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Statistical Analysis of Unemployment in Europe
Research · November 2015
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Applied Quantitative Methods
Statistical Analysis of Unemployment in Europe
MIFE (Winter Semester 2013/2014)
Submitted to Dr. Marco Rummer
Belen Villena Maria | Chaudhry Anika | Eckert Britta | Ilchyshyna Tetiana | Laricheva Tatyana | Shegay Olga
Contents
Introduction ............................................................................................................................................. 3
1. Literature Review................................................................................................................................ 4
1.1. Economic Environment ............................................................................................................ 4
1.2. Educational Factors ................................................................................................................... 5
1.3. Demographic Trends ................................................................................................................ 6
2. Methodology ....................................................................................................................................... 7
3. Regression Analysis .......................................................................................................................... 15
3.1. Economic Variables ................................................................................................................ 15
3.2. Educational Variables ............................................................................................................. 19
3.3. Demographic Variables .......................................................................................................... 23
Conclusion ............................................................................................................................................ 29
References ............................................................................................................................................. 31
2
Introduction
Unemployment is an economic indicator that refers to the number or proportion of people in an
economy who are willing and able to work, but are unable to get a job. In the Eurozone area
unemployment was reported to approximately 18.7 million, which is a number of concern. While
countries like Germany and Austria have unemployment rates around 5 per cent, those at the forefront
of Europe's debt crisis, such as Greece and Spain, have over one in four of their people out of work.
Eurostat estimates that 26.553 million men and women in the EU-28 (28 member states of the
European Union), of whom 19.241 million were in the euro area, were unemployed in November
2013. Compared with October 2013, the number of persons unemployed increased by 19 000 in the
EU-28 and by 4 000 in the euro area. Compared with November 2012, unemployment rose by 278
000 in the EU-EU28 and by 452 000 in the euro area (Eurostat, 2013).
Going back to the history, Isengard (2002) pointed that at the end of the 1980s due to a cyclical boom,
improvements were seen in the labor market condition, which led to a reduced unemployment in
1990s. During the period of 1990-1995 the unemployment rate rose in most of the European
countries. However, since the last two decades unemployment has been fluctuating sharply. Thus, it is
becoming increasingly difficult to ignore unemployment in today’s world.
In the modern world there are many factors, which may contribute to unemployment. Causes of
unemployment are varied and may relate to different aspects: economic, educational, demographic
and others, but the first three of them are considered as the most common and spread one. Knowledge
on the most powerful factors, which influenced unemployment in Europe in the recent time, is
important for the future efficient decision-making regarding measures to be taken.
The aim of the following project is to analyze the reasons of the unemployment situation in Europe in
2012. For this purpose, with the help of the regression analysis we will identify those factors, which
had the highest correlation with unemployment level. The analysis is performed in three separate
blocks - Economic factors, Educational factors and Demographic factors. The approach of using three
separate directions for analysis allows receiving the complete overview of the most significant
reasons of unemployment and, at the same time, it will show whether any of three blocks influence
unemployment more than other two.
3
1. Literature Review
1.1. Economic Environment
The situation in the economic environment directly influence on the companies’ decision on hiring or
dismissal of the work force. Gorlich et al (2013) stated: “There are a number of causes of
unemployment on many of which there is a fair amount of consensus.” Growth and GDP have huge
effects on the economy and, as a result, the unemployment. The latter fluctuates according to the
economic condition. Most studies find aggregate economic activity a major variable of the
unemployment. The evidence provides us with the fact that there is a negative relationship between
the unemployment rate and real GDP. Arthur Okun in the 20th century developed the idea that 1%
increase in unemployment causes a 2% fall in GNP (which is a part of the GDP). He made clear that
the changes in output are associated with changes in labor force participation, changes in number of
hours worked per person and changes in productivity (Okun, 1962). So, it is obvious that economic
growth negatively influences unemployment. However, economic growth may also have different
reasons. Some studies argue that economic growth is positively correlated with population growth.
Economic growth is largely dependent on population growth. More people are consuming more
resources each year in almost every category. Because of populations spending patterns and the
working age of the population, population is an important source of aggregate demand (Bjork, 1999).
Thus, if population growth may in some ways reduce unemployment, it contributes to economic
growth.
At the same time unemployment could be reduced by growth of productivity. Higher productivity
growth can lead to a reduced average unemployment. (Birk, 2002). The author points out that this
aspect is sometimes criticized from the point of view that growing productivity begins from
technological progress, which can contribute to substitution of the human work force by the
machinery. However, still the average effect in the long run is positive. The technological progress
depends on work of scientist in the field of research and development (R&D). Thus, for our project
influence of the technological progress could be measured by amount of population working in R&D
sector.
4
1.2. Educational Factors
One of the most well known educational factors, which should influence the unemployment, is, first
of all, the level of educational attainment. It is believed that the major benefit of education should be
with the lower risk of unemployment at higher educational levels. This assumption was subject of the
studies of many researchers. For example, Mincer agreed that educated people in the labor market
enjoy three advantages over less educated people, they obtain higher wages, better chances of
upwards mobility in income and occupation and greater employment stability. Educated workers have
lesser conditional unemployment and somewhat shorter duration of unemployment. (Mincer, 1991).
