• Research article
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  • Published: 15 January 2018

Long term unemployment, income, poverty, and social public expenditure, and their relationship with self-perceived health in Spain (2007–2011)

  • M. Puerto López del Amo González 1 ,
  • Vivian Benítez 1 &
  • José J. Martín-Martín 1  

BMC Public Health volume  18 , Article number:  133 ( 2018 ) Cite this article

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There is scant research that simultaneously analyzes the joint effects of long-term unemployment, poverty and public expenditure policies on poorer self-perceived health during the financial crisis. The aim of the study is to analyze the joint relationship between long-term unemployment, social deprivation, and regional social public expenditure on one side, and self-perceived health in Spain (2007–2011) on the other.

Longitudinal data were extracted from the Survey on Living Conditions, 2007–2010 and 2008–2011 (9105 individuals and 36,420 observations), which were then used to estimate several random group effects in the constant multilevel logistic longitudinal models (level 1: year; level 2: individual; level 3: region). The dependent variable was self-perceived health. Individual independent interest variables were long and very long term unemployment, available income, severe material deprivation and regional variables were per capita expenditure on essential public services and per capita health care expenditure.

All of the estimated models show a robust association between bad perceived health and the variables of interest. When compared to employed individuals, long term unemployment increases the odds of reporting bad health by 22% to 67%; very long-term unemployment (24 to 48 months) increases the odds by 54% to 132%. Family income reduces the odds of reporting bad health by 16% to 28% for each additional percentage point in income. Being a member of a household with severe material deprivation increases the odds of perceiving one’s health as bad by between 70% and 140%. Regionally, per capita expenditure on essential public services increases the odds of reporting good health, although the effect of this association was limited.

Conclusions

Long and very long term unemployment, available income and poverty were associated to self-perceived bad health in Spain during the financial crisis. Regional expenditure on fundamental public services is also associated to poor self-perceived health, although in a more limited fashion. Results suggest the positive role in health of active employment and redistributing income policies.

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The financial crisis, which started in 2008, has brought about a social crisis, which has worsened the health conditions of individuals throughout Europe [ 1 , 2 ], with particularly severe effects in Southern countries [ 3 , 4 ], due to the conjunction of recession and stark austerity policies [ 5 , 6 ].

In Spain the crisis has caused a dramatic increase in unemployment and poverty, while social protection policies have weakened [ 7 , 8 ]. Unemployment skyrocketed from 8.3% in 2007 to 28.1% in 2013, the last year of the crisis, whereas these deleterious effects were much more moderate in other European countries [ 9 ]. One of the main reasons for this sudden increase is the historical dualization of the Spanish labor market since the year 1984, when a binary divide was established between indefinite contracts, with high severance pay for laidoff workers, and temporary contracts with low severance compensation. Before the financial crisis, 88% of the 18.5 million yearly contracts were temporary. For the first few years of the crisis, the increase in unemployment affected mainly workers with the latter modality of contract, which went from 31.6% in 2007 to 21.9% in early 2013 [ 10 ]. In other words, the strong increase in unemployment was caused by the harsh dualization of the labor market and the asymmetrical impact of the financial crisis, and is probably not closely related to the health of workers. This labor dynamic is in agreement with current models used in institutions and in the theory of labor segmentation [ 11 ].

Between 2007 and 2011 long-term unemployment increased in Spain from 22% to 43% [ 12 ]. This increase, along with wage deflation, has caused an escalation of poverty and a drop in available income [ 13 ]. In the period 2007–2011, available income of Spanish households in constant 2011 Euros dropped from 26,773 to 22,146 [ 14 ] and severe material deprivation rose by 53% between 2009 and 2014, leaving 7% of the Spanish population in such a dire situation [ 15 ].

The existing literature does not agree on the definition of long-term unemployment. Brenner et al. (2016) or Romeu (2006) classified unemployment as long term when it lasts between one and two years, and as very long term when it lasts over two years [ 16 , 17 ]. In other works, long term designates unemployment over one year in length [ 18 , 19 ], over two years [ 20 , 21 ], between one and three years [ 22 ] or over five years [ 23 ]. For the purposes of our study we will follow the definition of Brenner et al. (2016) [ 16 ].

This evidence at the international level regarding the effect of long-term unemployment on health shows contradictory conclusions. Some studies found a negative impact of long-term unemployment on health [ 16 , 17 , 18 , 22 , 23 ] whereas others found no such link [ 20 , 24 ].

For instance, in 2013 Herbig et al. reviewed the existing literature to conclude that long-term unemployment increases mortality and the incidence of the most prevalent mental conditions [ 18 ]. Along the same lines, a recent study by Brenner et al. (2016) for all member states of the EU found that long-term unemployment is linked to bad self-perceived health, and that the longer the unemployment the higher the incidence of the perception of bad health [ 16 ].

Nevertheless, for example, Tøge et al. (2015), using the Survey on Living Conditions (SLC) from 28 European countries (2008–2011), applied fixed-effects regression models but failed to find an association between both variables [ 24 ].

In Spain, the only study linking unemployment length with perceived health after the 2008 crisis is the work carried out by Urbanos and González (2015) with data from the National Spanish Health Survey 2011–2012 [ 19 ]. Their results indicate that being unemployed has a detrimental effect on mental and self-perceived health, and that this effect increases the longer the unemployment spell lasts.

Socioeconomic conditions and the decrease in family income are linked to poor health indicators [ 25 , 26 ]. Aittomäki et al. (2012, 2014) used longitudinal data to analyze how health inequalities are associated with the specifics of the labor market and family income [ 27 , 28 ]. Poverty and material deprivation are risk factors [ 29 , 30 ] associated with poor perceived health [ 31 ] mental illness [ 32 ] both for the general population and for specific groups such as children or the elderly [ 33 , 34 , 35 ]. In Spain, the available evidence points at material deprivation as a risk factor for health [ 36 , 37 ].

The international literature has looked into the matter of social public expenditure and health-care expenditure and their impact on the health of individuals, and has found that they have a positive effect across countries both at the global [ 38 , 39 ], OECD [ 40 , 41 ], and European levels [ 1 , 42 , 43 , 44 , 45 , 46 , 47 ]. Conversely, Huijts et al. (2014) found that expenditure on active employment policies, unemployment benefits, and even total social expenditure had a very limited effect (even negative for women) in moderating the detrimental effects of unemployment on self-perceived health [ 48 ].

The main goal of the present study is to analyze the relationship between self-perceived health and two dimensions which are intimately linked with changes happened in the Spanish labor market after the financial crisis: long-term unemployment and social deprivation. In addition, a second goal at the regional level is to look into the relationship between regional social public expenditure and self-perceived health. The first goal requires considering four variables of interest: long- and very-long-term unemployment, income, and severe material deprivation. For the second goal two variables will be considered: per capita expenditure on fundamental public services and public per capita health-care expenditure. To this end, a longitudinal database was built using the Spanish SLC 2007–2011. Multilevel methods have been employed to integrate regional variables in order to produce a coherent hierarchy of data.

To the extent of our knowledge, no previous studies have dealt with the relationship between the financial crisis and the perceived health of the population using a set of variables that allows for the simultaneous consideration of long- and very-long-term unemployment, poverty and public social expenditure.

With the goal of observing the employment history of given individuals, a database was built from the longitudinal data files of the SLC between 2007 and 2010 and 2008–2011 [ 49 ]. Only individuals with continuous presence in the data during the four years were included. Individuals under 16 and over 65 were excluded. The longitudinal database includes 36,420 observations from 9105 individuals in 17 regions during the financial crisis of 2007–2011 in Spain. In the Spanish SLC, perceived health is a categorical variable with five possible answers (very good, good, fair, bad, and very bad), which in most models described in this study are collapsed into two (very good, good: good; fair, bad or very bad: bad).

Table  1 displays the individual and regional variables selected for each level.

The dependent variable was self-perceived health, as recorded in the SLC under “General health status”. Self-perceived health provides a multidimensional approach to health [ 50 , 51 , 52 ]; and is a good predictor of mortality [ 53 , 54 ], morbidity [ 55 ], disability, and use of health services [ 56 , 57 , 58 ]. For the purposes of our research, self-perceived health was collapsed as a dichotomous variable: good self-perceived health (very good or good) and bad self-perceived health (fair, bad, or very bad). This dichotomization follows the trend of most of the related literature [ 59 , 60 , 61 ], which allowed us to compare results against those of previous research.

Independent variables at the individual level include gender, activity status and education level, in accordance with previous studies on self-perceived health [ 18 , 60 , 61 , 62 , 63 ]. The variable “activity status” combines the answer categories as defined by the subject and the information provided by the question regarding monthly activity (employed, student, homemaker and/or caretaker, inactive (retired, disabled, and other forms of economic inactivity).

Unemployment variable has been categorized in being unemployed less than 12 months, being unemployed between 12 and 23 months, and being unemployed between 24 and 48 months. These categories correspond to what the literature refers to as long-term unemployment (between 12 and 23 months) and very-long-term unemployment (more than 24 months, −between 24 and 48 months-) [ 16 ].

Chronic disease may affect the odds of being unemployed and, in turn, a given individual may see their chronic condition worsen due to their losing their job or spending a long time unemployed. In order to check for robustness, our models have been tested with and without this variable [ 19 ].

Income level is one of the main components of the social gradient of health [ 64 , 65 ]. Given the evidence about the moderating role of family income on the link between employment status and individual health, we have introduced the independent variable “equivalent household income” [ 66 ]. This variable has been calculated by applying the OECD modified equivalence scale to available household income [ 67 ]. The variable “severe material deprivation” was introduced in the model because it is one of the components of the AROPE index (At Risk Of Poverty or social Exclusion). This indicator is obtained from the SLC and is harmonized at the European level: it includes people who declare being unable to afford at least four of a list of nine concepts listed in the Europe 2020 strategy, and who are therefore considered to be at risk of poverty [ 68 ].

Regionally, certain ecological variables have been introduced to account for public expenditure policies: expenditure on essential public services (education, health-care, and social protection [ 69 ], and public health-care expenditure [ 70 ].

Given that our study looks into the relationship between individual and regional variables and perceived health in a simultaneous way, we have employed a random group effects in the constant longitudinal multilevel logistic model. This approach is well suited for hyerarchical structures incorporating different levels of information, in which individuals share certain characteristics due to their belonging to the same higher level (the region), and repeated measurements are available over a certain time span, as it allows for the estimation of variance for each level.

In order to be able to contrast how health is related to long and very long unemployment, income and individual social deprivation, as well as its association with the regional social and economic context, in the present work we have estimated a series of longitudinal multilevel logistic models (level 1: year; level 2: individual; and level 3: region), with random intercept. These multilevel models address the lack of independence of ordinary least squares models through the inclusion of hyerarchical data, and avoid the ecological fallacy (in which aggregated data are interpreted at the individual level) and the atomistic fallacy (in which individual data are interpreted at the aggregated level) [ 71 ].

The multilevel logistic regression model points at the dependent variable Y ijk (perceived health; collapsed into good or bad health for year i) following a binomial distribution Y ijk ~ Binomial(1,π ijk ) with variance Y, conditioned on π, Var( Y ijk | π ijk ) = (1- π ijk ), where π ijk is the likelihood of presenting the feature of interest for year i , being i  = 2007, …, 2011, j the subject, j  = 1, ..., 9105, and being k the region, with k  = 1, …, 17.

Analytically:

where β 0 is the independent term, X ijk the explanatory variables at individual level j , and β h its associated coefficients; Z jk are the explanatory variables at the regional level k , and α m its associated coefficients. The error term divides the dependent variable into three parts, once for each hierarchical level.

In addition, and in order to confirm that the loss of information resulting from collapsing perceived health into fewer categories does not skew the results of the estimated odds ratios of the variables of interest, an ordered logit model was estimated with the self-perceived health in its original five categories. This longitudinal multilevel ordered logit model can be written in terms of a latent response y* ijk :

The ordinal of self-assessed health variable y ijk is related to the latent response via the threshold model: y ijk  = 1 if y* ijk  ≤  k 1 , y ijk  = 2 if k 1  <  y* ijk  ≤  k 2 , y ijk  = 3 if k 2  <  y* ijk  ≤  k 3 , y ijk  = 4 if k 3  <  y* ijk  ≤  k 4 and y ijk  = 5 if k 4  <  y* ijk where k parameters are the cutpoints, which will be estimated together with parameters β and α in the model.

In order to be able to estimate the extent to which the areas under analysis (regions) determine individual differences in health status, we calculate the variance partition coefficient (VPC) [ 72 ], and the median odds ratio (MOR) of the region as per the latent-variable method [ 73 ].

In total, 9 models were developed to estimate the relationship between the variables of interest and self-perceived health in Spain (2007–2011). Starting from the base model, the first three models treat chronic illness differently and use different subsamples (Table  3 ). Being Model 1 the base model, Model 2 controls for chronic illness, Model 3 excludes from the sample those individuals whose chronic illness appeared during the four follow-up years. Model 4 excludes those individuals who were unemployed at the beginning of the panel (in January 2007 for panel 2007–2010 and in January 2008 for panel 2008–2011), in order to avoid merging recently unemployed with long term unemployed people. By estimating this model we may check our results for robustness regarding the presence of individuals who were already unemployed at the beginning of the panel.

The following four models reproduce the previous sequence, but with a subsample including only those individuals who reported having good or very good health at the begining of the panel (Table  4 ). In other words, in these models none of the individuals who later found themselves unemployed, particularly for the long or very long term, reported to perceive their health as poor.

The goal of these models is testing our coefficients for sensitivity when good-health individuals are selected at the beginning of their panel. Some of them may fall ill and become unemployed for this reason, but among the unemployed the percentage of individuals reporting good health has increased (from 78.56% in 2007 to 82.44% in 2011; see Table  2 ). The last model, number 9, estimates an longitudinal, multilevel, ordered logit model in order to assess the extent to which the results found for the variables of interest are affected by the loss of information caused by collapsing self-perceived health from its five original categories into just two.

All models of multilevel regression were planned and executed using the STATA 14.0 statistical software package [ 74 ].

Table  2 shows the data regarding self-perceived health according to individual, family, and regional variables. The variable of interest at the individual level (activity status) shows that inactive (42.4%), homemaking (33.6%), or unemployed individuals (23.5%) report having worse health than those who are employed (14.6%) or studying (3.6%). Among the unemployed, the time spent with no employment affects how one’s health is perceived: the proportion of the unemployed who report good health when being unemployed for less than 12 months is higher (85.7%) than for those who have been unemployed for between one and two years (76.9%). This percentage is lowest among those who have been unemployed for between two and four years (72.16%).

As for the rest of individual variables, the analysis points at males having better self-perception of health (81.8%) than females (78.4%). Education level is associated with improved perception of one’s health, and age is linked with worsened perception of one’s health. As family income level increases, so does reported health, and being a member of a severely materially deprived household has a strong negative relationship with subjective health.

Table  3 shows the results of the first four multilevel models, which calculates the modulating effect of individual and regional variables on the association between unemployment and self-perceived health, depending on whether chronic illness is being controlled for or not (Models 1 and 2) or by dropping all the chronically ill people from the sample as in Model 3. As it has been described in the methodology section, in model 4 unemployed individuals at the beginning of panel are dropped.

Table  4 shows the results of the same sequence of models when the subsample contains those individuals who reported having good or very good health at the beginning of the panel. It also shows the results of the longitudinal, multilevel, ordered logit model.

The multilevel models used to estimate the association of long- and very-long-duration unemployment with the self-perceived health of individuals between 2007 and 2011 shows that long- and very–long-duration unemployment are associated with how health is subjectively perceived. Figure  1 shows the odds ratios for long- and very-long duration unemployment and other variables of interest in the main models.

By comparing the results of the first three estimations, it is apparent that there are no significant changes between the odds ratios of the variables of interest (Table  3 ). For instance, and regarding the long-term unemploment (between 12 and 23 months) and very-long-term unemployment (between 24 and 48 months) variables, odds ratios for models 1, 2, and 3 are 1.61, 1.41, and 1.37 for long-term unemployment, and 2.32, 1.81 and 1.72 for very-long-term unemployment. To sum up, after excluding chronically ill subjects from the sample the model remains stable regarding the relationship between long-term and very-long-term unemployment, income, poverty, and poor perceived health.

