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Labor unemployment costs and venture capital
investment
Lars Helge Hass*, Pauline M. Shum†, Monika Tarsalewska‡
ABSTRACT
This paper studies the effect of labor unemployment costs on venture capital
investments. We argue that lower labor unemployment costs reduce the wage
premium demanded by workers and therefore making investments by venture capital
funds more favorable. Additionally, higher unemployment benefits make labor force
adjustments easier which is especially important for venture capitalists who typically
invest in high-volatility industries. Using variation in unemployment benefits across
U.S. states we show that increases in unemployment benefits are significantly
associated with subsequent venture capital investment.
JEL classifications: G24, J21, J65, L26, M13, O31, O32, O52
Keywords: Employment protection regulations, dismissal costs, unemployment insurance
benefits, private equity, venture capital, entrepreneurship
* The Management School, Lancaster University, Bailrigg, Lancaster, LA1 4YX, United Kingdom, Phone: +44 1524
593981, Fax: +44 1524 847321, e-mail: [email protected] (Corresponding author). † Schulich School of Business, York University, 4700 Keele Street, Toronto, Ontario, Canada, Phone: +1 416 737
2100 66430, e-mail: [email protected] ‡ Exeter University Business School, Streatham Campus, Rennes Drive, Exeter, EX4 4ST, United Kingdom, Phone:
+44 1392 726256, email: [email protected].
Labor unemployment costs and venture capital
investment
ABSTRACT
This paper studies the effect of labor unemployment costs on venture capital
investments. We argue that lower labor unemployment costs reduce the wage
premium demanded by workers and therefore making investments by venture capital
funds more favorable. Additionally, higher unemployment benefits make labor force
adjustments easier which is especially important for venture capitalists who typically
invest in high-volatility industries. Using variation in unemployment benefits across
U.S. states we show that increases in unemployment benefits are significantly
associated with subsequent venture capital investment.
JEL classifications: G24, J21, J65, L26, M13, O31, O32, O52
Keywords: Employment protection regulations, dismissal costs, unemployment insurance
benefits, private equity, venture capital, entrepreneurship
This Version: April 15, 2016
1
1. Introduction
Modern theories emphasize the importance of innovation as a major determinant of
economic growth (Barro and Sala-i-Martin, 2004). Yet, the financing of innovation poses a
significant risk for investors as the entrepreneurial ventures are subject to high risk of failure.
Policy makers seek for factors that can spur financing for startups and enable growth of
entrepreneurial firms. Venture capital (VC) financing plays a major role in promoting start-ups
growth. They act as financial intermediaries and provide capital to entrepreneurial firms that are
not able to obtain financing from any other source. Firms that are backed by VCs have often
better performance and are more innovative1.
In this paper we study the effect of worker unemployment costs on VC financing and
investment. VC investments are very sensitive to changing economic environment and financial
risk (Nanda and Rhodes-Kropf, 2013; Nanda and Rhodes-Kropf, 2014). Given those investments
are typically based in volatile industries that offer rapid growth opportunities, VC investors need
to adjust the workforce subject to changing economic climate and opportunities that arise or
recover the portfolio companies at the time of failure. In order to compensate for a potential job
loss workers require a premium in the form of higher wages or benefits (Topel, 1984; Abowd and
Ashenfelter, 1981). In this paper we focus on the effect of unemployment insurance benefits on
the VC investment. We predict that lower unemployment insurance increases the costs of hiring
and hence the cost of investing in a portfolio company for VC investors. Thus, we would observe
less VC investment if the worker unemployment costs are higher.
There is very little focus on the effect the costs of workers’ unemployment have on VC
investments. On exception is a recent study by Bozkaya and Kerr (2014), yet in contrast to their
1 Kortum and Lerner, 2000; Krishnan et al. 2006, 2011; Kerr et al. 2014; Nahata et al. 2014; and Bernstein et al.
2015
2
study we intent to overcome two major challenges when studying the effect of labor regulation on
VC investments. First, it is difficult to measure the exposure of workers to unemployment risk.