This issue was examined by Brenke, who concluded that the cause of poor employment prospects is
the lack of education, the countries that provide relatively high level of education, tend to have less
difference in unemployment amongst youth and adult (Brenke, 2012). Also, this idea was supported
by Bolaji and Nneke, who stated: “Today young people are not ready to work because they are
lacking basic necessary skills, especially in maths and science, even worse is the fact that they lack
the ability to work in teams, think creatively, or interact effectively with colleagues or potential
customers. If young people are well prepared for the challenges, they have to face while working,
they will be much efficient and productive.” (Bolaji, Nneka, 2012).
From the other point of view, not only education of the young people is responsible for the
unemployment level, but also the adulthood learning or in another words - lifelong learning. The
effect of adult learning on the likelihood of employment has been investigated by Blundell, Dearden,
Goodman and Reed (2000), McIntosh (2004), Jenkins et al (2003), and Jenkins (2006), whereas the
effects of basic skills on employment probabilities were investigated by DeCoulon, MarcenaroGutierrez and Vignoles (2007). The provided researches presented the consistent results about the
benefits of adult learning in terms of getting a job. There is also evidence that an increase in basic
skills during adulthood is associated with the higher probability of being in employment (Sabates,
2008).
Additionally, it appears that unemployment is influenced by the public expenditures (spending) on
education. This could be concluded from the following statements. T. Plümper & C. J. Schneider,
based on the empirical analysis revealed that states which experienced relatively high unemployment
experienced the largest decline in university spending per student (Plümper, Schneider, 2007). It is
obviously, that amount of university spending per a student depends on the investment incentives in
5
education from public and private sector. According to OECD report, private household expenditure
on educational institutions is larger than the public spending, as it is influenced by the personal
employment and career expectations. As a result, the private spending incentives may definitely
influence unemployment situation (OECD, 2013).
1.3. Demographic Trends
Bell and Blanchflower (2011) highlighted that immigrants are more likely to lose their jobs and are
less likely to believe they can hold on to their posts. Chairman of migration watch Andrew Green
pointed out that mostly EU migrants are young; they are prepared to work for low wages and have
relatively good level of education. But because they are moving to areas, which have high labour
demand, they are pushing down other young people of that country into dole queue. Oxenfeldt (1943),
in his turn, argues that individuals confronted with unemployment and low prospects for wageemployment will turn to self-employment as a viable alternative.
According to Johansson (2000), Hurst and Lusardi, (2004), the unemployed tend to possess lower
endowments of the human capital and entrepreneurial talent needed to start and sustain a new firm.
This, in turn, would suggest that high unemployment may be associated with a low degree of selfemployment. They also pointed out that high unemployment rates may also imply lower levels of
personal wealth which also reduce the likelihood of becoming self-employed. Consequently, there are
not just theoretical reasons, but also empirical evidence. Albeit contested, that while unemployment
causes increased self-employment, self-employment causes reduced unemployment. There may be
both a positive effect of unemployment on self-employment (the “refugee” effect) and a negative
effect of self-employment on unemployment (the “entrepreneurial” effect).
Gibrat's Law implies that shifting employment from large to small enterprises should have no impact
on total employment, since the expected growth rates of both types of firms are identical. And, as a
result, restructuring the economy from large to small enterprises (including the self-employed) should
have no impact on the overall unemployment rate.
Gibrat’s Law states that the effect of self-employment rates on unemployment rates is the
“entrepreneurial” effect of increased entrepreneurial activity contributing to lower unemployment
6
rates. This means that effect of unemployment rates on self-employment rates is the push (“refugee”)
effect of recently unemployed workers starting their own venture to escape unemployment.
According to Friedman (1968), most recent theoretical work on unemployment has focused on the
search activities of the unemployed or on the unemployment of workers with permanent employment
contracts who are on temporary layoff. But most of the unemployed have truly lost their jobs. By and
large, workers become unemployed because their jobs are not permanent. The shorter the duration of
employment, the greater is the flow of workers into the pool of the unemployed. Friedman highlighted
that the decision of older persons to stop working is not the only explanation of low employment rates
at higher ages. Older employees are often confronted with low hiring rates and difficulties to re-enter
the labour market after a period of unemployment. Thus, more access to employment can only be
achieved by changing the attitudes of employers to hire older workers.
As Danzer (2010) stated, unemployment duration can increase due to longer transition from
unemployment to employment as hike in pension size leads to higher household income thus reducing
cost of job search for members of these household and giving them an incentive to search for job
longer. However, duration of unemployment spell can also decrease due to people exiting from
unemployment to inactivity, as pensions are now sufficient for retirement of working pensioners.
In the respect of pension increase and its influence on unemployment it is worth regarding the
research made by Danzer (2010). Though he mentions nothing about unemployment duration he
shows that labor supply of retirement age cohort should decrease due to hike in pensions. This will
more affect people who were entitled to small pensions before the change. Due to the structure of
pension system this people usually are having little working experience or are less educated. As more
working pensioners are exiting to inactivity we assume that unemployed pensioners may also stop job
search thus contributing to shortening of life unemployment duration.
2. Methodology
The following dataset contains information on 28 countries of European Union as well as two other
participants such as Norway and Turkey that have been combined together in order to target Europe.
