China Monitoring Report 2015 ) while POV and INC_INEQ lead to crime and violent factor in Colombia (Gordon 2016 ). The socio-economic factors including low provision of education, health, high POV, and food challenges lead to increase crime in Indonesia (Pane 2017 ), while generating employment opportunities and increasing wage rate in Malaysia may be beneficial to reduce crime–POV nexus in a given country (Mulok et al. 2017 ). Mexican economy is suffered with high rate of homicides that negatively affect labor market outcomes, while country inhibits by increasing strict laws to diminish violence (Kato Vidal 2015 ). The safety situation in Morocco is cumbersome, as one of the country reports shows that an increased rate in crime is about to increase up to 23% in 2016 (OSAC 2017 ). The number of other factors remains visible in selected sample of panel of countries, including rural POV and social exclusion that is considered the main factor of socio-economic crisis in Poland (European Commission 2008 ); POV, unemployment, and INC_INEQ chiefly attributed to crime rate in South Africa (Bhorat et al. 2017 ); politics, democracy, and INC_INEQ arise conflicts in Thailand (Hewison 2014 ); corruption and high unemployment are the major conflicts in Tunisia (Saleh 2011 ); and Uruguay economy needs policy actions to reduce POV by investment in children education, modernizing rural sector, and balancing the gender gap (Thamma 2017 ). Thus, these facts about crime and POV in different countries put a focus to study crime–POV nexus under PPG framework in this study for robust evaluation. Figure  2 in Appendix shows the plots of the studied variables at level.

The study used the following non-linear equations to determine the dynamic relationship between PPG factors and crime rate in a panel of countries, i.e.,

where GDPPC indicates per capita GDP, GDPPC 2 indicates square of per capita GDP, GINI indicates Gini coefficient—income inequality, EDUEXP indicates education expenditures, HEXP indicates health expenditures, POVHCR indicates poverty headcount ratio, TOP indicates trade openness, UNEMP indicates unemployment, and CRIME indicates crime rate.

Equations ( 1 ) to ( 3 ) assessed the possible inverted U-shaped relationships between crime rate and PCI, between POVHCR and PCI, and between GINI and PCI, while Eq. ( 4 ) reviewed the PPG reforms across countries. Arellano and Bond ( 1991 ) developed the differenced GMM estimator, whom argued that the GMM estimator eliminates country effects and controls the possible endogeneity of explanatory variables using the appropriate instrumental list that evaluated by Sargan–Hansen test. The process further involves two-step GMM iterations with the time updated weights and adopted the weighting matrix by White period. The tests for autocorrelations by AR(1) and AR(2) and the Sargan test by Sargan–Hansen of over-identifying restrictions are presented for statistical reliability of the given models. The differenced GMM is superior to the 2SLS and system GMM, i.e., 2SLS regression estimator is used when the known endogeneity exists between the variables, which are handled by including the list of instrumental variables at their first lagged. Thus, the possible endogeneity problem is resolved accordingly. The system GMM further be used instead of 2SLS as if there are more than one endogenous issues exist in the model, which is unable to resolve through 2SLS estimator. Finally, the differenced GMM estimator is used as its estimated AR(1) and AR(2) bound values that would be helpful to encounter the issues of serial correlation and endogeneity problem accordingly.

Using the GMM estimator, the study verified different possibilities of KC, i.e., if the signs and magnitudes of \(\beta_{1} > 0\) and \(\beta_{2} < 0\) , than we may confirm the crime-induced KC, poverty-induced KC, and inequality-induced KC. The inverted U-shaped relationship between crime rate and PCI verified ‘crime-induced KC’, between POVHCR and PCI verified ‘POV-induced KC’, and inverted U-shaped relationship between GINI and PCI verified ‘inequality-induced KC’. On the other way around, if \(\beta_{1} < 0\) and \(\beta_{2} > 0\) , then we consider the U-shaped KC between crime rate and PCI, between POV and PCI, and between GINI and PCI, respectively. There are three other situations we may observe with the sign and magnitude of \(\beta_{1}\) and \(\beta_{2}\) , i.e., (i) \(\beta_{1} < 0\) and \(\beta_{2} = 0\) , (ii) \(\beta_{1} > 0\) and \(\beta_{2} = 0\) , and (iii) \(\beta_{1} = 0\) and \(\beta_{2} = 0\) , referred the monotonically decreasing function, monotonically increasing function, and flat/no relationship with the crime-PCI, poverty-PCI, and inequality-PCI in a panel of cross-sectional countries. The study further employed social accounting matrix by impulse response function (IRF) and variance decomposition analysis (VDA) in an inter-temporal relationship between the studied variables for a next 10-year period starting from 2015 to 2024. As it name implies, VDA explains the proportional variance in one variable caused by the proportional variance by the other variables in a vector autoregressive (VAR) system, while IRF traces the dynamic responses of a variable to innovations in other variables in the system. Both the techniques use the moving average representation of the original VAR system. Figure  1 shows the theoretical framework of the study to clearly outline the possible relationship between the stated variables.

figure 1

Source: authors’ extraction

Research framework of the study.

Figure  1 shows the possible relationship between POV and crime rates in mediation of inequality, unemployment, and EG across countries. It is likelihood that POV increases inequality that leads to decrease in EG. The low-income growth further leads to increased unemployment, which causes high crime rates. This nexus is still rotated through crime rates that increase POV incidence across countries. The PPG process still works under the stated factors that need judicious income distribution to reduce crime rates.

The study further proceeds to evaluate the PPG reforms in a panel of selected countries. Kakwani and Pernia ( 2000 ) proposed an index of PPG called ‘PPG index’, which is evaluated by the growth elasticity and inequality elasticity with respect to POV. The same methodology is adopted in this study to assess the PPG and/or pro-rich growth reforms to assess the changes in the crime rate in a panel of countries. PPG defined as a state in which where the growth trickles down to the poor as compared to the non-poor. Poverty is largely affected by two main factors, i.e., higher growth rate may reduce the POV rates, while higher INC_INEQ reduces the impact of EG to reduce POV; therefore, the PPG index included the following mathematical illustrations, i.e.,

The study further assessed the pro-poorness of social expenditures and evaluates its impact to observe changes in IHR. The study shows the following mathematical illustrations that is extended from the scholarly work of Zaman and Khilji ( 2014 ); Kakwani and Pernia ( 2000 ) and Kakwani and Son ( 2004 ) i.e.,

where \(\alpha =\) 0, 1 and 2 indicate POVHCR, poverty gap and squared poverty gap, respectively, ‘P’ indicates FGT poverty measures, and ‘SOCIALEXP’ indicates social expenditures. Differentiating \(\eta_{\alpha }\) in Eq. ( 9 ) with respect to social expenditures gives more elaborated form of GEP, i.e.,

The elasticity of entire class of poverty measures \(P_{\alpha }\) with respect to Gini index is given by

which will be always positive only when \(S{\text{OCIALEXPE}} > z\) .Equations ( 10 ) and ( 11 ) are combined together to form TPE for all FGT poverty measures, i.e.,

or \(\delta_{\alpha } = \eta_{\alpha } + \xi_{\alpha }\) . Finally, pro-poorness of social expenditures estimated based on the following equation, i.e.,

Kakwani and Son ( 2004 ) presented the following bench mark applications to assess the pro-poor and/or anti-poor policies, i.e., the following value judgments regarding the PPG index ( \(\varphi\) ) are as follows, i.e.,

\(\varphi\)  < 0, growth is pro-rich or anti-poor,

0 <  \(\varphi\) \(\le\) 0.33, the process of PPG is considerable low,

0.33 <  \(\theta\) \(\le\) 0.66, the process of PPG is moderate,

0.66 <  \(\varphi\)  < 1.0, the process of EG considered as pro-poor, and

\(\varphi \ge\) 1.0, the process of EG is highly pro-poor.