Odds ratios for the association between long and very-long term unemployment, income, job insecurity, poverty, and self-perceived health in Spain (2007–2011). Source: Prepared by the authors based on data from the Survey on Living Conditions. Instituto Nacional de Estadística (2014). http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608 . Accessed 27 Dec 2016

In addition, model 4 exclude subjects who were unemployed at the beginning of the panel. The odds ratios for these models are also similar to those of the full sample concerning the three individual variables of interest and the two regional variables (Table  3 ). For instance, for the variables concerning long-term and very-long-term unemployment odds ratios are 1.43 and 1.60. The analysis of the subsample that included only those individuals reporting good or very good at the beginning of the panel shows that results remain stable (Table  4 , Fig.  1 ). In Model 8, in which subjects reported good health, and were not unemployed at the beginning of the panel, the odds ratio for the long-term unemployed (between 12 and 23 months) is 1.56, which increases for the very-long-term unemployed (between 24 and 48 months), 1.71.

The estimation performed with the longitudinal ordered logit model (Model 9) yields similar odds ratios in the variables of interest to those of the base model. For instance, concerning long-term and very-long-term unemployment, coefficients are 1.41 and 1.81 in Model 2, and 1.22 and 1.60 in the longitudinal ordered model. These results hold for all other variables of interest across all estimations.

In basic models, and controlling for chronic illness (numbers 2 and 6 ) , the household income variable was associated with a reduction of the odds of declaring bad health of 25% and 23% respectively for each additional percentage point in income. Being a member of a household with severe material deprivation increased by 95% and 113% respectively the odds of perceiving one’s health as bad compared with households not presenting severe material deprivation. In Tables  3 and 4 and the Fig.  1 , these data are consistent across the models. This is the case of Model 4, which excluded the unemployed at the beginning of the panel (28% increase for income and 97% decrease for severe material deprivation) and of the model dealing only with individuals in good or very good health, excluding the unemployed at the beginning of the panel (24% increase for income and 111% decrease for severe material deprivation, Model 8). The rest of individual variables behaved according to what has been described in the previous literature.

The analysis of VPC in the basic model number 2 showed that 1.4% of variance in the odds of reporting bad health can be attributed to the modulating effect that regional variables exert on the association between unemployment and self-perceived health. The calculation of the MOR shows that, when comparing two randomly selected regions, the likelihood of declaring bad health was 34% higher in one than in the other (in the median case).

Results regarding the influence of regional public expenditure on the association between long- and very-long-term unemployment and self-perception of health show that expenditure on essential public services is associated with better self perceived health: for each additional percentage point of increase in health-care, education, and social protection the odds of declaring bad health decreases by 0.01% (in every model, 1–9). Public health-care expenditure per capita did not yield statistically significant results.

Before discussing our results, some limitations must be acknowledged. Firstly, and given the bidirectional nature of the relationship between the variables of interest and perceived health, this study is not able to establish a causal relationship between the associations we have identified [ 75 ]. This becomes particularly relevant regarding the link between bad perceived health and long-term unemployment.

The literature has identified two processes linking bad perceived health and unemployment. On the one hand, the causal hypothesis suggests that unemployment is a risk factor for health. On the other, the selection hypothesis states that it is poor health which excludes workers from the labor market [ 76 , 77 , 78 ]. Three metaanalyses concluded that longitudinal studies provide enough evidence for both the causal and the selection hypotheses [ 79 , 80 , 81 ]. More recently, some studies have yielded certain evidence supporting the latter [ 82 , 83 ]. The work of Reeves et al. (2014) suggests that the financial crisis in Europe has had particularly severe effects on people with bad health, who are more prone to losing their jobs when market conditions worsen [ 84 ]. Heggebø and Dahl (2015), however, pointed out that while the selection effect has remained constant throughout time in the EU, in countries like Spain, where the financial crisis has brought about a swift increase in unemployment and high rates of unemployment population, a change has taken place in the breakdown of the unemployed population, which now includes a higher percentage of individuals who report to have good health [ 85 ]. This overrepresentation of the healthy among the unemployed can be interpreted as a consequence of a massive, sudden loss of employment, and supports the causal hypothesis. During the first years after the onset of the crisis (which is the period covered in our analysis), the destruction of employment affected temporary workers on a greater measure, since their lay-off costs are smaller than those of permanent workers [ 10 ].

Our results are consistent with this hypothesis. The percentage of unemployed individuals reporting good health has increased from 78.56% in 2007 to 82.44% in 2011. In addition, when only considering the subsample reporting good health at the beginning of the study, results show a robust association between long-term unemployment and bad perceived health, which increases with the time spent unemployed.

However, the selection effect may well play an important role in long-term unemployment, particularly when employers use poor health as an indicator for low productivity in their recruitment processes, in a context of low labor demand brought about by the financial crisis [ 84 ]. Subsequent studies should explore the evolution of unemployment since the end of the financial crisis (2014), in a context of sustained creation of jobs, and contemplate in their methodological approaches the need to analyze the endogenic nature of the association between unemployment and health, for example by using structural equation modeling [ 27 ].

Secondly, although self-perceived health is one of the best global health indicators, several significant dissonances have been described with objective indicators of morbimortality when populations have been compared [ 86 , 87 ]. Amartya Sen (2009) suggested employing a social context to examine the statistics on the perception of bad health, with a critical analysis of positional perspectives [ 88 ]. Contemplating some features of the labor market which may amount to risk factors for health, like job insecurity or involuntary part-time work, might provide a more thorough and detailed analysis of labor markets and their influence on health.

Yet another limitation originates from the SLC not including individual lifestyle information. In this regard it only records data concerning self-perceived health, chronic illness, and limitations for activity in daily life. This is however the only survey conducted in Spain to offer longitudinal information about the activity and employment status of individuals.

The present study offers evidence of the association between long- and very-long-term unemployment, loss of family income, and living in a household that is severely materially deprived with bad self-perception of health. All estimated models show similar and consistent results for all variables of interest.

According to our results, bad perceived health is associated with long- and very-long-term unemployment, and worsens as the time spent unemployed increases. This is in agreement with part of the literature published in this regard before the onset of the financial crisis [ 16 , 17 , 18 , 22 , 23 ] and with the work of Urbanos and González (2015), regarding the Spanish situation after the crisis [ 19 ].

Some evidence exists that certain health conditions and causes of mortality (such as suicide) increase due to the deleterious effect of recessions on mental health [ 2 , 5 ]. Ähs and Westerling (2005) compared self-perceived bad health during times of low (1983–1989) and high (1992–1997) rates of unemployment in Sweden and, after controlling for sociodemographic factors and long-term health conditions, differences in self-perceived health between the employed and the unemployed were higher at times of high unemployment [ 89 ]. Drydakis (2015) recently published his results regarding the negative impact of unemployment on the mental health and self-perceived health of Greek individuals in the period 2008–2013 [ 90 ].

However, our study revealed that, by following the professional history of individuals along four years since the onset of the crisis, a robust association appears between long- and very-long-term unemployment and the deterioration of the perception of their own health, after controlling for other individual and regional variables.

Our results also show that, after only one year of unemployment, perceived health worsens. One tentative explanation for this phenomenon is that a change in expectations takes place when the reality of being unemployed and losing income settles in and reveal itself as a permanent situation, thus increasing uncertainty about the future and causing stress and anxiety [ 91 ].

Labor policies aimed at reducing the long-term unemployment rate as a strategy to improve the health of the population are particularly attractive, since they are synergistic with macroeconomic policies of fiscal consolidation and sustained economic growth [ 92 ]. In a recent research, Doménech and González Páramo (2017) have shown how a reduction of 8% in structural unemployment would in the long term mean an increase in GDP and public expenditure per working-age population of more than 20% [ 93 ]. Additionally, according to the results of this study, health would likely be improved by the reduction of long-term unemployment and the reduction of social deprivation.

In the present study household income decreased the odds of reporting bad health by 16% to 28% (depending on the model) for each percentage point of income increase. Conversely, being member of a household with severe material deprivation affected the perception of health and increased the odds of perceiving one’s health as bad by 70% to 140% (depending on the model). These results are in agreement with several others that found a positive correlation between unemployment, low social and economic level, and bad health [ 25 , 28 , 78 , 94 , 95 , 96 , 97 ]. In Spain, the link between material deprivation and bad health was already proved in studies performed both prior to the onset of the financial crisis [ 36 ] and after [ 37 ].

The fact that severe material deprivation is associated with bad health is probably due to two mechanisms: an increase in the general susceptibility to illness and a set of specific factors, which increase the risk of death (healthy lifestyle, overweight, obesity, alcohol consumption, smoking, etc. [ 98 ]. The work of Ayllón and Gábos (2016) suggests that a vicious circle is established in which living in conditions of material deprivation for a long time erodes the human and social capital of individuals and worsens their health [ 99 ]. Long-term unemployment thus breeds poverty and material deprivation, which in turn decrease the chances of entering the labor market.

Regional per capita expenditure on essential public services is associated with better perceived health, although its influence is limited, whereas per capita expenditure on health-care did not show to have any significant relationship with self-perceived health. These results do not agree with those of other authors [ 40 , 41 , 100 ]. For instance, Ng and Muntaner (2015) found that expenditure on health-care, social services, and education reduced mortality rates in the provinces of Canada [ 101 ]. Huijts et al. (2014), on the other hand, did not find a link between social protection policies, health-care expenditure, and perceived health [ 48 ].

To conclude, this is the first longitudinal study carried out in Spain after the financial crisis to analyze the joint association of long-term unemployment, income, poverty, and severe material deprivation (closely derived from long-term unemployment) with bad perceived health. Our results provide robust evidence that long-term unemployment is related to bad health.

Finally, by using multilevel models we were able to find robust estimators regarding the relationship between social and health-care public expenditure policies in the Spanish regions and perceived health, which turned out to be limited in the case of the former and non significant for the latter.

Our results are particularly relevant for the design of public policies aimed at reducing the weight of social determinants in health. Specifically, these results should be considered when formulating active employment policies, safety nets for the long-term unemployed, and policies of redistribution focused on families with low-income levels and material deprivation.

Abbreviations

At Risk Of Poverty or social Exclusion

Median Odds Ratio

Survey on Living Conditions

Variance Partition Coefficient

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Acknowledgements

The authors are grateful to the Health Department of the Andalusian Board for the funding.

This work was supported by the Health Department of the Andalusian Board (2013–2015) under Grant PI-0682-2012.

The Health Department did not participate in the design of the study, collection, analysis, and interpretation of data nor in writing the manuscript.

The cost of translation of the work was supported by “Research Aid Program of the Faculty of Economics and Business Sciences of the University of Granada for the revision of scientific texts”.

Availability of data and materials

The dataset generated and analysed during the current study is available in the “Repositorio Institucional de la Universidad de Granada” repository, http://hdl.handle.net/10481/45608

Martín, J., Benítez, V. and López del Amo, M.P. 2017. “Longitudinal Life Conditions Survey Database 2007-2011. Influence of unemployment length, poverty and social public expenditure on self perceived health in Spain”. From the publicly accessible microdata of the Survey on Living Conditions. Spanish National Statistics Institute. Applied Economics Department, University of Granada: Granada. Available at: http://hdl.handle.net/10481/45608 . Accessed 30 march 2017.

The datasets analysed during the current study are available at:

Instituto Nacional de Estadística (2014). Encuesta de Condiciones de Vida. http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608 . Accessed 23 Dec 2016.

Fundación BBVA e Ivie (Instituto Valenciano de Investigaciones Económicas). Gasto en los servicios públicos fundamentales en España y sus comunidades autónomas (2002–2013). Mayo de 2015. http://www.fbbva.es/TLFU/tlfu/esp/areas/econosoc/bbdd/gastos_servicios_publicos_comunidades.jsp Accessed 23 Dec 2016.

Fundación BBVA e Ivie (Instituto Valenciano de Investigaciones Económicas). Gasto sanitario público en España. Agosto de 2013. http://www.ivie.es/es/banco/gasto-sanitario-publico.php . Accessed 23 Dec 2016.

The stata commands run to estimate the models in Stata 14 are:

xtset panelvar timevar [, tsoptions] (to declare longitudinal nature of data).

melogit depvar fe_equation [|| re_equation] [|| re_equation ...] [, options] (to estimate logit model).

meologit depvar fe_equation [|| re_equation] [|| re_equation ...] [, options] (to estimate logit ordered model).

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M. Puerto López del Amo González MPLAG, Vivian Benítez VB, José J. Martín JJM. All the authors above have taken part in the conception, design and writing of this article. More specifically, JJM coordinated the development of the research, the writing of the paper and its critical review; VB collected the data, estimated the multilevel models, reviewed the literature, and drafted the text; and MPLAG designed the project, reviewed the literature and drafted the text. All authors have contributed to the interpretation of results, have reviewed all aspects of the research, and have approved the final version.

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López del Amo González, M.P., Benítez, V. & Martín-Martín, J.J. Long term unemployment, income, poverty, and social public expenditure, and their relationship with self-perceived health in Spain (2007–2011). BMC Public Health 18 , 133 (2018). https://doi.org/10.1186/s12889-017-5004-2

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  • Long term unemployment
  • Self-reported health
  • Multilevel logistic longitudinal regression
  • Great recession
  • Social public expenditure
  • Social health determinants

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unemployment and poverty research paper

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Job market polarization and American poverty

  • Abu Bakkar Siddique   ORCID: orcid.org/0000-0002-9964-7511 1  

Journal for Labour Market Research volume  57 , Article number:  30 ( 2023 ) Cite this article

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The article posits that the puzzles of stagnating poverty rates amidst high growth and declining unemployment in the United States can be substantially explained by polarized job markets characterized by job quality and job distribution. In recent decades, there has been an increased number of poor-quality jobs and an unequal distribution of jobs in the developed world, particularly in the United States. I have calculated measures of uneven job distribution indices that account for the distribution of jobs across households. A higher value of the uneven job distribution indices implies that there are relatively large numbers of households with multiple employed people and households with no employed people. Similarly, poor-quality jobs are those jobs that do not offer full-time work. Two-way fixed-effect models estimate that higher uneven job distribution across households worsens aggregated poverty at the state level. Similarly, good-quality jobs help households escape poverty, whereas poor-quality jobs do not. This paper suggests that eradicating poverty requires the government to direct labor market policies to be tailored more toward distributing jobs from individuals to households and altering bad jobs into good jobs, rather than merely creating more jobs in the economy. This paper contributes by elaborating on relations of employment and poverty, addressing employment quality and distribution, and providing empirical evidence.

1 Introduction

Poverty in the United States is measured by a threshold, and people fall into and escape poverty for many reasons. Many consider employment as the primary policy solution to all forms of poverty, accounting for both falling into and out of poverty (Middleton and Loumidis 2002 ; Saunders 2006 ; Krishna 2007 ). Footnote 1 In the recent pre-pandemic time, the United States reached its highest level of employment in 50 years, with low unemployment. By many metrics, the job market was doing well (Kelly 2019 ). However, poverty in America has remained stagnant for many decades (Desmond 2018 ). What kind of jobs are available? Do they not pay enough to live on? Are these jobs not equally distributed? Footnote 2

Although it may seem logical that higher employment rates would reduce poverty, as the income and consumption of poor people largely come from their work, the relationship between employment and poverty is not straightforward. While some scholars assume that increasing employment reduces poverty (Cantillon et al. 2003 ), others believe that creating more non-subsidized jobs may lead to a higher number of low-paid jobs, leaving more working people in poverty (Kalleberg 2009 , 2011 ). In other words, when employment growth occurs at the cost of wage reduction it does not eradicate poverty (Sloane and Theodossiou 1996 ; Gardiner and Millar 2006 ). Moreover, poverty reduction may not be realized if employment growth occurs in sectors that do not accommodate many poor people (Ravallion and Datt 2002 ; Satchi and Temple 2009 ) or in industries that require higher levels of skills (Loayza and Raddatz 2010 ) and capital (Siddique 2016 ). Furthermore, poor people cannot always afford to be unemployed, and they are not necessarily unemployed people (Visaria 1981 ; Saunders 2002 ). Despite this complexity, there is little research exploring the relationship between employment and poverty, leading to a lack of evidence-based policy actions in this area. Specifically, there is a need for scientific empirical evidence on the relationship between job distribution, job quality, and poverty, and this paper makes an important contribution in these fields.

Despite strong economic performances, poverty rates in the United States have remained stubbornly high, and past attempts to explain this phenomenon have fallen short (Hoover et al. 2004 ; Hoynes et al. 2006 ; Edelman 2013 ; Meyers 2014 ; Pacas and Rothwell 2020 ). In this paper, I argue that job market polarization is a major factor that perpetuates poverty, even during periods of high employment and low unemployment. When jobs are distributed unequally across households and are of poor quality, with no full-time work available, the benefits of job growth fail to reach those who need them the most. This means that the “trickle-down” economy may not work for majority of poor Americans, who face both poor job quality and uneven job distribution. As a result, job growth in a polarized job market does not benefit jobless households, and poverty remains stagnant. Recent data shows that employment-rich households are experiencing faster employment growth than employment-poor households (England 2017 ), exacerbating the problem. In a more equitable, non-polarized job market, the unemployment rate would be the same for both individuals and households (Gregg et al. 2010 ).

