2. Data

The survey used to collect the data was designed to be consistent with the OECD survey of 2022 (OECD, 2022). The funding for the research work came partly from the Ministry of Justice and the DigiConsumers research project. The research findings will be used in the execution of the Finnish national strategy for financial literacy. The decision to follow the OECD survey influenced some decisions concerning the variables, especially the Big Three questions. However, the research team also included questions outside the OECD survey.

The data were collected by online survey. The prior Finnish 2014 survey data in Kalmi and Ruuskanen (Reference Kalmi and Ruuskanen2018) were collected using face-to-face interviews. Online surveys are a more common data collection method in financial literacy surveys, making the new Finnish survey more comparable to the other international surveys. However, the two Finnish surveys are not strictly comparable due to the differences in data collection methods.

The data were collected between January 25^th and February 6^th, 2023. The data were sent to a panel of survey respondents through a professional survey provider Innolink. Respondents are rewarded for participating in the surveys by collecting credits based on the length of each survey they respond to. After they have collected enough credits, these can be transformed into electronic gift cards for specified service providers.

The data collection period was quite unique: the war in Ukraine had been going on for almost a year at the time data were collected, food inflation was in double-digit figures, and the energy prices had risen quite considerably. Finland was also preparing for the national parliamentary elections in April 2023.

After the data collection was completed, the respondents’ response times were evaluated by this study’s authors. Very fast respondents were eliminated using a procedure described in Greszki, Meyer, and Schoen (Reference Greszki, Meyer and Schoen2015). This procedure eliminates those who responded shorter than a fraction of the median response time. We chose the threshold to be 50 percent of the median response time. This resulted in the removal of approximately 10 percent of the respondents. A detailed description of the differences between the removed and the non-removed samples is given in the Appendix Table A1.

To make the sample representative, after the elimination of the fast respondents, the weights were calculated using the population shares of (i) education attainment by each gender for each age group and region, (ii) gender composition for each age group and in each region, as well as (iii) age groups in each region. We similarly calculated the sample shares of these demographic characteristics. We divided the population share of demographic characteristics by their sample shares to get compensating weights for each demographic factor. We then multiplied compensating weights to obtain the sample weights for each respondent.

Summary statistics and frequency tables of the demographics and the variables on retirement planning and adverse economic outcomes are found in the Online Appendix, in Tables A1–A11.

3. Level of financial literacy in Finland

We examine financial literacy using variants of the Big Three financial literacy questions. Following Lusardi and Mitchell (Reference Lusardi and Mitchell2011), the following questions were asked in the survey to measure the level of financial literacy of the respondents:

1. i) Over 110€

2. ii) Exactly 110€

3. iii) Less than 110€

4. iv) It is impossible to know given the information provided

5. v) Don’t know

6. vi) Refused

1. i) More than today

2. ii) Less than today

3. iii) Don’t know

4. iv) Refused

1. i) True

2. ii) False

3. iii) Don’t know

4. iv) Refused

Although somewhat different, the questions are related to the canonical Big Three questions used by Lusardi and Mitchell (Reference Lusardi and Mitchell2011). The reason for these differences was the decision to follow the OECD (2022) survey as closely as possible.

The differences between the approaches are as follows: In the interest rate question, the numerical value is 110€ instead of 102€. This makes the question more difficult, as the respondent needs not only to understand the concept of interest rate but also the concept of compound interest rate. Also, consistent with the OECD questionnaire, there is an additional option, “It is impossible to know given the information provided.” This additional option is likely to reduce the proportion of correct answers. We have also added the condition of no taxes and fees for clarity. The differences in the inflation question are that our wording gives a monetary value (which is irrelevant to the task) and the rate paid to the savings and inflation rate are double that compared to the original Big Three. In the risk diversification question, the comparison is otherwise similar, but the statement is constructed to be true and not false, as in Big Three. This was again to keep the results comparable to the OECD formulation.

Summary statistics for these questions are provided in Table 1. It reports the share of respondents who answered each of the given alternatives to the three financial literacy questions. These are given for the full sample and just for those 25 to 65 years old.

Table 1.

