Abstract
Households’ stock market participation has significant effects on savings and on an economy’s financial development and performance. Yet participation into capital markets is limited and quite heterogonous both among and within several countries. This phenomenon represents an empirical puzzle whose understanding is rather incomplete. In this work, we exploited a combination of datasets for nine European countries and used different econometric specifications that allow to control for endogeneity of financial literacy and human capital, to assess the role of several variables in affecting the probability to participate in the stock market in year 2010. Besides socio-demographic variables, we found that financial literacy has a positive and significant effect on stock market participation, together with the level of human capital and social interaction. Country level differences are explained by such institutional factors as the effectiveness of the education system and by the attractiveness of the stock markets.
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Source: Authors’ calculation using SHARE database. Data are provided for 5-year cohort groups (apart from 1966 to 1975, due to low number of observations)
Source: Authors’ calculation using SHARE database. For details on financial literacy groups see Appendix 2
Source: Authors’ calculation using SHARE database
Source: Authors’ calculation using SHARE database. Income quintiles are country-specific
Source: Authors’ calculation using SHARE database. Wealth quintiles are country-specific
Source: Authors’ calculation using International Historical Statistics. X axis: 5-year average student–teacher ratio for 5-year cohort groups at age 6–15 (some older cohorts were merged with others in case of low number of observations or lack of data). Y axis: fraction of individuals participating in the stock market for each cohort group and country
Source: Authors’ calculation using Global Financial Database. X axis: 5-year averages of Sharpe-ratios for each 5-year cohort-groups and countries. Y axis: fractions of individuals participating in the stock market for each cohort group and country
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Notes
Also the significant increase of employee financial plans, either directly through employee share ownership (ESO) or indirectly through intermediaries like Employee stock ownership plans (ESOP) or profit sharing (PS) have provided a relevant support to individuals’ demand for risky assets. These financial plans, also linked to defined contribution schemes, have empirically proven to favor higher productivity, strengthen corporate governance and competitiveness of firms (Kaarsemaker et al. 2010; Soppe and Houweling 2014). Most importantly, these financial plans have also improved the financial outlook of workers, equipping them with more sophisticated financial techniques and thus resulting in better financial decisions in terms of savings, risky asset participation and retirement planning.
This paper uses data from SHARE wave 4 release 1.1.1, as of March 28th 2013 (https://doi.org/10.6103/SHARE.w4.111), and SHARELIFE release 1, as of November 24th 2010 (https://doi.org/10.6103/SHARE.w3.100). The SHARE data collection has been primarily funded by the European Commission through the 5th Framework Programme (Project QLK6-CT-2001-00360 in the thematic programme Quality of Life), through the 6th Framework Programme (Projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5- CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th Framework Programme (SHARE-PREP, N° 211,909, SHARE-LEAP, N° 227,822 and SHARE M4, N° 261,982). Additional funding from the US National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and the German Ministry of Education and Research as well as from various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions).”
Among few theoretical exceptions, see the model developed by Spataro and Corsini (2017).
In the recent years there has been burgeoning research on the measurement of financial literacy and its effects on household behaviour especially on retirement planning (Lusardi and Mitchell 2011; Van Rooij et al. 2012 among others) on savings and portfolio decisions (Jappelli and Padula 2013; Lusardi et al. 2017). For a review see Jappelli (2010).
Such differences among countries in stockholding could also arise from differences in cultural norms which are likely to be associated with apparent differences in risk tolerance levels or perception of the risk of the financial options. Additionally, changes in participation rates in risky asset markets could be influenced by the nature of society the worker belongs to. For example, in collectivistic societies, respondents are likely to receive financial assistance from their family and social networks; Pyles et al. (2016) showed that in these societies the perceived risk in financial decisions is lower than in individualistic societies (such as the Denmark, Sweden), where individuals are more likely to be left to fend for themselves. In the empirical analysis we take these issues into account by using control variables at both individual and cohort/country level.
We also tried to capture the difference in wages across different sectors; however, large number of missing values has forced us to drop the exercise.