The study utilized the PPG model for ready reference in this study.

This section presented the descriptive statistics in Table  1 , correlation matrix in Table  2 , dynamic system GMM estimates in Table  3 , IRF estimates in Table  4 , VDA estimates in Table  5 , while finally Table  6 shows the estimates for PPG in a panel of selected countries. Table  1 shows that GDPPC has a minimum value of US$ 199.350 and the maximum value of US$ 11257.600, with a mean and standard deviation (STD) value of US$ 4340.777 and US$ 2490.554, respectively. GINI has a minimum value of 25% and the maximum value of 64.790%, having an STD value of 8.580% with an average value of 45.095%. The minimum value of EDUEXP is about 0.998% of GDP and the maximum value of 7.657% of GDP, with an average value of 4.051% of GDP. The average value of HEXP per capita is about US$ 321.249 and a maximum value of US$ 1431.154, with an STD value of US$ 292.802. The maximum value of POVHCR is about 69% at US$1.90 a day with an average value of 12.394% at US$1.90 a day. The minimum value of trade is 13.753% of GDP and the maximum value of 220.407% of GDP, with an average value of 62.391% of GDP. The mean value for UNEMP is about 8.890% of total labor force with STD value of 6.010%. Finally, the minimum value of crime rate is about 0.439 per 100,000 inhabitants and the maximum value of 71.786 per 100,000 inhabitants, with an average value of 11.664 per 100,000 peoples. This exercise would be helpful to understand the basic descriptions of the studied variables in a panel of countries.

Figure  3 in Appendix shows the plots of the studied variables and found the stationary movement in the variables at their first difference. Table  2 presents the estimates of correlation matrix and found that GINI (i.e., r  = 0.264), EDUEXP ( r  = 0.243), HEXP ( r  = 0.730), TOP ( r  = 0.061), UNEMP (0.152) and CRIME ( r  = 0.031) have a positive correlation with the GDPPC, while POVHCR ( r  = − 0.599) significantly decreases GDPPC.

The results further reveal that GINI is affected by EDUEXP, HEXP, UNEMP and CRIME, while it considerably decreases by trade liberalization policies. EDUEXP, HEXP, PCI, TOP and UNEMP significantly decrease POVHCR, while crime rate has a positive correlation with the POVHCR. Finally, GINI have a greater magnitude, i.e., r  = 0.671, to influence CRIME, followed by UNEMP ( r  = 0.417), EDUEXP ( r  = 0.188), and POVHCR ( r  = 0.164) while trade liberalization policies support to decrease crime rates in a panel of countries. The study now proceeds to estimate the two-step system GMM for analyzing the functional relationship between socio-economic factors and crime rate. The results are presented in Table  3 .

The results of panel GMM show that GINI and UNEMP both have a significant and direct relationship with the CRIME, while TOP have an indirect relationship with CRIME in a panel of countries. The results imply that GINI and UNEMP are the main factors that increase CRIME, while trade liberalization policies have a supportive role to decrease crime rates across countries. Thorbecke and Charumilind ( 2002 ) evaluated the impact of income inequality on health, education, political conflict, and crime, and surveyed the different casual mechanism in between income inequality and its socio-economic impact across the globe. The policies have devised while reaching the conclusive relationships between them. Kennedy et al. ( 1998 ) concluded that social capital and income inequality are the powerful predictors of intentional homicides rate and violent crime in the US states. Altindag ( 2012 ) explored the long-run relationship between unemployment and crime rates in a country-specific panel dataset of Europe and found that unemployment significantly increases crime rates, while unemployment has a power predictor of exchange rate movements and industrial accident across the Europe. Menezes et al. ( 2013 ) confirmed the positive association between income inequality and criminality, as rational income distribution tends to decrease neighborhood homicides rate while it implies an increase in the intentional homicides rate in the surrounding neighborhoods.

In a second regression panel, the results confirmed the U-shaped relationship between POVHCR and GDPPC, as at initial level of EG, POV significantly declines, while at the later stages, this result is evaporated, as EG subsequently increases POVHCR that shows pro-rich federal policies across countries. The HEXP, however, significantly decreases POVHCR during the study time period. Dercon et al. ( 2012 ) investigated the relationship between chronic POV and rural EG in Ethiopia and argued that chronic POV is associated with the lack of education, physical assets and remoteness, while EG in terms of provide better roads and extension services may trickle down to the poor in a same way that the non-chronically poor benefited. Solinger and Hu ( 2012 ) examined the relationship between health, wealth and POV in urban China and found that wealthier cities prefer to allocate their considerable portion of savings for social assistance funds, while poorer places save the city money and work outside in a hope that the peoples would be better able to support themselves. Fosu ( 2015 ) examined the relationship between GIP triangle in sub-Saharan African countries and found that as a whole, South African countries lag behind the BICR (Brazil, India, China and Russia) group of countries; however, many of them in sub-Saharan African countries have outperformed India. The results further specified that PCI is the main predictor to reduce POV in sub-Saharan African countries; however, rational income distribution is a crucial challenge to reduce POV reduction through substantial growth reforms in a region. Kalichman et al. ( 2015 ) concluded that food poverty is associated with the multifaceted problems of health-related outcomes across the globe.

In a third regression panel, the results confirm an inverted U-shaped relationship between GDPPC and GINI that verified an inequality-induced KC in a panel of countries. The results imply that at initial level of economic development, GINI first increases and then decreases with the increased GDPPC across countries. CRIME, however, it is associated with the higher GINI during the studied time period. Kuznets ( 1955 ), Ahluwalia ( 1976 ), Deininger and Squire ( 1998 ), and others confirmed an inverted U-shaped relationship between INC_INEQ and PCI in different economic settings. Mo ( 2000 ) suggested different channelss to examine the possible impact of INC_INEQ on EG and found that ‘transfer channel’ exert the most important channel, while ‘human capital’ is the least important channel that negatively affects the rate of EG via INC_INEQ. Popa ( 2012 ) argued that health and education both are important predictors for EG, while POV and unemployment negatively correlated with the EG in Romania. Herzer and Vollmer ( 2012 ) confirmed the negative relationship between INC_INEQ and EG within the sample of developing countries, developed countries, democracies, non-democracies, and sample as a whole. In a similar line, Malinen ( 2012 ) confirmed the long-run equilibrium relationship between PCI and INC_INEQ and found that income inequality negatively affected the growth of developed countries.

