The Impact of the Pension Scheme on the Economic Growth of the
Country
DRITA LUZO, ZAMIRA VEIZI
Department of Accounting and Finance,
“Eqrem Çabej” University,
Rruga “Studenti”, Lagjia “18 Shtatori”, Gjirokastra,
ALBANIA
Abstract: - Albania’s pension system faces many challenges and many problems to be addressed. In 2014 the
next reform was undertaken in order to facilitate the pension scheme. The literature of the field is not
consolidated in the case of Albania, which is mainly due to the short history of poor management and statistics
of interest. Foreign literature is a great source of consultation, mainly for reforming the pension system as well
as the various schools for social protection.
The study aims to shed light on the assumption that the pension system is affected by economic growth. The
linear multifactor model is used for the study hypothesis, where it was shown that the pension system affects
the country's economic growth. The main components of the scheme are the income from the scheme, the
expenses, and funding from outside the pension scheme. Statistical analysis showed that only the expenditures
from the pension scheme indicate the economic growth of the country. Considering the lack of data and time
series after these 10 years of study, due to pension scheme problems and the pandemic period, the findings of
our study constitute a valuable contribution to the other interested people and researchers in this field, who
want to make further investigations and study analysis, financial institutions and governors.
Key-Words: - pension system, sustainability, income from the scheme, expenses of the scheme, funding from
the state budget, linear multifactor regression model, EViews9.
Received: March 24, 2023. Revised: August 27, 2023. Accepted: September 12, 2023. Published: September 22, 2023.
1 Introduction
The process of Albania's transition from the
centralized system to the free market system and the
adoption of the new system has had and continues to
have great difficulties. Many of these difficulties
consist of decision-making and the application of
various reforms in the economic, social, and
political fields. Although many reforms have been
successful, reforms in the pension system have left
much to be desired. One of the challenges that this
system must face is undoubtedly the aging of the
population, which causes a pronounced disorder in
the dependency ratio. The demographic change
brings into question the type of reform that this
system should follow, the parametric reforms, i.e.
the continuation of the PAYG system or the
transition to a fully funded system, [1], according to
the proposals of the World Bank.
Although the pension system in Albania has
undergone three important reforms, in 1993, 2002,
and 2005, according to [2], these reforms are not
sufficient to avoid a crisis in the following years
considering the demographic trend of the
population.
The variant proposed by the World Bank, [3],
the introduction of the multi-column system, also
applied in many European countries that have
managed to avoid the problem of an aging
population, seems suitable for many decades to
come.
In our country, the pension system can be
considered to suffer from some acute problems,
which require appropriate addressing.
Social security and especially pensions have
been a hot topic for a long time in academic,
political, and social circles. The issues under
discussion are so diverse and important that despite
all the great attention paid to pensions, a new paper
always finds a place and is welcome.
Despite the fact that there are numerous foreign
works on the effects of the pension scheme on
various factors of the economy, Albania has its
uniqueness in this scheme.
The aim of this study is the use of a linear
multifactor regression model to see the impact of the
pension scheme on the economic growth of the
country through three main factors: income,
expenses of the scheme, and the way it is financed.
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Considering the lack of data and time series, due to
pension scheme problems and the pandemic period,
the findings of our 10 years study constitute a
valuable contribution to the other interested people
and researchers in this field, who want to make
further investigations and study analysis, financial
institutions and governors.
The rest of the paper is structured as follows;
-Section 2 presents the literature review of many
researchers that have contributed to the field of
pensions, providing evidence on their studies and
conclusions and comparing them with the problems
our system faces.
-Section 3 contains the purpose and study objective
of the paper.
-Section 4 presents the methodology, the main
hypothesis in a research question form, and three
sub-hypotheses in help of the main raised
hypothesis.
-Section 5 presents data analysis and findings using
the linear multifactor model with the help of the
statistical software EViews9.
-Section 6 presents the conclusions of this study for
the period taken into consideration and serves as a
good opportunity for future researchers interested in
this field and decision-making institutions.
-Section 7 is for the limitations of the study.
The many problems that the pension scheme
has had over the years, encouraged us to study
this topic and look at the effects that this scheme
brings to the economic growth of the country and
its sustainability.
I emphasize that pensions are a very acute issue
in the economy of our country.
