literature review unemployment

Unemployment, Social Vulnerability, and Health in Europe pp 167–183 Cite as

The Effects of Youth Unemployment: A Review of the Literature

• V. L. Damstrup  

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Young adults and teenagers are engaged in work on a much smaller scale than older workers. Young people are engaged less in work because they are still in school, or they are involved in leisure activities. Some, on the other hand, would like to work, but find it difficult obtaining employment. The transition from school to employment is a process that involves searching and changing jobs before deciding on a more or less permanent employment. Today, more than ever, youths have a lower rate of employment, hence there has been much concern about the youth labor market.

• Young People
• Labor Force
• Vocational Training
• Young Person
• School Dropout

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Damstrup, V.L. (1987). The Effects of Youth Unemployment: A Review of the Literature. In: Schwefel, D., Svensson, PG., Zöllner, H. (eds) Unemployment, Social Vulnerability, and Health in Europe. Health Systems Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83112-6_12

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Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?

Hila axelrad.

1 Center on Aging & Work, Boston College, Chestnut Hill, MA 02467 USA

2 The School of Social and Policy Studies, The Faculty of Social Sciences, Tel Aviv University, P.O. Box 39040, 6997801 Tel Aviv, Israel

3 Department of Public Policy & Administration, Guilford Glazer Faculty of Business & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

Israel Luski

4 Department of Economics, The Western Galilee College, Akko, Israel

In this research we show that workers aged 30–44 were significantly more likely than those aged 45–59 to find a job a year after being unemployed. The main contribution is demonstrating empirically that since older workers’ difficulties are related to their age, while for younger individuals the difficulties are more related to the business cycle, policy makers must devise different programs to address unemployment among young and older individuals. The solution to youth unemployment is the creation of more jobs, and combining differential minimum wage levels and earned income tax credits might improve the rate of employment for older individuals.

Introduction

Literature about unemployment references both the unemployment of older workers (ages 45 or 50 and over) and youth unemployment (15–24). These two phenomena differ from one another in their characteristics, scope and solutions.

Unemployment among young people begins when they are eligible to work. According to the International Labor Office (ILO), young people are increasingly having trouble when looking for their first job (ILO 2011 ). The sharp increase in youth unemployment and underemployment is rooted in long-standing structural obstacles that prevent many youngsters in both OECD countries and emerging economies from making a successful transition from school to work. Not all young people face the same difficulties in gaining access to productive and rewarding jobs, and the extent of these difficulties varies across countries. Nevertheless, in all countries, there is a core group of young people facing various combinations of high and persistent unemployment, poor quality jobs when they do find work and a high risk of social exclusion (Keese et al. 2013 ). The rate of youth unemployment is much higher than that of adults in most countries of the world (ILO 2011 ; Keese et al. 2013 ; O’Higgins 1997 ; Morsy 2012 ). Official youth unemployment rates in the early decade of the 2010s ranged from under 10% in Germany to around 50% in Spain ( http://www.indexmundi.com/g/r.aspx?v=2229 ; Pasquali 2012 ). The youngest employees, typically the newest, are more likely to be let go compared to older employees who have been in their jobs for a long time and have more job experience and job security (Furlong et al. 2012 ). However, although unemployment rates among young workers are relatively higher than those of older people, the period of time they spend unemployed is generally shorter than that of older adults (O’Higgins 2001 ).

We would like to argue that one of the most important determinants of youth unemployment is the economy’s rate of growth. When the aggregate level of economic activity and the level of adult employment are high, youth employment is also high. 1 Quantitatively, the employment of young people appears to be one of the most sensitive variables in the labor market, rising substantially during boom periods and falling substantially during less active periods (Freeman and Wise 1982 ; Bell and Blanchflower 2011 ; Dietrich and Möller 2016 ). Several explanations have been offered for this phenomenon. First, youth unemployment might be caused by insufficient skills of young workers. Another reason is a fall in aggregate demand, which leads to a decline in the demand for labor in general. Young workers are affected more strongly than older workers by such changes in aggregate demand (O’Higgins 2001 ). Thus, our first research question is whether young adults are more vulnerable to economic shocks compared to their older counterparts.

Older workers’ unemployment is mainly characterized by difficulties in finding a new job for those who have lost their jobs (Axelrad et al. et al. 2013 ). This fact seems counter-intuitive because older workers have the experience and accumulated knowledge that the younger working population lacks. The losses to society and the individuals are substantial because life expectancy is increasing, the retirement age is rising in many countries, and people are generally in good health (Axelrad et al. 2013 ; Vodopivec and Dolenc 2008 ).

The difficulty that adults have in reintegrating into the labor market after losing their jobs is more severe than that of the younger unemployed. Studies show that as workers get older, the duration of their unemployment lengthens and the chances of finding a job decline (Böheim et al. 2011 ; De Coen et al. 2010 ). Therefore, our second research question is whether older workers’ unemployment stems from their age.

In this paper, we argue that the unemployment rates of young people and older workers are often misinterpreted. Even if the data show that unemployment rates are higher among young people, such statistics do not necessarily imply that it is harder for them to find a job compared to older individuals. We maintain that youth unemployment stems mainly from the characteristics of the labor market, not from specific attributes of young people. In contrast, the unemployment of older individuals is more related to their specific characteristics, such as higher salary expectations, higher labor costs and stereotypes about being less productive (Henkens and Schippers 2008 ; Keese et al. 2006 ). To test these hypotheses, we conduct an empirical analysis using statistics from the Israeli labor market and data published by the OECD. We also discuss some policy implications stemming from our results, specifically, a differential policy of minimum wages and earned income tax credits depending on the worker’s age.

Following the introduction and literary review, the next part of our paper presents the existing data about the unemployment rates of young people and adults in the OECD countries in general and Israel in particular. Than we present the research hypotheses and theoretical model, we describe the data, variables and methods used to test our hypotheses. The regression results are presented in Sect.  4 , the model of Business Cycle is presented in Sect.  5 , and the paper concludes with some policy implications, a summary and conclusions in Sect.  6 .

Literature review

Over the past 30 years, unemployment in general and youth unemployment in particular has been a major problem in many industrial societies (Isengard 2003 ). The transition from school to work is a rather complex and turbulent period. The risk of unemployment is greater for young people than for adults, and first jobs are often unstable and rather short-lived (Jacob 2008 ). Many young people have short spells of unemployment during their transition from school to work; however, some often get trapped in unemployment and risk becoming unemployed in the long term (Kelly et al. 2012 ).

Youth unemployment leads to social problems such as a lack of orientation and hostility towards foreigners, which in turn lead to increased social expenditures. At the societal level, high youth unemployment endangers the functioning of social security systems, which depend on a sufficient number of compulsory payments from workers in order to operate (Isengard 2003 ).

Workers 45 and older who have lost their jobs often encounter difficulties in finding a new job (Axelrad et al. 2013 ; Marmora and Ritter 2015 ) although today they are more able to work longer than in years past (Johnson 2004 ). In addition to the monetary rewards, work also offers mental and psychological benefits (Axelrad et al. 2016 ; Jahoda 1982 ; Winkelmann and Winkelmann 1998 ). Working at an older age may contribute to an individual’s mental acuity and provide a sense of usefulness.

On average, throughout the OECD, the hiring rate of workers aged 50 and over is less than half the rate for workers aged 25–49. The low re-employment rates among older job seekers reflect, among other things, the reluctance of employers to hire older workers. Lahey ( 2005 ) found evidence of age discrimination against older workers in labor markets. Older job applicants (aged 50 or older), are treated differently than younger applicants. A younger worker is more than 40% more likely to be called back for an interview compared to an older worker. Age discrimination is also reflected in the time it takes for older adults to find a job. Many workers aged 45 or 50 and older who have lost their jobs often encounter difficulties in finding a new job, even if they are physically and intellectually fit (Hendels 2008 ; Malul 2009 ). Despite the fact that older workers are considered to be more reliable (McGregor and Gray 2002 ) and to have better business ethics, they are perceived as less flexible or adaptable, less productive and having higher salary expectations (Henkens and Schippers 2008 ). Employers who hesitated in hiring older workers also mentioned factors such as wages and non-wage labor costs that rise more steeply with age and the difficulties firms may face in adjusting working conditions to meet the requirements of employment protection rules (Keese et al. 2006 ).

Thus, we have a paradox. On one hand, people live longer, the retirement age is rising, and older people in good health want or need to keep working. At the same time, employers seek more and more young workers all the time. This phenomenon might marginalize skilled and experience workers, and take away their ability to make a living and accrue pension rights. Thus, employers’ reluctance to hire older workers creates a cycle of poverty and distress, burdening the already overcrowded social institutions and negatively affecting the economy’s productivity and GDP (Axelrad et al. 2013 ).

OECD countries during the post 2008 crisis

The recent global economic crisis took an outsized toll on young workers across the globe, especially in advanced economies, which were hit harder and recovered more slowly than emerging markets and developing economies. Does this fact imply that the labor market in Spain and Portugal (with relatively high youth unemployment rates) is less “friendly” toward younger individuals than the labor market in Israel and Germany (with a relatively low youth unemployment rate)? Has the market in Spain and Portugal become less “friendly” toward young people during the last 4 years? We argue that the main factor causing the increasing youth unemployment rates in Spain and Portugal is the poor state of the economy in the last 4 years in these countries rather than a change in attitudes toward hiring young people.

OECD data indicate that adult unemployment is significantly lower than youth unemployment. The global economic crisis has hit young people very hard. In 2010, there were nearly 15 million unemployed youngsters in the OECD area, about four million more than at the end of 2007 (Scarpetta et al. 2010 ).

From an international perspective, and unlike other developed countries, Israel has a young age structure, with a high birthrate and a small fraction of elderly population. Israel has a mandatory retirement age, which differs for men (67) and women (62), and the labor force participation of older workers is relatively high (Stier and Endeweld 2015 ), therefore, we believe that Israel is an interesting case for studying.

The Israeli labor market is extremely flexible (e.g. hiring and firing are relatively easy), and mobile (workers can easily move between jobs) (Peretz 2016 ). Focusing on Israel’s labor market, we want to check whether this is true for older Israeli workers as well, and whether there is a difference between young and older workers.

The problem of unemployment among young people in Israel is less severe than in most other developed countries. This low unemployment rate is a result of long-term processes that have enabled the labor market to respond relatively quickly to changes in the economic environment and have reduced structural unemployment. 2 Furthermore, responsible fiscal and monetary policies, and strong integration into the global market have also promoted employment at all ages. With regard to the differences between younger and older workers in Israel, Stier and Endeweld ( 2015 ) determined that older workers, men and women alike, are indeed less likely to leave their jobs. This finding is similar to other studies showing that older workers are less likely to move from one employer to another. According to the U.S. Bureau of Labor Statistics, the median employee tenure is generally higher among older workers than younger ones (BLS 2014 ). Movement in and out of the labor market is highest among the youngest workers. However, these young people are re-employed quickly, while older workers have the hardest time finding jobs once they become unemployed. The Bank of Israel calculated the chances of unemployed people finding work between two consecutive quarters using a panel of the Labor Force Survey for the years 1996–2011. Their calculations show that since the middle of the last decade the chances of unemployed people finding a job between two consecutive quarters increased. 3 However, as noted earlier, as workers age, the duration of their unemployment lengthens. Prolonged unemployment erodes the human capital of the unemployed (Addison et al. 2004 ), which has a particularly deleterious effect on older workers. Thus, the longer the period of unemployment of older workers, the less likely they will find a job (Axelrad and Luski 2017 ). Nevertheless, as Fig.  1 shows, the rates of youth unemployment in Israel are higher than those of older workers.

Unemployed persons and discouraged workers as percentages of the civilian labor force, by age group (Bank of Israel 2011 ). We excluded those living outside settled communities or in institutions. The percentages of discouraged workers are calculated from the civilian labor force after including them in it

(Source: Calculated by the authors by using data from the Labor Force survey of the Israeli CBS, 2011)

We argue that the main reason for this situation is the status quo in the labor market, which is general and not specific to Israel. It applies both to older workers and young workers who have a job. The status quo is evident in the situation in which adults (and young people) already in the labor market manage to keep their jobs, making the entrance of new young people into the labor market more difficult. What we are witnessing is not evidence of a preference for the old over the young, but the maintaining of the status quo.

