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A joint initiative of Ludwig-Maximilians University’s Center for Economic Studies and the Ifo Institute
CESifo Conference Centre, Munich
Area Conferences 2013
CESifo Area Conference on
Applied Microeconomics
8 – 9 March
The Impact of Uncertainty on Small
Businesses: Evidence from Employment and
the Number of Businesses
Vivek Ghosal and Yang Ye
CESifo GmbH · Poschingerstr. 5 · 81679 Munich, Germany
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E-mail: [email protected] · www.CESifo.org
The Impact of Uncertainty on Small Businesses:
Evidence from Employment and the Number of Businesses
Vivek Ghosal1 and Yang Ye
Version: December 2012
Work-in-progress
Abstract
Within the broad context of theoretical models related to financing-constraints and real-options,
we examine the effects of economic uncertainty on small business employment and the number
of small businesses. We construct measures of economic uncertainty related to GDP growth,
inflation, the S&P500 stock price index, and fuel prices. Our findings are that economic
uncertainty has a negative impact on employment and the number of businesses, and the effects
are primarily felt by the relatively smaller businesses. The impact on large businesses are
generally non-existent or quantitatively smaller. We note implications of our findings and
comment on potential policies towards small businesses.
JEL codes: L11, D80, O30, G10, L40.
Keywords: uncertainty; small businesses; employment; number of firms; financing constraints;
credit constraints; real options; economic stabilization policy.
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Contact author: Vivek Ghosal, Professor, School of Economics, Georgia Institute of Technology, Atlanta, GA
USA. Email: [email protected] Web: http://www.econ.gatech.edu/people/faculty/ghosal
Yang Ye: Graduate student, School of Economics, Georgia Institute of Technology, Atlanta, GA USA.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
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1. Introduction
In this paper we use data on U.S. small businesses to examine the effects of economic
uncertainty on employment, as well as the total number of small businesses. While there is a
significant literature that has examined the impact of uncertainty on various aspects of firms’
decision-making and key choice variables, we are not aware of any systematic study which has
examined the uncertainty-employment link and for small businesses in particular. As we note
below, given the economic importance of the small businesses, examining the effects of
uncertainty on employment and number of small businesses may have important implications for
the study of economic cycles as well as framing of policy.
As we discuss in section 2, there are several channels identified in theory via which
uncertainty may have important effects on firms’ key decision variables, such as physical capital
investment outlays, entry and exit, employment and output. The broad classes of theories
include: (1) information asymmetries between borrowers and lenders which may affect the flow
of credit (e.g., Greenwald and Stiglitz, 1990); (2) real-options and the irreversibility of capital
expenditures (e.g., Dixit and Pindyck, 1994); (3) firms’ attitudes towards risk (e.g., Appelbaum
and Katz, 1986; Hartman, 1976); and (4) the convexity of the marginal product of capital (e.g.,
Abel, 1983). The first three channels tend to predict a negative effect of uncertainty on firms’
key decision variables. The fourth channel predicts positive effect. While there are a large
number of papers that have presented the core models and many refinements, in this paper we
focus on the credit-market and the real-options channels as these two have come to dominate the
literature in terms of gaining insights into firms’ decision-making under uncertainty.
While the theoretical channels present relatively clear predictions, empirical analysis,
using firm, industry or macroeconomic data, reveals that while uncertainty generally appears to
depress economic activity, the estimated quantitative effects and qualitative inferences vary
considerably across the studies and the particular decision variable under consideration
(investment, entry, output, among others). We take a quick look at the empirical literature in
section 2.
Our focus on small businesses is motivated by several factors. First, small businesses
play an important role in the economy in several dimensions. The U.S. Small Business
Administration (SBA) defines a small business as an independent business having fewer than
500 employees. According to the SBA, small businesses represent about 99 percent of all
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Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
employer firms, and employ about one-half of all private sector employees. Small businesses pay
about 43 percent of total U.S. private payroll and have generated 65 percent of net new jobs over
the past 17 years. Small businesses create more than half of the non-farm private GDP and made
up 97 percent of all identified exporters and produced 31 percent of export value in 2008. Small
businesses are also important to U.S. technology improvement. According to the SBA, small
businesses hire over 40 percent of high tech workers (scientists, engineers, computer
programmers, and others). Finally, small businesses generate patents more efficiently than large
firms: they produce 16 times more patents per employee than large patenting firms. Irrespective
of the specific dimension under consideration, small businesses play a vital role in the economy.
The importance of small businesses is a global phenomenon. As noted in ACCA (2010)
and di Giovanni et al. (2010), for example, the vast majority of businesses globally are very
small. They note that small businesses are special because they have several important
characteristics: they contribute disproportionately to local economic growth by improving
innovation ability; increase job opportunities; and contribute significant tax revenues. Given
these attributes, it has become commonplace in the aftermath of the current global economic
crisis to refer to SMEs as the backbone of the global economy.
Second, while the empirical literature on uncertainty is quite extensive, there have been
relatively few studies that have distinctly emphasized the effects of uncertainty on the economic
activity of small businesses. Examining this link is important, given the structural significance of
small business in the economy, and during economic expansions and contractions. Third, those
papers that have focused on the uncertainty-small business effects, have tended to be related to
investment (e.g., Ghosal and Loungani, 2000). Our primary focus is on the uncertaintyemployment link, along with the impact on the number of businesses, and the potential
distinction between the effects on small versus large businesses. Examining the effects of
economic uncertainty on employment and creation and survival of businesses is important as it
has taken on a larger than life significance following the 2007 onwards economic crisis, as well
as past crises, where employment effects have taken center stage in policy debates on how to
improve economic conditions.
Against this backdrop, we examine the effects of economic uncertainty on small
businesses. Our data on small businesses obtained from the SBA are US-wide, including all
small, and other, businesses in the US and their employment. Since the small business data
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portray a broader economy-wide picture, our uncertainty measures are constructed using
economy-wide variables related to GDP, inflation, stock prices, and fuel prices.
In section 2 we briefly note the central results from theory, present a cursory overview of
the empirical evidence, and note the key hypotheses. Section 3 discusses our data. In section 4
we outline construction of our measures of economic uncertainty. Sections 5 and 6 describe the
empirical specification we estimate, and present the results. Our findings are that economic
uncertainty negatively affects employment and the number of businesses, and the impact is
primarily concentrated in the relatively smaller business category. The impact on large
businesses is either non-existent or quantitatively smaller. Section 7 concludes with a discussion
of our findings and some policy issues.
2. Review of literature on the impact of uncertainty
The literature on the impact of uncertainty on firms decisions is quite extensive. Below we
briefly review selected papers and results in the theoretical and empirical literatures with an eye
towards spelling out the core hypotheses for our empirical analysis.
2.1 Review of theoretical results
In this section we briefly review some of the predictions from the theoretical literature and
highlight the implications for our empirical analysis. There are two strands of literature which offer
predictions on the impact on uncertainty on a range of firms’ decision variables. A large portion of
this literature has posed the problem as one of firms’ investment decisions under uncertainty. But
several researchers have examined the complementary issues related to production, employment,
R&D, among other variables. In our discussion below we note it as an investment problem, and then
note the implications for employment and number of businesses.
2.1.1 Uncertainty and financing constraints
Information asymmetry between borrowers and lenders may tend to constrain the flow of
credit. In an influential paper, Greenwald and Stiglitz (1990) model firms as maximizing expected
equity minus expected cost of bankruptcy and examine scenarios where firms may be equity or
borrowing constrained. A key result is that greater uncertainty exacerbates information asymmetries,
tightens financing constraints and lowers capital outlays. Since uncertainty increases the risk of
bankruptcy, firms cannot issue equity to absorb the risk. Those relying primarily on credit and
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Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
operating outside of equity markets, find the flow of credit constrained. Delli Gatti et al. (2003)
develop a model in which the financial conditions of businesses affect capital accumulation and entry
and exit which affect the distribution of firms differentiated by the equity ratio. In their model flow
of exiting firms is endogenously determined through bankruptcy while the flow of entering firms is
affected by stochastic factors. Lensink, Bo and Sterken (2001) provide a lucid discussion of
financing constraints in the general context of investment behavior, including the roles played by
uncertainty and sunk costs. Overall, this literature shows that periods of greater uncertainty widens
the information asymmetry, increases the likelihood of bankruptcy and exacerbates financing
constraints. Incumbent businesses who are more dependent on borrowing and adversely affected by
tighter credit will have greater likelihood of exit. Similarly, entry is likely to be impeded for potential
entrants who are more adversely affected by tighter credit conditions. Thus, periods of greater
uncertainty are expected to accelerate exits and retard entry leading to negative net entry – that is, a
decline in the number of firms in an industry.
The results are similar if we examine physical capital investment or employment.
Considering employment, for example, periods of greater uncertainty, by constraining credit flows,
limit the ability of existing firms to grow. Since new entry is dampened, this negatively affects
employment. As the likelihood of exit by businesses is greater, this also negatively affects
employment. Overall, periods of greater uncertainty are likely to lead to lower employment.
The literature points to not all businesses being equally affected by uncertainty and credit
market conditions. In different strands of this literature, the papers by Evans and Jovanovic (1989)
and Fazzari, Hubbard and Petersen (1988), for example, offer insights on small versus larger firm
effects. Gertler and Gilchrist (1994), for example, strongly emphasize the negative impact of
information asymmetric and credit constraints on smaller firms.
Tying the two literatures a prediction that emerges from is that periods of greater uncertainty,
via the credit market channel discussed above, is likely to have a dampening effect on employment
and the number of businesses. Given that the effects of financing constraints, at the margin, are more
likely to be borne by the smaller businesses, the prediction is that the negative effect of uncertainty
will be likely be greater on smaller businesses.
2.1.2 Uncertainty and real-options
An alternative channel examines the effects of uncertainty on firms’ decisions via the realoptions effects. The results in Caballero and Pindyck (1996), Dixit (1989) and Dixit and Pindyck
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
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(1994) are sufficient to generate the essential predictions for our empirical analysis, and we briefly
discuss the results.
Dixit (1989) shows that uncertainty and sunk costs imply an option-value of waiting and this
raises the entry trigger price and lowers the exit trigger price.2 For prices below the entry trigger, the
potential entrant holds on to its option to enter, and an incumbent firm does not exit at prices above
the exit trigger. Another way of looking at this is that to enter during periods of greater uncertainty,
firms would require a premium over the conventional Marshallian entry price. And incumbent firms
would wait longer to exit (i.e., let prices fall below average variable cost before they exit) as they
know that to re-enter the market they would have to re-incur the sunk entry costs.
The key results related to entry and exit can be summarized as follows: (1) numerical results
in Dixit (p.632-33) show that even small amounts of uncertainty are sufficient to generate significant
changes in entry and exit patterns; and (2) the numerical simulations in Dixit (1989, p.632-33) and
Dixit and Pindyck (Ch. 7, p.224-228; and Ch. 8.) show that an increase in uncertainty results in the
entry trigger price increasing by more than the decrease in the exit trigger. These results reveal that
entry is affected more than exit leading to negative net entry. In other words, periods of greater
uncertainty lead to a decrease in entry but exits, while lower, continue at a closer to normal pace
resulting in the industry experiencing a decrease in the number of firms.
The results are similar if we examine physical capital investment or employment. Dixit and
Pindyck (1994) present an extensive analysis of the detrimental effects of uncertainty on investment.
The effects on employment will be qualitatively similar. If greater uncertainty retards entry and
investment, it will also be expected to have a negative effect on employment.