2 Literature Review
Many authors and researchers have contributed to
pensions as a means of providing for and supporting
elderly people. In order to summarize these
contributions, it is appropriate to elaborate this
treatment according to its origin: social security, a
brief history of the evolution of social security, and
pensions in particular. So a review of the theoretical
and empirical literature related to the issue of
pensions also brings an exposition of the most
significant contributions in the field of pensions
from both foreign authors and Albanian researchers.
We begin this literature review with a brief history
of pensions.
Pensions are a product of social security. Social
security is the "food" of pensions, since social
security funds pensions. Based on this connection, it
is natural to consider social security as a proxy for
pensions themselves. The book "Theory and
Practice of Insurance", [4], has played a special and
irreplaceable role in terms of condensing literature
materials for consultation. On the other hand, [5],
[6], are among the rare Albanian authors who have
approached this topic and their work is very
valuable in terms of summarizing the movements
that have been made by different states in terms of
reforming the social security systems, as well as in
the plan of the analysis of pensions and their
connection with other factors and dimensions of the
economy. The study, [7], provides a valuable
comparative analysis of the pension systems in three
countries of South-Eastern Europe, including
Albania. These are the most prominent Albanian
authors dedicated to our field of study.
A very interesting discussion is brought by, [8],
in "Social Security Systems and the Neo-Liberal
Challenge". He tries to present the challenge that
social security schemes have to meet the standards
approved by the World Labor Organization.
According to the German researcher, [9], in his
full discussion in "The Privatization and
Marketization of Pensions in Europe: A Double
Transformation Facing the Crisis”, the adjustments
that have been made in the field of pensions in
European countries, have taken into account the side
of privatization and that of orientation towards the
market (marketization). Moreover, the study, [10],
has also contributed to the creation of an exposition
on the history of the insurance system in Russia.
Russia currently implements the pay-as-you-go
model supported by the World Bank. Similarly, the
study, [11], presents the problems of the Russian
social insurance system by grouping them into two
dimensions: the problem of the aging population as
well as the problem of financing the scheme.
The study, [12], estimated an aggregate savings
function for Sweden, including as regressors the
time lag of savings, income, and inflation. The
study, [13], began their theoretical discussion by
assuming that total wealth (including social security
wealth) is proportional to last year's net labor
income and, [14], in his studies emphasizes the fact
that there are differences in social security and
people savings.
In contrast to the analyses discussed so far, [15],
applies a consumption function approach to test the
effects of the social security rate.
3 The purpose and Study Objectives
The purpose of this paper is to analyze especially
the impact of the pension scheme on the economic
growth of the country. In view of the above purpose,
the paper has its main objective.
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The general objective of this paper is to verify
whether the pension scheme in Albania affects or
not the economic growth of the country.
4 Methodology and Study Hypotheses
The study, [16], approached the hypothesis of
whether pensions convey impacts on productivity,
savings, and economic growth.
The empirical model that has been taken into
account in this paper intends to capture the impact
of the pension system on the structural factors of the
economy. According to, [16], the pension system
affects the improvement of the factor of
productivity, savings, and growth of the economy.
Even for the latter, the author states that about a
quarter of the growth of the economy can be
dedicated to the contribution of pension reform.
The method for the investigation and analysis of
the raised objective is according to the empirical
approach. The model used in this study to test the
hypothesis is a multifactor linear econometric
model, which means that to explain a dependent
variable many independent variables are taken into
consideration.
Let the linear multifactor model be given,
p
iii XY
1
0
(1)
Or otherwise written
pp XXXY
22110
the relationship between the two
variables is correct (so as one increases,
we expect the other to increase as well).
Meanwhile, when the sign is negative, it
means that the relationship between the
variables is in the opposite direction (so
when the independent variable increases,
we expect the dependent variable to
decrease). Since there are several
independent or determining variables,
then we have several
parameters/coefficients. Each represents
the respective variable and is not
interpreted for the other variables.
β0 is the free constant and in most cases
has no economic interpretation while the
other β are the coefficients next to the
independent factors and can also be
known as partial regressors;
ε represents all other factors whose
effects have not been taken into
consideration. Ε as explained above
represents all other factors not considered
in the relevant model and is otherwise
known as the residual term or the error
term. When we study economic
phenomena through empirical
approaches, special attention should be
paid to the attributes of this component
of the model. Initially, the residual term
must be normally distributed.