The rate of employed Israelis covered by collective bargaining agreements increases with age: up to age 35, the rate is less than one-quarter, and between 50 and 64 the rate reaches about one-half. In effect, in each age group between 25 and 60, there are about 100,000 covered employees, and the lower coverage rate among the younger ages derives from the natural growth in the cohorts over time (Bank of Israel 2013 ). The wave of unionization in recent years is likely to change only the age profile of the unionization rate and the decline in the share of covered people over the years, to the extent that it strengthens and includes tens of thousands more employees from the younger age groups. 4

The fact that the percentage of employees covered by collective agreement increases with age implies that there is a status quo effect. Older workers are protected by collective agreements, and it is hard to dismiss them (Culpepper 2002 ; Palier and Thelen 2010 ). However, young workers enter the workforce with individual contracts and are not protected, making it is easier to change their working conditions and dismiss them.

To complete the picture, Fig.  2 shows that the number of layoffs among adults is lower, possibly due to their protection under collective bargaining agreements.

Dismissal of employees in Israel, by age. Percentage of total employed persons ages 20–75 and over including those dismissed

(Source: Israeli Central Bureau of Statistics, 2008, data processed by the authors)

In order to determine the real difference between the difficulties of older versus younger individuals in finding work, we have to eliminate the effect of the status quo in the labor market. For example, if we removed all of the workers from the labor market, what would be the difference between the difficulties of older people versus younger individuals in finding work? In the next section we will analyze the probability of younger and older individuals moving from unemployment to employment when we control for the status quo. We will do so by considering only individuals who have not been employed at least part of the previous year.

Estimating the chances of finding a job and research hypotheses

Based on the literature and the classic premise that young workers are more vulnerable to economic shocks (ILO 2011 ), we posit that:

H 1 : The unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes.

Based on the low hiring rate of older workers (OECD 2006 ) and the literature about age discrimination against older workers in labor markets (Axelrad et al. 2013 ; Lahey 2005 ), we hypothesis that:

H 2 : The difficulty face by unemployed older workers searching for a job stems mainly from their age and less from the characteristics of the labor market.

To assess the chances of younger and older workers finding a job, we used a logit regression model that has been validated in previous studies (Brander et al. 2002 ; Flug and Kassir 2001 ). Being employed was the dependent variable, and the characteristics of the respondents (age, gender, ethnicity and education) were the independent variables. The dependent variable was nominal and dichotomous with two categories: 0 or 1. We defined the unemployed as those who did not work at all during the last year or worked less than 9 months last year. The dependent variable was a dummy variable of the current employment situation, which received the value of 1 if the individual worked last week and 0 otherwise.

The regression allowed us to predict the probability of an individual finding a job. The dependent variable was the natural base log of the probability ratio P divided by (1 − P) that a particular individual would find a job. The odds ratio from the regression answers the question of how much more likely it is that an individual will find a job if he or she has certain characteristics. The importance of the probability analysis is the consideration of the marginal contribution of each feature to the probability of finding a job.

We used data gathered from the 2011 Labor Force Survey 5 of the Israeli Central Bureau of Statistics (CBS), 6 which is a major survey conducted annually among households. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. Given our focus on working age individuals, we excluded all of the respondents under the age of 18 or over the age of 59. The data sample includes only the Jewish population, because structural problems in the non-Jewish sector made it difficult to estimate this sector using the existing data only. The sample does not include the ultra-Orthodox population because of their special characteristics, particularly the limited involvement of men in this population in the labor market.

The base population is individuals who did not work at all during the past year or worked less than 9 months last year (meaning that they worked but were unemployed at least part of last year). To determine whether they managed to find work after 1 year of unemployment, we used the question on the ICBS questionnaire, “Did you work last week?” We used the answer to this question to distinguish between those who had succeeded in finding a job and those who did not. The data include individuals who were out of the labor force 7 at the time of the survey, but exclude those who were not working for medical reasons (illness, disability or other medical restrictions) or due to their mandatory military service. 8

Data and variables

The survey contains 104,055 respondents, but after omitting all of the respondents under the age of 18 or above 59, those who were outside the labor force for medical reasons or due to mandatory military service, non-Jews, the ultra-Orthodox, and those who worked more than 9 months last year, the sample includes 13,494 individuals (the base population). Of these, 9409 are individuals who had not managed to find work, and 4085 are individuals who were employed when the survey was conducted.

The participants’ ages range between 18 and 59, with the average age being 33.07 (SD 12.88) and the median age being 29. 40.8% are males; 43.5% have an academic education; 52.5% are single, and 53.5% of the respondents have no children under 17.

Dependent and independent variables

While previous studies have assessed the probability of being unemployed in the general population, our study examines a more specific case: the probability of unemployed individuals finding a job. Therefore, we use the same explanatory variables that have been used in similar studies conducted in Israel (Brander et al. 2002 ; Flug and Kassir 2001 ), which were also based on an income survey and the Labor Force Survey of the Central Bureau of Statistics.

The dependent variable—being employed

According to the definition of the CBS, employed persons are those who worked at least 1 h during a given week for pay, profit or other compensation.

Independent variables

We divided the population into sub-groups of age intervals: 18–24, 25–29, 30–44, 45–54 and 55–59, according to the sub-groups provided by the CBS. We then assigned a special dummy variable to each group—except the 30–44 sub-group, which is considered as the base group. Age is measured as a dummy variable, and is codded as 1 if the individual belongs to the age group, and 0 otherwise. Age appears in the regression results as a variable in and of itself. Its significance is the marginal contribution of each age group to the probability of finding work relative to the base group (ages 30–44), and also as an interaction variable.

This variable is codded as 1 if the individual is female and 0 otherwise. Gender also appears in the interaction with age.

Marital status

Two dummy variables are used: one for married respondents and one for those who are divorced or widowed. In accordance with the practice of the CBS, we combined the divorced and the widowed into one variable. This variable is a dummy variable that is codded as 1 if the individual belongs to the appropriate group (divorced/widowed or married) and 0 otherwise. The base group is those who are single.

This variable is codded as 1 if the individual has 13 or more years of schooling, and 0 otherwise. The variable also appears in interactions between it and the age variable.

Vocational education

This variable is codded as 1 if the individual has a secondary school diploma that is not an academic degree or another diploma, and 0 otherwise.

Academic education

This variable is codded as 1 if the individual has any university degree (bachelors, masters or Ph.D.) and 0 otherwise.

In accordance with similar studies that examined the probability of employment in Israel (Brander et al. 2002 ), we define children as those up to age 17. This variable is a dummy variable that is codded as 1 if the respondents have children under the age of 17, and 0 otherwise.

This variable is codded as 1 if the individual was born in an Arabic-speaking country, in an African country other than South Africa, or in an Asian country, or was born in Israel but had a father who was born in one of these countries. Israel generally refers to such individuals as Mizrahim. Respondents who were not Mizrahim received a value of 0. The base group in our study are men aged 30–44 who are not Mizrahim.

We also assessed the interactions between the variables. For example, the interaction between age and the number of years of schooling is the contribution of education (i.e., 13 years of schooling) to the probability of finding a job for every age group separately relative to the situation of having less education (i.e., 12 years of education). The interaction between age and gender is the contribution of gender (i.e., being a female respondent) to the probability of finding a job for each age group separately relative to being a man.

To demonstrate the differences between old and young individuals in their chances of finding a job, we computed the rates of those who managed to find a job relative to all of the respondents in the sample. Table  1 shows that the rate of those who found a job declines with age. For example, 36% of the men age 30–44 found a job, but those rates drop to 29% at the age of 45–54 and decline again to 17% at the age of 55–59. As for women, 31% of them aged 30–44 found a job, but those rates drop to 20% at the age of 45–54 and decline again to 9% at the age of 55–59.

Table 1

The rate of males and females who found a job (out of the entire group)

In an attempt to determine the role of education in finding employment, we created Model 1 and Model 2, which differ only in terms of how we defined education. In Model 1 the sample is divided into two groups: those with up to 12 years of schooling (the base group) and those with 13 or more years of schooling. In Model 2 there are three sub-groups: those with a university degree, those who have a vocational education, and the base group that has only a high school degree.

Table  2 shows that the probability of a young person (age 18–24) getting a job is larger than that of an individual aged 30–44 who belongs to the base group (the coefficient of the dummy variable “age 18–24” is significant and positive). Similarly, individuals who are older than 45 are less likely than those in the base group to find work.

Table 2

Chances of being employed—entire sample

Dependent variable: being employed

Included observations: 13,495

*  p  < 0.1, **  p  < 0.05, ***  p  < 0.01

Women aged 30–44 are less likely to be employed than men in the same age group. Additionally, when we compare women aged 18–24 to women aged 30–44, we see that the chances of the latter being employed are lower. Older women (45+) are much less likely than men of the same age group to find work. Additionally, having children under the age of 17 at home reduces the probability of finding a job.

A university education increases the probability of being employed for both men and women aged 30–44. Furthermore, for older people (55+) an academic education reduces the negative effect of age on the probability of being employed. While a vocational education increases the likelihood of finding a job for those aged 30–44, such a qualification has no significant impact on the prospects of older people.

Interestingly, being a Mizrahi Jew increases the probability of being employed.

In addition, we estimated the models separately twice—for the male and for the female population. For male and female, the probability of an unemployed individual finding a job declines with age.

Analyzing the male population (Table  3 ) reveals that those aged 18–24 are more likely than the base group (ages 30–44) to find a job. However, the significance level is relatively low, and in Model 2, this variable is not significant at all. Those 45 and older are less likely than the base group (ages 30–44) to find a job. Married men are more likely than single men to be employed. However, divorced and widowed men are less likely than single men to find a job. For men, the presence in their household of children under the age of 17 further reduces the probability of their being employed. Mizrahi men aged 18–24 are more likely to be employed than men of the same age who are from other regions.

Table 3

Chances of being employed—males and females separately

Table  3 illustrates that educated men are more likely to find work than those who are not. However, in Model 1, at the ages 18–29 and 45–54, the probability of finding a job for educated men is less than that of uneducated males. Among younger workers, this might be due to excess supply—the share of academic degree owners has risen, in contrast to almost no change in the overall share of individuals receiving some other post-secondary certificate (Fuchs 2015 ). Among older job seeking men, this might be due to the fact that the increase in employment among men during 2002–2010 occurred mainly in part-time jobs (Bank of Israel 2011 ). In Model 2, men with an academic or vocational education have a better chance of finding a job, but at the group age of 18–24, those with a vocational education are less likely to find a job compared to those without a vocational education. The reason might be the lack of experience of young workers (18–24), experience that is particularly needed in jobs that require vocational education (Salvisberg and Sacchi 2014 ).

Analyzing the female population (Table  3 ) reveals that women between 18 and 24 are more likely to be employed than those who are 30–44, and those who are 45–59 are less likely to be employed than those who are 30–44. The probability of finding a job for women at the age of 25 to 29 is not significantly different from the probability of the base group (women ages 30–44).

Married women are less likely than single women to be employed. Women who have children under the age of 17 are less likely to be employed than women who do not have dependents that age. According to Model 2, Mizrahi women are more likely to be employed compared to women from other regions. According to both models, women originally from Asia or Africa ages 25–29 have a better chance of being employed than women the same age from other regions. Future research should examine this finding in depth to understand it.

With regard to education, in Model 1 (Table  3 ), where we divided the respondents simply on the question of whether they had a post-high school education, women who were educated were more likely to find work than those who were not. However, in the 18–29 age categories, educated women were less likely to find a job compared to uneducated women, probably due to the same reason cited above for men in the same age group—the inflation of academic degrees (Fuchs 2015 ). These findings become more nuanced when we consider the results of Model 2. There, women with an academic or vocational education have a better chance of finding a job, but at the ages of 18–24 those with an academic education are less likely to find a job than those without an academic education. Finally, at the ages of 25–29, those with a vocational education have a better chance of finding a job than those without a vocational education, due to the stagnation in the overall share of individuals receiving post-secondary certificate (Fuchs 2015 ).

Thus, based on the results in Table  3 , we can draw several conclusions. First, the effect of aging on women is more severe than the impact on men. In addition, the “marriage premium” is positive for men and negative for women. Divorced or widowed men lose their “marriage premium”. Finally, having children at home has a negative effect on both men and women—almost at the same magnitude.