Second, one needs to distinguish between the impact of worker unemployment costs and other
factors that affect VC investments.
In order to address those issues we exploit changes in the U.S. state unemployment
insurance (UI) benefit laws. We examine the effect of those laws on VC investments 1950 to
2014. An increase in the UI benefits has an effect on works unemployment risk and cost. In
particular, higher unemployment benefit reduces the cost of layoffs and therefore reduces the
costs of investing in a portfolio firm. This identification approach allows us to identify the effect
of shocks to workers costs of unemployment on VC investment. In fact, previous studies show
that UI laws have important effect on workers’ behavior, aggregate labor supply, and firm
corporate policies (Topel and Welch, 1980; Topel, 1984; Meyer, 1990, 1995; Meyer and Mok,
2007; Gormley, Liu, and Zhou, 2010; Agrawal and Matsa, 2013; John et al. 2015).
We find that an increase in unemployment insurance benefits are both statistically and
economically associated with investment by venture capital funds. We find consistent results for
increases in the maximum amount of unemployment insurance benefits per week, total
unemployment benefits and the maximum duration as well as the total amount of venture capital
investment and the number of venture capital investments. Overall, we provide strong evidence
that labor market frictions can have a significant impact on venture capital financing.
We contribute to the literature in the following ways. First, we add to a literature on the
effects of labor market rigidities on corporate policies (Autor et al. 2007; Matsa, 2010;
Benmelech, Bergman, and Enriquez, 2012; Chen, et al. 2011a, 2011b). Bozkaya and Kerr (2014)
3
analyze the labor market regulations across European countries. They show that strict labor
regulations measured by the Employment Protection Index (EPI) hamper VC investments, while
the spending on unemployment insurance increase the VC investments. In contrast to their paper,
we focus on the U.S. sample. The data on state unemployment insurance laws in the U.S. offers
us a unique identification strategy. We therefore exploit changes in those laws as a source of
variation in the costs to employees during layoffs. We thus are able to clearly identify the causal
effects of labor regulation on VC investments.
Second, our work adds to the studies on the importance of labor market friction on
promoting innovation and investment.
Establishing these facts is key for investment and firm location decisions (e.g., Alcacer
and Chung, 2007), cluster formation (e.g., Delgado et al., 2010a,b; Glaeser et al. 2012), and
appropriate policy design.
The remainder of this paper is organized as follows. In section 2, we discuss prior
literature, institutional background and develop our hypothesis. In section 3, we describe our
sample selection process and research design. In section 4, we present our empirical results, and
in section 5, we conclude.
2. Literature review, institutional background and hypothesis development
2.1 Unemployment Insurance benefits
The unemployment insurance in the U.S. is designed to provide workers, who were
involuntarily laid off, monetary payments for a specific period of time. The basic framework for
unemployment insurance is nationwide and was created by Congress in 1935. However, each
4
state administers the program’s parameters, in particular is decides on the period and the amount
of benefits paid to unemployed workers.2
In each state the unemployment insurance benefits define who is eligible, what is the
benefit amount and duration. There is a lot of variation in unemployment insurance benefits
across states and over time. For example, the unemployment insurance benefit in California
increased around 7 time over four decades. There is also a large cross-sectional variation between
states. In 2010 the unemployment benefit in state Puerto Rico was $133, while in state
Massachusetts $943.
Unemployment insurance benefits have important effect on workers. It has been reported
that they affect unemployed workers consumption smoothing, saving and investment decisions
(Gruber, 1997; Gormley et al., 2010). They also have important implications for firm’s corporate
policies (Agrawal and Matsa, 2013). Also, mangers are more likely to let go workers when the
benefits increase and workers face lower unemployment costs (Topel, 1983).