Summary statistics for the Big 3 financial literacy questions

Note: DK refers to Don’t know. The asterisk* marks the correct answer to the question. All figures are weighted.

From Table 1, we can see that the question on risk diversification has the highest share of correct answers. The lowest share of correct answers is found for the interest rate question. The share of respondents who answered correctly to each individual question is slightly smaller in the overall sample than it is among those aged 25 to 65, indicating that the youngest and the oldest respondents have somewhat lower levels of financial literacy. The share of respondents who reported that they do not know the answer is roughly the same in both groups. 36.3 percent of respondents in the full sample answered all three questions correctly. The sample of respondents in previous assessments of adult financial literacy in Finland had a similar performance regarding answering all three questions correctly (OECD, 2016; Kalmi and Ruuskanen, Reference Kalmi and Ruuskanen2018). This leaves room for improving the level of adult financial literacy in Finland.

Compared to the previous work by Kalmi and Ruuskanen (Reference Kalmi and Ruuskanen2018) based on face-to-face interviews, the share of correct responses to interest rate and inflation questions is lower in the new sample, but correct responses risk diversification is quite a bit higher in the new sample. One reason for this might be that the last time the risk diversification question was posited in the way that the correct answer was false. The share of all three correct is almost the same as in the face-to-face interviews, and the share of “do not know” is much higher in the new sample. Due to the differences in data collection, the results are not strictly comparable.

At the time of the writing (May 2023), we do not yet know how the Finnish results will compare to the results of the OECD data collection round of 2022/2023, as the results will be released only in November 2023. However, compared with the 2018 survey results (OECD, 2020), it seems that the results will place Finland in the middle category among OECD countries – neither to the top nor to the bottom. This may be a reasonable position, as the data collection methods this time were more similar to those used in other countries than the ones used in 2014 and reported in Kalmi and Ruuskanen (Reference Kalmi and Ruuskanen2018).

Financial literacy is known to be quite unevenly distributed among the population. Next, we look at the heterogeneity in financial literacy. Tables 2a and 2b present the shares of respondents who answered either correctly or that they did not know in response to the three financial literacy questions. The answers are categorized by age, gender, education level, and employment status.

Table 2a.

Summary statistics by age, gender, education level, and employment status

Note: C refers to “correct,” DK to “don’t know.” All figures are weighted.

Table 2b.

Simple OLS regression of answering inflation correctly on the demographic variables

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

The group of working-age adults from 36 to 50 had the highest proportion of correct answers (55%) to the interest rate question. This share was lower, at 40.3 percent, for those younger than 35. Interest knowledge decreases for the oldest age group, thus forming an inverted U-shape with regard to age. Decreasing knowledge for the oldest respondents could reflect decreasing cognitive capabilities with old age. Both the young and the old have lower levels of financial literacy than those in their working age, although the share of having all three correct is much larger for the oldest age group than it is for the youngest.

These findings therefore show that the young have the weakest level of financial literacy. This could be due to lack of experience in financial decisions. It can be argued that people develop financial literacy through experience, and therefore, the young would have lower financial literacy simply because they have less experience. However, the understanding of central financial concepts as the Big Three is important when faced with large financial decisions such as taking a mortgage, highlighting the need for financial skills in making good choices in terms of choosing the right type of interest rate offered for the mortgage. Thus, it is concerning to find low levels of financial literacy for the young, since they will be faced with the consequences of making financial decisions without sound knowledge.

Another possible explanation for the lower level of financial literacy for the young could be cohort-specific effects. However, it is not possible to distinguish between cohort-specific and age-specific effects in this context. Yet, other studies, such as Lusardi and Mitchell (Reference Lusardi and Mitchell2011), have also found that the young and the old tend to have lower financial literacy than those in their prime working age.

Women have a lower share of correct answers to all financial literacy questions than men. The share of women who got all three questions correct is 26.3 percent, whereas for men it is almost 50 percent. The share of do not know answers is also higher for women than they are for men. These findings align with prior findings on gender differences in financial knowledge. It has been found in the literature that the lower levels of financial literacy of women could be due to a lack of self-confidence, as shown in Bucher-Koenen et al. (Reference Bucher-Koenen, Alessie, Lusardi and van Rooij2021).