Unemployment as a function of the business cycle

To determine whether unemployment of young workers is caused by the business cycle, we examined the unemployment figures in 34 OECD countries in 2007–2009, years of economic crisis, and in 2009–2011, years of recovery and economic growth. For each country, we considered the data on unemployment among young workers (15–24) and older adults (55–64) and calculated the difference between 2009 and 2007 and between 2011 and 2009 for both groups. The data were taken from OECD publications and included information about the growth rates from 2007 to 2011. Our assessment of unemployment rates in 34 OECD countries reveals that the average rate of youth unemployment in 2007 was 13.4%, compared to 18.9% in 2011, so the delta of youth unemployment before and after the economic crisis was 5.55. The average rate of adult unemployment in 2007 was 4% compared to 5.8% in 2011, so the delta for adults was 1.88. Both of the differences are significantly different from zero, and the delta for young people is significantly larger than the delta for adults. These results indicate that among young people (15–24), the increase in unemployment due to the crisis was very large.

An OLS model of the reduced form was estimated to determine whether unemployment is a function of the business cycle, which is represented by the growth rate. The variables GR2007, GR2009 and GR2011 are the rate of GDP growth in 2007, 2009 and 2011 respectively ( Appendix ). The explanatory variable is either GR2009 minus GR2007 or GR2011 minus GR2009. In both periods, 2007–2009 and 2009–2011, the coefficient of the change in growth rates is negative and significant for young people, but insignificant for adults. Thus, it seems that the unemployment rates of young people are affected by the business cycle, but those of older workers are not. In a time of recession (2007–2009), unemployment among young individuals increases whereas for older individuals the increase in unemployment is not significant. In recovery periods (2009–2011), unemployment among young individuals declines, whereas the drop in unemployment among older individuals is not significant (Table  4 ).

Table 4

Unemployment rate as a function of the business cycle

Dependent variable: the increase in the unemployment rate between 2007 and 2009, and between 2009 and 2011

Summary and conclusions

The purpose of this paper was to show that while the unemployment rates of young workers are higher than those of older workers, the data alone do not necessarily tell the whole story. Our findings confirm our first hypothesis, that the high unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes. Using data from Israel and 34 OECD countries, we demonstrated that a country’s growth rate is the main factor that determines youth unemployment. However, the GDP rate of growth cannot explain adult unemployment. Our results also support our second hypothesis, that the difficulties faced by unemployed older workers when searching for a job are more a function of their age than the overall business environment.

Indeed, one limitation of the study is the fact that we could not follow individuals over time and capture individual changes. We analyze a sample of those who have been unemployed in the previous year and then analyze the probability of being employed in the subsequent year but cannot take into account people could have found a job in between which they already lost again. Yet, in this sample we could isolate and analyze those who did not work last year and look at their employment status in the present. By doing so, we found out that the rate of those who found a job declines with age, and that the difficulties faced by unemployed older workers stems mainly from their age.

To solve both of these problems, youth unemployment and older workers unemployment, countries need to adopt different methods. Creating more jobs will help young people enter the labor market. Creating differential levels for the minimum wage and supplementing the income of older workers with earned income tax credits will help older people re-enter the job market.

Further research may explore the effect of structural and institutional differences which can also determine individual unemployment vs. employment among different age groups.

In addition to presenting a theory about the factors that affect the differences in employment opportunities for young people and those over 45, the main contribution of this paper is demonstrating the validity of our contention that it is age specifically that works to keep older people out of the job market, whereas it is the business cycle that has a deleterious effect on the job prospects of younger people. Given these differences, these two sectors of unemployment require different approaches for solving their employment problems. The common wisdom maintains that the high level of youth unemployment requires policy makers to focus on programs targeting younger unemployed individuals. However, we argue that given the results of our study, policy makers must adopt two different strategies to dealing with unemployment in these two groups.

Policy implications

In order to cope with the problem of youth unemployment, we must create more jobs. When the recession ends in Portugal and Spain, the problem of youth unemployment should be alleviated. Since there is no discrimination against young people—evidenced by the fact that when the aggregate level of economic activity and the level of adult employment are high, youth employment is also high—creating more jobs in general by enhancing economic growth should improve the employment rates of young workers.

In contrast, the issue of adult unemployment requires a different solution due to the fact that their chances of finding a job are related specifically to their age. One solution might be a differential minimum wage for older and younger individuals and earned income tax credits (EITC) 9 for older individuals, as Malul and Luski ( 2009 ) suggested.

According to this solution, the government should reduce the minimum wage for older individuals. As a complementary policy and in order to avoid differences in wages between older and younger individuals, the former would receive an earned income tax credit so that their minimum wage together with their EITC would be equal to the minimum wage of younger individuals. Earned income tax credits could increase employment among older workers while increasing their income. For older workers, EITCs are more effective than a minimum wage both in terms of employment and income. Such policies of a differential minimum wage plus an EITC can help older adults and constitute a kind of social safety net for them. Imposing a higher minimum wage exclusively for younger individuals may be beneficial in encouraging them to seek more education.

Young workers who face layoffs as a result of their high minimum wage (Kalenkoski and Lacombe 2008 ) may choose to increase their investment in their human capital (Nawakitphaitoon 2014 ). The ability of young workers to improve their professional level protects them against the unemployment that might result from a higher minimum wage (Malul and Luski 2009 ). For older workers, if the minimum wage is higher than their productivity, they will be unemployed. This will be true even if their productivity is higher than the value of their leisure. Such a situation might result in an inefficient allocation between work and leisure for this group. One way to fix this inefficient allocation without reducing the wages of older individuals is to use the EITC, which is actually a subsidy for this group. This social policy might prompt employers to substitute older workers with a lower minimum wage for more expensive younger workers, making it possible for traditional factories to continue their domestic production. However, a necessary condition for this suggestion to work is the availability of efficient systems of training and learning. Axelrad et al. ( 2013 ) provided another justification for subsidizing the work of older individuals. They found that stereotypes about older workers might lead to a distorted allocation of the labor force. Subsidizing the work of older workers might correct this distortion. Ultimately, however, policy makers must understand that they must implement two different approaches to dealing with the problems of unemployment among young people and in the older population.

Authors’ contributions

HA, MM and IL conceptualized and designed the study. HA collected and managed study data, HA and IL carried out statistical analyses. HA drafted the initial manuscript. MM and IL reviewed and revised the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have any no competing interests.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  5 .

Table 5

Gross domestic product, volume, annual growth rates in percentage.

Source: National Accounts at a Glance 2014, OECD, 2014. http://www.oecd-ilibrary.org/economics/national-accounts-at-a-glance-2014_na_glance-2014-en

1 For example, in the US, the UK and Portugal, we witnessed higher rates of growth during late 1990 s and lower rates of youth unemployment compared to 2011.

2 Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

3 Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

4 http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/RecentEconomicDevelopments/develop136e.pdf .

5 The Labor Force Survey is a major survey conducted by the Israeli Central Bureau of Statistics among households nationwide. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. The publication contains detailed data on labor force characteristics such as their age, years of schooling, type of school last attended, and immigration status. It is also a source of information on living conditions, mobility in employment, and many other topics.

6 The survey population is the permanent (de jure) population of Israel aged 15 and over. For more details see: http://www.cbs.gov.il/publications13/1504/pdf/intro04_e.pdf .

7 When we looked at those who had not managed to find a job at the time of the survey, we included all individuals who were not working, regardless of whether they were discouraged workers, volunteers or had other reasons. As long as they are not out of the labor force due to medical reasons or their mandatory military service, we classified them as "did not manage to find a job."

8 Until 2012, active soldiers were considered outside the labor force in the samples of the CBS.

9 EITC is a refundable tax credit for low to moderate income working individuals and couples.

Contributor Information

Hila Axelrad, Phone: 972 523524746, Phone: 617 459 4116, Email: li.ca.uat.xeuat@xaalih , Email: ude.cb@hdarlexa .

Miki Malul, Phone: 972-8-6472775, Email: li.ca.ugb.mos@lulam .

Israel Luski, Phone: 972-4-9015229, Email: li.ca.lilagw@llearsi .

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The Relationship Between Work and Health: Findings from a Literature Review

Larisa Antonisse and Rachel Garfield Published: Aug 07, 2018

• Issue Brief

A central question in the current debate over work requirements in Medicaid is whether such policies promote health and are therefore within the goals of the Medicaid program. Work requirements in welfare programs in the past have had different goals of strengthening self-esteem and providing a ladder to economic progress, versus improving health. This brief examines literature on the relationship between work and health and analyzes the implications of this research in the context of Medicaid work requirements. We review literature cited in policy documents, as well as additional studies identified through a search of academic papers and policy evaluation reports, focusing primarily on systematic reviews and meta-analyses. Key findings include the following:

KFF review: Research about the relationship between work and health finds only limited evidence that employment improves health, with some studies showing a positive impact and others showing no relationship or only limited effects.

• Job availability and quality are important modifiers in how work affects health; transition from unemployment to poor quality or unstable employment options can be detrimental to health.
• Selection bias in the research (e.g., healthy people being more likely to work) and other methodological limitations restrict the ability to determine a causal work-health relationship.
• The work-health relationship may differ for the Medicaid population compared to the broader populations studied in the literature, as Medicaid enrollees report worse health than the general population and face significant challenges related to social determinants of health.
• Limited job availability or poor job quality may moderate or reverse any positive effects of work.
• Work or volunteering to fulfill a requirement may produce different health effects than work or volunteer activities studied in existing literature.
• Loss of Medicaid coverage under work requirements could negatively impact health care access and outcomes, as well as exacerbate health disparities.

Introduction

On January 11, 2018, CMS issued a State Medicaid Director Letter  providing new guidance for Section 1115 waiver proposals that would impose work requirements (referred to as community engagement) in Medicaid as a condition of eligibility. On January 12, 2018, CMS approved the first work requirement waiver in  Kentucky , and three additional work requirement waiver approvals followed in  Indiana  (February 1, 2018), Arkansas (March 5, 2018), and New Hampshire (May 7, 2018). The new guidance and work requirement approvals reverse previous positions of both Democratic and Republican Administrations, which had not approved work requirement waiver requests on the basis that such provisions would not further the Medicaid program’s purposes of promoting health coverage and access. However, in both the new guidance and work requirement waiver approvals, CMS explains its policy reversal by maintaining that employment leads to improved health outcomes, and policies that condition Medicaid eligibility on meeting a work requirement will further this objective. Though the structure of work requirements is similar to those used in other programs, the administration’s stated goal of  improving health through Medicaid work requirements is different from the goals of welfare reform work requirements in the past, which were to strengthen self-esteem and provide a ladder to economic progress.

On June 29, 2018, the DC federal district court vacated HHS’s approval of the Kentucky Section 1115 waiver program. The court held that consideration of whether the waiver would promote beneficiary health in general is not a substitute for considering whether the waiver promotes Medicaid’s primary purpose of providing affordable health coverage and remanded to HHS to consider how the waiver would help furnish medical assistance consistent with Medicaid program objectives. However, the court also noted that plaintiffs and their amici assert that proclaimed health benefits of employment are unsupported by substantial evidence. Thus, there is likely to be ongoing debate and policy discussion over whether work requirements will further the aims of Medicaid.

To address whether work will further the aims of Medicaid, we examine the literature on the relationship between work and health and analyze the implications of this research in the context of Medicaid work requirements. Due to the large number of studies in this field spanning decades, this literature review focuses primarily (although not exclusively) on findings from other literature or systematic reviews rather than individual studies on these topics. We drew on studies cited in policy documents on work requirements in Medicaid, results of keyword searches of PubMed and other academic health/social policy search engines, and snowballing through searches of reference lists in previously pulled papers. In total, we reviewed more than 50 sources, the vast majority of which were published academic studies or program evaluations and most of which are reviews of multiple studies themselves. A more detailed description of the methods underlying this analysis is provided in the Methods box at the end of this brief.

What effect do health and health coverage have on work?

