INTRODUCTION
Research goals and objectives
This research aims to analyze the extent to which education and family background influence wage correlation Key factors include employment characteristics such as age, marital status, ethnicity, educational attainment, work experience, and geographical region Additionally, family background elements like parental education levels and the number of siblings are considered to provide a comprehensive understanding of wage disparities.
This thesis develops a model to analyze the influence of employment and family background on wages The findings indicate that education significantly affects wage levels and highlights the role of family background in shaping individual outcomes Based on these results, the study offers policy recommendations to address these disparities.
Research questions o • • • • • • o o o o o o o o 0 3
This research aims to address key questions regarding the influence of employment characteristics and family background on wage levels Specifically, it investigates how various employment factors impact earnings and examines the role of family background in shaping wage outcomes.
Specially, the thesis aims to answer questions below: i Is the educational attainment level a key factor of wage? ii.Does family background affect an employee's wage?
LITERATURE REVIEW
Previous empirical studies 0 • • • • • • • • • • • • • • • • 6
Worker skills significantly influence wage levels, as demonstrated by numerous empirical studies Buchinsky and Leslie (2009) developed a dynamic model to analyze individuals' educational investments, aiming to forecast future wage distributions Their research utilized data from the March Current Population Surveys in the U.S., focusing on males aged 14 to 65 who were either employed or enrolled in school from 1964 to 2004 The study emphasizes the importance of incorporating uncertainty about future wage distributions while maintaining certainty about individual parameters, such as the agent's utility function This innovative approach enhances the decision-making process regarding educational investments.
In our framework, a risk-averse individual considers both the uncertainty of future wage outcomes based on education and experience, as well as the variability in the estimated parameters of these conditional wage distributions.
The VAR-Gibbs model is selected for regression analysis due to its effectiveness in addressing two key issues Firstly, it examines how varying levels of risk aversion influence individuals' educational choices, revealing that higher risk aversion corresponds to lower educational investment, as education is perceived as a riskier investment compared to experience during the study period Secondly, the research explores the significance of financial resources in higher education decisions by assessing how changes in initial wealth can affect these educational choices.
Distinct forecasting methods reveal notable variations in school attendance rates, average education levels, and the duration required to attain a college degree The study highlights that individuals with greater initial wealth tend to accumulate education more rapidly.
Hamilton et al (2000) explored the impact of worker and employer heterogeneity on output distribution, revealing key insights into the relationship between profits and wages within firms Unlike Sattinger (1993), their study focuses on a labor market where workers and firms are vertically differentiated, maintaining a common level of general human capital while recognizing variations in individual skills This approach enables the analysis of unique worker characteristics that affect their suitability for different firms, despite having similar educational backgrounds Additionally, the research introduces a non-hierarchical assignment model, which complements existing hierarchical models by acknowledging the diverse job requirements of firms and the varied strengths of workers.
In the labor market, firms struggle to identify the specific skills of individual workers, leading to a scenario where those receiving less training often earn higher net wages This occurs because firms do not differentiate between workers based on their skill types, resulting in better-matched workers commanding higher wages despite similar levels of general human capital and productivity As the number of firms increases, competition for well-matched workers drives equilibrium wages higher In a situation where the number of firms is very large, wages approach the competitive level associated with common general human capital, while profits diminish Additionally, as the overall level of general human capital rises, gross productivity increases, and training costs for each worker decrease, leading to higher net wages, as confirmed by various empirical studies However, profits decline as firms lose some monopsony power, which offsets productivity gains.
When firms are fully aware of the quality of individual job matches before hiring, they can tailor offers based on workers' skill types This allows employers to focus on overall wage costs and training expenses, while employees consider their net wages after training costs The responsibility for training costs becomes a part of the negotiation process, making it less critical who pays them As a result, workers who receive more training may earn higher net wages, not necessarily due to increased productivity, but because their training costs at competing firms are lower Consequently, workers poorly matched with a firm may have better outside options than well-matched workers, enhancing their bargaining power.
Wage and schooling are also interesting issues for economists besides the U.S
In a study conducted by Meghir (2005), the impact of educational reform on final attainment and earnings was assessed using data from the Swedish Level of Living Survey, national education registers, and tax records spanning from 1985 to 1996 The research involved a diverse methodology, dividing the sample into two cohorts of pupils The first cohort consisted of 10,309 individuals born in 1948, comprising 5,235 men and 5,074 women.
