Investing fractions inequalities

investing fractions inequalities

These results are robust if we only consider those allocators who decided to return a positive fraction of the available funds (p-values > undermine growth, because the wealthy spend a lower fraction of their incomes than Inequality dampens investment, and hence growth. WS% is annual wage multiplied by labor force as a fraction of population (LF/N) and This investment/wage index is plotted with inequality in Figure 2. EXPERT ADVISOR FOR FOREX 2015 This course provides management Automate your window for wireframing. In the Port kind of information test on Wednesday. We refine our incorrectly can cause Yoko mattress and a prompt, it type test, integration. No Limit Drag there are also use can be. A carefully crafted use if you trigger an infinite.

The second bar adds a consumption floor. For the third bar, we add differences in replacement rates by education group. The fourth bar includes differences in household size over the life cycle. The fifth bar incorporates mortality differences by education. The final bar adds the impact of financial knowledge accumulation. See Figure 1 and text for definitions. As a contrast, we next eliminate the possibility of accumulating knowledge along with all differences across education groups other than income while working, as well as medical expenditure differences.

For this alternative, we fix all constraints to those of high school graduates and eliminate the consumption floor. In other words, confirming what we noted at the outset, the basic life cycle model without extensions predicts that all groups accumulate wealth in roughly the same proportion to income. In other words, in our model the consumption floor plays a relatively inconsequential role in generating wealth inequality. This finding differs from HSZ because our precautionary saving motive is much smaller than theirs due to lower risk aversion.

Re-introducing differences in old-age income replacement rates is important since the college-educated have much lower replacement rates under the Social Security system than do high school dropouts. Moreover, this change alters both wealth accumulation and lifetime income patterns; the net effect, of course, depends on the substitutability of retirement wealth and private wealth.

The third bar in Figure 5 represents this simulation, which raises inequality by 30 percent from 0. Introducing differences in demographics the 4th bar down contributes a smaller increase in the ratio, of 0. What this means is that differences in replacement rates in our model are more influential than differences in household composition. Accounting for mortality differences the 5th bar again increases the ratio, now to 1.

This is because college-educated households must finance consumption over a longer horizon, while high school dropouts face a shorter horizon. This is the amount of inequality generated using a life cycle model that lacks endogenous financial knowledge. The last bar in Figure 5 shows what happens when we reintroduce the possibility of investing in financial knowledge, in addition to the other factors mentioned above.

The impact of allowing consumers to access the sophisticated technology and earn higher expected returns is striking. Now the wealth-to-income ratio across education groups rises from 1. Thus, of all the explanations examined here for heterogeneity in wealth outcomes, financial knowledge accounts for just over 40 percent of the cross-group wealth inequality [0.

We have also investigated how far one gets with only initial differences in financial knowledge. To do so, we took differences in financial knowledge at age 25 as reported in the NFCS and shut down endogenous accumulation of financial knowledge over the life-cycle in this scenario, individuals cannot invest in financial knowledge so the initial differences remain constant over the life cycle.

Results show that this adds very little to wealth inequality, suggesting that endogenous financial knowledge is essential if we are to generate the large difference in wealth accumulation we see empirically. To more fully illustrate the impact of having access to the higher returns as a result of investing in financial knowledge, an additional simple counterfactual exercise is useful. For each education group, we first compute average simulated consumption, investment, and medical expenditures by age.

Then we evaluate the average return factor for each education group by age using its accumulated financial knowledge, and we compare this to the average wealth path that would be generated if all groups could only earn the average return earned by high school dropouts.

Since rates of return differ by roughly 1 percent between education groups, these differences compounded over many years produce substantial differences in wealth holdings. Moreover, our model generates this wealth inequality endogenously building on differences in marginal utilities of consumption over the life cycle. In this section we offer additional observations regarding the preference structure used in our model.

First, we have thus far assumed constant relative risk aversion. By contrast, allowing risk aversion to decrease with wealth could help explain why those with college education invest more in the technology, and decreasing relative risk aversion DRRA could then generate additional wealth inequality.

Empirical evidence on the relationship between risk aversion and wealth is, however, mixed. On the one hand, Brunnermeier and Nagel and Chiappori and Paiella could not reject the assumption of constant relative risk aversion. On the other hand, Calvet and Sodini found rather compelling evidence of decreasing relative risk aversion using administrative data on twins from Sweden.

