Why Do Firms Hold Cash?
Evidence from Demographic Demand Shifts1
Igor Cunha
Nova School of Business and Economics
igor.cunha@novasbe.pt
Joshua Pollet
University of Illinois at Urbana Champaign
pollet@illinois.edu
May 4, 2015
1 We
thank Manuel Adelino, Heitor Almeida, Claudia Custodio, Murillo Campello, Ran Duchin,
Douglas Fairhurst, Michael Faulkender, Miguel Ferreira, Janet Gao, Andrei Gonalves, Sandy Klasa,
Valeriy Sibilkov, Rui Silva, John Tsoukalas, Philip Valta, Michael Weisbach for helpful comments
Abstract
We study why companies hold cash by exploring shifts in demand induced by demographics.
These shifts generate exogenous variations both on the precautionary motive and the free cash
problem. We find that firms increase their cash holdings in advance of high demand growth.
Consistent with the precautionary motive, this relation is stronger for financially constrained
firms. Additionally, we find that firms decrease their cash holdings when facing low forecasted
demand growth. Consistent with an adjustment to reduce the free cash problem this relation
is stronger for firms with stronger governance, lower debt, and in low concentration industries.
Keywords: Cash Holdings, Precautionary Motive, Free Cash Problem, Agency,
Payout, Financing Policy
JEL CLASSIFICATIONS: G30, G32, G35
1
Introduction
The corporate finance literature sought to explain why firms hold cash. It is well documented that firms with high growth opportunities and high costs of external finance hold
more cash (Almeida, Campello, and Weisbach (2004), Faulkender and Wang (2006), Denis
and Sibilkov (2010), Farre-Mensa (2014) and Gao, Harford, and Li (2013)). There is also
abundant evidence that firms with agency problems have larger cash reserves, and use it for
the wrong reasons (Harford (1999), Dittmar, Mahrt-Smith, and Servaes (2003), Pinkowitz,
Stulz, and Williamson (2006), Dittmar and Mahrt-Smith (2007) and Harford, Mansi, and
Maxwell (2008)). The biggest challenge facing this literature is that the existing evidence
relies on cross-sectional regressions that can have identification issues. It is extremely challenging to attribute the findings to the factors that might cause a firm to hold cash and not
to factors that are correlated with cash holdings due to other unobserved characteristics of
the firm. Given this difficulty, the literature lacks a well-identified estimate describing the
relation between a firm’s decision to hold cash and measures of either financing frictions or
private benefits of control.
The major roadblock hindering a definitive answer to this question is the lack of exogenous
variation in the various motivations for holding cash. We use predictable demand shifts
generated by demographic patterns (presented in detail in DellaVigna and Pollet (2007) and
Dellavigna and Pollet (2013)) to address this issues. Our identification strategy relies on
the fact that certain firms are in industries linked to specific age cohorts (such as bicycle
and pharmaceutical). Figure 1 presents the distribution of household consumption of such
industries as a function of the age of the head of the household. Consumption pattern for
bicycles peaks when the age of the head of household is between 35 and 45 1 . The analogous
pattern for pharmaceutical consumption provides a useful contrast. This pattern indicates
that consumption increases with age. We explore the consumption age pattern of these goods
to forecast consumption up to 10 years in advance. We do so combining data on cohort size,
1
Presumably, the heads of household in this age range are the most likely to have children of bicycle-riding
age.
1
mortality, and fertility rates to form forecasts of the size of gender and one-year-age groups.
Variation generated by demand shifts is appropriate to study questions related to cash
holding. First, the predictable shifts in demand generated by demographics are not endogenously related to any of the firm’s fundamentals. Second, they only have an impact in the
future and do not affect the firm’s current cash flow. These demand shifts provide a novel
opportunity to avoid the classic endogeneity concerns and determine the drivers of changes
in firms’ cash holdings.
We begin our analysis by testing the identifying assumption that the changes to investment
opportunities created by predictable demand shifts in the long term are unrelated to the
firms’ current cash flow. This is important for two reasons: First, it provides evidence that
the future demand shifts induced by demographics are likely not driven by current macro
and firm fundamentals. Second, it assures us that our results are not contaminated by any
misspecification that could be generated by the relations between cash and cash flows. We
analyze linear specifications of a firm’s cash flow on demand shifts predicted by demographics.
We find that forecasted demand shifts are not associated with changes in firms’ current cash
flows. The absence of any relation between demand shifts in the more distant future and the
firm’s current cash flow rules out the possibility that omitted factors simultaneously affecting
cash holdings and current cash flow explain our results.
There are several explanations for the level of cash holdings. First, cash itself may not be
optimally selected by managers; instead the level of cash is simply the consequence of historical
cash flow patterns, combined with investment and payout policies. Second, managers with
properly aligned incentives may carefully choose the level of cash by balancing the expected
benefits and costs of holding cash. The main benefit of holding cash is the precautionary
motive linked to financing frictions, that is, a manager chooses to hold cash to avoid foregoing
profitable investment opportunities. Third, if the incentives of managers are not properly
aligned, agency conflicts may lead the manager to accumulate cash to pursue negative net
present value projects that increase the manager’s private benefits, the free cash problem.
Demand shifts are particularly useful to study the reasons why firms hold cash, because
2
they generate exogenous variation on both the precautionary motive and the free cash problem. High forecasted demand growth implies an improvement on firm’s investment opportunities and consequently an increase in the precautionary motive. Low forecasted demand
growth implies a deterioration of the firm’s investment opportunities. The cash firms have in
their books is no longer needed to fund good investments. Managers have the flexibility to
decide how to reallocate this cash, which can lead to the free cash problem.
We start our analysis of the cash holdings exploring shocks to the precautionary motive.
We analyze changes in cash levels of firms with high forecasted demand growth. We classify
firms as high demand growth if their forecasted demand growth is in the top quartile of the
forecasted demand growth distribution. Consistent with the precautionary motive of cash
holdings, we find that firms that have high demand growth forecasted for the long term (5
to 10 years) increase their cash holdings by 18%, which is both statistically and economically
significant. In addition, consistent with the recent literature that shows that lines of credit
are an important alternative source of liquidity (Sufi (2009), Disatnik, Duchin, and Schmidt
(2014) and Acharya, Almeida, and Campello (2013)) these firms also adjust their lines of
credit. We observe an increase of 15% on the lines of credit of firms experiencing high demand
growth forecasted for the long run.
We further explore the precautionary motive testing the effects of the forecasted demand
shifts on financially constrained firms. These firms have a higher benefit of holding cash in the
presence of good investment opportunities. Therefore, they should present a more pronounce
increase in their cash holdings when faced with an increase in their investment opportunities.
We find that the impact of high demand growth is stronger for more financially constrained
(smaller, with less tangible assets and without a credit rating) firms. In fact, the increase in
cash holdings of firms with high demand growth in the long term is almost exclusively driven
by firms without credit ratings.
An important assumption of our identification strategy is that demographic demand shifts
are not driven by other simultaneous factors. In order to further validate our exclusion
restriction, we run a falsification test using industries that have a high forecasted demand
3
growth, but that are not targeted to a specific age cohort. These firms are subjected to
the same macroeconomic and business conditions as the demographic sensitive firms, but
changes in demographics do not impact their investment opportunities. Our results indicate
that firms that are not sensitive to demographics do not adjust their cash holdings when
faced with forecasted demand shifts. The lack of statistical and economic significance in this
falsification test allow us to conclude that our results are not contaminated by a confounding
factor simultaneous to the demand shift.
Demographic demand shifts offer another feature that is extremely helpful on the analysis
of the reasons why firms hold cash. Dellavigna and Pollet (2013) find that, because managers
are involved in the long term planning of the firm, they are aware of the forecasted demand
shifts well in advance and use this information for their capital structure decisions. DellaVigna and Pollet (2007) show that investors, on the other hand, are not aware of the shifts
forecasted for the long run. These shifts have a strong predictive power of the stock returns
of demographic sensitive firms. Therefore, demand shifts forecasted for the long run (5 to 10
years) increase information asymmetry between managers and investors. The combination
of high investment opportunity and high information asymmetry offer the perfect setting to
explore questions related to cash holdings. This is precisely the situation at which theory
predicts that cash should be relevant. (Holmstrom and Tirole (1998), Tirole (2010))
We explore the sources of funds firms use for their cash holdings and find evidence consistent with an increase in information asymmetry. We find that predictable demand shifts have
a substantial impact on the firm’s payout policy. Firms with high long term demand growth
forecasted for the long run reduce their total payout over assets by 15% 2 . The increase in cash
holdings is generated by a reduction in payout rather than cuts in investment and R&D or
increases in equity or debt issuances. These results are consistent with the findings in Dellavigna and Pollet (2013). Even though, these firms have good investment opportunities in the
future, the information asymmetry obstruct their ability to raise external capital. The fact
2
Although firms reduce dividends, the changes in payout are mainly coming from a reduction in share
repurchases
4
that our setting is closely related to the theoretical framework is important for the external
validity of our findings. Our estimates should be applicable to other settings in which firms
have good investment opportunities, but are unable to communicate it to their investors.