Altogether, there are 30 countries that have been chosen to demonstrate the behavior of
unemployment rate (which is primarily annual unemployment rate determined by age and sex and
7
expressed as a percentage) and growth of unemployment rate taken these two variables are dependent.
In order to see the dynamics of development of unemployment rate and growth of unemployment
rate, two years have been chosen: 2003 and 2012.
It should be stated that the definitions of employment and unemployment, as well as other survey
characteristics follow the definitions and recommendations of the International Labor Organization
(ILO, 2013). The definition of unemployment is further precised in Commission Regulation (EC)
No 1897/2000.
This domain comprises collections of monthly, quarterly and annual averages of unemployed persons
and unemployment rates. Let us consider relevant definitions:

Unemployed persons are all persons 25 to 64 years of age who were not employed during the
reference week, had actively sought work during the past four weeks and were ready to begin
working immediately or within two weeks. Figures show the number of persons unemployed
in thousands.

The duration of unemployment is defined as the duration of a search for a job or as the length
of the period since the last job was held (if this period is shorter than the duration of search for
a job).

Employed persons are all persons who worked at least one hour for pay or profit during the
reference week or were temporarily absent from such work. This variable is needed for the
calculation of the unemployment rate, the long-term unemployment rate and the very longterm unemployment rate. For the unemployment rate, only persons from 25 to 64 years of age
are used.

The unemployment rate is the number of people unemployed as a percentage of the labor
force. The labor force is the total number of people employed and unemployed.
Unemployment is a negative phenomenon in any human society as it’s adversely affected in different
dimensions and directions. Thus, three main independent groups of variables have been identified,
namely:

Economic variables;

Educational variables;

Demographic variables.
8
Unemployment is highly dependent on economic activity; in fact, growth and unemployment can be
thought of as two sides of the same coin: when economic activity is high, more production happens
overall, and more people are needed to produce the higher amount of goods and services. And when
economic activity is low, firms reduce their workforce and unemployment rises. Despite the fact that
there are plenty of factors that contribute to economic activity, we have chosen those that might make
a significant impact on unemployment rate and growth of unemployment rate. Let us consider the
independent economic factors: e.g. GDP and unemployment are negatively correlated; as GDP
declines unemployment increases. Conversely, as GDP increases, unemployment will decrease. GDP
measures total output by an economy over a given time. Hence, if GDP falls, it means that less is
produced. And if less is produced, firms will need fewer inputs to conduct business, i.e. less labor and
capital. This results in a reduced workforce during contractionary periods. General Government
Surplus/Deficit is another reverse indicator of the unemployment rate. Observing a government
surplus in the economy means that GDP is also very sustainable (there is a massive production in the
economy meaning there is a huge demand for people to produce these goods) leading to a decrease in
the unemployment rate. Accordingly, if there is a government deficit in the economy, it can be
concluded that government cannot provide unemployed population with training facilities and, as a
result, there is a high rate of unemployment. Population Growth indicator affects the unemployment
rate also adversely. If there are more people purchasing goods, demand for goods will rise. In order to
meet the demand, there will be demand for people needed to produce these goods. This will result in
bigger fraction of people employed, thus reducing the unemployment rate. Taken another example of
Share Price Indexes variable, it can also be pointed out that usually rising (falling) unemployment
rates is followed by falling (rising) stock prices, and that unemployment rate can be used to predict
stock prices.
Referring to short-term periods, it is rationalized that rising (falling) unemployment leads to a (an)
decrease (increase) in demand for goods and services, and as a result, firms’ revenues, profits, and
stock prices will decline (increase). Employees involved in Research and Development are highly
qualified workers and a number of labor economists argue that each highly skilled employee can
create a set of jobs opportunities for low-skilled workers, e.g. assistants, co-workers, housekeeping
staff, etc. Additionally, when there is a high fraction of those employed in R & D, it stands for
innovation trend in a particular country. Consequently, innovation may create new jobs as well and
fosters economic growth that might positively influence unemployment rate.
9
Table 2.1. “Economic variables explanation”.
Economic variables
Explanation
GDP
Real GDP growth rate.
POPUL
Population Growth (taken as annual %).
SPI
Share Price Indexes (e.g. Dow Jones).
GGD_S
General Government Deficit/Surplus.
R&D
Persons
employed
in
Research
and
Development
expressed as a percentage of labor force.
Second block examines the relationship between education and working life. OECD countries depend
upon a stable supply of well-educated workers to promote economic development. Data on
unemployment rates and growth of unemployment rate – and how they evolve over time – thus carry
important information for policy makers about the potential supply of skills available to the labor
market and about employers’ demand for these skills.
Speaking about educational variables that might have impact on unemployment rate, the following
ones have been identified:
Table 2.2. “Educational variables explanation”.
Educational variables
LOW_EDUC
Explanation
Share of the population aged 25-64 years who have low
educational attainment (maximum - short secondary
education).
LIFE_EDUC
Share of population aged 25 to 64 who stated that they
received education or training in four weeks preceding the
survey.
PRSP_EDUC
Private spending on education (taken as % of GDP).
Let us consider a brief example: if there is more private spending on education, meaning students can
acquire e.g. better quality of education or be educated in better conditions and with better resources,
have ability to visit additional trainings and workshops, this will diminish the unemployment rate.
