Labor Market Experiences and Portfolio Choice:
Evidence from the Finnish Great Depression*
Samuli Knüpfer
London Business School and CEPR
Elias Rantapuska
Aalto University
Matti Sarvimäki
Aalto University and VATT
August 4, 2014
Abstract
Labor market experiences are a natural candidate for explaining heterogeneity in portfolio choice.
However, identifying their impact is challenging, because unobservables can influence the events
and circumstances people experience. We use plausibly exogenous variation in labor market
conditions during the Finnish Great Depression in the early 1990s to trace the long-run impact of
labor market experiences on portfolio choice. The results suggest that workers who experienced
adverse labor market conditions are less likely to invest in various types of risky assets. This
pattern is robust to a number of controls, including wealth and income, and it also is pervasive
across different types of workers. The consequences of labor market experiences span generations:
individuals whose parents have experienced adverse labor market conditions also avoid risky
investments.
JEL-classification: D31, E21, G11
*
E-mails: [email protected], [email protected], [email protected]. We thank Statistics Finland,
the Finnish Tax Administration, and the Finnish Armed Forces for providing us with the data. We also are grateful to
Ashwini Agrawal, Shlomo Benartzi, Janis Berzins, David Cesarini, Joao Cocco, Henrik Cronqvist, Francisco Gomes,
Harrison Hong, Kristiina Huttunen, Tullio Jappelli, Ron Kaniel, Markku Kaustia, Hyunseob Kim, Juhani Linnainmaa,
Ulrike Malmendier, Stefan Nagel, Stijn Van Nieuwerburgh, Alessandro Previtero, Miikka Rokkanen, Clemens Sialm,
Paolo Sodini, Robin Stitzing, and Johan Walden, as well as conference and seminar participants at the 4Nations Cup
2013, American Economic Association Meetings 2014, China International Conference in Finance 2013, Conference
of the European Association of Labour Economists 2013, Duisenberg Workshop in Behavioral Finance 2014,
European Conference on Household Finance 2013, European Finance Association Meetings 2013, Helsinki Finance
Summit 2013, Young Scholars Nordic Finance Workshop 2012, Aalto University, HECER, London Business School,
Research Institute of Industrial Economics, Stockholm School of Economics, and University of California at Berkeley
for valuable comments and suggestions. Antti Lehtinen provided excellent research assistance. Financial support from
Emil Aaltonen Foundation, Helsinki School of Economics Foundation, NASDAQ OMX Nordic Foundation, and OPPohjola Group Research Foundation is gratefully acknowledged.
1.
Introduction
The great heterogeneity in how individuals construct financial portfolios poses a challenge for
theory and empirical work. Studies using twin data deepen the puzzle by revealing that the large
degree of variation in portfolio choice cannot be attributed to genetically inherited traits or other
factors related to family background. Likewise, variation in observable characteristics, over and
above genetic makeup and family environment, does little to explain heterogeneity. 1 These
patterns suggest the answers to the heterogeneity puzzle may lie in the events and circumstances
individuals experience during their lifetimes.
Labor markets are a natural source of lifetime experiences. During the 2007–2009 Great
Recession, about one in six workers in the U.S. labor force experienced a job loss, and more than a
third of the workers expressed worries about layoffs, wage cuts, shorter hours, and difficulties of
finding a good job.2 Experiencing a job loss, having difficulties in job search, and seeing other
workers laid off can turn into formative experiences that permanently make individuals hold more
pessimistic beliefs, reduce their appetite for risk, or shatter trust in financial markets. This
formative experience hypothesis predicts that adverse labor market experiences make workers less
likely to invest in risky assets. Because these experiences vary widely in the population, they can
contribute to the large degree of unexplained variation in how individuals construct their
portfolios. When shared by many workers, they can also sideline large groups of individuals from
1
Bertaut and Starr-McCluer (2002), Calvet et al. (2007), Curcuru et al. (2010), Guiso and Sodini (2013), Haliassos
and Bertaut (1995), and Heaton and Lucas (2000) document portfolio heterogeneity. Barnea et al. (2010), Cesarini et
al. (2010), and Calvet and Sodini (2013) use register-based twin data to analyze the determinants of portfolio choice.
2
Farber (2011) documents the rate of involuntary job losses from 1981 to 2009, whereas Davis and von Wachter
(2011) report worker attitudes based on Gallup data.
1
participating in risky asset markets and generate systematic patterns in aggregate demand for risky
assets.
We use the Finnish Great Depression of the early 1990s and nearly two decades of
remarkably detailed register-based data to trace the long-run impact of labor market experiences
on portfolio choice. Finland’s registers allow us to precisely track employment histories of each
individual over nearly two decades and to link the employment records to equally impressive
information on portfolio choice. The high quality of the data circumvents measurement issues and
response biases that commonly plague survey-based studies. Our data set, which originates from
registers maintained by the Tax Administration, the Ministry of Labor, and various other
authorities, does not suffer from measurement error caused by recall biases in surveyed
employment histories or by imprecise reporting of asset holdings. 3
Identifying the impact of personal labor market experiences is challenging, because workers’
unobserved characteristics may determine selection into experiencing different labor market
conditions. 4 We solve the identification challenge by taking advantage of variation across local
labor markets caused by the Finnish Great Depression. The depth of the depression and its root
causes—the collapse of the Soviet Union in 1991 and a twin currency-banking crisis 5—meant
many local labor markets experienced unexpected and severe disruptions. This feature creates a
3
The main survey used to assess job losses in the United States, the Displaced Workers Survey (DWS), suffers
from recall bias due to its long recall period (five years in the early years of the survey, three in the later years). For
example, the number of displaced workers in 1987 is dramatically different when estimated based on answers to the
January 1988 wave (2.3 million) and to the January 1992 wave (1.3 million). See Appendix A in Congressional
Budget Office (1993) and Evans and Leighton (1995).
4
More risk-averse individuals may experience fewer labor market disruptions because they choose a safe career,
but at the same time, their risk aversion makes them less likely to invest in risky assets. This behavior would generate
a spurious positive correlation between labor market disruptions and risky investment.
5
From 1991 to 1993, Finland’s real GDP fell by 10% and the unemployment rate rose quickly from 3% to 16%.
The collapse of the Soviet Union in 1991 generated large output contraction in industries involved in the barter trade
between Finland and the USSR (Gorodnichenko et al. 2012). The export shock was amplified by a twin currencybanking crisis that is typically attributed to financial deregulation and credit expansion in the 1980s and to attempts to
defend the currency peg (Gulan et al., 2013; Honkapohja et al., 2009; Jonung et al., 2009).
2
source of plausibly exogenous variation and weakens the role unobserved worker characteristics
play in determining exposure to adverse labor market conditions.
Our measure of local labor market conditions stratifies the sample according to each worker’s
region, sector, and type of occupation, and calculates how the rate of unemployment in each
region-sector-occupation cell changed compared to years prior to the depression. When estimating
the impact of this variable on long-term portfolio choice, we control for fixed effects for each
dimension along which we calculate the rate of unemployment. We therefore identify the impact
of labor market conditions from variation in local labor markets while simultaneously controlling
for effects common to workers in regions, sectors, and occupations.
Our way of measuring labor market experiences is designed to capture factors in local labor
market conditions that are unrelated to worker characteristics. Indeed, a number of observable
worker characteristics measured prior to the depression do not correlate with labor market
experiences during the depression. Most importantly, labor market conditions during the
depression are not related to investment in risky assets prior to the depression. This falsification
exercise casts doubt on the ability of omitted variables to explain our results.