Demographic demand shifts also allow us to test how firms react to an increase in the free
cash problem (Jensen (1986), Jensen and Meckling (1976), Myers and Majluf (1984)). These
problems arise when the firm has worsening investment opportunities and cash is no longer
needed to avoid forging a profitable investment opportunity. Excess cash holdings can give
managers flexibility to engage in value destroying empire building. Low demand growth offers
an opportunity to study how firms change their cash policy when faced with an increase in the
free cash problem, because they are a negative shock to investment opportunities. We classify
firms as low demand growth if their forecasted demand growth is in the bottom quartile of
the forecasted demand growth distribution. Our baseline results provide weak evidence that
firms reduce their cash holdings when faced with a low demand growth.
We further explore the relation between the free cash and firm’s cash policy looking at
how firms with good corporate governance react to the negative shocks. We find evidence
that firms with good governance significantly decrease their cash when the free cash problems
are more likely to be an issue. Our results indicate that firms with good governance measured
using the G-Index decrease their cash holdings by 53% in these instances. Similarly, firms
with institutional control decrease their cash by 15% when faced with deteriorating investment
opportunity.
One possible reason why our evidence on the effect of worsening investment opportunity
on cash policy is weak is that there might be other factors mitigating the free cash problems
such as debt and competition. We further explore how the free cash problem affects firms’
cash policy isolating firms experiencing low demand growth but that present other factors that
attenuate the free cash problem. First, we isolate highly leveraged firms. The disciplinary
effects of debt are well documented (Jensen and Meckling (1976), Jensen (1986) and Hart
and Moore (1995)). Firms with high leverage do not have a free cash problem when faced
with a decrease in investment opportunity. We find that firms with interest coverage below
5
one do not decrease their cash holdings when faced with a low demand growth in the short
term. Once we isolate highly leveraged firms, the effects of low demand growth on cash
holdings become economically and statistically significant. Our estimates indicate that firms
with interest coverage above one cut their cash holdings by 17% when faced with a low short
term demand growth.
In our final analysis, we isolate firms in highly concentrated industries. Giroud and Mueller
(2010, 2011) show that competition mitigates manager’s financial slack and that strong corporate governance is only effective in noncompetitive industries. Fresard (2010) shows that
firms use cash holdings to increase their strategic aggressiveness in the product market. Firms
in highly concentrated industry that are experiencing low demand growth can use their cash
holdings to implement a competitive strategy to avoid new entries. Therefore, for these firms
the free cash problems is less pronounced. They actually have an important use for the cash,
despite the decrease in investment opportunities. Consistent with the existing literature, we
find that firms in highly concentrated industries do not decrease their cash levels when faced
with a low demand growth. Firms in less concentrated industries decrease their cash significantly. Our estimates indicate that firms in less concentrated industries decrease their cash
levels by 27% when faced with a low long term demand growth. Taken all together, our
evidence shows that firms take the free cash problem into account on their cash policy and
adjust their cash levels to avoid it.
Other authors have studied what factors affect firms’ cash holdings.3 There is an abundant
evidence that business risk is associated with higher levels of cash holdings (Kim, Mauer, and
Sherman (1998), Opler, Pinkowitz, Stulz, and Williamson (1999), Acharya, Almeida, and
Campello (2007), Pinkowitz and Williamson (2007), Bates, Kahle, and Stulz (2009), Lins,
Servaes, and Tufano (2010)) and that the use of diversification to reduce business risk is
associated with lower levels of cash (Duchin (2010) and Erel, Jang, and Weisbach (2014)). It
is also well documented that financial frictions play an important role in a firm’s decision to
hold cash (Almeida, Campello, and Weisbach (2004), Faulkender and Wang (2006), Denis and
3
Almeida, Campello, Cunha, and Weisbach (2013) provide a detailed summary of the state of the literature.
6
Sibilkov (2010), Farre-Mensa (2014) and Gao, Harford, and Li (2013)). The literature also
provide evidence on how the free cash problem affects firms’ cash holdings. Dittmar, MahrtSmith, and Servaes (2003) find that firms in countries with worst investors’ protection hold
more cash. In addition, investors respond more positively when cash enters firms with lower
agency problems (Dittmar and Mahrt-Smith (2007) and Pinkowitz, Stulz, and Williamson
(2006)), and when it leaves a firms with higher agency problems (Grullon and Michaely
(2004)). Harford, Mansi, and Maxwell (2008) find that firms with worst corporate governance
actually have lower cash levels because they spend it more quickly, especially in mergers and
acquisitions. Harford (1999) finds that firms with more cash are more likely to engage in value
destroying acquisitions. Azar, Kagy, and Schmalz (2014) show that the opportunity costs of
holding cash (cost of carry) is also an important determinant of the decision to hold cash.
Lastly, other authors found evidence of the strategic benefits of holding cash (Haushalter,
Klasa, and Maxwell (2007), Fresard (2010), Boutin, Cestone, Fumagalli, Pica, and SerranoVelarde (2013), Fresard and Valta (2013), and Morellec, Nikolov, and Zucchi (2013)).
This paper contributes to the literature by providing causal estimates of the reasons why
firms adjust their cash levels. The exogenous variations we use allow us to observe precisely
what kinds of changes make managers revise their cash policies. Our results are not contaminated by confounding factors in tests of the relation between investment opportunities and
cash. In addition, our tests are not contaminated by omitted factors that might explain the
firm’s innate propensity to save. In our setting, firms do not experience an increase in their
cash flows and are actively increasing their cash by increasing their retention ratio. Our tests
are also not contaminated by other choices of liquidity. The shocks explored in our tests increase the information asymmetry between firms and investors, which hinder the firm’s ability
to access external capital (Dellavigna and Pollet (2013)). Our results validate and extend the
intuition of previous work on the tradeoffs of cash holding. Our evidence indicate that firms
clearly understand the tradeoffs of holding cash, using it on their financing decisions when
needed, and reducing it to avoid the negative effects it might cause.
7
2
Methodology and Data
2.1
Demand shifts
We use the set of predictable demand shifts induced by demographics described in DellaVigna
and Pollet (2007, 2013). Please see those papers for a complete discussion of the methodology
for building the demand forecasts using demographic information and consumption patterns.
2.1.1
Cohort size
Data on cohort size, mortality, and fertility rates are combined to form forecasts for gender
and one-year-age group. This methodology uses information available as of year t to predict
the age distribution by gender for each year u > t while adjusting net migration. According to
DellaVigna and Pollet, these forecasts closely follow actual cohort sizes, except for forecasts
sufficiently far in the future that predicting future cohort size at birth becomes essential.
The fluctuations in the cohort size of the young provide substantial demand shifts to
the goods purchased by this group of people, such as toys and bicycles. The cohort sizes
of the older cohorts exhibit less variation. Demographic shifts induce the most variation in
demand for products consumed by the young and by young adults. In general, these forecasts
accurately predict the growth of various cohort sizes for the horizon of interest, during the
10 years after year t. These predicted growth rates also follow the official Census Bureau
forecasts using 2000 Census data.
2.1.2
Consumption patterns
DellaVigna and Pollet (2013) analyze all major expenditures on final consumption products
using data from the Survey of Consumer Expenditures, 1972-1973 and from the 1983 to 1984
cohorts of the ongoing Consumer Expenditure Survey. The consumption data is disaggregated into separate products with different age-consumption profiles. For instance, alcoholic
beverages includes separate categories for beer and hard liquor expenditures.
8
We reproduce Figure 1 from DellaVigna and Pollet (2013) to show how estimated age patterns of consumption reflect the age-related nature of different products. This figure presents
“the age profile for two goods using kernel regressions of household annual consumption on
the age of the head of household”. For both of these surveys of consumer expenditure, the
consumption pattern for bicycles peaks when the age of the head of household is between 35
and 45. Presumably, the heads of household in this age range are the most likely to have children of bicycle-riding age. The analogous pattern for pharmaceutical consumption provides
a useful contrast. This pattern indicates that consumption increases with age, especially in
the more recent survey.
DellaVigna and Pollet (2013) conclude that: 1) “consumption for each good depends
significantly on the age of the head of household”, 2) “these age patterns vary substantially
across goods”, and 3) “the age profile of consumption for a given good is quite stable across
time”. Therefore, the predictable changes in cohort size by age should induce predictable
changes in demand for various products.
