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Questions to Address Youth Unemployment

We need to spur fresh thinking in this field even as we test and evaluate diverse approaches that promote youth economic empowerment in developing countries.

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By Reeta Roy Sep. 14, 2010

Next week, more than 300 people will convene at the Global Youth Enterprise & Livelihoods Development Conference in Washington, DC. I will be joining several of our partners, other funders, international NGOs, and youth innovators to discuss tough questions and promising solutions related to employment and entrepreneurship for young people. 

Approximately 1.3 billion young people between the ages of 12 and 24 live in developing countries. The pace of economic growth in many of these countries will be insufficient to create the 1 billion jobs needed over the next decade as youth transition into the workforce. And while there is an increase in basic education levels, millions of young people in developing countries still face bleak employment opportunities. Young women have even higher rates of unemployment and face additional systemic, social and cultural barriers.

Thus, there is an urgent need for new approaches to create economic opportunities for young people.  If successful, the effects of youth employment and productivity will have inter-generational impact with multiplier effects from wealth creation and growth to social stability and new leadership.

This requires a continuum of interventions that equip young people to change their own lives. Access to education, knowledge, skills, social networks and capital are the building blocks of this change.  How do we enable young people to stay in school and complete their secondary education?  Is micro-franchising a potential solution to entrepreneurship and job creation? How do we expand technology applications to equip young people with employability skills and connect them to ideas, mentors and resources? What’s required to encourage financial institutions to sustainably offer youth-inclusive financial education and services to enable young people to save money, build assets and manage financial resources for their education or to start a business?

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We need to spur fresh thinking in this field even as we test and evaluate diverse approaches that promote youth economic empowerment in developing countries, particularly in Africa. We have much work ahead of us to generate approaches that work at scale.

We believe that the most compelling ideas will come from those with the greatest stake in finding solutions—young people themselves.  We’ve already seen an explosion of activities by young people around the world to tackle questions facing them, and we would like to tap into this innovation.  As funders and practitioners, we need ways to engage young people in identifying needs, developing solutions and delivering them in ways that are trusted and accessible by youth and their families. 

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• 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.

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

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.

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 .

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."

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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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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• Unemployment
• Older workers

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Unemployment among young workers during COVID-19

Subscribe to the economic studies bulletin, stephanie aaronson and stephanie aaronson senior associate director, division of research and statistics - federal reserve board francisca alba francisca alba former research analyst - economic studies.

September 10, 2020

• 10 min read

On June 8, the Business Cycle Dating Committee officially declared that the United States entered a recession in February. Young workers are typically hard hit in recessions, and research suggests that entering the labor market during a recession has a negative impact on future earnings and job prospects. In this post we examine the labor market experience of young workers since the onset of the pandemic and provide some thoughts on policy implications.

While the aggregate unemployment rate increased by 11.2 percentage points between February and April of this year (the local peak), unemployment rates among young workers increased by much more. For example, over the same time period, the unemployment rate for those aged 16-19 increased by 20.9 percentage points. The result is that while young people age 16-29 make up less than a quarter of the labor force, they accounted for about a third of the rise in the unemployment rate between February and April of this year. We also find disparities among young workers by education and by race, with Black and Hispanic workers and workers with lower levels of education experiencing larger increases in unemployment rates between February and April compared to white and college-educated workers. Moreover, we find that while between April and July the unemployment rates for young white 1 , and to a lesser extent Hispanic, workers have retraced a good part of their initial rise, the unemployment rate for young Black workers remains particularly elevated and was little changed in June and July.

That young workers have experienced a greater rise in unemployment during the recession is not surprising, as this is typically the case.  However, the extent to which young workers are bearing the brunt of the downturn is unusual.  This is partly attributable to the fact that, as is typically the case, younger workers were more likely to be laid off in both April and in May 2 , within industries, compared to their older counterparts. Moreover, this pattern has been exacerbated by the fact that, prior to the pandemic, young workers were more likely to work in service industries that were heavily impacted by social distancing policies required to slow the spread of the virus and reductions in consumer spending.

LABOR FORCE STATISTICS BY AGE:

Figure 1 shows the unemployment rate for five groups by age: 16+ (the aggregate unemployment rate widely reported in the media), 16-19, 20-24, and 25-29. As shown, the aggregate unemployment rate rose 11.2 percentage points between February and April (the local peak).  Meanwhile, the unemployment rate for the young increased by about 13 percentage points on average, with the largest increases occurring among the youngest workers. Between April and July the unemployment rate for the young decreased by an average of about 7 percentage points while the aggregate unemployment rate decreased by 4.5 percentage points; although, the unemployment rate for young workers in July still remains 7 percentage points higher, on average, than the aggregate.