The final regression shows that HEXP and TOP both significantly increase GDPPC, while POVHCR decreases the pace of EG, which merely be shown pro-rich federal policies in a panel of countries. Ranis et al. ( 2000 ) found that both the health and education expenditures lead to increased EG, while investment improves human development in a cross-country regression. Bloom et al. ( 2004 ) confirmed the positive connection between health and EG across the globe. Gyimah-Brempong and Wilson ( 2004 ) examined the possible effect of healthy human capital on PCI of sub-Saharan African and OECD countries and found the positive association between them in a panel of countries.

The statistical tests of the system GMM estimator confirmed the stability of the model by F-statistics, as empirically model is stable at 1% level of confidence interval. Sargan–Hansen test confirmed the instrumental validity at conventional levels for all cases estimated. Autocorrelations tests imply that except POVHCR model, the remaining three models including CRIME, GINI and GDPPC model confirmed the absence of first- and second-order serial correlation, and as a consequence, we verified our instruments are valid. As far as POVHCR model, we believed the results of Sargan–Hansen test of over identifying restrictions and AR(1) that is insignificant at 5% level, and confirmed the validity of instruments and absence of autocorrelation at first-order serial correlation. Table  4 shows the estimate of IRF for the next 10-year period starting from a year of 2015 to 2024.

The results show that the socio-economic factors have a mix result with the rate of crime, as POVHCR slightly increases with decreasing rate with the crime data, i.e., in the next coming years from 2016, 2018, 2019, and 2022, POVHCR exhibits a negative sign, while in the remaining years in between from 2015 to 2024, POVHCR increases crime rate. GINI will considerably increase crime rate from 2022 to 2024. UNEMP has a mixed result to either increase crime rate in one period while in the very next upcoming periods, it declines crime rate. Similar types of results been found with EDUEXP, HEXP and with the TOP; however, GDPPC will constantly increase the rate of crime in a panel of countries. In an inter-temporal relationship between POVHCR and other predictors, the results show that GDPPC would significantly decrease POVHCR for the next 10-year period; however, UNEMP, HEXP, and crime rate would considerably increase POVHCR. EDUEXP and TOP would support to reduce GINI for the next upcoming years, while remaining variables including crime rate, POV, UNEMP, HEXP, and GDPPC associated with an increased GINI across countries. The GDPPC will be influenced by crime rate, POVHCR, GINI, UNEMP, HEXP, and EDUEXP, while TOP would considerably to support GDPPC for the next 10-year time period. Figure  4 in Appendix shows the IRF estimates for the ready reference.

Table  5 shows the estimates of VDA and found that POVHCR will exert the largest share to influence crime rates, followed by GDPPC, TOP, HEXP, EDUEXP, GINI, and UNEMP. POVHCR would be affected by crime rate (i.e., 4.450%), UNEMP (1.751%), GDPPC (1.120%), GINI (1.043%), HEXP (0.639%), and EDUEXP (0.512%), and TOP (0.299%), respectively.

The results further reveal that GINI will affected by POVHCR, as it is explained by 7.680% variations to influence GINI for the next 10-year period. UNEMP, EDUEXP, and crime rate will subsequently influenced GDPPC about to 1.107%, 0.965%, and 0.312% respectively. The largest variance to explain UNEMP will be TOP, while the lowest variance to influence UNEMP will be GINI for the next 10-year period. Finally, GDPPC would largely influenced by HEXP, followed by UNEMP, CRIME, POVHCR, EDUEXP, TOP, and GINI for the period of 2015 to 2024. Figure  5 in Appendix shows the plots of the VDA for ready reference.

Finally, Table  6 presents the changes in crime rate by five different growth phases, i.e., phase 1: 1990–1994, phase 2: 1995–1999, phase 3: 2000–2004, phase 4: 2005–2009, and phase 5: 2010–2014. The results show that in the years 1990–1994, 1% increase in EG and INC_INEQ decrease POVHCR by − 0.023% and − 0.630%, which reduces TPE by − 0.629 percentage points. The PPG index surpassed the bench mark value of unity and confirmed the trickledown effect that facilitates the poor as compared to the non-poor. However, there is an overwhelming increase in the crime rate beside that the pro-poorness of EG, which indicate the need for substantial safety nets’ protection to the poor that escape out from this acute activities (Wang et al. 2017 ). In a second phase from 1995 to 1999, although EG decreases POVHCR by − 0.187; however, GINI has a greater share to increase POVHCR by 0.517% that ultimately increases TPE by 0.330%. This increase in the TPE turns to decrease PPG as 1.764, which shows anti-poor/pro-rich federal policies and low reforms for the poor that accompanied with the higher rates of crime in a panel of countries. The rest of the growth phases from 2000 to 2014 show anti-poor growth accompanied with the higher INC_INEQ and lower EG; however, crime rate decreases in the year 2000–2004 and 2010–2014 besides that the growth process is anti-poor across countries. The policies should be formulated in a way to aligned crime rate with the PPG reforms across countries (Vellala et al. 2018 ).

The results of PPE index confirmed an anti-poor growth from 1990 to 2004, while at the subsequent years from 2005 to 2014, education growth rate subsequently benefited the poor as compared to the non-poor, i.e., PPE index exceeds the bench mark value of unity. Crime rate is increasing from 1990 to 1999, and from 2005 to 2009, while it decreases the crime rate for the years 2000–2004 and 2010–2014. The good sign of recovery has been visible for the years 2010–2014 where the PPE growth supports to decrease crime rate in a panel of selected countries. Finally, the PPH index confirmed two PPG phases, i.e., from 1990 to 1994, and 2010 to 2014 in which crime rate increases for the former years and decreases in the later years. The remaining health phases from 1995 to 2009 show anti-poor health index, while crime rate is still increasing during the years from 1995 to 1999 and 2005 to 2009, and decreasing for the period 2000–2004. The results emphasized the need to integrate PPG index with the crime rate, as PPG reforms are helpful to reduce humans’ costs by increasing EG and social expenditures, and providing judicious income distribution to escape out from POV and vulnerability across the globe (Musavengane et al. 2019 ).

From the overall results, we come to the conclusion that social spending on education and health is imperative to reduce crime incidence, while it further translated a positive impact on POV and inequality reduction across countries (Hinton 2016 ). EG is a vital factor to reduce POV; however, it is not a sufficient condition under higher INC_INEQ (Dudzevičiūtė and Prakapienė 2018 ). INC_INEQ and unemployment rate both are negatively correlated with crime rates; however, it may be reduced by judicious income distribution and increases social spending across countries (Costantini et al. 2018 ). Trade liberalization policies reduce incidence of crime rates and improve country’s PCI, which enforce the need to capitalize domestic exports by expanding local industries. Thus, the United Nations SDGs would be achieved by its implication in the countries perspectives (Dix-Carneiro et al. 2018 ). The study achieved the research objectives by its theoretical and empirical contribution, which seems challenge for the developmental experts to devise policies toward more pro-growth and PPG.