Using stock market participation as the explanatory variable, we find that experiences of
adverse labor market conditions are strongly associated with less long-term investment in risky
assets. The estimates suggest the stock market participation rate was, more than a decade later, 2.9
to 3.1 percentage points lower for workers who experienced a one-standard-deviation worsening
of labor market conditions. The t-values, clustered at the level of local labor markets, range from –
4.6 to –5.0. The effect is large given that the unconditional stock market participation rate in our
sample is 21.2%. The reductions in risky investment also extend to other asset classes. Adverse
labor market conditions are associated with less investment in bonds, balanced funds, and
3
derivatives. These effects suggest labor market disruptions strongly affect the extensive margin of
having any risky investments.6
The estimates are remarkably stable across additional specifications that control for predepression stock market participation, family fixed effects, observable characteristics of the
individual’s parents, and cognitive ability measured in an IQ test taken by males in the mandatory
military service. The most conservative estimate, obtained using family fixed effects that control
for unobservable characteristics shared by members of the same family, suggests a 2.5 percentage
point reduction in stock market participation (t-value –4.9). These results reinforce the conjecture
that the depression generates plausibly exogenous variation in labor market experiences.
An explanation alternative to the formative experiences hypothesis posits that the labor
market disruptions’ long-term impact on labor income, unemployment risk, and wealth
accumulation drives the effects on portfolio choice. These factors play a key role in determining
risky investment in normative theories of household portfolio choice and may indirectly contribute
to the reduction in risky investment following adverse labor market conditions (see Campbell,
2006, and Guiso and Sodini, 2013, for reviews).
Several pieces of evidence suggest labor market experiences do not solely operate through the
indirect channel. First, adverse labor market conditions are associated with investment in risky
assets even in regressions that flexibly control for labor income, unemployment risk, and wealth
accumulation following the depression. Second, we find less risky investment also for the subset
of workers who were in a better position to navigate the consequences of the depression, and
whose risky investment should therefore have been less affected through the indirect channel.
6
We do not find an effect on the intensive margin: portfolio volatility, beta, and idiosyncratic risk are not reliably
related to labor market experiences. The strong impact of labor market conditions on the extensive margin may mask
the true effect on the intensive margin.
4
These workers include individuals who remained in full employment throughout the depression
and who had higher pre-depression income and wealth.
The influence of formative experiences may also extend to an intergenerational setting: we
ask how labor market conditions an individual’s parents experienced affect her investment in risky
assets. We find a 1.1 percentage point reduction in stock market participation for individuals
whose parents experienced a one-standard-deviation worsening of labor market conditions (t-value
–2.8). Because of their young age, these individuals have few firsthand labor market experiences,
suggesting that the consequences of labor market disruptions might be transmitted from one
generation to another.
Taken together, our findings lead us to conclude that labor market experiences—just one
dimension of the many different events and circumstances individuals may experience—generate
a significant degree of heterogeneity in household portfolios that worker characteristics observable
in a typical data set do not explain. This result is related to four strands of research. First, our
paper contributes to the literature that studies how personal experiences influence investment
decisions. Chiang et al. (2011), Choi et al. (2009), Kaustia and Knüpfer (2008), and Odean et al.
(2011) analyze the role prior investment experiences play in determining IPO subscriptions,
retirement savings decisions, and stock purchases. These papers focus on relatively short-term
experiences and do not address portfolio heterogeneity. In their analysis of cohort-specific
macroeconomic experiences, Malmendier and Nagel (2011, 2013) find that the history of
experienced stock returns and inflation is associated with investment decisions and beliefs. Our
focus is different because we study the permanent marks that labor market experiences leave in
households’ financial portfolios. These formative experiences are measured from the cross section
of labor market conditions and thus vary within cohorts. This within-cohort variation can
5
contribute to portfolio heterogeneity over and above any macroeconomic experiences captured by
cohort effects.
Second, we share a theme with the few papers that have used twin data to understand
portfolio heterogeneity. Barnea et al. (2010) and Cesarini et al. (2010) show that genetic factors or
family environments can explain only a small share of the variation in household portfolios. These
papers point to experiences as a catch-all explanation for the remaining variation, whereas our
paper measures specific experiences and investigates their impact on portfolio choice. The focus
on labor market conditions and their long-term impact also sets our paper apart from Calvet and
Sodini (2013), who use a twin design to estimate the relation between observable wealth and risky
investment.
Third, we add to the literature that studies intergenerational transmission of beliefs and
attitudes toward risk. Charles and Hurst (2003) document positive parent-child correlations in
wealth accumulation and stock market participation, whereas Dohmen et al. (2012) report similar
correlations in survey-based measures of risk attitudes and trust. Our results suggest that
intergenerational correlations may partly be driven by personal formative experiences whose
consequences carry over to future generations.
Finally, we contribute to the literature that studies the impact of labor market conditions on
long-run earnings and unemployment (e.g., Couch and Placzek, 2010; Davis and von Wachter,
2011; Jacobson et al., 1993; Kahn, 2010; Oreopoulos et al., 2012; Oyer, 2008; von Wachter et al.,
2011). Our paper adds a new dimension to the effects associated with living through adverse labor
market conditions.
The remainder of this paper is organized as follows. Section 2 introduces the timeline of the
events, the data sources, and the empirical strategy. Section 3 presents results on the impact of
labor market experiences on long-term portfolio choice. Section 4 analyzes the role of wealth
6
accumulation and labor market outcomes in generating the effects, and section 5 analyzes
intergenerational mechanisms. Section 6 concludes.
2.
Timeline, Data, and Empirical Strategy
2.1. Timeline
This section details the timing of the events and describes the measurement of the predepression control variables, the labor market conditions, and the post-depression outcomes. What
were the main features of the depression? Its scale was unusual: no other OECD country had
experienced such a severe economic contraction since the 1930s. Figure 1 depicts the real GDP
and the unemployment rate from 1980 to 2005. Real GDP fell by 10%, and the unemployment rate
increased from 3% to 16% between 1990 and 1993. Although GDP started recovering at the end
of 1993, it reached its 1990 level only in 1996. The unemployment rate remained above 10% until
1999.
The causes of the depression were twofold. First, the collapse of the Soviet Union in 1991
generated large output contraction. Gorodnichenko et al. (2012) discuss why the collapse was so
detrimental for Finland. Soviet trade accounted for about 20% of Finnish exports prior to the
depression, with some industries, such as shipbuilding and railroad equipment manufacturing,
exporting more than 80% of their goods to the USSR. The products exported to the USSR were
often specialized and could not be sold easily to other countries (e.g., railroad equipment
manufacturing using Russian track gauge). The overvalued terms of trade in the barter
arrangements that exchanged the exported goods for oil and gas meant the effective price of Soviet
imports was significantly lower than their market price, resulting in an up-hike in energy prices
when the Soviet Union collapsed. The trade shock was also largely unexpected. The Soviet Union
7
cancelled the trade arrangements on December 6, 1990, without a transitional period, and the
Finnish firms did not seem to anticipate the shock.
The second factor contributing to the depression was a twin currency-banking crisis
(Honkapohja et al., 2009; Jonung et al., 2009). A few years prior to the depression, regulation
concerning domestic bank-lending rates was abolished, and foreign private borrowing was
allowed. Foreign loans, which had significantly lower nominal interest rates, appeared attractive:
25% of new borrowing was in foreign currency. However, Finland’s currency remained pegged.
These policies led to a significant increase in capital inflows and bank lending, and while the
economy was booming in the late 1980s, the financial sector became fragile.
Attempts to moderate the boom and to defend the currency peg against speculative attacks led
to a sharp increase in real interest rates. The defense ultimately failed, and the currency was
devalued and floated in November 1991 and September 1992, respectively. Benefits to exporting
firms were trumped by increases in debt burdens denominated in foreign currency. Declines in
asset prices and increases in credit losses contributed to a banking crisis, which likely further
affected consumption and investment through the classic credit channel (Bernanke, 1983).
