May 9, 2014
Ray Boshara, Federal Reserve Bank of St. Louis (4:05)
- Keynote Address
Neil Howe, Founding Partner and President, LifeCourse Associates and President, Saeculum Research (36:03)
Keynote Q&A (11:04)
Extended Interview with Keynote Speaker Neil Howe (28:43)
Plenary One — A Micro and Macro Look at Younger Americans' Balance Sheets
- The State of the Balance Sheets of Younger Americans
Lisa Dettling, Board of Governors, Federal Reserve System (14:25)
- Links Between Younger Americans’ Balance Sheets and Economic Growth
William Emmons, Federal Reserve Bank of St. Louis (21:40)
Steve Fazzari, Washington University in St. Louis (18:14)
Plenary One Q&A (15:06)
Plenary Two — Student Loans
- Student Loans and the Economic Activity of Young Consumers
Meta Brown, Federal Reserve Bank of New York (21:12)
- Does Parents’ College Savings Reduce College Debt?
Melinda Lewis, University of Kansas (18:11)
Alex Monge-Naranjo, Federal Reserve Bank of St. Louis (19:01)
Plenary Two Q&A (18:22)
Concurrent Session I
Julie Birkenmaier, Saint Louis University (4:36)
- Toward Healthy Balance Sheets: The Role of Savings Accounts for Young Adults’ Asset Diversification and Accumulation
Terri Friedline, University of Kansas (22:23)
- Financial Decisions of Young Households During the Great Recession: An Examination of the SCF 2007-09 Panel
Wenhua Di, Federal Reserve Bank of Dallas (19:00)
John Sabelhaus, Board of Governors, Federal Reserve System (14:38)
Session One Panel Response (6:57)
Session One Q&A (5:41)
Concurrent Session II
- Impacts of Child Development Accounts on Change in Parental Educational Expectations: Evidence from a Statewide Social Experiment
Michael Sherraden, Washington University in St. Louis (13:09)
- Trends and Patterns in the Asset Holdings of Young Households
Ellen A. Merry, Board of Governors, Federal Reserve System (15:19)
Trina Williams Shanks, University of Michigan (7:06)
Session Two Q&A (28:58)
Plenary Three — Homeownership
Todd Swanstrom, University of Missouri–St. Louis (5:54)
- Homeownership and Wealth Among Low-Income Young Adults: Evidence from the Community Advantage Program
Blair Russell, Washington University in St. Louis (15:57)
- Aggregate and Distributional Dynamics of Consumer Credit in the U.S.
Don Schlagenhauf, Federal Reserve Bank of St. Louis (21:53)
John Duca, Federal Reserve Bank of Dallas (13:59)
Plenary Three Panel Response (6:40)
Plenary Three Q&A (8:46)
Plenary Four — Economic Mobility
Jason Purnell, Washington University in St. Louis (2:00)
- The Balance Sheets and Economic Mobility of Generation X
Diana Elliott, Pew Charitable Trusts (17:58)
- Coming of Age in the Early 1970s vs. the Early 1990s: Differences in Wealth Accumulation of Young Households in the United States, and Implications for Economic Mobility
Daniel Cooper, Federal Reserve Bank of Boston (17:11)
Bhashkar Mazumder, Federal Reserve Bank of Chicago (16:15)
Plenary Four Panel Response (3:14)
Plenary Four Q&A (19:02)
Closing Reflections: From Research to Policy
Michael Sherraden, Washington University in St. Louis (15:59)
Ray Boshara, Federal Reserve Bank of St. Louis (9:30)
Thank You / Adjourn
Julie Stackhouse, Federal Reserve Bank of St. Louis (5:59)
Below is a full transcript of this video presentation. It has not been edited or reviewed for accuracy or readability.
Wenhua Di: Good morning. I’d like to first thank the organizer of this conference, especially Ray and Brian to allow us to present for the first time a study young, financial decisions of young household during the Great Recession. This is a collaborative work and it’s with Sherry Ryan who is here at the panel, and William Green and Emily Ryder Perlmeter. The views expressed here are ours and do not represent those of the FDIC, the Dallas Fed or the Federal Reserve System. And it could possibly represent those of MYU. So financial decisions made at earlier stage of life could have significant impact on one’s remainder life of affable impact. So in this chart we divide households into nine age groups. And we want to get an idea of the rough, well a rough idea of the distribution of circumstances households are facing in 2007 on the five topics we’re going to analyze. The first one is banks, bank account status. As you could see over 80% of households of each age group have a bank account that they have either a checking account or a savings account. Having a relationship with mainstream financial institution can help young households to do financial transactions at a lower cost and have a safe place to put their, put part of their income for future use, facilitate their access to other products and services, such as loans or investment products, and also help them improve their financial management skills.