4 Conclusions and policy recommendations

This study investigated the dynamic relationship between socio-economic factors and crime rate to assess PPG reforms for reducing crime rate in a panel of 16 diversified countries, using a time series data from 1990–2014. The study used PCI and square PCI in relation with crime rate, POVHCR, and GINI to evaluate crime-induced KC, poverty-induced KC and inequality-induced KC, while PPG index assesses the federal growth reforms regarding healthcare provision, education and wealth to escape out from POV and violence. The results show that GINI and UNEMP are the main predictors that have a devastating impact to increase crime rate. Trade liberalization policies are helpful to reduce crime rate and increase PCI. Healthcare expenditures decrease POVHCR and amplify EG. The EG is affected by POVHCR, which requires strong policy framework to devise PPG approach in a panel of selected countries. The study failed to establish crime-induced KC and poverty-induced KC, while the study confirmed an inequality-induced KC. The results of IRF reveal that PCI would considerably increase crime rate, while crime rate influenced GINI and PCI for the next 10-year period. The estimates of VDA show that POVHCR explained the greater share to influence crime rates, while reverse is true in case of POVHCR. The study divided the studied time period into five growth phases 1990–1994, 1995–1999, 2000–2004, 2005–2009, and 2010–2014 to assess PPG, PPH, and PPE reforms and observe the changes in crime rates. The results show that there is an only period from 1990 to 1994 that shows PPG, while crime rate is still increasing in that period; however, in the years 2000–2004, and 2010–2014, crime rate decreases without favoring the growth to the poor. PPE and PPH assessment confirmed the reduction in the crime rates for the years 2010–2014. The overall results confirmed the strong correlation between socio-economic factors and crime rates to purse the pro-poorness of government policies across countries. The overall results emphasized the need of strong policy framework to aligned PPG policies with the reduction in crime rate across the globe. The study proposed the following policy recommendations, i.e.,

Education, health and wealth are the strong predictors of reducing crime rates and achieving PPG, thus it should be aligned with inclusive trade policies to reduce human cost in terms of decreasing chronic poverty and violence/crime.

The policies should be formulated to strengthen the pro-poorness of social expenditures that would be helpful to reduce an overwhelming impact of crime rate in a panel of countries.

GIP triangle is mostly viewed as a pro-poor package to reduce the vicious cycle of poverty; however, there is a strong need to include some other social factors including unemployment, violence, crime, etc., which is mostly charged due to increase in poverty and unequal distribution of income across the globe. The policies should devise to observe the positive change in lessen the crime rate by PPG reforms in a panel of selected countries.

The significant implication of the Kuznets’ work should be extended to the some other unexplored factors especially for crime rate that would be traced out by the pro-poor agenda and pro-growth reforms.

There is a need to align the positivity of judicious income distribution with the broad-based economic growth that would be helpful to reduce poverty and crime rate across countries.

The result although not supported the ‘parabola’ relationship between income and crime rates; however, it confirmed the U-shaped relationship between income and poverty. The economic implication is that income is not the sole contributor to increase crime rates while poverty exacerbates violent crimes across countries. There is a high need to develop a mechanism through which poverty incidence can be reduced, which would ultimately lead to decreased crime rates. The improvement in the labor market structure, judicious income distribution, and providing social safety nets are the desirable strategies to reduce crime rates and poverty incidence across countries, and

The results supported parabola relationship between economic growth and inequality, which gives a clear indication to improve income distribution channel for reducing poverty and crime rates at global scale.

These seven policies would give strong alignment to improve social infrastructure for managing crime through equitable justice and PPG process.

Availability of data and materials

The data are freely available on World Development Indicator, published by World Bank on given URL ID: https://datacatalog.worldbank.org/dataset/world-development-indicators .

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Acknowledgements

The authors are thankful for King Saud university research project number (RSP-2019/87) for funding the study. The authors are indebted to the editor and reviewers for constructive comments that have helped to improve the quality of the manuscript.

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Anser, M.K., Yousaf, Z., Nassani, A.A. et al. Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates. Economic Structures 9 , 43 (2020). https://doi.org/10.1186/s40008-020-00220-6

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Poverty, unemployment, and common mental disorders: population based cohort study

Scott weich.

a University Department of Psychiatry, Royal Free Hospital School of Medicine, London NW3 2PG, b Division of Psychological Medicine, University of Wales College of Medicine, Cardiff CF4 4XN

SW was the principal investigator, initiated the study, discussed core ideas and contributed to the generation of study hypotheses, prepared the application for funding, obtained the dataset, undertook data management and analysis, and drafted the manuscript; he will act as guarantor for the paper. GL initiated the study, discussed core ideas and contributed to the generation of study hypotheses, and participated in the preparation of the manuscript.

Objective: To determine whether poverty and unemployment increase the likelihood of or delay recovery from common mental disorders, and whether these associations could be explained by subjective financial strain.

Design: Prospective cohort study.

Setting: England, Wales, and Scotland.

Subjects: 7726 adults aged 16-75 living in private households.

Main outcome measures: Common mental disorders were assessed using the general health questionnaire, a self assessed measure of psychiatric morbidity.

Results: Poverty and unemployment (odds ratio 1.86, 95% confidence interval 1.18 to 2.94) were associated with the maintenance but not onset of episodes of common mental disorders. Associations between poverty and employment and maintenance of common mental disorders, however, were much smaller than those of cross sectional studies. Financial strain at baseline was independently associated with both onset (1.57, 1.19 to 2.07) and maintenance (1.86, 1.36 to 2.53) even after adjusting for objective indices of standard of living.

Conclusions: Poverty and unemployment increased the duration of episodes of common mental disorders but not the likelihood of their onset. Financial strain was a better predictor of future psychiatric morbidity than either of these more objective risk factors though the nature of this risk factor and its relation with poverty and unemployment remain unclear.

Key messages

  • The prevalence of the most common mental disorders, anxiety and depression, has been shown to be consistently associated with unemployment and measures of poverty, independent of occupational social class
  • Unemployment and poverty were associated with the maintenance of episodes of most common mental disorders but not their onset
  • Financial strain was a powerful independent predictor of both the onset and maintenance of episodes of common mental disorders, even after adjusting for more objective measures of standard of living
  • Over 12 months poverty and financial strain, but not unemployment, were associated with significant increases in psychiatric morbidity; the effect of poverty increased with the level of baseline morbidity
  • Further research is needed to better understand the nature of financial strain and its relation with unemployment and objective measures of standard of living

Introduction

Symptoms of anxiety and depression are common, co-occur frequently, and are continuously distributed in the general population. 1 , 2 At the top end of this distribution are disorders recognised by psychiatrists, with an estimated community prevalence of about 15%. 1 – 3 These common mental disorders 1 account for one third of days lost from work due to ill health 4 and one fifth of consultations in general practice in the United Kingdom. 5 Those affected have increased mortality rates 6 , 7 and clinically significant impairments in physical and social functioning. 8

Common mental disorders are most prevalent among those with a poor standard of living, 1 – 3 , 9 , 10 independent of occupational social class. 9 , 11 Longitudinal findings, however, have been inconsistent. Despite reports of associations between low income, 12 few possessions, 13 and onset of common mental disorders these risk factors have been more consistently associated with longer episodes. 13 – 15 Unemployment is also associated with the prevalence, 1 , 2 , 9 , 16 , 17 incidence, 16 , 17 and maintenance 16 of common mental disorders. Recent evidence suggests that the effects of unemployment and poverty on mental health may be mediated or modified by financial strain, 9 , 18 although this has never been evaluated prospectively.