Figure 2 summarizes the timeline of the events and the time of measurement of the key
variables. Our primary sample includes individuals born between 1955 and 1965. We measure
pre-depression control variables at the end of 1990, the year prior to the beginning of the
depression.7 We measure labor market conditions in 1991-93 and investigate their impact on
portfolio choice we observe, based on asset holdings, in 2005. We also use data on income,
7
We have also experimented with taking the control variables from the years 1987 or 1989. The results, reported in
Table IA1 in the Internet Appendix, are virtually unchanged.
8
unemployment incidence, and wealth accumulation in the 12-year post-depression period ranging
from 1994 to 2005.
2.2. Data
The data originate from three official primary sources that all include a personal identification
number used to merge the data.
Statistics Finland (SF). The first source provides us with the 5% random primary sample of
the population born between January 1, 1955, and December 31, 1965. The parents and siblings of
the subjects in the random sample supplement the data set.8 To be included in the final sample
used in the estimations, the subjects must be employed at the end of 1990, which leaves us with
31,191 individuals from the initial sample of 38,190 individuals. 9
The data from Statistics Finland pools together information from a number of administrative
registers held by different authorities. The unemployment data record the number of months a
subject was unemployed in a given year. The Ministry of Labor collects these data from the
Regional Employment Offices that register unemployed workers. The incentives to visit an
employment office are strong because the registration is a requisite for claiming unemployment
benefits. Other information from Statistics Finland includes field and level of education, sector
and region of the subject’s employer, year of birth, gender, marital status, and native language
(Finland has two official languages, Finnish and Swedish), drawn from the records at the Finnish
Tax Administration, the Ministry of Education, and the Population Register Center.
8
Table IA3 shows that the inclusion of siblings in the sample does not influence the results.
An individual can only leave the sample due to death. Regressions of mortality on labor market conditions,
reported in Table IA2, yield small and insignificant coefficients.
9
9
Finnish Tax Administration (FTA). The second data source records information on income,
assets, and liabilities of the individuals. The FTA collects these data to calculate the taxes levied
on income and wealth. We use the FTA’s data on labor income and the taxable values of assets
and outstanding liabilities. Stock ownership data are delivered to the FTA by Euroclear Finland,
and they contain the year-end number and value of shares held by an individual in each stock
listed on the Helsinki Stock Exchange. We link the stock ownership data with monthly stock
returns from the Helsinki Stock Exchange in our calculation of portfolio characteristics. The
mutual fund companies directly deliver the mutual fund ownership data to the FTA. These records
indicate the mutual funds in which an individual has invested and the year-end market value of
each holding. We supplement these data with information from the Mutual Fund Report (a
monthly publication detailing fund characteristics) on the asset class in which a fund invests and
monthly returns on each mutual fund. Grinblatt et al. (2014) provide additional details on the
mutual fund data. The stock and mutual fund holdings are available for the year 2005. We also
obtain a proxy for stock market participation from the FTA based on a worker reporting realized
capital gains in any of the years 1987-1990.
Finnish Armed Forces (FAF). Our third source provides a cognitive-ability test score that we
use as an additional control variable. Males in Finland must take a battery of tests when entering
the mandatory military service around the age of 19. We have comprehensive data on the 120question cognitive ability test beginning in the enlistment year of 1982. See Grinblatt et al. (2011)
for an extensive description of the test procedure.
2.3. Empirical Strategy
Our definition of labor market conditions takes advantage of the rich data that record the
region, the sector, and the type of occupation for all workers prior to the depression. We define
10
local labor markets using 10 sectors, 12 regions, and four types of occupations. 10 For the workers
in each of the 480 region-sector-occupation cells, we calculate the share of months they were
unemployed during the 1987-1990 pre-depression period and the 1991-1993 depression period.
Because we are interested in labor market shocks, we use the difference in the 1991-1993 and
1987-1990 unemployment shares as our main variable of interest. Intuitively, this variable
captures the change in labor market conditions in the region-sector-occupation cell caused by the
depression.
With this definition of labor market conditions at hand, our specification is
=
+
+
+
where i indexes individuals. The dependent variable
+
+
+ ,
(1)
indicates workers who invest in risky
assets (stocks in the baseline specification, bonds and other risky investments in robustness
checks).11
measures labor market conditions during the depression, and the vector of control
variables
contains observables measured prior to the depression. These control variables
contain indicators for deciles of income, total assets, and total liabilities, for the level and field of
education, 12 and for native language, gender, and marital status, all measured prior to the
10
The 10 sectors are agriculture, forestry, mining, and quarrying; manufacturing of food, textiles, and paper;
manufacturing of chemicals, metals, and machinery; energy, water, and construction; wholesale, trade,
accommodation, and food services; transportation and information technology; finance, insurance, and real estate;
business services; government, military, education, health, and social work; and non-profits and other. The four types
of occupation are blue collar, pink collar (clerical, sales, or administrative worker), white collar (professional worker),
and self-employed.
11
To facilitate interpretation, we estimate all the regressions using linear probability models. Table IA4 in the
Internet Appendix shows that logit models produce estimates that are similar to the OLS approach.
12
The nine fields are agriculture and forestry, business and economics, educational science, health and welfare,
humanities and arts, natural sciences, services, social sciences, and technology and engineering.
11
depression in 1990. Cohort dummies control for the birth cohort and age effects, although they are
not separately identifiable (Mankiw and Zeldes, 1991).
The three sets of fixed effects are for the worker’s region (
occupation (
), sector ( ), and type of
). Our estimation strategy thus identifies the effect solely from variation between
region-sector-occupation cells while controlling for unobserved characteristics of workers in each
region, each sector, and each type of occupation. The parameter
returns the influence of labor
market conditions on investment in risky assets if, conditional on controls and fixed effects,
selection into experiencing different labor market conditions is as good as randomly assigned.
This conditional independence assumption (e.g., Angrist and Pischke, 2009; Imbens and
Wooldrige, 2009) suggests that individuals identical along observable dimensions face the same
propensity to experience adverse labor market conditions.
2.4. Assessing the Conditional Independence Assumption
The main advantage of using the depression of the early 1990s is that it likely brings about
variation in labor market conditions that is unrelated to unobserved worker characteristics. We
evaluate this conjecture using information about the correlation of observable worker
characteristics with labor market conditions.
Figure IA1 in the Internet Appendix illustrates that the depression generated significant
shocks to local labor markets and that these shocks are not related to labor market conditions prior
to the depression. The great variation in labor market shocks likely comes from mass layoffs and
closures in establishments most heavily affected by the depression. These events are less
correlated with unobservable worker characteristics than downsizing in normal times, implying a
useful source of exogenous variation. Section 3 in the Internet Appendix uses auxiliary data to
12
show that a significant amount of variation in labor market conditions indeed comes from events
in which a large share of an establishment’s workforce lost their jobs.
Table 1 reports descriptive statistics of the sample and puts together evidence that suggests
our way of measuring labor market conditions captures variation that is unrelated to worker
characteristics. The four leftmost columns calculate the mean and standard deviation of each
characteristic in the years 1987 and 2005. The remaining columns report statistics that evaluate the
correlation of labor market conditions with observable worker characteristics. We first calculate
the average of each characteristic separately for local labor markets whose conditions are worse or
better than the median and find meaningful differences in many characteristics. However, the
differences become much smaller when we control for fixed effects for regions, sectors, and types
of occupations in the three rightmost columns in the table. This estimation is based on regressing
each characteristic in 1987 on our measure of labor market conditions during the 1991-1993
depression and fixed effects for the three dimensions of local labor markets. The marginal effects
are evaluated at a one-standard-deviation change in labor market conditions.