However, the pandemic unemployment rate has understated the extent to which workers are losing jobs , as more of those who have lost jobs have chosen to drop out of the labor force than is typically the case during a recession.  Our analysis suggests that this dynamic has been particularly prevalent among young workers. Figure 2 displays the labor force participation rate for these same age groups.  While the aggregate labor force participation rate decreased by 3.2 percentage points between February and April, the labor force participation rate for those between the ages of 16 and 29 dropped by about 6 percentage points on average. Moreover, these declines were much larger proportionally and relative to the rise in the unemployment rate, and they have been more sustained. Since April, the labor force participation rate for those between the ages of 16 and 29 has increased by an average of about 1.5 percentage points, similar to the increase in the aggregate.

LABOR FORCE STATISTICS BY RACE AND BY EDUCATION:

Overall, the young have been hit hard by the recession, but the impact also varies by race/ethnicity and education.  Figure 3 shows the unemployment rate since the start of the recession for young white, Black, and Hispanic workers. The unemployment rate rose more for young Black and Hispanic workers between February and April 3 .  Between April and July, young white and Hispanic workers started to make up ground, as their unemployment rate declined by an average of about 7 percentage points. However, unemployment rates for young Black workers only declined by about 2 percentage points.

The disparities by education are also stark.  These data, which are reported only for those over the age of 25 (by which time educational attainment is largely complete) show that the unemployment rate for those with a high school degree or less and for those with some college education rose by more than twice as much as for those with a college degree or more between February and April. Interestingly, since then, the unemployment rate for those with less than a college degree have come down—likely as the industries in which they work have recovered—while the unemployment rate for those with a college degree has been fairly flat.  That said, the unemployment rates for these lower skilled workers, especially those with a high school degree or less, remain higher than those with a college degree or more.

RECENT UNEMPLOYMENT BY INDUSTRY:

These results raise the question of why young workers have lost their jobs at much higher rates. The left panel of Figure 5 shows that between March and April, young workers were more likely to be laid off than older age workers in almost every industry, although in a few cases the differences are quite small (and the same dynamic is true between April and May). The differences are especially pronounced in mining, information, wholesale and retail trade, information, education and health services, leisure and hospitality, and other services sectors 4 . These dynamics are similar to those typically observed during a recession.  Employers may be more likely to layoff young workers for a variety of reasons, which depend on the culture of the industry, the nature of the work, and the cost structure.  For instance, firms may have policies of firing the most recent hires first, as a way to retain the morale and support of long-time workers.  In industries that require significant firm-specific knowledge, young workers with lower tenure would likely have less of this, which would make separating them from the firm less of a loss.

However, the pandemic appears to have introduced an additional economic challenge for young workers.  As the right panel of Figure 5 shows, in February, prior to the significant decline in economic activity due to the pandemic, young workers were significantly more likely to be working in many of the hardest hit industries, including leisure and hospitality (17.5 percent) and wholesale and retail trade (16.5 percent) compared to their older counterparts (7 percent and 10.8 percent respectively). These industries are not especially sensitive to economic downturns , so this is a dynamic that is unique to the pandemic and is different from a typical recession.

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Executive Summary

Youth bearing the brunt of rising unemployment

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The latest unemployment figures show once again youth are hardest hit during economic headwinds.

Last week, Stats NZ reported the official unemployment rate rose to 4.3 percent in the first quarter of this year.

In the year to March, official unemployment rose by 31,000 people with youth, 15-24 year olds, comprising more than half of that increase - rising by 21,000.

According to Stats NZ data, the rate of youth not in employment, education, or training (NEET) has fluctuated with the economy over the past two decades.

After experiencing relative stability in the early 2000s, the Global Financial Crisis triggered a dramatic spike in youth NEET rate, peaking at 17.8 percent for young women and 12.6 percent for young men.

In the three months to the end of March, the proportion of New Zealand youth not in employment, education, or training was sitting at 12.4 percent.

AUT Professor of Economics and NZ Policy Research Institute director Gail Pacheco speaks to Kathryn.

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• Published: 03 May 2024

A lifestyle screening tool for young children in the community: needs and wishes of parents and youth healthcare professionals

• Anne Krijger 1 , 2 ,
• Lieke Schiphof-Godart 3 ,
• Caren Lanting 4 ,
• Liset Elstgeest 2 , 5 ,
• Hein Raat 2 &
• Koen Joosten 1  

BMC Health Services Research volume  24 , Article number:  584 ( 2024 ) Cite this article

Metrics details

Youth healthcare has an important role in promoting a healthy lifestyle in young children in order to prevent lifestyle-related health problems. To aid youth healthcare in this task, a new lifestyle screening tool will be developed. The aim of this study was to explore how youth healthcare professionals (YHCP) could best support parents in improving their children’s lifestyle using a new lifestyle screening tool for young children.

We conducted four and seven focus groups among parents ( N  = 25) and YHCP ( N  = 25), respectively. Two main topics were addressed: the experiences with current practice of youth healthcare regarding lifestyle in young children, and the requirements for the lifestyle screening tool to be developed. The focus groups were recorded, transcribed verbatim and analysed using an inductive approach.

Both parents and YHCP indicated that young children’s lifestyles are often discussed during youth healthcare appointments. While parents felt that this discussion could be more in-depth, YHCP mainly needed clues to continue the discussion. According to parents and YHCP, a new lifestyle screening tool for young children should be easy to use, take little time and provide courses of action. Moreover, it should be attractive to complete and align with the family concerned.