Over 12 months we investigated whether poverty and unemployment increased the likelihood of onset of or delayed recovery from episodes of common mental disorders, and tested whether these associations could be explained by greater subjective financial strain in people who are poor or unemployed.

Subjects and methods

Data were collected for the British household panel survey, an annual survey of individuals in private households in England, Wales, and Scotland. The design and primary aims of this survey have been described elsewhere. 11 , 19 Only subjects who completed psychiatric assessments at two sets of interviews were included. The first set of interviews took place in 1991 (T1) and the second set (T2) 12 months later. The survey investigators complied with the ethical guidelines of the Social Research Association. 19 Specific ethical approval was not sought for this secondary analysis, which was based on anonymous data supplied from the data archive of the Economic and Social Research Council in accordance with its regulations.

Common mental disorders were assessed with the general health questionnaire, comprising 12 items. 20 We followed previous studies in treating common mental disorders as a single dimension. 3 , 21 The questionnaire was scored in two ways: by designating each item as absent or present (0 or 1) according to the method of the general health questionnaire 20 ; and according to severity (range 0 to 3) (the Likert method). Those scoring 3 or more (out of 12) by the general health questionnaire method were classified as cases. 20 Likert scores (range 0 to 36) more closely approximated a normal distribution and were used when the general health questionnaire score was treated as a continuous outcome.

To overcome likely colinearity between indices of standard of living, a poverty score comprising seven items was generated from variables previously judged to provide a comprehensive yet frugal assessment of each subject’s standard of living. 11 One point was scored for each of the following: (a) annual household income (adjusted for household size and composition 19 ) in the bottom fifth for region of residence (since the cost of living was expected to differ between regions); (b) no household access to car or van, (c) not saving from income (excluding money put by for bills but including life insurance, personal equity plans, share purchases, and holidays); (d) fewer than four domestic household appliances from a list of nine; (e) living in rented accommodation; (f) overcrowded accommodation (more than two household members per bedroom); and, (g) a home with two minor or any major structural problems such as dry rot. Where income sources could not be verified by documentary evidence missing data were imputed by the British household panel survey investigators 19 using methods that minimised any tendency to overpredict associations with income. 11 , 19 Items contributing to the poverty score were not weighted, given the absence of any rationale or method for doing so. Furthermore, cross sectional findings at T1 indicate that individual associations with the prevalence of common mental disorders differed little between items. 11

Subjective financial strain at T1 was assessed by asking: “How well would you say you are managing financially these days?”, responses to which were coded as: (a) living comfortably or doing alright; (b) just about getting by; or, (c) finding it difficult or very difficult.

Potential confounding variables selected from the dataset of the British household panel survey were registrar general’s social class by head of household based on current or most recent occupation, 11 , 19 plus marital status, education, employment, ethnic group, household size, responsibility for dependent children under the age of 16, number of current physical health problems, and region of residence.

Statistical methods

Data were analysed in two ways. Firstly, the sample was stratified by case status at T1 and separate analyses were carried out for onset (proportion of non-cases at T1 who were cases at T2) and maintenance (proportion of cases at T1 who were also cases at T2) of common mental disorders. Secondly, to evaluate the effects of exposures at T1 on change in psychiatric morbidity between sets of interviews without imposing an arbitrary case threshold, the general health questionnaire score at T2 was treated as a continuous outcome and adjusted for the general health questionnaire score at T1.

Univariate differences were tested using non-parametric χ 2 and Kruskal-Wallis tests as appropriate. Unadjusted and adjusted odds ratios and likelihood ratio χ 2 tests to assess confounding, effect modification, and departure from linear trends were calculated by means of logistic regression using statistical software (Release 4.0, Stata, TX). Regression analyses were conducted using the Huber-White sandwich estimator to control for the clustering of respondents within households. 22

Overall, 5511 (73.6%) of enumerated private households participated at T1 comprising 10 264 individuals aged 16 and over. The general health questionnaire was completed by 9064 individuals (94.3% of those interviewed) aged 16-75 at T1, of whom 7726 (85.2%) were re-interviewed at T2. The prevalence of common mental disorders was 24.6% (95% confidence interval 23.7% to 25.5%) at T1 and 26.3% (25.3% to 27.3%) at T2. The rate of onset of a mental disorder was 17.5% (16.5% to 18.4%) and the rate of maintenance was 54.2% (51.9% to 56.5%). Spearman’s rank order correlation coefficient for the general health questionnaire scores at T1 and T2 was +0.51 (P<0.0001). Of the cases at T2, 1011 (49.7%) were also cases at T1. Poverty scores were skewed to the right at T1 with 6571 (72.5%) of subjects scoring ⩽2.

Non-participation was associated with low socioeconomic status at both sets of interviews. Compared with the 1% sample of anonymised records from the 1991 census, those living in very large households and in households with no access to a car or van were underrepresented at baseline to a statistically significant degree. 19 Loss to follow up was associated with a higher mean poverty score (Kruskal-Wallis χ 2 =44.8, df=1, P=0.0001) and most individual indices of poverty, but not with financial strain (χ 2 =2.63, df=2, P=0.27). Those who were unemployed (n=588) (response rate 78.6%) were significantly less likely than those in work at T1 to participate at T2 (n=5577) (85.9%, P<0.0001).

Associations that were highly statistically significant were found between financial strain at T1 and poverty score (analysis of variance F=880.96, df=2, P<0.0001), unemployment (χ 2 =496.3, df=3, P<0.001), and caseness at T1 (χ 2 =599.6, df=2, P<0.0001).

Onset of episodes of common mental disorders

—A statistically significant association with the onset of common mental disorders was found for financial strain, but not for poverty score or unemployment at T1 (table ​ (table1). 1 ). Adjusting for potential confounders did not alter this association, which was not modified to statistically significant degree by age, sex, general health questionnaire score, or household income at T1.

Unadjusted odds ratio (95% confidence interval) for onset and maintenance of episodes of common mental disorders by poverty score, unemployment, and financial strain at T1, adjusted for age, sex, general health questionnaire score at T1, social class (by head of household), other potential confounders, * and other variables in table †

Maintenance of common mental disorders

—There was a statistically significant, non-linear (likelihood ratio χ 2 =8.10, df=3, P=0.04) association between poverty score and maintenance of common mental disorders (table ​ (table1), 1 ), which was partly confounded by age, sex, employment status, and general health questionnaire score at T1 (likelihood ratio χ 2 =7.77, df=3, P=0.05). Even after adjusting for these and other confounders, maintenance was significantly higher among those who were unemployed compared with those in work at T1, and among those reporting financial strain at T1 (table ​ (table1). 1 ). The associations between maintenance and both poverty score (likelihood ratio χ 2 =3.73, df=3, P=0.29) and unemployment (likelihood ratio χ 2 =5.72, df=2, P=0.06) were confounded by financial strain at T1. No statistically significant interactions were found between financial strain and age, sex, general health questionnaire score or low income at T1.