2.2 Venture Capital (VC) financing
While start-up firms are the driving force of the economy they often have difficulties to
finance their development. Financing has an important impact on those firms (Kerr and Nanda,
2009). Those firms mainly are in a very early stages of life and are based on innovative ideas and
concepts. Therefore, they pose a significant risk of failure for traditional investors and providers
of capital. VC investors act as financial intermediaries and provide financing to entrepreneurial
firms. They mitigate the conflicts between investors and entrepreneurs by closely monitoring and
advising to the start-up firms (Gompers and Lerner, 1998, 2001; Cumming and Johan; 2013).
Furthermore, firms that obtain VC financing are often more innovative, have better post-IPO
2 For detailed discussion please see Agrawal and Matsa (2013).
5
performance, and governance (Kortum and Lerner, 2000; Masulis and Nahata, 2009, 2011;
Krishnan, et al., 2011; Hochberg, 2012; Nahata et al., 2014; Bernstein et al., 2015). Therefore,
VC investors are perceived to be the best source of providing the financing and support to
entrepreneurial firms.
2.3 Hypothesis development
Involuntary unemployment imposes a significant costs on the employees such as for
example the costs of job search and mobility (Mortensen, 1986). Therefore, workers require
additional compensation given these high costs. In order to bear this unemployment risk they
must be ex ante compensated in the form of wage premium or benefits. These additional
payments are known in the literature as wage differentials (Abowd and Ashenfelter, 1981). The
unemployment risk and therefore the wage differential depends on multiple factors such as
unemployment likelihood, worker’s risk aversion, and the works costs during layoff period.
The compensation for unemployment affects the firm’s corporate policies. For example,
Agrawal and Matsa (2013) show that unemployment risk affects firm’s leverage. High financial
leverage increases the probability of a firm distress. Over the restructuring period firms quite
often need to layoff workers to overcome the bankruptcy. Therefore, firms with high financial
leverage should ex ante offer higher wage differentials. In fact, this is what Agrawal and Matsa
(2013) find. In particular, they show that higher unemployment benefits the less conservative
financial policies are.
The unemployment risk has therefore important implications on the firm financing. VC
investors provide financing to high risk firms. VC investments are subject to changing economic
environment and financial risk (Nanda and Rhodes-Kropf, 2013; Nanda and Rhodes-Kropf,
6
2014). VCs typically invest in volatile industries that offer growth opportunities. Hence, they
need to adjust the workforce in order to survive and meet the challenges of changing economic
climate, expand if opportunities arise, and restructure portfolio companies at the time of failure.
Typical VC investor would undertake net present value (NPV) analysis of the future costs
and profits before providing financing to the portfolio company. The wage differential would
represent an additional costs that the VC investors need to account for. These can be shown as
additional term to the of NPV analysis.
NPV[Investment] = NPV[Future Profits] – NPV[Costs] – ∆ Labor Expense (1)
The Labor Expense represents the additional costs associated with the unemployment
risk. It increases the overall investment costs in the portfolio company and therefore delays the
timing of the investment. Workers can be compensated for this risk in two ways. First, they can
be paid premium in addition to their base salary that imposes a direct cost on the VC investment.
Second, they can be compensated in the form of unemployment benefits paid during the layoff
periods. In this paper we focus on the effect of the second and we predict that higher
unemployment benefits lower the Labor Expense and therefore speed up the timing of the VC
investment in the portfolio company. Therefore, higher unemployment insurance, the lower the
costs of hiring that reduce the cost of investing in a portfolio company. Thus, we would observe
more VC investment if the worker unemployment costs are lower.
3. Sample selection and research design
In this section we outline the sample for our analysis, the construction of our measures
and our empirical research design.
7
3.1 Labor unemployment costs
We collect information on the maximum amount of benefits and the maximum duration
during which a worker might receive unemployment benefits from the U.S. Department of Labor.