To calculate the predicted demand growth for a particular consumption product, the
cohort size forecasts are combined with the estimated age profiles of consumption. While this
process contains several different types of estimation error, the cumulative impact of these
errors should only bias the results toward the null hypothesis of no relation.
2.1.3
Demographically Sensitive Firms
We also adopt the categorization of industries by the volatility of demand growth induced by
demographic shifts provided by DellaVigna and Pollet (2007). This categorization is necessary
to ensure that the analysis is targeted towards industries in which demographics play a substantial role. For example, the demand growth for utilities is unlikely to be affected by shifts
in the relative cohort sizes, while the demand for bicycles, motorcycles, and pharmaceutical
is likely to be affected by the relative size of various age cohorts. An industry is classified
as a member of the subset of Demographic Industries in each year t if the industry is one of
the 20 industries with the highest standard deviation of predicted demand growth during the
9
next 15 years.
2.2
Sample and Variable Construction
The sample of firms is derived from the Compustat database merged with the forecasted
consumption dataset. Our sample period goes from 1974 to 2004. We exclude financial (SIC
codes 6000-6999), utilities (SIC codes 4899-4950), and quasi-public firms (SIC codes greater
of equal to 8880) because the financing decisions of firms in these industries are likely to
be affected by regulations. In order to avoid that our results are affected by outliers and
measurement errors we apply a series of filters to the data: we drop firms with less than $10
million in assets, or less than $6 million in property, plant and equipment; we drop firms with
unrealistic (greater than 100%) growth of assets, or of property, plant and equipment, or of
sales; we drop firms with missing assets; we drop firms with negative market-to-book ratio;
we drop firms with negative book equity, negative deferred taxes and investment tax credit,
or negative short-term or long term debt; we winsorize all variables at the 1% level.
In our analysis the main outcome variables are the logarithm of cash flows, logarithm of
cash holdings, changes in cash holdings and logarithm of total payout. Log Cash Flow is
the logarithm of the ratio of income before extraordinary items plus depreciation to total
assets plus 1. Log Cash Holdings is defined as the logarithm of the ratio of cash and cash
equivalents (che) to total assets (at). Log Payout is the logarithm of the ratio of dividend
paid to common stocks (dvc), plus dividend paid to preferred stocks (dvp), plus purchased of
common and preferred stock (prstkc) to total assets (at). The controls used in our regression
are market-to-book ratio, tangibility, size, leverage R&D expenses and Investments, return
on assets, age, dividend ratio, and whether or not the firm is rated. Market-to-Book is define
as the ratio of market value of assets (market value of equity (prccf × csho), plus short term
debt (dlc), plus long term debt (dltt), plus preferred stocks (pstkl), minus deferred taxes and
investment tax credit (txditc)) to total assets (at). Tangibility is the ratio of total plant,
property and equipment (ppent) to total assets (at). Size is the logarithm of total assets (at).
10
Book Leverage is defined as the ratio of total debt (dlc + dltt) to total assets (at). R&D are
defined as the ratio of Research and Development Expense (xrd) to the lag of total assets.
Investment is define and the ratio of capital expenditures (capx) to the property planet and
equipment (ppent). Return on assets is the ratio of income before extraordinary items (ib) to
assets. Age is the number of years the firms appears on the data. Dividends is the ration of
dividends common/ordinary (dvc) plus dividends - preferred/preference (dvp) to total assets.
Rated is a dummy indicating the firm is rated by one of the large rating agencies.
2.3
Summary Statistics
Table 1 presents the summary statistics of the covariates used in the paper. Panel A uses
the sample of all Compustat firms after we apply the filters. Panel B uses the subsample
of Compustat firms for which we have forecasted demographic consumption data. Panel C
uses the subsample of demographic sensitive firms. A comparison of the results in each panel
shows that we are not selecting the firms on observed variables. The mean and median of all
covariates are similar across the three samples; there is no evidence that the firms exposed to
the investment opportunity shocks in our sample are different than the average firm in the
full sample.
3
Effects on Cash Flows
An important assumption of our identification strategy is that forecasted demand shifts do not
affect firm’s cash flow. These shifts only happen farther into the future and, therefore, should
not affect firm’s current operations. This is important to our tests, because it guarantees
that demographic shifts predicted to the future are not related to current macro and firm
fundamentals. In addition it also guarantees that our results are not contaminated by any
unobserved firm characteristic that might affect its propensity to save. For instance, if there is
an increase in cash flows we could simply be capturing a omitted characteristic of demographic
sensitive firms that make them more likely to save out of cash flows. We test this assumption
11
using the following specification:
Log Cash Flow i,t = β1 High Long Term Demand Growth + β2 High Short Term Demand Growth
+ β3 Low Long Term Demand Growth + β4 Low Short Term Demand Growth
+ β5 Xi,t + θt + µi + εi,t
(1)
Where Log Cash Flow is the logarithm of the ratio of cash flow (measured as earnings
before extraordinary items plus depreciation) to assets plus 1. High Short Term Demand
Growth and High Long Term Demand Growth are dummy variables that take the value of
one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10
and t + 5 respectively are at the top quartile of their distribution. Low Short Term Demand
Growth and Low Short Term Demand Growth are dummy variables that take the value of
one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10
and t + 5 respectively are at the bottom quartile of their distribution. Xi,t is a set of control
variables that include size, tangibility, R&D expenses, capex, return on assets, age, dividend
ratios, and whether or not the firm is rated
Table 2 presents the estimates of the effects of demand shift on cash flows. The results
show that high long term demand growth do not affect cash flows. The estimates are both
statistically and economically insignificant. Increase in demand in the short run are statistically significant, but economically insignificant. Low demand growths both in the short and
long run are not associated with changes to cash flows. The absence of a relation between
demand shifts and cash flows, rules out the possibility that omitted factors explain our results.
In our main specifications we cluster the standard errors by industry, because our shocks are
industry wide. In columns (1) and (2) we explore the fact that our demand shocks are at
a more granular industry level and control for industry fixed effects at the 3-digit SIC code
level. We also check if our results are robust if we cluster the standard errors using alternative
reference groups. Column (4) shows the results clustering by firm and column (5) clustering
12
by year. Our results are robust to both clustering alternatives.
4
Precautionary motive
We start our analysis of the reasons why firms hold cash looking at the effects of increases
in the precautionary motive on firms cash policy. The precautionary motive to hold cash
is associated with financing frictions. A manager chooses to hold cash to avoid foregoing
profitable investment opportunities. High forecasted demand growth allow us to study that
because it implies an improvement on firm’s investment opportunities and consequently an
increase in the costs of foregoing a profitable investment opportunity.
4.1
Effects on Cash
In order to measure the effects of consumption shifts on firm’s cash holdings we estimate the
following regressions:
Log Cash i,t = β1 High Long Term Demand Growth + β2 High Short Term Demand Growth
+ β3 Low Long Term Demand Growth + β4 Low Short Term Demand Growth
+ β5 Xi,t + θt + µi + εi,t
(2)
Where Log Cash is the logarithm of the ratio of cash and cash equivalents to assets. The
control variables are similar to the ones used on the previous section, but cash flows are also
included as a control.
Table 3 presents the estimates of the effect of demand shifts on firm’s cash holdings.
Consistent with a response to an increase in precautionary motive, firms increase their cash
holdings when they have an increase in their investment opportunities. Our regressions show
that firms with high long term demand growth increase their cash holdings by 18%, which is
both statistically and economically significant. In Panel B we verify that our results are not
driven by a specific characteristic of the omitted groups in our original regressions. Instead of
13
using firms that are not going through any demographic shocks as the omitted group, we use
all firms that are not going through a high long term demand growth. Changing the omitted
group does not affect our results, neither quantitatively nor qualitatively. In columns (1) and
(2) we explore the fact that our demand shocks are at a more granular industry level and
control for industry fixed effects at the 3-digit SIC code level. We also check if our results are
robust if we cluster the standard errors using alternative reference groups. Column (4) shows
the results clustering by firm and column (5) clustering by year. Our results are robust to
both clustering alternatives.
4.2
Event Study - Differences-in-Difference of Cash Holdings
We also observe the dynamics of the difference of cash levels of the group of firms experiencing high demand growth in the long run with respect to a control group. Our control
group is formed in the first year we forecast a high demand growth in the long run for our
treated firms 4 . We find a match firm in the same year and that is not going through any
other type of demographic shocks. We match the firms based on several covariates: size,
tangibility, R&D expenses, capex, return on assets, age, dividend ratios, cash levels, and
whether or not the firm is rated. We then run differences-in-difference regressions of the impact of high demand growth in the long run on cash levels. The only difference to a standard
differences-in-difference regression is that instead of having only one time dummy, we interact the treatment variable with several event time dummies. These estimates provide the
differences-in-difference between the cash levels of treated and control firms over the event
time. Our regressions include year and industry fixed effects and the standard errors are
clustered by industry.