Not surprisingly, research has demonstrated that being in poor health is associated with an increased risk of job loss or unemployment. 1 , 2 , 3 , 4 , 5 A meta-analysis of longitudinal studies on the relationship between health measures and exit from paid employment found that poor health, particularly self-perceived health, is associated with increased risk of exit from paid employment. 6 Another study that simultaneously examined and contrasted the relative effects of unemployment on mental health and mental health on employment status in a single general population sample found mental health to be both a consequence of and a risk factor for unemployment. However, the evidence for men in particular suggested that mental health was a stronger predictor of subsequent unemployment than unemployment was a predictor of subsequent mental health. 7 Additional research suggests that, in some cases, individual characteristics such as income, race, sex, or education level may mediate the relationship between poor health and unemployment. 8 , 9 10 Research also demonstrates that an unmet need for mental health or substance use disorder treatment results in greater difficulty with obtaining and maintaining employment. 11 , 12 , 13 , 14 , 15

Additional research suggests that, in addition, access to affordable health insurance and care, which may help people maintain or manage their health, promotes individuals’ ability to obtain and maintain employment. For example, in an analysis of Medicaid expansion in Ohio, most expansion enrollees who were unemployed but looking for work reported that Medicaid enrollment made it easier to seek employment, and over half of employed expansion enrollees reported that Medicaid enrollment made it easier to continue working. 16 Similarly, a study on Medicaid expansion in Michigan found that 69% of enrollees who were working said they performed better at work once they got coverage, and 55% of enrollees who were out of work said the coverage made them better able to look for a job. 17 A study on Montana’s Medicaid expansion found a substantial increase of 6 percentage points in labor force participation among low-income, non-disabled Montanans ages 18-64 following expansion, compared to a decline in labor force participation among higher-income Montanans. 18 National research found increases in the share of individuals with disabilities reporting employment and decreases in the share reporting not working due to a disability in Medicaid expansion states following expansion implementation, with no corresponding trends observed in non-expansion states. 19 Additional literature suggests that access to health insurance and care promotes volunteerism, finding that the expansion of Medicaid under the ACA was significantly associated with increased volunteerism among low-income adults. 20 , 21

What effect does work have on health and health coverage?

Overall, the body of literature examining whether work affects health shows mixed results, with some studies showing a positive effect of work on health yet others showing no relationship or isolated effects . A 2006 literature review found that, while “there is limited amount of high quality scientific evidence that directly addresses the question [of whether work is good for your health]… there is a strong body of indirect evidence that work is generally good for health and well-being.” 22 That assessment was based on comprehensive review of the literature, including other systematic reviews as well as narrative and opinion pieces. A more focused 2014 systematic review about the health effects of employment, which included 33 longitudinal studies, 23 found strong evidence that employment reduces the risk of depression and improves general mental health, yet it found insufficient evidence for an effect on other health outcomes due to a lack of studies or inconsistent findings of the studies. 24 A 2015 review of 22 longitudinal studies found an association between employment and re-employment with better physical health. 25

In contrast, research shows a strong association between unemployment and poor health outcomes, though researchers caution that these findings do not necessarily mean the reverse is true (e.g. employment causes improved health). The effect of unemployment on health has long been an area of research focus, and a substantial body of research from the U.S. and abroad consistently demonstrates a strong association between unemployment and poorer health outcomes, 26 , 27 , 28 , 29 30 , 31 , 32  with some evidence suggesting a causal relationship in which unemployment leads to poor health. 33 , 34 , 35 The bulk of the research in the unemployment and health field focuses on mental health outcomes. 36   Examples of negative health outcomes associated with unemployment include increases in depression, anxiety, mixed symptoms of distress, and low self-esteem. 37 , 38 A more limited body of research suggests an association of unemployment with poorer physical health (including increases in cardiovascular risk factors such as hypertension and serum cholesterol as well as increased susceptibility to respiratory infections), and mortality. 39 , 40 A 2006 literature review noted that there is continuing debate about the relative importance of possible mechanisms involved in this relationship, and adverse effects of unemployment may vary in nature and degree for different individuals in different social contexts. 41 Some evidence also indicates that cumulative length of unemployment is correlated with deteriorated health and health behavior. 42 However, despite the evidence of a relationship between unemployment and health, researchers caution against using findings to infer that an opposite relationship (employment causing improved health) exists. 43 , 44   In addition, researchers note that the literature on unemployment tends to study more negative than positive health outcome variables, 45 which may skew our understanding of the health effects of unemployment. 46

Another related area of research is studies examining the relationship between re-employment (i.e., returning to work) and health, which find some association between re-employment and mental health . A 2012 systematic review on this topic found support for a beneficial health effect of returning to work, with most of the 18 studies included in this review focusing on mental health-related outcomes. 47 The review also tried to assess to what extent the relationship was causal (i.e., reemployment caused health improvements) versus due to selection (e.g., people with poor health were more likely to remain unemployed) and concluded that both were at play. The review did not reach a definitive conclusion about mechanisms linking re-employment to improved health (due to lack of evidence), and it noted that it is still unclear whether health effects of reemployment are moderated by factors such as socioeconomic status, reason for unemployment, and the nature of employment. 48 The 2006 literature review described above also analyzed research findings on re-employment and found strong evidence that re-employment leads to improved psychological health and measures of general well-being, with a dearth of information on physical health and some but not all studies showing that re-employment/health relationship is at least partly due to health selection. However, these authors also cite evidence from numerous studies suggesting that “the beneficial effects of re-employment depend mainly on the security of the new job, and also on the individual’s motivation, desires, and satisfaction” 49

Research review: Low-quality, unstable and poorly paid jobs lead to or are associated with adverse health effects, suggesting that all jobs should not be expected to have similar effects on workers’ health.</p> <p>

Studies on work and health have found that the quality and stability of work is a key factor in the work-health relationship: research finds that low-quality, unstable, or poorly-paid jobs lead to or are associated with adverse effects on health. 50 , 51 , 52 , 53 , 54 , 55 , 56   For example, a 2014 meta-analysis of studies published after 2004 found that job insecurity can pose a comparable (and even modestly increased) risk of subsequent depressive symptoms compared to unemployment. 57 A 2011 longitudinal analysis found that while unemployed respondents had poorer mental health than those who were employed, the mental health of those who were unemployed was comparable or more often superior to those in jobs of poor psychosocial quality (based on measures of job control, perceived job security, and job demands and complexity) and the mental health of those in poor quality jobs declined more over time than the mental health of those who were unemployed. Moreover, while moving from unemployment into a high quality job led to improvement in mental health, the transitioning from unemployment to a poor quality job was more detrimental to mental health than remaining unemployed. 58 Additionally, a 2003 study that examined the association of different employment categories with physical health and depression found a consistent association between less than optimal jobs (based on economic, non-income, and psychological aspects of the jobs) and poorer physical and mental health among adults. 59

It is possible that the work-health association reflects people in good health being more likely to work, versus work causing good health. Some researchers caution against the possibility that selection bias has occurred in many of the studies on work and health. The existence of a “healthy worker effect”—in which relatively healthy individuals are more likely to enter the workforce whereas those with health problems are at increased risk to withdraw from and remain outside of the workforce—has been documented in multiple studies. 60 , 61 , 62 , 63 64 , 65   Authors of both individual studies and literature reviews on this topic explain that the healthy worker effect is difficult to control for even in studies that attempt to do so, and thus this effect may cause an overestimation of the findings in the literature on health effects of work. 66 , 67 As authors of a 2014 systematic review of studies on health effects of employment point out, there are no randomized controlled trials on this topic available in the literature because performing such trials would be unethical, 68 yet randomized controlled trials are the gold standard for determining a causal relationship.

Most study authors specifically note additional caveats to drawing broad conclusions about work and health. The 2006 review concluding a general positive effect of work on health emphasized three major provisos to this conclusion: (1) findings are about average or group affects, and a minority of people may experience contrary health effects from work, (2) the beneficial health effects of work depend on the nature and quality of work (described above), and (3) the social context must be taken into account, particularly social gradients in health (i.e. inequalities in population health status related to inequalities in social status) and regional deprivation. 69 These caveats could explain the seemingly contradictory findings about employment and unemployment: While unemployment is almost universally a negative experience and thus linked to poor outcomes, especially poor mental health outcomes, employment may be positive or negative, depending on the nature of the job (e.g., stability, stress, hours, pay, etc.). As discussed below, these provisos have implications for the applicability of research to Medicaid work requirements.

While work can help people access employer-sponsored health coverage, many jobs—especially low-wage jobs—do not come with an affordable offer of employer coverage. In 2017, just over half (53%) of firms offered health coverage to their employees, 70 and workers in low-wage firms are less likely than those in higher wage firms to be eligible for coverage through their employer. 71 In 2017, less than a third of workers who worked at or below their state’s minimum wage had an offer of health coverage through their employer. 72 Though most employees take up employer-sponsored coverage when offered, workers in low-wage firms are less likely to be covered by their employer even if coverage is offered, likely reflecting the fact that workers in such firms pay a larger share of the premium than workers in higher-wage firms. 73 The fact that work does not always lead to health coverage is further demonstrated by the large majority of uninsured people who are in a family with either a full-time (74%) or part-time (11%) worker. 74

What is the effect of volunteerism on health?

In the January 2018 guidance, CMS includes volunteering as a “community engagement” activity that may improve health outcomes, 75 and the Medicaid work requirement waivers approved to date all permit volunteer activities to count towards the required weekly/monthly hours of work activity.

However, there is limited existing evidence that volunteer activities benefit health outcomes. One literature review on the health effects of volunteering “did not find any consistent, significant health benefits arising through volunteering” based on experimental studies available at the time of the literature review. 76 The authors’ analysis of cohort studies revealed limited benefits of volunteering on depression, life satisfaction, and well-being (with no significant benefits on physical health). In addition, the cohort studies focused primarily on volunteers ages 50 and over, with some of the studies suggesting that the association between volunteerism and improved health outcomes may be limited to older volunteers and that that the health benefits of volunteering may diminish as hours of volunteering increase. 77 Another study (published in 2018) examined the health benefits of “other-oriented volunteering” (other-regarding, altruistic, and humanitarian-concerned volunteering) compared to “self-oriented volunteering” (volunteering focused on seeking benefits and enhancing the volunteers themselves in return). While the authors found beneficial effects of both forms of volunteer activity on health and well-being, other-oriented volunteering had significantly stronger effects on the health outcomes of mental and physical health, life satisfaction, and social well-being than did self-oriented volunteering. 78 As discussed below, this finding may indicate that health benefits of volunteering are likely to be weaker when individuals are compelled to engage in volunteering.

What does this research mean for Medicaid work requirements?

The body of literature summarized above includes several notable caveats and conclusions to consider in applying findings to a work requirement in Medicaid. Limitations and implications that are particularly relevant include:

Effects found for the general population may not apply to Medicaid, as the link between work and health is not universal across populations or social contexts. In general, the studies examined above analyze the relationship between work and health among broad populations of all income levels. However, several authors suggest that population differences may modify the relationship between work and health.  A 2003 study found that nationally, older adults, women, blacks, and individuals with low education levels were more likely to be employed in jobs viewed as “barely adequate” or “inadequate” (the types of jobs that the study found to be independently associated with poorer physical health and higher rates of depression) compared to other populations. 79 Authors of a 2006 literature review qualify their broad findings on the work/health relationship with the proviso that the social context must be taken into account (particularly social inequities in health and regional deprivation), and also cite evidence that the strong association between socioeconomic status and physical and mental health and mortality likely outweighs (and is confounded with) all other work characteristics that influence health. 80 Authors of a 2005 review on unemployment and health found a strong association between deprived areas, poor health, poverty and unemployment (although the exact relationship is not clear), and highlight the need for more research on the geographical dimension on unemployment and health. 81 These findings imply that the work/health relationship may differ significantly for the low-income Medicaid population, who report worse health status compared to the total US population and often face more significant challenges related to housing, food security, and other social determinants of health. 82 , 83 , 84 In addition, some volunteerism research suggests that the association between volunteerism and improved health outcomes may be limited to older volunteers, yet approved and pending Section 1115 Medicaid work requirement waiver requests all include exemptions for individuals above a certain age (which varies by state but ranges from 50 to 65 years). 85

Work or volunteering undertaken to fulfill a requirement may produce different health effects than work and volunteer activities studied in existing literature. For example, research on health effects of work requirements in Temporary Assistance for Needy Families (TANF) suggests that they did not benefit and sometimes negatively affected health among enrollees and their dependents. 86 Another study found that welfare reform was associated with increases in self-reported poor health and self-reported disability among white single mothers without a high school diploma or GED. 87 These adverse effects could reflect different relationships between work and health for low-income populations, as described above, or different effects of work undertaken voluntarily versus as a requirement. Authors of a 2006 literature review on work and health found that forcing claimants off benefits and into work without adequate supports would more likely harm than improve their health and well-being. 88 Similarly, most studies on volunteerism and health define volunteerism as an act of free-will (essentially, a voluntary act), a definition that may not be applicable to volunteer activity undertaken for the purpose of meeting work/community engagement requirements in order to maintain eligibility for Medicaid. Volunteer activities undertaken to retain Medicaid appear more closely aligned with the self-oriented form of volunteerism (volunteering focused on seeking benefits and enhancing the volunteers themselves in return), which research shows has weaker health effects than the other-oriented form (other-regarding, altruistic, and humanitarian-concerned volunteering).