The regressions reveal that the differences in income, unemployment, and wealth
accumulation are statistically insignificant (t-values 0.9, –0.2, and –1.8). The marginal effects
suggest that workers who experienced bad labor market conditions during the depression had 207
euros more labor income and spent 0.01 months less in unemployment, but had accumulated 310
euros less wealth in 1987. Education, native language, and cognitive ability test scores (available
for men who entered the military in or after 1982) also show insignificant correlations with labor
market conditions during the depression. The significant marginal effects of marital status and
gender suggest the shares of married workers and females are 1.7% and 2.1% lower, respectively,
in the group of individuals who experienced adverse labor market conditions. These workers also
are 0.2 years younger than other individuals. Taken together, the results in this section show that
13
the depression generated a large degree of variation in labor market conditions, and that this
variation is not strongly correlated with worker characteristics.
3.
Labor Market Experiences and Investment in Risky Assets
The regressions in this section evaluate how investment in risky assets is related to labor
market experiences. We first present a falsification exercise that analyzes how risky investment
prior to the depression is associated with labor market conditions during the depression. We then
estimate regressions that relate post-depression risky investment to labor market conditions during
the depression and supplement them with a battery of robustness checks that add control variables
and vary the definition of local labor markets. We also investigate alternative dependent variables.
The main independent variable in all of these regressions measures the labor market
conditions the individual experienced during the 1991-1993 depression. The pre-depression
controls come from the year 1990, one year prior to the beginning of the depression. These control
variables include the fixed effects we use to control for differences in unobserved characteristics
that make workers sort into regions, sectors, and types of occupation, and the observable worker
characteristics detailed in section 2.3.
The test statistics are based on two methods to cluster the standard errors. The parentheses
report t-values assuming clustering at the level of the region-sector-occupation cell we use to
compute labor market conditions. The t-values in brackets use a two-way approach that
simultaneously clusters at the region and sector levels.
3.1. Falsification Exercise
Table 2 explains stock market participation in 1990, prior to the depression, with labor market
conditions during the depression in 1991-1993. These regressions address the possibility that
14
unobserved characteristics that lead workers to invest in risky assets correlate with labor market
conditions. Because stock and mutual fund holdings are only observed in 2005, the pre-depression
stock market participation proxy is based on tax reporting of capital gains. The dependent variable
takes the value of one if a worker files a tax return containing realized capital gains in the years
1987-1990, and zero otherwise.13 The average participation rate using this variable equals 7.6% in
1990, which is in line with the 9.3% stock market participation rate in 1996 (Ilmanen and
Keloharju, 1999).
The four specifications, which vary the sets of control variables, indicate that labor market
conditions during the depression are not economically or statistically significantly related to predepression stock market participation. The full specification in the fourth column delivers a
coefficient of 0.074 (t-value 0.8). This estimate translates into a 0.074 × 0.046 = 0.003 higher
stock market participation rate in 1990 for a one-standard-deviation worsening of labor market
conditions during the 1991-1993 depression. The falsification exercise thus strongly supports the
conjecture that the way we measure labor market conditions does not capture differences in
omitted variables that make workers invest in risky assets.
3.2. Baseline Regressions
The baseline regressions estimate the impact of labor market conditions on long-term
investment in risky assets using the year 2005 to measure stock market participation. The
dependent variable takes the value of 1 if an individual owns equities either directly or through
mutual funds, and 0 otherwise (we later investigate other variables related to risky investment).
13
Sales of stocks and mutual funds likely constitute the bulk of these gains because the sales of owner-occupied
housing are exempt from capital gains taxation.
15
This variable is based on the comprehensive asset holdings reported by the Finnish Tax
Administration.
The regressions in Panel A of Table 3 show that experiences of labor market disruptions are
strongly negatively associated with long-run stock market participation. In the most exhaustive
specification in the fourth column, the coefficient on labor market conditions takes the value of –
0.625 and has a t-value of –5.0. The coefficient translates into a –0.625 × 0.046 = –0.029 drop in
stock market participation in 2005 for a one-standard-deviation worsening of labor market
conditions during the 1991-1993 depression. This effect is economically significant because the
average stock market participation rate in 2005 is 21.2%.
An alternative way to gain perspective on the magnitude of the effect is to consider a
specification that breaks the labor market conditions down into quintiles and indicates each
quintile with a dummy variable (bottom quintile omitted). The estimates from this approach,
formally reported in Table IA5 in the Internet Appendix, show that the difference in stock market
participation rates between the bottom and top quintile of labor market conditions equals –0.046
(t-value –3.0). The coefficients on the other quintile dummies monotonically decrease between the
two extremes.
The estimates in the other columns of Panel A in Table 3 are remarkably similar to the full
specification. The coefficients vary between –0.601 and –0.676, implying marginal effects ranging
from –0.028 to –0.031. In section 3 in the Internet Appendix, we compare these estimates with
each other to assess how much any omitted variables could potentially bias the results using the
approaches of Altonji et al. (2005) and Oster (2014). These calculations indicate that to
qualitatively alter our conclusions, selection on omitted variables would have to be more than four
times larger than selection on the set of controls included in Table 3.
16
3.3. Robustness Checks
This section presents robustness checks that evaluate how much the estimates change when
we control for variables that could plausibly introduce bias in the baseline specifications.
Parameter stability across these alternative specifications would further alleviate concerns about
having omitted a relevant control variable. We also discuss results from regressions that use
alternative ways to construct the local labor markets and estimates from region-by-region
regressions that address the agglomeration of sectors and occupations into regions.
Controlling for Additional Variables. The first specification in Panel B of Table 3 controls for
the worker’s pre-depression stock market participation, defined as in section 2.2. The inclusion of
this characteristic addresses any variables that correlate with determinants of stock market
participation but are not captured by the controls in the baseline specifications. These include risk
aversion and attitudes toward financial market participation.
The second column in Panel B runs a family fixed-effects regression that identifies the effect
of labor market experiences solely from variation within offspring of the same parents. This
analysis controls for work ethics, prudence, employment opportunities, and other latent traits to
the extent they are shared by the members of the same family. The siblings increase the sample
size to 73,055 individuals.
The third column in Panel B uses observable characteristics of the worker’s parents in lieu of
family fixed effects. In this specification, we return to the baseline sample and get a sample size of
21,989 workers because the parental variables are not available for individuals whose parents have
already retired from the labor force by 1990.
The fourth specification in Panel B adds the cognitive ability test score. Workers with better
cognitive skills may be able to hold onto their jobs regardless of the state of the economy, and
they also are more likely to participate in the stock market (Grinblatt et al., 2011). The sample of
17
5,988 individuals omits all conscripts who entered the military prior to the year 1982 and women,
who are not required to enlist.
The results of these regressions, reported in the four leftmost columns of Panel B, show that
the effects are remarkably robust to different assumptions about the source of the omitted-variable
bias. The marginal effects range from –0.031 to –0.027 (t-values from –5.1 to –2.1). The stability
of estimates across the four specifications reinforces the conclusion that our approach successfully
identifies the effect of labor market experiences on portfolio choice.
Alternative Labor Market Definitions. Although our definition of labor market conditions
makes intuitive sense and is backed by the nature of frictions that prevent mobility across local
labor markets, we also study other definitions that vary the dimensions according to which we
stratify the labor market. The three rightmost columns in Panel B of Table 2 show results for
groupings that use information on the worker’s educational background as the source of labor
market segmentation. The –0.026, –0.019, and –0.020 marginal effects using the alternative labor
market definitions are statistically significant and indicate the results are robust to alternative ways
of defining the local labor markets.