A regression model is not appropriate if its
residual term is not normally distributed. The
autocorrelation of the error term for economic
growth is also taken into consideration. A
regression model to be considered appropriate
must be supported by Fisher's F test and pass
these error term tests.
The method for evaluating the parameters or
coefficients of such a model is that of the
smallest squares (least square method).
It works on the principle of finding the
relationship that gives the least squared distance
between the actual observed values and the
model line. The parameters of the multifactor
regression model are those that are interpreted in
this analysis. The model parameters are
otherwise known as regression coefficients, βi.
For the case of this study, the linear multifactor
model is of the form:
FIEfG ,,
(3)
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FIEG 3210
(4)
where economic growth is marked with: G
pension expenses with: E
income from the pension scheme with: I
and funding from outside the pension scheme
or funding from the state budget with: F
In our study, economic growth (G) is the dependent
variable; while the pension expenses (E), income
from the pension scheme (I), and funding from
outside the pension scheme (F) are independent
variables.
Epsilon (ε) expresses the error term of the model.
The linear multifactor model is a sufficient tool to
answer the main hypothesis and its sub-hypotheses.
From hypothesis to variables
Based on the treatment of the literature of the field
and the researchers' considerations about the
phenomenon, the following hypothesis is deduced:
Hypothesis: The pension system in Albania
helps and supports economic growth.
This hypothesis can be formulated in the form of the
question:
Does the pension system support the economic
growth of the country?
This formulation brings together four different
components: pension scheme income, pension
expenditure, non-scheme funding, and economic
growth. The fact that there are several components
within the same hypothesis makes it necessary to
design several other sub-hypotheses, the control of
which provides answers to the raised hypothesis.
Thus the sub-hypotheses are segmented as follows:
Sub-hypotheses:
1. Income from the pension scheme affects
economic growth in Albania. (I)
2. Pension expenses affect economic growth
in Albania. (E)
3. Funding from outside the pension scheme
affects economic growth in Albania. (F)
These three sub-hypotheses will help us to answer
the main hypothesis.
The investigation and observation of these sub-
hypotheses will make it possible to derive a final
answer to the main hypothesis of the paper. The
study and control of the hypothesis by examining
the sub-hypotheses allow us to formulate the answer
to the hypothesis according to the specifics resulting
from the sub-hypotheses. In other words, if the
economic growth turns out to be affected only by
pension expenditures, then the answer to the main
hypothesis will be the version: the pension system
affects economic growth through the expenditure
component, but not through the income component
and financing from outside the scheme.
It is important to emphasize that with this
hypothesis we try to look at the impact that the
pension system has on economic growth and not to
determine which are the factors affecting the
economic growth of the country.
5 Data Analysis and Findings
Economic growth is the dependent factor for the
factors or components of the insurance system. Real
economic growth on an annual basis is obtained
from, [17], [18], and it is expressed as a percentage.
Income from the pension scheme is measured in
billions of ALL on an annual basis and it is provided
by the, [19], Social Insurance Institute. (S.S.I).
Annual expenses for pensions are in billions of ALL
and are received by S.S.I., [19], Funding from
outside the pension scheme means funding that
originates from the state budget in response to the
deficit or gap of the pension scheme.
Table 1. Data description and their source
Factors
Abbreviation
Description
Nature
Source
Economic
Growth
G
Real growth
on an annual
basis of
GDP
expressed in
(%)
%
INSTA
T, Bank
of
Albania
Income
from the
scheme
I
Annual
income
from the
pension
scheme
Billion
ALL
S.S.I*
Expenditu
res for
pensions
E
Annual
expenditures
for the
pension
scheme
Billion
ALL
S.S.I*
Financing
from
outside
the
scheme
F
Financing
level outside
the scheme
origination
from the
state budget
Billion
ALL
S.S.I*
* The data on the pension system has been obtained from
S.S.I.
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Table 2. Descriptive statistics of variables
Indicators
G
I
E
F
Average
0.042
42.35
67.36
25.57
Median
0.046
43.30
66.72
24.28
Maximum
0.075
61.22
96.79
40.07
Minimum
0.004
28.51
40.30
11.16
St. Dev.