The study analyzed 9,007 individuals born in 1953, comprising 4,525 men and 4,482 women, with earnings data collected from 1985 to 1996 A logit model assessed the impact of educational attainment, revealing an overall increase of 0.298 years in education, with significant effects Low-ability individuals tended to shift to the new compulsory education level, while high-ability individuals not only reached this level but also exceeded it significantly The overall impact of the educational reform on earnings was modest at 1.42 percent, significant only at the 10 percent level, yet it masked considerable variation among different groups Notably, individuals with unskilled fathers experienced a significant earnings increase of 3.4 percent due to the reform.
Some older studies have attempted to investigate impact of schooling on wage
Harmon and Walker (1995) analyzed the impact of education on wages and income using data from the U.K Family Expenditure Survey (FES), which included a sample of 34,336 employed males aged 18-64 from 1978 to 1986 To address the endogenous issues in their study, they employed an instrumental variable approach, focusing on the exogenous changes in the educational distribution resulting from the increases in the minimum school-leaving age in the UK, which occurred twice during the study period.
- - - - - - - of the working-age individuals in our data) to provide instruments for schooling By choosing this way, this research has a little bit different to previous studies such as
A study by Grist and Krueger (1991) utilized natural variations in data influenced by external factors affecting schooling decisions, reinforcing the conclusions of Ashenfelter and Krueger (1994) Their findings indicate that an additional year of education can lead to over a 15% increase in wages Additionally, it was observed that residents in larger cities tend to earn higher wages compared to those in other regions.
Social scientists across various disciplines have extensively researched the impact of family background on adults' economic and social status Numerous studies have investigated how family factors, including parental income, parental education, and the number of siblings, influence wage outcomes Levin et al contribute to this body of work by examining these relationships in detail.
In 2007, researchers introduced a novel method to assess brother correlation in earnings across two distinct time periods They utilized two separate cohorts of men from the National Longitudinal Surveys (NLS) to conduct their analysis.
The NLS data sets reveal significant changes in sibling correlations for young men born between 1957 and 1965 compared to those born between 1944 and 1952 Notably, the correlation in annual earnings has increased from 0.26 to 0.45, indicating a strong statistical significance This trend suggests that nearly half of the variance in earnings and wages for recent cohorts can be attributed to family and community influences While the sibling correlation reflects more than just parental income effects, it also points to a decline in intergenerational mobility among younger men Interestingly, there has been no change in the correlation of years of schooling between these cohorts, indicating that the stronger family influence on earnings is not linked to educational attainment.
The significant rise in returns to education between the two cohorts accounts for only a small fraction of the overall increase in the correlation of earnings among brothers.
OVERVIEW OF VIETNAM LABOUR MARKET
Employment by age and gender 0 0 0 0 0 0 0 ° 0 0
The employment-to-population ratio is a crucial indicator of economic activity in Vietnam, reflecting the percentage of employed individuals within relevant population groups This ratio has shown a steady increase over the years, rising from 62.73% in 2006 to 69.18% in 2008 Additionally, the youth population in Vietnam is relatively balanced in terms of gender distribution.
In 2008, the labor force participation rate for women was slightly higher at 50.33% compared to men's 49.67% Despite an increase in male participation ratios, the overall quantity decreased Women experienced a decline in ratios but maintained a larger proportion in the labor force than men This trend contrasts with findings from the Population and Housing surveys conducted between 1997 and 2008, indicating a concerning shift in gender participation dynamics.
Surveys indicate that all age and gender groups are experiencing declining labor force participation rates, with women making up a smaller percentage than men Last year's employment-to-population ratio was 53.3%, with men at 52% and women at 48% The VHLSS data shows a decrease in observations from 39,071 in 2006 to 35,154 in 2008, despite a slight increase in the labor force, which contributed to a higher workforce ratio among total samples The VHLSS particularly highlights rural areas, where female employment often represents a larger share, as many women engage in agricultural activities and serve as the primary earners for their families, often working as unpaid family workers or self-employed individuals This demographic significantly influences the overall sample size.
The Population and Housing survey encompasses the entire population of a country, providing comprehensive results rather than relying on a sample Notably, the most significant portion of the overall labor force growth is attributed to the youth demographic.