The United States has not also succeeded in reducing poverty despite high employment rates due to the poor-quality of available jobs. The job market in the United States has become increasingly bifurcated, which has significant implications for poverty reduction strategies and outcomes. This trend started in the 1980s as the service sector began to replace the manufacturing sector and technology started playing major roles in the labor market (Autor and Dorn 2013 ; Goos et al. 2014 ; Salvatori 2018 ). Since then, Americans have faced an increase in low-paying jobs with few hours, little job security, and no entitled benefits. These poor-quality jobs are prevalent across multiple industries and have impacted the daily lives of millions of Americans. Several studies have documented this trend, including Herzenberg et al. ( 2000 ), Desmond and Gershenson ( 2016 ), Kelly ( 2019 ), Newman ( 2009 ), and Kalleberg ( 2009 , 2011 ). Contrastingly, while some industries like technological offer attractive salaries, promotions, benefits, and even equity in the company, they struggle to find a sufficient pool of skilled applicants to fill their job openings (Goos and Manning 2003 ; Salvatori 2018 ; Kelly 2019 ), contributing to job market polarization. Therefore, economic prosperity alone cannot effectively reduce poverty if it does not generate an adequate number of good-quality jobs (Odhiambo 2011 ; Page and Shimeles 2015 ). Unfortunately, the majority of recently available jobs are low-paying, part-time contract positions that does not provide healthcare or other benefits. The rise of these poor-quality jobs contributes to the growth of the working poor and inequality, Footnote 3 which could be a potential explanation for the persistent high levels of poverty in the United States.

In this research paper, the impact of uneven job distribution and poor-quality jobs on poverty rates in the United States is examined. A longitudinal dataset was constructed by aggregating household-level data to the state-level to match with state-level variables, and a two-way fixed effect model was applied to estimate their effects. The results indicate that the eradication of poverty in the United States may depend on redistributing jobs from individuals to households and on improving quality of jobs. Rather than solely focusing on creating more jobs, policymakers should prioritize transforming existing poor-quality jobs into good-quality ones and ensuring their equitable distribution. It is important to note that creating more jobs is not discouraged, only the creation of poor-quality jobs. Moreover, the findings suggest that a high-growth economy and high employment rates are unlikely to alleviate poverty.

This paper makes a significant contribution by expanding our understanding about the intricate relationship between employment and poverty and presenting novel empirical evidence. Notably, this analysis introduces a fresh perspective by focusing on the United States context, which distinguishes it from previous research that primarily examined the United Kingdom and other European economies. In addition, it opens up avenues for further research into the potential implications of issues related to job distribution and job quality, not only on poverty but also on various aspects of socio-economic well-being.

The following section will delve into the intricate relationships between family dynamics, employment, and poverty. Data, measuring key variables, and empirical strategy will provide detailed insights into data, the metrics used for key variables of interest, and chosen empirical methodologies. Results: two-way fixed effect model will unveil the findings, and, finally, in Sect. 5, I will wrap up this paper with a conclusion.

2 Job distribution, job quality, and poverty

2.1 family is central to avoid poverty.

In most societies, the risk of poverty is unevenly distributed, affecting specific groups such as ethnic minorities, single parents, and people with disabilities more significantly than others. Unexpected events, such as illness, can also lead to poverty (Flaherty et al. 2004 ; Gardiner and Millar 2006 ). Vulnerable individuals often employ strategies like living with family members or relying on state transfer benefits and tax credits to prevent falling into poverty. Gardiner and Millar ( 2006 ) conducted a study in British society and found that over 30% of low-paid workers can escape poverty by depending on the income of other family members. More than 60% rely on the income of their partners and other adults in the household. Approximately 8% of low-paid workers manage to avoid poverty by working long hours to compensate for their low earnings, while around 13% achieve this by combining incomes from state transfers. Living with other individuals plays a crucial role in helping many low-income individuals mitigate the impact of poverty. Pooling together all sources of income is effective, as even a family member with relatively low earnings can improve the overall financial well-being of the household. It's important to note that a single individual earning a decent income has the potential to lift the entire family out of poverty.

Over the past four to five decades, the family structure in the United States has undergone significant transformations, marked by a notable decrease in marriage rates, a decline in the proportion of children born within marriages, and a rise in the number of children born outside of wedlock (Cancian and Haskins 2014 ; Thiede et al. 2017 ). Simultaneously, there has been a substantial increase in women entering the labor market, while many men, particularly those with lower levels of education, have faced diminishing employment opportunities (Cancian and Haskins 2014 ; Binder and Bound 2019 ). These shifts in family structure have had a considerable impact on poverty through their influence on labor market opportunities. Increased participation of women in the workforce may have a poverty-reducing effect if they entered the labor market to compensate for limited family income. Data from the United States Census and the American Community Survey show that families headed by single females with children have consistently experienced an average poverty rate of around 40% over the past four to five decades. In contrast, families headed by married couples with children have maintained a poverty rate of less than 8% during the same period. Moreover, the poverty rate for married couples without children has been even lower, averaging less than 4%. Conversely, families headed by single males and single females have experienced poverty rates higher than 15% on average (Cancian and Haskins 2014 ). Thus, families play a crucial role in shielding many individuals from falling into poverty.

When a single female gets married, the household's needs are likely to increase. However, this marriage also introduces a second earning adult to the household, potentially reducing the risk of poverty. Similarly, if she joins a joint family with another earner, similar benefits can arise. This phenomenon is known as economies of scale, where each additional person added to a household results in less than proportional increases in needs (Cancian and Haskins 2014 ; Reyes 2020 ). The current state of individual and household employment is influenced by various factors, including modernization, the prevalence of nuclear families, the feminization of labor markets, and an increasing number of individuals pursuing tertiary education (Corluy and Vandenbroucke 2017 ; Thiede et al. 2017 ). Considering these trends in family structure, I hypothesize that the unequal distribution of jobs among households may explain the persistent high levels of poverty in the United States. Surprisingly, there have been no studies to date that have specifically explored the distribution of employment across households and its associated consequences on poverty in the country.

2.2 Jobs quality, labor market policies, and poverty

The gap between good and bad jobs is widening in the United States, representing another dimension of job market polarization. The availability of good jobs that offer fringe benefits is declining, while the number of bad jobs without such benefits is increasing. In non-regulated and non-competitive labor markets, both good jobs and bad jobs can coexist. Acemoglu ( 2001 ) suggests that in a laissez-faire equilibrium, the labor market is biased towards poor-quality jobs due to a phenomenon known as "hold-up." According to his search model (Acemoglu 2001 ), the presence of diverse job creation costs results in differentiated compensation for similar workers. In this market, employers and employees share rents, thus breaking the relationship between wages and marginal productivity. Additionally, employers fail to internalize the externalities arising from rent-sharing, which could otherwise be addressed through market allocation. Capital-intensive firms that have made substantial sunk investments are compelled to negotiate and create significant positive pecuniary externalities for workers. Consequently, these firms tend to create a scarcity of good jobs while simultaneously generating an excess number of bad jobs.

There are also several policy factors that play a crucial role in determining the quality of jobs in the market. These factors may include but not limited to inadequate social security programs, such as unemployment benefits, and the absence of minimum wage laws. Both policies can potentially incentivize workers to wait for better job opportunities, consequently reducing firms' profits from creating low-quality jobs and shifting overall job compositions (Carter 1995 ; Acemoglu 1996 ). In the United States, due to the limited prevalence of social security programs, most workers cannot afford to remain unemployed while waiting for better job offers. This results in an oversupply of labor in the market, prompting firms to shift towards a higher proportion of poor-quality jobs. On the contrary, if workers have the protection of unemployment insurance, the cost of waiting for a better job would be less burdensome. Therefore, social protection programs can potentially reduce the labor supply by increasing reservation wages (Marinescu and Skandalis 2021 ).

Similarly, the absence of a minimum wage requirement is also contributing to the growth of poor-quality jobs. Firms find it more profitable to offer poor-quality jobs when there are no higher minimum wage laws in place. Conversely, setting a higher minimum wage in the economy would have compelled firms to pay higher wages for poor-quality jobs, making them less economically profitable and encouraging firms to improve their job compositions (Bulow and Summers 1986 ; Carter 1998 ).

Poor-quality work may not alleviate poverty but rather perpetuate it. To comprehend the implications for poverty, in addition to the economic theories discussed above, it is necessary to examine the policy changes that have promoted a flexible labor market over the last five decades. The Regulatory Flexibility Act (RFA) of 1980 was adopted on a bipartisan basis, reflecting liberal economic principles aimed at deregulating laws and enhancing business power with an aim to improve innovation and production. These flexible labor policies have encouraged the creation of low-quality jobs while reducing the number of high-quality jobs. Sectors that offer low-quality jobs experience high employee turnover (Albrecht and Vroman 1992 ; Carter 1998 ). During economic downturns, these positions can easily be terminated without incurring costs, only to be rehired when production needs increase (Kalleberg 2000 ; Van Arsdale 2013 ), offer no job security to these workers. In other words, the underlying logic of creating low-quality jobs is to pay only for the units of work performed by these positions, and thus, this policy change has direct implications for poverty.

Within these flexible labor law regimes, new third-party staffing firms (e.g., Kelly Services Inc., Robert Half, Toptal, etc.) have emerged to manage this flexible workforce, creating a triangular relationship and operating their businesses at the expense of labor wages (Van Arsdale 2013 ). Major corporations like Amazon, Microsoft, and Sheraton often outsource numerous positions to independent staffing companies, predominantly offering jobs with unpredictable schedules. Surprisingly, approximately 40% of hourly employees receive their work schedules only a week or less in advance (Desmond 2018 ). It is common to see cleaners, reception assistants, and security officers working at prominent corporations, yet these workers are not directly employed by these companies. Instead, they are engaged through independent contractors who take a share of their wages. These intermediary companies not only deduct a portion of the employees' wages offered by the host companies but also deny workers any opportunity for career advancement within the host company, regardless of their hard work. Consequently, many workers under these contracts do not even receive their full wages. By outsourcing these positions to independent contracting agencies, large organizations evade their responsibilities to provide healthcare and other security benefits.

In this triangular relationship, staffing firms negotiate wage and work conditions between employing firms and employees, often leaving employees with little to no voice. Consequently, employees frequently reject job offers, resulting in an increased pool of unemployed individuals, further enhancing labor market flexibility, and boosting company profit margins. The consequence of a flexible labor market is increased poverty, as a large pool of flexible labor enables staffing companies to exploit employees, leading to more individuals earning poverty-level wages and a disproportionate transfer of resources to businesses. The decline in regular employment by host companies and the growth in employment through these staffing firms are strategic policies employed by businesses to avoid the costs associated with adding people to their payrolls, such as health insurance, bonuses, and other human resource expenses, particularly during economic recessions (Van Arsdale 2013 ). The poverty implications of this changing employment era have not been thoroughly studied yet.

The growth of poor-quality jobs is also linked to various recent developments, including technological advancements, changes in work arrangements, the expansion of service sectors, and the decline of industrial employment. It is further associated with shifts in corporate governance and employer strategies (Kalleberg 2011 ). Moreover, it is intertwined with emerging trends such as privatization, marketization, and individualization (Keune 2013 ), along with the declining influence of trade unions (Farber and Levy 2000 ). Consequently, poverty reduction efforts have been stalled.

3 Data, measuring key variables, and empirical strategy

I utilized publicly available data in this study, primarily drawing from the American Community Survey (ACS). The ACS provides individual-level data with household and geographic codes, enabling precise estimates at the local administrative unit level and tracking long-term trends. This dataset is collected by the Census Bureau and is representative of the entire USA population, based on a 1% sample. To align with state-level macro-variables, the data was initially aggregated to the household level and subsequently to the state level. The household-level employment status data is derived from the ACS, while individual unemployment data at the state level is sourced from the Department of Labor and Training. Furthermore, the ACS data was instrumental in measuring households with good-quality jobs and poor-quality jobs. The ACS survey questionnaire includes multiple questions that inquire about the employment status of household members, facilitating the classification of households into full-time and part-time employment categories.

Other macroeconomic variables, such as GDP per capita and the estimated Theil inequality index, were acquired from the US Bureau of Economic Analysis (BEA). Data related to government expenditure and revenue were obtained from the National Association of State Budget Officers (NASBO) community. Human capital and educational attainment data were sourced from the US Census Bureau. Regarding poverty measures, I employed the official poverty measurement utilized by the Federal Government, which is based on the US Census Bureau's poverty threshold of $20,212 for a family consisting of two adults and one child in 2018. This poverty threshold has been adjusted for the number of children, meaning that households with more than one child have a higher poverty threshold. Table 1 in the paper presents the descriptive statistics of the data after aggregating it to the state level.

I aggregated individual and household-level data to the state level for several compelling reasons. Firstly, income is a flow variable that is commonly used to measure poverty, while employment / unemployment status represents the labor market conditions at a specific point in time, rendering it a stock variable. Consequently, if an individual is interviewed during a period of unemployment but remains employed for the rest of the year, their annual income may surpass the poverty line (Saunders 2002 ). Moreover, income is typically reported annually, whereas employment status is not regularly reported on an annual basis. Therefore, analyzing the impact of employment status on poverty without aggregation can lead to misleading results.

Secondly, employment status is usually analyzed at the individual level, whereas poverty measures consider the combined income of the entire family to determine the poverty level. As discussed earlier in this paper, an individual may have low or zero income but not meet the criteria for poverty according to Federal guidelines, as other family members may have higher earnings. Income within a household is shared, and it is the collective income that determines whether the individual or family qualifies for welfare benefits. Hence, measures of poverty and employment should ideally be aggregated at the family level over the entire year.

Thirdly, microdata sources like ACS exhibit variation over time but do not follow a panel format, which means they do not observe the same individual or household over an extended period. Without panel data, it is not possible to apply fixed-effect (FE) models, which allow for controlling observable and unobservable characteristics that remain constant over time (more details of empirical strategies are in Empirical strategies ). The two-way FE model is more suitable for statistical inference than the pooled cross-section model (Lechner et al. 2016 ). Lastly, public welfare expenditure is a crucial determinant of poverty. However, individual or household-level records in the ACS do not provide information about the benefits received from public welfare programs, such as cash assistance. Aggregating the data at the state level is necessary because states are the primary authorities responsible for disbursing these types of payments. Additionally, states play a crucial role in decision-making regarding most poverty reduction programs.

3.2 Measuring the index of uneven job distribution

The index of uneven job distribution indicates an increasing concentration of jobs at the individual level and a decreasing concentration of jobs at the household level. This index essentially reflects unequal job distribution between individuals and households, as opposed to the usual measures of job polarization in occupation, pay, and sector, as explained elsewhere (Goos and Manning 2003 ; Salvatori 2018 ; Jaimovich and Siu 2020 ). Footnote 4 In this paper, the index of uneven job distribution is measured using the method of Gregg & Wadsworth ( 2008 ), which discerns the discrepancy between individual-level joblessness and household-level joblessness. When a state's population consists of individuals 'i', the total number of households ‘H’ consists of individual household ‘h’, and person 'k' resides within household 'h', the total population 'P' can be expressed as:

When an individual person living in a household has no job, a binary outcome value of 1 ( \({n}_{ih}=1\) ) is assigned, and when an individual person living in a household has a job, a binary outcome value of 0 ( \({n}_{ih}=0\) ) is set. Therefore, the individual-level joblessness rate in the population will be:

Let's now consider household-level joblessness, where households can host both jobless individuals and individuals with a job. Such joblessness can now be grouped by households. When a household does not have any people with a job, a binary outcome value of 1 ( \({m}_{h}=1\) ) is assigned, and when a household has at least one person with a job, a binary value of 0 ( \({m}_{h}=0\) ) is set. Therefore, the household-level joblessness will be:

Here, 'm' essentially reflects the number of households with a value of 1 for \({m}_{h}\) weighted by the total number of households ' H '. A simple example will be highly useful here to understand the distinction between joblessness at the individual level and at the household level, and why this distinction should matter for poverty prevalence. Consider a small economy with only two households, each consisting of two people, for a total of four people. The economy offers only two jobs, leaving only two possible scenarios. In the first scenario, one person from each household is unemployed, denoted by \(N=[\left\{\mathrm{1,0}\right\}, \left\{\mathrm{1,0}\right\}]\) , with an associated joblessness rate of 50%. In the second scenario, both working individuals belong to the same household, leaving the other household with no employed individuals, denoted by \(N=[\left\{\mathrm{1,1}\right\}, \left\{\mathrm{0,0}\right\}]\) , with an associated joblessness rate of 50%. Despite both scenarios having the same unemployment rate, they have different rates of joblessness at the household level. In the first scenario, no households are jobless, whereas in the second scenario, 50% of households are jobless.