The classification is based on direct stockholding percentages. Italy and Austria are low participating countries with less than 7% of the respondents holding stocks. The medium participation country group represents countries whose direct participation levels are between 8–20% and the high participating countries ranges from 21 to 40%. Broadly speaking stockholding increases form Southern to Northern Europe, with Switzerland as an exception.
Interactions of country dummies with Sharpe-ratios were also attempted, although they were not significant. In fact, Sharpe-ratios are country-specific and the latter, together with the country—group dummies and student–teacher ratios (cohort/country level) already capture the between-country variability in participation rates.
We recall that in SHARE database education years range from age zero to 25 (see Table 1).
The relative mathematical ability at age10 shows almost equal effect on financial literacy and school years, while relative score of language skills at age 10 is a better predictor of school years rather than financial literacy.
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Acknowledgements
We are grateful to the participants at the Seminar at Department of Economics and Management, University of Pisa, at the «13th Workshop on Pension, Saving and Insurance, Paris», at the «Institutional and Individual Investors: Saving for old age» Conference, University of Bath, at GRASS IX Workshop, IMT Lucca, at XXIV MBF Rome Conference, at 52nd TIES conference, IIM Kozhikode, Michael Haliassos, Maurizio Fiaschetti, Angela Parenti, Davide Fiaschi, Thomas Renström and Valeria De Bonis, Alessandro Belmonte, Nebojsa Dimic for helpful insights and suggestions on a previous version of this paper. The usual disclaimer applies.
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Appendix
Appendix
Appendix 1
See Table 5.
Appendix 2: Financial Literacy in SHARE
The questions used to construct the financial literacy indicator are set out below. Possible answers are shown on cards displayed by the interviewer who is instructed not to read them out to respondents:
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1.
If the chance of getting a disease is 10%, how many people out of 1000 can be expected to get the disease? The possible answers are 100, 10, 90, 900 and another answer.
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2.
2. In a sale, a shop is selling all items at half price. Before the sale a sofa costs 300 euro. How much will it cost in the sale? The possible answers are 150, 600 and another answer.
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3.
3. A second hand car dealer is selling a car for 6000 euro. This is two-thirds of what it costs new. How much did the car cost new? The possible answers are 9000, 4000, 8000, 12,000, 18,000 and another answer.
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4.
4. Let’s say you have 2000 euro in a savings account. The account earns 10 per cent interest each year. How much would you have in the account at the end of the second year? The possible answers are 2420, 2020, 2040, 2100, 2200, 2400.
If a person answers (1) correctly she is then asked (3) and if she answers correctly again she is asked (4). Answering (1) correctly results in a score of 3, answering (3) correctly but not (4) results in a score of 4 while answering (4) correctly results in a score of 5. On the other hand, if she answers (1) incorrectly she is directed to (2). If she answers (2) correctly she gets a score of 2 while if she answers (2) incorrectly she gets a score of 1.
The questions were asked in national languages like German, Italian, Swedish, Danish and Dutch. As for the Austria, the language used was German. The respondents from Belgium questions were provided in French or Flemish and for the Switzerland, the questionnaires were provided in Italian, German or French.
The actual range of responses were as follows: For question 1, the range of response is five, question 2 has three alternative answers, question 3 have six responses and finally question 4 has seven responses.
Appendix 3
See Table 6.
The detailed methodology of calculating the Sharpe-ratios is the following. The data on the return index is computed from Global Financial Database. The returns on risky assets and safe asset returns are calculated separately from the return index by applying the formula
where Y1 is the current return index value and Y0 is the base return index. Then we calculate the average returns by subtracting the return of the risky asset (Rf) from the return from safe asset\(\left( {{R_0}} \right):\frac{{{R_f} - {R_o}}}{{stdev({R_f})}}.\)
Finally, the average returns are divided by the standard deviation of risky assets that is annualised by multiplying by \(\sqrt 2\)of the respective years.
Appendix 4
See Table 7.
Appendix 5
See Table 8.
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Thomas, A., Spataro, L. Financial Literacy, Human Capital and Stock Market Participation in Europe. J Fam Econ Iss 39, 532–550 (2018). https://doi.org/10.1007/s10834-018-9576-5
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DOI: https://doi.org/10.1007/s10834-018-9576-5