Limited job availability, low demand for labor, or poor job quality may moderate any positive health effects of employment. Authors of a 2014 systematic review of prospective studies on health effects of employment commented that most studies in this field do not adjust for quality of employment and include all kinds of jobs in their analysis (e.g. part- and full-time employment, self-employment, and both blue- and white-collared jobs) despite the possibility that different forms of employment have different health effects. 89 Under Medicaid work requirement programs, the population subject to Medicaid work requirements may have access to only low-wage, unstable, or low-quality jobs to meet the weekly/monthly hours requirement, as these are the types of positions adults with Medicaid who currently work hold. 90 In discussing the policy implications of their findings, multiple researchers have concluded that such policies could be detrimental to health, with authors of one study asserting that, “Policies that promote job growth without giving attention to the overall adequacy of the jobs may undermine health and well-being.” 91

Long-term effects of work on health are unclear. Much of the evidence on the work/health relationship is about short-term effects after about one year, which, as authors of one literature review point out, is a short period when assessing health impacts. 92 There is less evidence on longer-term effects over a lifetime perspective. 93 In addition, research on work requirements in other public programs shows little evidence of long-term impacts on employment or income. Studies on welfare recipients subject to work requirements generally have found that any initial increase in employment after an imposition of a work requirement faded over time. 94 , 95 , 96 After five years, one study showed those who were not required to work were just as likely or more likely to be working compared to those who were subject to a work requirement, suggesting that these work requirements had little impact on increasing employment over the long-term. 97 Other research has found that employment among people who left welfare was unsteady and did not lift them out of poverty. 98 Thus, even short-term effects are likely to disappear as short-term boosts in employment fade over time.

Loss of health insurance coverage due to not meeting reporting or work requirements under waivers could affect access to health care and health. Low-wage workers typically work in small firms and industries that often have limited employer-based coverage options, and very few have an offer of coverage through their employer. Work requirements in Medicaid could lead to large Medicaid coverage losses, especially among people who would remain eligible for the program but lose coverage due to new administrative burdens or red tape versus those who would lose eligibility due to not working. 99 Several studies on individuals leaving TANF following welfare reform show reductions in insurance coverage across this “welfare leaver” population, with significant decreases in Medicaid coverage that were not fully offset by the smaller increases in private coverage. 100 , 101 , 102 , 103 , 104 A study evaluating welfare-to-work interventions found that some programs led to a reduction in health insurance coverage for both children and parents. 105   Given the evidence of Medicaid’s positive impact on access to care and health outcomes, 106 as well as data demonstrating that uninsured individuals go without needed care due to cost at much higher rates than those with Medicaid coverage, 107 widespread coverage losses as a result of Medicaid work requirements are likely to result in adverse effects on health outcomes. In TANF evaluations, for example, studies found that children of TANF enrollees who lose benefits for failure to comply with a work requirement experience adverse health effects such as behavioral health problems 108 or hospitalization. 109

Policies that have disproportionate effects on certain Medicaid enrollees could widen health disparities. Data demonstrate the persistence of clear disparities in health insurance coverage, access to care, and health outcomes for certain vulnerable populations in the US, including people with disabilities (compared to their non-disabled counterparts) 110 and people of color (compared to whites). 111 Research shows that people with disabilities and people of color are face disproportionate challenges in meeting and are disproportionately sanctioned under existing work requirement programs. 112 , 113 If racial minority groups, people with disabilities, or other vulnerable populations face similarly disproportionate challenges in meeting work requirements when they are attached to the Medicaid program, these policies could result in wider disparities in health insurance coverage and health outcomes.

Looking Ahead

Taken as a whole, the large body of research on the link between work and health indicates that proposed policies requiring work as a condition of Medicaid eligibility may not necessarily benefit health among Medicaid enrollees and their dependents, and some literature also suggests that such policies could negatively affect health. While it is difficult to determine a causal relationship between employment and health status (largely due to challenges controlling for health selection bias and the inability to conduct randomized controlled trials on this topic), there is strong evidence of an association between employment and good health. However, research suggests that factors like job availability and quality, as well as the social context of workers, mediate the effect of work or work requirements on health. Given the characteristics of the Medicaid population, research indicates that policies could lead to emotional strain, loss of health coverage, or widening of health disparities for vulnerable populations. As debate considers the question of whether policies to promote health—versus health coverage—are the aim of the Medicaid program, the question of whether work requirements will promote health also will remain key to the ongoing debate over the legality of work requirements in Medicaid.

• Work Requirements
• ISSUE BRIEF

news release

• Does Employment Lead to Improved Health? New Research Review Finds Mixed Evidence with Caveats that Could Impact Applicability to Medicaid Work Requirements

Also of Interest

• Implications of a Medicaid Work Requirement: National Estimates of Potential Coverage Losses
• Implications of Work Requirements in Medicaid: What Does the Data Say?
• Explaining Stewart v. Azar: Implications of the Court’s Decision on Kentucky’s Medicaid Waiver

• Original Article
• Open access
• Published: 08 March 2018

Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?

• Hila Axelrad 1 , 2 ,
• Miki Malul 3 &
• Israel Luski 4  

Journal for Labour Market Research volume  52 , Article number:  3 ( 2018 ) Cite this article

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In this research we show that workers aged 30–44 were significantly more likely than those aged 45–59 to find a job a year after being unemployed. The main contribution is demonstrating empirically that since older workers’ difficulties are related to their age, while for younger individuals the difficulties are more related to the business cycle, policy makers must devise different programs to address unemployment among young and older individuals. The solution to youth unemployment is the creation of more jobs, and combining differential minimum wage levels and earned income tax credits might improve the rate of employment for older individuals.

1 Introduction

Literature about unemployment references both the unemployment of older workers (ages 45 or 50 and over) and youth unemployment (15–24). These two phenomena differ from one another in their characteristics, scope and solutions.

Unemployment among young people begins when they are eligible to work. According to the International Labor Office (ILO), young people are increasingly having trouble when looking for their first job (ILO 2011 ). The sharp increase in youth unemployment and underemployment is rooted in long-standing structural obstacles that prevent many youngsters in both OECD countries and emerging economies from making a successful transition from school to work. Not all young people face the same difficulties in gaining access to productive and rewarding jobs, and the extent of these difficulties varies across countries. Nevertheless, in all countries, there is a core group of young people facing various combinations of high and persistent unemployment, poor quality jobs when they do find work and a high risk of social exclusion (Keese et al. 2013 ). The rate of youth unemployment is much higher than that of adults in most countries of the world (ILO 2011 ; Keese et al. 2013 ; O’Higgins 1997 ; Morsy 2012 ). Official youth unemployment rates in the early decade of the 2010s ranged from under 10% in Germany to around 50% in Spain ( http://www.indexmundi.com/g/r.aspx?v=2229 ; Pasquali 2012 ). The youngest employees, typically the newest, are more likely to be let go compared to older employees who have been in their jobs for a long time and have more job experience and job security (Furlong et al. 2012 ). However, although unemployment rates among young workers are relatively higher than those of older people, the period of time they spend unemployed is generally shorter than that of older adults (O’Higgins 2001 ).

We would like to argue that one of the most important determinants of youth unemployment is the economy’s rate of growth. When the aggregate level of economic activity and the level of adult employment are high, youth employment is also high. Footnote 1 Quantitatively, the employment of young people appears to be one of the most sensitive variables in the labor market, rising substantially during boom periods and falling substantially during less active periods (Freeman and Wise 1982 ; Bell and Blanchflower 2011 ; Dietrich and Möller 2016 ). Several explanations have been offered for this phenomenon. First, youth unemployment might be caused by insufficient skills of young workers. Another reason is a fall in aggregate demand, which leads to a decline in the demand for labor in general. Young workers are affected more strongly than older workers by such changes in aggregate demand (O’Higgins 2001 ). Thus, our first research question is whether young adults are more vulnerable to economic shocks compared to their older counterparts.

Older workers’ unemployment is mainly characterized by difficulties in finding a new job for those who have lost their jobs (Axelrad et al. et al. 2013 ). This fact seems counter-intuitive because older workers have the experience and accumulated knowledge that the younger working population lacks. The losses to society and the individuals are substantial because life expectancy is increasing, the retirement age is rising in many countries, and people are generally in good health (Axelrad et al. 2013 ; Vodopivec and Dolenc 2008 ).

The difficulty that adults have in reintegrating into the labor market after losing their jobs is more severe than that of the younger unemployed. Studies show that as workers get older, the duration of their unemployment lengthens and the chances of finding a job decline (Böheim et al. 2011 ; De Coen et al. 2010 ). Therefore, our second research question is whether older workers’ unemployment stems from their age.

In this paper, we argue that the unemployment rates of young people and older workers are often misinterpreted. Even if the data show that unemployment rates are higher among young people, such statistics do not necessarily imply that it is harder for them to find a job compared to older individuals. We maintain that youth unemployment stems mainly from the characteristics of the labor market, not from specific attributes of young people. In contrast, the unemployment of older individuals is more related to their specific characteristics, such as higher salary expectations, higher labor costs and stereotypes about being less productive (Henkens and Schippers 2008 ; Keese et al. 2006 ). To test these hypotheses, we conduct an empirical analysis using statistics from the Israeli labor market and data published by the OECD. We also discuss some policy implications stemming from our results, specifically, a differential policy of minimum wages and earned income tax credits depending on the worker’s age.

Following the introduction and literary review, the next part of our paper presents the existing data about the unemployment rates of young people and adults in the OECD countries in general and Israel in particular. Than we present the research hypotheses and theoretical model, we describe the data, variables and methods used to test our hypotheses. The regression results are presented in Sect.  4 , the model of Business Cycle is presented in Sect.  5 , and the paper concludes with some policy implications, a summary and conclusions in Sect.  6 .

2 Literature review

Over the past 30 years, unemployment in general and youth unemployment in particular has been a major problem in many industrial societies (Isengard 2003 ). The transition from school to work is a rather complex and turbulent period. The risk of unemployment is greater for young people than for adults, and first jobs are often unstable and rather short-lived (Jacob 2008 ). Many young people have short spells of unemployment during their transition from school to work; however, some often get trapped in unemployment and risk becoming unemployed in the long term (Kelly et al. 2012 ).

Youth unemployment leads to social problems such as a lack of orientation and hostility towards foreigners, which in turn lead to increased social expenditures. At the societal level, high youth unemployment endangers the functioning of social security systems, which depend on a sufficient number of compulsory payments from workers in order to operate (Isengard 2003 ).

Workers 45 and older who have lost their jobs often encounter difficulties in finding a new job (Axelrad et al. 2013 ; Marmora and Ritter 2015 ) although today they are more able to work longer than in years past (Johnson 2004 ). In addition to the monetary rewards, work also offers mental and psychological benefits (Axelrad et al. 2016 ; Jahoda 1982 ; Winkelmann and Winkelmann 1998 ). Working at an older age may contribute to an individual’s mental acuity and provide a sense of usefulness.

On average, throughout the OECD, the hiring rate of workers aged 50 and over is less than half the rate for workers aged 25–49. The low re-employment rates among older job seekers reflect, among other things, the reluctance of employers to hire older workers. Lahey ( 2005 ) found evidence of age discrimination against older workers in labor markets. Older job applicants (aged 50 or older), are treated differently than younger applicants. A younger worker is more than 40% more likely to be called back for an interview compared to an older worker. Age discrimination is also reflected in the time it takes for older adults to find a job. Many workers aged 45 or 50 and older who have lost their jobs often encounter difficulties in finding a new job, even if they are physically and intellectually fit (Hendels 2008 ; Malul 2009 ). Despite the fact that older workers are considered to be more reliable (McGregor and Gray 2002 ) and to have better business ethics, they are perceived as less flexible or adaptable, less productive and having higher salary expectations (Henkens and Schippers 2008 ). Employers who hesitated in hiring older workers also mentioned factors such as wages and non-wage labor costs that rise more steeply with age and the difficulties firms may face in adjusting working conditions to meet the requirements of employment protection rules (Keese et al. 2006 ).