In 2006 and 2008, young people aged 15 to 24 represented 27.99% and 26.45% of the total workforce, respectively, with a noticeable decline in participation due to an increasing tendency to prolong education Male participation in the workforce exceeds that of females, indicating that men tend to enter the job market earlier Between 2006 and 2008, the youth labor force decreased by over 400 individuals, or 1.54% While the gap in labor force participation rates between men and women is smaller compared to many other countries, both genders experienced a decline in participation from 1997 to 2007, with the most significant drop occurring among young women.
Vietnam's employment-to-population ratio is relatively high regionally, though it lags behind East Asia As of 2009, women's participation in the workforce was 48%, compared to 52% for men, reflecting a consistent trend over the past 30 years (Population and Housing Survey 2009).
Vietnam's labor force has experienced a gradual decline compared to East Asia, Southeast Asia, and the Pacific; however, the pace of change in Vietnam is notably faster While East Asia has seen a significant decrease in male workforce participation, Vietnam has witnessed an opposite trend Similarly, Southeast Asia and the Pacific have recorded the highest reductions in female labor force participation (ILO, May 2009) Although the percentage of men in the workforce in Vietnam is comparable to other countries in the region, the proportion of women participating is notably higher For instance, women's workforce participation in Vietnam stands at 79.5%, compared to approximately 50% in the Philippines, Indonesia, and Korea (ILO, 1997-1998).
Figure 3.2: Workforce classified by age-bands (person) i 2,500
According to the VHLSS 2008 data, labor force participation among both women and men shows a gradual decline starting from ages 15-19, with noticeable differences in participation rates emerging at this stage The gap narrows between ages 40-44, where both genders experience a downward trend in labor involvement, and after age 55, women continue to engage in work, often contributing more than men The VHLSS focuses on the living standards of rural residents, highlighting that rural areas comprise a significant portion of the sample In these regions, women are often the primary breadwinners in agricultural activities and unpaid family work, remaining active in the workforce across all age groups without the typical retirement patterns seen in wage-earning jobs.
Table 3.2: Labour force divided by sex, regions(%) rããã ããã2aa6ããã ããã2ao8ããã1
' ' i Regions ã"ATC~exe~ ããMale -Fe~a1e _ ãA:ifsexes -M~1e ã:pe~a!eã -~
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According to Table 3.2, the North West region has the lowest labor force participation, while the Mekong River Delta shows a contrasting trend Interestingly, the South East and Red River Delta regions have even smaller percentages of participation Despite housing major cities like Hanoi, Ho Chi Minh City, and Haiphong, which collectively have populations of around 11 to 12 million, urban areas in Vietnam represent less than 24% of the total population aged 15 and over.
Between 2006 and 2008, the majority of the population resided in rural areas, but this trend has been gradually shifting as urbanization increases More people are migrating from the countryside to cities, leading to a steady rise in the proportion of the urban population within the labor force since 1997 This ongoing movement reflects the expanding geographic and demographic dimensions of urban areas.
We also find that women take part in workforce higher both in rural and urban areas.
Employment with education and training 0 0 0
Vietnamese youth exhibit high literacy rates, exceeding 90%, which have remained stable over the past 25 years despite reductions in state education subsidies following the Doi Moi reforms A significant portion of the workforce is literate, with a reported literacy rate of 86.3% in 2006 among 33,700 respondents This rate has shown a continuous increase, rising from 93.2% in 2008 to 93.5% in subsequent years.
The 2009 Population and Housing survey indicates that the implementation of compulsory primary education and initiatives to eliminate illiteracy have effectively increased the literacy rate While there was only a marginal rise in the workforce's literacy rate from 94.7% in 2006 to 94.8% in 2008, these efforts have contributed to overall educational improvements Detailed information regarding the labor force's education can be found in Table 3.3.
Table 3.3 highlights significant disparities in educational attainment across different regions The Mekong River Delta, known for its leadership in rice exports, has the highest number of individuals without any educational certificates and those who have only completed primary education In contrast, the Red River Delta boasts the highest number of high school graduates, including both lower and upper secondary levels.