This illustrates how aggregated individual-level job statistics fail to reflect the actual distribution of jobs across households, much like how typical income distribution measures fail to capture the necessary details of income distribution. To address this, we can measure the degree of uneven job distribution across households by calculating the difference between the actual rate of jobless households and the rate predicted under a counterfactual scenario in which jobs are randomly distributed among the working-age population. This difference can offer insight into the extent to which households experience joblessness beyond what would be expected under random job distribution.

Assuming that all other factors remain constant, it is more likely for fewer individuals to be present in a household where all occupants are employed if the family structure is nuclear, as opposed to extended. This is because households are categorized based solely on their size, and as family sizes decrease, the number of households without any employed individuals is expected to increase. Consequently, the size of workless households can be attributed to a combination of the individual-level unemployment rate and the number of individuals in the household.

where \({uneven job distribution}_{it}\) represents the extent of uneven job distribution in state i for year t . Meanwhile, \({JL}_{it}\) denotes the fraction of households that do not have any employed individuals in state i for year t , regardless their size of households. \({JL}_{it}^{e}\) represents the expected rate of jobless households in state i for year t if jobs were randomly distributed. We can present a counterfactual rate of households without any employed individuals, denoted as \({\hat{J} }_{k}\) for households with k as follows:

Assuming that joblessness is randomly distributed, every individual adult in a household \(k\) has an equal chance of being unemployed. Consequently, the probability of observing an adult in a jobless household (counterfactual rate) should be the same as the observed unemployment rate at the individual level. Similarly, the likelihood of a household with two adults being jobless would be twice the individual unemployment rate (Gregg and Wadsworth 2008 ). To benchmark the probabilities of equal job distribution, we can consider it as having an intuitive appeal similar to the criterion of income equality used in the Lorenz curve. Finally, we can aggregate the counterfactual households with no employed individuals and weigh them by the population, ( \({s}_{k}\) ), of households of size k :

So, the uneven job distribution is the distinction between observed and counterfactual jobless households.

The measure of uneven job distribution discussed here does not have any normative implications. A higher index of uneven job distribution indicates a larger proportion of households are jobless and greater job distribution inequality. Since job numbers are generally limited, positive values in the index can be considered as reflecting ‘Matthew effects,’ wherein additional jobs are concentrated among households who already have employment, rather than being spread out more evenly across population, particularly households with all members are jobless. Footnote 5 Conversely, negative value of uneven job distribution index suggests that there are fewer jobless households than would be expected if jobs were randomly distributed, which can be seen as a form of solidarity. If the expected and observed rates of jobless households are identical, the index of uneven job distribution should have a value of zero.

We never know the counterfactual jobless household rate. While the individual unemployment rate can be a reasonable proxy for the counterfactual jobless households, this will still be an extremely conservative measure of uneven job distribution. This is because there are more jobs available than the number of households in the country. Consider a state with a population of 1 million; there will be a million households if each household only has one person. If the state's unemployment rate is 5%, then both the individual unemployment rate and household joblessness rate will also be 5%. Since job numbers are generally fixed at a point in time, a negative or positive uneven job distribution index will only emerge when two or more individuals form a family, and some of them are employed while others are not. If one household has multiple jobholders, it comes at the expense of another family that has no employed individuals. The value of the index of uneven job distribution increases as more households have no employed individuals while others have multiple jobholders.

3.3 Description of uneven job distribution measures and poverty with data from United States

Let’s consider the data provided by the United States Census Bureau for 2018. According to their report, the United States had a population of 326.68 million people, with 60% of them falling within the working-age bracket, totaling approximately 196.01 million individuals. With an average family size of 2.53, we can estimate that there were around 129.13 million households in the country. In comparison to the number of households, there were approximately 197.31 million available jobs in the country for the year 2018. If these jobs were randomly distributed, every household should ideally have at least one employed member (as shown in Table 2 ). Therefore, the percentage of workless households can serve as a measure of uneven job distribution, which, while still conservative, is less so than the measure expressed in Eq. ( 3 ). In this paper, I will examine the association between both uneven job distribution measures and the poverty rate. The less conservative uneven job distribution measure will be referred to as Uneven Job Distribution Index 1 (UJD-1), while the more conservative measure mentioned in Eq. ( 3 ) will be known as Uneven Job Distribution Index 2 (UJD-2).

With an average family size of 2.53, we can estimate that there were around 129.13 million households in the country. In comparison to the number of households, there were approximately 197.31 million available jobs in the country for the year 2018. If these jobs were randomly distributed, every household should ideally have at least one employed member (as shown in Table 2 ). Therefore, the percentage of workless households can serve as a measure of uneven job distribution, which, while still conservative, is less so than the measure expressed in Eq. ( 3 ).

In this paper, I will examine the association between both measures of uneven job distribution and the poverty rate. The less conservative measure of uneven job distribution will be referred to as Uneven Job Distribution Index 1 (UJD-1), while the more conservative measure mentioned in Eq. ( 3 ) will be known as Uneven Job Distribution Index 2 (UJD-2). Figures  1 and 2 provide valuable insights into job distribution measures at the national level as well as within and across states. Figure  1 demonstrates a noticeable divergence between the unemployment rate at the individual level and the prevalence of jobless households from 2008 to 2018. The state level heterogeneities in the same divergence trend are presented in the Appendix A.

figure 1

Workless households and individual unemployment rate in the United States. This was calculated using American Community Survey (ACS) and Bureau of Labor Statistics (BLS) data

figure 2

Employment and poverty across states for the year 2018. The data sources are ACS and BLS

The main concern highlighted by these illustrations is the increasing rate of workless households (UJD-2) in the United States despite a stable decrease in the individual-level unemployment rate. Consequently, there has been a growing disparity between the rates of unemployment for individuals and jobless households (UJD-2) in the country. This divergence indicates that job growth in the United States over the past few decades has not benefitted workless households. States with higher employment growth have primarily witnessed benefits for individuals or households with already employed members, resulting in a lack of improvement in job distributions at the household level and, in fact, worsening the situation. Similar trends of employment growth predominantly benefiting households with employed individuals have been observed not only in the United States but also in the UK, Netherlands, and other developed economies (Gregg and Wadsworth 1994 , 2003 ; Beer 2001 ; Cantillon et al. 2003 ; Corluy and Vandenbroucke 2017 ).

While the growing gap in employment distribution between households and individuals may pose various issues concerning labor market performance in the country, the rising rate of workless households (even if it is a stagnant one) carries significant implications for poverty reduction strategies. As mentioned earlier, in an ideal scenario with a normative world of random employment distribution, the unemployment rate at the individual level and the rate of joblessness in households should be identical, and more importantly, with zero joblessness at the household level in the United States. Therefore, the values presented in Fig.  1 represent the extent of uneven job distribution in the United States. Despite positive job market outcomes at the individual level, the situation at the household level has been deteriorating. In this paper, I propose the hypothesis that this higher index of uneven job distribution may be responsible for the increased poverty levels in the country.

Figure  2 showcases the percentage of workless families and individual unemployment rates in relation to the poverty rate across states for the year of 2018. It also highlights the disparity between individual-level unemployment and workless households across different states. Notably, the individual-level unemployment rate is significantly lower than the rate of workless households. Several states, including West Virginia, Mississippi, Alabama, Kentucky, New Mexico, Louisiana, and others, exhibit considerably higher index of uneven job distribution, indicating a pronounced discrepancy between individual-level unemployment and workless households. Intriguingly, these states with higher rates of jobless families are also characterized by higher poverty rates, as depicted in Fig.  2 . In contrast, the states of Utah, Minnesota, New Jersey, Nebraska, Iowa, North Dakota, Wisconsin, Maryland, Colorado, New Hampshire, Connecticut, and others show the lowest index of uneven job distribution. These states demonstrate a more balanced distribution of job opportunities, with a smaller gap between individual-level unemployment and workless households, and lower poverty rate.

3.4 Measure of job quality

Measuring job quality, particularly when identifying households with poor-quality jobs, presents complex challenges. While it's true that low-paying jobs are often categorized as poor-quality jobs, as conventionally defined in economics (Acemoglu 2001 ), this criterion alone doesn't provide the most comprehensive measure of job quality. Job quality is multifaceted (Findlay et al. 2013 ), and the International Labor Organization (2019) offers a more comprehensive definition of a high-quality job, encompassing factors such as higher pay, job security, safety, work-life balance, fairness in employment, social protections, and various other socio-economic considerations. Additionally, scholars like Stecy-Hildebrandt et al. ( 2019 ) and Adamson and Roper ( 2019 ) emphasize additional indicators, including fringe benefits, job security, and favorable income trajectories as characteristics of high-quality jobs. It's important to recognize that job quality is also subjective and influenced by individual perceptions (Clarke 2015 ). In some cases, even a low-paying job can be viewed as a high-quality job if it includes in-work benefits, job security, full-time employment, and other favorable aspects, particularly if it is perceived as such. Many employees prioritize factors like job security, as it can have a more substantial positive impact on their overall well-being than wages alone.

This paper defines a 'good job' as one held by a household with at least one full-time worker who commits to 35 h or more of work per week, and I refer to such households as having 'good jobs.' Conversely, it defines a 'bad job' when a household has only part-time worker(s) putting in less than 35 h of work per week and has no full-time worker, and I refer to such households as having 'bad jobs.' This classification of full-time and part-time employment status at the household level can serve as proxies for assessing job quality at the household level. Moreover, it is expected that part-time and full-time work status is associated with other indicators of job quality such as wages, and it provides suitable quantitative measurement for regression analysis in this paper. While it can be argued that some women and students voluntarily choose part-time positions to allocate more time to family responsibilities (Walsh 1999 ; Hill et al. 2004 ; Pech et al. 2021 ) and education, it's crucial to recognize here that the measurement used in this paper operates at the household level, where it is reasonable to expect that at least one member of the household should be willing to take on a full-time role, unless the household is a single-parent household or its member(s) require special care (e.g., disability).

Furthermore, there is strong evidence to suggest that a significant proportion of part-time jobs are involuntary, with individuals preferring full-time contracts if they were given the opportunity (Tilly 1991 , 2010 ; Kalleberg 2009 ; Kauhanen and Nätti 2015 ). Involuntary part-time jobs represent a form of underemployment, as these part-time workers are actively seeking full-time positions but have not been able to secure them. Notably, part-time workers constitute more than one-fifth of the total workforce in the United States, and their presence in the labor market has been steadily increasing since 1970 (Tilly 1991 ; Fullerton et al. 2020 ). This growth in involuntary part-time employment is primarily driven by employer demands for flexible scheduling, cost minimization, and their predictability of available labor forces, rather than worker’s preferences (Tilly 1991 , 2010 ; Kalleberg 2009 ).

Two significant theories, neoclassical and institutionalist, can potentially elucidate the issue of job composition in terms of part-time and full-time employment, particularly regarding involuntary part-time positions. The neoclassical approach can be extended in two ways—building a microeconomic foundation to analyze market constraints and addressing the lack of aggregate demand in the macroeconomic environment. Bulow and Summers ( 1986 ), who developed the "efficiency wage" model based on a microeconomic foundation, suggest that firms pay full-time workers higher wages than their marginal product, while part-time workers receive lower wages at the market rate. The higher pay for full-time workers is efficient because it disincentivizes them from shirking, leading to lower employee turnover and increased productivity. In contrast, part-time employees, who face low wages and high turnover, become less productive even though they may be identical to full-time employees in all other aspects. In such situations, involuntary part-timers may consider the loss of their job and associated income to be significantly more detrimental than accepting lower wages. Full-timers are also treated as insiders, while part-timers are seen as outsiders (Lindbeck and Snower 1986 ). Insiders have the power to threaten to leave, leading to higher costs for firms related to hiring and training new employees, but part-timers lack such bargaining power.

In macroeconomic contexts, involuntary part-time employment or underemployment emerges as a natural consequence. During economic downturns, part-time employment may be the preferred choice for employers, enabling them to retain skilled employees at reduced wages while reducing the workforce. For instance, the recent COVID-19 pandemic vividly exemplified that, as part-time workers were more susceptible to job loss, full-time workers were better equipped to retain their employment. In such economic downturns, workers often find that accepting lower wages is a more viable option than losing their jobs, particularly in the absence of unemployment insurance.

The institutionalist approach, while acknowledging the logic of neoclassical perspectives, underscores that part-time employment is distinguished not only by hours worked but also by various other attributes. Institutional theories contend that a fundamental shift in the organizational environment, including elements such as labor unions and policies, is necessary to transition between different job compositions rather than merely adjusting to economic variables within the same framework (Woodbury 1987 ; Tilly 2010 ). In contrast to the neoclassical approach, which assumes that market actors act rationally and that labor market outcomes are determined by the standard demand–supply model, the institutional approach recognizes that a range of labor market outcomes is possible and that these outcomes are shaped by socially imposed norms and traditions. Given the tradition of strong capitalism and a history of limited policy regulations in place in the United States, employers enjoy greater flexibility in crafting job configurations that maximize their profit margins compared to other welfare-oriented nations.

These part-time positions consistently come with a wage penalty (Hirsch 2005 ; Baffoe-Bonnie and Gyapong 2018 ; Gallego-Granados 2019 ) and provide limited opportunities for career advancement, resulting in high turnover rates (Tilly 1991 ; Sloane and Theodossiou 1996 ). Part-time work status also implies limited access to social protections due to shorter lengths of service (Stecy-Hildebrandt et al. 2019 ) and is generally associated with lower rates of unionization (Anderson et al. 2006 ). Research has also demonstrated that the prevalence of part-time jobs, primarily driven by neoliberal market restructuring, contributes to wage disparities across race (Wilson and Roscigno 2016 ) and gender (Fuller 2005 ). In addition to the reduced income potential associated with part-time positions, studies by McDonald et al. ( 2009 ) have raised several other concerns, including limitations on career progression, restricted access to high-status roles, increased workloads, challenging work environments, and related issues. For these various reasons, part-time positions are a fair representation of poor-quality jobs, while full-time positions signify good-quality jobs.

Hence, it is reasserted here that economic prosperity alone may not effectively alleviate poverty unless it results in a sufficient number of good-quality jobs with full-time contracts for households. An increase in the number of families with good-quality jobs is expected to play a pivotal role in poverty eradication in the United States. The combined remuneration package associated with full-time employment has the potential to lift a household above the poverty threshold. Conversely, households relying solely on part-time jobs are likely to contribute to an increase in poverty rates across the states. These household-level indicators of job quality are anticipated to have a direct and significant impact on the economic status of people living in poverty, surpassing the influence of broader macro-level determinants, such as overall economic growth and employment levels.

3.5 Empirical strategies

The identification strategy of this paper is to estimate Eqs.  4 and 5 using the two-way fixed effect (TWFE) method for a strong balanced panel data that spans from 2008 to 2018. With a large number of independent clusters of observations (i.e. states), the coefficient of our variable of interests can be consistently estimated using a TWFE regression specification, and by clustering standard errors the conclusion will be an asymptotically valid inference (Roth et al. 2023 ).

Where \({Y}_{it}\) represents the poverty rate, \({UJD}_{it}\) represents either the uneven job distribution index-1 or index-2, and \({JQ}_{it}\) represents either the measure of good-quality jobs or poor-quality jobs in state i for year t . \({X}_{it}\) is a vector of characteristics that vary over time at the state level, including GDP per capita, GDP growth rate, state population size, regional inequality, per capita tax revenue, intergovernmental transfers, population demographics (such as age groups, immigration/citizenship status, and human capital), and others. \({\varepsilon }_{it}\) is the random error term. By applying the two-way fixed effect method, I examine the within-state variation to estimate the impact of polarized job distribution and job quality on the poverty rate.