Assessing the implications for our empirical analysis, we note the following. One of our
interests is to evaluate whether these effects may be different for small versus large firms. Previous
studies have shown that the vast majority of the entry and exit churning is for the small businesses
(e.g., see Audretsch (1995), Sutton (1997) and Caves (1998)). The literature shows that: (1) entrants
are typically small compared to incumbents and have high failure rates; (2) the typical exiting firm is
small and young; and (3) larger firms are older with higher survival rates. This implies that, via the
real-options channel, most of the negative effects of uncertainty on the number of businesses and
employment will likely be observed for the smaller businesses.
2
Dixit and Pindyck (1994) outline the theoretical framework for studying firms’ decision-making regarding
entry and exit under uncertainty and sunk entry costs. Hopenhayn (1992) and Pakes and Ericsson (1998), for
example, study firm dynamics with firm-specific uncertainty. These models, however, are best subjected to
empirical tests using micro-datasets as in Pakes and Ericsson, and is beyond the scope of our paper.
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2.3 Overview of empirical findings
The literature examining the effects of uncertainty is quite extensive. Studies vary
considerably in terms of the underlying variables they use to measure uncertainty, such as
profits, input prices, stock prices, inflation, GDP growth, economic policy news, survey of
businesses or forecasters, among others. Equally, there is a large variety in terms of the specific
constructs to measure uncertainty, such as conditional or unconditional variance (or standard
deviation), among others. Finally, some studies use micro-data at the firm level, while others use
data at the industry or macroeconomic level. The literature is quite expansive and it is difficult to
review the full spectrum of this literature.
To offer a perspective in a convenient format, we present a table in Appendix A which
summarizes selected papers. These are not meant to be comprehensive, but merely display the
variety of constructs used to measure uncertainty (GDP, inflation, prices, energy prices, stock
prices, among others), the level of aggregation of the studies (firm or industry level, economywide, or cross-country), and the estimated quantitative and qualitative effects. Regarding the
effects of uncertainty on small business, we briefly note some of the findings in the empirical
literature useful for our analysis. Using industry-level data, Ghosal and Loungani (2000) find
investment-uncertainty relationship is negative and this negative impact is greater in industries
dominated by small firms. Koetse and Vlist (2006) find that there are differential effects of
uncertainty on input and output variables. Bianco, Bontempi, Golinelli and Parigi (2012) find
family firms’ investments are significantly more sensitive to uncertainty than non-family firms.
2.4 Hypotheses
Based on our discussion of theory and the summary of the results in sections 2.1 and 2.2,
the following hypotheses emerge:
H1: Greater uncertainty is expected to negatively affect employment, and this effect may
be more pronounced in smaller businesses.
H2: Greater uncertainty is expected to negatively affect the number of businesses, and
this effect may be more pronounced for smaller businesses.
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3. Data description
For our econometric analysis, we use data from multiple sources. Below we provide
details of the datasets we use.
First, is the U.S. Small Business Administration (SBA) database. From the SBA database
we use annual data on the number of firms according to the size of firms from 1988 to 2011.
(Data prior to 1988 are not available.) The SBA database also provides employment data for
businesses by the size of firms. Second, U.S. macroeconomic data on real GDP and GDP
implicit price deflator are from the Federal Reserve Economic Data. The data on S&P 500 stock
price index is from Yahoo Finance. Finally, we obtain data on fuels and related products and
power PPI from the U.S. Bureau of Labor Statistics.
To consider size classes for our employment and number of businesses, we use the
following taxonomy to parse out the small business related effects versus other. The overall
sample we examine are:
1. ‘All’ businesses;
2. ‘Large’ businesses – these are businesses with ≥500 employees;
3. ‘Small’ businesses – these are businesses with <500 employees; and
4. ‘Smaller’ businesses – these are businesses with <20 employees.
The 500 employee cutoff is the one used by the U.S. SBA, and we use this as the baseline. We
consider an additional cutoff of <20 employees for the following reasons: (a) a 500 employee
firm is relatively large, so we wanted to consider an alternate cutoff for defining small; (b) data
with the <20 employee cutoff was available consistently for both our variables (employment and
number of businesses); and (c) a large percentage of the truly small business fall in this category.
In our estimation of employment and number of businesses specifications, we will
present estimates for each of the four groupings (1-4) noted above.
In Figures 1 and 2 we display the time paths for the employment levels and number of
businesses by size class. Table 1 presents the summary statistics for the employment and number
of businesses by size class.
4. Measures of uncertainty
To examine the impact of economic uncertainty on small businesses, we construct
alternate measures. As we discussed earlier, our objective is to examine the effects of economic
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
9
uncertainty on small businesses. Our data on small businesses are US-wide and include all small
and larger businesses in the US and their employment. Since the data are designed to portray a
broader picture, our uncertainty measures are created using economy-wide indicators.
The four measures of uncertainty we construct relate to uncertainty about:
1. real GDP growth. This indicates the overall state of the economy capturing demand and
supply effects;3
2. inflation rate. We measure inflation as the annual percentage growth of the GDP deflator.
Inflation uncertainty captures effects related to input and product prices, as well as
affecting firms’ real borrowing rates;4
3. stock prices. We use the S&P500 stock price index. As with real GDP, this is an indicator
of the overall state of the economy, and forward looking indicator of investor and
business confidence;5 and
4. real fuel price growth. Here we use the BLS price index of a range of commonly used
fuels by businesses. The nominal fuel price index is converted to real values after
deflating by the implicit GDP deflator. This variable serves to proxy a critical input –
fuels and energy – price for businesses.6
To create our measures of uncertainty, we use the following procedure. We assume that
firms use a forecasting equation to predict future values of the relevant variable, an economywide indicator in our case. As noted earlier, our objective is to examine the effects of economic
uncertainty on small businesses. Our data on small businesses is US-wide (i.e., all small
businesses in the US and their employment). Our uncertainty measures, therefore, are created
using economy-wide variables related to GDP, inflation, stock prices, and fuel prices.
As our baseline, we use an autoregressive, AR(2), specification as the forecasting model.
AR(n) models are based on Box and Jenkins (1970) formulation for forecasting economic
variables, and historically they have performed well in forecasting exercises (e.g., Meese and
Geweke (1984) and Marcellino, Stock and Watson (2003)). Given this aspect, and our annual
3
GDP uncertainty measures, in different forms, have been used by, for example, Driver et al. (2005), Asteriou et al.
(2005) and Bloom (2009).
4
Inflation has been used to construct uncertainty measures by, for example, Huizinga (1993), Fountas et al. (2006)
and Elder (2004).
5
Stock prices have been used to measure uncertainty by, for example, Bloom (2009), Chen et al. (2011), Bloom et
al. (2007), Greasley et al. (2006), and Stein et al. (2010).
6
Fuel and energy prices have been used to construct uncertainty measures by, for example, Koetse et al. (2006),
Kilian (2008) and Guo et al. (2005).
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
10
time-series data for the small business variables, we use this standard methodology to forecast
the relevant state variables.
The forecasting specification is:
where Z is either: (i) real GDP growth; or (ii) inflation rate; or (iii) S&P500 growth; or (iv) real
fuel price growth. From specification (1), the predicted values represent the forecastable
component. The residuals:
represent the unsystematic, or unforecastable, component. Since
values, we use the squared value of
can take positive or negative
, interpreted as the conditional-variance, as our measure
of uncertainty about the relevant variable. Using the residuals from forecasting equations and
conditional-variance to construct uncertainty measures is common: see, for example, the
insightful review in Lensink, Bo and Sterken (2001), and the references cited in the above
footnotes. If the forecasting specification is for GDP (i.e., Z is real GDP growth), then we denote
the uncertainty measure as:
Using this procedure for our four underlying economy-wide variables (Z), we obtain four
measures of uncertainty denoted by: (i)
; (ii)
; (iii)
; and (iv)
.7
To estimate the forecasting specification (1), we use available data on Z from 1960 to
2010. The rationale is as follows. The objective is obtain a good forecasting specification, which
is better done with a longer time-series ensuring that the parameters of the equation are estimated
precisely. While data on GDP, inflation and S&P 500 are available for earlier periods, the BLS
fuel price indices are available starting 1960. So we start the time-period for the equation in 1960
for which all of our variables (that we consider for Z) are available. The terminal period, 2010, is
7
While we report the results for an AR(.) specification, our results were robust to experiments with lag lengths.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
11
the same as the last period for which the small business data are available. This implies that the
generated time-series in our four uncertainty measures
,
,
and
are over
1962-2010 (two initial observations are lost due to use of lagged values).
A final remark we make is that while our AR(.) forecasting equation is fairly standard,
the length and frequency (annual from 1988-2010) of the available small business variables
restricts us from considering alternate procedures, such as using ARCH models, to construct
measures of uncertainty. While this may be viewed as a limitation, the SBA data on employment
and number of businesses are simply not available at a higher frequency. Since we need to
harmonize the estimation periods and frequency, we are restricted to using annual data to
forecast our specification (1).
5. Estimating Specification
To examine the hypotheses noted in section 2.4, we estimate autoregressive-distributed
lag AD(n, m) specification of the form:
where
is a measure of small business activity (in our case, employment or number of
businesses),
growth, and
or
or
is the measure of uncertainty measured in natural logarithms,
is real GDP
is the error term. As described earlier, the four measures of uncertainty are
or
. In specification (4), the autoregressive component has one lag, and
the distributed lag (for uncertainty and GDP) also contain one lag implying an AD(1,1) model.
Since we are interested in the shorter-term effects of uncertainty on small business
employment or number of businesses, and that the data displayed in Figures 1 and 2 show a clear
trend, we measure
in annual logarithmic first-differences (annual percentage changes).
is included in the specification to capture any persistence in the dependent variable. The
two real GDP growth variables,
and
, are included to control for overall economic
conditions.
As noted in the data section, the available data on Small Businesses are annual and cover
the period 1988-2010. We, therefore, estimate specification (4) over 1988-2010. While, as noted
earlier, our four generated uncertainty measures
,
,
and
are over 1962-
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
12
2010, we only use their values over 1988-2010 in estimating specification (4). Estimating (4)
informs us about the impact of uncertainty on employment and number of businesses, after
controlling for overall economic activity (GDP growth) and the dynamic lagged structure for the
included variables.8
In our examination of the effects of uncertainty on employment and number of
businesses, we will present estimates for four groups noted in our data section 3:
1. ‘All’ businesses;
2. ‘Large’ businesses – these are businesses with ≥500 employees;
3. ‘Small’ businesses – these are businesses with <500 employees; and
4. ‘Smaller’ businesses – these are businesses with <20 employees.
6. Estimation results << Preliminary >>
The results for the employment specifications are presented in tables 2.1 and 2.2. Table
2.1 presents estimates for ‘All’ and ‘Large’ businesses, and table 2.2 presents estimates for the
‘Small’ and ‘Smaller’ size classes. A similar format is followed for presenting the results on the
number of businesses in tables 3.1 and 3.2.
In table 2.1, the
are generally in the 80% range indicating good fit of the
specification. The estimates of the first-order autocorrelation coefficient, ρ, are relatively low.
The estimates show that uncertainty related to GDP, Inflation and Fuel prices have a negative
effect on ‘All’ business employment, but the quantitative effects vary. Turning to the ‘Large’
businesses, only Inflation and Fuel price uncertainty dampen employment, and the estimated
quantitative effects are smaller for the Large business group.
In the small business groups in table 2.2, the
vary in the 63% to 78% range. The
estimates of the first-order autocorrelation coefficient, ρ, are is relatively low for the ‘Smaller’
group, and somewhat higher for the ‘Small’ group. The estimates show that uncertainty related to
GDP, Inflation and Fuel prices have a negative effect on employment for the ‘Smaller’
businesses. None of the uncertainty effects are significant for the broader ‘Small’ business group.