Conclusions

According to parents and YHCP, a new lifestyle screening tool for young children could be useful to discuss specific lifestyle topics in more detail and to provide targeted advice.

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In early childhood (0–4 years), unhealthy lifestyle behaviours, such as poor dietary intake, limited physical activity, excessive screen time and insufficient sleep, have been associated with adverse health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Overweight and obesity are among the most prominent manifestations of these unhealthy behaviours [ 8 ]. According to the WHO, 5.1% of all children under five were overweight or obese in 2020 [ 9 ]. In the Netherlands, the prevalence of overweight (including obesity) in children aged 2–9 years was 15.5% in 2021; the prevalence of obesity was 4.8% [ 10 ]. These prevalence rates illustrate the large scale of current lifestyle behaviour problems. As lifestyle habits are formed early in life and may persist over time, lifestyle interventions in the early years hold the greatest potential for long-term health benefits [ 11 , 12 ].

In the Netherlands, preventive youth healthcare is a free service that aims to promote, protect and secure the health, growth and development of children up to the age of 18 [ 13 ]. From birth onwards, all children and their parents are offered regular consultations, vaccinations and counselling at local child health clinics. Youth healthcare professionals (YHCP) work in multidisciplinary teams and can refer to specialized care when needed. Among the core activities of YHCP is screening, for example for unhealthy lifestyle behaviour. By identifying unhealthy lifestyle behaviour in children, YHCP can provide targeted advice to parents to help them improve their children’s lifestyles. As up to 95% of young children are reached by Dutch youth healthcare, this provides an excellent setting for lifestyle improvement and prevention of adverse lifestyle-related health consequences [ 14 ]. In practice, however, lifestyle screening in young children appears to be complex and time-consuming, and no unambiguous screening method or tool is available.

In 2018, the Dutch Ministry of Health, Welfare and Sport published the National Prevention Agreement, which describes policies to tackle overweight, smoking and problematic alcohol use [ 15 ]. This agreement called for the development of a screening tool that would provide insight into the lifestyle of children aged 0–4 years and give parents practical support in mitigating the long-term risks of an unhealthy lifestyle. In previous phases of the project, we studied existing lifestyle screening tools for children and examined bottlenecks and patterns lifestyle behaviours in Dutch toddlers [ 16 , 17 , 18 ].

Linking to current work practices in youth healthcare and alignment with the needs and wishes of parents and YHCP may be critical to the ultimate success of the new lifestyle screening tool. To this end, we carried out a target group analysis before actually developing the new tool [ 19 ]. The aim of this paper is to describe (1) current practice of youth healthcare regarding lifestyle in young children, and (2) the requirements for the lifestyle screening tool under development, according to parents and YHCP.

Study design

We conducted focus groups among parents of children aged 0–6 years and YHCP working in Dutch youth healthcare. The use of focus groups allows for interaction between participants, which may lead to additional insight into the topics discussed [ 20 ]. Prior to the focus groups, participants completed a questionnaire assessing general characteristics. For the parents, this concerned their age, sex, education level, country of birth, number of children and age of their children. From YHCP, their profession (youth physician or youth nurse) and the healthcare centre they were appointed at were obtained. This study is reported as indicated by the COREQ (COnsolidated criteria for REporting Qualitative Research) Checklist (Additional File 1 ) [ 21 ].

Participants

Participants were recruited using convenience and purposive sampling between April and October 2021. Parents were able to sign up via a previously conducted survey that served as a first exploration of the topic of lifestyle among parents of young children. In addition, parent recruitment leaflets were posted at youth healthcare centres, nurseries and the Erasmus University Medical Centre. Personal networks of members of the research team were also contacted and snowball recruitment occurred through parents who had signed up. The inclusion criteria for parents were: (1) having at least one child between the ages of 6 months and 6 years, and (2) being able to provide informed consent. There were no exclusion criteria. As data collection took place, we noticed that parents with lower educational attainment and parents with a migrant background were under-represented. A final recruitment attempt was therefore made through ‘parent contact persons’ at schools in Rotterdam, the Netherlands, with a relatively large population of low-educated families and families with a migration background.

YHCP (both youth physicians and youth nurses) working with children between the ages of 6 months and 6 years were eligible for inclusion. They were recruited through the research team’s professional network and through JGZ Life! , an online current affairs program for professionals within youth healthcare.