Table 3.3: Labour force classified by education (%)
Region No Primary Lower Upper College University Master certificate secondary school and higher
The South East region boasts the highest university graduation rates in the country, while the South Central Coast excels in master's degree attainment In urban areas, the majority of individuals hold upper secondary school diplomas, whereas in rural regions, primary school graduates make up the largest demographic This disparity indicates that a significant portion of the population lacks education beyond upper secondary, which adversely impacts the quality of the workforce in rural areas, where they represent 76.26% of the labor force Overall, these educational trends highlight the need for improved skill development in rural communities.
Nearly 6% of individuals have attained a college degree or higher, with only 4.4% achieving a university-level education These statistics highlight a concerning trend in our workforce: while there is an abundance of labor, there is a significant shortage of highly skilled workers, resulting in low productivity across various industries.
According to Figure 3.3, over 95% of the working-age population in Vietnam consists of unskilled workers, as they have not received vocational training, excluding those with college degrees This situation presents significant challenges for the youth in the job market The Vietnam Youth Development Strategy 2010 emphasizes the urgent need to create more job opportunities Millions of young people in urban areas are actively seeking employment, with many working approximately 75% of the year The increase in the employment ratio from 2006 to 2008 can be attributed to the global crisis, which negatively impacted household incomes, leading many students to leave school and enter the workforce without proper training.
According to a survey by VCCI, ILO with observations from FDI companies, Young enterprise associations, VCCI's members and Human and Personnel
This year, a management club survey revealed that up to 50% of enterprises frequently retrain new recruits due to a mismatch between employment skills and company requirements The survey included companies from various sectors, such as printing, aquatic processing, textiles, electronics, and tourism There is a significant shortage of highly skilled workers, particularly in manufacturing, where 67% of positions remain unfilled Consequently, the majority of jobs tend to be subsistence-based, with manual labor accounting for 67.31% of total employment as reported in the VHLSS 2008.
Between 2006 and 2008, the percentage of employed workers receiving vocational training significantly declined, dropping from 4.26% in 2006 to just 1.81% in 2008 This decrease can be attributed in part to the global crisis, which prompted many students to leave school and enter the labor market Consequently, a large number of untrained individuals are joining the workforce, resulting in a surplus of workers qualified only for manual jobs, such as laborers or agricultural workers.
Manual labor constitutes a significant portion of total occupations; however, these jobs frequently have short durations, often lasting only a week or a month Many of these positions are seasonal and typically offer low wages.
Status-in-employment data categorize employed individuals into four groups: wage or salaried workers, self-employed individuals (either as employers or working independently), and unpaid family workers, also known as "contributing family workers." Unpaid family workers assist in family businesses or farms without receiving a salary, helping the business owner generate profits This group typically includes spouses, children, and extended family members such as grandparents, cousins, and aunts or uncles.
According to VHLSS data, employment status can be categorized into four groups: wage and salaried workers, employers, own-account workers, and unpaid family workers In 2006, the number of employed individuals identified within these categories was notably low.
In recent years, the percentage of employers has decreased slightly from 0.43% to 0.34% Wages and salaries workers remain the largest employment group, accounting for 44.72% to 45.33% of total employment Unpaid family workers have also shown a slight increase, rising from 44.11% to 44.38% Conversely, the proportion of self-employed workers has declined from 10.83% to 9.85% The combined figures for vulnerable employment, which includes unpaid family workers and self-employed individuals, decreased from 54.94% to 54.23% in 2008 Despite the gradual reduction in self-employment and unpaid family work, these groups still represent a significant portion of the workforce, with over half of all employed women classified as unpaid workers in family businesses, compared to their male counterparts.
Table 3.4: Distributions of status in employment by sex
waies an-cr -~ -3-s-~s4 ; _ 6-ci-6+ -39:Trj_ _ 6_o~-s9- salaries workers (%)
According to data from the VHLSS 2006-2008, a significant statistic reveals that over 50% of employed women in Vietnam did not receive any earnings for their work, highlighting a critical issue in gender wage disparity (refer to Appendix table AS for details).
We can say that women suffer more than men in terms of vulnerability in Vietnam because of 78% of their total employment was in the own-account and unpaid
family worker, compared with a still very high 75 per cent among men (ILO, 2007)
In Vietnam, a significant number of women are employed as unpaid family workers, highlighting the concentration of women's employment in unpaid roles This group, along with own-account workers, faces a lower likelihood of securing formal work arrangements, which often results in a lack of essential elements associated with decent employment, such as adequate social security and a voice in the workplace Consequently, high rates of vulnerable employment serve as an indicator of widespread poverty within the country.
