https://doi.org/10.1016/j.cya.2017.02.001
Paper Research
The impact of consumer behavior on financial security
of households in Poland
Impacto del
comportamiento del consumidor en la seguridad financiera de los hogares en
Polonia
Maria Piotrowska1
1 Wroclaw University of
Economics, Poland
Corresponding author: Maria Piotrowska, email: maria.piotrowska@ue.wroc.pl
Abstract
The
paper applies the concept of identity to investigate whether consumer behavior
matters for a household's financial security. It is assumed that considerable
part of households may express their identity through status-oriented
consumption. The research is carried out in two steps. First, the index of
financial security is built and used to determine the level of financial
security experienced by working-age families in Poland. Second, the simulation
results based on an econometric model are employed to find the answer to the
question: Does financial insecurity result more from the need to manifest
consumption at the higher level than average in an income-group of which people
are members, or people want to be distinguishable inside their own income-group
but they do not identify with a group having consumption at visibly higher
level, or from the need to improve self-image by bringing own consumption
closer to the pattern of a group with higher wealth status of which they are
not members? The source of data is the 2005-2009 Households Budget Surveys in
Poland. The findings offer empirical evidence for the relevance of consumer
behavior for financial security of households in Poland. Considerable part of
households expresses identity through conspicuous consumption. Both groups of
households, the insecurity rich and the insecurity poor, accept the same
ranking of status goods: a car on the first position, next homes (housing and
equipment) and clothes on the third place. Status-oriented consumption creates
life beyond means and pushes even relatively rich households towards financial
insecurity.
Keywords: Social identity, Status-oriented consumption,
Financial security.
JEL classification: D19, D14, D12, D10.
Resumen
El trabajo aplica el concepto de identidad
a investigar si el comportamiento del consumidor es importante para la
seguridad financiera de un hogar. Se asume que parte considerable de los
hogares pueden expresar su identidad por medio de un consumo orientado al
estatus. La investigación se realiza en dos pasos. Primero, se construye y se
usa el índice financiero para determinar el nivel de seguridad financiera que
gozan las familias en edad laboral en Polonia. Segundo, se emplean los
resultados de la simulación, basados en un modelo econométrico, para encontrar
la respuesta a la cuestión: ¿Es la inseguridad financiera un resultado más de
la necesidad de manifestar el consumo al nivel más alto que el promedio en un
grupo de ingresos del que son miembros las personas, o quieren las personas distinguirse
dentro de su propio grupo de ingreso pero no se identifican con un grupo con un
consumo a un nivel visiblemente más alto, o es resultado de la necesidad de
mejorar la autoimagen acercando más el propio consumo al patrón de un grupo con
un estatus de riqueza más alto del que ellos no son miembros? La fuente de los
datos es la Encuesta 2005-2009 de Presupuestos del Hogar en Polonia. Los
resultados ofrecen evidencia empírica para la relevancia del comportamiento del
consumidor para la seguridad financiera de los hogares en Polonia. Una parte
considerable de los hogares expresa su identidad mediante un consumo notorio.
Ambos grupos de hogares, los inseguros ricos y los inseguros pobres, aceptan la
misma clasificación de bienes del estatus: un carro en primer lugar, en
seguida, una casa (habitación y equipamiento) y ropa, en tercer lugar. El
consumo con orientación al estatus crea una vida más allá de los medios y
arrastra incluso a los hogares relativamente ricos hacia la inseguridad
financiera.
Palabras clave: Identidad social, Consumo orientado al estatus,
Seguridad financiera.
Códigos JEL: D19, D14, D12, D10.
Received: 09/02/2015
Accepted: 04/08/2016
Introduction
The household's identity
(a household is treated as the whole) shapes consumer behavior and affects the
household's choice of a consumption pattern. The concept of identity has been
introduced into economics by Akerlof and Kranton (2000, 2005) and by Davis (2003, 2007). Akerlof
and Kranton have focused on the social identity drawn
directly from social psychology and self-categorisation
theory. They have incorporated the social identity as an argument in the
utility function. Davis has employed the sociological approach to identity and
suggested to treat the individual as being active in creating a personal
identity. The purpose of the paper is to apply the concept of identity (mainly
social identity) to investigate the importance of consumer behavior for
financial security of households in Poland.
The
structure of the paper is as follows: the research concept is presented in the
first section; the methodology in the second; findings in the third and finally
the conclusions.
Research concept
Akerlof and Kranton,
in their book “Identity economics”, have given the following definition of the
term “identity” and its relations to social categories and norms: “People's
identity defines who they are-their social category. Their identities will
influence their decisions, because different norms for behavior are associated
with different social categories. First, there are social categories (…).
Second, there are norms for how someone in those social categories should or
should not behave. Third, norms affect behavior” ( Akerlof & Kranton, 2010 , p. 13). Social categories are
broad social science classifications used to describe widely recognized social
aggregates ( Davis, 2007 , p. 350). In the Akerlof-Kranton framework the social identity means that
individuals identify with people in same categories and differentiate
themselves from those in others ( Akerlof & Kranton,
2000, p. 720).
The social
identity is based on the idea of “identifying with” others, while the personal
identity on the idea of “identity apart from” others. Individuals have personal
identities as well as social identities, and these two concepts are related as
Davis has emphasized ( Davis, 2007 , p. 355). He has
suggested to see personal identity as being fundamental in the sense that
individual creates his/her identity, considering the utilities drawn from
multiple social identities. The Davis extension of the Akerlof
and Kranton utility function by incorporating,
additionally, personal identity, makes identity reflective in the sense that
the individual evaluates the utility created by social identity, taking into
account how this utility contributes to their personal identity.
Consumption
is related to the process of identity formation by signaling status. First,
consumption points at the reference groups with which people want to identify.
Second, relative status determines patterns of behavior (such as for example
white or blue collar habits) ( Herrmann-Pillath, 2008).
A
reference group is a group of people (or even a person) that significantly
influences an individual's behavior. Such an influence can appear when people
orient themselves to other than membership groups in shaping their behavior and
evaluations ( Merton & Rossi,
1949 ). Reference groups unmask people's preferences on behavior and
lifestyles, influence self-concept development, contribute to the formation of
values and attitudes, and generate pressure for conformity to group norms ( Bearden & Etzel, 1982). Kelley (1947) distinguished between reference
groups used as standards of comparison for self-appraisal (comparative) and
those used as a source of personal norms, attitudes and values (normative).
Based
on the work of Deutsch and Gerard
(1955) and Kelman (1961) , information,
utilitarian and value-expressive influences can be identified. Informational
influence can flow from a need to be a properly informed. Those who offer
information influence others. Utilitarian reference group influence appears if
an individual feels that it will be useful to meet the expectations of people
significant for him or her. Value-expressive influence is characterized by the
need for psychological association with a person or group and is reflected in
acceptance of positions expressed by others. This association can take two
forms: being like the reference group or liking for the reference group.
The
reference group construct is important in at least some types of consumer
decision making. Many individuals can manifest their identity thorough
consumption. They may derive utility from the consumption of commodities.
Consumption can be seen through status goods that are defined according to
their meanings, not to their functions. Their utility depends on the
interactions in social networks which manifest status orders. As Herrmann-Pillath (2008) emphasizes status goods
entirely depend on the cultural frame in the sense that everything can be a
status good. Even consumption of foodstuffs influences the process of identity
formation.
Akerlof and Kranton argue that individual utility is
partly determined by the extent to which one perceives to conform with certain
social types to which one strives to belong. In the same sense Potts, Cunningham, Hartley, and Ormerod
(2008) define social network goods as goods in which individual utility is
partly determined by the extent to which others also consume the same good.
Consumption of such social network goods leads toward collective patterns of
consumption.
Osberg (1998) points that that the
maintenance of social identity depends partially on whether or not individuals
have the discretionary income to purchase goods and services perceived as appropriate
to manifest their identity. Economic insecurity about outcomes can, therefore,
be highly threatening to the individual's identity.
The
paper addresses the inverse influence, it means, the influence of identity on a
household's financial security.1 The household's identity
(a household is treated as the whole) shapes consumer behavior and affects the
household's choice of a consumption pattern.
The
research is carried out under the assumptions that (1) households appreciate
consumption and then a group characterized by high levels of consumption will
have a higher status than a group characterized by low levels; (2) for some households consumption expenditure may be more important
than income, as a criterion, when they compare themselves.
The purpose
of the paper is to apply the concept of identity (mainly social identity) to
investigate whether consumer behavior matters for financial security of
households in Poland. People can be motivated to buy any good by a need to
manifest consumption at the higher level than the average in an income-group of
which people are members, or people want to be distinguishable inside of own
income-group but they do not identify with members of own groups having the
highest consumption expenditure. A need to improve his/her self-image by having
consumption at the highest level in own-income group can drive consumer
behavior of the other. Some people can feel very strongly a need to bring own
consumption closer to the pattern of a group with higher wealth status of which
they are not members. These people want to create the impression of attachment
to the group with higher consumption rather than to be associated with this
group. The research tries to reveal which of those needs is the most important
to explain the differentiation in financial security across households in
Poland. The findings should also reveal which goods are considered status goods
by households in Poland.