Data collection

Data collection was performed in Dutch between June and November 2021 and took place for parents and YHCP separately. Participants could indicate their availability on predefined time slots. We tried to have between four and eight participants per focus group, but twice we accepted that there would be two participants in a focus group and once three. Due to COVID-19 measures, the first focus groups were held online via MS Teams. However, at the end of 2021, COVID-19 measures were loosened and we were able to conduct the focus groups with lower-educated parents and parents with a migration background in dedicated parent rooms at their children’s schools. In addition to the information letter and written informed consent, the focus group moderator briefly explained the study aims and participants verbally reconfirmed their consent to audio recording, at the beginning of each focus group. During the focus groups, at least two members of the research team (CL (MD, PhD, extensive focus group experience), MdW (PhD, extensive focus group experience), LSG (PhD, extensive focus group experience), and AK (MD), all female researchers) were present and field notes were taken. The ‘parent contact persons’ were also present at the focus groups in the schools. We developed separate topic guides for parents and YHCP. The key questions for both parents and YHCP in this topic guide concerned: 1) the current practice of youth healthcare regarding young children’s lifestyle, and 2) the requirements for the lifestyle screening tool under development. Prior to topic two, the moderator summarized the main idea of the new lifestyle screening tool (i.e. asking parents questions regarding their children’s lifestyle preceding a youth healthcare visit, leading to tailored advice). The lifestyle screening tool had not been mentioned to the participants before, in order to avoid narrowed data collection for the first topic. All participating parents received a gift card as a token of appreciation; YHCP received an attendance fee.

Data analysis

All audio recordings were transcribed verbatim. The transcripts were coded using NVivo software (QSR International Pty Ltd. (2022) Nvivo (Release 1.7)) and analysed using an inductive thematic approach [ 22 ]. First, two researchers (AK and KK) openly coded two transcripts independently, one from parents and one from YHCP. These preliminary coding schemes were compared, refined and discussed with LSG until consensus about the axial coding framework was reached. Next, AK and KK coded the remaining transcripts. In consultation, new codes were added to the coding scheme and AK and KK checked the consistency of all coded transcripts. AK and LSG agreed that data saturation was achieved. Through a process of discussion, agreement was reached on overarching themes and key findings of the data. Descriptive characteristics of the study samples were summarized using Microsoft Excel 2016.

Participant characteristics

Seven focus groups were held among parents and four among YHCP. The average durations were 58 and 56 min for parents and YHCP, respectively. The characteristics of participating parents and YHCP are given in Table  1 . The average age of parents was 38.0 years (SD 4.4, range 31–46) and the majority were female (96%). The mean number of children per parent was 2.6 (SD 1.7, range 1–7). Most of the parents had been born in the Netherlands (64%) and had received a high level of education (44%). Five YHCP dropped out because the scheduled time did not suit them. Participating YHCP were predominantly female (96%). Most of them were working as a youth physician (68%) and the largest portion in the Western part of the Netherlands (48%).

Current practice of youth healthcare regarding young children’s lifestyle

Regarding the current practice of youth healthcare regarding young children’s lifestyles, the themes that arose were: (1) screening and discussing lifestyle, and (2) advising and informing. Parents stated that their child’s lifestyle is often discussed during youth healthcare appointments and that they appreciate this. The emphasis of the conversation is usually on nutrition, but the topics of physical activity and sleep are also commonly addressed. Parents value the open-ended, non-judgmental questions asked by YHCP to start the conversation. However, when asked to clarify their preferences, parents expressed that YHCP could ask more in-depth questions, such as how much the child is eating exactly or what the vegetable intake is like. According to the parents, this may provide YHCP with a better understanding of the situation from which to offer specific advice. It may also help to break down barriers that might prevent parents from sharing their concerns if only open-ended questions are asked.

Regarding parents’ preferences for screening and discussing lifestyle:

“If a child is growing well and following the curve, then it’s basically done. But you [the YHCP] could also zoom in on what they [the children] actually eat and what the fruit and vegetable intake is like.” Parent #5.

Moreover, parents indicated that the lifestyle conversation could be more in line with their needs and family situation. Barriers to discussing lifestyle, according to parents, are the relatively few appointments offered by youth healthcare services and time constraints. Some parents put forward that not all YHCP were equally open to alternative ways of eating or upbringing.

Parents reported receiving advice and information about their child’s diet, physical activity and screen use. In general, parents were satisfied with the advice they received. However, the advice and information was also repeatedly perceived as not being very comprehensive and not giving enough guidance on what is healthy. As facilitators in informing about lifestyle by YHCP, parents reported explaining guidelines and advice and providing information material to take home.

With regard to the way information is given, parents prefer a coaching, non-strict conversation with a holistic perspective. The presence of older children in the family is a major obstacle in compliance with lifestyle advice for their young children. In the focus groups with parents with lower levels of education and migrant backgrounds, the grandparents’ views on a healthy lifestyle and a healthy weight were also noted as disturbing factor.

Regarding the perception of older family members as a barrier to complying with lifestyle advice:

“When I go on holiday to my family, they say: ‘Oh he is cute, but skinny, so sad’.” [when in fact he has a normal weight] Parent #13.

For YHCP, the themes on the current practice of youth healthcare regarding young children’s lifestyles that arose: (1) screening and discussing lifestyle, and (2) advising and informing. YHCP indicated that the subject of lifestyle is discussed in the majority of appointments. Exceptions include appointments on indication, for example when vision or motor skills are examined only. When children are younger than one year old, lifestyle, particularly nutrition, is often addressed at the parent’s initiative. Parents may have questions themselves, and also expect talking about their child’s nutrition. After the first year of life, parents typically bring up the topic of nutrition only when they experience problems, such as the child not eating well or being a picky eater. YHCP stated that if parents do no mention lifestyle themselves, they will inquire about it as openly as possible.

