▪
relatively high percentage of single non-familial households (26%),
▪
relatively most often comparing to other clusters, the household is run by woman (37%),
dominate the households run by persons with secondary education (37%),average level of income (2159 PLN).
Cluster 4: Saving in cash
The last and most numerous cluster, is represented by almost half the analyzed population of seniors over 50 (46%). The saving portfolio of this group of households is dominated by cash (92%). Only every fifth representative of this cluster declare to have a personal current account (22%). Preference to accumulate money in form of cash may be due to the low number of banking services users in this cluster. More than 1/5 of the representatives of this focus does not use any bank service.
The value of saving is the lowest among all clusters. More than ¼ of households (28%) have savings not exceeding their monthly income. In every third household (35%) the value of savings amounts from monthly to three months income.
Saving in cash are indebted to the average level (19% repay some credit or loan), but they relatively least likely use mortgage - only every tenth representative of this cluster declare to repay mortgage. For half of the households representing this cluster, the debt does not exceed the three-month income. However, it should be noted that among Saving in cash there is also a relatively high proportion of households (10%) in which the value of loans exceed a 3-year income.
Saving in cash rather negatively evaluate their income situation. More than 60% of the households declare problems with making ends meet. Almost every fifth household thinks that its income situation has worsened in the last two years, and one in ten households acknowledges that income is not sufficient to cover current needs.
The households representing Saving in cash cluster are characterized by the following socio-economic features:
dominate households of retired (57%), relatively the highest percentage of pensioners’ households (7%), Relatively high percentage of single non-familial households (26%),
the relatively high age of the head of household, the highest percentage of households led by people aged 70-79 years (21%) and above 80 (16%),
the place of living is usually village (44%), relatively the smallest percentahe of households living in a big cities (6%),
low level of education of the head of household. every fourth household is run by persons with primary education or lower,
relatively the highest percentage of households run by widowers (27%), the lowest level of income among all clusters (1755 PLN).
4 SUMMARY
Segment of households run by seniors over 50 is a heterogeneous group, so it should not be treated it in the same way. Banking and financial institutions should try to adopt their products, taking into account the needs and preferences of older people and differentiate it depending on the specific socio-economic features, such as age, education level or level of wealth.
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The results of cluster analysis justify the perception of saving behaviors of households of people over 50 through the prism of the four clusters, which are distinguished on the basis of the composition of the savings portfolio: Saving in cash (46%), Saving the bank and avoiding cash (26%), Saving in bank and cash (25%), Diversified (3%).
The segment of older people usually use simple, safe and passive forms of saving. Much less popular are more complex products, with a greater degree of risk and requiring activity in the financial market. In the saving portfolio of more affluent and better educated households the occurrence of savings products with a higher degree of risk is a bit more common.
Key factors diversifying the saving portfolio of seniors are: age, the level of income and level of education.
Taking into account the demographic changes, such as an increase in the percentage of seniors with higher education, which translates into higher income, it can be assumed that the next generation of seniors will be more active on the market of financial products. Higher level of income and education translates into a reduction of risk aversion, which may contribute to the growth of interest in alternative, more aggressive ways of investing free funds.
References
Bakshi G.S. & Chen Z., (1994). Baby boom, population aging, and capital markets. Journal of Business, 67(2), 165-202. https://doi.org/10.1086/296629
Bejtelsmit V.L. & Bernasek A., (2001). Risk preferences and the investment decisions of the older Americans. AARP Public Policy Institute.
Clark, G. & Strauss, K., (2008). Individual pension-related risk propensities: the effects of socio-demographic characteristics and a spousal pension entitlement on risk attitudes. Ageing and society, 28, 847-874. https://doi.org/10.1017/S0144686X08007083
Collard S., (2009). Individual investment behaviour: a brief review of research. Personal Finance Research Centre, Bristol.
Czerwiński B., (2016). Zachowania seniorów z Polski Północnej na rynku usług finansowych w świetle wyników badań. Handel Wewnętrzny, 3(362), 76-87.
Diagnoza społeczna: zintegrowana baza danych. www.diagnoza.com, data pobrania: 2.06.2016r.).
Grzelak-Kostulska E., (2016). Seniorzy w Polsce w świetle procesów modernizacyjnych. Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń.
Kłobukowska J., (2016). Pozycja i ochrona seniorów na rynku bankowości detalicznej w perspektywie zrównoważonych finansów. Finanse i prawo finansowe, vol 3, nr 1, 71-82.
Morin R.A. & Suarez A.F. (1983), Risk aversion revisited, The Journal of Finance, 38. 1201–1216.
https://doi.org/10.1111/j.1540-6261.1983.tb02291.x
Olejnik I., (2014). Zachowania finansowe osób w wieku emerytalnym – integracja metod ilościowych i jakościowych. Handel Wewnętrzny, 1(348), 46-56.
Poczta-Wajda A., (2010). Nowoczesne techniki analityczne w kształceniu na studiach ekonomicznych.
Część pierwsza. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
Rytelewska G. & Kłopocka A., (2010). Wpływ czynników demograficznych na poziom i strukturę oszczędności gospodarstw domowych w Polsce. Bank i Kredyt 41/1, 57-80.
Sagan A., (2010). Metody statystyczne w marketingu i badaniach rynku. Kraków 4-5 marca 2010 – materiały szkoleniowe StatSoft w ramach kursu obsługi programu Statistica.
44
Smyczek S., (2001). Zachowania konsumentów na rynku usług bankowych. Wydawnictwo Akademii Ekonomicznej w Katowicach, Katowice.
Stanisz A., (2007). Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny. Tom 3. Analizy wielowymiarowe. StatSoft, Kraków.
Wójcik E., (2007). Polskie gospodarstwa domowe na rynku oszczędności. Bank i Kredyt, 07, 55-66.
Zalega T., (2016). Incomes and savings of Polish seniors in view of research outcomes. Problemy Zarządzania, vol.14, nr 2(59), t.1:135-155.
Contact information
Paulina Anioła-Mikołjaczak, Ph.D.
Poznan University of Life Sciences, Faculty of Economics and Social Sciences Address: Wojska Polskiego 28, 60-637 Poznań, POLAND
E-mail: aniola@up.poznan.pl
DOI ID: https://www.doi.org/10.7441/dokbat.2017.03