Figure 2 presents the dynamics of the difference of the cash levels of treated and control
firms. The results show that treated and control firms have similar parallel trends prior to the
shock. Between -2 and 1 the differences between the two groups are statistically insignificant.
On year 2, this difference becomes statistically significant. The difference remains statistically
4
Our shocks are path dependent, and are likely to be repeated after the first year
14
significant until year 5 and disappears after that. The pattern is consistent with our expectations. Firms start saving cash as soon as they realize the increase in investment opportunity.
The cash levels remain high until these firms are close enough to the shock at which point it
decreases its cash levels.
4.3
Lines of Credit
We also investigate whether or not firms change their lines of credit when faced with changes
in demand caused by demographics. Sufi (2009) and Disatnik, Duchin, and Schmidt (2014)
show that lines of credit are an important source of firm liquidity. Acharya, Almeida, and
Campello (2013) show that firms with low systematic risk are more likely to choose lines of
credit over cash, because banks are more likely to be able to provide them liquidity when
they need. Demographic demand shifts only affect demographic sensitive firms, therefore
the shocks we consider are not systematic, and therefore we expect that firms with high
demographic demand growth increase their lines of credit.
We obtain the data from the lines of credit from LPC-DealScan and we follow Acharya,
Almeida, and Campello (2013) on our variable construction. We use the sample of LPCDealScan data for which we can obtain the firm identifier gvkey. We drop utilities, quasi-public
and financial firms. We only consider short and long term credit lines, which have the variable
“loantype” equals to ”364-day Facility”, “Revolver/Line<1Yr”, “Revolver/Line>=1Yr”, or
“Revolver/Line”. We drop lines that seem to be repeated and add the credit lines of firms
that have more than one line of credit per year.
Table 4 presents the effects of demand shifts caused by demographics on lines of credits.
Panel A presents the results of the ratio of total lines of credit to assets. Firms with high
demand growth in the long run increase their LC-to-Assets ratio by 2.3 percentage points.
This implies an increase of 15% on their LC levels (the average LC-to-Assets is 12%), which
is both economically and statistically significant. Panel B presents the ratio of lines of credit
to the sum of lines of credit and cash. This ratio is called LC-to-Cash both on Sufi (2009) and
15
Acharya, Almeida, and Campello (2013). Firms with high demand growth in the long run
increase their LC-to-Cash ratios by 2.3 percentage points 6.2 percentage points. This implies
an increase of 17% on their LC-to-Cash ratios (the average LC-to-Cash is 35%), which is also
statistically and economically significant. The results are not robust to all specifications, but
they provide evidence consistent with an increase in the precautionary motive.
Although the increase in information asymmetry is likely to make external financing more
expensive for firms with high demand growth, they seem to be relying on lines of credit as an
alternative source of liquidity to finance their investment needs.
4.4
Financially Constrained Firms
In order to further investigate the precautionary motive to hold cash we observe the impact
of high demand growth (both in the short and the long run) on financing constrained firms.
These firms have a higher benefit of holding cash and should increase their cash holdings more
than financially unconstraint firms. We test this prediction using the following specification:
Log Cash i,t = β1 High LT Dem. Gro. × F inConst + β2 High ST Dem. Gro. × F inConstraint
+ β3 High LT Dem. Gro. + β4 High ST Dem. Gro. + β6 F inConst
+ β5 Xi,t + θt + µi + εi,t
(3)
Where FinConst are proxies of financial constraint. In this analysis we use three different
proxies: firm size, tangibility and if the firm has rating. Size is the logarithm of firm’s total
assets. Tangibility is the ratio of property, plant and equipment to assets. HasRating is a
dummy indicating that the firm has credit rating.
Table 5 presents the differential effects of demographic demand shifts on firm’s with different degrees of financial constraints. The results are consistent using the different proxies of
financial constraints. Financially constrained firms are largely responsible for the increase in
16
cash holdings observed in Table 3. Columns (3) and (6) show that the results are driven almost exclusively by firms without a credit rating. This result further validate the predictions
of the precautionary motive of cash holdings. Firms that have a higher benefit of holding
cash adjust their cash holdings more dramatically when faced with better investment opportunities. We run our regressions clustering the standard errors both by industry (columns
(1) to (3)) and by year (columns (4) to (6)) and our results are robust to both clustering
alternatives. In these regressions we explore the fact that our demand shocks are at a more
granular industry level and control for industry fixed effects at the 3-digit SIC code level.
4.5
Falsification Tests
An important assumption of our identification strategy is that demographic demand shifts are
not driven by other simultaneous factors. In order to further validate our exclusion restriction,
we run a falsification test using industries that have a high forecasted demand growth, but that
are not targeted to a specific age cohort. These firms are exposed to the same macroeconomic
changes and business conditions that happen simultaneously to the demographic demand
shifts. However, their demand is not age dependent and therefore, a forecasted increase in
demographics does not affect their investment opportunity set.
Table 6 presents the estimates of the impact of demographic demand shifts on firm’s cash
and payout policy for demographic insensitive firms. These firms do not adjust their cash
holdings when there is a demographic shift. The lack of statistical and economic significance
in this falsification test allow us to conclude that our results are not contaminated by a
confounding factor simultaneous to the demand shift. In columns (1) and (2) we explore the
fact that our demand shocks are at a more granular industry level and control for industry
fixed effects at the 3-digit SIC code level. We also check if our results are robust if we
cluster the standard errors using alternative reference groups. Column (4) shows the results
clustering by firm and column (5) clustering by year. Our results are robust to both clustering
alternatives.
17
4.6
Information Asymmetry - Source of Funds
Another interesting feature of our setting is the fact that demographic demand shifts in
the long run are associated with increases in the information asymmetry between investors
and managers. DellaVigna and Pollet (2007) show that there is investors inattention to
demographic demand shifts. Dellavigna and Pollet (2013) show that CEOs are aware of
the shifts and use the information asymmetry to time the market. Because of this increase
in information asymmetry, it should be more difficult for firms to finance their investments
opportunities using external sources. This is precisely the situation at which theory predicts
that cash should be relevant (Holmstrom and Tirole (1998), Tirole (2010)). We explore the
sources firms use for their cash management to verify this assumption.
4.6.1
Debt and Equity Issuances
We follow Lemmon and Roberts (2010) and study the effects of demographic demand shifts
on short and long term net debt issuances and on net equity issuances. Similar to Lemmon
and Roberts (2010) we define net long term debt issuances as the ratio of long-term debt
issues (dltis) minus long-term debt reductions (dltr) to the start of period total assets (at).
We define net short-term debt issuances as the ratio of change in current debt (dlcch) to startof-period assets. Net equity issuances is defined as the ratio of sale of common and preferred
stock (sstk) minus purchase of common and preferred stock (prstkc) to start-of-period assets.
Table 7 presents the results of the impact of demand shifts on equity and debt issuances.
Panel A presents the results for Net Long Term Debt Issuances. Panel B presents the results
for Net Short Term Debt Issuances. Panel C presents the results for Net Equity Issuances.
All three panels provide evidence that the changes in cash holdings are not due to changes in
capital structure.
18
4.6.2
Payout Policy
We also investigate the effects of demand shifts caused by demographics on firms’ payout
policy using the following specification:
Log Payout i,t = β1 High Long Term Demand Growth + β2 High Short Term Demand Growth
= β3 Low Long Term Demand Growth + β4 Low Short Term Demand Growth
+ β5 Xi,t + θt + µi + εi,t
(4)
Where Log Payout is the logarithm of the ratio of total dividends plus purchase of common
and preferred stock to total assets. Table 8 presents the estimates of effects of demand shifts on
payout policy. The results indicate that firms with high demand growth in the long run reduce
their payout over assets by 18%, which is both statistically and economically significant. The
increase in cash holdings is largely generated by a reduction in payout rather than cuts in
investment and R&D or increases in equity or debt issuances.5 In columns (1) and (2) we
explore the fact that our demand shocks are at a more granular industry level and control
for industry fixed effects at the 3-digit SIC code level. We also check if our results are robust
if we cluster the standard errors using alternative reference groups. Column (4) show the
results clustering by firm and column (5) clustering by year. Our results are robust to both
clustering alternatives.
These results are consistent with the findings in Dellavigna and Pollet (2013). They find
that, due to investors inattention, demand shift forecasted for the long run (5 to 10 years) increase information asymmetry. Even though, these firms have good investment opportunities
in the future, the information asymmetry obstruct their ability to raise external capital. For
this reason they rely mainly on internal sources to increase their cash holdings. Combined
with the evidence on payout, these results allow us to conclude that firms are actively saving
out of operation cash flows to increase their cash levels.