Financial knowledge increases strongly with education. For instance, the share of correct answers is 23.6 percent for those with less than high school level education, and it is over 60 percent for postgraduates. This finding makes sense, since higher education can be intuitively understood to correlate with higher financial literacy measured by the Big Three.

Furthermore, the self-employed get the largest share of correct answers in the employment categories, a common finding in the field. The self-employed need to know more about financial matters, because they have to take care of their taxes and other things much more independently than the average wage earner in the Finnish setting needs to. Not only do the self-employed have the highest share of correct answers but also they say they do not know much less frequently than the other employment groups. This could indicate that they are more confident in financial matters, perhaps due to more experience they have acquired through their business knowledge.

We next investigate the relationship between inflation knowledge and its determinants in more detail. In the recent decades, inflation has remained low, but has recently reappeared in high rates in many developed economies. First, in order to investigate the importance of inflation knowledge in day-to-day financial decisions, we look at the development of the inflation rate in Finland. Figure 1 shows the development of the Finnish inflation rate over time starting from the 1960s up to 2023. From this figure, we can see that high inflation has not been a concern in the economy for about 30 years, leaving the young more vulnerable to not understanding its effects on personal finance due to the lack of experience on inflation.

Figure 1.

Inflation rate in Finland, the Euro area, and the United States (CPI, annual %).

Source: World Bank (2023)

Figure 1 shows that the inflation rate in Finland has remained, until recently, below 5 percent since the early 1990s. Before that, in the 1960s, Finland’s inflation rate varied between 2 and 10 percent, while it rose close to 20 percent during the oil crisis in the 1970s. Inflation remained quite high during the 1980s as well, declining from over 10 percent down to about 3 percent throughout the decade. In the late 1980s, inflation increased beyond 5 percent and started to decline again during the Finnish banking crisis of the early 1990s.

After joining the Euro area during its inception in 1999, Finland’s inflation has mostly closely mirrored the Euro area. The annual inflation rate has since been well below 5 percent and in 2013–2021 very low, occasionally dipping into the deflation zone. However, as in the rest of the Eurozone countries and the United States, the inflation rate in Finland started to rise in the aftermath of the COVID-19 pandemic. The inflation rate in Finland rose above the European Central Bank’s target of 2 percent in late 2021 and has quite steadily increased close to 10 percent in late 2022.

With this background of the inflation environment of the past decades in mind, we turn back to the inflation knowledge figures in Tables 2a and 2b. From it, we observe that inflation knowledge indeed increases with age, although there is a small drop in knowledge for the oldest individuals. Older individuals had more experience with inflation during their lifetime in the 1970s and 1980s, which could be reflected in their higher inflation knowledge. The share of correct answers is almost 70 percent for those older than 65, who would have been in their twenties during the oil crisis in the mid-70s. In contrast, the share of those younger than 35 who answered the inflation question correctly is much lower, at 49.3 percent. These individuals have not really had experience with inflation, as those 35 today have been small children in the late 1980s to the early 1990s when Finland last went through a spell of high inflation.

Next, we look at how inflation knowledge correlates with the demographic variables. Table 2b reports the results of this simple OLS regression of getting the inflation question correct on age, gender, education, and employment, which are the demographic variables considered in Table 2a.

The results show that age and education are positively and significantly related to correctly answering the inflation question. The positive coefficient for age shows that the older the individual, the higher the score on inflation knowledge. Surprisingly though, the coefficient for age is only weakly significant. This suggests that the correlation between inflation knowledge and age is not very strong.

On the other hand, the higher the education level is, the greater the coefficient, suggesting that higher education predicts better inflation understanding. Unlike the coefficient for age, the coefficients on education are highly statistically significant. The negative and significant coefficient for the female dummy indicates that females have lower inflation knowledge than men even after accounting for other demographic variables.