Methodology
The research is carried out in two steps. First,
the index of financial security is built to determine the level of financial
security experienced by working-age families in Poland. Second, the simple
simulation based on the regression estimation is applied to find answers to the
research questions (presented above) related to the influence of consumer
behavior on financial insecurity.
The index of financial security of households
In the literature there are two concepts:
economic security and economic insecurity. Scientists define economic insecurity
concentrating on either existence of current losses ( Hacker,
2007 ) or
anxiety, fear connected with the possibility of occurrence of such losses in
the near future ( Anders & Gascon, 2007; Dominitz & Manski, 1997; Osberg, 1998 ). In contrary security is regarded as the
fulfillment of certain conditions which guarantee the individual wealth ( Beeferman, 2002; report By
a Thread: The New Experience of America's Middle Class (2007) prepared together by Demos: A Network for
Ideas & Action and The Institute on Assets and Social Policy at Brandeis
University; ILO Socio-Economic Security Programme).
Financial security is defined very
narrowly in the paper as the ability to achieve income necessary for covering
household needs at its suitable level and to create financial reserves to be at
disposal in case of unfavorable accidence (sickness, job loss, family
breakdown).
Source of data: Household Budget Survey
(HBS) conducted by the Polish Central Statistical Bureau for the panel of
2005–2006 and 2008–2009. Each household is included in the HBS over two years.
The total number of households in the panel 2008–2009 is equal to 8034. The
structure of the sample is as follows: 7049 households with income from hired
work (3981 manual worker households and 3068 non-manual worker households), and
985 households with income from self-employment. The total number of households
in the panel of 2005–2006 is equal to 7638.
Poland enjoyed very dynamic growth over
the period of 2005–2008. The research period is chosen to investigate a change
in consumer behavior that could matter for financial security between the
beginning of economic prosperity (2005–2006) and the first wave of the
financial crisis (2008–2009) three years later.
The methodology of the financial security
index covers:
- defining a sample;
- identifying
factors influencing financial security;
- setting
weights for the factors in the index;
- setting
for each area included in the index: (1) a threshold that would be optimal
to support overall financial security, and (2) a threshold that would
threaten it – finally, determining percentage of households that met these
thresholds.
- defining
criteria for considering the family: (1) secure, or (2) at high risk, or
(3) in-between these two groups;
- calculating
the index for each household.
Defining a sample
The research is based on a sample covering
households meeting two following criteria:
- main
income source – households which main income source of maintenance is:
income from hired work or income from self-employment (employees and
owners of small and medium-sized firms, lawyers, artists, journalists;
excluding farmers); all incomes are considered equivalent incomes; the
modified OECD scale is used: 1 for the first adult person in household,
0.5 for each next member of household – 14 years and over, 0.3 – for every
child under 14 years.
- age
range – age of household head: 25–64 (working age for a man with the
university's diploma)
The whole sample, based on these two
criteria, is divided into sub-samples called “the rich” and “the poor”. The
threshold for income is set as 150% of social minimum (adjusted to a household
size using the OECD scale). Social minimum is not a poverty line. It
constitutes income that allows to keep living standards at the minimum but fair
level, including not only biological but also social needs. Social minimum is
calculated by the Institute of Labour and Social
Studies. For example, in 2009 the 150% of social minimum for a 4-person family
was equal to 1844 PLN ≈ 461 EUR (an equivalent income per person
per month).
Identifying factors influencing financial security and setting the weights
and thresholds for them
The index covers three factors: financial
assets, housing and budget. All three factors are crucial for financial
security defined narrowly in this paper. Each factor is included in the index
with its weight that reflects its relevance for overall financial security. The
weight depends on the percentage of households that meets the threshold of risk
for financial security.
Assets are the key factor of financial
security. The problem occurs how to estimate household's assets when data on
savings, securities as well as on home equity are not available at a
household's level. It seems to be acceptable to investigate whether a household
has been able to generate savings over two succeeding years (a given household
is included in the HBS only over two years). Basing on this proposal the 2-year
sum of an increase in savings plus capital income has been applied as a proxy
of assets accumulated over two years; in details:
An increase in savings is calculated as a
surplus of available income over total consumer expenditures and loan repayment
and private insurances; in details:
income from hired work or income from self-employment
The asset factor is included in the index
as the number of months when a family could meet 75% of its essential living
expenses, using financial assets accumulated over two last years (the increase
in assets calculated as above).
Essential living expenses are expenditures
on food, housing (without spending on furniture and equipment), clothing,
transport (without purchases of cars and motors, bicycles), health care,
personal care, education, transport insurance, private health insurance.
Setting the thresholds is based on the
average number of months without income from hired work or self-employment.
This number of months depends on the situation in a labor market and it was
equal to 10 months in 2009. Therefore:
-
The optimal level for financial security –
the level of assets accumulated over two last years that allows a family to cover
75% of its essential living expenses for at least 150% of average number of
months without employment income or income from self-employment;
-
Risk for financial security – the level of
assets accumulated over two last years that allows to finance 75% of its
essential expenses for less than 50% of average number of months without
employment income or income from self-employment.
The housing factor means a percentage of
after-tax income spent on housing.
Housing expenses: mortgage principle and interest
for owned home/or vacation home, rent, insurance, maintenance, utilities, fuels
and public services.
In absence of the Polish definition of
housing affordability the thresholds are based on the definition used by the
Department of Housing and Urban Development in USA. This definition can be also
accepted in Polish conditions.
-
The optimal level for financial security –
less than 20% of after-tax income spent monthly on housing;
-
Risk for financial security – more than
30% of after-tax income spent monthly on housing
The budget factor is included into the
index as the ratio of the amount left at the end of the month after paying
taxes and covering living expenses to the amount that allows to make ends meet.
This amount should afford a family to cover the costs of expensive medicines,
to improve housing, or in general, to improve the quality of life or saving and
investing.
-
total consumer expenditures
-
principle and interest of loans and house
loans
-
house, life, health and other private
insurances
An amount that allows to make ends meet is
a base for the thresholds. In 2009 this amount was equal to 806 PLN per month/per
equivalent person (≈202 EUR).
-
The optimal level for financial security –
Amount left at the end of the month after paying taxes and covering living
costs is more than 150% of the amount that allows to make ends meet (the amount
adjusted to a family size);
-
Risk for financial security – Amount left
at the end of the month after paying taxes and covering living costs is less
than 50% of the amount that allows to make ends meet (the amount adjusted to a
family size).
Defining criteria for considering the
family secure, or at high risk
A family can enjoy financial security, if
at least two factors for this family meet the optimal threshold for financial
security. A family is exposed to financial insecurity, if at least two factors
for this family meet the threshold defined as risk for financial security. If a
family falls between these two groups it means that
the family is not at high risk but its financial security is fragile.
Calculating the
financial security index
The financial security index for each
household is calculated as follows:
(1)
where:
·
FS i – financial security index for i household; i = 1…N; N = 8034
·
A i – asset factor as the number of months
when a family could meet 75% of its essential living expenses, using financial
assets accumulated over two last years
·
H i – housing factor means the percentage of
after-tax income spent on housing
·
B i – budget factor as the ratio of the amount
left at the end of the month after paying taxes and covering living expenses to
the amount that allows to make ends meet.
·
wij – weight for j factor and i household: j = asset, housing, budget
Table 1:
Insecure households by an educational
level and a place of residence in the panels of: 2005–2006 and 2008–2009 (in
%).
Source
:
Own calculation based on the HBS.
A
Secondary level covers: post-secondary and
vocational secondary levels; primary level covers: basic vocational, lower
secondary and primary levels.
The values of each factor are normalized
relative to its average. The weights are based on the percentage of households
that met the threshold of risk to financial security. The weights are
normalized relative to their sum. The higher value of the index means the
higher level of financial security.
The econometric model
Characteristics
of the insecure poor and insecure rich
In the second step of the research – for
each panel data, 2005–2006 and 2008–2009 – the sample of insecure households
(determined in the first step) in each panel is divided into groups:
-
the insecure rich – households with an
equivalent income above 150% of social equivalent minimum in a given year ( N = 1085 for 2005–2006 and N = 1442 for 2008–2009)
-
the insecure poor–households with an
equivalent income below 150% of social equivalent minimum ( N = 4374 for 2005–2006 and N = 3092 for 2008–2009)
The group of the insecure poor covers
households with visibly lower level of education, living in villages and small
towns (see Table 1 ), while a considerable part of the
insecure rich has the university diplomas and lives in big and medium-size
towns.
Descriptive statistics of main variables for
the insecure poor and the insecure rich in the panels of 2005–2006 and
2008–2009 are presented in Tables
2 and 3 ,
respectively. Age of a head in both groups of households and in both data
panels is similar, more or less 44 years. A size of a household is a little bit
smaller for the insecure rich (3 persons in average) than for the insecure poor
(4 persons). Income per household is, of course, higher for the insecure rich
but it is worth to mention that a difference between mean income and median
income in both groups in both periods is rather small suggesting there is no
significant income inequality among each group. A little bit bigger difference
can be found in consumption expenditure, more visible for the insecure rich (a
ratio of mean expenditure to median expenditure equals 1.16 in 2005–2006 and
1.18 in 2008–2009 while a ratio of mean income to median income is equal to
1.08 and 1.07, respectively).
Visible differentiation among each group and
between two groups in both panels can be observed for a sum of increases in
savings as well as for loan burden and finally for financial security index.
The two-year sum of increases in savings
plus capital income is used to measure an ability of a household to increase
its assets. If a household is not able to generate any surplus of income over
its expenditure over two years, its financial security can be fragile. In both
periods the insecure rich experienced dissavings (a
decline in savings) to larger extend than the insecure poor. The statistics
show, however, the visible increase in dissavings
among the insecure poor in the period of 2008–2009. The high mean/median ratios
as well as the high values of variation coefficients point at considerable concentration
of dissavings in both groups of households. It means
there are households extremely insecure due to this reason.
Loan burden can be a cause of financial
insecurity only for a small number of households. Majority of insecure
households do not have any loans. However a small part of those who are in
debts can feel insecure. Variation of loan burden, especially in reference to
housing loans, is very high.
The statistics of financial security index
(the higher values of mean and median, higher financial security of households)
show that a mean (and median) level of financial security is much lower for the
insecure rich than insecure poor, while variation is stronger among the
insecure poor.
Variables
For each of these two groups of households
in each panel the econometric model is estimated with the financial security
index as the dependent variable. The reason to estimate the model for each
panel is that a given household is included in the HBS only over two years.
A household is an observation unit, it means that identity of the household refers to the
family's identity shaped by interactions between family members. An
individual's decision about whether or not to buy a good may be influenced by
the expectations of family members.
There are two groups of insecure
households, the insecure rich and the insecure poor. Each insecure household is
characterized by vector of expenses on consumer goods and services Gi=(Gi1…GiC)Gic, where i
means an insecure household, i = 1…N; and c means a category of consumer goods and
services, c = 1…C
A social group determined by the wealth
status, (the rich or the poor) is characterized by two kinds of the prototype
of consumption, Gjkc:
1)
Gjmc is equal to the mean
expenses (m) on c category of goods and services across
members of j group (the rich group or the poor group);
or
2)
Gjhc is equal to the average of ten highest
expenses (h) on c across members of j group
Gjkc, where j means a group (the rich = r, or the poor = p); k means a kind of the prototype for
consumption of c category of goods and services; k can be,
m , the mean expenses across members of the
whole rich group, r, (or of the whole poor
group, p) and h , the highest expenses (precisely, the
average of ten highest expenses) across members of the whole rich
group, r, (or of the whole poor
group, p ). The prototype of consumption is based
on the consumption of the rich, as the whole and on the consumption of the
poor, as the whole, not on the consumption of insecure households.
The research covers 12 categories of
consumption expenditure:
-
Food and non-alcoholic beverages
-
Alcoholic beverages, tobacco
-
Clothing and footwear
-
Housing, water, electricity, gas and other
fuels
-
Furnishings, household equipment and
routine maintenance of the house
-
Health
-
Transport
-
Communication
-
Recreation and culture
-
Education
-
Restaurants and hotels
-
Hygiene
The main independent variables measure the
2-year sums of weighted distances between the insecure household's expenses on
particular categories of consumer goods and services and the levels of expenses
described as the prototypes of consumption.
First, the distance between the insecure household's
expenses and the mean expenses in its income group (it means the rich or the
poor). Such the distance is assumed to reflect the need to be distinguishable
inside of own income-group by having consumption at higher level than the
own-group average but without liking for expenditure at the top.
(2)
Second, the distance between the insecure household's
expenses and the expenses made by the group with higher material status. This
distance reflects the need to create the impression of attachment to the group
with higher consumption rather than a desire to be associated with this group.
-
for the insecure poor – the distance
between the insecure household's expenses:
-
the mean expenses of the rich
(3)
or
-
the highest expenses of the rich
(4)
Third,
the distance between the insecure household's expenses and the highest expenses
in own group. This distance describes the need to improve household's
self-image by having consumption at the highest level in own group;
-
for the insecure rich – the distance
between the household's expenses and the highest expenses across members of the
rich:
(5)
-
for the insecure poor – the distance
between the household's expenses and the highest expenses across members of the
poor:
(6)
Fourth,
the distance between the insecure rich household's expenses and the highest
expenses of the poor (the highest expenses across members of the poor are at
visibly higher level than mean expenses for the rich, and of course, they are
lower than the highest expenditure made by the rich). This distance is assumed
to show that some households may evaluate that a group characterized by high
levels of consumption will have a higher status even such a group has lower
income. If consumption, not income, is the criterion of self-categorization
households who cannot afford the highest expenses made by the rich, they may
attempt to approach to the highest level of consumption manifested by the poor:
(7)
Finally,
for each panel and for each household the 2-year sum of weighted distances
between the household's expenses on each category of consumption expenditure
and the given prototype of consumption is calculated. The weight reflects a
relevance of the category of consumer goods and services in the structure of
equivalent consumption expenditure.
The regressions control for the following
variables: log of equivalent income, the educational level attained by a head,
age of a head, main income source, a size of family, a district where a
household lives, a place of permanent residence (large, medium-size, small
towns and village), consumer loan burden, housing loan burden.
The results from the model estimations
will allow:
-
to reveal which kind of the distance is
statistically significant for explaining financial insecurity in both groups of
households;
-
to determine other factors important for
explaining financial insecurity, like: income, loan burden, age, education,
membership to white or blue collars, a family size or a place where a household
lives
-
to carry out the simple simulation to rank
categories of consumption expenditure taking into account their influence on
financial security.
The estimation results of regressions
of financial security index
The estimation results of regressions of
financial security index are presented in Tables
4–7 . Under
each table, there are shown: (a) a histogram and descriptive statistics of the
residuals, including the Jarque–Bera
statistic for testing normality; (b) a plot of residuals; (c) the Breusch-Godfrey Serial Correlation LM Test; (d) the White
heteroskedasticity test. These diagnostics refer to the regression on variables
chosen due to the strongest correlation with financial security index. The Jarque–Bera test suggests a lack
of normality, the Breusch-Godfrey Serial Correlation
LM test reveals autocorrelation and the White test shows a presence of
heteroskedasticity.
All the regressions are estimated with the
inclusion of a constant.
The
bold values mean the coefficients included in the regression which is a base
for the simulation which results are presented in Tables
8 and 10.
Coefficient
estimates reported in the table.
**
Coefficient estimate significantly
different from zero at the 5% level.
***
Coefficient estimate significantly
different from zero at the 1% level.
All the regressions are estimated with the
inclusion of a constant.
The
bold values mean the coefficients included in the regression which is a base
for the simulation which results are presented in Tables
9 and 10.
Coefficient
estimates reported in the table.
*
Coefficient estimate significantly
different from zero at the 10% level.
**Coefficient estimate significantly
different from zero at the 5% level.
***Coefficient estimate significantly
different from zero at the 1% level.
All the regressions are estimated with the
inclusion of a constant.
The
bold values mean the coefficients included in the regression which is a base for
the simulation which results are presented in Tables
8 and 11.
Coefficient
estimates reported in the table.
***
Coefficient estimate significantly
different from zero at the 1% level.
All the regressions are estimated with the
inclusion of a constant.
The
bold values mean the coefficients included in the regression which is a base
for the simulation which results are presented in Tables
9 and 11.
Coefficient
estimates reported in the table.
**
Coefficient estimate significantly
different from zero at the 5% level.
***
Coefficient estimate significantly
different from zero at the 1% level.
Firstly, are the residuals normally
distributed? Under Tables 4–7 there are presented a histogram and
descriptive statistics of the residuals, including the Jarque–Bera statistic for testing normality. If the residuals are
normally distributed, the histogram should be bell-shaped and the Jarque–Bera statistic should not
be significant. Jarque–Bera
is a test statistic for testing whether the series is normally distributed. The
test statistic measures the difference of the skewness and kurtosis of the
series with those from the normal distribution. The reported Probability is the
probability that a Jarque–Bera
statistic exceeds (in absolute value) the observed value under the null
hypothesis. For each regression presented in Tables
4–7 the
zero probability value leads to the rejection of the null hypothesis of a
normal distribution.
This conclusion seems to be doubtful because,
in the presence of outliers, the power of the Jarque–Bera test is low. If the distribution is disturbed by
outliers (data points with really big positive or negative residuals) a value
of the test statistic becomes huge in comparison to the critical value. The
test suggests that the distribution considerably departs from the normal one.
However, a decision to reject the null hypothesis of a normal distribution is
influenced by outliers. In such a case, one should take into account that tails
of a distribution are the most important issue in carrying out statistical
inference. If a distribution in its ends is close to the assumed distribution,
the factual values of statistical tests will be equal to the assumed values. It
is much less important for statistical inference how the distribution looks in
its remaining part.
Each of the residual distributions shown
in the boxes under Tables 4–7 is influenced by outliers (compare mean
and maximum, minimum values in the descriptive statistics). It results in the
huge values of Jarque–Bera
statistics. However, the tails of these distributions are rather close to the
normal distribution (see the histograms in the boxes). It allows to assumed
that the residual distribution is normal for each regression shown in Tables
4–7
(taking into account that applying the M-method has not improved the values of
skewness and kurtosis).2
Secondly, the Breusch-Godfrey
LM test (see point c below each of Tables
4–7 )
shows autocorrelation. Neighboring error terms are correlated because the
values of the dependent variable (the financial security index) are ordered
from maximum to minimum. The next problem refers to heteroskedasticity. Have
the error terms for all observations a common variance (are they homoskedastic) or a varying variance (are the error terms
heteroskedastic)? One of the statistical assumptions underneath ordinary least
squares is that the error terms for all observations have a common variance.
Based on the White test statistics, the null of homoskedascity
is rejected (see the point d under each of Tables
4–7 ).
This means that the error term in each regression is heteroskedastic and
standard errors must be adjusted.
The common approach to dealing with both
autocorrelation of unknown form and heteroskedasticity is to use the HAC
Consistent Covariance (Newey-West). An idea is to stick with least squares
estimation, but to adjust the standard errors for heteroskedasticity and
autocorrelation of unknown form. In Tables
4–7 the
summary of the OLS (HAC Consistent Covariance (Newey-West)) estimation results
of financial security regressions are presented.
For purpose of comparison the full results
with unadjusted standard errors (the OLS estimation) and the results with
adjusted standard errors (the OLS – HAC Consistent Covariance (Newey-West)) are
reported in Appendix . As expected, the estimated coefficient
values do not change. But the adjusted standard errors (and associated t-statistics) – Tables
Ib, IIb, IIIb, IVb in Appendix – are different from the original regressions
– Tables Ia, IIa, IIIa, IVa – suggesting that autocorrelation and
heteroskedasticity are present and should be corrected (what is done).
Returning to the OLS (HAC Consistent
Covariance (Newey-West)) estimation results of financial security regressions –
Tables 4–7, (and Tables
Ib, IIb, IIIb, IVb in Appendix ) – a majority of coefficient estimates
on independent variables are significant at the 1% level, few ones at the 5%
level and only one at the 10% level ( p = 0.0777, Table
IIb in Appendix).
Diagnostic tests for the regression on variables
chosen due to the strongest correlation with the financial security index –
“the insecure POOR”, 2005–2006 ( Table
4):
a)
the histogram with the descriptive
statistics including the Jarque–Bera
statistic for testing normality
b)
the plot of residuals for the financial
security regression, the sample of “The Insecure POOR”, 2005–2006 (The symbol
“BEZ06” means the financial security index in 2006)
c)
The Breusch-Godfrey
Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test: |
|||
F-statistic |
508.1410
|
F-statistic |
508.1410
|
Obs*R-squared |
827.4356
|
Obs*R-squared |
827.4356
|
d)
the White heteroskedasticity
test
Heteroskedasticity Test: White |
|||
F-statistic |
26.52778
|
Prob. F(135,4238)
|
0.0000
|
Obs*R-squared |
2003.311
|
Prob. Chi-Square(135) |
0.0000
|
Scaled explained SS |
27,516.06
|
Prob. Chi-Square(135) |
0.0000
|
Diagnostic tests for the regression on
variables chosen due to the strongest correlation with the financial security index
– “the insecure RICH”, 2005–2006 ( Table
5):
a)
the histogram with the descriptive
statistics including the Jarque–Bera
statistic for testing normality
b)
the plot of residuals for the financial
security regression, the sample of “the insecure RICH”, 2005–2006 (The symbol
“BEZ06” means the financial security index in 2006)
c)
The Breusch-Godfrey
Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test: |
|||
F-statistic |
385.6713
|
Prob. F(2,1071) |
0.0000
|
Obs*R-squared |
454.2623
|
Prob. Chi-Square(2) |
0.0000
|
d)
the White heteroskedasticity
test
Heteroskedasticity Test: White |
|||
F-statistic |
9.287261
|
Prob. F(77,1007)
|
0.0000
|
Obs*R-squared |
450.5520
|
Prob. Chi-Square(77) |
0.0000
|
Scaled explained SS |
2988.193
|
Prob. Chi-Square(77) |
0.0000
|
Diagnostic tests for the regression on variables
chosen due to the strongest correlation with the financial security index –
“the insecure POOR”, 2008–2009 ( Table
6):
a)
the histogram with the descriptive
statistics including the Jarque–Bera
statistic for testing normality
b)
the plot of residuals for the financial
security regression, the sample of “The Insecure POOR”, 2008–20069 (The symbol
“BEZ09” means the financial security index in 2009)
c)
The Breusch-Godfrey
Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test: |
|||
F-statistic |
505.8184
|
Prob. F(2,3078) |
0.0000
|
Obs*R-squared |
764.8554
|
Prob. Chi-Square(2) |
0.0000
|
d)
the White heteroskedasticity
test
Heteroskedasticity Test: White |
|||
F-statistic |
128.0054
|
Prob. F(77,3014)
|
0.0000
|
Obs*R-squared |
2367.914
|
Prob. Chi-Square(77) |
0.0000
|
Scaled explained SS |
77,937.59
|
Prob. Chi-Square(77) |
0.0000
|
Diagnostic tests for the regression on
variables chosen due to the strongest correlation with the financial security
index – “the insecure RICH”, 2008–2009 ( Table
7):
a)
the histogram with the descriptive
statistics including the Jarque–Bera
statistic for testing normality
b)
the plot of residuals for the financial
security regression, the sample of “the insecure RICH”, 2008–20069 (The symbol
“BEZ09” means the financial security index in 2009)
c)
The Breusch-Godfrey
Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test: |
|||
F-statistic |
402.6629
|
Prob. F(2,1431) |
0.0000
|
Obs*R-squared |
519.2802
|
Prob. Chi-Square(2) |
0.0000
|
d)
the White heteroskedasticity
test
Heteroskedasticity Test: White |
|||
F-statistic |
11.78479
|
Prob. F(44,1397)
|
0.0000
|
Obs*R-squared |
390.3469
|
Prob. Chi-Square(44) |
0.0000
|
Scaled explained SS |
5567.550
|
Prob. Chi-Square(44) |
0.0000
|
Simulation of the decline in the household's financial security index
as the result of one-standard-deviation increase in the category of consumption
expenditure
The coefficient estimates on variables in
the financial security index regressions are used to carry out the simple
simulation of the type: “what if”. “Variables” mean the 2-year sum of weighted
distances between the household's expenses on each category of consumption
expenditure and the given prototype of consumption. The simulation is aimed at
estimating to what extent the household's financial security could decline as
the result of one-standard-deviation increase in the category of consumption
expenditure The exercise allows to rank the expenses on particular categories
of consumer goods and services taking into account their influence on financial
security of households. The calculation is based on the coefficient estimates
in the regression and it runs as follows: One-standard-deviation increase in
the category of consumption expenditure = parameter of variable × mean standard deviation of a variable. The
percentage decline in financial security is calculated under the assumption
that the value of a chosen variable increases by one standard deviation,
keeping remaining variables in the regression constant at their previous levels
for a given household. The percentage decline in financial security due to the
increase in a given variable is the average ratio of one-standard-deviation
increase in this variable (it means the increase in a given category of
consumption expenditure) to existing level of the household's financial
security index (the sum of the declines in household's financial security index
= 100%). The results of simulation are
presented in Tables 8–11.
Results of the research
The significance of variables in the regressions
The estimations of financial security
regressions show that status-oriented consumption (reflected by the distances
of the household's consumption expenditure from the consumption prototypes) is
a statistically significant cause that threatens financial security of both
groups of households: the rich and the poor (see Tables
4–7 ).
Purchasing for display keeps its dominant influence on financial insecurity
when several control variables have been included in the regressions. Among
these controls only income has been statistically significant in both periods,
while consumer loan burden contributed to financial insecurity a little bit
only in the first period, 2005–2006, and it was replaced in 2008–2009 by the
educational level with reference to the insecure poor (higher educational level
higher financial security) and by the place of permanent residence considering
the insecure rich (larger town as a place of residence higher financial
security). Other controls like, age, the family size, a source of income (hired
work or self-employment), housing loan burden were statistically insignificant
in both periods.
Table 8:
The results of simulation for “the insecure
POOR”, 2005–2006 and 2008–2009 – the decline in the household's financial
security index resulted from one-standard-deviation increase in the category of
consumption expenditure (the sum of the declines in household's financial
security index = 100%).
a
The POOR-households with equivalent
after-tax income per month lower than 150% of equivalent social minimum per
month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The average of 10 highest monthly
equivalent expenses across members of the poor/the rich
Table
9:
The results of simulation for “the
insecure RICH”, 2005–2006 and 2008–2009 – the decline in the household's
financial security index as a result of one-standard-deviation increase in a
category of consumption expenditure (the sum of the declines in household's
financial security index = 100%).
a
The RICH – households with equivalent
after-tax income per month higher than 150% of equivalent social minimum per
month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The average of 10 highest monthly
equivalent expenses across members of the poor/the rich.
Table 10:
The results of simulation for “the
insecure POOR” and for “the insecure RICH”, 2005–2006 – the decline in the
household's financial security index as a result of one-standard-deviation
increase in a category of consumption expenditure (the sum of the declines in
household's financial security index = 100%).
The bold value means the highest decline
in the household's financial security index as a result of
one-standard-deviation increase in a given category of consumption expenditure.
a
The POOR/RICH – households with equivalent
after-tax income per month lower/higher than 150% of equivalent social minimum
per month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The average of 10 highest monthly
equivalent expenses across members of the poor/the rich.
Table 11:
The results of simulation for “the
insecure POOR” and for “the insecure RICH”, 2008–2009 – the decline in the
household's financial security index as a result of one-standard-deviation
increase in a category of consumption expenditure (the sum of the declines in
household's financial security index = 100%).
The bold value means the highest decline
in the household's financial security index as a result of
one-standard-deviation increase in a given category of consumption expenditure.
a
The POOR/RICH – households with equivalent
after-tax income per month lower/higher than 150% of equivalent social minimum
per month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The average of 10 highest monthly
equivalent expenses across members of the poor/the rich.
The results of simulation
Tables 8–11 present the simulation results, it means the decline in the household's financial security index (or the increase in financial insecurity) resulted from one-standard-deviation increase in the category of consumption expenditure (the sum of the declines in the household's financial security index = 100%) for two groups, the insecure poor and the insecure rich, as well as in two periods: 2005–2006 and 2008–2009. These results allow:
- to rank expenditures on the consumption categories
taking into account their influences on households’ financial insecurity;
- to show the changes in the relevance of consumption
expenditures on particular categories over time;
- to find out the similarities and differences between
the insecure poor and the insecure rich;
- to reveal the dominant prototypes of consumption;
- to point at status goods.
Contribution of consumption expenses
to households’ financial insecurity
Tables 12 and 13 show the rankings of consumption
expenses considering their influences on households’ financial insecurity.
With reference to
financial insecurity experienced by the rich, expenses on the same categories
of consumer goods and services – (1) transport, (2) housing and (3) furnishings
plus household equipment–have three first ranks both in 2005–2006 as well as
2008–2009 (see Table 12 ). Expenses on these
goods and services have generated to largest extent financial insecurity among
members of the insecure rich group.
There
are two visible changes in the ranking over time. First, expenses on some
categories completely lost their influences on financial insecurity of the rich
in 2008–2009: first of all expenses on recreation and culture which had
relatively high, fourth rank in 2005–2006, moreover, expenses on education and
communication. Second, expenses on clothing and footwear as well as on food,
what is specially interesting considering the rich,
improved their ranks in 2008–2009, it means that expenses on these goods much
stronger generated financial insecurity of the rich than three years earlier.
The
relevance of expenses on particular categories of consumption expenditure for financial
insecurity experienced by the poor is better recognized in 2008–2009 than
2005–2006 (see Table 13 ). The differences in
the influences of expenses that have two first ranks in 2005–2006 are rather
small. In 2008–2009 the ranking became transparent. Expenses on (1) transport,
(2) housing, and (3) furnishings plus household equipment, were responsible for
financial insecurity across members of the poor group in 2008–2009. The
influence of expenses on recreation and culture has become much smaller (the
most visible change over time), while expenses on communication, education and
hygiene completely lost their impact on financial insecurity of the poor in
2008–2009.
Table 12: Ranking of consumption
expenses considering their influences on households’ financial insecurity – the
insecure RICH, 2005–2006 and 2008–2009.
Source: Table 9.
a The
RICH – households with equivalent after-tax income per month higher than 150%
of equivalent social minimum per month, 1635PLN ≈
409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b The average of 10 highest monthly
equivalent expenses across members of the poor/the rich.
Table 13: Ranking of consumption
expenses considering their influences on households’ financial insecurity – the
insecure POOR, 2005–2006 and 2008–2009.
Source: Table 8.
a The
POOR-households with equivalent after-tax income per month lower than 150% of
equivalent social minimum per month, 1635PLN ≈ 409EUR
in 2006 and 1844PLN ≈ 461EUR in 2009.
b The average of 10 highest expenses
across members of the poor/the poor.
The similarities and differences
between the insecure poor and the insecure rich
Tables 14 and 15 allow to compare the relevance of
the categories of consumption expenditure for financial insecurity between the
poor and the rich. There are four main similarities The most visible one is
that the same categories of consumer goods and services have the first three
ranks for both groups of households in both periods. There are expenses on: (1)
transport, (2) housing, and (3) furnishings plus household equipment.
Furthermore, the impact of expenses on recreation and culture, which generated
to considerable extent financial insecurity of both groups in 2005–2006, became
much weaker (for the poor) or statistically insignificant (for the poor) in
2008–2009. Expenses on clothing and footwear improved its rank in 2008–2009. In
general, there is some convergence in the influence of consumer goods and
services categories on financial insecurity of both groups. In 2005–2006 only the
first three ranks were occupied by the same categories while in 2008–2009 the
rankings were similar up to the sixth position.
There are some
differences. First, expenses on larger number of consumer goods and services
categories have generated financial insecurity of the poor than of the rich.
Second, expenses on such categories like: alcoholic beverages, tobacco;
restaurants and hotels; hygiene, were statistically insignificant in explaining
financial insecurity of the rich in both periods while they influenced
financial position of the poor at least in one period. Third, expenses on food
kept its position in the ranking for the insecure poor in both periods while
the influence of food expenses on financial insecurity of the rich increased in
2008–2009 in comparison to 2005–2006.
The dominant prototypes of consumption
Display consumption is influenced by
Veblen, snob and bandwagon effects, ( Liebenstein, 1950 ). Veblen effects are recognized when individuals
use product price as a means of ostentatiously displaying wealth; snob effects
stimulate consumers to buy an item because of its relative scarcity value; and
bandwagon effects intend people to purchase goods and services in order to be
identified with a particular social group. Cultural traditions and social
values have always shaped the pattern of status-directed consumption ( Mason,
1993 ).
Display consumption, however, is now heavily influenced by multinational companies
and television that create “international” culture and commercially-sponsored
value systems.
The findings suggest some patterns of
consumption. In the period of 2005–2006 mean expenditure of the rich was the dominant
prototype of consumption for the insecure poor (see Table
9 ).
There were only three exceptions to it: the highest expenses of the rich for
recreation, the highest expenses of the poor for transport and the mean expenses
of the poor for health. In general, consumer behavior of the insecure poor was
shaped by liking for the rich and the need to create the impression of
attachment to the group with higher consumption. Only prototype of expenses on
transport reveals the need to improve the household's self-image by having
consumption at the highest level in own group.
In 2008–2009 after four years of dynamic
growth and the increases in incomes of all groups, the highest expenditure of
the poor became the prototype of consumption for the insecure poor (it is worth
to remember at this moment that the highest expenditure of the poor is much
higher than mean expenditure of the rich). Consumer behavior of the insecure
poor was influenced by the need to improve the household's self-image by
approaching own consumption to the highest level recognized as possible to meet
by the poor.
Consumer behavior of the insecure rich has
been shaped mostly by the need to be distinguishable inside of own income-group
by having consumption at higher level than the own-group average. The dominant
prototype of consumption is mean expenditure of the rich in both periods.
Liking for the highest expenses has been shown with reference to a few
categories of goods and services.
Table
14:
Ranking of consumption expenses
considering their influences on households’ financial insecurity – the insecure
RICH and the insecure POOR, 2005–2006.
Source: Table 10.
Rank |
2005–2006 |
|||
Relevance of expenses made by the poor and the rich for their
financial insecurity |
||||
“The
insecure POOR” a |
“The
insecure RICH” a |
|||
Category of consumption expenditure |
Prototype of consumption |
Category of consumption expenditure |
Prototype of consumption |
|
1
|
a) Housing, water, electricity, gas and other fuels |
a) Mean expenditure of the rich |
Transport |
Highest expenditure of the poor |
2
|
a) Furnishings, household equipment and routine
maintenance of the house |
a) Mean expenditure of the rich |
Housing, water, electricity, gas and other
fuels |
Mean expenditure of the rich |
3
|
Food and non-alcoholic beverages |
Mean expenditure of the rich |
Furnishings, household equipment and routine
maintenance of the house |
Highest expenditure of the rich |
4
|
Clothing and footwear |
Mean expenditure of the rich |
Recreation and culture |
Highest expenditure of the rich |
5
|
Communication |
Mean expenditure of the rich |
Health |
Mean expenditure of the rich |
6
|
a) Health |
a) Mean expenditure of the poor |
a) Clothing and footwear |
a) Highest expenditure of the rich |
7
|
a) Restaurants and hotels |
a) Mean expenditure of the rich |
Food and non-alcoholic beverages |
Mean expenditure of the rich |
8
|
|
|
Communication |
Mean expenditure of the rich |
9
|
|
|
Alcoholic beverages, tobacco
insignificant |
–
|
10
|
|
|
Restaurants
and hotels insignificant
|
–
|
11
|
|
|
Hygiene insignificant |
–
|
a
The
POOR/RICH – households with equivalent after-tax income per month lower/higher than
150% of equivalent social minimum per month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The
average of 10 highest monthly equivalent expenses across members of the
poor/the rich.
Table
15:
Ranking of consumption expenses
considering their influences on households’ financial insecurity –the insecure
RICH and the insecure POOR, 2008–2009.
Source: Table 11.
Rank |
2008–2009 |
|||
Relevance of expenses made by the poor and the rich for their
financial insecurity |
||||
“The
insecure POOR” a |
“The
insecure RICH” a |
|||
Category of consumption expenditure |
Prototype of consumption |
Category of consumption expenditure |
Prototype of consumption |
|
1
|
Transport |
Highest expenditure of the rich b
|
Transport |
Highest expenditure of the rich |
2
|
Housing, water, electricity, gas and other
fuels |
Highest expenditure of the poor b
|
Housing, water, electricity, gas and other
fuels |
Highest expenditure of the poor |
3
|
Furnishings, household equipment and routine
maintenance of the house |
Mean expenditure of the poor |
Furnishings, household equipment and routine
maintenance of the house |
Mean expenditure of the rich |
4
|
Clothing and footwear |
Highest expenditure of the poor |
Clothing and footwear |
Mean expenditure of the rich |
5
|
Food and non-alcoholic beverages |
Highest expenditure of the poor |
Food and non-alcoholic beverages |
Mean expenditure of the rich |
6
|
a) Health |
a) Mean expenditure of the poor |
Health |
Mean expenditure of the rich |
7
|
Restaurants
and hotels |
Highest expenditure of the poor |
Education Insignificant |
–
|
8
|
Recreation and culture |
Highest expenditure of the poor |
Communication insignificant |
–
|
9
|
Communication insignificant |
–
|
Alcoholic beverages, tobacco
insignificant |
–
|
10
|
Education insignificant |
–
|
Restaurants
and hotels insignificant
|
–
|
11
|
Hygiene insignificant |
–
|
Hygiene insignificant |
–
|
a
The
POOR/RICH – households with equivalent after-tax income per month lower/higher than
150% of equivalent social minimum per month, 1635PLN ≈ 409EUR in 2006 and 1844PLN ≈ 461EUR in 2009.
b
The
average of 10 highest monthly equivalent expenses across members of the
poor/the rich.
The tendency in the dominant prototype of
consumption toward higher and higher expenses can strongly deepen financial
insecurity of the poor if the second wave of financial crisis visibly declines
economic activity in Poland. The question is still open how fast the insecure
poor as well as the insecure rich will able to change their consumer behavior
in a response to slower growth. When 50% households has not be able to generate
savings over two years, job loss could threaten standards of life.
The status goods
The findings point at a car as the main status
good accepted both by the poor and the rich. The desire of having a car has
intended many households to acquire expenditure beyond their means. Spending on
a car has been seen by conspicuous consumers as means of attaining or
maintaining their social status. A car plays its role, as a status good, very
well because it is a good consumed publicly and it can be seen and evaluated by
“relevant others”.
Housing is the second symbol of status-oriented
consumption. The size of a home or flat, its location, the standard of
facilities matter for improving the household's image. People want to live like
the reference group enjoying the highest spending on housing.
A trend toward the higher expenditure on
cars and housing results in the inability to save funds by quite considerable
fraction of households in both groups: the rich and the poor.
Furnishings and household equipment are
the third status good. What is interesting that the prototypes of consumption
for both the rich and the poor have shifted from the highest expenses to mean
expenses inside own income-group. Probably, the character of these goods is
responsible for such a change. Furnishings and household equipment are consumed
privately, and it is enough to be better than friends and relatives to be
distinguishable. It is not necessary to compare themselves to people spending a
lot on these goods.
The research has not been aimed at
distinguishing Veblen, snob and bandwagon effects. It is difficult to evaluate
causes for which clothing and footwear is the fourth status good for both the
rich and the poor. Designer clothes can be bought for Veblen and snob motives
as well as for bandwagon effects. However, the relatively high position of
clothes in status consumption may signal the growing importance of personal
display. Probably the shift of food to the fifth position in the ranking of
status goods for the rich suggests also expressing conspicuous consumption to
larger extent through personal lifestyle. But on the other side the opposite
argument can be drawn from the fact that recreation has lost its relevance, as
a status good, when prices of touristic services have gone visibly down as a
typical Veblen effect predicts.
It seems that up to now in Poland display
consumption is expressed through conspicuous expenditures on cars and homes
rather than through style and taste. In this sense consumer behavior in Poland
is more similar to conspicuous consumption in the United States than in France,
for example.
Poland shares the consumption patterns
with other transition countries in Europe and Central Asia.
Consumption patterns in Central and
Eastern European Countries (CEECs) have been shaped by two factors:
1)
bandwagon purchasing, driven by the
pervasive influence of multinational corporations and of global communication
networks – as a consequence interest in both conspicuous consumption and in
bandwagon effects has created a substantial demand for snob products among
those consumers for whom status consumption has been used as an expression of
individualism and personal distinction. ( Mason,
1993)
2)
the heritage from the Communist rule – it
is characterized by “the economy of permanent shortage” – household consumption
was very restricted and created an enormous hunger for goods and western
lifestyle ( Fammler, 2011, p. 19).
To reach west European economic wealth has
been a major policy goal in all CEE countries since their shift to a market
economy. Therefore the hunger for consumer goods and western lifestyle at CEEC
is easy to understand.
Mroz (2010, p. 14) emphasizes on the pervasive influence of
multinational corporations and of global communication networks: “ After decades of ascetic consumption, the Polish consumers will not be
easily persuaded to exercise self-restraint, the more so as the world of
industry, commerce, media and advertisement sends them compelling signals with
enticement to increased consumption ”.
The structure of household expenditure has
also changed and adapted to the western pattern: when the total available
household budget is growing, the percentage spent on satisfying basic needs is decreasing.
More and more money is spent for transport, recreation and housing. This stands
for all CEE region, with a slightly different time scale and break down of
household structure ( Fammler, 2011, Graph 3, p. 9). Zilahy and Zsóka (2012,
Figure 15, p. 14) call attention to an interesting indicator
of consumption, the purchasing and registration of private cars. The recession
(2008–2009) spitted both CEE and other, more developed countries: Hungary,
Estonia and Slovenia (CEE) as well as Spain, Finland and the U.K. (developed)
showed a marked decrease in registration, while other countries (e.g. Czech
Republic, Slovak Republic, Austria and France) were able to grow in this
respect.
The changes in consumption patterns
observed in Southeast Europe, Caucasus and Central Asia between 1995–2005 were
similar to the changes in CEECs (report, jointly prepared by the United Nations
Environment Programme ( UNEP ) and the European Environment Agency
(EEA), 2007) The additional income was used increasingly on housing and
utilities, transport and communication, home appliances and recreation.
Household consumption patterns varied
widely across countries in the period of 1995–2005. In the lower-income
countries of Central Asia and the Caucasus, greater proportions of household
expenditures were set aside for food. This was most pronounced in Tajikistan
and Armenia where food represented 64% and 57% of average household expenditures,
respectively. In Tajikistan, despite increases in incomes since the mid-1990s,
there remained little surplus for non-essentials in the average household. At
the other extreme, Croatia, which had the highest household expenditure per
capita across the regions, used the smallest proportion on food (33%) and the
highest on transport and communication and recreation, culture and healthcare.
Since the 1990s, European preoccupation
with the environment and with the need to protect and preserve natural resources
has been still continued to grow. It has become increasingly counterproductive
to indulge in wasteful expenditure, particularly when the waste had strong
environmental overtones and has been seen to diminish the overall quality of
life.
Nowadays financial incentives for “smart
consumption” are still largely missing in CEE countries. Environmental goods
and principles, e.g. energy efficiency of housing, have been only introduced to
the market after the energy shortage and the stricter EU legislative frame made
members states act. However, as soon as the cost burden lowers, people are
ready to consume much more environmental friendly goods ( Fammler, 2011, p. 19).
Conclusions
The findings offer
empirical evidence for the relevance of consumer behavior for financial
security of households in Poland. Considerable part of households expresses the
identity through conspicuous consumption. The need to be distinguishable inside
own income-group shapes the consumption prototype of the insecure rich while a
desire to improve self-image by approaching own consumption to the highest
expenses seems to be the dominant consumer behavior rather for the insecure
poor.
Both
groups of households accept the same ranking of status goods: a car on the
first position, next homes (housing and equipment) and clothe on the third
place. Status-oriented consumption creates life beyond means and pushes even
relatively rich households toward financial insecurity. The budget constrain is
beaten by the need to improve social status.
Appendix
Explanation
for the variables names in tables in Appendix:
BEZ06;
BEZ09 – financial security index in 2006 and 2009, respectively
The
name of each main independent variable covers:
The
symbols of consumption expenditure categories are as follows:
·
ZYW – Food and non-alcoholic beverages
·
AL – Alcoholic beverages, tobacco
·
OD – Clothing and footwear
·
UZM – Housing, water, electricity, gas and other fuels
·
WYM – Furnishings, household equipment and routine maintenance of the
house
·
ZD – health
·
TR – Transport
·
LA – Communication
·
RE – Recreation and culture
·
ED – Education
·
RH – Restaurants and hotels
·
HI – Hygiene
The
symbols of the consumption prototypes are as follows:
Mean expenditure of the poor |
Highest expenditure of the poor |
Mean expenditure of the rich |
Highest expenditure of the rich |
PM
|
PH
|
RM
|
RH
|
Names
of controls are as follows:
·
DOCHEK56; DOCHEK89 – a sum of equivalent income in two years (in 2005
and 2006; 2008 and 2009, respectively)
·
WO – district
·
KRE – consumer loan of burden
·
WYKSZ – level of education
·
KLM – place of permanent residence
Dependent variable: BEZ06 |
Method: least squares
|
Sample: 1 4374 |
Included observations: 4374 |
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−30.35643
|
0.586007
|
−51.80213
|
0.0000
|
ZYWRM
|
−3.625362
|
0.156623
|
−23.14709
|
0.0000
|
ALRM
|
−5.444067
|
0.449334
|
−12.11585
|
0.0000
|
ODRM
|
−7.030983
|
0.458682
|
−15.32867
|
0.0000
|
UZMRM
|
−5.884140
|
0.122813
|
−47.91138
|
0.0000
|
WYMRM
|
−9.617382
|
0.329135
|
−29.22020
|
0.0000
|
ZDPM
|
−2.853154
|
0.227384
|
−12.54773
|
0.0000
|
TRPH
|
−126.0093
|
2.570609
|
−49.01923
|
0.0000
|
LARM
|
−8.070014
|
0.633479
|
−12.73921
|
0.0000
|
RERH
|
−138.0362
|
5.330992
|
−25.89315
|
0.0000
|
EDRH
|
−206.6208
|
18.00510
|
−11.47568
|
0.0000
|
RHRM
|
−6.236869
|
0.624998
|
−9.979023
|
0.0000
|
HIPH
|
−42.01250
|
4.720310
|
−8.900369
|
0.0000
|
LOG(DOCHEK56)
|
4.830225
|
0.086024
|
56.14944
|
0.0000
|
WO
|
0.006237
|
0.002349
|
2.655484
|
0.0079
|
KRE
|
−0.135911
|
0.021658
|
−6.275169
|
0.0000
|
R-squared |
0.688618
|
Mean
dependent var |
−0.482501
|
Adjusted R-squared |
0.687546
|
S.D.
dependent var |
2.540544
|
S.E.
of regression |
1.420102
|
Akaike info criterion
|
3.542986
|
Sum
squared resid |
8788.733
|
Schwarz criterion |
3.566336
|
Log
likelihood |
−7732.510
|
Hannan-Quinn criter. |
3.551225
|
F-statistic |
642.5120
|
Durbin-Watson stat |
1.391143
|
Prob(F-statistic) |
0.000000
|
|
|
Dependent variable: BEZ06 |
Method: least squares
|
Date:
07/28/16 Time: 11:47 |
Sample: 1 4374 |
Included observations: 4374 |
HAC standard errors and covariance (Bartlett kernel,
Newey-West fixed bandwidth = 10.0000)
|
Variable
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−30.35643
|
1.165260
|
−26.05121
|
0.0000
|
ZYWRM
|
−3.625362
|
0.197115
|
−18.39213
|
0.0000
|
ALRM
|
−5.444067
|
0.610208
|
−8.921654
|
0.0000
|
ODRM
|
−7.030983
|
0.656322
|
−10.71271
|
0.0000
|
UZMRM
|
−5.884140
|
0.330203
|
−17.81976
|
0.0000
|
WYMRM
|
−9.617382
|
1.274751
|
−7.544515
|
0.0000
|
ZDPM
|
−2.853154
|
0.275811
|
−10.34459
|
0.0000
|
TRPH
|
−126.0093
|
12.99363
|
−9.697777
|
0.0000
|
LARM
|
−8.070014
|
0.947281
|
−8.519133
|
0.0000
|
RERH
|
−138.0362
|
7.068920
|
−19.52720
|
0.0000
|
EDRH
|
−206.6208
|
27.30312
|
−7.567663
|
0.0000
|
RHRM
|
−6.236869
|
1.806326
|
−3.452793
|
0.0006
|
HIPH
|
−42.01250
|
6.078170
|
−6.912032
|
0.0000
|
LOG(DOCHEK56)
|
4.830225
|
0.183549
|
26.31570
|
0.0000
|
WO
|
0.006237
|
0.002212
|
2.819596
|
0.0048
|
KRE
|
−0.135911
|
0.041128
|
−3.304574
|
0.0010
|
R-squared |
0.688618
|
Mean
dependent var |
−0.482501
|
Adjusted R-squared |
0.687546
|
S.D.
dependent var |
2.540544
|
S.E.
of regression |
1.420102
|
Akaike info criterion
|
3.542986
|
Sum
squared resid |
8788.733
|
Schwarz criterion |
3.566336
|
Log
likelihood |
−7732.510
|
Hannan-Quinn criter. |
3.551225
|
F-statistic |
642.5120
|
Durbin-Watson stat |
1.391143
|
Prob(F-statistic) |
0.000000
|
Wald F-statistic |
93.69255
|
Prob(Wald F-statistic)
|
0.000000
|
|
|
Dependent variable: BEZ06 |
Method: least squares
|
Sample: 1 1085 |
Included observations: 1085 |
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−40.78711
|
4.063164
|
−10.03826
|
0.0000
|
ZYWRM
|
−3.235094
|
0.908642
|
−3.560362
|
0.0004
|
ODRH
|
−35.28852
|
7.257759
|
−4.862179
|
0.0000
|
UZMRM
|
−5.045496
|
0.322711
|
−15.63473
|
0.0000
|
WYMRH
|
−93.30457
|
9.107039
|
−10.24532
|
0.0000
|
ZDRM
|
−5.411047
|
0.886550
|
−6.103489
|
0.0000
|
TRPH
|
−48.48994
|
1.811191
|
−26.77241
|
0.0000
|
LARM
|
−5.786614
|
1.827709
|
−3.166048
|
0.0016
|
RERH
|
−57.43489
|
6.367631
|
−9.019822
|
0.0000
|
EDRM
|
−3.502311
|
0.737846
|
−4.746672
|
0.0000
|
LOG(DOCHEK56)
|
5.711505
|
0.517919
|
11.02779
|
0.0000
|
KRE
|
−0.124274
|
0.028846
|
−4.308237
|
0.0000
|
R-squared |
0.546592
|
Mean
dependent var |
−2.116177
|
Adjusted R-squared |
0.541944
|
S.D.
dependent var |
5.352678
|
S.E.
of regression |
3.622685
|
Akaike info criterion
|
5.423307
|
Sum
squared resid |
14,081.89
|
Schwarz criterion |
5.478488
|
Log
likelihood |
−2930.144
|
Hannan-Quinn criter. |
5.444196
|
F-statistic |
117.5928
|
Durbin-Watson stat |
1.016838
|
Prob(F-statistic) |
0.000000
|
|
|
Dependent variable: BEZ06 |
Method: least squares
|
Date:
07/28/16 Time: 12:02 |
Sample: 1 1085 |
Included observations: 1085 |
HAC standard errors and covariance (Bartlett kernel,
Newey-West fixed bandwidth = 7.0000)
|
Variable
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−40.78711
|
5.134985
|
−7.942986
|
0.0000
|
ZYWRM
|
−3.235094
|
0.829615
|
−3.899511
|
0.0001
|
ODRH
|
−35.28852
|
10.46667
|
−3.371515
|
0.0008
|
UZMRM
|
−5.045496
|
0.654937
|
−7.703784
|
0.0000
|
WYMRH
|
−93.30457
|
15.62188
|
−5.972684
|
0.0000
|
ZDRM
|
−5.411047
|
1.248609
|
−4.333661
|
0.0000
|
TRPH
|
−48.48994
|
8.990782
|
−5.393295
|
0.0000
|
LARM
|
−5.786614
|
1.780349
|
−3.250269
|
0.0012
|
RERH
|
−57.43489
|
8.646987
|
−6.642186
|
0.0000
|
EDRM |
−3.502311 |
1.983627 |
−1.765610 |
0.0777 |
LOG(DOCHEK56)
|
5.711505
|
0.665573
|
8.581333
|
0.0000
|
KRE
|
−0.124274
|
0.009471
|
−13.12132
|
0.0000
|
R-squared |
0.546592
|
Mean
dependent var |
−2.116177
|
Adjusted R-squared |
0.541944
|
S.D.
dependent var |
5.352678
|
S.E.
of regression |
3.622685
|
Akaike info criterion
|
5.423307
|
Sum
squared resid |
14,081.89
|
Schwarz criterion |
5.478488
|
Log
likelihood |
−2930.144
|
Hannan-Quinn criter. |
5.444196
|
F-statistic |
117.5928
|
Durbin-Watson stat |
1.016838
|
Prob(F-statistic) |
0.000000
|
Wald F-statistic |
34.89781
|
Prob(Wald F-statistic)
|
0.000000
|
|
|
Only one coefficient estimate is significant at the 10% level (in bold).
Dependent variable: BEZ09 |
Method: least squares
|
Sample: 1 3092 |
Included observations: 3092 |
Variable
|
Coefficient |
Std. error |
t-Statistic
|
Prob. |
C
|
−13.09247
|
0.687612
|
−19.04050
|
0.0000
|
ZYWPH
|
−4.158351
|
0.395542
|
−10.51305
|
0.0000
|
ALPH
|
−23.39098
|
2.894077
|
−8.082361
|
0.0000
|
ODPH
|
−33.77853
|
2.290247
|
−14.74886
|
0.0000
|
UZMPH
|
−23.52979
|
0.743453
|
−31.64933
|
0.0000
|
WYMPM
|
−2.122668
|
0.107001
|
−19.83791
|
0.0000
|
ZDPM
|
−2.234426
|
0.264261
|
−8.455368
|
0.0000
|
TRRH
|
−182.4880
|
4.332439
|
−42.12131
|
0.0000
|
REPH
|
−12.27672
|
2.369590
|
−5.180947
|
0.0000
|
RHPH
|
−92.42022
|
14.27268
|
−6.475322
|
0.0000
|
LOG(DOCHEK89)
|
2.027749
|
0.091991
|
22.04302
|
0.0000
|
WYKSZ
|
0.054766
|
0.015515
|
3.529880
|
0.0004
|
R-squared |
0.566897
|
Mean
dependent var |
−0.440464
|
Adjusted R-squared |
0.565351
|
S.D.
dependent var |
1.935924
|
S.E.
of regression |
1.276315
|
Akaike info criterion
|
3.329704
|
Sum
squared resid |
5017.258
|
Schwarz criterion |
3.353132
|
Log
likelihood |
−5135.723
|
Hannan-Quinn criter. |
3.338118
|
F-statistic |
366.4981
|
Durbin-Watson stat |
1.190560
|
Prob(F-statistic) |
0.000000
|
|
|
Dependent variable: BEZ09 |
Method: least squares
|
Date:
07/28/16 Time: 12:12 |
Sample: 1 3092 |
Included observations: 3092 |
HAC standard errors and covariance (Bartlett kernel,
Newey-West fixed bandwidth = 9.0000)
|
Variable
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−13.09247
|
1.134965
|
−11.53557
|
0.0000
|
ZYWPH
|
−4.158351
|
0.609436
|
−6.823273
|
0.0000
|
ALPH
|
−23.39098
|
3.412402
|
−6.854695
|
0.0000
|
ODPH
|
−33.77853
|
4.610188
|
−7.326931
|
0.0000
|
UZMPH
|
−23.52979
|
2.675905
|
−8.793209
|
0.0000
|
WYMPM
|
−2.122668
|
0.324887
|
−6.533556
|
0.0000
|
ZDPM
|
−2.234426
|
0.382026
|
−5.848885
|
0.0000
|
TRRH
|
−182.4880
|
37.43383
|
−4.874950
|
0.0000
|
REPH
|
−12.27672
|
3.048266
|
−4.027444
|
0.0001
|
RHPH
|
−92.42022
|
23.98632
|
−3.853039
|
0.0001
|
LOG(DOCHEK89)
|
2.027749
|
0.179479
|
11.29795
|
0.0000
|
WYKSZ
|
0.054766
|
0.017040
|
3.214052
|
0.0013
|
R-squared |
0.566897
|
Mean
dependent var |
−0.440464
|
Adjusted R-squared |
0.565351
|
S.D.
dependent var |
1.935924
|
S.E.
of regression |
1.276315
|
Akaike info criterion
|
3.329704
|
Sum
squared resid |
5017.258
|
Schwarz criterion |
3.353132
|
Log
likelihood |
−5135.723
|
Hannan-Quinn criter. |
3.338118
|
F-statistic |
366.4981
|
Durbin-Watson stat |
1.190560
|
Prob(F-statistic) |
0.000000
|
Wald F-statistic |
30.41461
|
Prob(Wald F-statistic)
|
0.000000
|
|
|
Dependent variable: BEZ09 |
Method: least squares
|
Sample: 1 1442 |
Included observations: 1442 |
Variable
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−11.91631
|
2.367413
|
−5.033475
|
0.0000
|
ZYWPH
|
−4.654467
|
1.137328
|
−4.092459
|
0.0000
|
ODRM
|
−2.262381
|
0.403977
|
−5.600274
|
0.0000
|
UZMPH
|
−20.42455
|
1.288235
|
−15.85467
|
0.0000
|
WYMRM
|
−2.812936
|
0.249265
|
−11.28492
|
0.0000
|
ZDRM
|
−1.808704
|
0.492559
|
−3.672052
|
0.0002
|
TRRH
|
−89.79425
|
2.551026
|
−35.19927
|
0.0000
|
LOG(DOCHEK89)
|
1.693634
|
0.286955
|
5.902099
|
0.0000
|
KLM
|
0.128450
|
0.038001
|
3.380158
|
0.0007
|
R-squared |
0.560992
|
Mean
dependent var |
−1.526226
|
Adjusted R-squared |
0.558541
|
S.D.
dependent var |
3.898688
|
S.E.
of regression |
2.590382
|
Akaike info criterion
|
4.747709
|
Sum
squared resid |
9615.542
|
Schwarz criterion |
4.780625
|
Log
likelihood |
−3414.098
|
Hannan-Quinn criter. |
4.759996
|
F-statistic |
228.8967
|
Durbin-Watson stat |
1.248172
|
Prob(F-statistic) |
0.000000
|
|
|
Dependent variable: BEZ09 |
Method: least squares
|
Date:
07/28/16 Time: 12:21 |
Sample: 1 1442 |
Included observations: 1442 |
HAC standard errors and covariance (Bartlett kernel,
Newey-West fixed bandwidth = 8.0000)
|
Variable
|
Coefficient |
Std. Error |
t-Statistic
|
Prob. |
C
|
−11.91631
|
4.027146
|
−2.958997
|
0.0031
|
ZYWPH
|
−4.654467
|
1.472689
|
−3.160522
|
0.0016
|
ODRM
|
−2.262381
|
0.690814
|
−3.274948
|
0.0011
|
UZMPH
|
−20.42455
|
1.786031
|
−11.43572
|
0.0000
|
WYMRM
|
−2.812936
|
0.397395
|
−7.078431
|
0.0000
|
ZDRM
|
−1.808704
|
0.691530
|
−2.615509
|
0.0090
|
TRRH
|
−89.79425
|
14.60889
|
−6.146549
|
0.0000
|
LOG(DOCHEK89)
|
1.693634
|
0.503966
|
3.360615
|
0.0008
|
KLM
|
0.128450
|
0.041572
|
3.089798
|
0.0020
|
R-squared |
0.560992
|
Mean
dependent var |
−1.526226
|
Adjusted R-squared |
0.558541
|
S.D.
dependent var |
3.898688
|
S.E.
of regression |
2.590382
|
Akaike info criterion
|
4.747709
|
Sum
squared resid |
9615.542
|
Schwarz criterion |
4.780625
|
Log
likelihood |
−3414.098
|
Hannan-Quinn criter. |
4.759996
|
F-statistic |
228.8967
|
Durbin-Watson stat |
1.248172
|
Prob(F-statistic) |
0.000000
|
Wald F-statistic |
25.89485
|
Prob(Wald F-statistic)
|
0.000000
|
|
|
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Notes.
1 The paper is a part of the research project financed
by the National Centre for Science in Poland (DEC-2011/01/B/HS4/03239).
2 One approach to dealing with outliers is to use the
M-estimation which addresses dependent variable outliers where the value of the
dependent variable differs markedly from the regression model norm (large
residuals). However, this method has given worse values of skewness and
kurtosis for three regressions (POOR Insecure 2005–2006 – OLS (M-estimation):
skewness = −1.840114 (−2.273517) and
kurtosis = 28.67266 (29.66801); RICH
Insecure 2005–2006 – OLS (M-estimation): skewness = −1.445126 (−3.211370) and kurtosis = 14.56294 (19.31410); POOR Insecure 2008–2009 –
OLS (M-estimation): skewness = −2.357050
(−4.613211) and kurtosis = 67.34200
(73.60050). Only for RICH Insecure 2008–2009 the value of skewness is improved
but the value of kurtosis is worse – OLS (M-estimation): skewness = −2.008 (−0.738046) and kurtosis = 29.88561 (38.34048).
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