Regarding the current method of screening and discussing lifestyle during a youth healthcare visit:

“Well, I basically just ask at every consultation: ‘How is the diet?’. And then we talk about that.” YHCP #5.

In addition to nutrition, YHCP may discuss with parents their children’s physical activity, screen time, sleep, as well as family stressors, parenting and parental lifestyle. Sometimes this conversation is initiated on the basis of a child’s growth curve or specific items in the electronic health record, such as supplemental vitamin D intake. Several YHCP also mentioned that tools, such as a waiting room poster that displays the number of sugar cubes in various sugar-sweetened beverages, frequently spark discussion. However, the demand-driven way of working within Dutch youth healthcare and time constraints make it sometimes challenging to discuss lifestyle with parents, especially when YHCP feel there are no “starting points”, such as unhealthy weight, for the conversation.

Regarding the absence of “starting points” to proceed with the conversation:

“So, when I ask ‘How is the diet?’, and the answer is ‘Good’, yes, then it gets difficult. Because indeed, how much further should you ask? If I see a child having overweight or obesity, then I really have a starting point for a conversation, but when I see a child with a healthy weight who is developing well, yes… Then I’ll let it go, then I won’t ask any further questions. So, I’m probably missing a lot of things.” YHCP #7.

Regarding time constraints during consultations:

“You have several things to do and this [discussing lifestyle] is just a small part of it. In that respect, I believe I absolutely miss children who may have an unhealthy diet but are otherwise healthy-weighted. But because you just have 20 minutes and there are so many things you need to give attention to, that goes wrong sometimes.” YHCP #14.

Nevertheless, when YHCP notice “red flags”, such as abnormal growth or overweight, they probe further. While it is easier to start the conversation about lifestyle in this case, YHCP find it more difficult to continue this conversation. Reasons for this are mainly parent-related: some parents may find the topic of lifestyle too sensitive, they may not be open to a conversation about it, or are unaware of the lifestyle recommendations for a specific age.

With regard to advising and informing parents about lifestyle of their children, YHCP indicated a list of facilitators and barriers. Above all, it was stated that advice or information given should be tailored to the family concerned. To facilitate this, YHCP reported that provided advice and information should be in line with the parents’ knowledge, skills, financial resources, environment, and culture. Additionally, using existing tools and information sources, such as flyers from the Dutch Nutrition Centre, and offering feasible advice was considered helpful. Most barriers were related to these facilitators. In addition, the resistance of parents to advice was also raised as a major concern.

Regarding parental resistance to lifestyle advice:

“But here again, if parents notice that their child is overweight but refuse to do anything about it, it is better to ask parents again when they begin to worry about it. (.) However, it gives me mixed feelings, because the child has no choice. (.) So, I still find that very difficult.” YHCP #16.

Requirements for a new lifestyle screening tool

The requirements that emerged from the parents were divided into requirements for themselves and for their children (Table  2 ; Fig.  1 ). Six themes were identified in terms of requirements for the parents themselves: (1) usability, (2) time investment, (3) alignment with family, (4) visual attractiveness, (5) effectiveness, and (6) child privacy. Usability mainly concerned completing the tool at a suitable place (e.g. at home or waiting room) and in a practical way (digitally or on paper). Although opinions varied on the best place and method, parents agreed that the time investment should be minimal and certainly no longer than ten minutes. To align the lifestyle screening tool with the family, parents requested that the tool be tailored to the family’s needs and values in terms of socio-economic status, skills and family culture. Parents preferred a visually appealing tool that provides an overview of a child’s lifestyle.

Regarding effectiveness and visual attractiveness:

“And that’s why I thought of a spider web, because then you can show the relationship between the different elements, and as professional you can also say: ‘Hey, I’m noticing something here’.” Parent #2.

As for effectiveness, major concerns for parents were that the purpose of the tool should be clear to them and that YHCP act upon the answers parents provide. Moreover, the tool should mainly facilitate and support the conversation with the YHCP and not be strict and patronizing. While the higher-educated parents emphasized the importance of using the tool holistically and without judgment, the parents with a lower education and/or migration background indicated that they would prefer outcomes with more direction. The use of a traffic light system, for example, in which healthy behavior is marked green and less healthy behavior orange or red, would give them guidance and motivation to improve.

Regarding outcomes with a clear direction as part of the theme ‘effectiveness’:

“Of course! When I get a warning like ‘your child can do much better’ (…), you just do your best!” Parent #14.

Some parents mentioned that a tool would have been helpful before the age of one, whereas others stated that they had more questions during toddlerhood and such a tool would therefore be more effective from the age of 12 months and older. Finally, parents considered it critical to ensure the safety of the data they would provide with the tool.