There is a long-standing historical division between the North-Eastern part and South-Western parts of Finland. This divide dates back to the division of Finland between Sweden and Russia in a peace treaty in the 14^th century. However, it has been suggested that this geographic divide was in existence for centuries, even millennia before that time, where the North-East constituted of mainly hunting-oriented societies, and the South-West focusing on agriculture (Palo, Reference Palo2020). Therefore, as indicated in Palo (Reference Palo2020), even the gene pool of individuals on different sides of this historic divide is different. These differences are still apparent today, where for instance, health outcomes are on average poorer in the East and then in the North of the country, and there are worse socio-economic outcomes in general such as higher unemployment levels. The population is mostly concentrated in the South and the West, where the East and the North of Finland are much more sparsely populated. The country’s major cities are found in the South and the West. Here, we look at summary statistics for the Big Three financial literacy questions for different regions in Finland, which are presented in Tables 3a and 3b.

Table 3a.

Summary statistics for South, West, East, and North Finland

Note: C refers to “correct” and DK to “don’t know.” All figures are weighted.

Table 3b.

Distribution of answers to self-reported financial literacy by age, gender, education level, and employment status

Note: All figures are weighted. Self-reported financial literacy score varies from 1 (Very bad) to 7 (Very good).

Inflation knowledge is highest in North Finland, where 69.9 percent of the respondents answered the inflation question correctly. By contrast, 63 percent of the respondents answered this question correctly in South Finland, which was the lowest. West Finland scored the highest for the interest rate question, while it was lowest in North Finland. The understanding of risk, on the other hand, is highest in East Finland.

It may look like there is a financial literacy gap between the North (North Finland Province) and the rest of Finland (i.e., South, West, and East Finland provinces). We have tested this by checking the financial literacy of those respondents who live in the North and those who live in the rest of Finland. In the former group, the (weighted) average Big 3 score is 1,899 (std. dev. = 1.180), while it was 1.827 (std. dev. = 1.079) in the latter group. The difference in the (weighted) Big 3 scores between these two groups was not statistically significant according to an adjusted Wald test (p = 0.490).Footnote ¹ The same conclusion can be reached if we only compare the Big 3 scores between North Finland and South Finland provinces (p = 0.534 according to an adjusted Wald test).

Taken together, these region-specific measures of financial literacy do not reveal any large differences between the regions. This can be seen as reassuring, as even though the population of Finland is mainly concentrated in the South and to the West, and there are other differences between the regions, the levels of financial literacy have not fallen behind in the less populated North and East.

The survey further asked about how the respondents would rank themselves on their financial knowledge. The survey question was formulated as follows:

The responses included the options “don’t know” and “I don’t want to answer.” The distribution of this measure is reported by demographic groups in Table 3b.

We can see that the most commonly reported level of self-reported financial literacy for the full sample is at the value 4, indicating neither bad nor good knowledge. The average score for the whole sample is slightly above this at 4.4. The level of self-reported financial knowledge rises with age and education, which is in line with the actual knowledge measures found in Tables 2a and 2b. The fact that self-reported financial knowledge increases with age possibly indicates more confidence in one’s knowledge with more life experience.

Women have a slightly lower level of self-reported financial knowledge than men. Less than 18 percent of women assess their financial knowledge to be good or very good (values 6 and 7), whereas this figure is over 26 percent for men. Women also self-assess their financial knowledge to be average more often than men do. The self-employed and the retired have clearly larger shares of top assessments than those who are not employed. The self-employed may be more confident in the knowledge gained from their experience as an entrepreneur, whereas the retired may feel confident due to accumulated life experience in general. These findings also reflect the figures obtained from the knowledge questions.

4. Financial literacy and economic outcomes

In this section, we link financial literacy to selected economic outcomes. The outcomes we investigate are retirement planning and three measures of adverse economic outcomes. These include subjective over-indebtedness, coping with a major expense and facing an income shock, of which the latter two can be seen as measures of financial fragility. In prior literature, Clark, Lusardi and Mitchell (Reference Clark, Lusardi and Mitchell2021) use facing an emergency expense as a measure of financial fragility. The outcome that most closely aligns with theirs is the variable measuring coping with a major expense, here the size of the respondents’ monthly income. In addition, we include another measure of financial fragility in the form of losing one’s main source of income. This variable is of interest since many people lost their main source of income during the pandemic. Although Finland has extensive government support for unemployment, it is nevertheless of interest to investigate how this variable plays out in relation with financial literacy. We next present these economic outcome measures in more detail. Their summary statistics regarding financial literacy are presented in Table 4.