10
Secondly, if there is a big fraction of population who is willing to be educated further while having
current job, this means that people will be able improve their initial level of educational attainment or
get a deeper specialized knowledge in certain areas, or even obtain education in the new field of
knowledge. Everything listed would improve their competiveness on the labor market. Consequently,
this will diminish the unemployment rate.
And finally, if there is a big fraction of those who have low educational attainment or even left at the
very beginning from school or university, this means that there will be many people on the labor
market with a low competiveness, so this will probably increase the unemployment rate.
The last Demographic block includes such indicators as migration, fraction of self-employed
population, and duration of working life. Migration affects the unemployment rate because the less
mobile a population is, the higher the unemployment rate will be. This is because a population that is
immobile will remain in the area of high unemployment where jobs are not available and continue to
search for jobs, which are acceptable to them. Searching for a job that meets an individual’s needs
and qualifications is a process, which often takes time. This creates a situation here more people are
unemployed for longer periods of time. If there is a big fraction of those who are self-employed, the
unemployment rate very likely will be diminishing as instead of being unemployed, people are
working on their own.
Speaking about Duration of working life indicator, it can be outlined that the shorter the duration of
employment, the greater is the flow of workers into the pool of the unemployed thus increasing the
unemployment rate. The probability of older persons who stop working does not explain low
employment rates at higher ages. Older employees are often confronted with low hiring rates and
difficulties to re-enter the labor market after a period of unemployment.
Table 2.3 “Demographic variables explanation”.
Demographic variables
Explanation
MIGR
Crude rate of net migration plus adjustment (in %).
WLIFE
Duration of working life (measured in years).
SELFEMPL
Share of self-employed population (in %).
Having analyzed the potential effects each block of variables might have on unemployment rate, the
following confronting Hypotheses have been identified:
11
Table 2.4. “Economic factors Hypotheses”.
Hypotheses on economic factors
H0: β2=0
H1: β2≠0
GDP growth in a country does not influence Real GDP growth contributes to decreasing
unemployment as economic growth creates more
unemployment.
jobs.
Population growth has no influence on Population growth leads to a decrease in the
unemployment rate.
unemployment.
General Government deficit has no impact General
government
deficit
stimulates
unemployment.
on unemployment.
The fraction of population employed in The fraction of population employed in R&D
R&D does not affect unemployment rate.
could
diminish
the
overall
level
of
unemployment.
Share
price
indices
do
not
unemployment.
affect Share price indices, when high, could reduce the
level of unemployment.
Basically, it is expected that the increase of such variables, as GDP, population growth, fraction of
population employed in R&D and share price indices, should reduce unemployment. Regarding
GDP, it is clear that increasing welfare of the country will lead to extension of demand for the work
force and, thus, the unemployment should be reduced.
Regarding population growth, it is assumed that if there are more people buying goods, demand for
goods will rise and, therefore, there will be an increase in demand for the workforce, required to
produce more goods. Next, the growing number of specialists in R&D will mean that these high
skilled workers will create new jobs (assistants, co-workers, housekeeping staff), secondly, that there
is a lot of innovation activity which fosters economic growth. The last assumption is that the growth
of the share prices indices will mean that the companies are more profitable, consequently they will
be more willing to hire additional staff.
Finally, we have one variable, which should increase unemployment - General Government Deficit.
The increase of this factor means that country is losing its welfare level and, as a result, companies
will need to cut down their costs, which should live to massive dismissal of the work force.
12
Table 2.5.“Educational factors Hypotheses”.
Hypotheses on educational factors
H0: β2=0
H1: β2≠0
The level of the educational attainment has The low educational attainment has a significant
no effect on unemployment.
negative impact on unemployment.
The level of activity in a further learning The level of activity in a further learning within
within the working life does not influence the working life reduces unemployment.
unemployment.
Private expenditures are not able to change High private expenditures on education decrease
unemployment.
unemployment.
In this block it is expected that the increase of such variables, as activity in further learning (lifelong
learning) and the private expenditures on education should reduce unemployment. Regarding the
lifelong learning, it is assumed that people, who have acquired more training and practical skills, will
be more competitive on the labor market and will have more chances to find an adequate job. Also,
such people are more likely to take higher level of jobs, leaving more low skilled jobs to those less
qualified, therefore lowering the unemployment rate.
Concerning the private expenditures on education, it is expected that growth of this variable, will
mean that people are getting education of a high quality, which will again contribute to their
competiveness on the labor market. Consequently, unemployment should be reduced.
Finally, there is also one variable, which should be positively correlated with unemployment – the
low educational attainment. It is assumed, that if more people will not reach the final stages of the
education (completion of the university education, etc.), on the labor market the demand for low
qualified jobs will considerably exceed the supply of such vacancies, which will result in higher
unemployment rate.
13
Table 2.6.“Demographic factors Hypotheses”.
Hypotheses on demographic factors
H0: β2=0
Migration
rate
does
H1: β2≠0
not
affect Migration rate changes have an impact on the
unemployment.
unemployment rate.
Self-employment rate has no influence on The rate of self-employed population diminishes
unemployment.
unemployment.
Duration of working life does not influence Duration
unemployment.
of
working
life
influences
unemployment.