The requirement for the child comprised including relevant topics in the tool. The parents suggested nutrition, physical activity and sleep as the most relevant topics. Screen time was not mentioned.

YHCP devised requirements for the new lifestyle screening tool for themselves, for the parents, and for the children (Table  2 ; Fig.  1 ). As for requirements for YHCP themselves, three themes were identified: (1) usability, (2) time investment, and (3) courses of action. Usability referred to several factors, including using the tool as a conversation aid, embedding it into the current working method and electronic health record, and utilizing existing tools and resources for providing advice and information. Regarding time investment, the most frequently mentioned concern for YHCP themselves was that the instrument should not lead to time loss during the appointment. Lastly, the YHCP mentioned that the tool should offer them courses for action, for example by providing a score, offering cues for the conversation or contributing to counselling.

Regarding using the tool as a conversation aid:

“Could it be a starting point for the conversation you are already having anyway, but in a certain way, from that starting point?” YHCP #2.

According to the YHCP, the requirements for the parents were subdivided into: (1) usability, (2) alignment with family, (3) attractiveness, and (4) effectiveness. YHCP expressed that the tool should have high usability for parents too, for example by enabling quick and digital completion. In addition, the YHCP above all felt that a new lifestyle screening tool should align with the family, particularly in terms of the parents’ needs, socio-economic status, skills, and culture. Other requirements for parents for the tool included it being attractive, i.e. visually appealing and not too strict or patronizing, as well as being effective, for example by increasing parents’ knowledge and awareness of their child’s lifestyle.

The overarching theme of the requirements for the children according to YHCP was effectiveness. YHCP mentioned that a new lifestyle screening tool would be effective for children if it covers relevant topics and is used at appropriate ages. Healthy and unhealthy dietary intake and physical activity were most frequently mentioned as relevant topics, but screen time, sleep and smoking also emerged. YHCP agreed that a lifestyle screening tool should be applied before lifestyle patterns become ingrained, so for example at the age of one year, or even earlier.

Overlapping and individual themes that emerged for parents, children and YHCP according to parents and YHCP

This study describes the experiences of current practice in Dutch youth healthcare regarding lifestyle in young children and the requirements for a new lifestyle screening tool according to parents and YHCP. A new lifestyle screening tool was considered desirable by both groups.

Parents reported that they were generally satisfied with the current practice in youth healthcare regarding the lifestyle of young children. They appreciated the open start to the lifestyle conversation, but required a more in-depth approach from the YHCP, both in continuing the conversation and in providing advice and information. This finding is in line with Swedish research in which parents indicated the desire to receive more information and advice regardless of their identified needs [ 23 ]. Parents in our study felt that this could be overcome by further questioning on specific lifestyle topics, and also by providing more explanation and background to the guidelines and advice, or by offering information materials to take home. Asking specifically about the habitual quantity of fruit consumption or hours of screen time, for example, may not be in line with the demand-driven approach used in Dutch youth healthcare. A common conversation technique within this demand-driven methodology starts from the parents’ concerns in order to actively engage them in the conversation [ 24 ]. This technique is based on the idea that care can then be tailored to parents’ needs and that parents will be more motivated to make changes if they themselves perceive certain issues as problems. YHCP also experienced that sometimes they want to continue a conversation about lifestyle with the parent, but they lack “starting points” or tools to do so. In their view, the demand-driven approach then conflicts with the need to work preventively. A lifestyle screening tool could address this concern by first asking an open-ended question about the parent’s perspective and then eliciting more specific information about certain lifestyle topics. In this way, both the parent and the YHCP are given a helping hand to guide and deepen the conversation, discuss topics that might not otherwise be covered, and allow the parent to get specific advice.

Although the nuance of the themes was slightly different for parents and YHCP, we found considerable overlap between the requirements of both groups. Above all, for parents as well as YHCP, a new lifestyle screening tool for young children should be easy to use, take little time, and provide concrete courses of action. Furthermore, for parents, tool usage should align to the family in question and be (visually) attractive to use. In our view, these requirements may also be relevant to other innovations within youth healthcare. Support that matches personal experiences, preferences and practices that is culturally sensitive was also expressed as a need in a Dutch study that examined parents’ perspectives regarding youth health care in the first two years [ 25 ]. For the lifestyle screening tool, for example, this could mean that the advice given by the YHCP takes into account the family’s food culture and financial resources. In another Dutch study on psychosocial and lifestyle assessment of childhood obesity, care professionals also stated that visual materials are helpful in conversations with parents [ 26 ].

Higher-educated parents and YHCP felt that screening tool outcomes should not be too judgmental, whereas parents from less educated or migrant backgrounds needed more clarity in the answers given and were open to a more directive approach. As children of parents with lower education levels or migrant backgrounds are more likely to have unhealthy lifestyles, there is more to be gained especially there [ 27 , 28 ]. A ‘traffic light’ system indicating healthy and unhealthier behavior, as suggested by these parents, may therefore be a useful, clarifying and effective feature of the new lifestyle screening tool.