Thus, we have a paradox. On one hand, people live longer, the retirement age is rising, and older people in good health want or need to keep working. At the same time, employers seek more and more young workers all the time. This phenomenon might marginalize skilled and experience workers, and take away their ability to make a living and accrue pension rights. Thus, employers’ reluctance to hire older workers creates a cycle of poverty and distress, burdening the already overcrowded social institutions and negatively affecting the economy’s productivity and GDP (Axelrad et al. 2013 ).

2.1 OECD countries during the post 2008 crisis

The recent global economic crisis took an outsized toll on young workers across the globe, especially in advanced economies, which were hit harder and recovered more slowly than emerging markets and developing economies. Does this fact imply that the labor market in Spain and Portugal (with relatively high youth unemployment rates) is less “friendly” toward younger individuals than the labor market in Israel and Germany (with a relatively low youth unemployment rate)? Has the market in Spain and Portugal become less “friendly” toward young people during the last 4 years? We argue that the main factor causing the increasing youth unemployment rates in Spain and Portugal is the poor state of the economy in the last 4 years in these countries rather than a change in attitudes toward hiring young people.

OECD data indicate that adult unemployment is significantly lower than youth unemployment. The global economic crisis has hit young people very hard. In 2010, there were nearly 15 million unemployed youngsters in the OECD area, about four million more than at the end of 2007 (Scarpetta et al. 2010 ).

From an international perspective, and unlike other developed countries, Israel has a young age structure, with a high birthrate and a small fraction of elderly population. Israel has a mandatory retirement age, which differs for men (67) and women (62), and the labor force participation of older workers is relatively high (Stier and Endeweld 2015 ), therefore, we believe that Israel is an interesting case for studying.

The Israeli labor market is extremely flexible (e.g. hiring and firing are relatively easy), and mobile (workers can easily move between jobs) (Peretz 2016 ). Focusing on Israel’s labor market, we want to check whether this is true for older Israeli workers as well, and whether there is a difference between young and older workers.

The problem of unemployment among young people in Israel is less severe than in most other developed countries. This low unemployment rate is a result of long-term processes that have enabled the labor market to respond relatively quickly to changes in the economic environment and have reduced structural unemployment. Footnote 2 Furthermore, responsible fiscal and monetary policies, and strong integration into the global market have also promoted employment at all ages. With regard to the differences between younger and older workers in Israel, Stier and Endeweld ( 2015 ) determined that older workers, men and women alike, are indeed less likely to leave their jobs. This finding is similar to other studies showing that older workers are less likely to move from one employer to another. According to the U.S. Bureau of Labor Statistics, the median employee tenure is generally higher among older workers than younger ones (BLS 2014 ). Movement in and out of the labor market is highest among the youngest workers. However, these young people are re-employed quickly, while older workers have the hardest time finding jobs once they become unemployed. The Bank of Israel calculated the chances of unemployed people finding work between two consecutive quarters using a panel of the Labor Force Survey for the years 1996–2011. Their calculations show that since the middle of the last decade the chances of unemployed people finding a job between two consecutive quarters increased. Footnote 3 However, as noted earlier, as workers age, the duration of their unemployment lengthens. Prolonged unemployment erodes the human capital of the unemployed (Addison et al. 2004 ), which has a particularly deleterious effect on older workers. Thus, the longer the period of unemployment of older workers, the less likely they will find a job (Axelrad and Luski 2017 ). Nevertheless, as Fig.  1 shows, the rates of youth unemployment in Israel are higher than those of older workers.

(Source: Calculated by the authors by using data from the Labor Force survey of the Israeli CBS, 2011)

Unemployed persons and discouraged workers as percentages of the civilian labor force, by age group (Bank of Israel 2011 ). We excluded those living outside settled communities or in institutions. The percentages of discouraged workers are calculated from the civilian labor force after including them in it

We argue that the main reason for this situation is the status quo in the labor market, which is general and not specific to Israel. It applies both to older workers and young workers who have a job. The status quo is evident in the situation in which adults (and young people) already in the labor market manage to keep their jobs, making the entrance of new young people into the labor market more difficult. What we are witnessing is not evidence of a preference for the old over the young, but the maintaining of the status quo.

The rate of employed Israelis covered by collective bargaining agreements increases with age: up to age 35, the rate is less than one-quarter, and between 50 and 64 the rate reaches about one-half. In effect, in each age group between 25 and 60, there are about 100,000 covered employees, and the lower coverage rate among the younger ages derives from the natural growth in the cohorts over time (Bank of Israel 2013 ). The wave of unionization in recent years is likely to change only the age profile of the unionization rate and the decline in the share of covered people over the years, to the extent that it strengthens and includes tens of thousands more employees from the younger age groups. Footnote 4

The fact that the percentage of employees covered by collective agreement increases with age implies that there is a status quo effect. Older workers are protected by collective agreements, and it is hard to dismiss them (Culpepper 2002 ; Palier and Thelen 2010 ). However, young workers enter the workforce with individual contracts and are not protected, making it is easier to change their working conditions and dismiss them.

To complete the picture, Fig.  2 shows that the number of layoffs among adults is lower, possibly due to their protection under collective bargaining agreements.

(Source: Israeli Central Bureau of Statistics, 2008, data processed by the authors)

Dismissal of employees in Israel, by age. Percentage of total employed persons ages 20–75 and over including those dismissed

In order to determine the real difference between the difficulties of older versus younger individuals in finding work, we have to eliminate the effect of the status quo in the labor market. For example, if we removed all of the workers from the labor market, what would be the difference between the difficulties of older people versus younger individuals in finding work? In the next section we will analyze the probability of younger and older individuals moving from unemployment to employment when we control for the status quo. We will do so by considering only individuals who have not been employed at least part of the previous year.

3 Estimating the chances of finding a job and research hypotheses

Based on the literature and the classic premise that young workers are more vulnerable to economic shocks (ILO 2011 ), we posit that:

H 1 : The unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes.

Based on the low hiring rate of older workers (OECD 2006 ) and the literature about age discrimination against older workers in labor markets (Axelrad et al. 2013 ; Lahey 2005 ), we hypothesis that:

H 2 : The difficulty face by unemployed older workers searching for a job stems mainly from their age and less from the characteristics of the labor market.

To assess the chances of younger and older workers finding a job, we used a logit regression model that has been validated in previous studies (Brander et al. 2002 ; Flug and Kassir 2001 ). Being employed was the dependent variable, and the characteristics of the respondents (age, gender, ethnicity and education) were the independent variables. The dependent variable was nominal and dichotomous with two categories: 0 or 1. We defined the unemployed as those who did not work at all during the last year or worked less than 9 months last year. The dependent variable was a dummy variable of the current employment situation, which received the value of 1 if the individual worked last week and 0 otherwise.

3.1 The model

i—individual i, P i —the chances that individual i will have a full or part time job (at the time of the survey). \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{X}}_{\text{i}}\) —vector of explanatory variables of individual i. Each of the variables in vector \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{X}_{i}\) was defined as a dummy variable with the value of 1 or 0. β—vector of marginal addition to the log of the odds ratio. For example, if the explanatory variable was the log of 13 years or more of schooling, then the log odds ratio refers to the marginal addition of 13 years of education to the chances of being employed, compared with 12 years of education or less.

The regression allowed us to predict the probability of an individual finding a job. The dependent variable was the natural base log of the probability ratio P divided by (1 − P) that a particular individual would find a job. The odds ratio from the regression answers the question of how much more likely it is that an individual will find a job if he or she has certain characteristics. The importance of the probability analysis is the consideration of the marginal contribution of each feature to the probability of finding a job.

3.2 The sample

We used data gathered from the 2011 Labor Force Survey Footnote 5 of the Israeli Central Bureau of Statistics (CBS), Footnote 6 which is a major survey conducted annually among households. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. Given our focus on working age individuals, we excluded all of the respondents under the age of 18 or over the age of 59. The data sample includes only the Jewish population, because structural problems in the non-Jewish sector made it difficult to estimate this sector using the existing data only. The sample does not include the ultra-Orthodox population because of their special characteristics, particularly the limited involvement of men in this population in the labor market.

The base population is individuals who did not work at all during the past year or worked less than 9 months last year (meaning that they worked but were unemployed at least part of last year). To determine whether they managed to find work after 1 year of unemployment, we used the question on the ICBS questionnaire, “Did you work last week?” We used the answer to this question to distinguish between those who had succeeded in finding a job and those who did not. The data include individuals who were out of the labor force Footnote 7 at the time of the survey, but exclude those who were not working for medical reasons (illness, disability or other medical restrictions) or due to their mandatory military service. Footnote 8

3.3 Data and variables

The survey contains 104,055 respondents, but after omitting all of the respondents under the age of 18 or above 59, those who were outside the labor force for medical reasons or due to mandatory military service, non-Jews, the ultra-Orthodox, and those who worked more than 9 months last year, the sample includes 13,494 individuals (the base population). Of these, 9409 are individuals who had not managed to find work, and 4085 are individuals who were employed when the survey was conducted.

The participants’ ages range between 18 and 59, with the average age being 33.07 (SD 12.88) and the median age being 29. 40.8% are males; 43.5% have an academic education; 52.5% are single, and 53.5% of the respondents have no children under 17.

3.4 Dependent and independent variables

While previous studies have assessed the probability of being unemployed in the general population, our study examines a more specific case: the probability of unemployed individuals finding a job. Therefore, we use the same explanatory variables that have been used in similar studies conducted in Israel (Brander et al. 2002 ; Flug and Kassir 2001 ), which were also based on an income survey and the Labor Force Survey of the Central Bureau of Statistics.

3.5 The dependent variable—being employed

According to the definition of the CBS, employed persons are those who worked at least 1 h during a given week for pay, profit or other compensation.

3.6 Independent variables

We divided the population into sub-groups of age intervals: 18–24, 25–29, 30–44, 45–54 and 55–59, according to the sub-groups provided by the CBS. We then assigned a special dummy variable to each group—except the 30–44 sub-group, which is considered as the base group. Age is measured as a dummy variable, and is codded as 1 if the individual belongs to the age group, and 0 otherwise. Age appears in the regression results as a variable in and of itself. Its significance is the marginal contribution of each age group to the probability of finding work relative to the base group (ages 30–44), and also as an interaction variable.

3.6.2 Gender

This variable is codded as 1 if the individual is female and 0 otherwise. Gender also appears in the interaction with age.

3.6.3 Marital status

Two dummy variables are used: one for married respondents and one for those who are divorced or widowed. In accordance with the practice of the CBS, we combined the divorced and the widowed into one variable. This variable is a dummy variable that is codded as 1 if the individual belongs to the appropriate group (divorced/widowed or married) and 0 otherwise. The base group is those who are single.

3.6.4 Education

This variable is codded as 1 if the individual has 13 or more years of schooling, and 0 otherwise. The variable also appears in interactions between it and the age variable.

3.6.5 Vocational education

This variable is codded as 1 if the individual has a secondary school diploma that is not an academic degree or another diploma, and 0 otherwise.

3.6.6 Academic education

This variable is codded as 1 if the individual has any university degree (bachelors, masters or Ph.D.) and 0 otherwise.

3.6.7 Children

In accordance with similar studies that examined the probability of employment in Israel (Brander et al. 2002 ), we define children as those up to age 17. This variable is a dummy variable that is codded as 1 if the respondents have children under the age of 17, and 0 otherwise.

3.6.8 Ethnicity

This variable is codded as 1 if the individual was born in an Arabic-speaking country, in an African country other than South Africa, or in an Asian country, or was born in Israel but had a father who was born in one of these countries. Israel generally refers to such individuals as Mizrahim. Respondents who were not Mizrahim received a value of 0. The base group in our study are men aged 30–44 who are not Mizrahim.

We also assessed the interactions between the variables. For example, the interaction between age and the number of years of schooling is the contribution of education (i.e., 13 years of schooling) to the probability of finding a job for every age group separately relative to the situation of having less education (i.e., 12 years of education). The interaction between age and gender is the contribution of gender (i.e., being a female respondent) to the probability of finding a job for each age group separately relative to being a man.