However, it is important to note that, given our observational data, this analysis does not intend to interpret the \(\beta\) coefficient as a strictly causal relationship between employment variables and the poverty rate outcome. The state fixed effect, \({\delta }_{i}\) , estimate accounts for both observable and unobservable time-invariant factors that may impact poverty rates, such as colonial history and geographic locations. The year fixed effect, \({\mu }_{t}\) , captures any unusual time trends, such as financial crises, that may also influence the poverty rate. However, the two-way fixed-effect model cannot address problems that may arise if employment distribution and quality and poverty are simultaneously related. For instance, it may not account for scenarios where higher poverty leads to either higher or lower uneven job distribution or job quality, thus failing to discern any such reverse effect. To tackle this issue, we would require an instrument that is strongly correlated with poverty but has no association with our employment variables, and then apply an instrumental variables regression approach. Unfortunately, our data lacks such an instrumental variable that fulfills both of these conditions for a good instrument. This concern regarding internal validity remains unaddressed in this paper.

Furthermore, while the two-way fixed-effect model accounts for observed and unobserved time-invariant factors, it does not consider omitted time-varying factors, such as union and firm density in the local area. These factors could not be controlled for due to the absence of long-panel data used in this paper. Similarly, measurement errors pose potential threats to estimating unbiased coefficients. For instance, using part-time and full-time working status at the household level as proxies for job quality may not accurately capture the true measures of job quality and may introduce bias into the estimates. If alternative measures of job quality in terms of pay and benefits were available, the results might differ from those presented in this paper, thus posing a threat to the external validity of these results in this paper.

4 Results: two-way fixed effect model

Figure  3 presents the scatterplot matrix depicting the relationships among the key variables of interest. Both the less conservative and more conservative measures of uneven job distribution are positively associated with the poverty rate. Conversely, there exists a strong negative relationship between poverty and good-quality jobs (households with full-time workers), while the relationship between poverty and poor-quality jobs (families with only part-time workers) is positive. All of these relationships confirm our earlier predictions in this paper that an unequal job distribution across households is a significant predictor of persistent poverty in the United States. Similarly, bad jobs also serve as a predictor of higher poverty, whereas good jobs act as a predictor of lower poverty in the country. While these scatterplots provide a visual representation of the basic strength, direction, and nature of the relationship between poverty and variables related to job distribution and quality, the following sections delve into the empirical findings obtained through the application of two-way fixed effect methods.

figure 3

Scatterplot of uneven job distribution, job quality, and poverty

4.1 Uneven job distributions and poverty

Table 3 presents the two-way fixed effect estimates of the effect of uneven job distribution on the poverty rate. The poverty rate is measured as the average poverty across all races, based on the Federal poverty guideline at 100%. Uneven job distribution consists of two measures: the more conservative index-2 from Eq. ( 3 ) and the less conservative index-1, which represents the share of jobless households as discussed in section III. The estimates in Table 3 are presented as percentage point estimates for both job distribution indices. The results strongly support the prediction made earlier in this paper that higher job market polarization in the form of job distribution leads to a higher poverty.

Notably, the uneven job distribution index-1 has a significantly larger impact on the poverty rate compared to uneven job distribution index-2. The interpretation of these coefficients is straightforward. A one percentage point increase in uneven job distribution index-1 will result in approximately a 0.50 percentage point increase in the poverty rate. On the other hand, a one percentage point increase in uneven job distribution index-2 will lead to an approximately 0.25 percentage point increase in the poverty rate. This disparity in magnitudes between index-1 and index-2 can be attributed to the subtraction of unequal job distribution from index-1 to obtain index-2. This difference reinforces the hypothesis that as the level of uneven job distribution increases, the poverty rate also increases. In other words, this provides strong evidence that equal distribution of jobs is a necessary condition to reduce poverty in the United States. Furthermore, these results indicate that the higher employment levels in the United States did not benefit all families, especially low-income families. The findings persist in terms of both effect size and significance level across all model specifications.

The most vulnerable group of people in every society comprises those living in families where no one is employed (Gallie et al. 2000 ; Cantillon et al. 2003 ). Our results in this paper align with the findings from earlier literature. Even after controlling for a comprehensive set of variables as presented in Table 3 , the effect size remains large and statistically significant which is an alarming issue for the country. Förster (2000) reported raw figures indicating that the average poverty rate among households with a working-age head but only one employed member is approximately 36% across 16 OECD countries. In contrast, the corresponding figures are only about 13% for families with one employed household member and merely 3% for households with two employed members. It is important to note that the coefficient presented in this paper cannot be directly compared to the raw numbers reported in Förster's (2000) work. Nonetheless, our coefficients suggest that a ten-percentage point increase in jobless families (uneven job distribution index-1) leads to a substantial five-percentage point increase in poverty.

While factors such as the share of children, elderly individuals, non-white population, and non-professional occupations also contribute to a higher overall poverty rate in the USA, their magnitudes are considerably smaller compared to the job distribution measures. Moreover, variables negatively associated with poverty rates, such as GDP per capita, human capital, and public expenditures, also exhibit relatively small effects. Previous studies that excluded these job distribution measures in their analyses of poverty had less predictive power as a result. Appendix B presents a comparison of aggregated household-level employment (such as job distribution index) versus aggregated individual-level unemployment rate. The uneven job distribution index at the household level exhibits more than three times the predictive power to explain poverty compared to the individual unemployment rate.

A higher percentage of the non-white population and individuals outside the working age (such as children and older people) is positively associated with the poverty rate, which aligns with earlier literature (Bradbury et al. 2001 ; Hoover et al. 2004 ). This result suggests that a portion of children and individuals outside the working age lack the support of working-age individuals within their families. It is worth noting that the fraction of immigrants and non-white individuals has been increasing over the past few decades. Data indicates that recent immigrants, on average, have lower levels of education and fewer skillsets compared to native individuals, resulting in a higher proportion of immigrants earning lower incomes and living in poverty (Hoynes et al. 2006 ; Siddique et al. 2022 ). Alternatively, the influx of immigrants may reduce job market opportunities for native individuals. Consequently, if this argument holds, the overall association between the share of immigrants and the poverty rate will be positive, although the evidence in the literature is mixed (Llull 2017 ). Moreover, the increasing share of non-white and immigrant populations leads to greater ethnic diversity within states, which in turn contributes less to public income and resources (Siddique 2021 , 2022 ). This indirect effect may further hinder poverty reduction efforts.

On average, historically non-white populations face relative disadvantages in this country. Therefore, an increasing fraction of the non-white population can be seen as a predictor of higher poverty, indicating the ongoing disadvantages faced by non-white individuals in society (Siddique 2022 ). However, the statistical association between immigration status (citizenship) and poverty is not significant, except in model 3.2. This is because although recent immigrants experience higher poverty rates compared to earlier immigrant cohorts, their share in the overall population of the United States is relatively small (the non-citizen population is 4.91% as shown in Table 1 ) to significantly impact the state-level poverty rate. The growth rate among non-white children surpasses that of white children, and non-white children are more likely to experience poverty due to their parents' lower economic resources (Mordechay and Orfield 2017 ; Siddique 2022 ). Therefore, a higher share of non-white children may contribute to an overall increase in poverty. However, the share of non-white children is not statistically significant, suggesting that the effects may have been absorbed by the share of the non-white population as a whole.

Spatial variations and economic development can potentially play a role in determining poverty and inequality (Glasmeier 2002 ; Khan and Siddique 2021 ). As the spatial differences between states are likely to be fixed during our study period and are accounted for by fixed effects estimates, I also control for within-state spatial economic inequality, measured by Theil regional inequality, along with GDP per capita. The results show that higher GDP per capita is negatively associated with poverty and is statistically significant, while the Theil local inequality index is not statistically significant. The insignificant coefficient for regional inequality is small because regional inequality within states has remained relatively stable throughout the study period (Khan and Siddique 2021 ). Although a higher GDP per capita is negatively associated with the poverty rate, the relationship between the two variables is weak in terms of effect size. This finding is consistent with earlier literature in the United States and across countries (Adams 2004 ; Hoynes et al. 2006 ).

The proportion of non-management/professional jobs also serves as a predictor of higher poverty, although its impact is less consistent. On average, 62% of employment falls within this occupational category. In addition, I have included controls for various measures of human capital or educational attainment and government expenditure. Human capital has always played a crucial role in explaining economic growth and poverty. It directly influences employment and growth patterns by providing the skills necessary for the growth process, thereby impacting poverty (Gutierrez 2007). Controlling for human capital takes into account the reverse impact of poverty on employment. Limited human capital may prevent many low-income families from accessing good job opportunities. By including measures of human capital or educational attainment, we can capture the impact of poverty on employment, if any. Most educational achievements are negatively associated with the poverty rate, with the exception of those who have completed less than the 9th grade and the share of high school graduates or higher. Education below the 9th grade is likely insufficient in terms of human capital to prevent poverty. Increasing the percentage of high school graduates among individuals aged 25 years and above (who are likely to be part of the workforce) helps in poverty prevention, whereas the share of high school graduates among the overall population does not have the same effect, as many individuals in this group are not part of the labor force. Those who are not in the labor force do not utilize their human capital to earn income. Overall, the relationship between educational attainment and poverty aligns with the existing literature, which suggests that higher levels of education, such as a bachelor's degree or associate degree, help people escape poverty (Assari 2018 ).

Government taxes and transfers play a vital role as income sources for the poor. While a higher share of public expenditure is negatively associated with the poverty rate, a higher percentage of public revenues does not show the same relationship (models 3.5–6). The link between public spending and poverty is well-established in the literature (Hidalgo-Hidalgo and Iturbe-Ormaetxe 2018 ); however, there are debates regarding which types of public expenditures effectively help the poor escape poverty (Fan et al. 2000 ; Jung et al. 2009 ). Furthermore, I have included controls for two different measures related to welfare expenditures: the Temporary Assistance to Needy Families (TANF) program and other cash assistance. The TANF program does not have a significant impact on poverty reduction, whereas the other cash assistance program shows a significant negative effect on the poverty rate. Government transfers can have both direct and indirect consequences on family earnings. The immediate impact is that government transfers provide households with cash and other benefits, which directly affect income and poverty. However, there is an indirect effect as well, where households may adjust their behavior due to the availability of government transfers, potentially reducing their incentive to work and resulting in lower incomes (Schoeni and Blank 2000 ). Thus, the indirect impact may offset the direct effect. Moreover, estimating the immediate impact of government benefits on poverty can be challenging due to the various types of benefits and the definition of the poverty level used in this study. The TANF program, for example, provides cash benefits to low-income households with children. Assuming no behavioral changes due to TANF, it should directly increase the incomes of poor families. However, its impact on poverty reduction may be limited since TANF transfers phase out at income levels around the poverty line (Hoynes et al. 2006 ). Therefore, these estimates do not show any effects of TANF on the poverty rate.

4.2 Job quality and poverty

Table 4 presents the impact of job quality measures on the overall poverty rate at the state level. Good-quality jobs have a significant effect on poverty reduction, while poor-quality jobs have a significant opposite effect on poverty (increasing effect) across all model specifications in Table 4 . As previously defined, "good-quality jobs" refer to households with at least one full-time employee, while "poor-quality jobs" encompass households with only part-time employment. The model specifications in Table 4 are identical to those in Table 3 , except that here, I replaced the uneven job distribution index-1 and index-2 with measures of good-quality jobs and poor-quality jobs.

The interpretation of this result is simple: a one percentage point increase in the share of households with good-quality jobs reduces the poverty rate by 0.42 to 0.44 percentage points. On the other hand, a one percentage point increase in the share of households with poor-quality jobs increases the poverty rate by 0.35 to 0.38 percentage points. This evidence demonstrates that job quality matters for poverty reduction. The existence of a substantial share of poor-quality jobs in the economy, concentrated in households that have no other good jobs, is responsible for the higher persistent poverty rate in the country. In addition to uneven job distribution, as we have seen in the earlier section, job quality is another factor that can explain the persistent level of poverty in the United States. The significance level and size of coefficients are consistent, and the estimated model has high goodness of fit measures (R-square = 0.81 and 0.76 in models 4.5–6). After controlling for both state and year fixed effects and gradually including control variables, no inconsistencies in terms of the size of the coefficients and their significance levels have been noticed. These findings provide robust support for partial causal evidence that job quality plays a critical role in determining the poverty rate in the country: bad jobs increase poverty, and good jobs reduce poverty.

When people are working but still living below the poverty line, it is referred to as "poverty in work," as we have observed in the case of households with poor-quality jobs, which aligns with earlier evidence (Burkhauser and Finegan 1989 ). Due to the lack of extensive unemployment insurance, minimum wage protections, and the diminishing presence of trade unions in the USA, most poor individuals cannot afford to remain unemployed and wait for offers of good jobs. Consequently, they are compelled to accept these bad jobs regardless of their quality and level of compensation (Berry and Sabot 1978 ; Visaria 1981 ; Acemoglu 2001 ; Saunders 2002 ). The existence of bad jobs stems from employers being able to find an adequate labor supply to fill these positions. Therefore, the presence of bad jobs and higher poverty rates are closely intertwined. If these bad jobs persist in the labor market, it is unlikely that poverty will disappear from the economy.

Previous literature attempted to explain why the poverty rate remained persistent in the United States despite positive economic indicators (Hoover et al. 2004 ; Hoynes et al. 2006 ). However, they failed to adequately consider the role of employment quality and employment distribution, which are the primary sources of income for the poor. If jobs are not evenly distributed and predominantly consist of poor-quality positions, a rapidly growing economy is unlikely to help poor individuals escape poverty. A $1,000 increase in GDP per capita only reduces the poverty rate by 0.001 percentage points, which is minimal compared to the impact of uneven job distributions and job quality on poverty, as demonstrated in Tables 3 and 4 . These results suggest that the "trickle-down" economy does not effectively benefit the poor if employment, their main source of earnings, does not prioritize their well-being. In the United States, the role of uneven job distributions and job quality in determining poverty is more significant compared to other developed economies, as alternative means of support for the poor, such as transfers and redistribution, are relatively limited in this country (Gilens 2009 ; Garfinkel et al. 2010 ). Therefore, without sufficient redistribution programs, higher levels of uneven job distributions and the prevalence of low-quality jobs may explain why the income poverty rate remains high and persistent in the country, even during periods of robust economic growth and low unemployment.

5 Conclusions

The poverty rate in the United States has persistently remained high, posing a challenge to understand the underlying reasons. This paper aims to explore whether higher levels of uneven job distribution and the prevalence of poor-quality jobs contribute significantly to poverty in the country. The findings reveal that both uneven job distribution and poor-quality jobs play an important role in explaining poverty in the United States.

An increase of one percentage point in the uneven job distribution index—1, which represents workless households, leads to an almost 0.48 percentage point increase in the poverty rate. This suggests that for a state with an average poverty rate of 14 percent, a 1 percent increase in workless households will result in a corresponding rise of approximately 3.43 percent in the poverty rate. Similarly, a one percentage point increase in the share of households with poor-quality jobs results in a 0.35 percentage point increase in the poverty rate. This implies that in a state where the average poverty rate stands at 14 percent, a mere one percentage point rise in households with only part-time workers can potentially contribute to an increase in poverty by approximately 2.5 percent. In contrast, a mere one percentage point increment in the proportion of households with good-quality jobs results in a poverty reduction of 0.42 percentage points. Consequently, in a state with an average poverty rate of 14 percent, this translates to a significant decline in poverty by approximately 3 percent.

In comparison to other commonly studied variables in the literature, such as individual unemployment rate, GDP per capita, public expenditure, and human capital, which are expected to impact the poverty rate, the role of job distribution and job quality is significantly prominent and noteworthy. The evidence presented in this paper can serve as a valuable resource for public policy debates aimed at reducing poverty in developed economies. It sheds light on the reasons behind the stagnation in poverty reduction efforts in the United States. Both uneven job distribution and poor-quality jobs are structural problems that hinder progress in achieving poverty reduction targets. This is not to say that safety-net programs and other measures do not alleviate poverty—they likely lift millions of households above the poverty line each year. However, I find that the most effective anti-poverty solutions lie in the availability of good-quality jobs and their equitable distribution among households.

In the absence of policy guidance, the issue of uneven job distribution is likely to worsen in the future, potentially hindering the achievement of poverty reduction goals. Traditionally, when the first person from a family enters the labor market, it is more likely to be a male, unless the family is headed by a single mother. Subsequently, the second person from a family to enter the labor market is more likely to be a woman. When a woman becomes the second earner due to financial hardship, the family's economic situation improves, increasing their chances of surpassing the poverty threshold (Blackburn and Bloom 1994 ). However, in recent decades, a significant portion of the female labor force, particularly those married to well-paid men, has joined the workforce (Stier and Lewin 2002 ; Averett et al. 2021 ). There is a positive correlation between husbands' and wives' incomes (Averett et al. 2021 ). As female labor market opportunities expand, high-earning women tend to marry high-earning men, resulting in a rise in households with two high-earning individuals. Such structural forces play a role in determining employment opportunities, and as a result, the likelihood of both spouses in some families being unemployed may increase (De Graaf and Ultee 2000 ; Stier and Lewin 2002 ).