Table 4.

Financial literacy and measures of retirement planning and adverse economic outcome variables

Note: All figures are weighted.

Adverse economic outcomes

In this section, we present the three measures of adverse economic outcomes: perceived over-indebtedness, major expense, and facing a large income shock.

Having too much debt can lead to having less flexibility in the household budget, therefore highlighting the need to investigate the relation of this measure with financial literacy. This particular question is also included in the OECD questionnaire.

Perceived over-indebtedness

The respondents are asked to evaluate how much they agree or disagree with the following statement:

• I have too much debt right now

They evaluated this statement on a 1–5 scale, where 1 indicated a complete disagreement with the statement and 5 a complete agreement. The respondents also had the possibility to answer Don’t know or refuse to answer the question, which were again coded as missing. The variable too much debt takes the value of 1 if the respondents chose the value 4 or 5.

In total, slightly over 30 percent say that they have too much debt. The share of correct answers to the financial literacy questions is higher for those who do not perceive to have too much debt, whereas the share of incorrect answers is slightly higher for those who state they have too much debt. Indebted respondents are also more likely to indicate that they do not know the answer. These findings thus point to the direction that individuals with higher financial literacy accumulate less debt that they perceive excessive.

Facing a major expense

If you, personally, faced a major expense today – equivalent to your own monthly income – would you be able to pay it without borrowing the money or asking family and friends for help?

1. i) I certainly would be able to pay

2. ii) I probably would be able to pay

3. iii) I probably would not be able to pay

4. iv) I certainly would not be able to pay

5. v) The question does not apply to me, I have no personal income

6. vi) Don’t know

7. viii) Refused

This variable takes on the value 1 when the individual states that they could not pay an unexpected bill that is equal in size to their monthly net income without asking for help from family or friends or without borrowing money to pay the bill. The individuals who answered that they probably or definitively could not pay were coded as 1, and those who answered that they probably or definitively could were coded as 0. Individuals, who refused to answer or did not know or said the question did not apply to them because they have no income, were coded as missing.

43.9 percent of the respondents would not be able to pay for a major expense. We find that financial literacy is higher among those who stated they would be able to pay for a major expense. Those who stated that they could not <>had a slightly higher share of incorrect answers. On the other hand, the share of “do not know” answers was higher for those who could not pay a major expense.

Income shock

If you lost your main source of income, how long could you continue to cover living expenses, without borrowing any money or moving into another apartment?

1. i) Less than a week

2. ii) At least a week, but not one month

3. iii) At least one month, but not three months

4. iv) At least three months, but not six months

5. v) More than six months

6. vi) Don’t know

7. vii) Refused

This variable measures how long the individual could cope with losing their main source of income. It can therefore be seen as another measure of financial fragility. It takes the value 1 when the respondent states that if they lost their main source of income, they could cover their expenses without having to move or borrow money for less than one month. If the individual could cover their expenses for more than a month without having to move or borrow money, it takes the value 0. The responses stating refused and don’t know have been coded as missing observations.

Considering the income shock, 32.4 percent of respondents were classified as financially fragile. Financial literacy among the fragile is lower than for those not fragile. Considering the interest rate question, only 38.6 percent of those financially fragile responded correctly, whereas that share is 58.7 percent of the ones not considered fragile. The share of incorrect answers was larger for the fragile, especially concerning the interest rate question. The share of incorrect answers for the inflation and risk questions was only slightly larger for the fragile, and the share of do not know answers was larger for the financially fragile.

In conclusion, the summary statistics in Table 5 indicate that financial literacy is somewhat higher among those who can better deal with the economic outcomes investigated in this study. Taken together, we thus expect financial literacy to play an important role in relation to adverse economic outcomes. We go on to explore this relationship further in the regression framework.

Table 5.

OLS estimates of retirement planning on financial literacy

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

Dependent variable is a dummy indicating planning for retirement. 19% of respondents stated that they had tried to find out how much to save for their retirement. Those who did not know, were already retired, or refused to answer were coded as missing. All regressions include controls for age squared, the number of children, and regional controls for North, East, South, and West Finland.