Psychiatric morbidity as a continuous outcome

—Statistically significant independent associations were found between general health questionnaire score at T2 and both poverty score and financial strain at T1, but not unemployment, after adjusting for general health questionnaire score at T1 and other potential confounders (table ​ (table2). 2 ). On stratifying by general health questionnaire score at T1 the association between poverty score at T1 and general health questionnaire score at T2 increased with general health questionnaire score at T1. Among those with poverty scores of 4 or more at T1 (n=1078), the regression coefficient for general health questionnaire score at T2 increased from 0.05 (SE 0.29) (P=0.86) for those with general health questionnaire scores in the bottom quarter of the study sample at T1 to 1.82 (SE 0.37) (P<0.001) for those with general health questionnaire scores in the top quarter. No statistically significant interactions were found between general health questionnaire score and either unemployment or financial strain at T1.

Mean (SE) general health questionnaire score (GHQ) at T1 (Likert scoring method), and regression coefficients (SE), B, for general health questionnaire score at T1 and T2 by poverty score, unemployment, and financial strain at T1, adjusted for age, sex, social class (by head of household), other potential confounders * , other variables in table † , and general health questionnaire score at T1 ‡

Poverty and unemployment were associated with longer episodes of common mental disorders but not their onset. The observed effect sizes for the maintenance of such episodes were modest and smaller than expected given the strength of cross sectional associations. 1 – 3 , 9 – 11 While the absence of associations between onset and either unemployment or poverty differs from several previous studies, 12 , 17 these findings are consistent with studies in New Zealand and the United Kingdom. 13 – 15 By contrast, financial strain at T1 was strongly associated with both onset and duration of episode.

Unemployment was associated with maintenance of common mental disorders but not with a statistically significant increase in general health questionnaire scores between sets of interviews. Previous studies have found that psychiatric morbidity increases during the first six months of unemployment and then plateaus, 16 while finding work has the opposite effect. 16 Thus, our finding may have been explained by the inclusion of both people who were unemployed long term at T1, and those who found work between sets of interviews among those people classified as unemployed.

Although confounding by psychiatric morbidity at T1 contributed to the discrepancy in the magnitude of cross sectional 11 and longitudinal socioeconomic gradients in rates of common mental disorders, it is difficult to distinguish definitively between the antecedents and consequences of these conditions, even in a cohort study. Some psychiatric morbidity at T1 may have been caused by earlier poverty or unemployment, and adjustment for baseline general health questionnaire score in longitudinal analyses may therefore underestimate the contribution of these exposures. 15 Similarly, the possibility cannot be excluded that the large socioeconomic gradient in the prevalence of these disorders may be partly due to social selection operating over periods longer than 12 months.

Methodology

The study was limited by use of the general household questionnaire rather than a standardised clinical interview. Associations between poverty and common mental disorders are generally larger in studies using standardised clinical interviews. 2 , 10 Since the general health questionnaire is sensitive to recent change in psychological functioning, false positives might have included individuals with mild or transient psychological disturbance, which should have biased associations towards the null. Although physical ill health also leads to false positives, all study findings were adjusted for the number of current physical health problems. Those in lower occupational grades 21 may underreport psychiatric symptoms on the general health questionnaire compared with responses to a standardised clinical interview. While this may have led to underestimates of socioeconomic gradients in rates of common mental disorders it could not explain the discrepancy between cross sectional and longitudinal findings.

Though modest, non-participation at baseline was associated with low socioeconomic status while loss to follow up was greatest among those who were cases, unemployed or living in poverty, or both, at T1. Since being unemployed or living in poverty are both risk factors for common mental disorders, non-participation was most likely to have biased longitudinal associations towards the null. This was clearly not the case for financial strain at T1, which was not associated with non-response at T2. Finally, statistical power was greatly diminished even in this large study by stratifying the sample according to case status at T1, and this is reflected in confidence intervals which include potentially important associations.

Conclusions

Poverty and unemployment increase the prevalence of common mental disorders by maintaining episodes rather than by precipitating their onset. Financial strain was strongly associated with both onset and maintenance of common mental disorders and was neither confounded nor modified by more objective risk factors. Although it is most likely that financial strain was simply the most accurate measure of standard of living it may also represent an aspect of personality such as proneness to pessimism or worry. There is a need to better understand the nature of this risk factor and its relation with poverty and unemployment if we are to meet the major public health challenge of reducing the prevalence of these costly and disabling disorders. 23

Acknowledgments

This study was started while SW was studying for the MSc in Epidemiology at the London School of Hygiene and Tropical Medicine. The data were made available through the Economic and Social Research Council’s data archive and were originally collected by its research centre on microsocial change at the University of Essex. Neither the original collectors of the data nor the archive bear any responsibility for the analyses or interpretations presented here. We thank Professor Anthony Mann for his help in obtaining financial support for this study, and Andrew Sloggett for his comments on the analyses and study findings.

Funding: Wellcome Trust (grant No 045048).

Conflict of interest: None.

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The share of New York City residents who could not afford basic essentials jumped dramatically in 2022, with one in four children living in poverty, a new report found.

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By Stefanos Chen

After several years of declining poverty, New York City saw a sharp reversal in 2022, when it experienced its largest yearly increase in the poverty level in a decade.

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Twenty-three percent of the city’s residents were unable to afford basic necessities like housing and food, according to a new report by a research group at Columbia University and Robin Hood, a large philanthropic organization. In 2021, that number was 18 percent.

The number of New Yorkers living in poverty, nearly two million in all, included one in four children.

The findings mark a major setback for New York City, where expanded government aid during the coronavirus pandemic had helped to counteract job losses, rising rents and high inflation.

With most of those programs ending, poverty has risen nationwide, but the surge has been especially clear in New York, said Christopher Wimer, the director of the Center on Poverty and Social Policy at the Columbia School of Social Work and a co-author of the report.

The national poverty rate in 2022 was 12.4 percent, up from 7.8 percent in 2021, the largest one-year jump on record, according to the United States Census Bureau. New York City’s rate was nearly double the national average, and there are signs that the gap is widening, Dr. Wimer said.

“It’s dispiriting,” Dr. Wimer said. “We’re going in the wrong direction.”

The biggest reason for the increase in poverty, both nationally and in New York, was the end of pandemic-era policies like the expanded child tax credit, enhanced unemployment insurance and cash payments that helped low-income families keep up with rising costs, Dr. Wimer said.

The steep rise in the number of New Yorkers living in poverty, which grew by 500,000 residents in 2022, underscores wide and longstanding disparities.

Black, Latino and Asian New Yorkers were roughly twice as likely as white residents to live in poverty, according to the report, and women were more likely than men to be unable to afford their basic necessities.

The report, part of a study that began in 2012, was based on surveys of a representative sample of more than 3,600 New York City residents that were conducted in 2022 and 2023.

The researchers used a metric called the supplemental poverty measure, which considers both income and noncash support like food stamps, as well as the local cost of living.

It differs from the Census Bureau’s official poverty measure, which only counts cash resources, but versions of the supplemental measure are also widely used by government officials, including in reports put out by the city.