Examining the broad inferences from the estimates in tables 2.1 and 2.2, uncertainty
appears to dampen employment, and this effects is primarily concentrated in the smaller
businesses.
8
Our experiments using longer lag lengths did not provide additional insights into the effects of uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
13
Next we turn to the specifications for the number of businesses in tables 3.1 and 3.2.
In table 3.1, the
for ‘All’ businesses specifications are in the 73% to 82% range, while they
are only in the 13% to 29% range for the ‘Large’ businesses, indicating a rather low explanatory
power for the latter group. The estimates of the first-order autocorrelation coefficient, ρ, are
relatively low. The estimates show that uncertainty related to GDP and Inflation have a negative
effect on the number of ‘All’ business, but the quantitative effects vary. Fuel price or S&P500
uncertainty does not appear to affect the number of ‘All’ businesses. Turning to the ‘Large’
businesses, only Fuel price uncertainty appears to decrease the number of these businesses.
For the smaller businesses specifications in table 3.2, the
are in the 60% range for
the ‘Smaller’ group, and in the 90% range for the ‘Small’ group. The estimates of the first-order
autocorrelation coefficient, ρ, are uniformly low. The estimates show that uncertainty related to
GDP and have a negative effect on the number of ‘Smaller’ business, but Fuel price and S&P500
uncertainty does not appear to affect this group. Turning to the ‘Small’ group, GDP and Inflation
uncertainty have a negative effect on the number of businesses.
Based on the estimates in tables 3.1 and 3.2, uncertainty has a negative impact on the
number of businesses, and this effect appears to be generally concentrated in the smaller business
groups.
The overall inferences we draw from the set of estimates presented in tables 2 and 3 are
that economic uncertainty dampens employment and the number of businesses, and the effects
appear to be concentrated in the Smaller (<20 employees) and Small (<500 employees) groups.
At broad brush, our findings appear supportive of the results from the theoretical models we
discussed in sections 2.1 and 2.2.
Before closing this section we note that the estimated specifications include real GDP
growth, and model the dynamics of the included variables via lagged effects. Real GDP growth
is probably the single most important control variable in either employment or number of
businesses specifications. With increasing GDP growth, business opportunities are expected to
expand allowing for creation of more jobs as well as new businesses. The fact that we find
effects related to uncertainty even after controlling for GDP growth and the lagged dynamics,
makes our finding even more noteworthy.
<< Additional results and checks of robustness to be added >>
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
14
7. Discussion of results and conclusions
Our findings on the negative impact of economic uncertainty on employment in small
businesses as well as the number of small businesses are particularly revealing as these results
appear even after controlling for GDP growth and appropriate controls for lagged dynamics of
the included variables. The underlying theories we reviewed related to potential financingconstraints and the real-options channels. Given that the data we use from the U.S. Small
Business Administration is relatively aggregated, it is difficult to disentangle which of these two
channels may be playing the more dominant role. It is fair to assume that both channels are
important in determining the outcomes.
As was noted earlier, there exists a significant literature on financing-constraints. This
literature has noted important differences between smaller and larger businesses, and point to
smaller firms being relatively credit-constrained (e.g., Fazzari, Hubbard and Petersen, 1988;
Gertler and Gilchrist, 1994; Evans and Jovanovic, 1989; Lensink, Bo and Sterken, 2001; Ghosal
and Loungani, 2000; Himmelberg and Petersen, 1994; Winker, 1999; and Audretsch and Elston,
1997).
Viewing our estimation results and the above information on financing constraints
collectively, part of our results for the smaller business is undoubtedly emerging from the
financing-constraints channel. If this is true, then important policy implications emerge,
primarily in the form of initiatives and instruments designed to partly ease the credit-constraints
faced by smaller businesses.
To the question as to why governments might pay special attention to small businesses,
there are several responses. First, we noted earlier in the paper that a large fraction of
employment and businesses fall into the smaller categories. Second, a number of emerging
structural factors – such as those related to globalization and banking sector consolidation – are
likely to favor large businesses relative to the smaller ones. These considerations alone provide
important economic policy justification.
As with many governments worldwide, the U.S. has recently implemented policies and
programs to help small businesses bridge the capital and market gap and encouraged publicprivate partnerships to support small business and entrepreneurship by, for example: (a)
supporting more than $53 billion in SBA loan guarantees to more than 113,000 small businesses;
(b) awarding more than $221 billion in Federal contracts to small businesses (FY 2009 through
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
15
April 30, 2011); and (c) awarding more than $4.5 billion in research funding through the Small
Business Innovation and Research Program during FY 2009 and FY 2010.9
Such initiatives, along with appropriate lending policies, can help ease some of the
financing-constraints faced by smaller businesses.10 By doing so, and in the context of this paper,
such policies may also help alleviate some of the negative impact of uncertainty on smaller
businesses.
9
The National Economic Council (2011) and Sheets and Sockin (2012) provide extensive discussion on the
importance of small businesses and policy.
10
The papers by Audretsch and Elston (1997, 2002), for example, provide important insights in this dimension from
German policy initiatives.
16
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
References
Abel, Andrew. “Optimal Investment under Uncertainty,” American Economic Review, 1983,
228–233.
Appelbaum, Elie, and Eliakim Katz. “Measures of Risk Aversion and Comparative Statics of
Industry Equilibrium,” American Economic Review, 1986, 524–529.
Association of Chartered Certified Accountants (ACCA). “Small Business: A Global Agenda,”
ACCA Accountants for business Report, 2010.
Asteriou, Dimitrios, and Simon Price. “Uncertainty, Investment and Economic Growth:
Evidence from a Dynamic Panel,” Review of Development Economics, 2005, 277–288.
Audretsch, David. Innovation and Industry Evolution. Cambridge: MIT Press, 1995.
Audretsch, David, and Julie Elston. “Does Firm Size Matter? Evidence on the Impact of
Liquidity Constraints on Firm Investment Behavior in Germany,” International Journal of
Industrial Organization, 2002, 1–17.
Audretsch, David, and Julie Elston. “Financing the German Mittelstand,” Small Business
Economics, 1997, 97-110.
Baker, Scott, Nicholas Bloom and Steven Davis. “Has Economic Policy Uncertainty Hampered
the Recovery?” Chicago Booth Paper, No. 12-06, 2012.
Baker, Scott, Nicholas Bloom and Steven Davis. “Measuring Economic Policy Uncertainty,”
University of Chicago and Stanford University, 2012.
Bianco, Magda, Maria Elena Bontempi, Roberto Golinelli and Giuseppe Parigi. “Family Firms’
Investments, Uncertainty and Opacity,” Small Business Economics, 2012, 1-24.
Bloom, Nicholas. “The Impact of Uncertainty Shocks,” Econometrica, 2009, 623-685.
Bloom, Nicholas. “The Impact of Uncertainty Shocks: Firm Level Estimation and a 9/11
Simulation,” CEP Discussion Paper, No. 718, 2006.
Bloom, Nick, Stephen Bond and John van Reenen. “Uncertainty and Investment Dynamics,”
Review of Economic Studies, 2007, 391-415.
Bo, Hong, and Elmer Sterken. “Volatility of the Interest Rate, Debt and Firm Investment: Dutch
Evidence,” Journal of Corporate Finance, 2002, 179–193.
Box, G.E.P., and , G.M. Jenkins. Time Series Analysis, Forecasting and Control. San
Francisco: Holden Day, 1970.
17
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Bulan, Laarni. “Real Options, Irreversible Investment and Firm Uncertainty: New Evidence from
U.S. Firms,” Review of Financial Economics, 2005, 255-279.
Caballero, Ricardo, and Robert Pindyck. “Investment, Uncertainty and Industry Evolution,”
International Economic Review, 1996, 641-662.
Caves, Richard. “Industrial Organization and New Findings on the Turnover and Mobility of Firms,”
Journal of Economic Literature, 1998, 1947-1982.
Crain, Nicole, and W. Mark Crain. “The Impact of Regulatory Costs on Small Firms,” Small
Business Administration, 2010.
Delli Gatti, Domenico, Mauro Gallegati, Gianfranco Giulioni and Antonio Palestrini. “Financial
Fragility, Patterns of Firms’ Entry and Exit and Aggregate Dynamics,” Journal of Economic
Behavior and Organization 51, 2003, 79-97.
Di Giovanni, J., Levchenko, A. A. and Rancière, R. “Power Laws in Firm Size and Openness to
Trade: Measurement and Implications,” IMF Working Paper No. 10/109.
Driver, Ciaran, Paul Temple and Giovanni Urga. “Profitability, Capacity, and Uncertainty: A
Model of UK Manufacturing Investment,” Oxford Economic Papers, 2005, 120–141.
Driver, Ciaran, and Brendan Whelan. “The Effect of Business Risk on Manufacturing Investment,”
Journal of Economic Behavior and Organization 44, 2001, 403-412.
Evans, David and Boyan Jovanovic. “An Estimated Model of Entrepreneurial Choice under Liquidity
Constraints,” Journal of Political Economy, 1989, 808-827.
Fazzari, Steven, Glenn Hubbard and Bruce Petersen, “Financing Constraints and Corporate
Investment.” Brookings Papers on Economic Activity, 1988, 141-195.
Folta, Timothy, and Jonathan O’Brien. "Entry in the Presence of Dueling Options," Strategic
Management Journal, 2004, 121-138.
Folta, Timothy. “Governance and Uncertainty: The Trade-off Between Administrative Control
and Commitment,” Strategic Management Journal, 1998, 1007-1028.
Fuss, Catherine, and Philip Vermeulen. “Firms’ Investment Decisions in Response to Demand
and Price Uncertainty,” Applied Economics, 2008, 2337-2351.
Gertler, Mark, and Simon Gilchrist. “Monetary Policy, Business Cycles, and the Behavior of Small
Manufacturing Firms,” Quarterly Journal of Economics, 1994, 309-340.
Ghosal, Vivek, and Prakash Loungani. “The Differential Impact of Uncertainty on Investment in
Small and Large Businesses,” Review of Economics and Statistics, 2000, 338-343.
18
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Ghosal, Vivek, and Prakash Loungani. “Product Market Competition and the Impact of Price
Uncertainty on Investment: Some Evidence From US Manufacturing Industries,” Journal of
Industrial Economics, 1996, 217-228.
Ghosal, Vivek. “The Effects of Uncertainty and Sunk Costs on Firms’ Decision-Making:
Evidence from Net Entry, Industry Structure and Investment Dynamics.” In The Economics of
Imperfect Markets, Giorgio Calcagnini and Enrico Saltari (Eds.), Contributions to Economics,
Springer: New York, 2009, Chapter 9, 167 – 182.
Ghosal, Vivek. “Does Uncertainty Influence the Number of Firms in an Industry?” Economics
Letters 50, 1996, 229 – 237.
Ghosal, Vivek. “Demand Uncertainty and the Capital-Labor Ratio: Evidence from the U.S.
Manufacturing Sector,” Review of Economics and Statistics 73, 1991, 157 – 162.
Greasley, David, and Jakob Madsen. “Investment and Uncertainty: Precipitating the Great
Depression in the United States,” Economica, 2006, 393–412.
Greenwald, Bruce, and Joseph Stiglitz. “Macroeconomic Models with Equity and Credit
Rationing,” in Hubbard, R. Glenn. ed., Asymmetric Information, Corporate Finance, and
Investment. Chicago: University of Chicago Press, 1990, 15-42.
Guo, Hui, and Kevin Kliesen. “Oil Price Volatility and U.S. Macroeconomic Activity,” Federal
Reserve Bank of St. Louis Review, 2005, 669-83.