5. Regressions and discussion

In this section, we study the link between financial literacy and retirement planning, over-indebtedness, and financial fragility. Following Lusardi and Mitchell (Reference Lusardi and Mitchell2011), the main explanatory variables include first a variable that indicates whether the individuals got all the big three financial literacy questions correct, second, a continuous variable of the number of big three questions correct, and finally, each of the big three questions on their own to measure the contribution of each question. Other controls include the respondents’ age and its quadratic, gender, education level indicators, and regional dummies.

Further controls include a dummy variable cohabitation/married, which takes the value one when the respondent reports they live with a spouse and zero otherwise. Therefore, this variable includes married couples and those couples who are cohabiting and are not necessarily married, which is common in Finland.

We also control for the number of children, which affects household expenses. The number of children is added as their own categorical variables, with the reference group being no children. The categories included are one, two, three, four, or more children. The maximum number of children in the sample was six children. The child in this definition refers to only children younger than 18 years of age.

We further added controls for income and wealth. The personal income of the respondent was added in four categories as up to 10 000€ per year, 10 to 29 999€ per year, 30 to 49 999€ per year, and 50 000€ or more per year.Footnote ² The survey asked the respondents to place themselves into given income categories. Using this, we constructed the categories above based on the most evenly distributed observations for each group. The lowest income group, those earning less than 10 000€ per year, was used as the reference group. We used home ownership as a measure of wealth. The indicator variable for home ownership includes those individuals who stated that they own their home, including both the ones who own their home debt-free and those with a mortgage.

Finally, measures of the individual’s employment status were included in the regressions. The self-employed are entered as their own category. The category not working includes those who are retired, looking for a job, are on parental leave, are unable to work, are studying, or are not working or looking for a job. Those working include both full-time and part-time workers, and it is used as the reference category.

Table 5 Footnote ³ reports the OLS estimates for retirement planning. The results do not give evidence for a strong link between retirement planning and financial literacy. Only interest knowledge is weakly significantly related to retirement planning, and this estimate is negative. This suggests that being more knowledgeable on interest rates, one would be less likely to have done retirement planning.

The empirical analysis thus did not find a strong link between retirement planning and financial literacy. This was similar to prior results in Kalmi and Ruuskanen (Reference Kalmi and Ruuskanen2018). The lack of a statistically significant relationship may be due to the nature of the Finnish pension system, which is of defined benefit type. Finnish citizens have limited means to influence their statutory pension earning, so there are few incentives to take action.

Of the other control variables, having higher education and income, as well as being self-employed give weakly significant estimates that are positively related to retirement planning. Relative to living in Southern Finland, living in the East or the North of the country is negatively and weakly significantly related to retirement planning. Gender is not significant, unlike in Kalmi and Ruuskanen (Reference Kalmi and Ruuskanen2018), where women were more likely to have been engaged in retirement planning.

Table 6 reports the OLS estimates when the dependent variable measures over-indebtedness, namely, the respondents who somewhat agree or completely agree with the statement “I have too much debt.” Answering correctly to all the financial literacy questions, as well as the number of correct answers are both negatively and significantly related to the outcome variable. However, none of the individual questions are significant.

Table 6.

OLS estimates of having too much debt

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

Dependent variable is a dummy indicating that the respondent has too much debt. 31.3% of respondents stated that they completely agree or somewhat agree with the statement “I have too much debt.” All regressions include controls for age squared, the number of children, and regional controls for North, East, South, and West Finland.

Financial literacy was thus found to have a negative relationship with perceived over-indebtedness, pointing to the direction that higher financial literacy may protect against adverse debt outcomes, as suggested by Lusardi and Tufano (Reference Lusardi and Tufano2015).

Table 7 presents the OLS estimations where the dependent variable indicates whether the respondent could not pay for a major expense. Here, the first two controls for financial literacy are negatively and significantly related to the event of not being able to come up with a sum equivalent to one’s monthly net income for a sudden expense. All the individual knowledge questions are also negatively related to the dependent variable, but only the risk question is statistically significant, suggesting that the understanding of risk diversification has an important link with financial fragility.