Regional Analyses. In Table IA6 in the Internet Appendix, we experiment with regressions
run separately for each of the 12 regions. These analyses address the possibility that the
agglomeration of sectors and occupations into particular regions makes the fixed effects
ineffective in controlling for shared worker characteristics along the dimensions we use to stratify
the labor markets. All coefficients from the region-by-region regressions imply sizeable reductions
in risky investment. A joint significance test of the region-by-region coefficients rejects the
hypothesis that all the coefficients are zero (F-statistic 16.6, p-value 0.001). Table IA7 in the
Internet Appendix further shows that about half of the variation in local labor market conditions
cannot be explained by fixed effects defined for each pair of the three dimensions we use to
18
stratify the sample. These analyses indicate that agglomeration patterns do not make the fixed
effects ineffective in controlling for unobserved worker characteristics and that a large share of
variation in the labor market conditions variable is unique to each local labor market.
3.4. Other Measures of Risky Investment
In this section, we use additional dependent variables to evaluate how the effects extend to
other margins in the data. These analyses help us understand whether the patterns arise from
factors specific to the stock market or reflect more general changes in worker perceptions.
Malmendier and Nagel (2011) show that individuals’ participation decisions in a particular asset
market are related to their return experiences in that market. Because labor market experiences are
not related to any particular asset market, we would expect to find effects that extend to different
dimensions of risky investment. 14
Panel A in Table 4 replaces the stock market participation indicator with dummies for
investment in fixed income funds, balanced funds, or derivatives. The latter two refer to mutual
funds that invest in fixed income and equity, and to options and warrants written on publicly listed
stocks. The regressions show that adverse labor market conditions reduce investment in all
categories of risky assets. The marginal effects for fixed income and balanced funds equal –0.009
and –0.007 (t-values –3.0 and –2.1), respectively. These estimates are sizeable given the
unconditional investment rates of 0.056 and 0.074.
Do the adversely affected workers who nonetheless choose to participate in the stock market
shun away from riskier securities? Panel B in Table 4 investigates the impact of labor market
experiences on the riskiness of the financial portfolios held by the stock market participants. We
14
Barseghyan et al. (2011), Barsky et al. (1997), Cutler and Glaeser (2005), Dohmen et al. (2011), Einav et al.
(2012), and Wolf and Pohlman (1983) investigate the consistency of decision-making across different context.
19
measure riskiness by calculating return volatilities and other measures from the time series of
portfolio returns to circumvent any challenges that arise from categorizing securities into the least
and most risky categories.
The regressions in Panel B show that portfolio volatility is not correlated with labor market
experiences. This pattern is also true for market beta and share of idiosyncratic risk calculated
from regressions of an investor’s portfolio returns on the European stock market index. 15 The
strong association of labor market experiences with stock market participation, however,
potentially hampers the interpretation of these estimates. Nevertheless, the tests in this section
show clearly that labor market conditions influence the extensive margins of participating in
various risky asset classes.
4.
Wealth Effects and Labor Market Outcomes
This section analyzes how the impact of labor market conditions on wealth and labor market
outcomes contributes to the effect of labor market experiences on portfolio choice. Normative
portfolio theory suggests the labor market disruptions’ effects on wealth accumulation, labor
income, and employment can indirectly affect portfolio choice. Lower levels of wealth
accumulation can curb risky investment if there are fixed costs of stock market participation
(Vissing-Jorgensen, 2003; Gomes and Michaelides, 2005) and leverage—often originating from
having taken out a mortgage—can crowd out investment in risky assets (Chetty and Szeidl, 2010;
Cocco, 2005; Yao and Zhang, 2005). Lower levels of permanent income and background risks
15
Specifically, we use the euro-denominated MSCI Europe index as the market portfolio and the 36 most recent
monthly return observations in the estimation of portfolio risk measures. Table IA8 in the Internet Appendix shows
that the choice of the market index does not drive the results.
20
related to income variability also predict less risky investment (Bodie, Merton, and Samuelson,
1992; Cocco et al., 2005; Heaton and Lucas, 1997; Merton, 1971; Viceira, 2001).
In the two sections that follow, we first present evidence that explicitly controls for wealth
accumulation, labor income, and unemployment. Because the intimate link between wealth
accumulation and investment in risky assets may confound this estimation approach, we also
analyze interactions of the effect with pre-depression worker characteristics to gain perspective on
the role wealth and labor market outcomes play in generating our results.
4.1. Controlling for Wealth, Income, and Unemployment
The regressions in this section add post-depression controls
=
+
+
+
+
The coefficient on labor market experiences
+
to the estimation equation:
+
+
.
(2)
now returns the association of labor market
experiences with portfolio choice while holding contemporaneous measures of wealth
accumulation and labor market outcomes constant. In our setting, as in others with naturally
occurring data,16 no plausibly exogenous variation in the post-depression variables would allow us
to give the remaining estimate a causal interpretation. For example, workers who were sidelined
from the stock market during the good years that followed the depression likely accumulated less
wealth. Controls for contemporaneous wealth would thus partly capture the effect of labor market
conditions on risky investment and yield conservative estimates of
. Nevertheless, we believe
that exploring these regressions helps us understand if wealth accumulation and labor market
16
See Malmendier and Nagel (2011) and Sullivan and von Wachter (2009) for similar approaches in the contexts of
macroeconomic experiences and job displacement.
21
outcomes play a decisive role in generating the impact of labor market conditions on portfolio
choice.
We implement the regressions in Panel A of Table 5. The first specification includes decile
dummies for different dimensions of wealth accumulation. For the year 2005, we measure the
values of total assets and total liabilities, as well as the values of holdings in real estate, forest,
foreign assets (excluding equity), and private equity (usually a business). These asset-type
variables separate the effect of asset composition from wealth accumulation (as in Grinblatt et al.,
2011) and address the possibility that labor market experiences not only affect the level of wealth,
but also background risks associated with holding different types of assets.
The second specification adds the average, standard deviation, and growth rate of labor
income, whereas the fourth specification also includes the share of months spent in
unemployment. We measure these variables over the post-depression years of 1994-2005. The last
specification adds fixed effects for worker’s sector, region, and type of occupation, measured in
2005.
In the first specification, where we control for post-depression wealth accumulation, the
coefficient estimate equals –0.379 (t-value –3.4). The marginal effect for a one-standard- deviation
worsening of labor market conditions equals 1.8 percentage points. In the full specification in the
fourth column, this marginal effect equals 1.1 percentage points (t-value –2.2).17 These results
suggest wealth accumulation and labor market outcomes, observed after the depression, cannot
fully explain the reduction in risky investment following adverse labor market conditions.
17
Table IA9 in the Internet Appendix reports an alternative specification that aggregates the post-depression
variables at the level of the local labor markets.
22
4.2. Using Interactions to Investigate Wealth Effects
An alternative way to understand the role wealth accumulation and labor market outcomes
play in generating the results is to consider how the effect of labor market experiences varies
between different types of workers. Building on previous studies that show depressions hit
different types of workers in different ways (Couch and Placzek, 2010; Jacobson et al., 1993;
Oreopoulos et al., 2012; von Wachter et al., 2011), we analyze workers whose labor market
prospects were likely less affected by the depression. If wealth, income, and unemployment are
the sole drivers of portfolio choice, the reductions in risky investment should be the smallest for
the least affected workers.
Table 6 reports results from regressions in which labor market conditions are interacted with
various worker characteristics. The first column compares workers who remained in employment
throughout the depression to workers who experienced unemployment. The following four
columns split the sample according to the pre-depression income, wealth, level of education, and
age. These cutoffs are approximately at the median of each distribution except for the split for
education that indicates workers who had completed at least a high school degree (the
matriculation examination) prior to the depression. The table omits the coefficients for the main
effects in columns 2 through 5, because they are captured by the decile dummies for income,
wealth, and education and by the cohort dummies
The results show that none of the interactions are statistically significant. This finding does
not reflect a power issue due to a small number of observations, because most of the groups
indicated by the interactions comprise about half of the total workforce. The only exception to this
rule is the one-fifth of workers with high-school degrees. Their interaction coefficient is the only
estimate whose magnitude stands out from others, albeit far away from passing the hurdle of
statistical significance (t-value 0.9).