Hartman, Richard. “Factor Demand with Output Price Uncertainty,” American Economic Review,
1976, 675–681.
Himmelberg, Charles, and Bruce Petersen. “R & D and Internal Finance: A Panel Study of Small
Firms in High-Tech Industries,” Review of Economics and Statistics, 1994, 38-51.
Hopenhayn, Hugo. “Entry, Exit and Firm Dynamics in Long Run Equilibrium,” Econometrica, 1992,
1127-1150.
Huizinga, John. “Inflation Uncertainty, Relative Price Uncertainty, and Investment in U.S.
Manufacturing,” Journal of Money, Credit and Banking, 1993, 521-549.
Kilian, Lutz. “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They
Matter for the U.S. Economy?” Review of Economics and Statistics. 2008, 216-240.
Koetse, Mark J., Arno J. van der Vlist and Henri L. F. de Groot, “The Impact of Perceived
Expectations and Uncertainty on Firm Investment,” Small Business Economics, 2006, 365-376.
Lensink, Robert, Hong Bo and Elmer Sterken Investment. Capital Market Imperfections, and
Uncertainty: Theory and Empirical Results. Edgar Elgar, 2001.
19
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Lensink, Robert, Paul van Steen and Elmer Sterken. “Uncertainty and Growth of the Firm,”
Small Business Economics, 2005, 381-391.
Marcellino, Massimiliano, James Stock and Mark Watson. “Macroeconomic Forecasting in
the Euro Area: Country Specific versus Euro Wide Information,” European Economic
Review, 2003, 1-18.
Meese, Richard, and John Geweke. “A Comparison of Autoregressive Univariate Forecasting
Procedures for Macroeconomic Time Series,” Journal of Business and Economic
Statistics, 1984, 191-200.
National Economic Council. “The Small Business Agenda: Growing America’s Small
Businesses to Win the Future,” 2011.
Oriani, Raffaele, and Maurizio Sobrero. “Uncertainty and the Market Valuation of R&D within a
Real Options Logic,” Strategic Management Journal, 2008, 343-361.
Pakes, Ariel, and Richard Ericson. “Empirical Implications of Alternate Models of Firm Dynamics,”
Journal of Economic Theory, 1998, 1-45.
Saemundsson, Rognvaldur, and Asa Lindholm Dahlstrand. “How Business Opportunities
Constrain Young Technology-Based Firms from Growing into Medium-Sized Firms,” Small
Business Economics, 2005, 113-129.
Schmitt, John, and Nathan Lane “An International Comparison of Small Business Employment,”
Center for Economic and Policy Research (CEPR) Reports and Issue Briefs, 2009.
Sheets, Nathan, and Robert Sockin. “Does Size Really Matter?: The Evolving Role of Small
Firms in the U.S. Economy,” Empirical & Thematic Perspectives, Citi Research, 2012.
Stein, Luke, and Elizabeth Stone. “The Effect of Uncertainty on Investment: Evidence from
Options,” Stanford University Working Paper, 2010.
Sutton, John. “Gibrat’s Legacy,” Journal of Economic Literature, 1997(a), 40-59.
U.S. Small Business Administration. Advocacy: the voice of small business in government.
“Frequently Asked Questions about Small Business Finance,”
http://www.sba.gov/sites/default/files/files/Finance%20FAQ%2082511%20FINAL%20for%20w
eb.pdf
Winker, Peter. “Causes and Effects of Financing Constraints at the Firm Level,” Small Business
Economics, 1999, 169-181.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
20
Figures: employment levels (fig. 1.1-1.4) and number of businesses (fig. 2.1-2.4) by size class
Figure 1.1. Total employment
120
100
80
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 1.2. Employment in large (≥500 employees) businesses
65
55
45
35
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 1.3. Employment in small (<500 employees) businesses
65
60
55
50
45
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 1.4. Employment in small (<20 employees) businesses
22
21
20
19
18
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
21
Figure 2.1. Total number of businesses
6.5
6.0
5.5
5.0
4.5
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 2.2. Number of large (≥500 employees) businesses
0.020
0.018
0.016
0.014
0.012
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 2.3. Number of small (<500 employees) businesses
6.5
6.0
5.5
5.0
4.5
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Figure 2.4. Number of small (<20 employees) businesses
5.5
5.0
4.5
4.0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
22
Tables
Table 1. Summary Statistics
Variable
By size class
Employment
EMP
Size: All
EMP
Size: Large w ≥500 employees
EMP
Size: Small w <20 employees
EMP
Size: Small w <500 employees
Number of businesses
FIRMS
Size: All
FIRMS
Size: Large w ≥500 employees
FIRMS
Size: Small w <20 employees
FIRMS
Size: Small w <500 employees
Mean
Std. Dev.
106.540
10.665
51.999
6.797
20.134
1.048
54.563
3.900
5.539
0.347
0.016
0.002
4.956
0.305
5.523
0.345
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
23
Tables: Estimates << Preliminary estimates – additional results to be added >>
Table 2.1. Employment Specifications
Dependent variable:
Size Group: All Businesses
Const
S1
-0.0251*
(0.060)
-0.3356*
(0.031)
0.6972*
(0.001)
0.7932*
(0.001)
0.0031*
(0.048)
-0.0032*
(0.011)
-
Size Group: Large Businesses
S2
-0.0584*
(0.006)
-0.3338*
(0.052)
0.5971*
(0.001)
0.8326*
(0.001)
-
S3
-0.0295*
(0.001)
-0.1424
(0.279)
0.5680*
(0.001)
0.7371*
(0.001)
-
S4
-0.0485*
(0.001)
-0.4121*
(0.018)
0.4462*
(0.001)
0.9239*
(0.001)
-
-
-
-
-
-
-
-
-
-
-0.0005
(0.694)
-0.0027*
(0.022)
-
-0.0059
(0.739)
-0.0307
(0.838)
0.7417*
(0.001)
0.7056*
(0.001)
0.0041
(0.130)
-0.0024
(0.167)
-
-
-
-
-
-
-0.0005
(0.427)
-0.0011
(0.174)
-
-
-
-
0.837
0.814
0.798
-
S1
S2
-0.0497*
(0.035)
0.0802
(0.360)
0.5890*
(0.001)
0.6046*
(0.003)
-
S3
-0.0240*
(0.011)
0.1824*
(0.008)
0.6098*
(0.001)
0.5634*
(0.002)
-
S4
-0.0382*
(0.001)
0.0212
(0.832)
0.5119*
(0.001)
0.6823*
(0.001)
-
-
-
-
-
-
-
-
-
-0.0010
(0.550)
-0.0019*
(0.096)
-
-
-
-
-
-0.0039*
(0.002)
-0.0017
(0.177)
0.869
-
-
0.0001
(0.920)
-0.0012
(0.145)
-
-
-
-
0.802
0.774
0.767
-0.0026*
(0.079)
-0.0017
(0.159)
0.795
Ρ
0.110
0.126
-0.061
-0.224
0.083
0.183
0.078
-0.069
Notes:
1. p-values (two-tailed test), based on efficient standard errors, are reported in parentheses. An * denotes
significance at least at the 10% level. All specifications are estimated using data over 1988-2010.
2. The first-order autocorrelation coefficient is denoted by ρ.
3. The variable definitions are as follows. (As noted in section 5, the uncertainty terms are measured in logarithms.)
= annual percentage change in employment.
= annual percentage change in real GDP.
= gdp uncertainty.
= inflation uncertainty.
= S&P 500 uncertainty.
= fuel price uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
24
Table 2.2. Employment Specifications
Dependent variable:
Size Group: Small<20 Employees Businesses
Const
S1
-0.0406*
(0.001)
-0.0536
(0.773)
0.4056*
(0.001)
0.0986
(0.495)
-0.0012
(0.325)
-0.0023*
(0.011)
-
Size Group: Small<500 Employees Businesses
S2
-0.0356*
(0.043)
0.1118
(0.617)
0.4386*
(0.001)
0.0359
(0.817)
-
S3
-0.0154*
(0.001)
0.1600
(0.413)
0.4435*
(0.001)
0.0826
(0.580)
-
S4
-0.0237*
(0.011)
-0.0202
(0.913)
0.3880*
(0.001)
0.1542
(0.192)
-
-
-
-
-
-
-
-
-
-
-0.0019*
(0.064)
-0.0006
(0.620)
-
S1
-0.0532*
(0.008)
-0.1196
(0.701)
0.6390*
(0.003)
0.3619 *
(0.024)
-0.0006
(0.765)
-0.0030
(0.106)
-
-
-
-
-
-
-0.0004
(0.427)
-0.0008
(0.246)
-
-
-
-
0.727
0.632
0.615
-
S2
-0.0560*
(0.045)
0.0753
(0.775)
0.5855*
(0.001)
0.3721*
(0.010)
-
S3
0.0344*
(0.001)
-0.3362*
(0.065)
0.5429 *
(0.001)
0.7774 *
(0.001)
-
S4
-0.0332 *
(0.099)
-0.0785
(0.869)
0.5628*
(0.001)
0.4896 *
(0.001)
-
-
-
-
-
-
-
-
-
-0.0025
(0.139)
-0.0008
(0.640)
-
-
-
-
-
-0.0028*
(0.002)
-0.0001
(0.912)
0.715
-
-
-0.0003
(0.655)
-0.0015
(0.103)
-
-
-
-
0.684
0.658
0.718
-0.0031
(0.174)
0.0006
(0.773)
0.683
ρ
0.146
-0.015
0.019
0.085
-0.272
-0.414
-0.046
-0.332
Notes:
1. p-values (two-tailed test), based on efficient standard errors, are reported in parentheses. An * denotes
significance at least at the 10% level. All specifications are estimated using data over 1988-2010.
2. The first-order autocorrelation is denoted by ρ.
3. The variable definitions are as follows. (As noted in section 5, the uncertainty terms are measured in logarithms.)
= annual percentage change in employment.
= annual percentage change in real GDP.
= gdp uncertainty.
= inflation uncertainty.
= S&P 500 uncertainty.
= fuel price uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
25
Table 3.1. Number of Businesses Specifications
Dependent variable:
Size Group: All Businesses
S1
Const
S2
Size Group: Large Businesses
-0.0186
(0.207)
0.6352*
(0.002)
0.3736*
(0.001)
-0.2613*
(0.008)
-
S3
-0.0091*
(0.049)
0.5691*
(0.001)
0.3950*
(0.001)
-0.1813*
(0.031)
-
-
-
-
-
-
-
-
-
-
-0.0021*
(0.013)
0.0005
(0.600)
-
S1
-0.0065
(0.846)
-0.4450
(0.013)
0.3575
(0.318)
0.6893*
(0.086)
0.0035
(0.332)
-0.0030
(0.387)
-
-
-
-
-
-
-0.0006
(0.215)
-0.0005
(0.385)
-
-
-
-
0.729
0.732
0.703
-0.0201*
(0.059)
0.4916*
(0.004)
0.3278*
(0.001)
-0.1249
(0.160)
-0.0021*
(0.018)
0.0002
(0.868)
-
S4
S2
-0.0133
(0.813)
-0.3788*
(0.045)
0.1198
(0.682)
0.8189 *
(0.063)
-
S3
-0.0295*
(0.062)
-0.3725*
(0.018)
0.1125
(0.734)
0.7453*
(0.030)
-
S4
-0.0488*
(0.064)
-0.4430*
(0.015)
0.0684
(0.830)
0.6541*
(0.027)
-
-
-
-
-
-
-
-
-
-0.0013
(0.776)
0.0009
(0.831)
-
-
-
-
-
-0.0007
(0.368)
0.0005
(0.630)
0.682
-
-
-0.0017
(0.387)
0.0028
(0.244)
-
-
-
-
0.158
0.132
0.218
-0.0046
(0.384)
0.5705*
(0.007)
0.3985*
(0.001)
-0.1686*
(0.098)
-
-0.0065*
(0.029)
-0.0025
(0.499)
0.290
ρ
0.120
-0.006
-0.054
0.035
-0.228
-0.231
-0.344
-0.346
Notes:
1. p-values (two-tailed test), based on efficient standard errors, are reported in parentheses. An * denotes
significance at least at the 10% level. All specifications are estimated using data over 1988-2010.