Table 7.

OLS estimates of facing a major expense

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

Dependent variable is a dummy indicating that the respondent could not pay an unexpected expense. 43.9% of respondents stated that they probably or for sure could not come up with a sum equivalent to their monthly net income to pay for an unexpected expense. All regressions include controls for age squared, the number of children, and regional controls for North, East, South, and West Finland.

Table 8 reports the OLS estimates where the dependent variable is the other measure of financial fragility, in this case experiencing an income shock. Having all three financial literacy questions correct is again negatively and significantly related to this outcome variable, as is the number of correct answers. Of the individual financial literacy questions, the question on risk is again negatively and significantly related to the outcome variable. The other two are also negative but not significant. Financial literacy is thus found to play an important role in protecting individuals from being financially fragile.

Table 8.

OLS estimations of financial fragility (income shock)

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

Dependent variable is a dummy indicating that the respondent could cover their living expenses for less than one month if they lost their main source of income. 32.4% of respondents stated that they could cover their living expenses for less than one month without borrowing money or moving. All regressions include controls for age squared, the number of children, and regional controls for North, East, South, and West Finland.

We ran a robustness check on the measure of financial fragility in Table 9, where instead of one month, we altered the definition of fragile to cover those individuals who could cope with the loss of their main source of income for up to three months. It can be argued that those individuals who could cope with the loss of their main source of income for up to three months could also be considered financially fragile. Here, the results became more significant, where now all measures of financial literacy were negatively and significantly related to the outcome variable. This suggests that financial literacy correlates even more strongly with the broader definition of financial fragility.

Table 9.

Robustness check for financial fragility (income shock)

Notes: Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

Dependent variable is a dummy indicating that the respondent could cover their living expenses for less than three months if they lost their main source of income. 55.1% of respondents stated that they could cover their living expenses for less than three months without borrowing money or moving. All regressions include controls for age squared, the number of children, and regional controls for North, East, South, and West Finland.

The regression Tables 5–8 were also run solely for the non-retired individuals aged 25–65. These results were mainly consistent with the findings presented above, although some differences were found with the restricted sample. Concerning the financial literacy controls, the results stayed the same for all four economic outcome variables in the restricted sample. The only change was for the case of facing a major expense. When we ran the major expense regressions on the population restricted to non-retired people aged 25 to 65, the results remained mostly the same, but the measure on interest was found to be negative and significant (see Table A4 in the Appendix). All of the tables on the non-retired population aged 25–65 can be found in the Appendix, in Tables A2–A5.

In conclusion, the results suggested a strong correlation between financial literacy and financial fragility. Having higher financial literacy was found to be correlated with being better able to handle major expenses and cope with income shocks.

Of the individual financial literacy measures, the understanding of risk was negatively and significantly related to both measures of financial fragility. Specifically, risk knowledge was found significant in facing major expenses and fragility to income shocks. Individuals with a better understanding of risk may be better prepared for major expenses and income losses.

We further found that the inflation question, although negative, was not significantly related to any of the economic outcome variables studied.

Moreover, higher education, living with a spouse, and home ownership can be seen as protective factors against financial fragility. These factors may provide additional resources and support, helping individuals better manage their financial situation and reduce fragility to financial shocks. Having children, on the other hand, can increase the financial burden on a family. In line with this observation, we found the number of children to be positively related to the adverse economic outcomes. Not surprisingly, our regression results also indicate that low-income earners are more likely to experience financial fragility. Also, having higher wealth, measured by home ownership, is associated with lower fragility. These results are consistent with earlier studies from other countries (see Lusardi and Mitchell, Reference Lusardi and Mitchell2011, Reference Lusardi and Mitchell2023).

While financial literacy is studied here in the OLS framework, we acknowledge that financial literacy can be an endogenous variable, meaning that it can be correlated with the error term. A usual approach to address this concern is turning to instrumental variables estimation. Unfortunately, we do not have a reasonable instrumental variable available and therefore leave this approach out of this study. In fact, the coefficients produced by the IV method are usually larger than the OLS estimates. Thus, the OLS estimates provided in this study may even underestimate the effects of financial literacy on economic outcomes.