In the last block of the analysis the changes in such variables, as the migration rate, the selfemployment rate and the duration of working life should significantly influence unemployment are
expected. Concerning the migration rate, it is known that it reflects the activity of the unemployed
people in relocation to the areas with readily available job vacancies. This contributes to equilibrium
in the demand and the supply on labor markets of both areas – the area of migration destination and
the area of the migrants’ previous location. Thus, it assumed that if the migration rate is growing, the
unemployment situation will be improved. At the same time, if the migration rate is declining, the
unemployment will become even more severe.
Regarding the self-employment, it is assumed that increase of this variable will mean that more
people, who could meet with the unemployment on the labor market, have decided to turn to the selfemployment as a viable alternative. Consequently, the unemployment rate should decrease. And the
other way, if the rate of self-employment decreases, the demand on the labor market will grow; as a
result, unemployment rate will increase.
Finally, it expected that the duration life should influence on the unemployment. The assumption is
that under the conditions of short duration of working life people are not able to accumulate the
considerable pensions by the end of their career and, thus, they will be willing to continue their
employment. This will result in a growing demand on labor market and increase of unemployment.
And in the other way – the longer is duration of working life, the higher pensions people will get - so
they will not return to employment after retirement. So, the unemployment should decrease in this
case.
14
Theoretically, all of these variables have an effect on unemployment rate and growth of
unemployment rate, but this research will prove how many of them demonstrate a significant impact
on two dependent variables.
There are two types of analysis: short and long run. Short run analysis is used to determine the effects
on unemployment rate, which in its turn, is used as a basis for calculation of multiple regressions for
2012. Long-run regressions are aimed at depicting the results that have been influencing the
development of growth of unemployment rate during 2003-2012. As we have only 30 observations,
we used the 10% rejection region in order to have more possibility for the acceptance of Ho
Hypothesis, thus our degrees of freedom will be calculated as follows:
Degrees of freedom (df) = Number of observations – Number of variables – 1
equals
28 = 30 – 1 – 1
3. Regression Analysis
3.1. Economic Variables
In the next step it is tested whether unemployment is influenced by economic factors.
In literature review there could be found evidence for the following variables to influence
unemployment:

Gross domestic product (GDP)

Population growth (POPUL)

General government deficit /surplus (GGD_S)

Share price index (SPI)

Share of employees in research and development (RD)
In order to verify these evidences single regressions were run on each of these variables. The critical
value for the t-test according to the t-table is 1.701. This means that if column t of Stata’s regression
gives a number that is either larger than 1.701 or smaller than -1.701 this variable lies in the tails of the
t-distribution,
is
therefore
significant
and
does
influence
the
dependent
variable.
15
If the number in column t is between – 1.701 and 1.701 the variable lays in between the tails, is
therefore insignificant and does not have an influence on the dependent variable.
With this in mind the significant variables are the following:
Table 3.1.1. “T-values for significant economic variables”.
Variable
T-value
GDP
-2,08
POPUL
-1,83
GGD_S
-4,14
SPI
-2,93
R&D
-2,89
The graphs for correlation of SPI with unemployment and GGD_S with unemployment give evidence
for their high significance.
-10
0
10
20
30
Figure 1. “General government Deficit / Surplus 2012”
-10
-5
0
5
GGD_S2012
UNEMP2012
10
15
Fitted values
16
0
5
10
15
20
25
Figure 2. “Share Price Index 2012”
0
50
100
SPI2012
150
UNEMP2012
200
Fitted values
All the variables that are significant according to literature also turned out to be significant in our
single regressions.
But what happens if all these variables are put together in one multiple regression?
Table 3.1.2. “Economic Variables 2012 in a multiple regression”.
1
2
3
4
5
Stata
parameters
GDP2012
POPUL2012
GGD_S2012
SPI2012
RD2012
Coefficient
-0.331547
0.9984052
-0.5234771
0.009158
-3.081025
Cons
13.22372
t
-0.81
-1.71
-2.35
-0.41
-1.64
P ˃ |t|
0.426
0.254
0.028
0.683
0.113
R
2
Adj. R
0.5382
2
0.4420
White is a general test for heteroscedasticity shows the following result:
17
Table 3.1.3. “Test for heteroscedasticity”.
. imtest, white
White's test for Ho: homoskedasticity
against Ha: unrestricted heteroskedasticity
chi2(20)
Prob > chi2
=
=
25.58
0.1802
Cameron & Trivedi's decomposition of IM-test
Source
chi2
df
p
Heteroskedasticity
Skewness
Kurtosis
25.58
7.95
0.20
20
5
1
0.1802
0.1589
0.6576
Total
33.73
26
0.1421
A result of 18 per cent implies that this regression does not suffer from heteroscedasticity and the
regression can be interpreted without any further adjustments.
In the multiple regression R² is 53.82 per cent, which means that 53.82 per cent of unemployment
development can be explained by those five variables. The small difference between R² and adjusted
R² is a good sign as it says that R² is not only high because of the amount of variables but because of
the quality of variables.
The critical value for the t-test is 1.711 with 10 per cent confidence interval and 24 degrees of
freedom. So, it turns out that in multiple regression only GGD_S with a t-value of -2.35 is
significant.
How can it be explained that four of these five variables are significant in the single regressions but
not in the multiple regression? This might be due to multicollineartiy that means that some variables
are highly correlated so that they level each other out if they are put together in a multiple regression.