2. The first-order autocorrelation coefficient is denoted by ρ.
3. The variable definitions are as follows. (As noted in section 5, the uncertainty terms are measured in logarithms.)
= annual percentage change in number of businesses.
= annual percentage change in real GDP.
= gdp uncertainty.
= inflation uncertainty.
= S&P 500 uncertainty.
= fuel price uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
26
Table 3.2. Number of Businesses Specifications
Dependent variable:
Size Group: Small<20 Employees Businesses
S1
Const
S2
-0.0210
(0.240)
0.5124*
(0.024)
0.3447*
(0.007)
-0.2439*
(0.013)
-
-0.0068
(0.198)
0.4854*
(0.011)
0.3604*
(0.001)
-0.1699*
(0.051)
-
-0.0052
(0.266)
0.4700*
(0.024)
0.3683*
(0.001)
-0.1658*
(0.066)
-
-
-
-
-
-
-
-
-
-
-0.0020*
(0.032)
0.0001
(0.885)
-
S1
-0.0185*
(0.001)
0.6303*
(0.001)
0.1954*
(0.003)
0.0917*
(0.070)
-0.0003
(0.638)
-0.0010 *
(0.034)
-
-
-
-
-
-
-0.0004
(0.396)
-0.0005
(0.453)
-
-
-
-
0.618
0.603
0.564
-0.0197
(0.140)
0.3991*
(0.058)
0.2806 *
(0.004)
-0.1099
(0.251)
-0.0024*
(0.021)
0.0003
(0.793)
-
S3
Size Group: Small<500 Employees Businesses
S4
S2
-0.0180*
(0.043)
0.6370*
(0.001)
0.1966*
(0.002)
0.1088 *
(0.029)
-
S3
-0.0098*
(0.001)
0.6498*
(0.001)
0.1805*
(0.005)
0.1456*
(0.001)
-
S4
-0.0106*
(0.009)
0.6553*
(0.001)
0.1631*
(0.003)
0.1425 *
(0.001)
-
-
-
-
-
-
-
-
-
-0.0006
(0.213)
-0.0004
(0.521)
-
-
-
-
-
-0.0007
(0.462)
0.00002
(0.987)
0.545
-
-
-0.0003
(0.188)
-0.0002
(0.448)
-
-
-
-
0.919
0.901
0.899
-0.0011*
(0.027)
0.0003
(0.521)
0.918
ρ
0.024
-0.101
-0.035
-0.004
-0.175
-0.224
-0.187
-0.273
Notes:
1. p-values (two-tailed test), based on efficient standard errors, are reported in parentheses. An * denotes
significance at least at the 10% level. All specifications are estimated using data over 1988-2010.
2. The first-order autocorrelation is denoted by ρ.
3. The variable definitions are as follows. (As noted in section 5, the uncertainty terms are measured in logarithms.)
= annual percentage change in number of businesses.
= annual percentage change in real GDP.
= gdp uncertainty.
= inflation uncertainty.
= S&P 500 uncertainty.
= fuel price uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
27
Appendix A: Selected empirical findings on the impact of uncertainty
The table below highlights the variables which are used to measure uncertainty, specific
constructs for uncertainty, and empirical findings. The papers included below are not meant to
represent a comprehensive survey, but merely to show the diversity of research in this area.
Table A.1. Selected papers examining the effects of uncertainty
Paper
Data
Estimation method
State variables
Uncertainty measure
Estimation results
Lensink, Robert, Paul van Steen and
Elmer Sterken. “Uncertainty and
Growth of the Firm,” Small Business
Economics, 2005, 381-391.
Survey of 1,097 Dutch firms in
1999.
Sales. Return on
Investment.
Logit model.
Coefficient of variation.
Uncertainty has a negative impact on
the size of investment, no matter what
the type of investment is used.
In the level-level regression, one unit
increase in measured uncertainty is
estimated to reduce the investment
(which is scaled by sales) by 0.288. In
the logit model, for one unit increase in
uncertainty, the log odds of investment
decreased by 0.878.
Koetse, Mark J., Arno J. van der Vlist
and Henri L.F. de Groot. “The Impact
of Perceived Expectations and
Uncertainty on Firm Investment,”
Small Business Economics, 2006,
365-376.
Survey of 135 plant locations in
Netherlands in 1998.
Wages. Energy prices.
Output prices.
Tobit model.
Survey based.
Bo, Hong, and Elmer Sterken.
“Volatility of the interest rate, debt
and firm investment: Dutch
evidence,” Journal of Corporate
Finance, 2002, 179–193.
Data for 41 Dutch listed firms
from 1984 to 1995.
Interest rate.
Driver, Ciaran, and Brendan Whelan.
“The Effect of Business Risk on
Manufacturing Investment,” Journal
of Economic Behavior and
Organization, 2001, 403-412.
Disaggregated survey data of
Ireland in 1995.
Conditional variance.
Panel Data, ARCH model, Fixed
effect estimation.
The paper analyzes the channel
how uncertainty affects firms’
investment by comparing the
percentage of different
respondents in the survey
questions.
Future demand and
future price
Future unit input cost
Capacity
Delay risk
Subjective descriptions
For small firms, that there are important
differences between the effects of
uncertainty about input and output
variables.
The regression results show that one
point increase in wage uncertainty is
associated with 0.06 unit decrease in
predicted value of investment to sales;
while the increasing of output price
uncertainty by one point is estimated to
increase the predicted value of ratio of
aggregate firm investment to sales by
2.44% point.
Using fixed effect estimation, they find
that an increase in the interest rate of 1
standard deviation leads to an expected
decrease in the investment to capital
ratio by 0.27 standard deviations
holding other explanatory variables
constant. One standard deviation
increase in the interaction between debt
and the volatility of the interest rate
leads to an increase in the investment to
capital ratio by 0.07 standard
deviations.
No strong effect of risk due to
convexities, though where price-taking
behavior dominated, as in the Food
sector, there seemed a weaker negative
effect on investment from demand or
price uncertainty.
Disequilibrium effect recorded in the
survey could cause an upward
investment bias for those firms worried
about capital shortage. However, in
practice firms may not invest more than
under certainty because the effect is
blunted by risk aversion.
Risk did affect the timing of
investment for between a quarter and a
third of the sample. The greatest
28
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Oriani, Raffaele, and Maurizio
Sobrero. “Uncertainty and the Market
Valuation of R&D within a Real
Options Logic,” Strategic
Management Journal, 2008, 343-361.
Data for 290 manufacturing
firms in UK from 1989 to 1998.
Bianco, Magda, Maria Elena
Bontempi, Roberto Golinelli and
Giuseppe Parigi. “Family Firms’
Investments, Uncertainty and
Opacity,” Small Business Economics,
2012, 1-24.
Data for 2,959 Italian private
companies from 1996 to 2007.
Bloom, Nick, Stephen Bond and John
Van Reenen. “Uncertainty and
Investment Dynamics,” Review of
Economic Studies, 2007, 391-415.
Data for 672 UK manufacturing
firms from 1972 to 1991.
Ghosal, Vivek, and Prakash
Loungani. “Product Market
Competition and the Impact of Price
Uncertainty on Investment: Some
Evidence From US Manufacturing
Industries,” Journal of Industrial
Economics, 1996, 217-228.
Data for 254 US 4-digit SIC
manufacturing industries from
1958 to 1989.
Fuss, Catherine, and Philip
Vermeulen. “Firms' Investment
Decisions in Response to Demand
and Price Uncertainty,” Applied
Economics, 2008, 2337-2351.
Survey of 279 firms from 19872000, and another survey of 319
firms from 1987-1999.
Bulan, Laarni. “Real Options,
Irreversible Investment and Firm
Uncertainty: New Evidence from U.S.
Firms,” Review of Financial
Economics, 2005, 255-279.
Data for 2,901 US firms from
1964 to 1999.
Panel Data, Hedonic model
Industry output. Patents.
Absolute percentage
difference. Inverse of
the median age.
Sales.
Coefficient of variation.
Panel data, GMM
Panel data, GMM
Panel data, fixed effect – IVE.
Stock returns.
Std. deviation of daily
stock returns.
Product price.
Rolling regression based
conditional std.
deviation.
Expectations of future
demand and prices.
Theil index.
Panel data, GMM.
Equity returns.
Standard deviation.
Panel data, 2SLS.
caution in respect of timing was in the
Hi-tech sector which was also the
sector with the greatest damage from
delay.
They find that increased market
uncertainty reduces the value of R&D
investment until a certain level of
market uncertainty is reached; whereas
beyond that level it augments the value.
To be specific, this is a U-shaped
relationship between Market
Uncertainty and Value of investment.
Also, they find an inverted U-shaped
relationship between technological
uncertainty and the value of R&D
capital.
Family firms’ investments are
significantly more sensitive to
uncertainty than nonfamily firms and
the greater sensitivity to uncertainty is
basically due to the greater opacity of
family firms and to their higher risk
aversion, rather than to the degree of
sunk fixed capital as is typical in the
literature on investment decisions.
A reduction in the degree of
uncertainty from the third to the first
quartile of its sample distribution
induces an increase in planned
investments of 1.69% for the median
family firm, while investment plans of
non-families are nearly unchanged;
also, a 0.27-0.36% increase in
investment plans associated with an
equal uncertainty reduction.
“Cautionary effects” of uncertainty are
large – going from the lower quartile to
the upper quartile of the uncertainty
distribution typically halves the first
year investment response to demand
shocks.
A negative relationship between
investment and price uncertainty only
exists in competitive industries.
One percentage increases in price
uncertainty is estimated to cause the
ratio of gross industry investment (I/K)
decrease by 0.358 for most competitive
industries.
Demand uncertainty at the time of
planning depresses planned and
subsequent realized investment.
One standard deviation increases in
demand uncertainty is estimated to
reduce 6% of the average investment
ratio.
Increased industry uncertainty and
firm-specific uncertainty display a
pronounced negative effect on firm
investment consistent with real options
behavior.
A one standard deviation increase in
industry uncertainty reduces a firm’s
investment-to-capital ratio by 6.4%. A
one standard deviation increase in firm-
29
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
specific uncertainty decreases firm
investment by 19.3%. In contrast, a one
standard deviation increase in Tobin’s q
increases investment by only 5%.
Ghosal, Vivek, and Prakash
Loungani. “The Differential Impact of
Uncertainty on Investment in Small
and Large Businesses,” Review of
Economics and Statistics, 2000, 338343.
Data for 330 US SIC 4-digit
manufacturing industries from
1958 to 1991.
Profits.
Panel data, IVE.
Rolling regression based
conditional std.
deviation..
Huizinga, John. “Inflation
Uncertainty, Relative Price
Uncertainty, and Investment in U.S.
Manufacturing,” Journal of Money,
Credit and Banking, 1993, 521-549.
Data for 450 U.S. SIC 4-digit
manufacturing industries from
1954 to 1989.
Real wage. Output
price. Real materials
price.
Bivariate ARCH model.
Conditional std.
deviation.
Stein, Luke C.D., and Elizabeth C.