23
The insignificant interactions imply the portfolio effects are pervasive across various subsets
of workers. This pattern in turn is consistent with the reduction in risky investment not being an
artifact of wealth and income effects. A less benign interpretation is that our sample does not show
the wealth and income heterogeneity required for this conclusion to hold. Table IA10 in the
Internet Appendix reports that the income effects are strongly heterogeneous. For example, a onestandard-deviation worsening in labor market conditions results in a (-0.984 + 0.421) × 0.046 = –
0.026 decrease in long-term labor income for workers who remained in employment throughout
the depression. The same marginal effect equals –0.984 × 0.046 = –0.046 for the workers who
experienced a spell of unemployment. Similar conclusions apply to workers with above-median
income and above-median wealth.
The two remaining interactions in Table 6 are useful for other purposes. First, they help rule
out an alternative interpretation that relates our results to asset market experiences. Regions that
suffer from poor economic conditions likely see the stocks tied to the local economy, often held
predominantly by local residents, decline in value. If workers update their beliefs about future
stock returns, or if their risk preferences change due to changes in stock prices, the correlation
between local labor market conditions and stock prices can potentially contribute to the effects we
find.
If the updating from stock prices takes place at the local level, the workers who do not hold
these assets might update their beliefs the least. However, the interaction coefficient for predepression stock market participants in specification 6 shows that the effect of labor market
conditions on portfolio choice is not statistically significantly different for workers who had no
stock holdings prior to the depression (t-value 0.7). This result suggests the asset market channel
is unlikely to fully explain the influence of labor market conditions on long-run portfolio choice.
24
The second additional insight that arises from the interactions is that the reduction in risky
investment also applies to workers who moved away from the region in which they lived during
the depression. Keeping in mind that workers may be induced to move due to adverse labor
market conditions, specification 7 nevertheless shows that the interaction coefficient for these
workers is small in magnitude and statistically insignificant (t-value –0.4). Thus, the workers who
left the region in which they experienced the labor market during the depression appear to have
been affected in the same way as the workers who stayed in the region.
5.
Intergenerational Effects
In this section, we ask whether workers’ labor market experiences also influence their
offsprings’ investment decisions. These analyses speak to the transmission of heterogeneity across
generations: beliefs and attitudes passed on to future generations may slow down learning from
new data and lead to interesting asset-pricing implications.
To analyze these intergenerational effects, we examine a sample of individuals who were
born between 1972 and 1982 and were thus 8 to 18 years old in 1990. Because of their young age,
these individuals had little firsthand experiences of labor markets during the depression. However,
their parents’ experiences, perhaps through word-of-mouth communication or observational
learning, may affect their own decisions to invest in risky assets.
Table 7 regresses these younger cohorts’ stock market participation on the labor market
conditions their parents experienced during the depression. The first column repeats the baseline
specification from column 4 in Table 3 by measuring the labor market conditions the individual’s
father experienced during the depression. Similarly, the pre-depression control variables are now
defined for the father. The three columns in the middle add post-depression controls used in Table
25
5, which addresses the alternative hypothesis that labor market disruptions lead to changes in
wealth accumulation and labor market outcomes and these effects are transmitted to children. The
last column adds a control for the father’s stock market participation, which captures the
intergenerational effects that directly work through the father’s post-depression stock market
participation.18
The regressions in Table 7 are consistent with labor market experiences having an
intergenerational impact. The first column yields a 1.1 percentage point reduction in long-term
stock market participation for a one-standard deviation worsening in the father’s labor market
conditions during the depression (t-value –2.8). The remaining columns that add the postdepression controls yield 0.8-0.9 percentage point reductions in stock market participation. These
effects are statistically significant with t-values ranging from –2.8 to –2.4, which suggests the
effect is not solely driven by intergenerational correlations in wealth accumulation and labor
market outcomes.
Table IA11 in the Internet Appendix separately estimates the impact of the father’s labor
market experiences on younger and older children to account for the possibility that children in
different developmental stages are differentially susceptible to the influence of adverse labor
market conditions. The specifications with the full set of pre-depression controls yield reductions
of 1.1 and 1.4 percentage points in stock market participation for children who were 8-12 years
and 13-18 years old in 1990, respectively (t-values –2.1 and –2.3). We also investigate the impact
18
The inclusion of the father’s stock market participation addresses the possibility that the effects work through the
parents being less likely to bequeath or gift stocks or equity mutual funds following adverse labor market conditions.
We also directly investigate this possibility by using Euroclear Finland’s data on gifts and bequests. We estimate that
0.34 percentage points of the cohorts born in 1972 through 1982 became stock market participants through gifts and
bequests in 1995 through 2005. Under the extreme assumption that all the transfers came from parents who did not
experience adverse labor market conditions, gifts and bequests can explain only 0.3 / 1.1 = 27% of the effect of
parents’ labor market experiences on children’s stock market participation.
26
of the mother’s labor market experiences and find a marginal effect of –0.9 percentage points (tvalue –2.4).
These results are consistent with the idea that the consequences of labor market experiences
are transmitted from parents to offspring. This finding implies that the effects of personal
experiences carry over to future generations.
6.
Conclusion
We show that labor market experiences have a long-lasting impact on investment in stocks
and other risky assets. We establish this result by taking advantage of the Finnish Great
Depression in the early 1990s and rich register-based data spanning almost two decades. The
severity and suddenness of the depression make it a useful source of plausibly exogenous variation
in local labor market conditions, and therefore alleviate concerns about omitted correlates of
exposure to labor market disruptions.
We find an economically and statistically significant reduction in risky investment for the
workers whom the depression most adversely affected. The estimates are remarkably similar in
specifications with many different sets of control variables. Parameter stability is not surprising,
because a falsification exercise and other tests indicate that worker characteristics do not strongly
correlate with our measure of labor market conditions. Assessing omitted-variable bias by the
degree of selection on observable worker characteristics also supports the conclusion that any
remaining omitted-variable bias is an unlikely explanation for our results.
The explanatory power of labor market experiences does not come solely from their impact
on wealth accumulation, labor income, and employment, indicating they matter over and above
the factors documented in previous work. Given that the effects are confined neither to a particular
27
risky asset class nor to a subset of workers, the candidate explanations for the effects are
permanent changes in risk preferences, beliefs, or trust in financial markets.
Our paper speaks to the importance of formative experiences in generating heterogeneity in
household portfolios. We corroborate earlier findings on the role of personal experiences by
showing that labor market experiences are an important determinant of portfolio choice. We also
uncover an intergenerational channel through which the consequences of formative experiences
are passed on to future generations. These effects have implications for understanding belief and
preference formation. For example, the rate of learning from publicly available data may be
slower in an economy in which individuals are affected by personal experiences. The parent-child
correlations further suggest that the heterogeneity caused by personal experiences can persist in
the long run.
The focus on labor market experiences in our paper is partly dictated by the ability to identify
the effect more cleanly than in other settings. Formative experiences relating to other areas of life,
such as health and family, can also influence how individuals construct their financial portfolios.
Identifying the effects of these experiences provides avenues for future research.