5
We test the effects of the demand shifts on Investment and R&D and found not significant impact.
19
These results are important for two reasons, first it helps alleviate concerns about the
external validity of our results. Even though demographic demand shifts are a very particular
type of demand shock, the proximity of our setting to the theoretical framework allows us
to expect that our finding are applicable in other settings. Firms with good investment
opportunities, but that, for some reason, are unable to communicate them to its investors are
likely to also rely on internal funds to finance this idea. Second, these results rule out the
possibility that the changes in cash holdings are driven by changes in capital structure, and
therefore cash is of first order importance on firm’s financing decisions.
5
Free Cash Problems
The free cash problem is not a major concern in firms with good investment opportunities.
These problems arise when the firm has worsening investment opportunities and excess cash
can give manager flexibility to engage in value destroying empire building. Managers in these
firms might choose to invest in a negative NPV project that has higher private benefits. Low
demographic demand growth allows us to test how firms react when they face worsening
investment opportunity. Our baseline results in table 3 provide weak evidence that firms
experiencing low demand growth reduce their cash levels. The results in this table are economically significant, but only marginally statistically significant. In order to further explore
the effects of the increase in the free cash problems we separate firms based on two things:
the likelihood that investors will notice the free cash problem, and the extend to which free
cash is indeed a problem for the firm.
5.1
Corporate Governance
We start our analysis of the effects of the free cash problem on cash holdings separating firms
based on their corporate governance level. We run regressions with a specification similar to
the one fond in equation (1) separated by firms with different levels of corporate governance.
First, we separate firms with good governance according to the G-Index (Gompers, Ishii, and
20
Metrick (2003)). Second we separate firms based on whether or not they have institutional
investor control.
Table 9 presents the results of the impact of low demand growth on firms with good and
bad corporate governance. We run this regression for four different sub-samples: Column
(1) presents the results for the sub-sample of firms that have Gompers Ishii and Metrick
(2003) corporate governance index below 6 (Low G-Index=1 ), also known as the Democracy
Portfolio, those are firms with good corporate governance according to the index. Column
(2) presents the results for the sub-sample of firms that have Gompers Ishii and Metrick
(2003) corporate governance index above 6 (Low G-Index=0 ), those are firms with worst
corporate governance according to the index. Data for the G-Index is only available between
1990 to 2004. Column (3) presents the results for the sub-sample of firms that either have
institutional holding above median, or above median institutional holding concentration (Institutional Control=1 ). Therefore, either institutional investors own a large portion of the
firm, or ownership is highly concentrated in the hands of a few institutional investors. In both
situations, institutional investors are likely to be able to interfere in the firm. Conversely, column (4) presents the results for the sub-sample of firms that do not have institutional holding
above median, nor have above median institutional holding concentration (Institutional Control=0 ). The data for institutional ownership comes from the Thomson Reuters Institutional
(13f) Holdings. These data is available for our full sample period (from 1974 to 2004). Panel
A presents the results using industry fixed effects. In these tests we explore the fact that our
demand shocks are at a more granular industry level and control for industry fixed effects at
the 3-digit SIC code level. Panel B presents the results using firm fixed effects. Although we
understand that using firm fixed effects is the appropriate way to measure this effect, we also
add the regressions with industry fixed effects because restricting the sample and adding firm
fixed effects produce imprecise estimates of the effects of low demand growth. Results using
only industry fixed effects are very similar to the results using firm fixed effects, but they are
more precisely measured.
The results are consistent using the two measures. We find that firms with better corporate
21
governance decrease their cash significantly when facing low demographic demand growth in
the short run. Firms with low G-Index cut their cash by 53%, while firms with institutional
control cut it by 15%. We find no effects when the shifts are in the long run, which is consistent
with investor’s inattention to long run shifts. Therefore, better monitored firms present lower
cash holdings when their investors realize that their investment opportunity set is decreasing.
5.2
Highly Leveraged Firms
One possible reason why our evidence on the effect of worsening investment opportunity on
cash policy is weak is that there might be other factors mitigating the free cash problems such
as debt and competition. We further explore how the free cash problem affects firms’ cash
policy isolating firms experiencing low demand growth but that present other factors that
attenuate the free cash problem. First, we isolate highly leveraged firms. The disciplinary
effects of debt are well documented (Jensen and Meckling (1976), Jensen (1986) and Hart and
Moore (1995) ). Firms with high leverage do not have a free cash problem when faced with a
decrease in investment opportunity. In order to test that, we look at how firms with different
interest coverage (measured as the ratio of earnings before interest and taxes to interest
expenses) react when faced with slow demand growth using the following specification.
Log Cash i,t = β1 Low LT Dem. Gro. × Low Int Cov + β2 Low ST Dem. Gro. × Low Int Cov
+ β3 Low LT Dem. Gro. + β4 Low ST Dem. Gro. + β6 Low Int Cov
+ β5 Xi,t + θt + µi + εi,t
(5)
Where Low Interest Coverage is a dummy variable indicating that the firm’s interest
coverage is below one. Table 10 shows the estimates of these regressions. The interaction
term shows that firms with low interest coverage, experiencing a decrease in their demand
in the short run hold 44% more cash than firms with lower leverage levels experiencing a
22
similar drop in demand. These difference is both statistically and economically significant.
The joint test of the total effect of the low demand growth in the short run are not robust to
all specifications, but it provides weak evidence that this firms are actually increasing their
cash levels when exposed to these negative shocks. The disciplinary effects of debt attenuate
the free cash problems inside these firms, for this reason these firms do not have to decrease
their cash levels as a response to the negative shock.
Once we isolate highly leveraged firms the effects of the low demand growth become clearer.
Firms with lower leverage decrease their cash holdings if their demand growth is slowing down
in the short run. Our results indicate that these firms reduce their cash by 17% in response to
the low demand growth in the long run. In columns (1) and (2) we explore the fact that our
demand shocks are at a more granular industry level and control for industry fixed effects at
the 3-digit SIC code level. We also check if our results are robust if we cluster the standard
errors using alternative reference groups. Column (4) shows the results clustering by firm and
column (5) clustering by year. Our results are robust to both clustering alternatives.
5.3
Concentrated Industries
In our final analysis we explore how competition can attenuate our results for the effects of the
free cash problems. Giroud and Mueller (2010, 2011) show that competition mitigates manager’s financial slack and that strong corporate governance is only effective in noncompetitive
industries. Fresard (2010) shows that cash holdings provide a competitive edge. Firms with
more cash are better able to protect themselves against an increase in the product market
competition. Industries having low demand growth experience an increase in competition.
The size of the pie is smaller, reducing each firm’s individual slice. Our setting allows us
to test the use of cash for strategic reasons, because our shifts happen a few years into the
future. Therefore, firms have time to prepare for the increase in competition. Therefore, for
these firms the free cash problems is less pronounced. They actually have an important use
for the cash, despite the decrease in investment opportunities. The strategic use of cash is
23
only present in highly concentrated industries, where firms are not price takers. Incumbents
in these industries have to prepare to go for a price war to avoid new entries. In order to test
this prediction, we explore the impact of low forecasted demand growth in industries with
different concentration levels using the following specification:
Log Cash i,t = β1 Low LT Dem. Gro. × High Concentration Industry
(6)
+ β2 Low ST Dem. Gro. × High Concentration Industry
+ β3 Low LT Dem. Gro. + β4 Low ST Dem. Gro. + β6 High Concentration Industry
+ β5 Xi,t + θt + µi + εi,t
(7)
Where High Concentration Industry is a dummy indicating that the firm’s industry has
a concentration above the median. Our measure of concentration is the ratio C-4 from the
Census Manufacturers. This ratio is the fraction of the industry’s revenue that is generated
by the four largest firms in the industry. Table 11 present the estimates of the impact of low
demand growth on firms in industries with different concentrations. Consistent with Fresard
(2010), we find that firms in highly concentrated industries hold significantly more cash than
firms in less concentrated industries when faced with a low demand growth. The interaction
between Low LT Dem. Gro and High Conc. shows that they actually hold 39% more cash,
which is both statistically and economically significant. The joint test of the total effect of the
low demand growth in the long run on firms in highly concentrated industries are not robust
to all specifications, but they provide weak evidence that this firms are actually increasing
their cash holdings. The differences are significantly only for low forecasted demand in the
long run (5 to 10 years before the shock), which is consistent with Fresard (2010). They are
accumulating to use it when they need it and not as a response to an entry.
Once we separate firms in highly concentrated industries the effects of the low demand
growth become clearer. Firms in industries with below median concentration decrease their
cash holdings if their demand growth is slowing down in the long run. Our results indicate
24
that these firms are reducing their cash by 27% in response to the low demand growth in the
long run. In columns (1) and (2) we explore the fact that our demand shocks are at a more
granular industry level and control for industry fixed effects at the 3-digit SIC code level. We
also check if our results are robust if we cluster the standard errors using alternative reference
groups. Column (4) shows the results clustering by firm and column (5) clustering by year.