The department publishes each year in January and July a report on significant provisions of state
unemployment insurance laws. This report contains information on the amount of benefits,
coverage and taxes in the case of involuntary unemployment. Whereas several provisions are
very similar across states (i.e. the waiting period for receiving benefits is typically 0 or 1 week)
the maximum weekly benefit workers can receive and the maximum duration workers can
receive this benefit vary substantially. For example, in 2015 the weekly maximum benefit is $235
in Missouri and $1,047 in Massachusetts. Similarly, the maximum duration of unemployment are
between 14 weeks in Florida and 30 weeks in Massachusetts. Therefore, in our analysis we
employ three different measures to proxy for labor unemployment costs. Max_benefits is the
maximum amount in USD that a worker may receive during involuntary unemployment per
week. Max_weeks is the number of weeks a worker can receive benefits. To capture overall labor
unemployment costs we calculate Max_total_benefits as the product of the maximum amount of
benefits times the maximum number of weeks. To limit the potential influence of outliers we use
the natural logarithm of our variables in the later analysis. Although states also provide also a
minimum amount of unemployment benefits in their laws we focus on the maximum amount as
minimum amount is less economically meaningful. Additionally, to make data collection feasible
we focus only on the July issue of significant labor provisions as states typically adjust their
unemployment benefit regulations in this period. Altogether, we collect information on labor
unemployment benefits for the years 1976 until 2007.
8
3.2 Venture capital investments
We obtain information on venture capital investments from Thomson Reuters
VentureXpert. This database contains detailed information on the investments by venture capital
firms as well the venture capital firms themselves and has been extensively used in the venture
capital literature, e.g. XXX. For each investment the database contains the name, industry and
location of the company invested in, the amount invested, the round of investment, the stage of
the investment (early stage/seed, expansion, later stage) and the status of the company (acquired,
liquidated, IPO). Additionally, the database contains information on each venture capital fund
including location, total funds invested, type and foundation year. Our first measure for venture
capital investments is the total sum of investments. Therefore, we calculate for each year and for
each state the total sum of USD invested by all venture capital firms (VC) as VC_investments. As
second measure we use the number of venture capital investments, independent of the amount
invested. Therefore, Num_deals is the number of venture capital investments in a particular state
in a particular year. In our empirical analysis we use XXX investments in the period 1976 until
2007 in 51 different U.S. states. To limit the potential influence of outliers we use the natural
logarithm of our variables in the later analysis.
9
3.3 Other control variables
In order to control for contemporaneous macroeconomic effects that could potentially
confound our empirical analysis we follow Aggarwal and Matsa (2013) and use the state-level
growth in GDP (GDP_growth) and unemployment rate (Unemployment_rate) to control for these
effects. State-level GDP growth is calculated as natural logarithm of current GDP in current USD
divided by last year’s GDP in current USD and is obtained from the Bureau of Economic
Analysis Regional Economic Accounts. Information on state unemployment rates are from the
Local Area Unemployment Statistics of the U.S. Department of Labor.
[Insert Table 1 Here]
Table 1 presents the descriptive statistics of our sample. In total we have 1,352 state-year
observations. The mean of venture capital investment per state per year is 11.04. Total venture
capital investment has risen from 214 in 1976 to 626 in 2007 with a peak of 640 in 2001. Besides
variability over time venture capital investment also varies across geography. States with the
lowest mean venture capital investment are Arkansas with 1.5 and Wyoming with 1.79, whereas
Massachusetts and California have the highest average venture capital investments with 14.02
and 15.35, respectively. These figures are relatively similar if we look at the number of venture
capital investments instead of the amount.
If we look at our independent variables, we see that labor unemployment benefits have
risen over time from an average of USD 4.62 in 1976 to USD 5.94 in 2007. Also unemployment
benefits vary substantially geographically. The minimum average unemployment benefits are
paid in Missouri with USD 4.97 whereas the highest are paid in Massachusetts with USD 5.99.
Compared to the benefits payments, the variation in the maximum number of weeks where
benefits can be claimed is rather limited, especially over time.