28
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Table 1
Descriptive statistics
This table reports descriptive statistics of the individuals in the sample. Income and wealth variables are inflation
adjusted using the Consumer Price Index from Statistics Finland using 2005 as the base year. Months unemployed
calculates the annual number of months a worker spent in unemployment. Taxable net worth is the difference between
total taxable assets and total liabilities. The cognitive ability test score comes from an IQ test administered by the
Finnish Armed Forces to 5,988 men who entered the army in or after 1982. It is measured on a stanine scale where 1
indicates the lowest score, 5 is the average, and 9 is the highest score. The four leftmost columns show the mean and
standard deviation of each characteristic in the years 1987 and 2005. The remaining columns stratify the sample
according to labor market conditions, which are measured as the mean share of months spent in unemployment in a
region-sector-occupation cell. Unconditional means report the average values of the characteristics in local labor
markets where the labor market conditions were worse and better than the median. The conditional means are based
on regressing each characteristic in 1987 on labor market conditions during the 1991-1993 depression and on fixed
effects for regions, sectors, and occupations. The t-values are based on clustering at the region-sector-occupation
level. The marginal effect is the coefficient multiplied by the standard deviation of labor market conditions.
Full sample
1987
Mean
Standard
deviation
Stratified by labor market conditions
2005
Unconditional means
Regression-based
in 1987
conditional means in 1987
Mean Standard
Bad
Good
Marginal Coeff. t-value
deviation conditions conditions
effect
Income and wealth
Labor income (euros)
16,477
8,918
30,189
20,422
16,842
16,115
Months unemployed
Net worth (euros)
0.39
2,908
1.45
13,833
0.73
20,251
2.39
84,805
0.49
2,486
0.30
3,326
78.9
12.4
4.1
4.6
40.8
33.0
19.8
20.8
78.9
4.0
6.6
10.5
40.8
19.5
24.8
30.7
88.2
9.3
1.7
0.7
69.6
15.6
6.5
8.3
0.02
0.3
–0.3
–0.02
40.0
19.9
46.7
5.0
1,960.4
5.3
49.0
39.9
49.9
21.8
2.9
1.9
58.0
15.6
49.4
36.3
36.1
22.2
34.4
3.8
1,960.6
5.0
43.9
17.5
59.0
6.2
1,960.2
6.0
–1.7
0.6
–2.1
–0.2
0.2
–0.03
Education
Basic or vocational (%)
High school (%)
Bachelor (%)
Graduate (%)
207
4,475
(0.90)
–0.01 –0.14 (–0.24)
–310 –6,705 (–1.78)
0.3 (0.02)
5.7 (0.46)
–5.6 (–0.57)
–0.4 (–0.04)
Other characteristics
Married (%)
Cohabits (%)
Female (%)
Swedish-speaking (%)
Birth year
Cognitive ability
34
–36.7
13.9
–46.1
–3.3
3.3
–0.7
(–2.16)
(1.25)
(–2.54)
(–0.43)
(3.82)
(–0.56)
Table 2
Falsification exercise
This table reports coefficient estimates and their associated t-values from regressing a proxy for an individual’s stock
market participation decision in 1990 on labor market conditions during the 1991-93 depression. The dependent
variable takes the value of 1 if an individual reports any capital gains in 1987-90, and 0 otherwise. Pre-depression
controls are measured in 1990, one year prior to the onset of the 1991-93 depression. Specification 1 controls for 12
region dummies, 10 sector dummies, and three dummies for type of occupation (blue collar, pink collar, white collar,
self-employed). Specification 2 adds decile dummies for labor income (using average income from 1987-89), wealth
(0 omitted category), and liabilities (0 omitted), whereas specification 3 adds dummies for four levels of education
and nine fields of education. Specification 4 includes 11 cohort dummies and dummies for females, native language,
and marital status. The t-values reported in parentheses are based on clustering at the level of the local labor markets.
The marginal effect is the coefficient multiplied by the standard deviation of the labor market conditions variable.
Dependent variable
Specification
Labor market conditions during the depression
Pre-depression stock market participation
1
2
3
4
0.060
(0.65)
0.097
(1.07)
0.095
(1.06)
0.074
(0.83)
Sector
Region
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Type of occupation
Income, assets, and liabilities deciles
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Level and field of education
Demographics
No
No
No
No
Yes
No
Yes
Yes
Standard deviation of labor market conditions
0.046
0.046
0.046
0.046
Mean participation rate
Marginal effect
0.076
0.003
0.076
0.004
0.076
0.004
0.076
0.003
Adjusted R2
Number of labor market cells
0.015
475
0.036
475
0.038
475
0.041
475
31,191
31,191
31,191
31,191
Pre-depression controls
Number of individuals
35
Table 3
Labor market experiences and stock market participation
This table reports coefficient estimates and their associated t-values from regressions that explain an individual’s
stock market participation decision. The dependent variable takes the value of 1 if an individual holds stocks directly
or through mutual funds, and 0 otherwise. In Panel A, the pre-depression controls follow the structure of Table 2.
Columns 1 through 3 in Panel B extend the set of controls in specification 4 in Panel A. Pre-depression stock market
participation is based on the proxy defined in Table 2. Family fixed effects indicate siblings that belong to the same
family. Parental variables are the pre-depression controls defined for an individual’s mother and father. Cognitive
ability comes from an IQ test administered by the Finnish Armed Forces, and it enters the regression as a standardized
variable that has a mean of zero and standard deviation of one. Columns 5 through 7 in Panel B modify the definition
of the local labor market. Specification 5 replaces the sectors with nine fields of education, and the types of
occupations with four levels of education. Specification 6 is a combination of sectors and levels of education, whereas
specification 7 segments the labor market according to fields of education and types of occupation. The t-values
reported in parentheses are based on clustering at the level of the local labor markets, and the brackets report two-way
clustered t-values at the region and sector level. The marginal effect is the coefficient multiplied by the standard
deviation of the labor market conditions variable.
Panel A: Base line regressions
Dependent variable
Specification
Stock market participation
1
2
3
4
–0.676
–0.617
–0.601
–0.625
(–4.71)
[–3.23]
(–4.55)
[–3.97]
(–4.78)
[–4.10]
(–5.04)
[–4.12]
Sector
Region
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Type of occupation
Income, assets, and liabilities deciles
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Level and field of education
Demographics
No
No
No
No
Yes
No
Yes
Yes
0.046
0.046
0.046
0.046
0.212
–0.031
0.212
–0.029
0.212
–0.028
0.212
–0.029
0.069
475
0.103
475
0.114
475
0.121
475
31,191
31,191
31,191
31,191
Labor market conditions
Pre-depression controls
Standard deviation of labor market conditions
Mean participation rate
Marginal effect
Adjusted R2
Number of labor market cells
Number of individuals
36
Panel B: Additional control variables and alternative labor market definitions
Predepression
stock
market
participation
Specification
Additional controls
Family
Parental Cognitive
fixed
variables
ability
effects
Alternative labor markets
Region- Region- Regionfield of
sectorfield of
education- level of educationlevel of education occupation
education
1
2
3
4
5
6
7
Labor market conditions
–0.629
(–5.09)
–0.532
(–4.86)
–0.679
(–4.78)
–0.495
(–2.09)
–0.713
(–3.05)
–0.423
(–2.64)
–0.522
(–2.72)
Mean participation rate
Sd of labor market conditions
Marginal effect
0.212
0.046
–0.029
0.185
0.048
–0.025
0.222
0.046
–0.031
0.232
0.054
–0.027
0.212
0.036
–0.026
0.212
0.044
–0.019
0.212
0.038
–0.020
0.122
0.244
0.138
0.132
0.121
0.121
0.118
475
31,191
480
73,060
469
21,993
427
5,988
302
31,187
444
31,186
418
31,177
Adjusted R2
Number of labor market cells
Number of observations
37
Table 4
Alternative measures of investment in risky assets
This table explains alternative measures of risky investment with labor market conditions. The set of pre-depression
controls employed in all specifications corresponds to column 4 in Table 3. Panel A indicates workers who hold fixed
income mutual funds, balanced funds that combine fixed income with equity investments, or derivatives written on
publicly listed stocks. Panel B restricts the sample to workers who invest in the stock market and estimates the impact
of labor market conditions on the return volatility, beta, and idiosyncratic risk of their financial portfolios. Volatility is
based on 36 observations of monthly portfolio returns. Beta is estimated by regressing the monthly portfolio returns
on the MSCI Europe index, whereas idiosyncratic risk measures the share of portfolio variance attributable to variance
in the index. The t-values reported in parentheses are based on clustering at the level of the local labor markets. The
marginal effect is the coefficient multiplied by the standard deviation of the labor market conditions variable.