0.021
9.25
18.72
9.56
Skewness
-0.270
0.35
0.08
0.00
Kurtosis
1.973
2.59
1.73
1.70
Jarque-Bera
0.673
0.327
0.817
0.843
Probability
0.714
0.849
0.665
0.656
Sum
0.502
508.14
808.31
306.87
Sum of squares
0.005
941.70
3855.46
1005.68
Observations
12
12
12
12
Source: author's calculations
This issue is written about the descriptive
analysis of the data of the paper, to familiarize the
reader with the reality of the research problem.
Descriptive statistics are considered to be one of the
most useful elements that fulfill this requirement.
Descriptive statistics summarize a range of
information about a given variable. Thus, part of
descriptive statistics is the mean, median, minimum
and maximum, standard deviation, coefficient of
asymmetry and Kurtosis, coefficient of normal
distribution, and a number of data for the variable.
Let's take a look at each of these statistics in turn.
The mean is one of the most commonly used
indicators when talking about the generalization of
the sample in its population. It is calculated through
the formula,
n
xxx
n
x
xn
n
i
i
211
(5)
In this paper, the data are all distributed over time,
otherwise known as time series, therefore, the
average calculated for the time series is the simple
one and not the weighted average.
The median is a type of average that only
requires the data to be ordered from smallest to
largest. The median of a time series is exactly the
midpoint of the data when it is ordered from
smallest to largest.
The minimum is the smallest value of a
variable or time series, while the maximum is its
largest value.
The standard deviation is another type of
mean, which is calculated similarly to the mean
formula. Specifically,
n
xxxxxx
n
xx
n
n
i
i
x
22
2
2
11
2
)()()(
)(
(6)
The coefficient of asymmetry is a statistic that
informs on the asymmetry of the distribution of the
variable compared to the normal distribution. It is
calculated in this way,
3
11
1
n
i
i
x
n
n
xx
n
A
(7)
The calculation of the asymmetry coefficient
includes the third moment and does not stop only at
the first or second moment. The first moment is
related to the mean, the second moment is related to
the variance and standard deviation, and the third
moment is related to the skewness of the series.
Following the coefficient of asymmetry, the
coefficient of Kurtosis provides information on the
distribution of extreme values of a variable,
compared to the normal distribution.
4
11
1
n
i
i
x
n
n
xx
n
K
(8)
The Jarque-Bera coefficient is a statistical test
of whether the series is normally distributed or not.
This statistic measures the difference between the
Skewness and Kurtosis of the series with the normal
distribution. It is calculated,
4
)3(
6
2
2x
xx
K
A
n
BeraJarque
(9)
If we take each of the variables we will see that
for the case of economic growth (G), the average is
about 4% with a maximum value of 7.5% and a
standard deviation of about 2.1%. The Jarque-Bera
criterion indicates that economic growth is normally
distributed. On the other hand, the income variable
(I) for pensions has an average of 42.35 billion
ALL, with a maximum of 61.22 billion ALL and
with a normal distribution, since the Jarque-Bera
criterion results in 0.33 with a probability of 0.85.
Expenditures for pensions, (E) have an average of
67.4 billion ALL with a maximum of 96.79 billion
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ALL, with a minimum of 40.3 billion ALL.
Likewise, pension expenses have a normal
distribution. This variable reflects a much higher
volatility compared to the other variables and this is
reported by the standard deviation, which is 18.72,
where the same statistical indicator for the other
variables is lower. Meanwhile, financing from
outside the scheme (F) is presented in the last
column of the following table. Its average value is
25.57 billion ALL, and the maximum value is 40.07
billion ALL and this coincides with the year 2013.
This variable is also normally distributed.
In addition to the analysis of the description, it
is also interesting to look at the progress of the
variables over the years. For this thing, the
presentation by means of the following figures helps
us. What stands out the most is that the variables of
the pension system have an increasing tendency
from year to year. The exception here is variable F,
which has reflected a decrease for the last year. Next
to each figure, information is provided in the form
of boxes for quartiles, mean, and median.
Expenditure on pensions reflects a clear and stable
upward trend from year to year, addressing the
alarm regarding the difficulties of managing the
pension scheme. The continuous increase in
expenses means an increase in the obligation of the
scheme to the beneficiaries of the pension scheme.