Therefore, it is crucial to establish public policy guidelines for the recruitment process that prioritize the common benefits over private benefits in order to reduce poverty in our society. One effective policy approach could be implementing a preference system similar to the veteran preference policy, where job applicants from workless families are entitled to preferences over applicants from households with already employed individuals in recruitment from competitive lists. Similar strategies should also be followed during firm’s downsizing times where employee retention offer should prioritize individual whose household does not have a second earning individual. This preference system can be implemented in the job market without sacrificing efficiency since candidates must still meet the minimum qualifications. It can be further strengthened by restricting job offers to employees' spouses, a practice that some institutions such as universities have recently adopted, although it overlooks the overall societal benefits. While achieving comprehensive job distribution efforts may pose challenges in the short term, even limited success would yield significant societal equity gains in the long-term.

Similarly, in the absence of a minimum wage, unemployment insurance, and trade unions, the proportion of poor-quality jobs will continue to rise and coexist with good-quality jobs. As a result, a high poverty rate will also persist. There is a significant disconnect between the booming labor market and the well-being of the people, particularly those at the bottom. The labor market is trapped in a cycle of bad jobs. The continuous rise of employment in gig economies may make the employment rate appear impressive, but without proper regulation and policies, the economy will keep producing more poor-quality jobs. This growth in poor-quality jobs is a byproduct of the massive scale development of the service sector, such as healthcare, leisure, hospitality, and restaurants, which predominantly hire people on a part-time basis and pay low wages. This trend also coincides with the declining manufacturing sector.

To reduce poverty, it is not only important to stop creating new poor-quality jobs, but also to replace the current poor-quality jobs with good-quality ones. Both direct and indirect policy guidance is necessary. The direct approach may include policy guidance by setting minimum work conditions and wages for all jobs in the market. Setting higher standards and a higher minimum wage would not only directly regulate job quality but also reduce incentives for firms to create more poor-quality jobs. Poor-quality jobs would be less beneficial for them compared to creating more good-quality jobs.

The indirect method should consist of increasing the coverage of unemployment insurance, investing in education to ensure equitable access to higher education for all, and allowing trade unions to function within each institution. Unemployment insurance should enable people to wait for better job offers instead of immediately accepting poor-quality jobs. It would also reduce the labor supply in the market, which would further push firms to raise pay and improve job quality. Similarly, increasing access to higher education is another way to create demand for good-quality jobs and reduce the supply of recipients of poor-quality jobs. This would leave firms with no choice but to produce more good-quality jobs. Highly skilled workers typically demand higher job quality than low-skilled workers (Cortés and Tessada 2011 ). Historically, unions have played a significant role in protecting workers' interests, and strengthening workers' unions can extend institutional regulations to represent worker interests and generate collective pressure to improve job quality (Simms 2017 ).

There are more full-time, good-quality jobs in the United States than the total number of households, as demonstrated in this paper. Therefore, their equitable distribution among families can play a significant role in eradicating poverty. While achieving a completely even distribution of jobs across households may not be immediately feasible due to structural constraints, combined and simultaneous efforts to allocate jobs from individuals to households and implement policies to enhance job quality would help alleviate poverty. Future research should explore methods to improve the distribution of jobs among households and transition from low-quality to high-quality jobs without sacrificing efficiency, as this paper did not adequately address these aspects, which fall beyond its scope.

Data are from the US Census Bureau, American Community Survey (ACS), World Development Indicators, and the Department of Labor.

Availability of data and materials

The associated data are publicly available, and the Stata code can be requested at [email protected] for reproducing and replicating the results.

Standard literature on poverty identified a wide range of factors that are responsible for poverty such as lack of education (Hofmarcher 2021 ), industrialization (Kimura and Chang 2017 ), technology adoption (Comin et al. 2010 ), redistribution (Jouini et al. 2018 ), and others. Read Ravallion ( 2012 ), McMillan ( 2016 ), and Rosenzweig ( 2012 ) for a more standard explanation of poverty.

In economics, this is called the productivity-pay gap when economic expansion does not broaden social uplift.

According to the Bureau of Labor Statistics, in 2016, there were about 7.6 million “working poor” who spent at least half the year either working or looking for employment.

For example, job polarization in occupation means higher growth of job in the highest-wage and lowest-wage occupations while wiping out the mid-waged occupational jobs (Goos and Manning 2003 ).

The Matthew effect is related to a statement from the Gospel of St Matthew—“For to all those who have, more will be given.” In recent times, this concept is used to present form of self-reinforcing inequality in income, wealth, political power, prestige, and others (Rigney 2010 ).

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Acknowledgements

I would like to express my gratitude to Drs. M. Kugler, K. Reinert, N. Koizumi, and S. Slavov for their invaluable guidance. Additionally, I extend my thanks to the two anonymous reviewers for their insightful and thorough referee reports. Special thanks to Prof. Christian Pfeifer, the Associate Editor of JLMR, for overseeing and managing the peer-review process for this manuscript and feedbacks. Your collective contributions have significantly enriched the quality of this work.

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1.1 Appendix A

See Fig.  4 .

figure 4

Workless households and individual unemployment rate by states. This was calculated using ACS and BLS data

1.2 Appendix B

Household-level versus aggregate-level measures.

This paper highlights an important issue regarding the significance of household-level employment measures compared to aggregate-level employment measures. To support this argument, a comparison is made between the predictive abilities of the job distribution index, job quality measure, and individual-level unemployment rate. Since the predictive power of two-way fixed effect estimates is largely driven by state and year fixed effects, I employ an ordinary least square model and compare their predictive power to offer suggestive evidence. In determining the statistical model's explanatory power, R-square is widely used and has been employed here (Hagerty and Srinivasan 1991 ; Foster et al. 1997 ; Choodari-Oskooei et al. 2012 ). Table 5 presents various estimates using the uneven job distribution index, job quality measures, and individual unemployment rate separately, as well as their estimates jointly. Remarkably, the uneven job distribution index, measured at the household level, exhibits more than three times the predictive power for poverty compared to the individual unemployment rate (R-square of 0.66 versus 0.19 in columns 5.1 to 5.2). Interestingly, including the individual unemployment rate alongside the uneven job distribution does not improve the R-square value. Additionally, it is important to note that good-quality jobs demonstrate higher explanatory power (R-square 0.60) as well, while poor-quality jobs have limited explanatory power. Therefore, the key takeaway from this analysis is that household-level employment distribution holds significantly greater explanatory power in understanding poverty than the commonly used economic indicator of the unemployment rate.

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Siddique, A.B. Job market polarization and American poverty. J Labour Market Res 57 , 30 (2023). https://doi.org/10.1186/s12651-023-00356-5

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unemployment and poverty research paper

Entrepreneurship Development as a Tool for Employment Creation, Income Generation, and Poverty Reduction for the Youth and Women

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  • Abdijabbar Ismail Nor 1  

Unemployment and poverty are global challenges, especially in emerging countries. In Somalia, both poverty and unemployment are major problems that are widespread among the youth who constitute more than 70% of the population. Despite the efforts to eradicate, both unemployment and poverty still remain major challenges in the country. Empirical studies have shown that entrepreneurship development plays an important role in socio-economic development. Therefore, this study aims to determine the contribution of entrepreneurship training and education, as well as SMEs’ development to job creation, income generation, and poverty reduction for the youth and women. The study applied a cross-sectional research design using a questionnaire to collect the data from a sample of 120 respondents from fresh graduates and other entrepreneurs who benefited from entrepreneurship training and education of the Next Economy Program provided by SIMAD Innovation Lab and ILO-SIYB Training packages as well as the entrepreneurship training programs provided by Irise-hub. The study applied partial least square structural equation modeling (PLS-SEM) to test the hypotheses through Smart-PLS (v.3.3.9). The study revealed that entrepreneurship training and education and SMEs’ development have a significant positive relationship with job creation, income generation, and poverty reduction. Therefore, the study findings have significant implications for entrepreneurs, entrepreneurial institutions, practitioners, and policymakers in entrepreneurship development. This study highlights the role of entrepreneurship development as a strategic tool for socio-economic development.

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Nor, A.I. Entrepreneurship Development as a Tool for Employment Creation, Income Generation, and Poverty Reduction for the Youth and Women. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01747-w

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Urban Poverty and Neighborhood Effects on Crime: Incorporating Spatial and Network Perspectives

Corina graif.

Department of Sociology and Criminology, The Pennsylvania State University

Andrew S. Gladfelter

Stephen a. matthews.

Department of Sociology and Department of Anthropology, The Pennsylvania State University

Research on neighborhoods and crime is on a remarkable growth trajectory. In this article, we survey important recent developments in the scholarship on neighborhood effects and the spatial stratification of poverty and urban crime. We advance the case that, in understanding the impact of neighborhoods and poverty on crime, sociological and criminological research would benefit from expanding the analytical focus from residential neighborhoods to the network of neighborhoods individuals are exposed to during their daily routine activities. This perspective is supported by reemerging scholarship on activity spaces and macro-level research on inter-neighborhood connections. We highlight work indicating that non-residential contexts add variation in criminogenic exposure, which in turn influence offending behavior and victimization risk. Also, we draw on recent insights from research on gang violence, social and institutional connections, and spatial mismatch, and call for advancements in the scholarship on urban poverty that investigates the salience of inter-neighborhood connections in evaluating the spatial stratification of criminogenic risk for individuals and communities.

Introduction

Since the beginning of the 20th century, urban scholars have extensively studied the role of urbanism and poverty in increasing crime. Rapid urban growth and population mobility together with stark socioeconomic differentiations across the urban space were, from the early years of the Chicago School, associated with the breakdown of social control and increased crime ( Zorbaugh 1929 ). Classic ecological studies showed that neighborhoods with high poverty near commercial and industrial districts exhibited the highest levels of delinquency and criminality ( Shaw and McKay 1942 ). These levels persisted over decades even when neighborhood population groups changed dramatically, indicating that structural conditions like neighborhood poverty contributed to delinquency and crime above and beyond individual disposition.

In the late-20 th century, industrial restructuring and suburban flight has exacerbated the spatial differentiation of resources and concentration of unemployment among the low-skilled. In The Truly Disadvantaged , Wilson (1987) noted that unemployment and poverty clustered and that together these ‘concentration effects’ weakened family bonds and institutional ties, undermining the formal and informal capacity for crime control. Scholars today refer to areas of high poverty as areas of concentrated disadvantage . The Great Recession of 2008 added greater strain to struggling low-income urban communities across the country and recent studies increasingly connect economic distress (e.g. foreclosures) to higher crime ( Ellen et al. 2013 ; see Arnio and Baumer 2012 for an exception).

Building on a century old tradition of research, research on neighborhoods and crime in the past decade has shown remarkable growth. More than 250 articles were published on this topic in 2012 alone ( Figure 1 ). The scholarship on place, space, and geography in relation to crime exhibited similar trajectories. Combined, this literature demonstrates that neighborhood poverty and related social and economic conditions are closely related to multiple indices of criminal exposure and offending. Specifically, studies find that neighborhood poverty and associated structural factors continue to predict multiple crime-related outcomes, including: individuals’ exposure to violence ( Bingenheimer et al. 2005 ; Sampson et al. 1997 ); risk of victimization ( Burchfield and Silver 2013 ); adolescent violent crime ( De Coster et al. 2006 ; Zimmerman and Messner 2010 ); aggression ( Molnar et al. 2008 ); arrests for violent behavior (Ludwig et al. 2000); domestic violence ( Benson et al. 2003 ); incarceration ( Rodriguez 2013 ); and recidivism ( Kubrin and Stewart 2006 ). With few exceptions, these patterns tend to hold in multiple cities and in nationally representative studies.

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Yearly Publication Count by Keyword Combination for the Past 10 Years Based on ISI Web of Science Search Results

Most studies implicitly assume that exposures to risk (e.g. criminal offending or victimization) are sufficiently represented by attributes of the neighborhood of residence. Chaix (2009) refers to this as the "residential trap." However, the residential focus ignores the fact that individuals' routines, in the aggregate, expose them to different neighborhoods on a daily basis. Few studies have examined the implications of routine exposures to multiple, non-residential neighborhood locations for crime. In this paper, we address this gap and advance a case for the idea that a more complete understanding of neighborhood effects on crime will greatly benefit from moving beyond the traditional focus on residential exposure to research based on an individual’s exposure to networks of neighborhoods . In building our argument, we draw on classic and modern theorizing on neighborhood effects and the spatial differentiation of poverty and crime, and integrate it with re-emerging literature on activity spaces, inter-neighborhood connections, and social and institutional networks.

Neighborhood effects on crime

In this section, we review some of the core mechanisms and associated theoretical perspectives that have been proposed to account for observed neighborhood effects. We discuss issues related to the definition of the neighborhood, scales of spatial exposures, and the spatial clustering of neighborhood disadvantage across urban environments.

Internal neighborhood mechanisms

Several major theoretical perspectives shed light on some of the possible mechanisms underlying the neighborhood effects on crime. First, one of the oldest theoretical perspectives, social disorganization, posits that ecological conditions like socioeconomic disadvantage, racial heterogeneity, and residential mobility, erode neighborhood social control and facilitate crime ( Shaw and McKay 1942 ). Social control largely operates through local ties to other individuals and institutions (Bursik and Grasmick 1993). A later extension of this theory proposes that independent of social ties, collective efficacy—a combination of social cohesion, trust and the ability of neighborhood residents to realize common goals and values—reduces neighborhood crime ( Sampson et al. 1997 ). Second, a perspective that is gaining increasing traction recently, routine activities states that crimes are most common when motivated offenders intersect in time and space with attractive targets in the absence of guardianship ( Cohen and Felson 1979 ). Often, routine activities components are assessed through measures such as unemployment rate (a proxy for motivated offenders) and time spent out of the household (low level of guardianship). Third, subcultural theories propose that local value structures can promote crime ( Fischer 1975 ), the focus being on "urban," "street" and a "southern" culture of violence. Finally, relative deprivation or strain approaches suggest that socioeconomic standing relative to peers or neighbors may influence offending behavior ( Merton 1938 ). In empirical tests of these theories, indicators of social disorganization and routine activities are found to exhibit the strongest and most consistent effects on crime (for a meta-analysis, see Pratt and Cullen 2005 ). Valuable reviews of social disorganization research and related theoretical thinking on neighborhood effects together with important suggestions for future directions are offered by Sampson et al. (2002) and Kubrin and Weitzer (2003) .

The main social mechanisms have been summarized by Sampson and collaborators (2002) under four categories: social ties and local interactions , referring to local interpersonal networks of friends and kin and neighborly exchanges; norms and collective efficacy , based on different dimensions of culture, social cohesion, trust, and social control ( Sampson et al. 1997 ); institutional resources , which include neighborhood organizations, family wellbeing support centers, youth centers and the like; and routine activities ( Cohen and Felson 1979 ), referring to the mix of residential, commercial or industrial land use and also the pattern of daily routine activities which facilitate access to local desirable targets by potential offenders living outside the neighborhood. The latter encompasses spatial mismatch theory ( Kain 1968 ), which highlights the distance between home and workplaces among population subgroups, a phenomenon also understood as institutional isolation (Wilson 1987).

These dimensions of neighborhood processes, while analytically distinct, are empirically related. Yet few studies have examined the nature and extent of these relationships. Sampson and Graif's study (2009b) is an exception that investigated the social networks of friends and kin and reciprocal exchange, collective efficacy (the ability of residents to realize common goals), culture (norms of conduct for different age groups), institutional engagement (neighborhood activism, involvement in local organizations, schools, and churches) and neighborhood leader contacts within and outside the community. They concluded that "as residents seem to disengage and are more cynical in disadvantaged communities, community leaders become more intensely involved in seeking resources, often from afar" ( Sampson and Graif 2009b , p. 1601). Independent of disadvantage, another study found that internal community network structures are positively associated with trust among leaders and among residents ( Sampson and Graif 2009a ). When networks extending outside the community shape the density of internal networks ( Sampson 2012 ), we might expect additional improvements in trust and other dimensions of social order. This literature implies important, yet understudied, relationships between the private or parochial levels of control and the external, public level ( Hunter 1985 , detailed below) with consequences for control of crime especially in disadvantaged communities.