In the following correlation coefficient matrix it is striking that GDP is highly correlated with
GGD_S and SPI, POPUL is highly correlated with RD and SPI is highly correlated with GDP and
GGD_S. These correlations makes each variable less significant in the multiple regression with
GDP, POPUL, SPI and RD dropping out of significance while GGD_S stays significant, even though
18
less significant, as it was by far the most significant and thus influential variable in the single
regressions.
Summing up, our regressions mirror the dominant opinion in literature. Those variables that most
scientists consider as significant for the development of unemployment were also significant in our
test: GDP, population growth, general government deficit / surplus, share prices index and share of
employees in research and development. The fact that four out of five variables become insignificant
in the multiple regression is due to multicollinearity.
Table 3.1.4. “Multicollinearity between economic variables 2012”.
GDP2012
POPUL2012
GGD_S2012
SPI2012
RD2012
GDP2012
1,000
-0.0996
0.5220
0.4782
-0.0630
POPUL2012
-0.0996
1,000
0.0917
0.1781
0.4002
GGD_S2012
0.5220
0.0917
1,000
0.4786
0.2594
SPI2012
0.4782
0.1781
0.4786
1,000
0.3851
RD2012
-0.0630
0.4002
0.2594
0.3851
1.000
With five economic variables accounting for 53.82 per cent of the unemployment development
economic factors can be considered as very influential. All the alternative hypotheses for economic
variables based on the common view in literature and logical conclusions can be proven which
means that all the H0 hypotheses have to be rejected.
3.2. Educational Variables
The purpose of the second block of the analysis is proving that the educational background of the
population is not less significant than the general economic or demographic situations across the
countries. Basically, the results from this block have to provide the evidences that a personal
willingness to learn or to study and the personal financial expenditures on the education might
significantly influence the level of unemployment.
19
The analysis is started from the single regressions with each of the following variables: ratio of the
population with the low educational attainment, ratio of the lifelong learning population and the
private spending on the education. Results of these calculations are provided in the table below
(Table 3.2).
Table 3.2.1. “Educational Variables 2012”.
1
Stata
Population with the
parameters
low educational
attainment (%)
2
3
Lifelong learning
Private spending on the
population (%)
education (%)
Coefficient
0.0610093
-0.2643826
-0.3209682
Cons
8.952618
13.14662
10.66941
t
1.03
-2.25
-0.14
P ˃ |t|
0.310
0.032
0.887
0.0367
0.1534
0.0007
0.023
0.1231
-0.0349
R2
2
Adj. R
Taking values of R2 and adjusted R2, it appears that only lifelong learning variable showed more or
less acceptable results, so there may be correlation between this variable and the unemployment.
Further, the t-test (with the critical value of 1.701) is performed to check the significance of the
variables. This measurement allows confirming that the lifelong learning variable is a significant
factor, which influenced unemployment level in 2012. The correlation in this case was negative. In
general, the regression model’s formula looks like:
Unemployment = 13,15 – 0,26 Lifelong learning
In other words, if the lifelong learning rate grows by 1 % then unemployment rate declines by
0,26%. The graph provided below supports this conclusion.
20
5
10
15
20
25
Figure 3. “Simple regression graph of lifelong learning rate 2012”.
0
10
20
30
LIFE_EDUC2012
UNEMP2012
Fitted values
According to these results, the zero hypothesis was rejected and the following alternative hypothesis
was accepted: the more people try to use any opportunity to improve their knowledge during all their
working life, the less of them will face the threat of unemployment.
At the same time, the remaining two variables – ratio of the population with the low educational
attainment and the ratio of private spending on the education - are insignificant in their simple
regressions. The graphs of these two regressions describe the same situation.
5
10
15
20
25
Figure 4. “Low education rate 2012”.
0
20
40
LOW_EDUC2012
UNEMP2012
60
80
Fitted values
21
5
10
15
20
25
Figure 5. “ Private expenditures on education”
0
.5
1
PRSP_EDUC2012
UNEMP2012
1.5
2
Fitted values
At the next stage of the analysis, a multiple regression is performed in order to summarize the
available effects of all educational variables. It was expected that the variables, which were
insignificant in the single regressions, would remain insignificant. If - not, it will clear then that
such unexpected results should be addressed to the multicollinearity or to the heteroscedasticity.
Results of these calculations are provided in the table below.
Table 3.2.2. “Educational Variables 2012 in a multiple regression”
1
Stata parameters
Population with the
low educational
attainment (%)
Coefficient
0.0510361
2
3
Lifelong learning
Private spending on
population (%)
the education (%)
-0.2596132
-1.43904
8.952618
Cons
t
0.87
-2.13
-0.66
P ˃ |t|
0.391
0.043
0.513
R2
0.1846
Adj. R2
0.0906
22
From the first view, the value of R2 and the adjusted R2 are satisfactory and even better, so the
regression explains some part of the data variability. However, at the same time the low value of
Adj. R2 indicates that this regression contains many insignificant variables, which do not really
improve the regression’s explanatory power.