This is due to the increase in the number of
beneficiaries from year to year and the fact that
beneficiaries receive pension increases from time to
time. On the other side of the balance lies the
income from the pension scheme. Thus, the increase
in expenses has been accompanied by an increase in
income. Especially during the last year presented, an
out-of-trend increase in income has been noticed,
defining a narrowing in the gap between expenses
and income within the pension scheme.
Economic growth has a different trend than the
indicators of the pension system. Until 2008,
economic growth experienced a steady and
continuous rise, but after this year, economic growth
experienced a decline with a return in 2014. In the
time span from 2003 to 2014, the economic growth
values for 2013 and 2014 are considered by box plot
as outliers. The multifactor regression model does
nothing but attempt to formulate a relationship that
may exist between economic growth and other
indicators of the pension system. In other words, the
model tends to show that the figures of these
variables have a common behavior and that they are
related to each other. In this paper, the data are
distributed over time, otherwise known as time
series.
Fig. 1: Time series trend
Table 3. Time series of the variables in focus
of this study
Year
Economic
growth
Income
Expenses
Funding
outside
the
scheme
G
I
E
F
2003
0.0580
28.50800
40.30369
11.16307
2004
0.0570
32.30410
44.50286
13.64157
2005
0.0570
32.89208
49.00474
16.12006
2006
0.0540
35.64271
53.78254
22.06770
2007
0.0590
39.10185
58.34577
21.20785
2008
0.0750
42.56100
62.90900
20.34800
2009
0.0330
44.03800
70.54000
26.50200
2010
0.0380
44.65800
76.01000
31.35200
2011
0.0310
47.87900
80.42800
32.54900
2012
0.0170
48.60400
84.88100
36.27700
2013
0.0044
50.73800
90.80700
40.06900
2014
0.0190
61.21600
96.79100
35.57500
* The data on the pension system has been obtained from
S.S.I
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The model that was built and passed all
statistical inference (statistical diagnosis) tests is as
follows. From the point of view of the mathematical
formulation, the model would be written:
FIEfG ,,
(10)
FIEG 3210
(11)
FIEG
05.1
002.0
457.0 0023.0
99.1
0039.0
146.2 017.0 02.0001.0008.0038.0
(12)
The part in parentheses shows the standard error
of the corresponding coefficient as well as the actual
value of the student's criterion. The first figure is the
standard error and the second figure indicates the
Student's criterion. This is a more complete way of
reading the model as we are provided with a lot of
details and information on it.
Meanwhile, from the point of view of the
EViews9 program language, the model would be
reported as in Table 4 (APPENDIX).
This representation obtained by the computer
program also provides information for other basic
tests of the model. From these tests, the level of R-
squared (R-squared), which results in a level of
0.67, should be highlighted. R-square is known as
the coefficient of determination and means the
measure of the variation of economic growth (G)
from the other variables included in the model,
which in our case are expenses (E), income (I), and
financing outside the scheme (F). The level of 0.67
is a significant measure when we consider all the
submissions we have made about the complexity of
the relationship of economic growth with other
factors in an economy. The 0.67 level is interpreted:
about 67% of the variation in economic growth is
explained by the variation of the independent factors
we are considering.
Next to the coefficient of determination, is
Fisher's statistic (F-statistic) and the probability of
this statistic (F-statistic). In simple words, the
Fisher statistic provides information about whether
the R-squared level is significant or not. Since the
probability of this statistic results in the level of
0.039, then we can state that the model is
significant, which means that the considered factors
are important in determining the variation of
economic growth. The level of 0.039 reads: the
coefficient of determination is significant with a
confidence level of 96.1%. This level of confidence
is very satisfying. If this test was greater than 0.05
(or 95% confidence level), then discussion of the
model would be stopped and another model would
be tried.
The Durbin-Watson statistic reflects information
on the model's error term. For our model, this
statistic results above the 2.9 level, and for this
level, we do not have enough information and this
forces us to do further in-depth investigations later
by means of the autocorrelation test of the error
term (APPENDIX, Table 5 and Table 6).
Series control deals with the fact that the built
model must reflect the appropriate and necessary
characteristics to represent a suitable model from a
mathematical-statistical point of view.