Despite great advancements on the theoretical and empirical testing of neighborhood level mechanisms, we agree with Kubrin and Weitzer's (2003 , p. 387) assessment that "compared to the large number of studies on the effects of intra-neighborhood factors on crime, surprisingly little attention has been given to the role of exogenous determinants, and very little is known about the connections and interactions between internal and external factors. This would be a fruitful avenue for future research, and would rightly expand the scope of social disorganization theory in a more macro direction." Below, we present important recent developments relevant to bridging the internal-external mechanisms gap and offer additional suggestions for the future.

Neighborhood definitions and scales of spatial exposures

Over 40 years ago, Hunter and Suttles (1972) stressed the importance of multiple scales of measurement. They identify four scales: the “face-block," where residents tend to know each other; the " defended neighborhoods, " the smallest areas with distinct identities recognized by outsiders and insiders; the “ community of limited liability, ” where local participation depends on residents' attachment to community; and the “ expanded communities of limited liability,” a large geographic area in which groups of residents come together only when needed to gain larger traction on specific political or economic decisions. Importantly, each of these traditions uses pre-defined, administratively-bounded areas. Since the 1970s much of the measurement of neighborhoods in crime research spanned the meso-to-macro scales, from census tracts ( Graif and Sampson 2009 ) to community areas ( Sampson and Graif 2009a , 2009b ) to counties ( Messner and Anselin 2004 ). More recent studies have made important advances at the micro-level too, illustrating the importance of local network groupings ( Hipp et al. 2012 ), blocks ( Hipp 2007 ) and street segment dynamics ( Weisburd et al. 2004 ) in shaping crime.

A limited but growing number of studies, however, have adopted a different framework altogether—eliminating dependence on administrative boundaries. These researchers define neighborhoods egocentrically, as the geographic context around an individual's residence or around a block independent of neighborhood administrative boundaries ( Hipp and Boessen 2013 ). The features of the surroundings that are closest geographically to the focal residence are assumed to be most influential ( Tobler 1970 ). Work in geography also has used kernel density analyses and routines that treat the world as a continuous surface ( Matthews 2011 ). A major advantage of these analytic frameworks is an acknowledgment that access to resources is often facilitated by geographic proximity (e.g. access to youth services may decrease delinquent behavior) independent of artificially defined neighborhood boundaries.

The bounded neighborhood approach and the respondent-centered approach fed recurrent debates about the "proper" definition of the neighborhood. We believe this is a false dichotomy that may distract from thinking in an integrative way about local social processes. Similarly, the debates over the correct geographic scale of the neighborhood mask an important point: certain features of the surrounding non-residential areas may matter above and beyond the residential neighborhood, however defined. We revisit the four types of mechanisms noted by Sampson and colleagues (2002) with respect to the immediate neighborhoods of residence in the first column of Table 1 . Additionally, we expand further to illustrate how these types of processes may interact in shaping individuals' victimization experiences and offending behavior with features of a) the broader area surrounding the immediate neighborhood of residence (the extended neighborhood, column two) and b) the neighborhoods frequented as part of peoples' daily routine activities (e.g. the neighborhood of workplace or of close friends, column three). These examples may be translated into research hypotheses in future studies.

Examples of Neighborhood Mechanisms from Extended Spatial and Network Perspectives

The spatial embeddedness of neighborhoods

It has long been shown, in multiple cities, that poverty and crime are both associated with each other and exhibit spatial clustering ( Peterson and Krivo 2010 ). In addition, social processes like neighborhood trust and collective efficacy also cluster in space, and the spatial covariation between poverty and neighborhood processes remained strong over the past four decades ( Sampson and Graif 2009a ). Moreover, the associations between neighborhood poverty and crime tend to be similar for multiple neighborhoods that are geographically proximate to each other, even though they vary from one section of the city to another ( Graif and Sampson 2009 ).

Given the progress in highlighting the ecological levels of covariation between poverty and crime, it is surprising that advances in our collective understanding of spatial dynamics at the ecological level have not been integrated into the analytical framework of neighborhood effects on individuals (for an exception, see Sampson et al. 1999 ). This gap is related to the fact that we still know little about the processes underlying observed spatial clustering ( Kubrin and Weitzer 2003 ). These patterns are in part attributed to measurement issues and in part to processes of contagion or diffusion, whereby nearby crime activity spills over neighborhood boundaries ( Anselin et al. 2000 ; Tita and Griffiths 2005 ). Other processes assumed to explain clustering are residents' daily movement and increased exposures to risk factors in nearby neighborhoods. To the extent that effects of spatial proximity are in large part due to overlapping activity spaces, a more general form of interdependence—which transcends geographic proximity while subsuming some aspects of it—may be inter-neighborhood connections forged as a result of individuals’ frequent movement (e.g. daily commuting) across space.

Non-residential neighborhoods and routine activity spaces

Individuals routinely travel outside the neighborhood of residence for leisure and work. Pathways of movement across large distances may increase variability of access to resources, institutions, information, and people in ways that may affect crime. Furthermore, much of the time spent in the neighborhood of residence is spent inside the home, when the objective risk of committing crime or being victimized is arguably low (Bureau of Labor Statistics 2013). Despite increasing calls for definitions of neighborhood context that take into account individuals' daily activity patterns ( Cagney et al. 2013 ; Matthews 2011 ; Matthews and Yang 2013 ), most social science literature still relies on census tract of residence as the operational definition for the neighborhood of influence. However, research on the journey to crime indicates that up to 70 percent of crimes are committed by individuals outside their neighborhood of residence ( Bernasco 2010 ; Wikström 1991 , p. 213-223). Moreover, compared to violent crime, property crimes are committed further away from offenders' neighborhoods of residence (White 1932). Additionally, Bernasco (2010) finds that locations where offenders lived in the past are more likely to be chosen as the location of current offending. Evidence on the importance of non-residential contexts in the study of crime is thus becoming increasingly more salient.

The argument that researchers need to focus on relevant contexts other than the neighborhood of residence is not new to sociology (e.g. Foley 1950 ). McClenahan (1929) was one of the first to argue that urban residents' activities are rarely located within the immediate vicinity of the home. Routine activity patterns have been shown to matter for individuals' outcomes. Inagami and colleagues (2007) suggest that the negative effects on health of living in disadvantaged neighborhoods may be confounded and suppressed by exposure to non-disadvantaged, non-residential neighborhoods in the course of routine daily activities (i.e. grocery shopping). More recently, both qualitative and quantitative research in sociology has highlighted the importance of nonresidential contexts ( Matthews, 2011 ). Other disciplines too have started to adopt activity space approaches and are beginning to focus on nonresidential neighborhoods ( Cagney et al. 2013 ; Zenk et al, 2011 ). To date however, few studies have assessed the impact of individual activity spaces on the propensity to commit crime or become the victim of crime.

One notable exception is a recent study of youth in a UK city ( Wikström et al. 2010 ), which showed that more than 54% of respondents’ awake time was spent outside their home area ( Figure 2 ). Those with higher propensities for crime were exposed more frequently to criminogenic settings outside their home and school areas and, in such settings, were more likely to become involved in criminal behavior. More than half of the respondents’ crimes were committed at locations central to their routine activities. These findings highlight the importance of designing new studies that do not rely on residential contexts as the only purveyor of contextual effects.

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Examples of Cross-Neighborhood Activity Spaces

Source: Adapted from Wikström et al. 2010 . With kind permission from Springer.

Networks of neighborhood exposures

The work on the importance of non-residential neighborhoods on crime and victimization provides some evidence for the necessity to study neighborhoods not as isolated, independent places but rather as parts of a larger, interconnected network of places. This type of perspective also has roots in earlier sociology, geography, and planning (see Matthews 2008 ). In the early sixties sociologists wrote about the “community without propinquity” or spatially dispersed communities ( Webber 1963 ) . Later Wellman (1979) discussed “community liberation”, extended social networks, and long distance communications such as “networks in the global village” ( Wellman 1999 ) which provides a bridge to the “new mobilities research” paradigm ( Larsen et al. 2012 ). We reintroduce and emphasize the idea that neighborhoods are part of a larger system of resource exchanges, in the form of networks, between places.

Our network perspective also draws on Hunter’s (1985) discussion of the importance of three core types of relational networks in shaping neighborhood social control. The “private” social order refers to intimate informal primary ties within a neighborhood (e.g. kin and friends) which can control crime through the threat of social disproval or other forms of deprivation. The “parochial” order is given by the broader connections with local institutions such as schools, churches, or community organizations, characterized by weaker attachment than the primary networks but nonetheless important. Finally, the “public” social order describes a community’s connections to external organizations and institutions that facilitate the mobilization of resources, mediate the ability of local networks to control local crime, and sometimes even enable the foundation of local institutions (also Taub et al. 1977 ).

The literature to date has predominantly focused on the private or parochial dimension of crime control, with little attention to the public dimension (Bursik and Grasmick 1993). Interestingly, the private and parochial ties often extend across space to create the foundation for public control. In a creative approach to neighborhoods as inter-related friendship networks, Hipp and colleagues (2012) show that while a high proportion of teens’ friends are predictably spatially clustered, many ties cross neighborhood boundaries over large geographic distances. To the extent that individuals’ contextual exposures are defined through their interactions, these findings underscore that a focus on only the administrative area of residence would miss substantial exposures to many friends’ neighborhoods. 1

The extent of between-neighborhood connections in Chicago are investigated in a recent monograph, Sampson's (2012) Great American City , as a function of residential mobility and nominations of influential people. Sampson (2012 , pp. 309-310) finds it surprising "how little neighborhood networks have actually been studied, as opposed to being invoked in metaphorical terms. … [P]rior research is dominated by a focus on individual connections and an “egocentric” perception of social structure. … [R]arely has social science documented variations between communities in social networks, much less the citywide structure."

A recent article by Slocum and colleagues (2013) addresses in part this gap by showing that organizations whose function it is to bridge to the larger community and secure resources for the local residents (e.g. community boards, political groups, economic development centers) are significantly associated with lower violent and property crime, even after controlling for multiple features of the community. Still, few empirical studies exist that show how neighborhoods are connected and more specifically how these ties matter for crime related outcomes. Just as residential neighborhood contexts matter to individuals through their connections with institutions and organizations within it ( Tran et al. 2013 ), similarly, involvement in non-residential neighborhoods may be consequential for criminal behavior or victimization risk.

Broader social phenomena highlight the importance of the interconnectedness of neighborhoods. For example, economic declines have been found to play a role in increasing violence ( Catalano et al. 2011 ; Ellen et al. 2013 ) but the evidence tends to be mixed and little is understood about the underlying mechanisms. We suggest that through plant closures and mass layoffs, recessions may sever critical interaction pathways (i.e. resource exchange in the form of labor) between neighborhoods. Despite a long tradition of research on spatial mismatch in employment prospects ( Kain 1968 ), understanding violence in the context of a neighborhood's connectivity to or isolation from other particularly influential communities in the city is underexplored.

In sum, as individuals move about space and across neighborhoods within urban contexts, patterns of behavior aggregate to create functional ties between sets of neighborhoods. Such ties may turn out to be as important for neighborhood change as spatial proximity is observed to be. In other words, underlying (or complementing) the spatial clustering of poverty and crime among neighborhoods in a city may be a broader network structure of interdependence governed by how people routinely move through the urban landscape. To the extent that communities are connected to others who are successful in dealing with crime, those strategies and tools may be transmitted through such ties (i.e. innovation diffusion).

Methodological considerations in the study of neighborhood networks

The empirical study of "networks of neighborhoods" is relatively new and underdeveloped. While we cannot offer definite approach to the study of neighborhood networks, we provide some guidance based on prior research and the emergence of data and methods to study complex networks. We highlight five relevant macro-level studies and their commonalities and differences along six dimensions: the nodes (neighborhoods) and the ties (relationships between nodes) as units of analysis; the levels of analysis; the type of analysis; the type of data used; and the questions of interest. This information is intended to provide readers with an overview of the types of methodological choices when designing a study of neighborhood networks.

In the networks of neighborhood approach, the nodes, or units of analysis, are frequently a geographic subdivision. In our selected examples, the operational definition of nodes range from administrative definitions of "neighborhoods" of the Paris Commune in the late nineteen century and community areas in contemporary Chicago ( Gould 1991 ; Sampson 2012 ) to tracts ( Schaefer 2012 ) and more complex units like gang turfs ( Papachristos et al. 2013 ). The definition and measurement of the ties between nodes - arguably the main focus of a networks approach to neighborhoods – vary as a function of the research question. In Gould's (1991) study, ties were represented by the number of men living in a neighborhood serving in the same military units as residents of another neighborhood. Papachristos and colleagues (2013) and Schaefer (2012) represented ties as gang violence and criminal co-offending relationships between places, respectively. Sampson (2012) measured ties as nominations by political leaders of people in the city who they believed they could rely on to "get things done" in their community. Thus, the core requirement of a tie is that it represent a form of meaningful interaction or relationship between nodes (see Table 2 ).

Selected Macro-level Applications of a Network of Neighborhoods Approach

The level of analysis is typically macro because of the interest in inter - neighborhood interactions or how neighborhoods are connected. All the studies we selected examined exchanges between macro-level units defined as a neighborhood. Data sources vary depending on the topic of interest, though some common themes emerge from the types of data used. Three of the studies ( Gould 1991 ; Papachristos et al. 2013 ; Schaefer 2012 ) used archival records whereas Sampson (2012) used a prospectively longitudinal survey format to collect data. Other types of data that link places to other places - including but not limited to resource exchange (e.g. financial exchange; commuting to work), criminal exchange (e.g. court records of co-offenders’ neighborhoods of residence; police reports linking offenders’ or victims’ address and crime location), or political exchange (e.g. nominations of "movers and shakers;" political interactions) - can be used to assess neighborhood networks. As an example, one ongoing study (Author 2013) uses police records and administrative data to connect employers’ location and employees’ neighborhood of residence to examine the extent to which commuting to violent neighborhoods increases victimization rates among the residents of a focal neighborhood ( Figure 3 ).

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Inter-Neighborhood Networks and Exposure to Violence

Source: Adapted from Author (2013).

The leftmost map represents Chicago’s 77 community areas while the middle and the rightmost maps are network representations of the communities in the highest thirtile (red nodes) and lowest thirtile of violence (green nodes), respectively. Community areas are represented as nodes and situated in geographic space according to the latitude and longitude coordinates of their centroids. Ties between nodes represent workers living in one neighborhood and commuting to the other. The arrows point toward the neighborhood of work and show the extent to which communities of similar violence level are connected to each other or not. This ecological perspective on networks of communities opens the field to new perspectives on age-old questions related to structural embeddedness, selection and exclusion, displacement of crime, and the diffusion of norms relevant for crime control (see also column 3 of Table 1 ).

With respect to modeling and analytical approaches, all of the selected studies use a combination of GIS mapping, spatial, and network analyses. These methods are used to assess how different types of neighborhoods are distributed over space, to calculate the geographic distance between them, and to assess the association between social and spatial distance on the one hand and prevalence of inter-neighborhood connections on the other. The network analyses use two different approaches: a) a nodal approach ( Gould 1991 ) where the outcome and most covariates are modeled egocentrically at the nodal level while the dyadic relationships are only summarized in the form of a network autocorrelation term, similar to a spatial autocorrelation term, and, b) a dyadic and structural approach, using exponential random graph models (ERGMs), where the outcome is at the tie level as are many covariates, but nodal attributes and structural properties of the overall network are also included ( Papachristos et al 2013 ; Schaefer 2012 ).

Not represented among these examples, but nonetheless an important approach for the future that allows for changes in the network structure over time, is modeling using SIENA ( Snijders 2001 ). A relatively recent development, SIENA is gaining traction in examining longitudinal networks at the individual level but little research so far has made use of it in examining change in a network of neighborhoods. For instance, one type of question that this strategy would help address in the future is whether increases in neighborhood unemployment contribute to subsequent increases in co-offending relationships between any two neighborhoods or whether co-offending occurs before or independent of increases in unemployment. Other types of questions may focus on the diffusion of crime between neighborhoods (how shots fired across neighborhoods may lead to further shootings in retaliation) or on crime displacement (how policing in a neighborhood pushes crime into new places) (Tita and Cohen 2004). In sum, while the macro-level study of networks of neighborhoods is still in its early stages, existing examples are encouraging.