Unemployment
Age-bands Number of worker Percent
According to the VHLSS 2008, 4,269 individuals aged 15 to under 60 are unemployed, representing 21.29% of the total labor force This significant figure is influenced by the inclusion of rural residents, where unemployment rates are highest Additionally, the report highlights that the age group of 15 to 29 is particularly affected by this issue.
In 2009, the unemployment rate reached 3.05% of the total labor force, with 1,504,888 individuals not employed, marking the highest ratio from 1997 to 2009 Among the unemployed, those aged 15-19 represented the largest demographic, accounting for nearly 75% of this group Out of a total of 4,269 unemployed individuals, 75 were unable to find jobs, highlighting the significant challenges faced by younger job seekers in the labor market.
According to the report, individuals aged 15-29 represent nearly 50% of the unemployment rate, highlighting a significant demographic impact Additionally, there is a stark contrast in technical training attainment between rural and urban areas, with urban residents lacking specific training facing higher unemployment rates compared to their rural counterparts.
Individuals with technical training have greater employment opportunities in urban areas The following figure illustrates the employment rates compared to the labor force from 1997 to 2009.
Figure 3.4: Workforce vs employed workers (thousand of person) c 30,000
Source: MOLISA labour and employment surveys
We can conclude that unemployment is not a senous issue of Vietnam economy The challenges are type of jobs and skills of employment
THE MODEL
Prior to 1992, various empirical studies examined intergenerational income correlations, but many faced biases due to OLS estimation Solon's groundbreaking research in 1992 significantly advanced the understanding of the link between family background and employment earnings Previous studies often relied on sibling or father-son correlations, yet they struggled with biases stemming from the inability to distinguish between permanent and transitory status variations, neglecting life-cycle stages, and using overly homogeneous samples According to Solon and Bielby and Hauser (1977), which relied on sons' recollections of parental income, the errors-in-variables bias was likely exacerbated Although Bielby and Hauser attempted a minor adjustment for response error, it was based on the questionable assumption of no correlation between response errors across different recollection times.
Behrman and Taubman (1985) analyzed a sample of white male twins born between 1917 and 1927, highlighting a downward bias due to two significant exceptions The primary issue was the absence of direct measures of long-term status, as many studies relied solely on single-year earnings or income data.
Samples in this study were not selected randomly, and while some research from this period employs Instrumental Variable (IV) estimation, Solon (1992) and Card (1998) demonstrated that using family background variables as instruments can introduce additional bias to Ordinary Least Squares (OLS) results, even if these variables do not independently affect earnings.
This occurs if the family background variables are correlated with unobserved
- - - - - - ability If one uses the family background variables solely as instruments, the bias is worse than OLS
John Bound et al (1993) emphasize the importance of carefully selecting instruments for estimation, as a weak correlation between instruments and wages can lead to significant bias in Instrument Variable (IV) estimates (Harm, 1995) To mitigate this bias, Card recommends controlling for family background variables, which allows Ordinary Least Squares (OLS) to yield less biased estimates of the returns to education compared to OLS estimates that do not account for these background factors.
In this study, I aim to analyze the impact of education and family background on wage employment using Ordinary Least Squares (OLS) methodology A key strength of my research is the utilization of cross-sectional data from two different years, which helps to mitigate bias associated with single-year estimates The sample is drawn from the Vietnam Household Living Standards Survey (VHLSS), ensuring a sufficiently large and randomly selected dataset Additionally, I incorporate parental education and the number of siblings as control variables to account for background influences.
A A A f3JXlf'eniS _ educatim+ ~sibilings+ f3sDcan _irdex+8 4 urlxTn+u; with ui is residual The dependent variable is natural logarithm of nominal hourly wage Betts
(200 1) used the log of nominal wages as the dependent variable in calculating determinants of wage
1 Age is one of characteristic belonging to demographic factors of a person
Age increases respectively wage ( Harmon, 1995) A year is added, an employee's wage will increase 13.38% (Betts, 2001)
Ethnicity is a crucial variable in studying employment behavior, as it falls under demographic characteristics In my research, I utilize ethnicity as a dummy variable to examine whether Kinh workers receive higher wages compared to other workers.