In light of these rigorous rules, each of the
series used in our study was checked for:
• The normal distribution of the series – I touched
on this elsewhere above, where we identified
whether or not the series used in modeling are
normally distributed. For this, the Jarque-Bera test
as well as the Skewness and Kurtosis indicators
comes to our aid. The Skewness and Kurtosis
indicators provide information on how skewed the
series under consideration are compared to a
normally distributed series.
• Autocorrelation of the series. The use of time-
distributed variables (time series) in econometric
modeling must meet some preliminary conditions.
This stage becomes even more difficult when the
time series has the behavior of a continuous cycle.
Specifically, the series of economic growth (G)
manifests a trend as noted in the description of the
series. Such an analysis contributes to finding the
horizon of the time series cycle according to, [20].
Autocorrelation analysis, as shown in the
accompanying table, reports that economic growth
suffers from first-order autocorrelation. The partial
correlation column informs this by illustrating it
with asterisks (*) that have crossed the dotted line.
The other orders of autocorrelation are within the
range marked by the dots, implying that we must
operate with the first-order autoregressive. Table 5
and Table 6 (APPENDIX), presents the summary of
the autocorrelation test for economic growth
considering autocorrelation at the level, i.e. no
difference.
This test or control was done for all the series
taken into consideration and the same result was
obtained for expenses (E), together with income (I)
and financing outside the scheme (F).
Now we have to look at the importance of each
variable in relation to economic growth. Besides the
importance of the variables, the direction of the
relationship between economic growth (G) and
other variables is of special interest. That is read
from the sign of the beta coefficient. Let's deal with
them one by one.
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The free constant results at the level of 0.038 and as
explained, we do not interpret it.
- Expenditures for pensions (E) reported a
negative coefficient of -0.008. This means: with an
increase of one unit in pension expenses, economic
growth is expected to decrease by 0.008 units (or
increase by -0.008 units). The importance of the
variable is reported by the corresponding
probability, which in this case is calculated at the
level of 0.0869 or 8.69%. However, it is acceptable
to consider as important that variable whose
probability is calculated to a smaller extent than 0.1
or 10%. Thus, pension spending is a variable that
determines the negative impact on economic growth
and it is important in explaining its variation.
- Income (I) results with a positive coefficient at
the level of 0.001. A positive coefficient (beta>0)
means a positive relationship between pension
scheme income and economic growth. This supports
the logic that an increase in income from the
pension scheme comes as a result of two factors: a)
an increase in the amount of contribution per
contributor and b) an increase in the number of
contributors to the scheme. On the other hand, the
importance of this variable in explaining economic
growth was calculated at the level of 0.6616, which
means that it is not significant. So the income from
the pension scheme turns out to be not important in
explaining the variation of economic growth, they
do not play a role in determining the progress or
fluctuations of economic growth.
- Financing outside the scheme (F) results in a
negative coefficient of -0.002. Here we need to
make a small prefix. Financing outside the scheme
is calculated as the difference between expenses and
income, otherwise, it expresses the gap between
these two indicators. Taking these results into
consideration, we can say that this variable
determines in itself an impact composed of expenses
and income from the pension scheme together.
The closer the values predicted by the model are to
the actual values of the dependent variable (G), the
more accurate and appropriate the model is. The
chart nr.2 below will help us to illustrate this fact.
The line with the name (g) represents the economic
growth according to its true values, while the line
with the name (GF) represents the economic growth
according to the forecast or according to the
estimation of the model. What we notice is that the
values provided by the model deviate very little
from the true value of economic growth. There are
some points where the value of the model (GF)
matches that of the economy's growth (g), and the
deviation of the points that do not match perfectly is
very small. The largest value of the deviation or
inaccuracy of the model is recorded for the year
2010. For this year, the model calculates a smaller
value for economic growth by comparing it with the
real one. This shows that the multifactor model
conceived and built to estimate economic growth
through the components of the pension system is
suitable as it is supported by economic logic,
presents good qualities in relation to mathematical
rigor, and reflects high accuracy in relation to the
forecast. So the components of the pension system
(expenditures for pension beneficiaries as an
important factor) can be used to predict the values
of economic growth as they are important for the
latter.
.00
.01
.02
.03
.04
.05
.06
.07
.08
03 04 05 06 07 08 09 10 11 12 13 14
GF g
Fig. 2: Graphic presentation of the forecast
compared to the current values of economic growth
6 Conclusions
In conclusion, we can say that the pension
system participates in the determination of
economic growth by means of the expenditure
factor for pension beneficiaries.