The t-test (in this case the critical value of t for 10% is 1.706) supports our expectations. Among the
3 presented variables only 1 has the significant result, which allows us to reject one of our zero
hypothesis. Thus, it appeared that the rate of unemployment in 2012 was negatively correlated with
share population, which continued further education (for example, trainings) within the working
age. If the share of the lifelong learning population increased by 1 %, then the unemployment rate
would decrease by 0.26 pp. Thus, the alternative hypothesis for this variable is still not rejected.
The remaining two variables again showed insignificant results, so it should be concluded that there
was no high multicollinearity. The test for the heteroscedasticity of the multiple regression confirms
the absence of the heteroscedasticity in the model (Prob > chi2 = 0.5091, which was more than
10%). Therefore there was no need to robust our regression.
Finally, the long-run period was analyzed. For this purpose, the growth rates of the same 3 variables
and the growth rate of the unemployment were used. However, the low values of R2 and adjusted
R2 and the absence of the significant results of t-test, didn’t allow reveal any influence of the
variables in long-run period.
Summarizing the results of the analysis of the educational block, it should be pointed out that only
one of three-settled alternative hypothesis is accepted. The unemployment was indeed influenced by
the level the lifelong learning. Unfortunately, there was no evidence that the low educational
attainment among the population and the private education expenditures changed significantly the
level of unemployment.
3.3. Demographic Variables
In the final, third block of analysis demographic factors are taken into account in order to prove if
human population statistics could demonstrate the influence on unemployment rate along with
economic and educational environment.
23
Firstly, single regressions with 3 variables for 2012 are run separately. It showed that migration rate,
duration of working life and self-employed population rate are significant as in the t-test all the
variables showed higher t-results in comparison with the critical value of 1.701.
It explains that if the migration rate falls by 1 % then unemployment rate will increase by 0,62%. It
supports the idea that the less young people migrate in the country, then the less of them are
employed by the companies and, consequently, unemployment rate rises. According to scatterplot on
the regression line, it is examined that there is good fit between unemployment rate and migration
rate.
Migration rate (R² = 41 %, Adj. R² = 39 %, t = - 4,45) had negative correlation with unemployment
rate.
The regression model’s formula is:
Unemployment = 11,23 – 0,62 Migration
0
5
10
15
20
25
Figure 5. “Simple regression graph of migration rate 2012”.
-10
0
10
20
MIGR2012
UNEMP2012
Fitted values
Duration of working life (R² = 11 %, Adj. R² = 7 %, t = - 1.87) also had negative impact on
unemployment rate.
The regression model’s formula is:
Unemployment = 30,82 – 0,586 Working life
24
If the number of years spent by population on working during the whole life decrease by 1 year then
unemployment rate will rise by 0,586 %. It is obvious that according to the literature review and the
stated hypothesis, if people work fewer years then the overall rate of unemployed people should
increase.
5
10
15
20
25
Figure 6. “Simple regression graph of duration of working life 2012”.
30
32
34
36
WLIFE2012
UNEMP2012
38
40
Fitted values
Finally, the simple regression for self-employment population rate was analyzed. (R² = 11 %, Adj. R²
= 8 %, t = 1.89) In comparison with previous models, this variable has positive correlation.
The regression model’s formula is:
Unemployment = 4,59 + 1,036 Self-employment
If the percentage of people who are self-employed will go up by 1% then the unemployment rate will
increase by 1,036%. It is considered to appear uncommon, because if people start to work for
themselves, so more of them are active and the unemployment rate should basically decrease.
However, this variable should be analyzed in the multiple regression. The graphic of simple
regression is the following:
25
5
10
15
20
25
Figure 7. “Simple regression graph of self-employed population rate 2012”.
4
6
SELFEMPL2012
UNEMP2012
8
10
Fitted values
As a second step, the multiple regression of independent demographic variables is observed. It
allowed estimating how unemployment rate predicts all of the three variables together. The results of
multiple regression model are provided in the following table.
Table 3.3.1. “Demographic Variables 2012”.
1
2
3
Migration rate
Duration of working life
Self-employed
(%)
(years)
population rate (%)
Coefficient
-0,577
-0,453
0,398
t
-4,26
-1,81
0,88
P ˃ |t|
0,000
0,083
0,384
Stata parameters
R2
0,5139
Adj. R2
0,4578
Taken together, the three demographic variables explain about 51% of the variance (R2 = 0,5139).
Meanwhile, the value of Adj. R2 of 46% shows that our regression contains moderately significant
variables, which could prove the regression’s explanation. In the t-test the critical value for “t” with
10% is 1.706. In overall, 3 independent variables there are 2, which show significant results. These
are migration rate (t = - 4,26) and duration of working life (t = -1,81). Both of them are negatively
correlated with unemployment rate in 2012.
26
The formula for regression model is the following:
Unemployment rate = 24,66 – 0,577 Migration – 0,453 Working life +
0,398 Self-employed
It shows if the migration rate drops by 1%, then the unemployment rate increases by 0,58% - if
duration of working life and self-employment rate do not change. The second significant variable
shows that if duration of working life decreases by 1 year then the unemployment rate increases by
0,45 % - if migration rate and self-employment rate do not change. It proves the results of simple
regressions, however self-employment rate becomes insignificant.
The presence of heteroscedasticity and multicollinearity within the proceeded regression model was
checked. However, the test for the heteroscedasticity of our regression confirmed the absence it (Prob
> chi2 = 0.52, which was more than 10%). Moreover, there is no evidence of high level of
multicollinearity between the variables: only 28 % between
duration of working life and self-
employment rate, 23 % between self-employment rate and migration rate, which is quiet acceptable
in the regression model.