3 Married: this is an important variable belongs to demographic characteristics (Dang et al 2005) A married worker will be got higher salary than others ( Betts, 200 1)
4 Educational attainment: employment's education directly affects skills of labour Empirical studies show that employment is skilled or higher education attainment will get higher wage (Hamilton et al 2000; Betts,
5 South east: residences live in center or big city often tendency to be paid higher wage (Dang et al, 20005; Harmon, 1995)
6 Parents' education: education of mother is important directly effect on children's knowledge as well as job selection decision at adulthood It results in positive relation to wage (Betts, 2001)
7 Siblings: number of siblings will negative affect employees' wages (Betts,
Research indicates that individuals with multiple siblings tend to seek employment and start working earlier than those with fewer siblings This trend suggests that having many brothers or sisters may influence early career engagement.
8 Urban: allocation residence also impacts differently on wage Betts, 2001 found that workers who live in central areas are paid higher wage
The Duncan Index serves as a crucial variable representing family background, specifically measuring the socioeconomic status of the job held by the head of a household Scored according to the Duncan Index of 1961 and the Social Occupation Index of 1992, this variable is anticipated to have a positive correlation with wage increases (Betts, 2001).
Determinant Variable Definition Expected I sign
Age Age The age of the employee (over 15 + to under 60)
Ethnicity Ethnicity ã The ethnicity of employee
rvia~Ieltl [95% Conf Interval]
Employment encompasses various forms, including paid work in cash or kind, self-employment to generate personal income, and activities related to production, business, and services for one's family without receiving a wage or salary, as defined by MOLISA.
Employed individuals are defined as those who have worked for at least one hour during a specified reference period, earning a wage or salary (paid employment) or generating profit through self-employment or family contributions This includes individuals who possess a job but were temporarily absent from work during the reference period The term "employed" encompasses all persons aged 15 and older within the labor force who have engaged in any of these employment forms within the past seven days Specifically, employed youth refers to individuals aged 15-24 who are participating in the labor force.
A job is defined as a paid position of regular employment, typically associated with wage and salaried workers However, the common understanding has broadened this definition to include any work-related task Consequently, anyone engaged in work—whether as a paid employee, self-employed individual, or unpaid family worker—can be considered as having a job, according to the International Labour Organization (ILO).
The labour force, or economically-active population, consists of individuals aged 15 and above who are either employed or actively seeking employment Conversely, those outside the labour force are classified as the economically-inactive population, which includes individuals aged 15 and older who are neither employed nor unemployed.
The labour market is a virtual space where job seekers compete for employment opportunities while employers strive to attract talent Analysts assess the effectiveness of this market through various statistics, including the employment-to-population ratio and the unemployment rate These metrics help to evaluate the dynamics of labour supply and demand, highlighting the reasons behind any discrepancies that prevent perfect equilibrium in the market.
Own-account workers: persons who are self-employed, with no employees working for them
An unemployed individual is defined as someone who, during a specific short reference period, is without work, available for work, and actively seeking employment through various methods Additionally, a person who is not currently employed but has made arrangements for future paid or self-employment is also classified as unemployed, according to the International Labour Organization (ILO).
Unemployment: a measure of the total number of unemployed persons
An unpaid family worker, also known as a contributing family worker, refers to an individual who contributes labor to a family-owned business without receiving any payment This type of work is typically performed in a business that is owned or managed by a family member, who is classified as an own-account worker according to the International Labour Organization (ILO).
Vulnerable employment: for purposes of this report, the sum of own-account workers and contributing family workers (unpaid family workers) (ILO)
Work, as a verb, refers to engaging in economic activity or providing labor for the production of goods and services As a noun, "work" is often used interchangeably with "job" and "employment," indicating that an individual supplying labor might say they "have work," "have a job," or "have employment."
Working-age individuals not participating in the labor force encompass all men aged 15 to 60 and women aged 15 to 55 who are neither employed nor classified as unemployed.
' Table Al: Construction of Duncan index variable
Group of occupation Score Occupation codes in
~ Table A2: Youth labour force participation rates(%)
Source: MOLISA labour and employment surveys and GSO population estimates
Table A3 :Labour force classified by sexes and regions
Total I Male I I Total I Male I Female
Regions Peo le) Peo le) %) Peo le %) Peo le) Peo le % Peo le) %)
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' Table A4: Unemployed workers by age-bands (person)
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