Other factors such as income from the pension
scheme as well as financing from outside the
scheme do not play a role in determining
economic growth. In this way, we have
answered the main hypothesis of this paper.
The hypothesis that income from the pension
scheme affects economic growth in Albania is
rejected. The model did not support this
formulation.
The same conclusion results for the case of the
hypothesis regarding the impact of funding from
outside the pension scheme on economic growth
in Albania.
On the other hand, the built empirical model
accepts the hypothesis that pension expenses
affect economic growth in Albania.
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As a result of accepting even one of the sub-
hypotheses, the main hypothesis is accepted. So,
based on the detailed analysis and rigorously
following the empirical procedures, we conclude
that the pension system in Albania helps in
determining and supporting the country's
economic growth.
Decision-makers and policy-makers should take
into consideration and pay special attention to the
fact that the continuous increase in pension
expenses harms the country's economic growth.
Therefore, belonging to, [12], the alternative of
orienting the pension market towards the private
sector (second and third columns) is of great
importance, because it not only relieves the
impasse of the pension scheme but also
"facilitates" economic growth. In light of this
argument, it is claimed that in the future, the
pension system should be oriented toward the
private sector. This system will have a great
influence on the economic growth of the country.
In the prism of income and financing from the
state budget, according to, [21], [22], it is
important to carefully observe the issue of the
number of contributors as well as the issue of the
number of contributions.
The multifactor model conceived and built to
evaluate economic growth through the
components of the pension system is suitable as
it is supported by economic logic, presents good
qualities in relation to mathematical rigor, and
reflects high accuracy in relation to the forecast.
So the components of the pension system
(expenditures for pension beneficiaries as an
important factor) can be used to predict the
values of economic growth as they are important
for the latter.
This study constitutes a valuable database and it
is important for further studies and all
researchers interested in the field of pensions.
7 Limitations of the Study
This work has its limitations in terms of data
collection and time series.
The study is my unique and original Ph.D.
work, never been published before. The time series
are from the years 2003-2014, as I have emphasized
during the paper.
After these years of study, the pension system in
our country has faced many problems and great
difficulty in collecting consecutive time series.
The worldwide Covid-19 pandemic emphasized,
even more, the gap in this scheme as well as the
difficulty of data collection.
The study of the pension system in Albania
faces many challenges and difficulties. Due to the
implementation of a multifactor linear model, which
was used in our study, there is a need to find and
examine statistical data that are spread through the
years. The biggest difficulty is related to the small
database of the factors under study. This is a very
big obstacle for both qualitative and quantitative
studies, especially for the latter.
I emphasize that this study will serve as
valuable literature for future researchers interested
in the field of pensions.
References:
[1] World Bank (2008). The World Bank Pension
Conceptual Framework
[2] Social Security Institute. Law. nr.7703 "On
Social Security in the Republic of Albania".
Tirana, (1993).
[3] The World Bank. (2014, April 24). Retrieved
February 2, 2016, from The World Bank
Website:
http://www.worldbank.org/sq/news/speech/20
14/04/29/tahseen-sayed-remarks-albania-
pension-reforms.
[4] Bundo.Sh dhe Lito.G: “Theory and practice of
insurances”, Tirana (2014)
[5] Gjini, Valbona “Pension reforms in Albania”.
Dissertation, Economic Faculty, University of
Tirana, (2013).
[6] Hysa, Eglantina “Social Insurances in
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changes in individual choises”
(Microeconomic treatment and analysis).
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Tirana, (2013).
[7] Xhumari, Merita: “Pension Trajectories in the
Western Balkans”. Comparative study of
three cases: Albania, Macedonia and Kosovo
1990-2010. Dissertation (2010).
[8] Bakvis, Peter (2005). “Social Security Systems
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ICFTU/Global Unions – Washington Office.
[9] Ebbinghaus, Bernhard. The Privatization and
Marketization of Pensions in Europe: A
Double Transformation Facing the Crisis.
Mannheim University. Germany.