Stone. “The Effect of Uncertainty on
Investment: Evidence from Options,”
Stanford University Working Paper,
2010.
Data for 2,230 US
manufacturing firms from 1996
to 2009.
Stock price.
Folta, Timothy, and Jonathan P.
O’Brien. “Entry in the Presence of
Dueling Options,” Strategic
Management Journal, 2004, 121-138.
Data for 2,230 US
manufacturing firms from 1996
to 2009.
Expected volatility.
Panel data, 2SLS.
Panel data, 2SLS.
Data on 17,897 firms from 1980
to 1999.
GARCH model.
Industry’s contribution
to GDP.
Square root of
conditional variance.
Investment-uncertainty relationship is
negative and this negative impact is
greater in industries dominated by
small firms.
The estimated profit uncertainty in
small firms is in -0.80 ranges, while
that for large firms are around -0.14.
Therefore, it appears that as large-firm
dominance increases, the impact of
uncertainty on investment gets
quantitatively weaker.
A one-standard-deviation increase in
estimated conditional standard
deviation of the real wage from is
estimated to reduce the predicted value
of ratio of capital spending to output,
by 0.72.
A one-standard-deviation increase in
estimated conditional standard
deviation of output price would
eventually lower the expected value of
ratio of capital spending to output, by
2.9.
A one-standard-deviation increase in
estimated conditional standard
deviation of profit rate is estimated to
immediately increase predicted value of
ratio of capital spending to output, by
0.74. and if sustained would, ceteris
paribus, increase the predicted value by
1.99.
They find a negative and statistically
significant relationship between
uncertainty and investment that is
robust across a variety of
specifications. The coefficient estimates
are larger in magnitude after addressing
the endogeneity of the uncertainty
measure, suggesting potential reverse
causation that biases the OLS estimates
towards zero.
Given a standard deviation of
implied volatility of 0.24 in data
sample, a one standard deviation
increase in uncertainty is associated
with a 0.7% decline in the quarterly
investment rate. After adding Tobin’s q
into the model, a one standard deviation
increase in market-wide uncertainty is
associated with a 0.1% decline in a
firm’s investment rate.
They find the effect of uncertainty on
entry is non-monotonic and U-shaped,
for the coefficient for Uncertainty2 is
positive (1.00), while the coefficient for
Uncertainty is negative (-4.16).
When there are potentially strong
early mover advantages (i.e., Scale
Advantages at 95th percentile), the
effect of Uncertainty on entry turns
positive at about the 54th percentile of
Uncertainty. In contrast, the effect of
30
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Uncertainty does not turn positive until
around its 94th percentile for most
industries.
Baker, Scott, Nick Bloom and Steven
J. Davis. “Has Economic Policy
Uncertainty Hampered the
Recovery?” Chicago Booth Paper,
No. 12-06, 2012.
Index of economic policy
uncertainty, news-based proxy,
government purchases data,
disagreement about future
indexes from 1985 to 2011. And
tax code expiration data is from
1991 to 2011.
Economic policy
uncertainty, news-based
proxy, government
purchases data,
disagreement about
future indexes, and
merge them into a new
proxy.
VAR model
Aggregating the above
Components to Obtain
an Index of Economic
Policy Uncertainty
Baker, Scott, Nicholas Bloom and
Steven Davis. “Measuring Economic
Policy Uncertainty,” University of
Chicago and Stanford University,
2012.
Constructing index data about
political uncertainty from 19852009.
VAR model
Newspaper coverage of
policy-related economic
uncertainty. Number of
federal tax code
provisions set to expire
in future years.
Disagreement among
economic forecasters.
Constructing an index
from three types of
underlying components.
Bloom, Nicholas. “The Impact of
Uncertainty Shocks,” Econometrica,
2009, 623-685.
Firm-level data from 2548 firms
U.S. firms from 1981 to 2000.
Panel data, VAR model,
SMM estimation
Driver, Ciaran, Paul Temple and
Giovanni Urga. “Profitability,
capacity, and uncertainty: a model of
UK manufacturing investment,”
Oxford Economic Papers, 2005, 120–
141.
Aggregate data of UK
manufacturing on two capital
assets (machinery and building)
from 1972 to 1999.
Asteriou, Dimitrios and Simon Price.
“Uncertainty, investment and
economic growth: evidence from a
dynamic panel,” Review of
Development Economics, 2005, 277–
288.
Data of 59 industrial and
developing countries from 1966
to 1992.
Profits growth, stock
returns, TFP growth,
GDP forecasts.
Standard deviation.
Output growth.
Time-series conditional
volatility.
GARCH model
Panel data, PMG, MG, GARCH
GDP per capita growth.
Conditional variance.
Their analysis indicates that the
historically high levels of policy
uncertainty in 2010 and 2011 mainly
reflect concerns about tax and monetary
policy and secondarily a broader range
of other policy-related concerns.
Policy-related concerns now account
for a large share of overall economic
uncertainty.
A rise in policy uncertainty, similar
in magnitude to the actual change since
2006, is associated with substantially
lower levels of output and employment
over the following 36 months.
Policy related uncertainty played a role
in the slow growth and fitful recovery
of recent years, and they invite further
research into the effects of policyrelated uncertainty on economic
performance.
VAR estimates show that a policy
uncertainty shock equal in size to actual
increase in the index value from 2006
to 2011 foreshadows drops in private
investment of 16 percent within 3
quarters, industrial production drops of
4 percent after 16 months, and
aggregate employment reductions of
2.3 million within two years.
Uncertainty appears to dramatically
increase after major economic and
political shocks like the Cuban missile
crisis, the assassination of JFK, the
OPEC I oil-price shock, and the 9/11
terrorist attacks.
Uncertainty shocks appear to have
large real effects, based on the
assumption that firms have non-convex
adjustment costs: the uncertainty
component alone generates a 1% drop
and rebound in employment and output
over the following 6 months, and a
milder long-run overshoot in these
economic shocks.
The GARCH model shows uncertainty
variable for the full sample are
estimated to be negatively significant at
the 5% level for machinery, and
negative but not significant for
building.
Using PMG estimation, they find the
estimated coefficients were -0.360 for
the industrial and -0.053 for the
developing countries, which showed a
negative relationship between growth
and uncertainty in both cases. For the
effect of uncertainty on investment, the
PMG results revealed a significant
negative relationship for both subgroups, with higher negative
magnitudes for the industrial countries
(-3.280) than that of developing
31
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
countries (-0.081).
Bloom, Nick. “The Impact of
Uncertainty Shocks: Firm Level
Estimation and a 9/11 Simulation,”
CEP Discussion Paper, No. 718,
2006.
Data of 579 US manufacturing
firms from 1991 to 2000.
Greasley, David, and Jakob B.
Madsen. “Investment and
Uncertainty: Precipitating the Great
Depression in the United States,”
Economica, 2006, 393–412.
Data of investment information
in US from 1920 to 1938.
Kilian, Lutz. “Exogenous Oil Supply
Shocks: How Big Are They and How
Much Do They Matter for the U.S.
Economy?” Review of Economics
and Statistics. 2008, 216-240.
Monthly production data for all
OPEC countries and for
aggregate non-OPEC oil
production since 1973.
Monthly share returns.
Standard deviations.
Panel data, time-varying
second moment model
Tobin’s q
Real stock prices.
Squared monthly
proportional change.
Oil production.
Exogenous variation.
OLS
Guo, Hui, and Kevin L. Kliesen. “Oil
Price Volatility and U.S.
Macroeconomic Activity,” Federal
Reserve Bank of St. Louis Review,
2005, 669-83.
Data of daily price of U.S. 1month futures and 12- month
futures contracts from 1983 to
2004.
Oil prices.
Realized variance series.
Forecasting regression
John Elder. “Another Perspective on
the Effects of Inflation Uncertainty,”
Journal of Money, Credit & Banking,
2004, 911-928.
Data of U.S. output growth rate,
inflation, CPI, commodity price
from 1966 to 2000.
Cross-section data,
MGARCH-M VAR model
Inflation
Conditional variance
Monthly neighborhood
returns
Conditional variance
Stilianos Fountas, Menelaos
Karanasos and Jinki Kim. “Inflation
Uncertainty, Output Growth
Uncertainty and Macroeconomic
Performance,” Oxford Bulletin of
Economics and Statistics. 2006, 319-
Monthly data for the G7 on PI
and the IPI.
Inflation
Output growth
Bivariate GARCH model, VAR
model
Conditional variance
Uncertainty appears to vary strongly
over time, temporarily rising by up to
200% around major shocks like the
Cuban Missile crisis, the assassination
of JFK and 9/11.
Based on a model with a time
varying second moment, it finds
temporary impact of a second moment
shock is different from the typically
persistent impact of a first moment
shock. While the second moment effect
has its biggest drop in month 1 and has
completely rebounded by month 5, a
persistent first moment shock will
generate a drop in activity lasting
several quarters.
For investment, the results here show
that the effects of heightened
uncertainty surrounding the expected
marginal profitability of capital,
measured by share price volatility, can
explain around 80% of the actual fall in
the business fixed investment ratio in
1930. Thus, to a large extent, the
investment slump led the declines in
income.
Using this approach and the new
exogenous oil supply shock measure, it
finds statistically significant evidence
of a sharp drop in real GDP growth five
quarters after an exogenous oil supply
shock and of a spike in CPI inflation
three quarters after the shock. And it is
shown that exogenous oil supply
shocks made remarkably little
difference overall for the evolution of
U.S. real GDP growth and CPI inflation
since the 1970s.
A volatility measure constructed using
daily crude oil futures prices has a
negative and significant effect on future
GDP growth over the period 19842004. Moreover, the effect becomes
more significant after oil price changes
are also included in the regression to
control for the symmetric effect.
The main empirical result is that
uncertainty about inflation has
significantly reduced real economic
activity over the post-1982 period, with
the effect concentrated after a twomonth lag.
Using MGARCH-M VAR model,
they find that one standard deviation
increase in inflation uncertainty has
tended to reduce real economic activity
over three months by about 22 basis
points in the post-1982 period.
First, inflation does cause negative
welfare effects, directly and indirectly.
Secondly, in some countries, e.g.
Canada and the UK, more inflation
uncertainty provides an incentive to
Central Banks to surprise the public by
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
32
343.
raising inflation unexpectedly. Thirdly,
in contrast to the assumptions of some
macroeconomic models, business cycle
variability and the rate of economic
growth are related. More variability in
the business cycle leads to more output
growth.
The Impact of Uncertainty on Small Businesses:
Evidence from Employment and the Number of Businesses
Vivek Ghosal1 and Yang Ye
Version: February 18 2013
Work-in-progress
Abstract
Within the broad context of theoretical models related to real-options and financing-constraints,
we examine the effects of economic uncertainty on small business employment and the number
of small businesses. We construct a range of measures of economic uncertainty derived from
survey forecasts, as well as those constructed from our estimation of forecasting models for GDP
growth, inflation, S&P500 stock price index, and fuel prices. Our findings are that economic
uncertainty has a negative impact on employment and the number of businesses, and the effects
are primarily felt by the relatively smaller businesses. The impact on large businesses are
generally non-existent or weaker. We note the linkages of our findings to the underlying theories,
and comment on potential policy implications.
JEL codes: L11, D80, O30, G10, L40.
Keywords: uncertainty; small businesses; employment; number of firms; financing constraints;
credit constraints; real options; economic stabilization policy.