To demonstrate the differences between old and young individuals in their chances of finding a job, we computed the rates of those who managed to find a job relative to all of the respondents in the sample. Table  1 shows that the rate of those who found a job declines with age. For example, 36% of the men age 30–44 found a job, but those rates drop to 29% at the age of 45–54 and decline again to 17% at the age of 55–59. As for women, 31% of them aged 30–44 found a job, but those rates drop to 20% at the age of 45–54 and decline again to 9% at the age of 55–59.

In an attempt to determine the role of education in finding employment, we created Model 1 and Model 2, which differ only in terms of how we defined education. In Model 1 the sample is divided into two groups: those with up to 12 years of schooling (the base group) and those with 13 or more years of schooling. In Model 2 there are three sub-groups: those with a university degree, those who have a vocational education, and the base group that has only a high school degree.

Table  2 shows that the probability of a young person (age 18–24) getting a job is larger than that of an individual aged 30–44 who belongs to the base group (the coefficient of the dummy variable “age 18–24” is significant and positive). Similarly, individuals who are older than 45 are less likely than those in the base group to find work.

Women aged 30–44 are less likely to be employed than men in the same age group. Additionally, when we compare women aged 18–24 to women aged 30–44, we see that the chances of the latter being employed are lower. Older women (45+) are much less likely than men of the same age group to find work. Additionally, having children under the age of 17 at home reduces the probability of finding a job.

A university education increases the probability of being employed for both men and women aged 30–44. Furthermore, for older people (55+) an academic education reduces the negative effect of age on the probability of being employed. While a vocational education increases the likelihood of finding a job for those aged 30–44, such a qualification has no significant impact on the prospects of older people.

Interestingly, being a Mizrahi Jew increases the probability of being employed.

In addition, we estimated the models separately twice—for the male and for the female population. For male and female, the probability of an unemployed individual finding a job declines with age.

Analyzing the male population (Table  3 ) reveals that those aged 18–24 are more likely than the base group (ages 30–44) to find a job. However, the significance level is relatively low, and in Model 2, this variable is not significant at all. Those 45 and older are less likely than the base group (ages 30–44) to find a job. Married men are more likely than single men to be employed. However, divorced and widowed men are less likely than single men to find a job. For men, the presence in their household of children under the age of 17 further reduces the probability of their being employed. Mizrahi men aged 18–24 are more likely to be employed than men of the same age who are from other regions.

Table  3 illustrates that educated men are more likely to find work than those who are not. However, in Model 1, at the ages 18–29 and 45–54, the probability of finding a job for educated men is less than that of uneducated males. Among younger workers, this might be due to excess supply—the share of academic degree owners has risen, in contrast to almost no change in the overall share of individuals receiving some other post-secondary certificate (Fuchs 2015 ). Among older job seeking men, this might be due to the fact that the increase in employment among men during 2002–2010 occurred mainly in part-time jobs (Bank of Israel 2011 ). In Model 2, men with an academic or vocational education have a better chance of finding a job, but at the group age of 18–24, those with a vocational education are less likely to find a job compared to those without a vocational education. The reason might be the lack of experience of young workers (18–24), experience that is particularly needed in jobs that require vocational education (Salvisberg and Sacchi 2014 ).

Analyzing the female population (Table  3 ) reveals that women between 18 and 24 are more likely to be employed than those who are 30–44, and those who are 45–59 are less likely to be employed than those who are 30–44. The probability of finding a job for women at the age of 25 to 29 is not significantly different from the probability of the base group (women ages 30–44).

Married women are less likely than single women to be employed. Women who have children under the age of 17 are less likely to be employed than women who do not have dependents that age. According to Model 2, Mizrahi women are more likely to be employed compared to women from other regions. According to both models, women originally from Asia or Africa ages 25–29 have a better chance of being employed than women the same age from other regions. Future research should examine this finding in depth to understand it.

With regard to education, in Model 1 (Table  3 ), where we divided the respondents simply on the question of whether they had a post-high school education, women who were educated were more likely to find work than those who were not. However, in the 18–29 age categories, educated women were less likely to find a job compared to uneducated women, probably due to the same reason cited above for men in the same age group—the inflation of academic degrees (Fuchs 2015 ). These findings become more nuanced when we consider the results of Model 2. There, women with an academic or vocational education have a better chance of finding a job, but at the ages of 18–24 those with an academic education are less likely to find a job than those without an academic education. Finally, at the ages of 25–29, those with a vocational education have a better chance of finding a job than those without a vocational education, due to the stagnation in the overall share of individuals receiving post-secondary certificate (Fuchs 2015 ).

Thus, based on the results in Table  3 , we can draw several conclusions. First, the effect of aging on women is more severe than the impact on men. In addition, the “marriage premium” is positive for men and negative for women. Divorced or widowed men lose their “marriage premium”. Finally, having children at home has a negative effect on both men and women—almost at the same magnitude.

5 Unemployment as a function of the business cycle

To determine whether unemployment of young workers is caused by the business cycle, we examined the unemployment figures in 34 OECD countries in 2007–2009, years of economic crisis, and in 2009–2011, years of recovery and economic growth. For each country, we considered the data on unemployment among young workers (15–24) and older adults (55–64) and calculated the difference between 2009 and 2007 and between 2011 and 2009 for both groups. The data were taken from OECD publications and included information about the growth rates from 2007 to 2011. Our assessment of unemployment rates in 34 OECD countries reveals that the average rate of youth unemployment in 2007 was 13.4%, compared to 18.9% in 2011, so the delta of youth unemployment before and after the economic crisis was 5.55. The average rate of adult unemployment in 2007 was 4% compared to 5.8% in 2011, so the delta for adults was 1.88. Both of the differences are significantly different from zero, and the delta for young people is significantly larger than the delta for adults. These results indicate that among young people (15–24), the increase in unemployment due to the crisis was very large.

An OLS model of the reduced form was estimated to determine whether unemployment is a function of the business cycle, which is represented by the growth rate. The variables GR2007, GR2009 and GR2011 are the rate of GDP growth in 2007, 2009 and 2011 respectively ( Appendix ). The explanatory variable is either GR2009 minus GR2007 or GR2011 minus GR2009. In both periods, 2007–2009 and 2009–2011, the coefficient of the change in growth rates is negative and significant for young people, but insignificant for adults. Thus, it seems that the unemployment rates of young people are affected by the business cycle, but those of older workers are not. In a time of recession (2007–2009), unemployment among young individuals increases whereas for older individuals the increase in unemployment is not significant. In recovery periods (2009–2011), unemployment among young individuals declines, whereas the drop in unemployment among older individuals is not significant (Table  4 ).

6 Summary and conclusions

The purpose of this paper was to show that while the unemployment rates of young workers are higher than those of older workers, the data alone do not necessarily tell the whole story. Our findings confirm our first hypothesis, that the high unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes. Using data from Israel and 34 OECD countries, we demonstrated that a country’s growth rate is the main factor that determines youth unemployment. However, the GDP rate of growth cannot explain adult unemployment. Our results also support our second hypothesis, that the difficulties faced by unemployed older workers when searching for a job are more a function of their age than the overall business environment.

Indeed, one limitation of the study is the fact that we could not follow individuals over time and capture individual changes. We analyze a sample of those who have been unemployed in the previous year and then analyze the probability of being employed in the subsequent year but cannot take into account people could have found a job in between which they already lost again. Yet, in this sample we could isolate and analyze those who did not work last year and look at their employment status in the present. By doing so, we found out that the rate of those who found a job declines with age, and that the difficulties faced by unemployed older workers stems mainly from their age.

To solve both of these problems, youth unemployment and older workers unemployment, countries need to adopt different methods. Creating more jobs will help young people enter the labor market. Creating differential levels for the minimum wage and supplementing the income of older workers with earned income tax credits will help older people re-enter the job market.

Further research may explore the effect of structural and institutional differences which can also determine individual unemployment vs. employment among different age groups.

In addition to presenting a theory about the factors that affect the differences in employment opportunities for young people and those over 45, the main contribution of this paper is demonstrating the validity of our contention that it is age specifically that works to keep older people out of the job market, whereas it is the business cycle that has a deleterious effect on the job prospects of younger people. Given these differences, these two sectors of unemployment require different approaches for solving their employment problems. The common wisdom maintains that the high level of youth unemployment requires policy makers to focus on programs targeting younger unemployed individuals. However, we argue that given the results of our study, policy makers must adopt two different strategies to dealing with unemployment in these two groups.

6.1 Policy implications

In order to cope with the problem of youth unemployment, we must create more jobs. When the recession ends in Portugal and Spain, the problem of youth unemployment should be alleviated. Since there is no discrimination against young people—evidenced by the fact that when the aggregate level of economic activity and the level of adult employment are high, youth employment is also high—creating more jobs in general by enhancing economic growth should improve the employment rates of young workers.

In contrast, the issue of adult unemployment requires a different solution due to the fact that their chances of finding a job are related specifically to their age. One solution might be a differential minimum wage for older and younger individuals and earned income tax credits (EITC) Footnote 9 for older individuals, as Malul and Luski ( 2009 ) suggested.

According to this solution, the government should reduce the minimum wage for older individuals. As a complementary policy and in order to avoid differences in wages between older and younger individuals, the former would receive an earned income tax credit so that their minimum wage together with their EITC would be equal to the minimum wage of younger individuals. Earned income tax credits could increase employment among older workers while increasing their income. For older workers, EITCs are more effective than a minimum wage both in terms of employment and income. Such policies of a differential minimum wage plus an EITC can help older adults and constitute a kind of social safety net for them. Imposing a higher minimum wage exclusively for younger individuals may be beneficial in encouraging them to seek more education.

Young workers who face layoffs as a result of their high minimum wage (Kalenkoski and Lacombe 2008 ) may choose to increase their investment in their human capital (Nawakitphaitoon 2014 ). The ability of young workers to improve their professional level protects them against the unemployment that might result from a higher minimum wage (Malul and Luski 2009 ). For older workers, if the minimum wage is higher than their productivity, they will be unemployed. This will be true even if their productivity is higher than the value of their leisure. Such a situation might result in an inefficient allocation between work and leisure for this group. One way to fix this inefficient allocation without reducing the wages of older individuals is to use the EITC, which is actually a subsidy for this group. This social policy might prompt employers to substitute older workers with a lower minimum wage for more expensive younger workers, making it possible for traditional factories to continue their domestic production. However, a necessary condition for this suggestion to work is the availability of efficient systems of training and learning. Axelrad et al. ( 2013 ) provided another justification for subsidizing the work of older individuals. They found that stereotypes about older workers might lead to a distorted allocation of the labor force. Subsidizing the work of older workers might correct this distortion. Ultimately, however, policy makers must understand that they must implement two different approaches to dealing with the problems of unemployment among young people and in the older population.

For example, in the US, the UK and Portugal, we witnessed higher rates of growth during late 1990 s and lower rates of youth unemployment compared to 2011.

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http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/RecentEconomicDevelopments/develop136e.pdf .

The Labor Force Survey is a major survey conducted by the Israeli Central Bureau of Statistics among households nationwide. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. The publication contains detailed data on labor force characteristics such as their age, years of schooling, type of school last attended, and immigration status. It is also a source of information on living conditions, mobility in employment, and many other topics.

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Until 2012, active soldiers were considered outside the labor force in the samples of the CBS.

EITC is a refundable tax credit for low to moderate income working individuals and couples.

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Authors’ contributions

HA, MM and IL conceptualized and designed the study. HA collected and managed study data, HA and IL carried out statistical analyses. HA drafted the initial manuscript. MM and IL reviewed and revised the manuscript. All authors read and approved the final manuscript.

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Hila Axelrad

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Department of Public Policy & Administration, Guilford Glazer Faculty of Business & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

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Axelrad, H., Malul, M. & Luski, I. Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?. J Labour Market Res 52 , 3 (2018). https://doi.org/10.1186/s12651-018-0237-9

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Received : 22 September 2017

Accepted : 25 February 2018

Published : 08 March 2018

DOI : https://doi.org/10.1186/s12651-018-0237-9

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Unemployment rate in Moscow in 2020 one of lowest among world's largest cities

MOSCOW, April 9. / TASS /. One of the lowest unemployment rates among the largest cities in the world was recorded in Moscow at the end of last year, it amounted to 2.5%, according to the official website of the mayor and the government of the capital, released on Friday.