Specification
Panel A: Alternative participation variables
Fixed income
Balanced funds
funds
Derivatives
1
2
3
Labor market conditions
–0.193
(–2.97)
–0.150
(–2.10)
–0.233
(–2.92)
Mean dependent variable
0.056
0.074
0.011
0.046
–0.009
0.046
–0.007
0.046
–0.011
Standard deviation of labor market conditions
Marginal effect
Adjusted R2
Number of observations
Specification
0.018
0.012
0.043
31,191
31,191
31,191
Panel B: Portfolio risk conditional on stock market participation
Volatility
Beta
1
2
Idiosyncratic risk
3
Labor market conditions
0.116
(1.39)
0.468
(1.23)
0.118
(0.79)
Mean dependent variable
Standard deviation of labor market conditions
Marginal effect
Adjusted R2
Number of observations
0.186
0.041
0.005
0.072
6,622
1.248
0.041
0.019
0.099
6,622
0.620
0.041
0.005
0.284
6,622
38
Table 5
Controlling for wealth accumulation and labor market outcomes
This table controls for the impact labor market experiences may have on wealth accumulation, unemployment, and
income following the depression. The set of pre-depression controls employed in all specifications corresponds to
column 4 in Table 3. The post-depression values of assets and liabilities are from the year 2005, as well as the four
asset-type variables for the values of real estate, forest, foreign assets (excluding equity), and private equity (typically
a business). The mean and standard deviation of income, income growth, and months spent in unemployment are
calculated from the annual observations in 1994-2005. All the continuous post-depression control variables are broken
down into decile dummies. The t-values reported in parentheses are based on clustering at the level of the local labor
markets. The marginal effect is the coefficient multiplied by the standard deviation of the labor market conditions
variable.
Dependent variable
Specification
Labor market conditions
Pre-depression controls
Post-depression controls
Assets
Liabilities
Asset types
Mean income
Standard deviation of income
Income growth
Months unemployed
Sector, region, and type of occupation
Mean participation rate
Standard deviation of labor market conditions
Marginal effect
Adjusted R2
Number of individuals
Stock market participation
1
2
3
4
–0.379
(–3.37)
–0.308
(–2.78)
–0.288
(–2.58)
–0.240
(–2.24)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.212
0.046
–0.018
0.206
31,191
0.212
0.046
–0.014
0.213
31,191
0.212
0.046
–0.013
0.213
31,191
0.212
0.046
–0.011
0.217
31,191
39
Table 6
Interacting labor market conditions with worker characteristics
This table augments the specification in column 4 of Table 3 by adding interaction variables for various worker
characteristics. Workers with no unemployment during the depression did not experience unemployment spells longer
than one month in 1991-93. Workers above median income and median wealth are in the top half of the income and
wealth distributions in 1990. Age below median refers to workers who were born in or after 1961. Workers who held
stocks in 1990 are defined as in Table 2. Movers live in a region in 2005 that was not their place of residence in 1990.
The t-values reported in parentheses are based on clustering at the level of the local labor markets. The marginal effect
is the coefficient multiplied by the standard deviation of the labor market conditions variable.
Dependent variable
Stock market participation
Specification
Interactions that assess wealth effects
1
Labor market conditions
× No unemployment during the depression
N=22,664
× Income above median
N=18,703
× Wealth above median
N=17,545
× High school education
N=6,242
× Age below median
N=15,132
× No stock holdings prior to the depression
N=2,360
× Moved after the depression
N=7,925
No unemployment during the depression
2
3
4
Other interactions
5
–0.550 –0.577 –0.625 –0.645 –0.596
(–3.81) (–4.12) (–4.96) (–5.30) (–4.20)
0.060
(0.46)
–0.070
(–0.69)
–0.003
(–0.03)
0.189
(0.92)
–0.060
(–0.60)
6
–0.621 –0.575
(–5.02) (–4.51)
0.112
(–0.70)
–0.044
(–0.44)
0.039
(3.47)
No stock holdings prior to the depression
–0.030
(1.92)
Moved after the depression
Sd of labor market conditions
Mean participation rate
Adjusted R2
Number of labor market cells
Number of individuals
7
–0.037
(–3.52)
0.046
0.212
0.123
475
31,191
0.046
0.212
0.121
475
31,191
40
0.046
0.212
0.121
475
31,191
0.046
0.212
0.121
475
31,191
0.046
0.212
0.121
475
31,191
0.046
0.212
0.122
475
31,191
0.046
0.212
0.123
475
31,191
Table 7
Parents’ labor market experiences and child’s portfolio choice
This table explains an individual’s stock market participation decision with the labor market experiences of her father.
The sample consists of individuals born in 1972-82. The dependent variable is the stock market participation
indicator, and the independent variable measures the labor market conditions the individual’s father experienced
during the depression. Specification 1 corresponds to column 4 in Table 3. The remaining specifications add postdepression controls similar to Table 5, calculated for the individual and her father. Specification 6 further includes an
indicator for the father’s stock market participation. The t-values reported in parentheses are based on clustering at the
level of the local labor markets. The marginal effect is the coefficient multiplied by the standard deviation of the labor
market conditions variable.
Dependent variable
Specification
Stock market participation
1
2
3
4
5
Father’s labor market conditions
–0.704
(–2.76)
–0.566
(–2.54)
–0.553
(–2.52)
–0.538
(–2.47)
–0.519
(–2.40)
Father’s pre-depression controls
Yes
Yes
Yes
Yes
Yes
Father’s post-depression controls
Wealth controls
No
Yes
Yes
Yes
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Wealth controls
Income and employment controls
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Sector, region, and type of occupation
No
No
No
Yes
Yes
0.155
0.015
0.155
0.015
0.155
0.015
0.155
0.015
0.155
0.015
–0.011
0.091
29,331
–0.009
0.252
29,331
–0.009
0.261
29,331
–0.008
0.261
29,331
–0.008
0.269
29,331
Income and employment controls
Sector, region, and type of occupation
Stock market participation
Child’s post-depression controls
Mean participation rate
Standard deviation of labor market conditions
Marginal effect
Adjusted R2
Number of individuals
41
20
2
Real GDP
15
1.6
10
1.4
Unemployment rate
Real GDP index (base year 1980)
Unemployment rate
1.8
5
1.2
1
1980
0
1985
1990
1995
2000
2005
Figure 1. Unemployment rate and real GDP growth around the Finnish Great Depression
The graph depicts the annual rate of unemployment and annual real GDP growth in Finland from 1980 to 2005. The
shaded area highlights the Finnish Great Depression from 1991 to 1993.
42
Depression
Portfolio choice
Cohorts born
1955
1965
1990 1991
1993
2005
Pre-depression controls
Figure 2. Timeline of events and measurement
The sample consists of subjects born between 1955 and 1965 who are employed at the end of 1990 when the predepression controls are measured. Labor market conditions are measured during the 1991-93 depression, and the
portfolio choice variable comes from the year 2005.
43