1
Contact author: Vivek Ghosal, Professor, School of Economics, Georgia Institute of Technology, Atlanta, GA
USA. Email: [email protected] Web: http://www.econ.gatech.edu/people/faculty/ghosal
Yang Ye: Graduate student, School of Economics, Georgia Institute of Technology, Atlanta, GA USA.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
2
1. Introduction
In this paper we use data on U.S. small businesses to examine the effects of economic
uncertainty on employment, as well as the total number of small businesses. While there is a
significant literature that has examined the impact of uncertainty on various aspects of firms’
decision-making and key choice variables, we are not aware of any systematic study which has
examined the uncertainty-employment link and for small businesses in particular. As we note
below, given the economic importance of the small businesses, examining the effects of
uncertainty on employment and number of small businesses may have important implications for
the study of economic cycles as well as framing of policy.
As we discuss in section 2, there are several channels identified in theory via which
uncertainty may have important effects on firms’ key decision variables, such as physical capital
investment outlays, entry and exit, employment and output. The broad classes of theories
include: (1) information asymmetries between borrowers and lenders which may affect the flow
of credit (e.g., Greenwald and Stiglitz, 1990); (2) real-options and the irreversibility of capital
expenditures (e.g., Dixit and Pindyck, 1994); (3) firms’ attitudes towards risk (e.g., Appelbaum
and Katz, 1986; Hartman, 1976); and (4) the convexity of the marginal product of capital (e.g.,
Abel, 1983). The first three channels tend to predict a negative effect of uncertainty on firms’
key decision variables. The fourth channel predicts positive effect. While there are a large
number of papers that have presented the core models and many refinements, in this paper we
focus on the credit-market and the real-options channels as these two have come to dominate the
literature in terms of gaining insights into firms’ decision-making under uncertainty.
While the theoretical channels present relatively clear predictions, empirical analysis,
using firm, industry or macroeconomic data, reveals that while uncertainty generally appears to
depress economic activity, the estimated quantitative effects and qualitative inferences vary
considerably across the studies and the particular decision variable under consideration
(investment, entry, output, among others). We take a quick look at the empirical literature in
section 2.
Our focus on small businesses is motivated by several factors. First, small businesses
play an important role in the economy in several dimensions. The U.S. Small Business
Administration (SBA) defines a small business as an independent business having fewer than
500 employees. According to the SBA, small businesses represent about 99 percent of all
3
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
employer firms, and employ about one-half of all private sector employees. Small businesses pay
about 43 percent of total U.S. private payroll and have generated 65 percent of net new jobs over
the past 17 years. Small businesses create more than half of the non-farm private GDP and made
up 97 percent of all identified exporters and produced 31 percent of export value in 2008. Small
businesses are also important to U.S. technology improvement. According to the SBA, small
businesses hire over 40 percent of high tech workers (scientists, engineers, computer
programmers, and others). Finally, small businesses generate patents more efficiently than large
firms: they produce 16 times more patents per employee than large patenting firms. Irrespective
of the specific dimension under consideration, small businesses play a vital role in the economy.
The importance of small businesses is a global phenomenon. As noted in ACCA (2010)
and di Giovanni et al. (2010), for example, the vast majority of businesses globally are very
small. They note that small businesses are special because they have several important
characteristics: they contribute disproportionately to local economic growth by improving
innovation ability; increase job opportunities; and contribute significant tax revenues. Given
these attributes, it has become commonplace in the aftermath of the current global economic
crisis to refer to SMEs as the backbone of the global economy.
Second, while the empirical literature on uncertainty is quite extensive, there have been
relatively few studies that have distinctly emphasized the effects of uncertainty on the economic
activity of small businesses. Examining this link is important, given the structural significance of
small business in the economy, and during economic expansions and contractions. Third, those
papers that have focused on the uncertainty-small business effects, have tended to be related to
investment (e.g., Ghosal and Loungani, 2000). Our primary focus is on the uncertaintyemployment link, along with the impact on the number of businesses, and the potential
distinction between the effects on small versus large businesses. Examining the effects of
economic uncertainty on employment and creation and survival of businesses is important as it
has taken on a larger than life significance following the 2007 onwards economic crisis, as well
as past crises, where employment effects have taken center stage in policy debates on how to
improve economic conditions.
Against this backdrop, we examine the effects of economic uncertainty on small
businesses. Our data on small businesses obtained from the SBA are US-wide, including all
small, and other, businesses in the US and their employment. Since the small business data
4
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
portray a broader economy-wide picture, our uncertainty measures are constructed using
economy-wide variables related to GDP, inflation, stock prices, and fuel prices.
In section 2 we briefly note the central results from theory, present a cursory overview of
the empirical evidence, and note the key hypotheses. Section 3 discusses our data. In section 4
we outline construction of our measures of economic uncertainty. Sections 5 and 6 describe the
empirical specification we estimate, and present the results. Our findings are that economic
uncertainty negatively affects employment and the number of businesses, and the impact is
primarily concentrated in the relatively smaller business category. The impact on large
businesses is either non-existent or quantitatively smaller. Section 7 concludes with a discussion
of our findings and some policy issues.
2. Review of literature on the impact of uncertainty
The literature on the impact of uncertainty on firms decisions is quite extensive. Below
we briefly review selected papers and results in the theoretical and empirical literatures with an
eye towards spelling out the core hypotheses for our empirical analysis.
2.1 Review of theoretical results
In this section we briefly review some of the predictions from the theoretical literature
and highlight the implications for our empirical analysis. There are two strands of literature
which offer predictions on the impact on uncertainty on a range of firms’ decision variables. A
large portion of this literature has posed the problem as one of firms’ investment decisions under
uncertainty. But several researchers have examined the complementary issues related to
production, employment, R&D, among other variables. In our discussion below we note it as an
investment problem, and then note the implications for employment and number of businesses.
2.1.1 Uncertainty and financing constraints
Information asymmetry between borrowers and lenders may tend to constrain the flow of
credit. In an influential paper, Greenwald and Stiglitz (1990) model firms as maximizing
expected equity minus expected cost of bankruptcy and examine scenarios where firms may be
equity or borrowing constrained. A key result is that greater uncertainty exacerbates information
asymmetries, tightens financing constraints and lowers capital outlays. Since uncertainty
5
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
increases the risk of bankruptcy, firms cannot issue equity to absorb the risk. Those relying
primarily on credit and operating outside of equity markets, find the flow of credit constrained.
Delli Gatti et al. (2003) develop a model in which the financial conditions of businesses affect
capital accumulation and entry and exit which affect the distribution of firms differentiated by
the equity ratio. In their model flow of exiting firms is endogenously determined through
bankruptcy while the flow of entering firms is affected by stochastic factors. Lensink, Bo and
Sterken (2001) provide a lucid discussion of financing constraints in the general context of
investment behavior, including the roles played by uncertainty and sunk costs. Overall, this
literature shows that periods of greater uncertainty widens the information asymmetry, increases
the likelihood of bankruptcy and exacerbates financing constraints. Incumbent businesses who
are more dependent on borrowing and adversely affected by tighter credit will have greater
likelihood of exit. Similarly, entry is likely to be impeded for potential entrants who are more
adversely affected by tighter credit conditions. Thus, periods of greater uncertainty are expected
to accelerate exits and retard entry leading to negative net entry – that is, a decline in the number
of firms in an industry.
The results are similar if we examine physical capital investment or employment.
Considering employment, for example, periods of greater uncertainty, by constraining credit
flows, limit the ability of existing firms to grow. Since new entry is dampened, this negatively
affects employment. As the likelihood of exit by businesses is greater, this also negatively affects
employment. Overall, periods of greater uncertainty are likely to lead to lower employment.
The literature points to not all businesses being equally affected by uncertainty and credit
market conditions. In different strands of this literature, the papers by Evans and Jovanovic
(1989) and Fazzari, Hubbard and Petersen (1988), for example, offer insights on small versus
larger firm effects. Gertler and Gilchrist (1994), for example, strongly emphasize the negative
impact of information asymmetric and credit constraints on smaller firms.
Tying the two literatures a prediction that emerges from is that periods of greater
uncertainty, via the credit market channel discussed above, is likely to have a dampening effect
on employment and the number of businesses. Given that the effects of financing constraints, at
the margin, are more likely to be borne by the smaller businesses, the prediction is that the
negative effect of uncertainty will be likely be greater on smaller businesses.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
6
2.1.2 Uncertainty and real-options
An alternative channel examines the effects of uncertainty on firms’ decisions via the
real-options effects. The results in Caballero and Pindyck (1996), Dixit (1989) and Dixit and
Pindyck (1994) are sufficient to generate the essential predictions for our empirical analysis, and
we briefly discuss the results.
Dixit (1989) shows that uncertainty and sunk costs imply an option-value of waiting and
this raises the entry trigger price and lowers the exit trigger price.2 For prices below the entry
trigger, the potential entrant holds on to its option to enter, and an incumbent firm does not exit
at prices above the exit trigger. Another way of looking at this is that to enter during periods of
greater uncertainty, firms would require a premium over the conventional Marshallian entry
price. And incumbent firms would wait longer to exit (i.e., let prices fall below average variable
cost before they exit) as they know that to re-enter the market they would have to re-incur the
sunk entry costs.
The key results related to entry and exit can be summarized as follows: (1) numerical
results in Dixit (p.632-33) show that even small amounts of uncertainty are sufficient to generate
significant changes in entry and exit patterns; and (2) the numerical simulations in Dixit (1989,
p.632-33) and Dixit and Pindyck (Ch. 7, p.224-228; and Ch. 8.) show that an increase in
uncertainty results in the entry trigger price increasing by more than the decrease in the exit
trigger. These results reveal that entry is affected more than exit leading to negative net entry. In
other words, periods of greater uncertainty lead to a decrease in entry but exits, while lower,
continue at a closer to normal pace resulting in the industry experiencing a decrease in the
number of firms.
The results are similar if we examine physical capital investment or employment. Dixit
and Pindyck (1994) present an extensive analysis of the detrimental effects of uncertainty on
investment. The effects on employment will be qualitatively similar. If greater uncertainty
retards entry and investment, it will also be expected to have a negative effect on employment.
Assessing the implications for our empirical analysis, we note the following.
One of
our interests is to evaluate whether these effects may be different for small versus large firms.
2
Dixit and Pindyck (1994) outline the theoretical framework for studying firms’ decision-making regarding
entry and exit under uncertainty and sunk entry costs. Hopenhayn (1992) and Pakes and Ericsson (1998), for
example, study firm dynamics with firm-specific uncertainty. These models, however, are best subjected to
empirical tests using micro-datasets as in Pakes and Ericsson, and is beyond the scope of our paper.
7
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Previous studies have shown that the vast majority of the entry and exit churning is for the small
businesses (e.g., see Audretsch (1995), Sutton (1997) and Caves (1998)). The literature shows
that: (1) entrants are typically small compared to incumbents and have high failure rates; (2) the
typical exiting firm is small and young; and (3) larger firms are older with higher survival rates.
This implies that, via the real-options channel, most of the negative effects of uncertainty on the
number of businesses and employment will likely be observed for the smaller businesses.
2.3 Overview of empirical findings
The literature examining the effects of uncertainty is quite extensive. Studies vary
considerably in terms of the underlying variables they use to measure uncertainty, such as
profits, input prices, stock prices, inflation, GDP growth, economic policy news, survey of
businesses or forecasters, among others. Equally, there is a large variety in terms of the specific
constructs to measure uncertainty, such as conditional or unconditional variance (or standard
deviation), among others. Finally, some studies use micro-data at the firm level, while others use
data at the industry or macroeconomic level. The literature is quite expansive and it is difficult to
review the full spectrum of this literature.