In 2022, under the supplemental measure, a family of New York City renters made up of two adults and two children was considered below the poverty line if it made less than about $44,000. The poverty threshold for a single adult renter was $20,340.

A major reason for the disparities seen among those living in poverty is the lopsided jobs recovery, said James Parrott, the director of economic and fiscal policy at the Center for New York City Affairs at the New School.

Dr. Parrott, a former chief economist for New York City, was not involved with the poverty report, but broadly agreed with its findings.

“A lot of the progress made in the prepandemic years in reducing poverty and child poverty has been undone with diverging unemployment rates by race and ethnicity,” Dr. Parrott said.

While the city said in October that it had recovered all the jobs lost during the pandemic, the positions that have returned have largely been in low-paying industries.

The retail sector, which pays around $54,000 a year and employs a large share of Black, Latino and Asian workers, has shed more jobs than any other industry, Dr. Parrott said. But the industry that is hiring the most employees, home health care, pays workers far less — around $32,100 a year. The median household income in New York City is about $75,000.

The average unemployment rate in 2023 among Black New Yorkers was 9.3 percent, more than three times higher than among white residents, according to Dr. Parrott.

“The Covid-19 pandemic took a disproportionate toll on our most vulnerable neighbors,” said Charles Lutvak, a spokesman for Mayor Eric Adams. But he pointed to a number of initiatives, including investments in a summer youth employment program and the expansion of the city’s earned-income tax credit, as signs of progress.

A full 25 percent of children in New York City lived in poverty in 2022, the highest rate since 2015, according to the report.

It was a sharp reversal from 2021, when the expansion of the federal child tax credit program cut child poverty in the city by 30 percent, said Chloe Sarnoff, the director of policy research and initiatives at Robin Hood.

The program temporarily increased the annual tax credit to up to $3,600 from $2,000 for each qualifying child under 6 years old, and up to $3,000 for older children. But Congress did not extend the benefits.

The need for public aid is clear at Grand Street Settlement, a nonprofit social services group in Lower Manhattan and Brooklyn that has seen its food pantry lines swell to 2,800 people a month, up from 500 before the pandemic.

A growing child care crisis is fueling the rising poverty rate. “If we’re going to reduce poverty in the city of New York, we have to invest in child care,” said Robert Cordero, the group’s chief executive, adding that dwindling support from the city for its free preschool program is making it harder for parents to make ends meet.

Shavon Johnson, 30, who lives in public housing on the Lower East Side, is a recent widow who was fired in September from her job as a dog food cook, where she made $20 an hour. She said she was let go because she couldn’t get to work on time and still drop her 4-year-old son, Dominique, off at school.

Now she is enrolled in a medical assistant program in the hopes of becoming a nurse — a goal she couldn’t accomplish without the free day care program offered by Grand Street Settlement, which enables her to afford other necessities.

“I would be homeless” if not for the program, she said.

The report recommended permanently expanding public benefits such as the federal child tax credit and New York’s Empire State Child Tax Credit, a credit for state residents that was first passed in 2006.

Robin Hood said it supported expanding the Empire State tax credit to a maximum benefit of $1,000 a year, per child, up from $330, and eliminating income criteria that disproportionately leaves out Black and Hispanic families.

The changes could lift up to 76,000 children out of poverty, according to an analysis by the Center on Poverty and Social Policy at Columbia.

The report also supported zoning reforms that would increase the supply of affordable housing, and an expansion of rental assistance vouchers to help keep low-income residents in their homes.

The City Council said on Wednesday that it planned to join a lawsuit ordering the city to comply with laws that would expand the voucher program, known as CityFHEPS. Mayor Adams has opposed such an expansion, arguing that it would be too costly.

Dulce Tellez, 22, is a teaching assistant for middle school students in Long Island City, Queens, where she is paid about $32,000 a year. After taxes, she said she cleared about $1,600 a month.

Every month she spends $1,000 for a babysitter and another $1,000 for her share of the rent in an apartment in Bushwick, Brooklyn, where she lives with family. She is also chipping away at more than $4,000 in student debt.

Since her expenses exceed her monthly take-home pay, she sometimes picks up shifts at a gelato shop, or works extra hours at her school.

She estimates that 60 percent of the teaching assistants she works with have to take on hourly side jobs, including as security guards or hotel receptionists.

“It makes it seem normal,” she said. “But it shouldn’t be normal.”

Audio produced by Tally Abecassis .

Stefanos Chen is a Times reporter covering New York City’s economy. He previously covered real estate in the city for over a decade. More about Stefanos Chen

New York City poverty spiked in 2022, new report says 

People attend a food bank pop-up Thanksgiving distribution event in...

People attend a food bank pop-up Thanksgiving distribution event in Brooklyn in 2022. Two million New Yorkers were living in poverty that year, according to the Poverty Tracker Annual Report.

  Credit: Getty Images for Food Bank For N/Michael Loccisano

Poverty in New York City saw its largest single-year increase in over a decade, with nearly 1 in 4 New Yorkers struggling to meet their basic needs and children among the hardest hit, according to a report released this week.

That means 2 million New Yorkers were living in poverty, 500,000 more than in the year prior, according to the Poverty Tracker Annual Report , the sixth of its kind released by the philanthropic foundation Robin Hood, which collaborated with Columbia University’s Center on Poverty and Social Policy. The report covers 2022, the most recent year for which full statistics are available.

Of the 2 million, 420,000 were kids, 160,000 more impoverished children than in 2021.

A single renter is considered to be living in poverty if the person's income is about $20,000 or less; for a family of four — two adults, two kids — the number is about $44,000 or less, said Chloe Sarnoff, Robin Hood’s director of policy research and initiatives. That threshold, called the supplemental poverty measure, factors in necessities like housing, food, utilities and clothing, adjusts for localized costs, and counts tax credits, food stamps and other government benefits.

The report found that 1 in 4 children in New York City were living in poverty, as were nearly 1 in 4 adults. 

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“This translates to an increase in the poverty rate from 18% in 2021 to 23% in 2022 and puts the city’s poverty rate at nearly double the national average (12%),” Robin Hood found. 

Sarnoff said that prior to the pandemic, there was a slow but steady decline in poverty, and during the pandemic, poverty was kept at bay by a slew of government programs, including expanded unemployment insurance, stimulus checks, a child tax credit and a moratorium on residential evictions.

“Then those expansions to policy expired, and we see the biggest year-over-year increase in the poverty rate since we’ve been doing this study,” she said.

Among the findings, comparing 2022 to 2021:

  • The poverty rate overall increased to 23% from 18%. 
  • The child poverty rate increased to 25% from 15%. 
  • Most New York City residents — 56% — lived in poverty or with low incomes. 

Sarnoff said households that make up to 200% of the poverty measure are considered to have low incomes. Perhaps they don’t struggle to make rent or pay for their next meal, but “you’re still more likely to suffer material hardships, such as difficulty affording housing every month, food, utilities and medical care.”

Citing U.S. Census Bureau data, Newsday reported last year that Long Island had a poverty rate in 2022 in the single digits , but that figure uses a stricter definition of poverty than the Robin Hood-Columbia study, not the supplemental poverty measure.