10
Table 2 presents the correlations four our variables. We find a statistically positive
association between all our measures of lagged labor unemployment costs and our venture capital
investment measures. This indicates that lower unemployment costs, i.e. more generous state
unemployment benefits are univariate associated with more venture capital investments.
[Insert Table 2 Here]
Figures 1 and 2 graphically show the unilateral association between labor unemployment
costs and venture capital invest. Both figures suggest a positive association between labor
unemployment costs, here measured by total maximal unemployment benefits and both the
amount of venture capital investments and the number of investments.
[Insert Figures 1 and 2 Here]
3.4 Research design
Our research design follows closely Aggarwal and Matsa (201), who study the relation
between labor unemployment risk and firms’ capital structure decisions. More specifically, we
estimate the following regression models:
𝑉𝐶_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠𝑖𝑡 = 𝛼 + 𝛽1𝐿𝑎𝑏𝑜𝑟_𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡_𝑐𝑜𝑠𝑡𝑠𝑖𝑡 + 𝛽2𝐺𝐷𝑃_𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 +
𝛽3𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡_𝑟𝑎𝑡𝑒𝑖𝑡 + 𝛾𝑖 + 𝛿𝑡 + 휀𝑖𝑡, (1)
𝑁𝑢𝑚_𝑑𝑒𝑎𝑙𝑠𝑖𝑡 = 𝛼 + 𝛽1𝐿𝑎𝑏𝑜𝑟_𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡_𝑐𝑜𝑠𝑡𝑠𝑖𝑡 + 𝛽2𝐺𝐷𝑃_𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 +
𝛽3𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡_𝑟𝑎𝑡𝑒𝑖𝑡 + 𝛾𝑖 + 𝛿𝑡 + 휀𝑖𝑡, (2)
where Labor_unemployment_costs is one of our three measures Max_benefits,
Max_weeks and Max_total_benefits. To control for time-invariant state characteristics we include
state fixed effects () and to control for time trends we include year fixed effects ().
11
GDP_growth and Unemployment_rate are included to control for contemporaneous
macroeconomic shocks that could also affect venture capital investments.
4. Findings
In this section we present the results of our empirical analysis. Table 3 shows our results
for the amount of venture capital investments. Models (1) – (3) shows a positive association
between all three measures of labor unemployment benefit generosity and the amount invested by
venture capital firms. However, the relation is statistically insignificant for the maximum number
of weeks benefits are obtainable, likely due to the limited variation of this measure. In models (4)
(6) we additionally control for contemporaneous changes in state-level macroeconomic
conditions besides time invariant state fixed effects. We find that an increase in unemployment
benefits is both statistically and economically positively associated with venture capital
investments for the maximum amount of benefits available as well as total unemployment
benefits.
[Insert Table 3 Here]
In Table 4 are the results when we look at the number of venture capital deals. Similarly,
to the amount invested we find a consistent positive relationship between unemployment benefits
and the number of venture capital deals, i.e. if a state increases its unemployment benefits the
number of venture capital deals increases in the subsequent year.
[Insert Table 4 Here]
12
5. Conclusion
This paper showed that labor market frictions can have a strong impact on financing of
young companies. Public policy therefore can spur investments in growing companies and
subsequent job creation and innovation by reducing labor unemployment costs.