Our results are robust to both clustering alternatives. Taken all together, our evidence shows
that firms take the free cash problem into account on their cash policy and adjust their cash
levels to avoid it.
6
Conclusion
Forecasted demand shifts create exogenous variation on firms’ investment opportunities. We
explore this variation combined with the differences in foresight of managers and investors to
determine the reasons why firms adjust their cash holdings. We show that managers increase
their cash holding in anticipation of a demand shift in the long run. This result is stronger
for financially constrained firms, which is consistent with managers reacting to an increase
in precautionary motive. We also find that firms with better governance, and institutional
control, where managers are better monitored by investors, decrease cash holding as when
investment opportunities deteriorate. Furthermore, firms reduce their cash levels in situations
in which the free cash is clearly a problem.
Our work contributes to the cash holdings literature providing, to our knowledge, the
first identified results of the reasons firms adjust their cash holdings. We show that shifts in
investment opportunities have causal effects on firm’s cash holdings not related to omitted
firms characteristics or simultaneous factors. Our results validate and extend the intuition of
previous work on the tradeoffs of holding cash. They allow us to conclude that cash holdings
are an important part of the corporate decisions and are not simply a byproduct of the firms’
capital structure, tax and investment decisions. Firms clearly understand what are the costs
and benefits of holding cash, and adjust their cash levels each time there is an imbalance
between them.
25
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Table 1: Summary Statistics
This table displays summary statistics across different samples used in the paper. Panel A presents the
summary statistics for the sample of compustat firms. Panel B presents the summary statistics for the
compustat firms that have demographic demand data. Panel C presents the summary statistics for the
sample of firm sensitive to demographic demand shifts.
Mean
Median
Standard
Deviation
Observations
5.220
0.120
1.261
0.197
0.293
0.072
0.398
0.020
0.283
4.956
0.061
0.968
0.154
0.243
0.066
0.353
0.000
0.201
1.647
0.159
0.961
0.182
0.243
0.049
0.232
0.043
0.331
91830
91830
91830
91830
91830
91830
91830
91830
91830
4.929
0.053
0.967
0.196
0.270
0.074
0.405
0.000
0.207
1.717
0.138
0.966
0.188
0.240
0.050
0.230
0.037
0.312
21642
21642
21642
21642
21642
21642
21642
21642
21642
4.996
0.061
0.931
0.179
0.252
0.064
0.282
0.000
0.204
1.686
0.154
1.125
0.181
0.244
0.044
0.176
0.048
0.286
6302
6302
6302
6302
6302
6302
6302
6302
6302
Panel A: Compustat
Size
Cash
Book-to-Market
Book Leverage
Market Leverage
Cash Flows
Tangibility
R&D
Investment
Panel B: Firms with demographic data
Size
Cash
Book-to-Market
Book Leverage
Market Leverage
Cash Flows
Tangibility
R&D
Investment
5.235
0.102
1.268
0.234
0.313
0.079
0.443
0.014
0.282
Panel C: Demographic sensitive firms
Size
Cash
Book-to-Market
Book Leverage
Market Leverage
Cash Flows
Tangibility
R&D
Investment
5.389
0.116
1.313
0.217
0.303
0.066
0.317
0.022
0.270
30
Table 2: Cash Flows
This table presents the estimates of linear regressions of the impact of demand shifts on cash flow. The dependent variable is
the logarithm of the ratio of cash flow to assets. High Short Term Demand Growth and High Long Term Demand Growth are
dummy variables that take the value of one if the firm’s forecasted annualized demand growth between t and t + 5 and between
t + 10 and t + 5 respectively are at the top quartile of their distribution. Low Short Term Demand Growth and Low Short Term
Demand Growth are dummy variables that take the value of one if the firm’s forecasted annualized demand growth between t
and t + 5 and between t + 10 and t + 5 respectively are at the bottom quartile of their distribution. Clustered standard errors
are reported in parentheses. ***,**,* indicates significance at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
Dependent Variable Log(Cash Flow over Assets+1)
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
-0.000
(0.002)
-0.001
(0.002)
0.003
(0.002)
-0.000
(0.001)
0.000
(0.002)
-0.000
(0.001)
0.003*
(0.002)
0.001
(0.001)
0.000
(0.001)
-0.000
(0.001)
0.004***
(0.001)
0.000
(0.001)
0.000
(0.001)
-0.000
(0.001)
0.004***
(0.001)
0.000
(0.001)
0.000
(0.002)
-0.000
(0.001)
0.004**
(0.002)
0.000
(0.001)
5,996
0.195
5,533
0.572
5,533
0.401
5,533
0.0228
5,533
0.776
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
31
Table 3: Cash Holdings
This table presents the estimates of linear regressions of the impact of demand shifts on cash holdings. The dependent variable is
the logarithm of the ratio of cash and cash equivalent to assets. High Short Term Demand Growth and High Long Term Demand
Growth are dummy variables that take the value of one if the firm’s forecasted annualized demand growth between t and t + 5
and between t + 10 and t + 5 respectively are at the top quartile of their distribution. Low Short Term Demand Growth and Low
Short Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted annualized demand growth
between t and t + 5 and between t + 10 and t + 5 respectively are at the bottom quartile of their distribution. Panel A presents
regressions including all four demand shift variables. Panel B presents the results of regressions including only High Long Term
Demand Growth and excluding the other demand shift variables. Clustered standard errors are reported in parentheses. ***,**,*
indicates significance at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
0.149**
(0.068)
-0.044
(0.078)
-0.006
(0.059)
-0.107
(0.075)
0.164**
(0.072)
-0.039
(0.076)
0.003
(0.062)
-0.092
(0.071)
0.184**
(0.071)
-0.062
(0.070)
0.011
(0.061)
-0.108*
(0.064)
0.184***
(0.065)
-0.062
(0.057)
0.011
(0.057)
-0.108
(0.066)
0.184***
(0.038)
-0.062
(0.044)
0.011
(0.041)
-0.108*
(0.056)
5,984
0.235
5,504
0.356
5,504
0.121
5,504
0.121
5,504
0.676
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
Panel B: High Long Term Demand Growth Alone
High Long Term Demand Growth
0.140** 0.157**
(0.065)
(0.072)
0.171**
(0.070)
0.171***
(0.065)
0.171***
(0.036)
Dependent Variable Log Total Cash Over Assets
Panel A: All Demand Shifts
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
5,984
0.237
5,504
0.357
5,504
0.119
5,504
0.119
5,504
0.675
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
32
Table 4: Lines of Credit
This table presents the estimates of linear regressions of the impact of demand shifts on lines of credit. In Panel A the dependent
variable is the ratio of lines of credit to assets. In panel B, the dependent variable is the ratio of lines of credit to the sum of lines
of credit plus cash. High Short Term Demand Growth and High Long Term Demand Growth are dummy variables that take the
value of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are
at the top quartile of their distribution. Low Short Term Demand Growth and Low Short Term Demand Growth are dummy
variables that take the value of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10
and t + 5 respectively are at the bottom quartile of their distribution. Panel A presents regressions including all four demand
shift variables. Panel B presents the results of regressions including only High Long Term Demand Growth and excluding the
other demand shift variables. Clustered standard errors are reported in parentheses. ***,**,* indicates significance at the 1%,
5% and 10% level respectively.