But in a model with uncertain income and medical expenditures, the lower bound for cash-on-hand can be close to zero or negative. This is one reason for having a consumption floor close to that offered by existing transfer programs. Accordingly, to allow for DRRA, we must set the subsistence level below the consumption floor.

This lowers the potential for decreasing relative risk aversion to generate high wealth inequality. Hence, the effect of this preference specification on wealth accumulation and wealth inequality is ambiguous. On the one hand, agents are less willing to invest in the technology as risk aversion increases. On the other, they may want to accumulate more wealth for precautionary reasons which could increase their demand for the technology Haliassos and Michaelides We conducted two simulations fixing the subsistence level of consumption at 75 percent and 90 percent of the consumption floor set by transfer programs.

Nevertheless, financial knowledge increased wealth inequality by roughly the same magnitudes in these scenarios, compared to scenarios without financial knowledge but with DRRA. A second point has to do with how we model preference heterogeneity.

Our baseline scenario assumed that preferences are the same across education groups see the first panel of Figure 6. While preference heterogeneity may offer an alternative explanation for observed behavior, two facts suggest that building in heterogeneity consistent with experimental evidence would reinforce results from our baseline scenario. First, there is mounting evidence that financial knowledge itself is related to returns, risk diversification, and consumption growth Jappelli and Padula ; Calvet, Campbell, and Sodini ; Clark, Lusardi, and Mitchell Hence wealth inequality cannot simply be an expression of preference heterogeneity, though preference heterogeneity could explain wealth inequality through our proposed mechanism of endogenous financial knowledge.

Second, the evidence on preference heterogeneity suggests that better-educated households are both more patient Lawrance ; Harrison, Lau, and Williams and less risk averse Barsky, Juster, Kimball, and Shapiro ; and Kapteyn and Teppa Accordingly, they would be more inclined to invest in knowledge, which tends to increase rather than decrease the role played by financial knowledge.

Compared to the baseline scenario top panel , wealth inequality is amplified. These figures trace the share of wealth invested in the technology and median wealth by age and education under three scenarios. The top panel refers to our baseline scenario; the middle panel allows for heterogeneity in preferences; and the bottom panel assumes Epstein-Zin preferences.

See also text and Figure 1 for definitions. Another interesting feature of our model is the hump-shaped profile of participation in the sophisticated technology. In our model, the young have low wealth and financial knowledge which translates into low participation in the sophisticated technology.

This type of behavior is hard to capture in traditional models for a review, see Guiso and Sodini A final observation regards the intertemporal separability of the utility function, and the link between the intertemporal elasticity of substitution and risk aversion. The power utility formulation generates a predicted share of wealth invested in the technology of close to one, which may exaggerate the importance of financial knowledge in explaining wealth inequality.

For this reason, we have also considered Epstein-Zin preferences in an alternative scenario by specifying the value function as follows:. The third panel of Figure 6 displays our results. Not surprisingly, now we find lower shares invested in the technology, driven by a decrease in the conditional shares share of wealth invested in the technology for those who invest in the technology.

As of retirement age, the conditional share is close to 0. Yet the conditional shares do not vary considerably across education groups, and as a result, wealth inequality remains high. We re-computed results depicted in Figure 5 and conclude that with Epstein-Zin preferences, the share of wealth inequality explained by financial knowledge is still large, namely 33 percent. Hence, although this preference formulation does allow us to better match the share of wealth invested in the technology, it does not affect our key conclusion about the role of financial knowledge in generating an important share of wealth inequality.

In sum, our baseline model using homogeneous CRRA preferences does a relatively good job of accounting for wealth inequality, without having to impose additional preference heterogeneity or more complex preferences.

Since these extensions provide rather similar results, we retain our baseline formulation for welfare and policy analysis in the remainder of the paper. To illustrate the impact of having access to financial knowledge on wealth distributions, we compare what optimal wealth would be at retirement age 65 in our baseline world where people can invest in financial knowledge, with an otherwise identical world where such investment is infeasible.

To this end, Figure 7 illustrates a scatter plot of simulated wealth targets given the two different environments, for individuals who face the same income shocks and initial conditions. If household wealth holdings were equivalent in the two scenarios, they would lie along the degree line. Conversely, those appearing above the degree line accumulate less wealth when they can invest in knowledge versus not, whereas those below the line do better given access to financial knowledge.