When the primary interest focuses on individual behaviors, experiences, and outcomes related to crime and victimization, studies of neighborhood network effects may combine network analytic tools with more typical approaches to the study of neighborhood effects or peer influences. Just like exposure to a network of delinquent friends affects individuals' attitudes and delinquent behavior, exposure to criminogenic places in which individuals spend considerable time (whether their own neighborhood of residence or outside it) may shape their attitudes and behavior. The mechanisms of peer influence on individual behavior may only in part overlap, if at all, with the mechanisms of place influence. Yet, the methodological advancements in assessing the role of one's network of peers ( Kreager et al. 2011 ) may also be valuable to scholars interested in assessing the role of an individual's network of neighborhoods.

The logic of the typical multilevel approach, for instance, as used in estimating effects of peer groups or of residential neighborhoods on individual attitudes and behavior related to crime and victimization may be also used to estimate the effects of a network of neighborhoods. The core difference consists in assessing criminogenic exposures based not only on where respondents live but based on the neighborhoods they frequent when they hang out with friends, go to school, shop, or commute to work. Exposures to each place can be weighted by the time respondents report (or are observed) to spend there or by another index representing functional ties (e.g. the number of friends they know in each place). GPS, smartphones, and tracking technologies enable collection of data that allows for weighting by the duration of exposure to a place. Alternatively, researchers may account for the time spent in traditional “nodes” such as home, work, and school as captured through activity logs ( Basta et al. 2010 ). To account for individuals' exposures to multiple non-nested places, multiple-membership models may constitute a valuable strategy ( Browne et al. 2001 ).

Related modeling strategies include the use of network lagged variables in hierarchical linear models. This would be similar to the use of spatial lag variables in multilevel analyses (see Crowder and South 2011 ; Sampson et al. 1999 ) but instead of geographic proximity it would model the lag as a function of existing network ties. For different examples of modeling social and spatial networks we direct the reader to Entwisle and colleagues (2007) and Larsen and colleagues (2012) .

Conclusions and directions for the future

In this article, we surveyed classic and recent studies on neighborhood effects and on the spatial stratification of poverty and urban crime. We argue that for a more complete understanding of the impact of neighborhoods and poverty on crime, sociological research would benefit from expanding the analytical focus from the residential neighborhoods to the network of neighborhoods (residential and non-residential) that individuals use during the course of their routine daily activity.

The reemergence of scholarship on activity spaces offers much promise for studies of non-residential contexts and crime. These non-residential contexts may add variation in criminogenic exposure, which would in turn influence their offending behavior ( Wikström et al. 2010 ) and victimization risk. We proposed that non-residential exposures may be thought of as a part of a " network of neighborhood exposures " that includes the neighborhood contexts of the workplace, school, friends' homes, recreation activities, or wherever individuals tend to spend their time on a routine basis.

Our approach is also related to insights on the importance of inter-neighborhood connections over large geographic distances directly or indirectly implied in studies of residential mobility ( Sampson 2012 ), extra-local organizational connections and involvement ( Sampson and Graif 2009a , 2009b ; Slocum et al. 2013 ), daily commuting distances ( Zenk et al. 2011 ), and spatial mismatch ( Holzer 1991 ; Kain 1968 ). We suggest that the criminogenic role of chronic unemployment resulting from the spatial mismatch between the location of jobs and the location of housing may be in part due to the absence of positive externalities of inter-neighborhood connections that may be forged through daily mobility across the urban landscape. We believe that our collective understanding of the causal relationship between neighborhood poverty, inter-neighborhood networks and crime will be greatly advanced by creative designs applied to studies of the recent Great Recession and economic decline more generally. More research is needed on how changes to activity spaces due to plant closures shape neighborhoods and crime and what happens when communities become disconnected as a result of economic restructuring.

Our principal purpose was to highlight the importance of studying how neighborhoods are related across space for advancing our collective understanding of macro-level patterns of neighborhood crime as well as individual attitudes and behavior. However, the study of inter-neighborhood connectivity is important for our understanding of urban stratification across space above and beyond crime. For instance, Krivo and colleagues (2013) , using the Los Angeles Families and Neighborhoods Survey (L.A. FANS) data, found that social inequality is reproduced through daily activities. That is, people living in socioeconomically advantaged neighborhoods similarly tend to conduct activities in (i.e. work, recreation, shopping, dining) neighborhoods that are non-overlapping with those in which disadvantaged populations conduct activities. Individuals from disadvantaged areas rarely enter non-disadvantaged parts of the city.

The comprehensive overview of the state of the field in the last decade and the discussion of the historical and theoretical context of the scholarship on urban poverty, neighborhoods, and crime left little room for addressing other important and recurrent issues in the field such as selection bias, ecological fallacy, and neighborhood change. We recommend several excellent reviews for more detailed discussions on these ( Kim et al. 2013 ; Kirk and Laub 2010 ; Kubrin and Weitzer 2003 ; Matthews and Yang 2013 ; Pratt and Cullen 2005 ; Sampson et al. 2002 ). We differ from previous reviews in our focus on a network approach to understanding neighborhood exposures. We call for new and creative research designs and analytical approaches to understanding urban crime that transcend the typical focus on the neighborhood of residence to include a focus on the broader context of routine activities. We also call for advancements in research on urban poverty that investigate the salience of inter-neighborhood connections in evaluating criminogenic risk for individuals and communities.

Acknowledgments

The first author thanks the Social Science Research Institute and the Population Research Institute at Penn State (NIH grant # R24 HD041025) for support during the writing of this article.

1 While a focus on networks as neighborhoods is valuable, we would also caution that the absence of friendships could alternatively activate criminogenic processes like alienation and anomie.

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Title: conceptualizing predictive conceptual model for unemployment rates in the implementation of industry 4.0: exploring machine learning techniques.

Abstract: Although there are obstacles related to obtaining data, ensuring model precision, and upholding ethical standards, the advantages of utilizing machine learning to generate predictive models for unemployment rates in developing nations amid the implementation of Industry 4.0 (I4.0) are noteworthy. This research delves into the concept of utilizing machine learning techniques through a predictive conceptual model to understand and address factors that contribute to unemployment rates in developing nations during the implementation of I4.0. A thorough examination of the literature was carried out through a literature review to determine the economic and social factors that have an impact on the unemployment rates in developing nations. The examination of the literature uncovered that considerable influence on unemployment rates in developing nations is attributed to elements such as economic growth, inflation, population increase, education levels, and technological progress. A predictive conceptual model was developed that indicates factors that contribute to unemployment in developing nations can be addressed by using techniques of machine learning like regression analysis and neural networks when adopting I4.0. The study's findings demonstrated the effectiveness of the proposed predictive conceptual model in accurately understanding and addressing unemployment rate factors within developing nations when deploying I4.0. The model serves a dual purpose of predicting future unemployment rates and tracking the advancement of reducing unemployment rates in emerging economies. By persistently conducting research and improvements, decision-makers and enterprises can employ these patterns to arrive at more knowledgeable judgments that can advance the growth of the economy, generation of employment, and alleviation of poverty specifically in emerging nations.

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The upside of recessions

New research confirms it: The worse the economy gets, the longer we live. But why?

There's a reason governments spend so many taxpayer dollars digging their economies out of recessions. Families lose their homes. Children go malnourished. New grads spend years struggling to get their careers back on track, forgoing marriage and kids and homeownership. But a growing body of research suggests that recessions are good for at least one thing: longevity. Puzzlingly, it appears that economic downturns actually extend people's lives.

The latest evidence comes from " Lives vs. Livelihoods ," a new paper by four researchers led by the renowned health economist Amy Finkelstein. They found that during the Great Recession, from 2007 to 2009, age-adjusted mortality rates among Americans dropped 0.5% for every jump of 1 percentage point in an area's unemployment rate. The more joblessness, the longer people lived — especially adults over 64 and those without a college education.

"These mortality reductions appear immediately," the economists concluded, "and they persist for at least 10 years." The effects were so large that the recession effectively provided 4% of all 55-year-olds with an extra year of life. And in states that saw big jumps in unemployment, people were more likely to report being in excellent health. Recessions, it would seem, help us stay fitter, and live longer.

The question, of course, is why. The economists ruled out a lot of possible explanations. Laid-off workers weren't using their free time to exercise more, or cutting back on smoking or drinking because money was tight. Infectious diseases like influenza and pneumonia kept right on spreading, even though fewer people were going to work and dining out. Retirees didn't seem to be getting better care, even though rising unemployment rates made it easier for nursing homes to staff up. So what could the explanation be? How does higher unemployment lead to longer life?

The answer was pollution. Counties that experienced the biggest job losses in the Great Recession, the economists found, also saw the largest declines in air pollution, as measured by levels of the fine particulate matter PM2.5. It makes sense: During recessions, fewer people drive to work. Factories and offices slow down, and people cut back on their own energy use to save money. All that reduced activity leads to cleaner air. That would explain why workers without a college degree enjoyed the biggest drops in mortality: People with low-wage jobs tend to live in neighborhoods with more environmental toxins. It would also explain why the recession reduced mortality from heart disease, suicide, and car crashes — causes of death all linked to the physical and mental effects of PM2.5. Overall, the economists found, cleaner air was responsible for more than a third of the decline in mortality during the Great Recession.

An economy firing on all cylinders creates more jobs — but it also generates all sorts of unseen but harmful side effects.

The new paper, along with other research into recessions, provides an important reminder that economic growth isn't — and shouldn't be — the only measure of our collective well-being. If recessions save lives, that comes with a corollary: Boom times cost lives. An economy firing on all cylinders creates more jobs — but it also generates all sorts of unseen but harmful side effects. "Our findings suggest important trade-offs between economic activity and mortality," the authors conclude. That's economist-speak for two very bad choices: Would you prefer wealth that kills you, or poverty that keeps you alive?

It's that dilemma that has given rise to what's known as the degrowth movement — the idea that the gross domestic product doesn't provide us with an accurate read on human progress. Sure, economic growth provides jobs. But it doesn't tell us anything about the health of our children or the safety of our neighborhoods or the sustainability of our planet. What's the point of having all this money, the degrowthers ask, if it's making us worse off?

I'm sympathetic to that line of reasoning — up to a point. But I don't think that actually shrinking the economy, as some degrowthers advocate, is a good idea. Lower growth inevitably leads to higher unemployment, and that's not a trade-off we should be willing to accept. I grew up in Japan, a country degrowthers often point to as a model for slower growth. It's true that Japan is politically stable, clean, and safe even though its economy has stalled for 30 years. But there's something about long-term economic stagnation that saps a country's hope. Nothing changes — in politics, in culture, in society — even when everyone knows it's bad. Without realizing it, I had settled into this national inertia, the belief that nothing could be done. It was only in 2012, when I moved to San Francisco, that I started to feel real agency over the direction of my life. Everyone around me believed they could change the world, and the sense of optimism was contagious.

The degrowth movement presents us with a false choice. The solution to bad growth isn't less growth. It's better growth. With stronger regulation and smarter innovation, I'm confident we can find ways to create jobs without destroying the environment and shortening our lives. If the new research tells us anything, it's that we still have a long way to go in striking a healthy balance between economic growth and social welfare. We shouldn't have to choose between working and living.

Aki Ito is a chief correspondent at Business Insider.

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    Abstract. Poverty is commonly defined as a lack of economic resources that has negative social consequences, but surprisingly little is known about the importance of economic hardship for social outcomes. This article offers an empirical investigation into this issue. We apply panel data methods on longitudinal data from the Swedish Level-of ...

  6. The relationship between unemployment and wellbeing: an updated meta

    Previous meta- analyses. Building on narrative reviews on the topic, Murphy and Athanasou (Citation 1999) provided the first meta-analysis on the relationship between unemployment and mental health based on 9 longitudinal studies published between 1986 and 1996.Although the authors noted that, on average, the unemployed suffer from poorer mental health than their employed counterparts, the ...

  7. Poverty and Unemployment

    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.".

  8. Relationship Analysis between Unemployment and Poverty in 33 ...

    The research found that: (i). Wage Rate & GRDP Growth has a negative and significant effect, while inflation does not have a significant effect on the unemployment rate; (ii). The level of subsidies has a negative and significant effect on the Poverty Level, the Human Development Index has a positive and significant effect on the Poverty Level ...

  9. Long term unemployment, income, poverty, and social public expenditure

    There is scant research that simultaneously analyzes the joint effects of long-term unemployment, poverty and public expenditure policies on poorer self-perceived health during the financial crisis. The aim of the study is to analyze the joint relationship between long-term unemployment, social deprivation, and regional social public expenditure on one side, and self-perceived health in Spain ...

  10. The Far-Reaching Impact of Job Loss and Unemployment

    Abstract. Job loss is an involuntary disruptive life event with a far-reaching impact on workers' life trajectories. Its incidence among growing segments of the workforce, alongside the recent era of severe economic upheaval, has increased attention to the effects of job loss and unemployment. As a relatively exogenous labor market shock, the ...

  11. Job market polarization and American poverty

    The article posits that the puzzles of stagnating poverty rates amidst high growth and declining unemployment in the United States can be substantially explained by polarized job markets characterized by job quality and job distribution. In recent decades, there has been an increased number of poor-quality jobs and an unequal distribution of jobs in the developed world, particularly in the ...

  12. Unemployment and social exclusion

    Hence, the loss of a job represents a potential source of stress, and can lead to emotional and physical distress, isolation and alienation. 3 These economic and social consequences of unemployment are expected to contribute to, or be accompanied by, the subjective feeling of social exclusion. The aim of this paper is to shed light on the ...

  13. Notes on Policy and Practice

    Unemployment and the Poverty Status of Individuals. Over the nine-year period 1967-75, two-fifths of individuals living in households were at some time members of a household headed by an unemployed worker (see table 1). Individuals in households headed by unemployed workers averaged 28.7 weeks in which the household head was unemployed.

  14. Unemployment-Poverty Trade-offs

    Agénor examines the potential trade-offs that may arise between poverty alleviation and unemployment reduction. He discusses various analytical arguments that may provide a rationale for their exis...

  15. (PDF) A Systematic Literature Review and Analysis of Unemployment

    This paper presents a comprehensive literature review using a content analysis approach to investigate the reasons for the unemployment problem across many countries and identifies proposed ...

  16. Entrepreneurship Development as a Tool for Employment ...

    Unemployment and poverty are global challenges, especially in emerging countries. In Somalia, both poverty and unemployment are major problems that are widespread among the youth who constitute more than 70% of the population. Despite the efforts to eradicate, both unemployment and poverty still remain major challenges in the country. Empirical studies have shown that entrepreneurship ...

  17. (PDF) A STUDY ON UNEMPLOYMENT IN INDIA

    Abstract and Figures. Unemployment is a persistent problem in India, with significant social and economic consequences. This paper provides an overview of the current state of unemployment in ...

  18. Poverty, unemployment, and common mental disorders: population based

    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 ...

  19. EconPapers: Leslie Moscow McGranahan

    Also in LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library (2004) View citations (37) 2000. The Earned Income Credit and Durable Goods Purchases JCPR Working Papers, Northwestern University/University of Chicago Joint Center for Poverty Research Also in

  20. PDF and Poverty Reduction Decent Work and Poverty Reduction Decent Work

    "Poverty anywhere constitutes a danger to prosperity everywhere" Declaration of Philadelphia, 1944 The ILO's work on poverty reduction is grounded in social justice and its twin concepts of entitlements and equity. The starting point is the fact that for poor people, work is the main and often only way to get and stay out of poverty. In

  21. New ILO research identifies policies to tackle poverty and inequality

    International Day for the Eradication of Poverty. New ILO research identifies policies to tackle poverty and inequality. Combining active labour market policies with income support makes both measures more effective in tackling poverty and helping people find decent work, a new report finds.

  22. Urban Poverty and Neighborhood Effects on Crime: Incorporating Spatial

    In The Truly Disadvantaged, Wilson (1987) noted that unemployment and poverty clustered and that together these 'concentration effects' weakened family bonds and institutional ties, undermining the formal and informal capacity for crime control. Scholars today refer to areas of high poverty as areas of concentrated disadvantage.

  23. Conceptualizing predictive conceptual model for unemployment rates in

    Although there are obstacles related to obtaining data, ensuring model precision, and upholding ethical standards, the advantages of utilizing machine learning to generate predictive models for unemployment rates in developing nations amid the implementation of Industry 4.0 (I4.0) are noteworthy. This research delves into the concept of utilizing machine learning techniques through a ...

  24. Recessions Actually Make People Live Longer

    They found that during the Great Recession, from 2007 to 2009, age-adjusted mortality rates among Americans dropped 0.5% for every jump of 1 percentage point in an area's unemployment rate.

  25. Lesser-Known Papers by a Well-Known Researcher of Russian Society: Yury

    Researchers in such fields as education, urban poverty, unemployment, the control of crime and drug abuse, and even health have discovered that successful outcomes are more likely in civically ...