In the second decision we look at is liquid savings. As this chart shows, the share of households that live in liquid asset poverty, that is having inadequate liquid savings that sustain income above federal poverty level for 3 months decrease in monotonically as the age group gets older. The amount needed is about $5,200 in 2007, about $500 in 2009 and about half of the American households do not have that much savings to cushion financial shocks according to the threshold. And younger households are more likely to have high debt-to-asset ratios when they first take out loans to buy a home or take out loans to get a college education, although they anticipate higher future income or possible home appreciation. These returns are uncertain and households with—well, that are higher leveraged can be financially vulnerable. And you can see here the share of households, at middle age they have the highest—well, they have the highest share of mortgage. And then the share of a lower, well, younger and older groups have lower share. And then younger households are more likely to have education loans than older households.
The fifth decision we look at is participation in the stock market. And for a young household it’s usually challenging to diversify their portfolio because they don’t really have much wealth. And then they, or they have to put their wealth into home or some other, like a one type of asset. And then this is, this chart shows that in general older households are more likely than younger households to participate in the stock market. And the actual allocation of portfolio really depends on the only age or life decision, life cycle decisions but also risk preferences and their experience with financial shocks. Studies show that inexperienced investors could overreact to the financial shocks and become more risk adverse.
After two years the distribution changed somewhat. And this chart shows the changes. As you could see that events that changed to event status didn’t really have a clear pattern. And for liquid asset poverty, similarly, there are some ups and downs, which suggest that some of these remain vulnerable, exiting and entering liquid asset poverty at different age in their life. And their higher share of younger households, especially those in their 20s and 30s, and lower share of older households who have a mortgage, who obtain a mortgage or they, well, between the two years—and for older households it’s possible that they pay off their loans, and this just, and that changes. And there are a higher share of households across the age spectrum with education loans. This is consistent to findings of several studies presented yesterday. In middle-age households who have higher percentage entering the stock market.
So I’m going to talk about what are the outcomes of household decisions during the Great Recession, and how does a household life cycle decision age as approximately influence financial decisions and what factors contributed to choices at younger versus older households? And the data is the Survey of Consumer Finances 2007 to 2009 panel. The sample size is 3,857 with five implicates. And re-interview rate is about 89%. And about a third responded in 2007 were 40 years or younger.
We are going to focus on these choices on all of these five decisions, but I’m going to just give a brief overview of the value of the assets and debts of these populations. With lower level of assets and higher level of debts, younger households have much lower net worth than the older households. And between 2007 and 2009 on average net worth and asset values fell for both younger and older households with value of the liquid assets rising slightly for both groups, debt increased for younger families and slightly decreased for older families, and mortgage debt rose for younger households and fell for older families. And education loan that increased for both age groups.
In the next five slides I’m going to show you, the four possible within family outcomes of these decisions. And for older, for younger versus older families from 2007 to 2009. And so each slide has four quadrants. For example, in the banking account status model, younger, about 84, 84.6% of younger households, they were banked in both periods. And about 4.5% they were not banked in 2007, but became banked in 2009. And compared to the older group, they’re 91% of older households have bank accounts, both here and about 2.9% became banked. The other two outcomes are shown there, but they’re not going to be the focus of our study, and then the results can actually be derived mathematically from our model.
And there are more, well, younger households have a higher percentage who stay in a liquid asset poor than older households. And, but they are more likely to get out of asset poverty, liquid asset poverty between the two years. And younger households are less—well, they have lower percentage who obtain a loan, a mortgage between the two years that they are—well, sorry. They’re less likely, they have lower percentage, have mortgage loans in both period, but they’re more like, they have higher percentage of, percentage of getting into a mortgage loan between these two years. And younger households have higher percentage of having the education loan in both period, and they are also having, well, they also have a higher share of obtaining education loan between the two years. And younger households have a lower percentage being in the stock market between the two-year, in both years. And they are slightly, well they have a slightly lower percentage of obtaining, of entering stock market between the two years.
Those are the dynamics of these households between the two years. And in a multi-varied framework we use a bivariate probit model considering the correlation among the two periods and also we address the standard error for the five implicates with the repeated implicate inference method. And we’re trying to figure out whether age actually contributes to these differences. And we have other variables to explain the differences. We control for economic factors, which include family income, education, homeownership and employment. Demographic factors include age, marital status, number of children, race and ethnicity. And behavioral and change factors include loss of jobs, drop in liquidity, becoming uncovered by health insurance, becoming unmarried or separated or divorced, becoming long-term financial planners, stated risk preferences, becoming extensive credit shopper. And I will just provide some highlights of our results.
With the full sample we have a variable under 40 and under. And for all of these five decisions in separate models. And this variable, this dummy variable is statistically significant for all models for both outcomes we analyzed, except for the bank status model. For example, in the liquid asset poverty model the partial effect is about four percentage point. The baseline, well the baseline of probability of stay in the poverty, the liquid asset poverty is about 41%. That means younger, the probability of younger households in both years and liquid asset poverty is about 45%. And the baseline for entering asset, liquid asset poverty is about 10%, adding the 5 percentage point, it’s about 15%. That is the probability for younger households to get into liquid asset poverty. And younger households are less likely than older households to have a mortgage in both periods and less likely to get a mortgage loan, which is different than the outcome we show.
They are more likely to have a student loan in both period and also more likely to obtain a loan. And they are less likely to have, to have stock in their portfolio and less likely to enter stock market just between the two years.
So to further investigate how these age variables actually influence the decisions through other variables, so we run the back by vary probing model for both groups, sub-groups, separately. And I won’t have time to go through all of them, but I will just highlight a few. For bank status, no surprise, there are a lot of common factors, income homeownership adaptation positively related to bank status ownership, and being black and I think Hispanic are plus, are negatively related to bank status ownership in both period and also become banked in the second year. And loss in liquidity, loss in health insurance coverage and willingness to take risks only affect the older households for being banked in both years. And only health insurance coverage loss is a significant for a model becoming banked for the older households as well.
Liquid asset poverty has a very different story. Income, medication, not surprisingly, they are negatively related. And being black or Hispanic is positively related to being asset poor in both periods, but they are, well, and they are only affecting the older households to get into, get into liquid asset poverty for the black. And being black actually is negatively associated with entering liquid asset poverty between the two years, and the Asian and other has a different story compared to the white. They’re less likely to be liquid asset poor in both periods, but they’re more likely to enter liquid asset poverty between 2000 and 2009.
Other, well, divorce and separation also only affect the young, and all other factors only affect the older. And mortgages, I would just, well again, race and ethnicity, black, being black, Hispanic, Asian, other races comparing to white, they’re likely, they are less likely to have a mortgage in both periods, and for black they, actually they’re positive related to having a mortgage and add, between the two years. Only for the younger group. And other variables are affecting the older groups only.
Education loan, the only variable that’s come in between these two groups that is significant is becoming an extensive credit shopper, which is similar to the mortgage outcome. So being the credit shopper is positive effect. It’s actually next of the socialist getting a loan.
And again, races in this thing, have some impact on the younger group and other variables only affecting the older.
And stock market, not surprisingly, willingness to take risks is a positive related, positively effecting the outcome. And again, race explained the differences between the younger and older group, especially in, well, in the outcome of having stocks in both years.
So I’m going to skip the summary. Basically younger groups and older groups are very different. And some variables tend to explain, well, some of the variables are common and similar between the two groups and some variables are not, especially race, ethnicity, marital status and so on.
So I’d like to highlight a few things we are considering right now. This is the first draft of this paper, and we only use 40 and under, which is a quite arbitrary cut-off for age, and as you have seen, the distributions could be quite complex for different decisions. So we’re going to explore different age cut-offs and also explore other options that is associated with these decisions. For example, we, the same research team, we’re also working on a savings account, ownership paper, and then for those of retirement age, households, they have much lower percentage having a savings account, and we found that actually more than half of them have other options versus only 11% of younger households under 30-years-old. They have other, well they use other options. And we are going to investigate further race, ethnicity, dynamics because that’s really interesting for each group. There is different dynamics for all these decisions. And we are going to refine the specification of the mortgage and educational models because right now it’s pretty simplistic and we are going to allow interactions among these financial decisions.
And we’re also going to compare studies that analyze financial decisions at stable times and different recessions and studies that follow age cohorts, like some of the presentation we heard yesterday. Thank you.