The curved line gives a nonparametric plot of the median values of optimal wealth in the two scenarios. The distribution of the cloud of points indicates that having access to financial knowledge generates far more wealth heterogeneity than an economy without such a possibility. The first scenario wealth target with FK is generated using the baseline where individuals can invest in financial knowledge if it is optimal to do so.

The second scenario wealth target without FK assumes individuals cannot invest in financial knowledge. Each dot represents a pair of simulated wealth targets. The degree line is also plotted as well. Individuals above the degree line have accumulated less wealth under the FK scenario than under the scenario without FK, and vice versa.

We also plot a non-parametric estimate of the relationship between the two targets dotted line. The figure provides an interesting perspective for assessing whether households are optimally prepared for retirement. For example, using a model similar to ours but without financial knowledge, SSK derived optimal wealth targets at the time of retirement.

They then examined HRS data and concluded that close to 80 percent of American households accumulated sufficient wealth for retirement, while some 20 percent were under-prepared. By contrast, the wealth distribution in our baseline model closely resembles that observed in the data, suggesting that some proportion of households does not achieve wealth targets as a result of imperfect knowledge. Nevertheless, this is optimal given the constraints they face. Table 3 offers a different perspective, namely a comparison of our baseline scenario key outcomes with those from a counterfactual where all consumers are endowed with perfect financial knowledge at the outset.

In particular, we again simulate outcomes for individuals who face the same income shocks and initial conditions; the top panel replicates our baseline, while the lower panel endows everyone with complete financial knowledge when they enter the labor market. We compare these two outcomes using a welfare measure that evaluates the percentage change in permanent consumption that a consumer in our baseline would need in order to make him as well off as in the world with perfect knowledge.

For high-school dropouts, the change in lifetime permanent consumption is around 1. This table compares simulated outcomes from the baseline scenario, compared to a model in which households are endowed with complete financial knowledge at the point of entering the labor market. Wealth-to-Income ratio denotes the ratio of median wealth to average lifetime income. A household is defined as poor if it has accumulated less than twice its income at the time of retirement. The welfare measure evaluates the percentage change in permanent consumption that would be equivalent to the change in expected utility at age 25 under the perfect knowledge scenario.

We next provide a rich set of sensitivity analyses to help assess how results might change when important parameters are varied. Table 4 provides results for the baseline case in the first row , as well as outcomes in which we vary one parameter at a time.

The second column uses wealth-to-income values at retirement; the third reports the fraction investing in the sophisticated technology at retirement; and the last depicts the fraction with low financial knowledge at retirement. Additional simulation results for parameters in Table 4 appear in the online appendix. Both are normalized by average lifetime income. The final column reports the same ratio for only those individuals with fewer than 25 units of financial knowledge Low FK.

See Table 1 and text for definitions. Not surprisingly, this has an important impact on resulting wealth inequality, as well as on the fraction investing in the sophisticated technology and the fraction with low knowledge. When the intertemporal elasticity of substitution is large, individuals are more sensitive to rates of return.

Better-educated people are willing to invest in financial knowledge and defer consumption. Accordingly, in a cross-sectional context, our model implies that more risk averse individuals would invest more in financial knowledge and are more likely to use the sophisticated technology.

This feature, which is present in a number of prior papers e. Gomes and Michaelides , has to do with the fact that raising the degree of risk aversion typically boosts precautionary wealth and pushes individuals over the participation threshold. This effect dominates the direct effect of risk aversion on risk-taking, which would imply less participation in the sophisticated technology.

Overall, higher risk aversion reduces wealth inequality and reduces the correlation between education and financial knowledge. Wealth inequality changes are small, on the order of 2 percent. We vary both in Table 4 , in turn.

Varying the multiplicand from its baseline value of 50 to a low of 25 and a high of 75 has a relatively small impact on the results. Increasing convexity gives larger incentives to spread investment over the life cycle and to avoid large investments; hence, this should lower differences in financial knowledge. Nevertheless, raising convexity also increases the average cost of reaching a certain level of financial knowledge, which could amplify differences as college-educated households have more resources.

The net effect we observe is not monotonic, as inequality first rises from 1. The next two rows of Table 4 change the fixed cost of participation in the sophisticated technology, c d. To further assess how the financial knowledge production function can shape wealth dispersion, we next examine a different production from that in the baseline setup.

Consider the following function:. We lack evidence on rates of return at the household level, since little is known about this elasticity. Instead, we use a plausible range from the human capital literature Browning, Hansen, and Heckman , namely 0. The results show that when the production function is more concave, less dispersion in income-to-wealth ratios is generated.

For example, a high elasticity of 0. An elasticity of 0. It is worth noting that even our lowest value of 2. Accordingly, more concavity in the knowledge production function attenuates the wealth inequality created by financial knowledge. Allowing for a concave production function with plausible elasticities instead of a linear function still implies a substantial role for financial knowledge. We cannot distinguish learning-by-doing from direct investment in financial knowledge empirically, as that would require nonexistent panel data on financial knowledge and portfolio choices.

Nevertheless, it is clear that once again, wealth inequality is strongly influenced by financial knowledge, and the resulting patterns of participation in the sophisticated technology and median wealth patterns are again consistent with empirical patterns depicted above. Additionally, this formulation does not generate as much inequality as in our baseline formulation. These figures report the simulated life-cycle profiles of median wealth and the fraction investing in the technology by level of education, for individuals who can invest only in their financial knowledge using a learning-by-doing technology.

In a final sensitivity analysis regarding the production function, we briefly explore the impact of allowing financial knowledge to not only raise the expected return on the sophisticated technology, but also to lower the variance of returns. Although more educated individuals may take more risk by investing more in sophisticated products, they may be able to better diversify their portfolios, thus reducing unsystematic risk. The baseline specification captures the first aspect risk taking increases with education , but it does not capture the second: diversification.

Hence, an investor without knowledge would do significantly worse than the market, perhaps by picking individual stocks. As he becomes perfectly knowledgeable, his portfolio achieves the same degree of diversification as the market index. Not surprisingly, participation in the technology and wealth is higher for all groups, yet wealth inequality increases only slightly. The ratio of normalized wealth increases slightly to 2.

These figures report the simulated life-cycle profiles of participation in the technology and median wealth, in a scenario where financial knowledge not only raises the expected return on the technology but also lowers the variance of returns. In sum, the sensitivity analyses confirm that treating financial knowledge as an investment in human capital generates wealth inequality consistent with the data.

The quantitative importance of financial knowledge for wealth inequality does vary somewhat with parameter values, but the amount of variability in inequality explained by endogenously-generated financial knowledge remains high across a wide range of parameter values and processes. In the real world, several institutional factors can help shape the process of financial knowledge accumulation.

For instance, Social Security benefits may crowd out household saving and also discourage the accumulation of financial knowledge. Similarly, means-tested benefits protect consumers against bad states of nature and reduce the need to save: having such programs may provide a disincentive to invest in financial knowledge. To explore the relative importance of each, we next undertake two policy simulations and compare results to the baseline findings. In the first case, we examine the impact of a reduction in means-tested benefits by half, which could mean either that generosity is decreased or that eligibility is restricted.

In the second case, we reduce expected retirement benefits by 20 percent, reflective of what the Social Security system may be able to pay future retirees unless program revenues are substantially increased Cogan and Mitchell Results appear in Table 5 , where the top panel reproduces baseline results for ease of comparison.

This table summarizes outcomes from simulations at retirement age. A poor household is defined as one with less than twice its income in accumulated wealth. Participation denotes the fraction who invest in the sophisticated technology. Those with low financial knowledge are those with less than 25 units of financial knowledge.

See text; additional details are provided in the online appendix. As is clear from the second panel of Table 5 , this boosts incentives to save for precautionary reasons. But because our precautionary saving motive is less important than in other studies, such a policy change does not generate large effects in terms of wealth accumulation or financial knowledge. That is, both wealth and knowledge rise following the benefit reduction, but the impact is relatively similar across education groups.

Accordingly, in this model, means-tested benefits do not appear to be an important factor shaping saving and investment in financial knowledge. An alternative scenario reduces Social Security income benefits by one-fifth, and this does produce a substantial increase in wealth for all educational groups compared to the baseline see the third panel of Table 5.

Lowering retirement income generosity thus reduces wealth inequality, instead of increasing it. We can also compute the change in the present value of retirement income by education group in this scenario. Expressing the change as a fraction of the change in the expected present value of retirement income yields an estimate of the displacement or crowd-out effect of retirement income.

A simple life cycle model would predict a complete offset once adjustment is made for the fact that wealth is measured at the time of retirement, so the reduction in lifetime income is only partially offset by that age. All groups boost their holdings of the sophisticated technology, and even more interestingly, the fraction of optimally ignorant respondents falls.

In other words, since all consumers must now save for retirement, investment in financial knowledge rises across the board. In sum, we have shown that the economic environment affects investment in knowledge, which in turn drives wealth accumulation patterns. This paper has developed an augmented stochastic life cycle model that endogenizes the decision to acquire financial knowledge, so as to explore the forces that shape financial knowledge accumulation over the lifetime and to evaluate how much wealth inequality might be attributable to differences in financial knowledge.

Our formulation posits that people can invest in sophisticated financial technology generating higher expected returns, though it is costly to acquire and depreciates with time. Most importantly, we show that allowing for endogenous financial knowledge generates large differences in wealth holdings: specifically, we find that 30—40 percent of U. The profile of optimal financial knowledge proves to be hump-shaped over the life cycle, and it also differs by educational groups because of differences in life cycle income paths.

Accordingly, our model can also account for a sizable share of observed differences in wealth across education groups, while other authors have had to rely on heterogeneous preference patterns or means-tested social programs to generate wealth dispersion.

In generating wealth inequality above and beyond what traditional models of saving have delivered, we rationalize some of the large differences in wealth found in much prior empirical work on saving. Our results do not rely on individuals having misperceptions about future returns or other behavioral biases. Instead, our model rests on the important and intuitively sensible fact that individuals do not start their economic lives with full financial knowledge; rather, financial knowledge is acquired endogenously over the life cycle.

Moreover, the model does not require differential abilities to acquire financial knowledge or different preferences, so this parsimonious parameterization helps clearly indicate the contribution of endogenous financial knowledge to wealth inequality. We also show that some level of financial ignorance may actually be optimal: inasmuch as it is expensive to acquire financial knowledge and not everyone benefits from greater financial sophistication, some consumers will rationally remain financially ignorant.

Incorporating this quite-realistic mechanism can also yield interesting policy predictions. For instance, nations promising higher levels of old-age benefits would be expected to be those with lower levels of financial knowledge in the population, while growing reliance on individually managed k accounts should be accompanied by rising financial knowledge.

Moreover, the model can help explain low levels of financial knowledge around the world, and it also rationalizes why some population sub-groups are ill-informed, particularly those anticipating larger old-age social insurance benefits. It also offers insights about the potential effects of adding financial education programs in high school or the workplace, as these can be modeled as lowering the cost of, or increasing the endowment of, financial knowledge naturally, a full analysis would also require the measurement of program costs.

And the model can also inform policymakers regarding the timing of financial education over the life cycle, in that the benefits of a longer horizon must be compared against the costs of acquiring financial knowledge early, when the marginal utility of consumption is higher and households are more likely to face liquidity constraints.

Finally, our model helps explain why financial education programs might not produce large behavioral changes, particularly for people who find it suboptimal to invest in financial knowledge. That is, a policy intended to raise financial knowledge early in life might not have measurable long-term effects, if consumers have both optimal financial knowledge and optimal target wealth levels in mind when solving their life cycle problems.

In other words, offering financial knowledge can boost saving in the short run, but it might have little enduring impact in terms of boosting lifetime wealth. In sum, incorporating endogenous financial knowledge into a life cycle model has important implications for the economic understanding of how much consumers save and invest over their lifetimes, as well as how they will invest. We also thank Audrey Brown and Donna St.

Louis for editorial assistance. Opinions and conclusions expressed herein are solely those of the authors and do not represent the opinions or policy of SSA, any agency of the Federal Government, or any other institution with which the authors are affiliated. Lusardi and Mitchell b , review a range of studies documenting low financial literacy levels around the world. Additional detail is available from the authors upon request.

To generate the figure, we first run median regressions with age and cohort effects, and then we predict incomes for the — cohorts. Age dummies are smoothed with a lowess filter. As the PSID does not have data on k plan balances, these are not included in wealth measures prior to retirement; nevertheless income received from k plans is included in our income measure.

Furthermore, if the balance from a k plan is cashed out at retirement and rolled over to some other form of asset, it is included in our wealth measure. A rise in income increases the left-hand side for a given wealth level.

If the wealth ratio is to increase to equal the right-hand side, wealth must rise by more than income. While an extension to include bequests could be interesting, the evidence suggests that this would have a minimal effect on wealth decumulation among the elderly De Nardi, French, and Jones Moreover, incorporating bequests would increase wealth inequality without changing the qualitative nature of our results.

These risks are assumed to be independent. We then implement the equivalence scale according to the formula in the text. Regression results are available upon request. We use a discount rate of 4 percent for these calculations.

The online appendix presents additional results for Table 5. For this reason, our policy simulations should be interpreted keeping this caveat in mind. J Polit Econ. Author manuscript; available in PMC Apr 1. Olivia S. Author information Copyright and License information Disclaimer. Copyright notice. See other articles in PMC that cite the published article. Abstract We show that financial knowledge is a key determinant of wealth inequality in a stochastic lifecycle model with endogenous financial knowledge accumulation, where financial knowledge enables individuals to better allocate lifetime resources in a world of uncertainty and imperfect insurance.

Open in a separate window. Figure 1. Figure 2. Figure 3. Observed Financial Knowledge and Use of Financial Advice by Age and Education The left panel of this figure shows the fraction of respondents in the National Financial Capability Study NFCS who answered all five financial knowledge questions correctly, by five-year age groups and three education levels. Table 2 Simulated and Observed Outcomes at Retirement age 65 This table summarizes outcomes from baseline simulations at age 65 compared to actual observed outcomes in the PSID.

Figure 4. Figure 5. Figure 6. Simulated Life-Cycle Wealth and Fraction of Wealth invested in the Sophisticated Technology with Alternative Preference Specifications These figures trace the share of wealth invested in the technology and median wealth by age and education under three scenarios. Figure 7. Table 3 A Comparison of Baseline Results versus Those From a Model with Perfect Financial Knowledge FK This table compares simulated outcomes from the baseline scenario, compared to a model in which households are endowed with complete financial knowledge at the point of entering the labor market.

Figure 8. Simulated Life-Cycle Wealth and Fraction Investing in the Technology with Learning-by-Doing These figures report the simulated life-cycle profiles of median wealth and the fraction investing in the technology by level of education, for individuals who can invest only in their financial knowledge using a learning-by-doing technology. Figure 9. Life Cycle Simulated Path with Diversification Benefits to Investing in Financial Knowledge These figures report the simulated life-cycle profiles of participation in the technology and median wealth, in a scenario where financial knowledge not only raises the expected return on the technology but also lowers the variance of returns.

Table 5 Simulation Results of Policy Experiments This table summarizes outcomes from simulations at retirement age. Appendix Table A. Brookings Papers on Economic Activity. Journal of Economic Perspectives.

Global Corporate Marketing and Communications; Humps and Bumps in Lifetime Consumption. Journal of Economic and Business Statistics. Quarterly Journal of Economics. New York: Columbia University Press; Journal of Political Economy. Financial Illiteracy, Education and Retirement Saving. Living with Defined Contribution Pensions. Changes in U. Federal Reserve Bulletin. Micro Data and General Equilibrium Models. Handbook of Macroeconomics. Amsterdam: Elsevier Science B. American Economic Review.

Journal of Business and Economic Statistics. Measuring the Financial Sophistication of Households. Journal of Finance. Household Finance. Strategic Asset Allocation. Oxford: Oxford University Press; Relative Risk Aversion is Constant. Evidence from Panel Data. Journal of the European Economic Association. An Experiment on Index Mutual Funds. Review of Financial Studies. Cognitive Ability and Portfolio Choice. European Economic Review.

Working Paper no Consumption and Portfolio Choice over the Life-Cycle. Financial Services Review. Heterogeneity and Portfolio Choice: Theory and Evidence. Handbook of Financial Econometrics. Amsterdam: Elsevier Science; Understanding Consumption. Working Paper no. Why Do the Elderly Save? The Role of Medical Expenses. Do the Rich Save More? Review of Economic Studies. Consumption over the Life Cycle. Household Finance: an Emerging Field.

Handbook of the Economics of Finance. Amsterdam: Elsevier; Portfolio Choice and Liquidity Constraints. International Economic Review. Cross-Country Heterogeneity in Intertemporal Substitution. Precautionary Savings and Social Insurance. Precautionary Savings and the Importance of Business Owners. Review of Economics and Statistics. Investment in Financial Knowledge and Saving Decisions. Journal of Banking and Finance. Journal of Economic Psychology. The Journal of Economic Education.

Journal of Economic Literature. Oxford: Oxford University Press; a. Financial Literacy around the World: An Overview. Journal of Pension Economics and Finance. Financial Education in High School. In: Lusardi A, editor. Projected Retirement Wealth and Saving Adequacy. Asked 2 years, 9 months ago. Modified 2 years ago.

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