According to international experts, the employment rate in Moscow was 79.9%. "The international group Euromonitor conducted a study that showed that in 2020 the unemployment rate in Moscow, calculated according to the methodology of the International Labor Organization, was 2.5%. It was lower only in Beijing - 1.4%. In other cities, the unemployment rate exceeded in Moscow from two to seven times. Thus, in Delhi it was slightly higher than 3%, in Tokyo - 3.8%, in London - 4.5%, in Paris - 7.6%, in Berlin - 7.9%, in Rio de Janeiro - 8.2%, in New York - 8.8%, in Istanbul - 17.5%, "the statement said. The unemployment rate in Moscow has been declining since 2015. By the end of 2019, it was only 2.2%.

Due to a balanced approach to the introduction of restrictive measures and a program to support urban business with a total volume of about 90 bln rubles, Moscow managed to avoid a significant increase in the indicator during the pandemic: compared to the pre-crisis value in 2020, the unemployment rate in the Russian capital increased by only 0.3 percentage point and amounted to 2.5%," said the Deputy Mayor of Moscow for Economic Policy and Property and Land Relations Vladimir Efimov. According to the Minister of the Moscow Government, Head of the Department of Economic Policy and Development Kirill Purtov, last year Moscow became the leader among the top 10 cities in the world in terms of employment. "The employment rate in Moscow, according to Euromonitor, was 79.9%. In London this figure in 2020 was 78.4%, in Beijing - 78.2%, in Tokyo - 76%, in Berlin - 75.9 %, in Paris - 72.4%, in New York - 67.8%. The employment rate in Rio de Janeiro was 59.6%, in Istanbul - 47.7%, in Delhi - 37.4%," he explained.

As reported on the website of the mayor and the government of Moscow, the capital is actively supporting people who have been left without work, they are paid benefits, help in finding a job. In addition, the city has the largest state personnel operator for job search - the capital's employment service, where 300,000 vacancies are presented.

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A Systematic Literature Review and Analysis of Unemployment Problem and Potential Solutions

Investments in training and education are one of the most important things that can help people acquire the required skills and knowledge for employment. However, in this changing environment, with a lot of emerging technologies, the major challenges facing many people is keeping up with the needed market skills and investing in upskills. 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 solutions and suggestions to handle this problem and particularly the problem of skills mismatch. The results indicated that the previous solutions were inadequate as they used reactive strategies, thus, people cannot respond quickly to change in the market and acquire the required skills immediately.

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V. I. Lenin

The tasks of the working women’s movement in the soviet republic, speech delivered at the fourth moscow city conference of non-party working women, september 23, 1919.

Delivered: 23 September, 1919 First Published: Pravda No. 213, September 25, 1919 ; Published according to the text of the pamphlet, V. I. Lenin, Speech at the Working Women’s Congress, Moscow, 1919, verified with the Pravda text Source: Lenin’s Collected Works , 4th English Edition, Progress Publishers, Moscow, 1965, Volume 30, pages 40-46 Translated: George Hanna Transcription/HTML Markup: David Walters & Robert Cymbala Copyleft: V. I. Lenin Internet Archive (www.marx.org) 2002. Permission is granted to copy and/or distribute this document under the terms of the GNU Free Documentation License

Comrades, it gives me pleasure to greet a conference of working women. I will allow myself to pass over those subjects and questions that, of course, at the moment are the cause of the greatest concern to every working woman and to every politically-conscious individual from among the working people; these are the most urgent questions—that of bread and that of the war situation. I know from the newspaper reports of your meetings that these questions have been dealt with exhaustively by Comrade Trotsky as far as war questions are concerned and by Comrades Yakovleva and Svidersky as far as the bread question is concerned; please, therefore, allow me to pass over those questions.

I should like to say a few words about the general tasks facing the working women’s movement in the Soviet Republic, those that are, in general, connected with the transition to socialism, and those that are of particular urgency at the present time. Comrades, the question of the position of women was raised by Soviet power from the very beginning. It seems to me that any workers’ state in the course of transition to socialism is laced with a double task. The first part of that task is relatively simple and easy. It concerns those old laws that kept women in a position of inequality as compared to men.

Participants in all emancipation movements in Western Europe have long since, not for decades but for centuries, put forward the demand that obsolete laws be annulled and women and men be made equal by law, but none of the democratic European states, none of the most advanced republics have succeeded in putting it into effect, because wherever there is capitalism, wherever there is private property in land and factories, wherever the power of capital is preserved, the men retain their privileges. It was possible to put it into effect in Russia only because the power of the workers has been established here since October 25, 1917. From its very inception Soviet power set out to be the power of the working people, hostile to all forms of exploitation. It set itself the task of doing away with the possibility of the exploitation of the working people by the landowners and capitalists, of doing away with the rule of capital. Soviet power has been trying to make it possible for the working people to organise their lives without private property in land, without privately-owned factories, without that private property that everywhere, throughout the world, even where there is complete political liberty, even in the most democratic republics, keeps the working people in a state of what is actually poverty and wage-slavery, and women in a state of double slavery.

Soviet power, the power of the working people, in the first months of its existence effected a very definite revolution in legislation that concerns women. Nothing whatever is left in the Soviet Republic of those laws that put women in a subordinate position. I am speaking specifically of those laws that took advantage of the weaker position of women and put them in a position of inequality and often, even, in a humiliating position, i.e., the laws on divorce and on children born out of wedlock and on the right of a woman to summon the father of a child for maintenance.

It is particularly in this sphere that bourgeois legislation, even, it must be said, in the most advanced countries, takes advantage of the weaker position of women to humiliate them and give them a status of inequality. It is particularly in this sphere that Soviet power has left nothing whatever of the old, unjust laws that were intolerable for working people. We may now say proudly and without any exaggeration that apart from Soviet Russia there is not a country in the world where women enjoy full equality and where women are not placed in the humiliating position felt particularly in day-to-day family life. This was one of our first and most important tasks.

If you have occasion to come into contact with parties that are hostile to the Bolsheviks, if there should come into your hands newspapers published in Russian in the regions occupied by Koichak or Denikin, or if you happen to talk to people who share the views of those newspapers, you may often hear from them the accusation that Soviet power has violated democracy.

We, the representatives of Soviet power, Bolshevik Communists and supporters of Soviet power are often accused of violating democracy and proof of this is given by citing the fact that Soviet power dispersed the Constituent Assembly. We usually answer this accusation as follows; that democracy and that Constituent Assembly which came into being when private property still existed on earth, when there was no equality between people, when the one who possessed his own capital was the boss and the others worked for him and were his wage-slaves-that was a democracy on which we place no value. Such democracy concealed slavery even in the most advanced countries. We socialists are supporters of democracy only insofar as it eases the position of the working and oppressed people. Throughout the world socialism has set itself the task of combating every kind of exploitation of man by man. That democracy has real value for us winch serves the exploited, the underprivileged. If those who do not work are disfranchised that would be real equality between people. Those who do not work should not eat.

In reply to these accusations we say that the question must be presented in this way—how is democracy implemented in various countries? We see that equality is proclaimed in all democratic republics but in the civil laws and in laws on the rights of women—those that concern their position in the family and divorce—we see inequality and the humiliation of women at every step, and we say that this is a violation of democracy specifically in respect of the oppressed. Soviet power has implemented democracy to a greater degree than any of the other, most advanced countries because it has not left in its laws any trace of the inequality of women. Again I say that no other state and no other legislation has ever done for women a half of what Soviet power did in the first months of its existence.

Laws alone, of course, are not enough, and we are by no means content with mere decrees. In the sphere of legislation, however, we have done everything required of us to put women in a position of equality and we have every right to be proud of it. The position of women in Soviet Russia is now ideal as compared with their position in the most advanced states. We tell ourselves, however, that this is, of course, only the beginning.

Owing to her work in the house, the woman is still in a difficult position. To effect her complete emancipation and make her the equal of the man it is necessary for the national economy to be socialised and for women to participate in common productive labour. Then women will occupy the same position as men.

Here we are not, of course, speaking of making women the equal of men as far as productivity of labour, the quantity of labour, the length of the working day, labour conditions, etc., are concerned; we mean that the woman should not, unlike the man, be oppressed because of her position in the family. You all know that even when women have full rights, they still remain factually downtrodden because all housework is left to them. In most cases housework is the most unproductive, the most barbarous and the most arduous work a woman can do. It is exceptionally petty and does not include anything that would in any way promote the development of the woman.

In pursuance of the socialist ideal we want to struggle for the full implementation of socialism, and here an extensive field of labour opens up before women. We are now making serious preparations to clear the ground for the building of socialism, but the building of socialism will begin only when we have achieved the complete equality of women and when we undertake the new work together with women who have been ’emancipated from that petty, stultifying, unproductive work. This is a job that will take us many, many years.

This work cannot show any rapid results and will not produce a scintillating effect.

We are setting up model institutions, dining-rooms and nurseries, that will emancipate women from housework. And the work of organising all these institutions will fall mainly to women. It has to be admitted that in Russia today there are very few institutions that would help woman out of her state of household slavery. There is an insignificant number of them, and the conditions now obtaining in the Soviet Republic—the war and food situation about which comrades have already given you the details—hinder us in this work. Still, it must be said that these institutions that liberate women from their position as household slaves are springing up wherever it is in any way possible.

We say that the emancipation of the workers must be effected by the workers themselves, and in exactly the same way the emancipation of working women is a matter for the working women themselves. The working women must themselves see to it that such institutions are developed, and this activity will bring about a complete change in their position as compared with what it was under the old, capitalist society.

In order to be active in politics under the old, capitalist regime special training was required, so that women played an insignificant part in politics, even in the most advanced and free capitalist countries. Our task is to make politics available to every working woman. Ever since private property in laud and factories has been abolished and the power of the landowners and capitalists overthrown, the tasks of politics have become simple, clear and comprehensible to the working people as a whole, including working women. In capitalist society the woman’s position is marked by such inequality that the extent of her participation in politics is only an insignificant fraction of that of the man. The power of the working people is necessary for a change to be wrought in this situation, for then the main tasks of politics will consist of matters directly affecting the fate of the working people themselves.

Here, too, the participation of working women is essential —not only of party members and politically-conscious women, but also of the non-party women and those who are least politically conscious. Here Soviet power opens up a wide field of activity to working women.

We have had a difficult time in the struggle against the forces hostile to Soviet Russia that have attacked her. It was difficult for us to fight on the battlefield against the forces who went to war against the power of the working people and in the field of food supplies against the profiteers, because of the too small number of people, working people, who came whole-heartedly to our aid with their own labour. Here, too, there is nothing Soviet power can appreciate as much as the help given by masses of non-party working women. They may know that in the old, bourgeois society, perhaps, a comprehensive training was necessary for participation in politics and that this was not available to women. The political activity of the Soviet Republic is mainly the struggle against the landowners and capitalists, the struggle for the elimination of exploitation; political activity, therefore, is made available to the working woman in the Soviet Republic and it will consist in the working woman using her organisational ability to help the working man.

What we need is not only organisational work on a scale involving millions; we need organisational work on the smallest scale and this makes it possible for women to work as well. Women can work under war conditions when it is a question of helping the army or carrying on agitation in the army. Women should take an active part in all this so that the Red Army sees that it is being looked after, that solicitude is being displayed. Women can also work in the sphere of food distribution, on the improvement of public catering and everywhere opening dining-rooms like those that are so numerous in Petrograd.

It is in these fields that the activities of working women acquire the greatest organisational significance. The participation of working women is also essential in the organisation and running of big experimental farms and should not take place only in isolated cases. This i5 something that cannot be carried out without the participation of a large number of working women. Working women will be very useful in this field in supervising the distribution of food and in making food products more easily obtainable. This work can well be done by non-party working women and its accomplishment will do more than anything else to strengthen socialist society.

We have abolished private property in land and almost completely abolished the private ownership of factories; Soviet power is now trying to ensure that all working people, non-party as well as Party members, women as well as men, should take part in this economic development. The work that Soviet power has begun can only make progress when, instead of a few hundreds, millions and millions of women throughout Russia take part in it. We are sure that the cause of socialist development will then become sound. Then the working people will show that they can live and run their country without the aid of the landowners and capitalists. Then socialist construction will be so soundly based in Russia that no external enemies in other countries and none inside Russia will be any danger to the Soviet Republic.

Collected Works Volume 30 Collected Works Table of Contents Lenin Works Archive

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