Table 3.3.2. “Multicollinearity between demographic variables 2012”.
Self-employed
Unemployment
Migration
Duration of
rate
rate
working life
Unemployment rate
1,000
-0,6437
-0,3326
0,3367
Migration rate
-0,6437
1,000
0,0651
-0,2258
-0,3326
0,0651
1,000
-0,2818
0,3367
-0,2258
-0,2818
1,000
Duration of working
life
Self-employment rate
population
rate
As a third step, regressions of growth rates are observed in order to see how the changes of variables
in 10 years period (from 2003 to 2012) influenced unemployment growth rate. The growth rate is
calculated by dividing the values of 2012 by the values of 2003 and is measured in percentages.
27
Table 3.3.3. “Growth rates from 2003 to 2012”.
1
Stata
Parameters
Migration rate
2
Duration of working
life
3
Self-employment rate
Coefficient
-0,143
-1,679
-1,190
t
-2,28
-0,53
-2,31
P ˃ |t|
0,031
0,601
0,029
R2
0,3435
Adj. R2
0,2678
The value of R2 is 34 %, which could moderately explain the data variability. The adjusted R2 of 27
% provides an explanatory result to the regression model. In t-test the critical value for “t” with 10%
is 1.706, thus it shows that migration rate (t = -2,28) and self-employment rate (t = -2,31) are
significant in 10 years period. Both of these variables are negatively correlated with unemployment
rate.
The formula for regression model is the following:
Unemployment = 4,43 – 0,143 Migration – 1,679 Working life –1,19 Selfemployed
In the long run, if migration rate drops by 1 %, then the unemployment rate increases by 0,143 % - if
duration of working life and self-employment rate do not change. If self-employed population rate
decreases by 1 % then the unemployment rate increases by 1,19 % -if migration rate and duration of
working life do not change. In this case, self-employment rate proves the literature review and
hypothesis that the more people are working for own business then more of them are considered as
active population and, as a result, unemployment rate decreases.
To sum up, the demographic block analysis showed that three variables – migration rate, duration of
working life and self-employed population rate – separately have correlation with unemployment
rate. The first two variables explain the influence on dependent variable logically and approve the
literature review; however the self-employment rate doesn’t explain anything. With this purpose
multiple regression for 2012 was proceeded for the short-run results and growth rate regression for 10
years period for the long-run results. It appeared that for the short-term period migration rate and
28
duration of working life are significant. The unemployment rate is negatively correlated with the
mentioned variables and these 2 variables also prove the previous researches. To remind, the less
young migrants are in the country, the less of them are employed and it leads to growing
unemployment. And the less years people spend on working during their whole life the higher is the
unemployment rate. In the long-run, migration rate and self-employment rates have negative impact
on unemployment growth rate. And in this case, self-employment rate proved the finding and the
stated hypothesis that the more people are self-employed the lower is the unemployment rate.
Conclusion
Through this project we hoped to shed light on the problems people are facing in terms of
unemployment. Euro zone unemployment was a record high 12.2% in September. Augusts' number
was revised up to 12.2% from an earlier estimate of 12.0%. Economists were hoping for a 12.0%
rate. Spain's unemployment rate stood at a horrific 26.6%, and Italy's climbed to 12.5%. Germany, on
the other hand, saw its unemployment rate slip to 5.2%.
According to regression analysis, economic factors are overall significant, as all the alternative
hypothesis were accepted) mainly because of share price index, general government deficit/surplus
and share of employees in research and development. “The Europe 2020 strategy sets the target of
improving the conditions for research and development, in particular with the aim of increasing
combined public and private investment levels for R&D to 3 % of GDP’ by 2020” European
commission. By investing into research and development European commission is very sure they can
reduce unemployment rate, as it will increase the availability of jobs into the research and
development sector, plus due to further development and innovation, new jobs will be created.
As far as the educational factors are concerned, only one hypothesis of three was accepted. The
lifelong learning ration has appeared to be significant. As already mentioned in the literature by
Mincer (1991), the longer the people are in education the better chances they have of upwards
mobility and they have greater degree of employment stability. There is clear evidence that an
increase in basic skills during adulthood is associated with the higher probability of being in
employment. Enhancing the skills by getting trainings or further education can definitely help an
individual to get a hold of their position and can also help them get promotions for a better future.
29
According to the demographical factors, migration rate and duration of working life are highly
significant. Andrew Plummer (2012) pointed that there are around 4 million unfilled vacancies in
EU, if the group of people with matching skills and abilities are entering the labor market,
unemployment will reduce. According to these statements one can realize that the EU immigrants are
highly qualified and they can be employed even by offering them lower wages. At the same time
Plummer mentioned that there are enough unfilled vacancies in Europe, if this appropriate group of
people enter the labour market, unemployment can be reduced. Generally, all three hypothesis of this
block were accepted.
To summarize, the economic and the demographic blocks had more significant results than the
educational block. Nevertheless, it doesn’t mean that in reality the education should not be taken into
account. For the improvement of the unemployment situation in Europe measures in all three
directions should be taken simultaneously.
30
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