Matthew Chayes

Matthew Chayes, a Newsday reporter since 2007, covers New York City.

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Social risk and vulnerability assessment of the hazardous hydrological phenomena in the Krasnodar region of Russia

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Methods and results of social vulnerability and risk assessment are presented in the article. It is explored if modified methodology of the United Nations University (World risk index) can be used on different scale levels: regional, municipal and settlement. It was estimated that, despite the low value of the World risk index for Russia, southern coastal and mountain regions have high values of the risk index for hydrological phenomena because of higher frequency of the hazardous events, higher population density, and high social vulnerability. The Krasnodar region (in the south-western part of Russia) was chosen for a detailed analysis. A municipal risk index was developed, and municipal districts in the Kuban river mouth were identified as territories with the highest risk. For verification of the index results, the percentage of vulnerable people was estimated based on opinion polls. The results can be used in further risk calculation for other hazardous phenomena.

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Men in police uniform arresting a young man on the street.

Lagos: drugs, firearms and youth unemployment are creating a lethal cocktail in Nigeria’s commercial capital

unemployment and poverty research paper

Senior Lecturer, Department of Urban and Regional Planning, Olabisi Onabanjo University

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Adewumi I. Badiora does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Lagos is the most populous city in Africa and a regional economic giant, having west Africa’s busiest seaport. It is the centre of commercial and economic activities in Nigeria.

The city’s population is estimated to be 20 million people. The existence of informal settlements makes it difficult to come up with a more precise number.

Lagos has grown rapidly since Nigerian independence in 1960, when its estimated population was 763,000 people. In the 1980s, its population reached 2.7 million. The government of Lagos state estimates that 86 young migrants arrive every hour.

This rapid urbanisation has been poorly managed. The result is crumbling public infrastructure, poor sanitation, poverty, and shortages of employment opportunities, food, social services, housing and public transport.

These challenges combine to make the city susceptible to criminal activities. Organised crime and violent conflicts are a public safety and security challenge.

The issue of crime has been with Lagos for years. In 1993, the Nigerian government described Lagos as the “crime capital of the country” with the emergence of the “ Area Boys ”, a group of social miscreants.

The 2017 statistics on reported crime incidences in Nigeria by the National Bureau of Statistics shows that Lagos has remained in a class of its own. Lagos State had the highest percentage share of total cases reported with 50,975 (37.9%) cases recorded.

I have been researching various aspects of crime and insecurity in Nigeria, particularly in the country’s south-west. I currently lead the African Cities Research Consortium safety and security domain research in Lagos.

I contributed to a recent paper about residents’ experiences and perceptions of safety in six African cities: Nairobi, Bukavu, Freetown, Mogadishu, Lagos and Maiduguri.

My research identified various drivers of insecurity in Lagos. They included youth migration and unemployment; inequality and poverty; the visible network of organised youth criminal groups; proliferation of small arms and drugs; inadequate preparedness of the city government; police corruption; the high rate of out-of-school children; and poor urban planning.

I argue that for residents to feel secure, the government needs to include these drivers in approaches to solving security challenges in Lagos.

Unemployment, firearms and drugs

In my African Cities Research Consortium safety and security domain research in Lagos, unemployment and the proliferation of small firearms and drugs stand out as trends.

A survey on Navigating Unemployment in Lagos, Nigeria revealed that 48.31% of the respondents were unemployed and the majority were between 25 and 34 years old.

In Lagos, youth of 18-40 years make up about half of the population , equalling over ten million people facing high rates of unemployment. I do not have current unemployment data but in its fourth quarter 2020 nationwide survey, the National Bureau of Statistics estimated a 37.14% unemployment rate in Lagos, and 4.52% underemployment rate.

According to my research participants, drug abuse and illicit arms have become serious issues. Some of the city precincts in communities such as Ikorodu, Somolu, Agege, Bariga, Ojo, Oshodi, Mushin and Badagry have become warehouses and destinations for firearms and drugs.

A recent survey published by ENACT Transnational on organised crime in Africa has shown that between 2010 and 2017, the largest supply of live ammunition transported into Nigeria illegally was intercepted at Lagos. This was made up of 21,407,933 items of live ammunition and 1,100 pump action guns.

Most of the illegal weapons pass through ports in west Africa; some are imported over land borders. While the country’s law forbids random possession of firearms, my research respondents say it is surprisingly common for young miscreants to carry firearms in Lagos.

The police have confirmed that hooligans acquire illicit firearms from local blacksmiths who make them, and from corrupt security officers.

In 2022, the National Drug Law Enforcement Agency discovered a warehouse in a residential estate in Ikorodu with 1.8 tonnes of cocaine. This was the largest single cocaine seizure in the country’s history.

In November 2023, security agents intercepted cannabis in Ibeshe, Iworoshoki and Badagry, and in January 2024, the drug law enforcement agency intercepted cannabis at Ikeja.

Impacts of unemployment, small arms and drugs in Lagos

Findings from my research in Lagos show respondents perceive high levels of violent crime in the city. Youth aged 13 to 40 are mostly the perpetrators.

While there are no accurate statistics of daily violent crime incidences, residents are complaining .

In 2022, the police reported that no fewer than 345 people were murdered in Lagos – the highest number in years.

Young people have formed themselves into street gangs. My research respondents spoke of violent encounters in which their assailants used firearms and were often under the influence of alcohol or drugs or both. This was the experience of 18 respondents, out of a sample of 50 randomly selected respondents.

Some respondents described street gangs in Lagos who are constantly high on drugs and have no regard for human life. Other respondents said drugs were accessible and affordable even for unemployed youth. Respondents believed that a combination of a large youth population, unemployment and easy access to drugs and illicit firearms was proving deadly.

Preventing and treating the issues

The crime triangle in Lagos – youth unemployment, drugs and illicit arms – requires urgent attention.

My study in Lagos shows that a widespread sense of economic hopelessness exacerbates the use of drug and firearms by young people in Lagos. Youth who embrace this culture of violence are those who feel that they have no stake in the city and no trust in the government to provide opportunities for them.

Thus, the state and communities must address the lack of opportunities and alternatives, reaching out to marginalised youth and providing them with an environment in which they can lead a fulfilling life. An effective strategy is one that provides legitimate activities and job opportunities for them.

Government action is required to ensure that opportunities exist for training in a trade or life skill. This would enable youth to make better choices and find productive employment. They could be socially responsible and play an active role in the city rather than becoming a threat in their communities.

Government has the authority to control the supply and use of firearms and drugs.

Special operations should be directed at drug addicts and unlicensed firearms carriers. The approach should be to disrupt the market for illicit arms and drugs.

Security agencies can work with communities to discover new dealing locations and make buyers feel vulnerable and uncomfortable through sting operations – pretending to be dealers or users.

Urban planning approaches could also be applied such as inclusive planning of informal settlements, installation of security cameras and street lighting, limiting access to problematic streets through road changes, removal of transport stops used by drug and firearms users and their dealers, and improved signage.

  • Youth unemployment
  • informal settlements
  • Small arms proliferation

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