(1)
Panel A - Dependent Variable: Lines of Credit
High Long Term Demand Growth
-0.005
(0.012)
Low Long Term Demand Growth
-0.011
(0.009)
High Short Term Demand Growth
0.003
(0.011)
Low Short Term Demand Growth
0.003
(0.009)
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
(3)
over Assets
0.015
0.023*
(0.013)
(0.014)
-0.007
-0.009
(0.010)
(0.010)
0.005
0.009
(0.012)
(0.012)
-0.004
-0.004
(0.010)
(0.010)
(4)
(5)
0.023
(0.020)
-0.009
(0.015)
0.009
(0.014)
-0.004
(0.016)
0.023***
(0.006)
-0.009
(0.013)
0.009
(0.014)
-0.004
(0.014)
2,971
0.180
2,516
0.193
2,516
0.119
2,516
0.119
2,516
0.537
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
to Cash
0.059
(0.038)
-0.071**
(0.032)
-0.017
(0.031)
0.020
(0.032)
0.062*
(0.037)
-0.080**
(0.031)
-0.010
(0.028)
0.025
(0.033)
0.062
(0.045)
-0.080**
(0.034)
-0.010
(0.029)
0.025
(0.029)
0.062**
(0.024)
-0.080***
(0.024)
-0.010
(0.022)
0.025
(0.025)
2,948
0.242
2,499
0.280
2,499
0.178
2,499
0.178
2,499
0.550
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
Panel B - Dependent Variable: Lines of Credit
High Long Term Demand Growth
0.039
(0.042)
Low Long Term Demand Growth
-0.063**
(0.030)
High Short Term Demand Growth
-0.012
(0.027)
Low Short Term Demand Growth
0.017
(0.029)
Observations
R2
(2)
Table 5: Financial Constraints
This table presents the estimates of the impact of demographic demand shifts on cash for firms with different levels of financial
constraint. The dependent variable is the logarithm of the ratio of cash and cash equivalent over assets. High Short Term Demand
Growth and High Long Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted annualized
demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the top quartile of their distribution. Low
Short Term Demand Growth and Low Short Term Demand Growth are dummy variables that take the value of one if the firm’s
forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the bottom quartile of
their distribution. Size is the log of firm’s assets. Tangibility is the ratio of property plant and equipment to assets. HasRating
is a dummy variable indicating that the firm has credit rating . Clustered standard errors are reported in parentheses. ***,**,*
indicates significance at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
(6)
0.256***
(0.091)
-0.016
(0.099)
0.684***
(0.147)
0.109
(0.171)
-0.097***
(0.024)
-0.016
(0.028)
0.369***
(0.104)
-0.117
(0.093)
0.256***
(0.056)
-0.016
(0.058)
Dependent Variable Log of Total Cash over Assets
High LT Dem. Gro.
High ST Dem. Gro.
High LT Dem. Gro. × Size
High STDem. Gro × Size
0.684***
(0.243)
0.109
(0.185)
-0.097**
(0.042)
-0.016
(0.028)
High LT Dem. Gro × Tangibility
0.369**
(0.164)
-0.117
(0.171)
-0.687
(0.580)
0.372
(0.526)
High ST Dem. Gro × Tangibility
High LT Dem. Gro × HasRating
-0.391**
(0.191)
0.098
(0.154)
High ST Dem. Gro × HasRating
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Cluster
-0.687**
(0.314)
0.372
(0.243)
-0.391***
(0.075)
0.098
(0.105)
5,510
0.398
5,510
0.404
5,510
0.392
5,510
0.398
5,510
0.404
5,510
0.392
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Year
Yes
Yes
Yes
Year
Yes
Yes
Yes
Year
34
Table 6: Falsification Tests
This table presents the estimates of the impact of demographic demand shifts on cash holdings and payout policy for firms
insensitive to demographic demand shifts. In Panel A the dependent variable is the logarithm of the ratio of total cash and
cash equivalents to assets. In Panel B the dependent variable is the logarithm of the ratio of total dividends plus purchase of
common an preferred stock to assets. High Short Term Demand Growth and High Long Term Demand Growth are dummy
variables that take the value of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10 and
t + 5 respectively are at the top quartile of their distribution. Low Short Term Demand Growth and Low Short Term Demand
Growth are dummy variables that take the value of one if the firm’s forecasted annualized demand growth between t and t + 5
and between t + 10 and t + 5 respectively are at the bottom quartile of their distribution. Clustered standard errors are reported
in parentheses. ***,**,* indicates significance at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
Panel A: Dependent Variable Log Total Cash Over Assets
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
0.017
(0.073)
0.050
(0.050)
-0.020
(0.082)
-0.002
(0.056)
0.030
(0.058)
0.031
(0.048)
-0.082
(0.063)
0.007
(0.054)
0.005
(0.056)
0.043
(0.053)
-0.058
(0.066)
0.012
(0.055)
0.005
(0.052)
0.043
(0.041)
-0.058
(0.061)
0.012
(0.047)
0.005
(0.053)
0.043
(0.043)
-0.058
(0.049)
0.012
(0.036)
13,431
0.112
12,257
0.217
12,257
0.142
12,257
0.0708
12,257
0.671
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
35
Table 7: Debt and Equity Issuances
This table presents the estimates of the impact of demographic demand shifts on debt and equity issuances. The dependent
variable is the logarithm of the ratio of total dividends plus purchase of common an preferred stock to assets. High Short Term
Demand Growth and High Long Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted
annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the top quartile of their
distribution. Low Short Term Demand Growth and Low Short Term Demand Growth are dummy variables that take the value
of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at
the bottom quartile of their distribution. Clustered standard errors are reported in parentheses. ***,**,* indicates significance
at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
-0.010
(0.012)
-0.011
(0.015)
0.008
(0.012)
0.006
(0.013)
-0.001
(0.013)
-0.012
(0.014)
0.007
(0.013)
0.007
(0.013)
0.003
(0.012)
-0.009
(0.014)
-0.001
(0.014)
0.008
(0.013)
0.003
(0.011)
-0.009
(0.012)
-0.001
(0.013)
0.008
(0.013)
0.003
(0.010)
-0.009
(0.013)
-0.001
(0.016)
0.008
(0.013)
5,504
0.0416
5,070
0.104
5,070
0.109
5,070
0.00502
5,070
0.288
0.013
(0.016)
0.005
(0.009)
-0.019
(0.012)
0.008
(0.008)
0.013
(0.014)
0.005
(0.011)
-0.019
(0.012)
0.008
(0.010)
0.013
(0.018)
0.005
(0.011)
-0.019
(0.013)
0.008
(0.010)
Panel A: Log(Net Long Term Debt Issuances + 1)
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Panel B: Log(Net Short Term Debt Issuances+1)
High Long Term Demand Growth
0.020
0.019
(0.013)
(0.016)
Low Long Term Demand Growth
-0.002
0.002
(0.008)
(0.008)
High Short Term Demand Growth -0.025* -0.029**
(0.013)
(0.013)
Low Short Term Demand Growth
0.004
0.003
(0.008)
(0.008)
Observations
R2
2,314
0.0594
2,239
0.0584
2,239
0.036
2,239
0.036
2,239
0.185
Panel C: Log(Net Equity Issuances+1)
High Long Term Demand Growth
0.001
(0.006)
Low Long Term Demand Growth
0.001
(0.008)
High Short Term Demand Growth
-0.006
(0.010)
Low Short Term Demand Growth
0.006
(0.008)
-0.002
(0.006)
-0.001
(0.008)
-0.005
(0.010)
0.001
(0.007)
-0.004
(0.009)
-0.001
(0.009)
-0.007
(0.010)
0.002
(0.009)
-0.004
(0.011)
-0.001
(0.008)
-0.007
(0.009)
0.002
(0.010)
-0.004
(0.009)
-0.001
(0.008)
-0.007
(0.010)
0.002
(0.011)
5,651
0.103
5,197
0.161
5,197
0.067
5,197
0.067
5,197
0.285
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
Table 8: Payout Policy
This table presents the estimates of the impact of demographic demand shifts on payout policy. The dependent variable is the
logarithm of the ratio of total dividends plus purchase of common an preferred stock to assets. High Short Term Demand Growth
and High Long Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted annualized demand
growth between t and t + 5 and between t + 10 and t + 5 respectively are at the top quartile of their distribution. Low Short Term
Demand Growth and Low Short Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted
annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the bottom quartile of their
distribution. Clustered standard errors are reported in parentheses. ***,**,* indicates significance at the 1%, 5% and 10% level
respectively.
(1)
(2)
(3)
(4)
(5)
Dependent Variable Log of Total Payout over Assets
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Firm Fixed-Effects
Cluster
-0.193***
(0.069)
0.010
(0.063)
0.036
(0.060)
-0.090
(0.092)
-0.172**
(0.078)
0.030
(0.060)
0.056
(0.064)
-0.059
(0.069)
-0.181**
(0.083)
0.025
(0.062)
0.089
(0.061)
-0.091
(0.071)
-0.181**
(0.080)
0.025
(0.075)
0.089
(0.072)
-0.091
(0.076)
-0.181***
(0.065)
0.025
(0.041)
0.089
(0.072)
-0.091
(0.059)
4,758
0.247
4,387
0.409
4,387
0.172
4,387
0.172
4,387
0.623
No
Yes
Yes
No
Industry
Yes
Yes
Yes
No
Industry
Yes
Yes
No
Yes
Industry
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
37
Table 9: Free Cash Problems
This table presents the estimates of the impact of demographic demand shifts on cash for firms with different levels of corporate
governance. The dependent variable is the logarithm of the ratio of cash and cash equivalent over assets. Low Short Term Demand
Growth and Low Short Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted annualized
demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the bottom quartile of their distribution.