13
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17
Figure 1: Labor unemployment benefits and venture capital investments
05
10
15
20
Natu
ral lo
ga
rith
m o
f V
C in
ve
stm
ents
in U
SD
7 8 9 10Natural logarithm of maximum amount of total unemployment benefits in USD, L
Maximum unemployment benefits and VC investments
18
Figure 2: Labor unemployment benefits and venture capital investments
02
46
81
0
Natu
ral lo
ga
rith
m o
f nu
mb
er
of V
C d
ea
ls
7.5 8 8.5 9 9.5 10Natural logarithm of maximum amount of total unemployment benefits in USD, L
Maximum unemployment benefits and VC deals
19
Table 1: Descriptive statistics
N Mean Median Std.Dev. Min Max IQR
Log (VC_investments) 1352 11.05 11.15 2.569 4.007 17.83 3.637
Log (Num_deals) 1352 3.389 3.332 1.857 0 9.058 2.777
Log (Max_total_benefits) 1352 8.692 8.696 0.447 7.401 10.16 0.591
Log (Max_benefits) 1352 5.424 5.438 0.443 4.143 6.759 0.623
Log (Max_weeks) 1352 3.269 3.258 0.0433 3.258 3.664 0
Unemployment_rate 1352 5.765 5.450 1.901 2.300 15.37 2.308
GDP_growth 1352 6.674 6.136 3.472 -9.027 20.21 4.379
20
Table 2: Correlation table
Log
(VC_investmentst)
Log
(Num_dealst)
Log
(Max_total_benefitst-1)
Log
(Max_benefits t-1)
Log
(Max_weeks t-1)
Unemployment_
ratet
GDP_
growtht
Log (VC_investmentst) 1
Log (Num_dealst) 0.916*** 1
Log (Max_total_benefitst-1) 0.577*** 0.470*** 1
Log (Max_benefits t-1) 0.584*** 0.468*** 0.993*** 1
Log (Max_weeks t-1) -0.0190 0.0821*** 0.189*** 0.0741*** 1
Unemployment_ratet -0.254*** -0.131*** -0.320*** -0.330*** 0.0830*** 1
GDP_growtht -0.263*** -0.138*** -0.420*** -0.434*** 0.110*** -0.0443* 1
21
Table 3: Labor unemployment benefits and venture capital investments
(1) (2) (3) (4) (5) (6)
Log
(VC_investmentst)
Log
(VC_investmentst)
Log
(VC_investmentst)
Log
(VC_investmentst)
Log
(VC_investmentst)
Log
(VC_investmentst)
Log (Max_total_benefitst-1) 1.050** 1.241**
(0.458) (0.482)
Log (Max_benefitst-1) 1.074** 1.475***
(0.482) (0.488)
Log (Max_weekst-1) 1.588 0.328
(1.452) (1.513)
Unemployment_ratet -0.086* -0.089* -0.070
(0.046) (0.046) (0.049)
GDP_growtht 0.006 0.008 -0.002
(0.013) (0.013) (0.014)
Constant -1.705 1.654 1.304 -2.633 0.397 6.018
(3.620) (2.214) (4.829) (3.737) (2.163) (4.969)
N 1422 1422 1422 1352 1352 1352
adj. R2 0.732 0.731 0.729 0.757 0.757 0.752
Standard errors in parentheses
State and year fixed effects included. Standard errors adjusted for clustering at the state level. * p < .10, ** p < .05, *** p < .01
22
Table 4: Labor unemployment benefits and venture capital investments
(1) (2) (3) (4) (5) (6)
Log (Num_dealst) Log (Num_dealst) Log (Num_dealst) Log (Num_dealst) Log (Num_dealst) Log (Num_dealst)
Log (Max_total_benefitst-1) 0.728*** 0.878***
(0.231) (0.267)
Log (Max_benefitst-1) 0.733*** 0.983***
(0.256) (0.269)
Log (Max_weekst-1) 1.285 0.694
(0.994) (1.052)
Unemployment_ratet -0.081*** -0.082*** -0.067**
(0.030) (0.030) (0.030)
GDP_growtht 0.003 0.004 -0.002
(0.009) (0.009) (0.010)
Constant -4.718** -2.338* -3.251 -5.337** -2.907** -0.766
(1.822) (1.193) (3.271) (2.021) (1.179) (3.410)
N 1338 1338 1338 1276 1276 1276
adj. R2 0.683 0.682 0.679 0.712 0.712 0.705
Standard errors in parentheses
State and year fixed effects included. Standard errors adjusted for clustering at the state level. * p < .10, ** p < .05, *** p < .01