To offer a perspective in a convenient format, we present a table in Appendix A which
summarizes selected papers. These are not meant to be comprehensive, but merely display the
range of variables used to measure uncertainty (GDP, inflation, prices, energy prices, stock
prices, among others), the specific statistical constructs to capture uncertainty (unconditional
variance, conditional variance derived from regression estimates, survey measures), the level of
aggregation of the studies (firm or industry level, economy-wide, or cross-country), and the
estimated quantitative and qualitative effects. Regarding the effects of uncertainty on small
business, we briefly note some of the findings in the empirical literature useful for our analysis.
Using industry-level data, Ghosal and Loungani (2000) find investment-uncertainty relationship
is negative and this negative impact is greater in industries dominated by small firms. Koetse and
Vlist (2006) find that there are differential effects of uncertainty on input and output variables.
Bianco, Bontempi, Golinelli and Parigi (2012) find family firms’ investments are significantly
more sensitive to uncertainty than non-family firms.
8
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
2.4 Hypotheses
Based on our discussion of theory and the summary of the results in sections 2.1 and 2.2,
the following hypotheses emerge:
H1: Greater uncertainty is expected to negatively affect employment, and this effect may
be more pronounced in smaller businesses.
H2: Greater uncertainty is expected to negatively affect the number of businesses, and
this effect may be more pronounced for smaller businesses.
3. Data description
For our econometric analysis, we use data from multiple sources. Below we provide
details of the datasets we use.
First, is the U.S. Small Business Administration (SBA) database. From the SBA database
we use annual data on the number of firms according to the size of firms from 1988 to 2011.
(Data prior to 1988 are not available.) The SBA database also provides employment data for
businesses by the size of firms. Second, U.S. macroeconomic data on real GDP and GDP
implicit price deflator are from the Federal Reserve Economic Data. The data on S&P 500 stock
price index is from Yahoo Finance. Finally, we obtain data on fuels and related products and
power PPI from the U.S. Bureau of Labor Statistics.
To consider size classes for our employment and number of businesses, we use the
following taxonomy to parse out the small business related effects versus other. The overall
sample we examine are:
1. ‘All’ businesses;
2. ‘Large’ businesses – these are businesses with ≥500 employees;
3. ‘Small’ businesses – these are businesses with <500 employees; and
4. ‘Smaller’ businesses – these are businesses with <20 employees.
The 500 employee cutoff is the one used by the U.S. SBA, and we use this as the baseline. We
consider an additional cutoff of <20 employees for the following reasons: (a) a 500 employee
firm is relatively large, so we wanted to consider an alternate cutoff for defining small; (b) data
with the <20 employee cutoff was available consistently for both our variables (employment and
number of businesses); and (c) a large percentage of the truly small business fall in this category.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
9
In our estimation of employment and number of businesses specifications, we will
present estimates for each of the four groupings (1-4) noted above.
In Figures 1 and 2 we display the time paths for the employment levels and number of
businesses by size class. Table 1 presents the summary statistics for the employment and number
of businesses by size class.
4. Measures of uncertainty
As noted earlier, Appendix A summarizes selected papers and displays the range of
variables used to measure uncertainty (GDP, inflation, prices, energy prices, stock prices, survey
opinions, among others), and the specific statistical constructs to capture uncertainty
(unconditional variance, conditional variance derived from regression estimates, survey forecast
variance). The broader literature reveals a wide range of variables and methods to capture
uncertainty. In the spirit of this literature, we construct six alternate measures to examine the
impact of economic uncertainty on small businesses.
As we discussed earlier, our objective is to examine the effects of economic uncertainty
on small businesses. Our data on small businesses are US-wide and include all small and larger
businesses in the US and their employment, as well as the total number of such businesses. Since
the data are designed to portray a broader picture, our uncertainty measures are created using
economy-wide indicators.
First, we use the Forecasts for the Survey of Professional Forecasters provided by the
Federal Reserve Bank of Philadelphia. From the survey forecasts, we use index of industrial
production and forecasts for the level of real GDP to construct the two measures of uncertainty.
Our measures of uncertainty are the within-year variance of survey forecasts. These two
measures of uncertainty are labeled
and
, with sf denoting survey forecast.
Second, we use forecasting specifications to construct four additional measures related to
uncertainty about:
1. real GDP growth. This indicates the overall state of the economy capturing demand and
supply effects;3
3
GDP uncertainty measures, in different forms, have been used by, for example, Driver et al. (2005), Asteriou et al.
(2005) and Bloom (2009).
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
10
2. inflation rate. We measure inflation as the annual percentage growth of the GDP deflator.
Inflation uncertainty captures effects related to input and product prices, as well as
affecting firms’ real borrowing rates;4
3. stock prices. We use the S&P500 stock price index. As with real GDP, this is an indicator
of the overall state of the economy, and forward looking indicator of investor and
business confidence;5 and
4. real fuel price growth. Here we use the BLS price index of a range of commonly used
fuels by businesses. The nominal fuel price index is converted to real values after
deflating by the implicit GDP deflator. This variable serves to proxy a critical input –
fuels and energy – price for businesses.6
To create our measures of uncertainty, we use the following procedure. We assume that
firms use a forecasting equation to predict future values of the relevant variable, an economywide indicator in our case. As noted earlier, our objective is to examine the effects of economic
uncertainty on small businesses. Our data on small businesses is US-wide (i.e., all small
businesses in the US and their employment). Our uncertainty measures, therefore, are created
using economy-wide variables related to GDP, inflation, stock prices, and fuel prices.
As our baseline, we use an autoregressive, AR(2), specification as the forecasting model.
AR(n) models are based on Box and Jenkins (1970) formulation for forecasting economic
variables, and historically they have performed well in forecasting exercises (e.g., Meese and
Geweke (1984) and Marcellino, Stock and Watson (2003)). Given this aspect, and our annual
time-series data for the small business variables, we use this standard methodology to forecast
the relevant state variables.
The forecasting specification is:
4
Inflation has been used to construct uncertainty measures by, for example, Huizinga (1993), Fountas et al. (2006)
and Elder (2004).
5
Stock prices have been used to measure uncertainty by, for example, Bloom (2009), Chen et al. (2011), Bloom et
al. (2007), Greasley et al. (2006), and Stein et al. (2010).
6
Fuel and energy prices have been used to construct uncertainty measures by, for example, Koetse et al. (2006),
Kilian (2008) and Guo et al. (2005).
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
11
where Z is either: (i) real GDP growth; or (ii) inflation rate; or (iii) S&P500 growth; or (iv) real
fuel price growth. From specification (1), the predicted values represent the forecastable
component. The residuals:
represent the unsystematic, or unforecastable, component. Since
values, we use the squared value of
can take positive or negative
, interpreted as the conditional-variance, as our measure
of uncertainty about the relevant variable. Using the residuals from forecasting equations and
conditional-variance to construct uncertainty measures is common: see, for example, the
insightful review in Lensink, Bo and Sterken (2001), and the references cited in the above
footnotes. If the forecasting specification is for GDP (i.e., Z is real GDP growth), then we denote
the uncertainty measure as:
Using this procedure for our four underlying economy-wide variables (Z), we obtain four
measures of uncertainty denoted by: (i)
; (ii)
; (iii)
; and (iv)
.7
To estimate the forecasting specification (1), we use available data on Z from 1960 to
2010. The rationale is as follows. The objective is obtain a good forecasting specification, which
is better done with a longer time-series ensuring that the parameters of the equation are estimated
precisely. While data on GDP, inflation and S&P 500 are available for earlier periods, the BLS
fuel price indices are available starting 1960. So we start the time-period for the equation in 1960
for which all of our variables (that we consider for Z) are available. The terminal period, 2010, is
the same as the last period for which the small business data are available. This implies that the
generated time-series in our four uncertainty measures
,
,
and
are over
1962-2010 (two initial observations are lost due to use of lagged values).
A final remark we make is that while our AR(.) forecasting equation is fairly standard,
the length and frequency (annual from 1988-2010) of the available small business variables
7
While we report the results for an AR(.) specification, our results were robust to experiments with lag lengths.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
12
restricts us from considering alternate procedures, such as using ARCH models, to construct
measures of uncertainty. While this may be viewed as a limitation, the SBA data on employment
and number of businesses are simply not available at a higher frequency. Since we need to
harmonize the estimation periods and frequency, we are restricted to using annual data to
forecast our specification (1).
5. Estimating Specification
To examine the hypotheses noted in section 2.4, we estimate autoregressive-distributed
lag AD(n, m) specification of the form:
where
is a measure of small business activity (in our case, employment or number of
businesses),
growth, and
or
or
is the measure of uncertainty measured in natural logarithms,
is real GDP
is the error term. As described earlier, the four measures of uncertainty are
or
. In specification (4), in our estimation we found that for several
specifications a second autoregressive lag was significant, whereas in others only one lag was
significant. Rather than alternating the lag length, we parsimoniously used two autoregressive
lags for all specifications.
Since we are interested in the shorter-term effects of uncertainty on small business
employment or number of businesses, and that the underlying data on employment and the
number of businesses contained a clear trend, we measure
differences (annual percentage changes).
in annual logarithmic first-
and
are included in the specification
to capture any persistence in the dependent variable and the variable’s own dynamics. The two
real GDP growth variables,
and
, control for overall economic conditions.
As noted in the data section, the available data on Small Businesses are annual and cover
the period 1988-2010. We, therefore, estimate specification (4) over 1988-2010. While, as noted
earlier, our four generated uncertainty measures
,
,
and
are over 1962-
2010, we only use their values over 1988-2010 in estimating specification (4). Estimating (4)
informs us about the impact of uncertainty on employment and number of businesses, after
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
13
controlling for overall economic activity (GDP growth) and the dynamic lagged structure for the
included variables.8
In our examination of the effects of uncertainty on employment and number of
businesses, we will present estimates for four groups noted in our data section 3:
1. ‘All’ businesses;
2. ‘Large’ businesses – these are businesses with ≥500 employees;
3. ‘Small’ businesses – these are businesses with <500 employees; and
4. ‘Smaller’ businesses – these are businesses with <20 employees.
6. Estimation results << Preliminary >>
The results for the employment specifications are presented in tables 2.1 and 2.2. Table
2.1 presents estimates for ‘All’ and ‘Large’ businesses, and table 2.2 presents estimates for the
‘Small’ and ‘Smaller’ size classes. A similar format is followed for presenting the results on the
number of businesses in tables 3.1 and 3.2.
In table 2.1, the
are in the 75% to 87% range indicating good fit of the specification.
The estimates of the first-order autocorrelation coefficient, ρ, are relatively low on average. The
estimates show that uncertainty related to GDP, Inflation, S&P500 and Fuel prices have a
negative effect on ‘All’ business employment, but the timing (contemporaneous versus lagged)
and quantitative effects and vary. Turning to the ‘Large’ businesses, only Inflation and Fuel price
uncertainty dampen employment, and the estimated quantitative effects are a bit smaller for the
Large business group.
For the small business groups in table 2.2, the
are in the 62% to 78% range. The
estimates of the first-order autocorrelation coefficient, ρ, are generally quite low. The estimates
show that aside from the mixed inferences from the Inflation and S&P500 based measures,
uncertainty related to the two survey-based measures, and GDP, Inflation and Fuel prices have a
negative effect on employment for the ‘Small’ and ‘Smaller’ businesses.
Examining the broad inferences from the estimates in tables 2.1 and 2.2, uncertainty
appears to dampen employment, and the effects are primarily concentrated in the smaller
businesses.
Next we turn to the specifications for the number of businesses in tables 3.1 and 3.2.
8
Our experiments using longer lag lengths did not provide additional insights into the effects of uncertainty.
14
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
In table 3.1, the
for ‘All’ businesses specifications are in the 70% to 75% range, while they
are only in the 40% to 55% range for the ‘Large’ businesses, indicating lower explanatory power
for the latter group. