Diversity of LSE Students’ Consumer Behaviour
and its Potential Explanations
LSEGROUPS 2012 - Group 10:
Rajlakshmi De
Fan Jiang
Mirela Oana
Long Ying Tang
1
Acknowledgement:
The authors would like to thank Elizabeth Stubbins Bates for the constructive
comments and support, Dr Sally Stares for the technical feedback and
LSEGROUPS organisers for planning an undergraduate research opportunity
we have valued very much.
2
Abstract
A thorough understanding of consumer behaviour diversity is important both to
businesses and to academic inquiries into people’s decision-making. Past
literature has debated the correlations between regional influence and spending
habits and between gender and spending habits. However, there is insufficient
research relating these factors of regional culture and gender to university
students’ consumer behaviour. Using the student population at the London
School of Economics and Political Science (LSE), this paper addresses the
question of, “What variables affect LSE students’ consumer behavior?” After
hypothesising that there would be correlations between gender and expenditure
behaviour and between region and expenditure behaviour, the relationships
between these variables were analysed through multiple regressions. The
control variables were LSE degree type, relationship status, sources of funding,
type of accommodation, and an approximated total expenditure. Findings were
highly statistically significant for both relationships between gender and leisure
travel spending and between regional influence and food expenditure.
Keywords: consumer behaviour, expenditure diversity, LSE, gender, world
region.
3
1. Introduction
Consumers exhibit a variety of spending patterns, and this “consumer behaviour diversity” is a powerful
mechanism within both businesses and social sciences. Diversities of consumption choices are often
measured within businesses, where consumption decisions affect demand functions, revenue streams,
profit functions, and marketing techniques. Social sciences, economics and psychology measure
consumer expenditures as a way to understand individuals’ preferences or utility gains from consuming
different goods. Because of the difficulty of ascertaining individuals’ innate preferences, these disciplines
have used consumption decisions to reveal preferences. This research paper measures consumer
behaviour diversity with a different goal of understanding if categories of expenditure (dependent
variables) are correlated with factors of gender and the region where an individual has spent the most
time (independent variables), while controlling for other pertinent variables. The significance of this
research lies at the intersection of the approaches between businesses and social sciences: to find
differences between genders and world regions with regards to spending is important both to business
development and to furthering the understanding of individuals’ innate preferences.
The student population at the London School of Economics and Political Science (LSE) is diverse with
regards to gender and world region, and provides a population for measuring expenditure diversity. To
explore relationships among the independent, dependent and control variables, we asked the question,
“What variables affect LSE students’ consumer behavior?” Rather than formulating hypotheses for all
possible combinations of explanatory and response variables, we hypothesised broadly that there would
be correlations between gender, region, and expenditure categories. Our hypothesis also included the
control variables of relationship status, LSE degree type, funding sources, living arrangements, and
approximated total spending. The hypothesis was tested using data from surveys of 97 LSE students. F
Tests and T Tests were used to reduce the size of regressions, and finally, multiple regression analysis
was used to obtain two statistically significant findings. With regards to gender, women spent £700 more
than men on annual leisure travel, which corresponds to a significance level of α = 0.005. With regards to
region, individuals selecting Asia spent more on weekly food than individuals selecting Eastern Europe
4
or Western Europe, and the difference was statistically significant at a level of less than α = 0.001. These
statistically significant findings on gender and region were found only in certain categories of
expenditure.
2. Literature Review
2.1 Regional influences and consumer behaviour diversity
Culture binds consumers from the same region into similar lifestyles and cultural habits (Pietrykowshi,
2009:15); thus diversity in regional culture leads to diversity in consumer behaviour. This is consistent
with Anderson’s theory of “imagined community”, where economies are bound by consumers’ regional
culture. Xu et al. echoed Anderson’s theory by using the example of similar spending habits within the
Asian-American group (Xu et. al, 2004:95). Xu et al. further suggested that parental acculturation and
ethnic-friendship networks would influence one’s regional identity and consumption behaviour, while
Luna and Gupta emphasised that regional cultural rituals have limited individuals’ lifestyles and
consumption practices (Luna and Gupta, 2001:59). These scholarly publications have confirmed the
correlation between regional diversity and consumer behaviour diversity.
However, globalisation has potentially reduced the effect of regional culture on consumer behaviour.
Kvidal argued that the increasing level of global homogeneity has created a “global culture” (Kvidal,
2011:59). In this viewpoint, diversity in regional culture does not have an impact on consumer behaviour
diversity. However, the impact of globalisation on cultural and consumption homogenisation requires
further research.
2.2 Gender and consumer behaviour diversity
Gender, as a social phenomenon, also has an impact on consumer behaviour diversity (Alagöz and
Burucuoglu, 2011:94). There are two social characteristics associated with gender: gender identity
(masculinity and femininity) and gender role attitude (the financial provider or care giver) (Fischer and
Arnold, 1994:166).
5
Empirical findings show diversity in spending habits can be caused by differences in gender identity
(Firat, 1994: 205). Fisher and Gainer used the example of spending on organised sports to illustrate that
consumption behaviour is dependent on one’s gender identity (Fischer and Gainer, 1994:85). They
argued that men tend to dominate the more violent forms of organised sports such as American football,
while women tend to spend on organised sports that reflect femininity, such as dancing. This
demonstrates the correlation between one’s gender identity and consumption decisions. Alagöz and
Burucuoglu supported this argument and further denote masculine and feminine identities of men and
women as variables that determine consumers’ needs and wants (Alagöz and Burucuoglu, 2011:96) and
cause consumption behaviour diversity.
Furthermore, gender role attitudes contribute to consumption behaviour diversity. Pietrykowski directly
asserts that consumers are gendered agents (Pietrykowski, 2009:3). Nelson supports Pietrykowski and
further notes the traditional roles of men and women. Nelson emphasises the domestic roles of men as
“financial providers” and women as “shoppers” within a family (Nelson, 1993:3). This potentially
explains the findings that show women spend more (Ellwood and Shekar, 2008:192) and leads some
credence to gender role identity determining the consumption behaviour diversity.
However, a number of scholars opposed the above arguments because of variations within findings. Stern
suggested that gender identity is not bipolar in nature (Stern, 1988:85). Fischer and Arnold further
suggest that males could be feminine while females could be masculine (Fischer and Arnold, 1994:168).
There is also an increasing egalitarian element within gender role. As suggested by Buss and Schaninger,
historical events have changed the role of women in society (Buss and Schaninger, 1987:293). Costa also
suggested that an increasing domestic role for men (Costa, 1994:8). However, the correlation between
consumer behaviour diversity and the level of social equality between men and women is still open for
further research.
6
2.3 Diversity of university students’ consumer behaviour
University study incorporates an experience of living away from home for many students, who may
manage their own finances. Literature on university students’ spending decisions is mainly self-published
and not peer-reviewed. Among the scholarly resources, age can be an influence on spending habits
(Kapoor et al., 2007). The year of study may influence money management: seniors are better planners
than freshmen (Stollak et al., 2011).
Sources of income are also considered to affect consumer behaviour, but this has not been examined in a
student context. The United States Bureau of Labor Statistics has designed the Consumer Expenditure
Survey in which individuals indicate all sources of income received during the previous 12 months.
Using the same concept, we controlled for students’ sources of funding when analysing other variables’
relationships with spending behaviour.
Our study focuses on diversity with regards to gender, regional influence, and five control variables that
may shape consumers’ behaviour. Our research design adds details and demographic diversity to the
existing literature, which consists of, an unpublished student paper from the Philippines that depicts
student budgeting behaviour (Rose et al., 2008).
Although research has shown the relationships between the independent variable of either region or
gender and the dependent variable of consumer behaviour, an understanding about the diversity of factors
that shape university students’ consumer behaviour as a distinct group has not been developed in the
current literature. This research paper strives to address this missing link.
7
3. Methodology
3.1 Choice of variables and survey design
The first aspect of the research design was to define concepts that may be correlated with LSE students’
expenditure behaviour, leading to a list of 11 possible factors and 12 expenditure categories. In the
interests of feasibility, both within the survey design and the eventual analysis, we narrowed our lists to 6
factors and 6 expenditure categories. Using the themes within the literature review and factors we felt had
intuitive relevance, we limited the list to the factors of gender, world region, relationship status, LSE
degree type, funding sources, and living arrangements, and broad concepts like “world region” were
defined specifically in the survey.
For the expenditure variables, the focus was on categories that would elicit variation among students,
excluding standardised costs like tuition. We also avoided private expenditures like healthcare and
medication, because of the importance of not invading privacy and of obtaining informed consent. Our
final survey included the categories of food, transportation, clothing, housing, recreation, and leisure
travel. By conceptualising our survey design as “factors” and “expenditure categories,” we had the
orderly framework of explanatory, or independent, variables and response, or dependent, variables.
3.2 Ethical considerations
Two levels of ethical considerations were imperative to our project. Firstly, there were the general ethical
guidelines such as informed consent, avoiding deception, not harming participants, and not invading
privacy (Bryman, 2012: 135). These considerations permeated the survey phrasing and the physical
interactions with participants. It was important to phrase questions neutrally, and to make sure all
potential respondents—LSE students—are able to choose from our survey choices. Though there are not
many data points from regions such as Africa or South America, we included all regions on the survey to
avoid the methodological flaw of under-inclusiveness.
8
Secondly, due to the private nature of personal finance, we framed our research questions around
“expenditures,” which is less intrusive than “income” or “savings”. The more sensitive data on income
and savings would have been useful for analysis, but we recognised the ethical-analytical trade-off
present in the research question and chose to rely on specific expenditures instead. Even questions on
expenditure, however, may feel intrusive to some participants, and if a relationship between reticence on
matters of personal finance and expenditure behaviour exists, our analysis may be affected by nonresponse bias. We chose to use ranges of values for expenditure, to facilitate the participants’ survey
completion, another trade-off between not burdening participants and achieving more precise
correlations.
3.3 Reliability, replicability, and validity
In evaluating our research, we have identified potential threats to reliability, replicability, and validity.
Potential threats to reliability may include uneven price changes or time-dependence of LSE students’
preferences. There are no procedural threats to replicability because the survey design could be copied
and reused to test the diversity of LSE students’ consumer behaviour and its potential explanations at
another point in time by other researchers. However, because we were unable to rely purely on random
sampling, replication may expose sampling bias inherent in using tactics of convenience sampling and
snowballing to obtain participants.
The internal validity of our results is affected by any unaccounted variables that have causal relationships
on expenditure. We lowered this risk by controlling across the six independent variables. Understanding
LSE students’ consumer diversity was central to our research, but focusing on this specific population
greatly threatens the external validity of our results. Possible reticence towards disclosing personal
finances reappears in questions of ecological validity. A participant who is uncomfortable sharing data on
personal finances may provide answers that are not reflective of true expenditures. Though we have
striven to not be intrusive, the unnaturalness of having to answer a survey may mean that findings have
limited ecological validity (Bryman, 2012: 48).
9
4. Data Analysis and Discussion
With regards to sample sizes, there were not enough participants from the category of “Other Region,”
which included Africa, Australia, North America and South America. For the subsequent analyses, only
the regions with substantive data—Asia, Western Europe, and Eastern Europe—were considered.
Multiple Regressions
Multiple linear regression analysis on SPSS was used to find the relationships between gender, region,
and expenditure behaviour. Four sets of exogenous variables are measured and act as control variables in
the regressions. However, statistically, the inclusion of many less relevant variables make regression
analysis inefficient and lead to high standard errors and low significance levels. Therefore the analysis
required a method to reduce the regressions to only the most pertinent variables. We tested the
significance of each set of control variables on different categories of expenditure using the F Test and T
Test, allowing a selection of control variables for each expenditure category. Also, the sum of other
categories of spending is added as control variable as a proxy for the total spending. The limitation of
using a proxy is that the total spending power cannot be perfectly controlled in the regressions.
Gender
With regards to gender, women’s average monthly total expenditure on the six categories is £1348 which
is £200 higher than men (Appendix 2, Table 1). This is consistent to existing empirical findings (Ellwood
and Shekar, 2008:192). As our study is based on a group of students and most of the respondents are not
married, the argument of women’s role as family “shopper” may not apply (Nelson, 1993:3). A
regression for women respondents is run to explore the potential factors that influence women’s spending
behaviour. It shows that female students spend £289 more each month if they are in a relationship
(significance 10%) while no significant influence of relationship status is found on men (Appendix 3,
table 6&7). This reflects that women’s care giver role (Pietrykowski, 2009:3) may apply by taking care of
their partners which contributes to this difference.
10
In the regressions, women are spending more on housing (by £74), clothing (by £17) and recreational
activities (by £9) per month, but these relationships are statistically insignificant of more than 50%
(Appendix 3, table2, 3, and 5). Gender has a significant influence only on expenditure in leisure travel.
According to the regression, women spend £700 more than men on annual leisure travel, which
corresponds to a very high significance level of 0.5% (Appendix 3, table 4). This unexpected result may
affect by inadequate sample size or potential sample bias using non-random sampling, but with such a
high significance level, it still suggests that LSE’s female students are more willing to spend on leisure
travel than male students.
Collins showed that Australian men tend to travel more often for business, but women travelled more
often for leisure (Collins, 2002). Further theoretical and statistical research is needed on the relationship
between gender and leisure travelling. Our finding may also be explained by a specific LSE culture or
feature, which cannot be generalised to other populations.
Region
In the data, the spending of Asian students is very high with average total spending on the six categories
of £1341.3 per month outweighing £1118.4 for West Europe and £1235.1 for East Europe (Appendix 2,
table 2). Specifically, students from Asia spend £43 more weekly on food compared with Western
European students; such difference was statistically very significant at a level of 0.1% (Appendix 3, table
1). It is also found that Asian students spent £683 more than Eastern European students on leisure travel
at a significance level of 11% (Appendix 3, table 4).
Asians’ high spending could reflect the strong spending power of their countries. Despite the financial
crisis, Asian economies remain in boom and have comparatively higher growth rates than Europeans.
GDP growth rate in Asia is averaged 7% in both 2011 and 2012 after reaching of 8.3% in 2010 according
to IMF. The high growth rates make people perceive themselves to be richer and this wealth effect cause
increase in consumer behaviour (Darby, 1987:883).
11
The theory of “imagined community” states that the life-style in different regions can cause regional
similarity in respect of consumer behaviour (Anderson, 1991:8). The differences of spending on food
between Asians and Europeans can be interpreted by diversities in culture. The dining experience in Asia
places great value on social and cultural reasons, rather than economic reasons (Yan, 2008:505).
Of students selecting Asia, 72.7% rely on only family support as their source of funding compared to
15.6% for West Europe and 25% for East Europe (Appendix2, table 3). Having family support as the
only source of funding sets a threshold of family income to these students. According to LSE statistics,
the tuition fees and expected living expenses are around £22,500 per year for international students.
However, using China as an example, the annual family disposable income was £17,200 for the 95th
percentile and £55,900 for the 99th percentile (Chinese Family Financial Study 2012). It shows that less
than 5% of Chinese families can afford to support a child studying in LSE. The relatively affluent family
circumstances may associate with the generous spending behaviour of Asian students in LSE, but this
would need further research to verify the correlation.
5. Conclusion
This paper has investigated the diversity of LSE students’ consumer behaviour across gender and world
region, while controlling for other pertinent variables. Although the analysis was limited to correlations
and not causations, it found highly statistically significant relationships between gender and leisure travel
expenditure and between region and food expenditure. With less statistical significance, relationships
were found between region and recreational expenditure and between region and leisure travel
expenditure.
Nonrandom sampling, non-response bias, and the overall time frame of this research were limitations to
the internal validity of these results. In addition, due to the specificity of LSE students, the external
validity of the findings must be questioned. To strengthen the internal validity, future analysis should
investigate the causal relationships of our findings. We hope that future scholars will conduct a
12
comprehensive investigation of students from various universities in various locations. This may provide
stronger external validity and achieve a thorough understanding of student consumer behaviour diversity.
13
Bibliography:
Alagöz, S. B. and Burucuoglu, M. (2011) Gender: As a Purchasing Decision Variable and a
Research at Karamanoglu Mehmetbey University, European Journal of Economics, Finance
and Administrative Sciences, 39, 94-100
Anderson, B. (1991) Imagined Communities: Reflections on the Origin and Spread of
Nationalism, London, Verso
Bryman, A. (2012) Social Research Methods: 4th edition, Oxford University Press
Buss, W. C. and Schaninger, C. (1987) An overview of dyadic family behavior and sex roles
research: A summary of findings and an agenda for future research, in Houston, M. (eds),
Review of Marketing, Chicago: American Marketing Association, 293-324
Collins, D. and Tisdell, C. (2002), Gender and Differences in Travel Life Cycles, Journal of
Travel Research, 41, 133-143
Costa, J.A. (1994) Gender Issues and Consumer Behavior, California,Sage Publications, 1-10
Darby, M.R. (1987) Wealth Effect, in Durlauf, S.N., Blume, L.E. (eds) The New Palgrave: A
Dictionary of Economics, 4, 883-884
De Guzman, M. R., Fojas, G., Inamac, J., Lee, N., Sangria, K., and Tia,T., A Descriptive
Research on Allowance and Budget of Second Year Students of University of Santo Tomas’
College of Fine Arts and Design. 28 Jun.2012 <http://www.scribd.com/doc/85174266/ADESCRIPTIVE-RESEARCH-ON-ALLOWANCE-AND-BUDGET-OF-SECOND-YEARSTUDENTS-OF-UNIVERSITY-OF-SANTO-TOMAS-COLLEGE-OF-FINE-ARTS-ANDDESIGN>
Dolin, J. and Chroninger, E.W.A., Teen Spending Survey.
<http://www.losalamosjjab.com/storage/Teen_Spending_Report.pdf>
28
Jun.2012
Ellwood E. and Shekar, S. (2008) Wonder Woman: Marketing Secrets for the Trillion-Dollar
Customer, New York, Palgrave Macmillan
Firat, A. F. (1994) Gender and Consumption: Transcending the Feminine?, Coasta, J.A. (eds)
Gender Issues and Consumer Behavior, California, Sage Publications, 205-228
Fischer, E. and Arnold, S.J. (1994) Sex Gender Identity, Gender Role Attitudes, and
Consumer Behavior, Psychology & Marketing, 2, 163-182
Fischer, E. and Gainer, B. (1994) Masculinity and the Consumption of Organized Sports, J.A.
Coasta (eds) Gender Issues and Consumer Behavior, California, Sage Publications, 84-103
IMF (2011) World Economic and Financial Surveys: Regional Economic Outlook, Asia and
Pacific Managing the Next Phase of Growth April 2011, 28 Jun.2012
< http://www.imf.org/external/pubs/ft/reo/2011/apd/eng/areo0411.pdf>
14
Kapoor, J., Dlabay, L. and Hughes, R. (2007) Personal Finance: Student Edition: 8th edition,
USA, McGraw-Hill Higher Education, e-book, 27 Jun.2012
Kelley School of Business (Indiana University – Bloomington), IU Student Spending Habits
Survey Results Fall 2004. 28 Jun. 2012
<http://bloomington.in.gov/media/media/application/pdf/1470.pdf>
Kvidal, T. (2011) Tensions of Consumer Individualism: Norwegian Identity in the Context of
Globalization, Nordicon Review, 32, 111-124
Luna, D. and Gupta, S.F. (2001) An integrative framework for cross-cultural consumer
behavior, International Marketing Review, 18, 45-69
Nelson, J.A. (1993) The study of choice or the study of provisioning? Gender and the
definition of economics, in Ferber, M.A. and Nelson, J.A. (eds) Beyond Economic Man:
Feminist Theory and Economics, Chicago, University of Chicago Press
Pietrykowshi, B. (2009) The Political Economy of Consumer Behavior: Contesting
Consumption, London, Routledge
Pine,K., Sheconomics - Report on a survey into female economic behaviour and the emotion
regulatory role of spending,2009. University of Hertfordshire. 28 Jun. 2012
<http://www.sheconomics.com/downloads/womens_emotions.pdf>
Stern, B. (1988) Sex role self concept measures and marketing: A research note, Psychology
and Marketing, 5, 85-99
Stollak, M., Vandenberg, A., Steiner, K. and Richards, J. (2011) Student budgeting and
spending behaviors: a comparative study, Journal of Behavioral Studies in Business,3
The People’s Bank of China (2012), Chinese Family Financial Study中國家庭金融調查報
告, 28 Jun.2012 < http://money.sohu.com/s2012/jrdcbg2012/>
U.S.Bureau of Labor Statistics, Consumer Expenditure Surveys, Washington, DC 202120001. 28 Jun.2012 < http://www.bls.gov/cex/>
Yan, Y. (2008) Of Hamburger and Social Space: Consuming McDonald’s in Beijing, in
Counihan, C., Esteril, P.V. (eds) Food and Culture: A Reader, London, Routledge, 500-522
Xu, J., Shim, S., Lotz, S. and Almeida, D. (2004) Ethnic Identity. Socialization Factors, and
Culture-Specific Consumption Behavior, Psychology & Marketing, 21, 93-112
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Appendix 1: Survey Design
Research Survey: LSE Students
We are LSE undergraduates conducting a research project through the undergraduate initiative of LSEGROUPS. Please take our survey if you were an LSE student of any level during the 2011-2012 academic
year. The survey should take less than 5 minutes. Thank you for your time. We truly appreciate it.
All responses will be confidential and we will anonymise your data fully if we refer to it in our research. You
may withdraw your participation at any time by contacting us at LSEGroups10@gmail.com and your data will
then not be used in the research.
Do you consent to having your responses used as data for our LSE-GROUPS project?
All responses will be confidential and we will anonymise your data fully if we refer to it in our research.
□
□
Yes, I consent. I understand I can withdraw my participation at any time and my data will not be
used in the research.
No. I do not consent.
Gender
□ Male
□ Female
In which region have you spent the most years of your life?
□ Africa
□ Asia
□ Australia/Oceania
□ East Europe
□ West Europe
□ North America
□ South America
What was your relationship status for the majority of the 2011-2012 academic year?
□ Single
□ In a Relationship
□ Married
□ Other
What course did you undertake during the 2011-2012 academic year?
□ General Course
□ Undergraduate
□ Masters
□ Other
□ PhD
Please indicate your sources of income during the 2011-2012 academic year. Check all that
apply.
□ Employment during the academic year
□ Family support
□ Financial Aid
□ Government grants
□ Loans
□ Personal savings
□ Scholarship
What was your main type of residence during the 2011-2012 academic year?
□ Lived with family
□ Private accommodation
□ Student accommodation
16
Please estimate your typical weekly expenditure on food during the academic year.
Please answer WEEKLY.
□
□
□
□
□
□
□
£0 - £25
£25 - £50
£50 - £100
£100 - £150
£150 - £200
£200 - £250
over £250
Please estimate your typical weekly expenditure on transportation during the academic year,
including trains, underground, buses, bikes, and cabs.
Please answer WEEKLY.
□
□
□
□
□
£0 - £25
£25 - £50
£50 - £75
£75 - £100
over £100
Please estimate your typical monthly expenditure on clothing during the academic year.
Please answer MONTHLY.
□
□
□
□
□
□
£0 - £50
£50 - £100
£100 - £200
£200 - £500
£500 - £1500
over £1500
Please estimate your typical monthly expenditure on housing during the academic year,
including rent, electricity, gas, heating, and water.
Please answer MONTHLY.
□
□
□
□
□
□
□
£0 - £200
£200 - £400
£400 - £600
£600 - £800
£800 - £1500
over £1500
Do not pay rent or mortgage
Please estimate your typical monthly expenditure during the academic year on the recreation
activities of movies, performances, hobbies, and socialising.
Please answer MONTHLY.
□
□
□
□
□
□
□
£0 - £20
£20 - £50
£50 - £100
£100 - £150
£150 - £200
£200 - £250
over £250
Please estimate your annual expenditure during the academic year on leisure travel, including
flights, accommodation, and tourism costs.
Please answer ANNUALLY.
□
□
□
□
□
£0 - £200
£200 - £500
£500 - £1000
£1000 - £3000
over £3000
17
Appendix 2: Descriptive Statistics on average monthly total speeding
Table 1: Average Spending for Male and Female
Gender
Average monthly total
spending
Number
St. Deviation
Female
1348.108
63
632.8629
Male
1147.598
34
473.8721
Total
1277.826
97
587.4564
Table 2: Average Spending for World Regions
World Region
Average monthly total
spending
Number
Std. Deviation
Africa
1430.000
2
655.2523
Asia
1341.345
44
550.3390
East Europe
1235.069
12
769.0523
West Europe
1118.437
32
478.5369
North America
1500.333
5
593.5406
South America
1978.750
2
1540.9035
Total
1277.826
97
587.4564
Table 3:
Asia
East Europe
West Europe
Other Regions
No. receiving solely
family support
32
3
5
1
Total Number
Percentage (%)
44
12
32
9
72.7
25.0
15.6
11.1
Diagram 1: Average Monthly Total Spending
18
Diagram 2: Average Weekly Expenditure on Food
Diagram 3: Average Annual Expenditure on Leisure Travel
19
Appendix 3: Regression coefficients tables
Table 1: Dependent Variable: Weekly Expenditure on Food
Model
Unstandardised
Coefficients
B
(Constant)
Standardised
Coefficients
Std. Error
t
Sig.
Beta
68.365
14.225
4.806
.000
3.053
11.438
.027
.267
.790
East Europe
-29.434
20.437
-.150
-1.440
.153
West Europe
-42.978
12.401
-.372
-3.466
.001
Other Region
-9.337
19.520
-.050
-.478
.634
In Relationship
3.073
11.723
.028
.262
.794
Master
7.010
11.127
.064
.630
.530
.030
.011
.262
2.670
.009
Gender
Total Expenditure Except
Food
Table 2: Dependent Variable: Monthly Expenditure on Housing
Model
Unstandardised Coefficients
B
(Constant)
Std. Error
464.187
118.789
Gender
-60.670
88.741
East Europe
165.624
West Europe
Other Region
Live With Family
Private Accommodation
Standardised
Coefficients
t
Sig.
Beta
3.908
.000
-.075
-.684
.496
157.069
.116
1.054
.295
151.988
101.787
.187
1.493
.139
106.418
143.638
.084
.741
.461
-446.356
234.407
-.210
-1.904
.060
-17.883
86.006
-.023
-.208
.836
.238
.125
.220
1.900
.061
Total Expenditure
Except Housing
Table 3: Dependent Variable: Monthly Expenditure on Clothing
Model
Unstandardised
Coefficients
B
Standardised
Coefficients
Std. Error
(Constant)
27.664
38.141
Gender
-7.360
27.709
East Europe
-30.488
West Europe
t
Sig.
Beta
.725
.470
-.028
-.266
.791
48.074
-.067
-.634
.528
-16.200
29.538
-.061
-.548
.585
Other Region
-21.729
46.369
-.051
-.469
.641
Master
-25.833
26.459
-.102
-.976
.332
.067
.025
.287
2.719
.008
Total Expenditure Except
Clothing
20
Table 4: Dependent Variable: Annually Expenditure on Leisure Travel
Model
Unstandardised
Coefficients
B
Standardised
Coefficients
Std. Error
(Constant)
1466.701
444.110
Gender
-702.022
238.816
East Europe
-683.317
West Europe
t
Sig.
Beta
3.303
.001
-.290
-2.940
.004
422.237
-.165
-1.618
.109
-295.570
315.301
-.121
-.937
.351
Other Region
178.705
426.328
.045
.419
.676
Employment
-421.775
333.150
-.133
-1.266
.209
345.980
312.918
.122
1.106
.272
-125.841
467.592
-.029
-.269
.789
120.751
431.823
.032
.280
.780
Loans
-685.079
350.075
-.228
-1.957
.054
Personal Savings
-272.783
306.105
-.096
-.891
.375
Scholarship
-385.365
296.895
-.141
-1.298
.198
.012
.200
.006
.062
.951
Family Support
Financial Aid
Government Grants
Total Expenditure Except
Travel
Table 5: Dependent Variable: Monthly Expenditure on Recreational Activities
Model
Unstandardised
Coefficients
B
Standardised
Coefficients
Std. Error
(Constant)
65.159
29.429
Gender
-3.998
15.439
-37.801
West Europe
Other Region
t
Sig.
Beta
2.214
.030
-.029
-.259
.796
28.157
-.160
-1.343
.183
3.524
21.187
.025
.166
.868
-28.398
28.068
-.127
-1.012
.315
7.830
15.827
.059
.495
.622
Master
15.588
15.060
.118
1.035
.304
Employment
37.375
21.835
.208
1.712
.091
-22.792
20.274
-.142
-1.124
.264
Financial Aid
-1.267
30.117
-.005
-.042
.967
Government Grants
-5.462
28.505
-.026
-.192
.849
.141
22.721
.001
.006
.995
-5.341
19.667
-.033
-.272
.787
3.737
19.200
.024
.195
.846
.021
.013
.187
1.624
.108
East Europe
In Relationship
Family Support
Loans
Personal Savings
Scholarship
Total Expenditure Except
Recreational Activities
21
Table 6: Dependent Variable: Monthly Total Spending on the Six Categories FOR WOMEN
Model
Unstandardised
Coefficients
B
(Constant)
Standardised
Coefficients
Std. Error
1203.380
287.679
East Europe
-98.702
295.958
West Europe
-572.990
Other Region
t
Sig.
Beta
4.183
.000
-.047
-.334
.740
245.289
-.403
-2.336
.024
135.385
259.939
.073
.521
.605
In Relationship
288.865
174.360
.229
1.657
.104
Master
373.208
164.143
.298
2.274
.028
-1196.241
448.436
-.413
-2.668
.010
77.868
171.148
.061
.455
.651
340.246
248.926
.209
1.367
.178
7.809
229.798
.005
.034
.973
Financial Aid
-191.652
357.545
-.076
-.536
.594
Government Grants
1187.351
458.818
.469
2.588
.013
Loans
-594.941
269.832
-.366
-2.205
.032
209.900
216.295
.137
.970
.337
-104.126
203.021
-.073
-.513
.610
Live With Family
Private Accommodation
Employment
Family Support
Personal Savings
Scholarship
Table 7: Dependent Variable: Monthly Total Spending on the Six Categories FOR MEN
Model
Unstandardised
Coefficients
B
(Constant)
Standardised
Coefficients
Std. Error
1896.251
367.288
East Europe
116.709
409.961
West Europe
-114.815
Other Region
t
Sig.
Beta
5.163
.000
.059
.285
.779
229.332
-.121
-.501
.623
438.385
654.582
.159
.670
.512
In Relationship
-121.099
242.180
-.126
-.500
.623
Master
-181.414
204.587
-.189
-.887
.387
Live With Family
-930.613
307.588
-.641
-3.026
.007
Private Accommodation
-307.033
184.882
-.306
-1.661
.114
101.982
276.451
.070
.369
.717
Family Support
-342.819
317.085
-.296
-1.081
.294
Financial Aid
-300.371
394.492
-.182
-.761
.456
Government Grants
-195.670
252.089
-.159
-.776
.448
114.846
279.472
.094
.411
.686
Personal Savings
-117.660
218.597
-.102
-.538
.597
Scholarship
-259.888
283.315
-.212
-.917
.371
Employment
Loans
22
Table 8: Dependent Variable: Monthly Total Spending on the Six Categories
Model
Unstandardised
Coefficients
B
Standardised
Coefficients
Std. Error
(Constant)
1488.291
222.965
gender
-103.558
127.773
East Europe
-118.038
West Europe
t
Sig.
Beta
6.675
.000
-.084
-.810
.420
231.863
-.056
-.509
.612
-337.910
172.084
-.274
-1.964
.053
Other Region
185.319
228.579
.093
.811
.420
In Relationship
141.709
129.681
.120
1.093
.278
Master
122.050
124.938
.104
.977
.332
-861.614
262.126
-.385
-3.287
.002
-11.875
129.385
-.010
-.092
.927
Live With Family
Private Accommodation
Employment
79.521
183.688
.050
.433
.666
Family Support
-109.094
178.173
-.076
-.612
.542
Financial Aid
-386.689
250.938
-.173
-1.541
.127
337.040
233.455
.177
1.444
.153
-208.069
186.471
-.137
-1.116
.268
214.542
159.734
.150
1.343
.183
-191.658
159.462
-.138
-1.202
.233
Government Grants
Loans
Personal Savings
Scholarship
23