While focus groups are used to gain in-depth knowledge of people’s perceptions, beliefs and opinions about a particular (health) issue, they are also useful in identifying the target population’s needs and wishes when developing innovations [ 29 , 30 , 31 ]. Although most of the findings of other focus group studies are specific to the innovations, they all concluded that the use of focus groups supports the development itself and future acceptability [ 32 , 33 , 34 , 35 ].

Strengths and limitations

Strengths of this study include conducting focus groups with both stakeholder groups, namely parents and YHCP. This qualitative approach allowed for the collection of enhanced opinions and data from person-to-person interactions, as well as comparison of the two groups [ 20 ]. Our efforts to reach more parents with a lower education level and non-Dutch background increased the transferability of our findings. Credibility was raised by the data being coded independently by two researchers. The use of convenience sampling in parent recruitment is a study limitation, which was partly due to COVID-19 regulations in place at the time. COVID-19 regulations also required most of the focus groups to take place online. While this may not have affected the data quality, face-to-face interviews may be preferable when discussing socially sensitive topics, such as lifestyle [ 36 ]. As most of the parents in our study had several children, we should be aware that the results may be different for first-time parents. Lastly, the limited involvement of fathers, the lack of diversity of cultural backgrounds and the limitation to Dutch-speaking parents should be taken into account when interpreting the results in the context of the Netherlands as a whole.

Young children’s lifestyles are often discussed during youth healthcare appointments. While parents felt that these conversations could be more in-depth, YHCP needed resources to carry on the conversation. A lifestyle screening tool may be able to respond to these desires. According to parents and YHCP, this screening tool should be easy to use, take little time and offer courses of action. For parents in particular, the tool should be attractive to complete and align with the family in terms of parental needs, socio-economic status, skills and culture. These preferences need to be considered throughout the development of the new lifestyle screening tool to increase its effectiveness, acceptability and value in improving the lifestyle of young children. To reach the group that would benefit most from lifestyle improvements, i.e. families from lower socio-economic backgrounds, it is crucial to specifically address their needs.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Youth healthcare professionals

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Acknowledgements

The authors thank all participating parents and youth healthcare professionals. We thank Marianne de Wolff for helping us to conduct the focus groups and Karin Kruit for her help with the coding of the transcripts.

This study was conducted as part of the project ‘Nutrition and lifestyle screening tool for youth healthcare 2019–2022′ and funded by the Dutch Ministry of Health, Welfare and Sport (VWS).

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Conceptualisation: KJ, LE, CL, AK; Data collection: CL, AK, LS-G; formal analysis: AK, LS-G; interpretation of results: AK, LS-G, CL, LE, HR, KJ; writing – original draft preparation: AK; writing – review and editing: AK, LS-G, CL, LE, HR, KJ. All authors read and approved the final manuscript.

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The Medical Ethical Committee of the Erasmus University Medical Centre Rotterdam approved the study protocol and declared that the study was not subject to the Dutch Medical Research Involving Human Subjects Act (WMO) (reference number MEC-2021-0539). The study was conducted in accordance with the principles of the Declaration of Helsinki. All participants were informed about the study aims by an information letter and verbally before the start of the focus group. They all gave written informed consent to the audio recording and to the anonymous processing and use of the data for this specific project.

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Krijger, A., Schiphof-Godart, L., Lanting, C. et al. A lifestyle screening tool for young children in the community: needs and wishes of parents and youth healthcare professionals. BMC Health Serv Res 24 , 584 (2024). https://doi.org/10.1186/s12913-024-10997-y

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This study investigates the nature and causes of youth unemployment in Nigeria, with the aim of proffering evidence-based workable solutions as policy recommendation. Its contribution to the literature on youth unemployment is the joint examination of the nature and causes of youth unemployment, which gives a holistic view and provides sufficient background for designing holistic solutions to the problem of youth unemployment in Nigeria. The study employs a Vector Autoregressive (VAR) model. This describes the spillovers of youth employment among different sectors (Agriculture, Industry and Services) in Nigeria; thus, explains whether the nature of youth unemployment in Nigeria is frictional or not. The study also adopts Panel Autoregressive Distributed (PARDL) model to analyze the short-run and long-run significance of the determinants of youth unemployment, such output level, macroeconomic uncertainties and labour market flexibility. This helps to determine the main causes of youth unemployment in Nigeria and whether the youth unemployment is cyclical or structural in nature. The results suggest that the nature of youth unemployment in Nigeria is non-cyclical, partly frictional, but largely structural. This may explain why youth unemployment is increasing in Nigeria despite government remedial efforts; as government focused on frictional youth unemployment remedial policies and dispelled the potential of youth unemployment being structural in nature. The recommended solutions are fiscal and monetary policy easing and demand-side subsidy programme to dealing with structural youth unemployment. The study also shows the need to enforce relevant extant labour laws and regulations to stem the tide of youth unemployment and underemployment in Nigeria.

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Data availability.

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Acknowledgements

Comments from the Editor and three anonymous reviewers are gratefully acknowledged. The authors also acknowledge the intellectual contributions of Tirimisiyu F. Oloko, Ahamuefula E. Ogbonna, Idris A. Adediran and Kazeem O. Isah.

The authors wish to acknowledge the research support received from the Tertiary Education Trust Fund (TETFund) of the Nigerian Government, under the Research Fund (RF) Project (2019).

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Olubusoye, O.E., Salisu, A.A. & Olofin, S.O. Youth unemployment in Nigeria: nature, causes and solutions. Qual Quant 57 , 1125–1157 (2023). https://doi.org/10.1007/s11135-022-01388-8

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Accepted : 23 March 2022

Published : 28 April 2022

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DOI : https://doi.org/10.1007/s11135-022-01388-8

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23% decrease in the number of unemployed

The number of unemployed Muscovites in 2022 decreased by 23% compared to 2021. This was announced on January 24, 2023 by the Deputy Mayor of Moscow for Economic Policy and Property and Land Relations Vladimir Efimov.

According to him, by January 1, 2023, 28.1 thousand unemployed were registered in Moscow , which is a record low over the past few years, and the unemployment rate fell to 0.39% (the minimum value since February 2020). There are 14 job offers per candidate in Moscow.

According to the Department of Economic Policy and Development of the city of Moscow , in January-December 2022, the largest difference between the number of hired and dismissed people was recorded in the field of computer and software development - 24.7 thousand people. In the research and development industry, 10.7 thousand more employees were employed than laid off, in the housing and utilities sector - 5.3 thousand, in the field of repair and installation of machines and equipment - 3.4 thousand, in the production of computers, electronics , optics - 2.7 thousand.

The Moscow mayor's office notes that despite the sanctions and the departure of a number of large foreign companies, the labor market in the capital remained stable. This was ensured by a balanced employment policy, measures to support enterprises and businesses, as well as an active program to replace products of foreign brands with goods from manufacturers from Russia and friendly countries, the mayor's office said.

Unemployment rate of just 0.4%

Since the beginning of 2022, the number of unemployed in Moscow has decreased by more than 20%. As of December 6, the unemployment rate was 0.4%, the lowest since March 2020.

Moscow topped the ranking of G20 cities with the lowest unemployment rate

Moscow topped the ranking of the largest G20 cities with the lowest unemployment rate. Maria Bagreeva, head of the department of economic policy and development of the capital, spoke about this at the end of August 2022.

The rating was compiled by the Moscow Analytical Center to compare the unemployment rate in various countries and cities using the methodology of the International Labor Organization (ILO). The list includes Moscow and 20 other cities from the G20 countries, comparable to it in terms of population, size and structure of the economy.

The remaining places in the top five were taken by:

• Tokyo , in which ILO unemployment for the six months was 2.8%;
• Sydney - 3.2%;
• New York - 4.4%;
• Riyadh - 4.5%.

The highest unemployment rate was recorded in cities Brazil - above 10%. Almost all G-20 megacities in 2021 - the first six months of 2022 year showed a consistent recovery in the labor market after the peaks of unemployment during the pandemic, and in coronavirus some cities it almost halved.

According to him, in January - June 2022, unemployment in Moscow , calculated according to the ILO method, amounted to 2.2%. The indicator returned to the levels of the first half of 2020, which indicates the recovery of the capital's economy after the pandemic and its high resilience to the crisis caused by sanctions. [2]

2021-2022: Unemployment in Moscow has tripled

The level of officially registered unemployment in Moscow as of January 1, 2021 was 1.32%, and a year later the figure fell to 0.46%. This was announced on February 1, 2022 by the Deputy Mayor of Moscow for Economic Policy and Property and Land Relations Vladimir Efimov.

According to him, unemployment in the Russian capital has been consistently declining since October 2020, when a peak of 3.03% was recorded. By the beginning of 2022, there were 33.6 thousand officially registered unemployed in Moscow.

Data from the HeadHunter portal indicate that in the capital's skilled labor market, the level of tension at the end of 2021 decreased by 41% compared to December 2020. This indicator is the ratio of the number of resumes and vacancies published on the job search site. By December 2019, values ​ ​ fell by 45%, indicated on the website of the mayor of Moscow.

According to the head of the Department of Economic Policy and Development of the city of Moscow, Kirill Purtov , by the beginning of 2022, the level of tension is 5.1 resumes for a vacancy (the indicator remains from January 2019), while the peak fell on May 2020, when it was 17.2.

According to the portal "Work in Russia ," the number of employees hired for the period from April 2020 to December 2021 exceeded the number of dismissed by 584 thousand people. The maximum excess of those accepted over those dismissed for the entire time since the beginning of the pandemic was recorded in the field of software development - 82.1 thousand people, wholesale trade - 73.1 thousand. people, construction - 66.9 thousand people, healthcare - 57.7 thousand people, retail - 47.3 thousand people. [3]

• ↑ In 2022, the number of unemployed Muscovites decreased by 23 percent
• ↑ Moscow has the lowest unemployment among the largest G20 megacities
• ↑ Vladimir Efimov: the unemployment rate in Moscow has almost tripled