Column (1) presents the results for the sub-sample of firms that have Gompers Ishii and Metrick (2003) corporate governance
index below 6 (Low G-Index=1 ), also known as the Democracy Portfolio, those are firms with good corporate governance
according to the index. Column (2) presents the results for the sub-sample of firms that have Gompers Ishii and Metrick (2003)
corporate governance index above 6 (Low G-Index=0 ), those are firms with worst corporate governance according to the index.
Our sample for these two variables goes only from 1990 to 2004. We also use data from Thomson Reuters Institutional (13f)
Holdings. These data is available for our full sample period (from 1974 to 2004). Column (3) presents the results for the
sub-sample of firms that either have institutional holding above median, or above median institutional holding concentration
(Institutional Control=1 ). Therefore, either institutional investors own a large portion of of the firm, or ownership is highly
concentrated in the hands of a few institutional investors. In both situations, institutional investors are likely to be able to
interfere in the firm. Conversely, column (4) presents the results for the sub-sample of firms that do not have institutional
holding above median, nor have above median institutional holding concentration (Institutional Control=0 ). Panel A presents
the results using industry fixed effects and Panel B presents the results using firm fixed effects. Standard errors clustered by
industry are reported in parentheses. ***,**,* indicates significance at the 1%, 5% and 10% level respectively.
(1)
Low G-Index
=1
(2)
Low G-Index
=0
(3)
Institutional
Control = 1
(4)
Institutional
Control = 0
-0.462
(0.480)
0.045
(0.324)
0.815
(0.487)
-0.673***
(0.200)
0.206
(0.131)
0.087
(0.139)
-0.069
(0.108)
-0.134
(0.111)
0.249***
(0.068)
0.114*
(0.062)
0.133**
(0.064)
-0.144**
(0.065)
-0.014
(0.088)
-0.126
(0.190)
-0.273**
(0.125)
0.048
(0.171)
175
0.654
Yes
Yes
Yes
2,770
0.438
.
Yes
Yes
Yes
3,941
0.390
.
Yes
Yes
Yes
1,599
0.406
.
Yes
Yes
Yes
-0.548
(0.351)
0.552**
(0.253)
0.532
(0.369)
-0.530**
(0.236)
0.260***
(0.097)
0.018
(0.080)
-0.026
(0.073)
-0.059
(0.085)
0.223**
(0.087)
-0.057
(0.076)
0.081
(0.066)
-0.145*
(0.075)
0.043
(0.070)
-0.063
(0.103)
0.053
(0.107)
-0.091
(0.137)
175
0.00377
2,770
0.0225
3,941
0.0332
1,599
0.000654
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Panel A: Industry Fixed Effects
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Industry Fixed-Effects
Panel B: Firm Fixed Effects
High Long Term Demand Growth
Low Long Term Demand Growth
High Short Term Demand Growth
Low Short Term Demand Growth
Observations
R2
Controls
Year Fixed-Effects
Firm Fixed-Effects
Table 10: Highly Levered Firms
This table presents the estimates of linear regressions of the impact of demand shifts on cash holdings for firms with different
leverage levels. The dependent variable is the logarithm of the ratio of cash and cash equivalent over assets. High Short Term
Demand Growth and High Long Term Demand Growth are dummy variables that take the value of one if the firm’s forecasted
annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the top quartile of their
distribution. Low Short Term Demand Growth and Low Short Term Demand Growth are dummy variables that take the value
of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at
the bottom quartile of their distribution. Low Interest Coverage is a dummy variable indicating that the firm’s interest coverage
(measured as the ratio of earning before interest and taxes to interest expenses) is below one. Clustered standard errors are
reported in parentheses. ***,**,* indicates significance at the 1%, 5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
Dependent Variable Log Total Cash Over Assets
Low Long Term Demand Growth
Low LT Dem. Gro. x Low Int Cov
Low Short Term Demand Growth
Low ST Dem. Gro. x Low Int Cov
Low Interest Coverage
Observations
R2
-0.005
(0.082)
-0.053
(0.125)
-0.114
(0.073)
0.225
(0.147)
-0.334***
(0.076)
-0.024
(0.079)
0.019
(0.140)
-0.152**
(0.062)
0.371**
(0.159)
-0.257***
(0.061)
6,347
0.236
5,266
0.298
-0.038
-0.038
-0.038
(0.075)
(0.060)
(0.053)
0.005
0.005
0.005
(0.147)
(0.138)
(0.121)
-0.172*** -0.172*** -0.172***
(0.059)
(0.063)
(0.061)
0.439*** 0.439*** 0.439***
(0.161)
(0.148)
(0.148)
-0.278*** -0.278*** -0.278***
(0.069)
(0.065)
(0.048)
5,266
0.114
Controls
No
Yes
Yes
Year Fixed-Effects
Yes
Yes
Yes
Industry Fixed-Effects
Yes
Yes
No
Firm Fixed-Effects
No
No
Yes
Cluster
Industry Industry Industry
Joint Test: Low ST Dem. Gro + Low ST Dem. Gro.×Low Int Cov
Joint
0.111
0.219
0.267
(0.188)
(0.188)
(0.181)
39
5,266
0.114
5,266
0.654
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
0.267**
(0.157)
0.267**
(0.144)
Table 11: Industry Concentration
This table presents the estimates of linear regressions of the impact of demand shifts on cash holdings for firms in industries
with different concentrations. The dependent variable is the logarithm of the ratio of cash and cash equivalent over assets. High
Short Term Demand Growth and High Long Term Demand Growth are dummy variables that take the value of one if the firm’s
forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are at the top quartile of
their distribution. Low Short Term Demand Growth and Low Short Term Demand Growth are dummy variables that take the
value of one if the firm’s forecasted annualized demand growth between t and t + 5 and between t + 10 and t + 5 respectively are
at the bottom quartile of their distribution. High Concentration Industry is a dummy variable indicating that the firm’s is in a
high concentration industry. Clustered standard errors are reported in parentheses. ***,**,* indicates significance at the 1%,
5% and 10% level respectively.
(1)
(2)
(3)
(4)
(5)
-0.267***
(0.095)
0.361***
(0.130)
-0.077
(0.069)
-0.008
(0.118)
-0.229
(0.181)
-0.267***
(0.097)
0.361***
(0.110)
-0.077
(0.100)
-0.008
(0.143)
-0.229
(0.201)
-0.267***
(0.090)
0.361***
(0.092)
-0.077
(0.089)
-0.008
(0.089)
-0.229*
(0.130)
4,803
0.127
4,803
0.0712
4,803
0.672
Yes
Yes
No
Yes
Firm
Yes
Yes
No
Yes
Year
0.0938
(0.0719)
0.0938*
(0.0548)
Dependent Variable Log Total Cash Over Assets
Low Long Term Demand Growth
Low LT Dem. Gro. x High. Conc
Low Short Term Demand Growth
Low ST Dem. Gro. x High. Conc
High Concentration Industry
Observations
R2
-0.230** -0.245***
(0.094)
(0.093)
0.352*** 0.365***
(0.135)
(0.126)
-0.014
-0.061
(0.099)
(0.074)
-0.093
-0.001
(0.133)
(0.131)
-0.222
-0.147
(0.182)
(0.170)
5,650
0.258
4,803
0.365
Controls
No
Yes
Yes
Year Fixed-Effects
Yes
Yes
Yes
Industry Fixed-Effects
Yes
Yes
No
Firm Fixed-Effects
No
No
Yes
Cluster
Industry Industry Industry
Joint Test: Low LT Dem. Gro + Low LT Dem. Gro.×High Conc.
Joint
0.122
0.121
0.0938
(0.117)
(0.111)
(0.103)
40
Figure 1: Figure 1 DellaVigna and Pollet (2013)
This figure reproduces figure 1 from Dellavigna and Pollet (2013). The figure is described in their
paper as follows: “This figure displays a kernel regression of normalized household consumption for
each good as a function of the age of the head of household. The regression uses an Epanechnikov
kernel and a bandwidth of 5 years. Each line for a specific good uses an age-consumption profile
from a different consumption survey. Expenditures are normalized so that the average consumption
for all ages is equal to one for each survey-good pair.”
41
Figure 2: Difference in Cash Holdings Levels around High Forecasted Demand Growth
-.1
-.05
0
.05
This figure presents the difference in the cash levels of firms experiencing a high demand growth in
the long run and a control group. The control group is comprised of firms matched with high long
term demand firms. The matching is based on size, tangibility, R&D expenses, capex, return on
assets, age, dividend ratios, cash levels and whether or not the firm is rated. We match the firms
on the first year we forecast a high demand growth in the long run for treated firms (event time 0).
The difference and standard errors clustered by industry are obtained from the regression estimates
of the effects of the interaction of event time dummies and High Long Term Demand Growth on the
log of cash over assets.
-3
-2
-1
0
1
2
3
Event Time
90% Conf. Interval
4
5
6
Treated minus Control
42
7