[10] Williamson, John B.; Stephanie A. and
Michelle L. Maroto (2006). “Howling The
political economy of pension reform in
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Drita Luzo, Zamira Veizi
E-ISSN: 2224-2899
2056
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Russia: Why partial privatization?” Journal
of Aging Studies. Elsevier
[11] Nazarov, Vladimir dhe Sergei Germanovich
Sinelnikov-Murylev (2012). A Strategy for
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ct_id=2111085
[12] Markowski, A. dhe E. Palmer (1979): Social
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[13] Feldstein, M. dhe A. Pellechio (1979): Social
security and household wealth accumulation:
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[14] Feldstein, M. (1980): International
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[15] Manger, R.P. (1986): Consumption Behaviour
and the Effects of Government Fiscal Policies.
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growth? Central Bank of Chile. 29-30
[17] INSTAT (Albanian Institute of Statistics)
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3136.html
http://databaza.instat.gov.al/pxweb/sq/DST/
[18] Bank of Albania
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type and urban/rural schemes, 2011
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budapest/documents/publication/wcms_79863
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"On the Administration and Calculation of
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[20] Enders, W. (2015) Applied Econometric Time
Series, 4th Edition, Wiley, Hoboken, NJ,
pp.7-70.
[21] Edlira Luci & Dorina Kripa, 2010. "Does The
Albanian Pension System Work?," The
Annals of the "Stefan cel Mare" University of
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[22] Joshua D. Rauh (2010). Are state public
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http://ssrn.com/abstract=1596679
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Drita Luzo was responsible for the literature
review, methodology, and supervision.
- Zamira Veizi was responsible for gathering the
data from different sources of information and
processed them with the statistical software
EViews9 and edited the paper.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
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APPENDIX
Table 4. Multifactorial regression model
Dependent Variable: D(G)
Method: Least Squares
Sample (adjusted): 2004 2014
Included observations: 11 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.037629
0.017535
2.146021
0.0690
D(E)
-0.007718
0.003879
-1.989820
0.0869
D(I)
0.001059
0.002317
0.456937
0.6616
D(F)
-0.002112
0.002014
-1.049064
0.3290
R-squared
0.674569
Mean dependent var
-0.003545
Adjusted R-squared
0.535099
S.D. dependent var
0.015996
S.E. of regression
0.010907
Akaike info criterion
-5.923628
Sum squared resid
0.000833
Schwarz criterion
-5.778938
Log likelihood
36.57995
Hannan-Quinn criter.
-6.014834
F-statistic
4.836650
Durbin-Watson stat
2.935110
Prob(F-statistic)
0.039531
Table 5. Autocorrelation in the level for economic growth
Table 6. First-order autocorrelation for economic growth
Partial Correlation
AC
PAC
Q-Stat
Prob
. |***** |
. |***** |
1
0.653
0.653
6.5111
0.011
. |***. |
. | . |
2
0.394
-0.056
9.1242
0.010
. |* . |
. | . |
3
0.207
-0.050
9.9219
0.019
. | . |
. *| . |
4
0.001
-0.174
9.9220
0.042
. **| . |
. **| . |
5
-0.250
-0.289
11.425
0.044
.***| . |
. *| . |
6
-0.412
-0.174
16.186
0.013
. **| . |
. |** . |
7
-0.289
0.262
18.995
0.008
. **| . |
. *| . |
8
-0.278
-0.149
22.251
0.004
. **| . |
. *| . |
9
-0.262
-0.085
26.099
0.002
. *| . |
. *| . |
10
-0.190
-0.081
29.119
0.001
. *| . |
. | . |
11
-0.074
-0.037
30.028
0.002
Autocorrelation
Partial Correlation
AC
PAC
Q-Stat
Prob
.***| . |
.***| . |
1
-0.375
-0.375
2.0157
0.156
. *| . |
. **| . |
2
-0.092
-0.271
2.1487
0.342
. |* . |
. | . |
3
0.132
-0.017
2.4598
0.483
. | . |
. |* . |
4
0.073
0.132
2.5695
0.632
.***| . |
.***| . |
5
-0.404
-0.362
6.4585
0.264
. |* . |
. **| . |
6
0.110
-0.268
6.8036
0.339
. | . |
. *| . |
7
0.041
-0.196
6.8630
0.443
. | . |
. | . |
8
-0.019
-0.030
6.8804
0.550
. | . |
. | . |
9
0.016
0.066
6.8990
0.648
. | . |
. *| . |
10
0.018
-0.163
6.9456
0.731
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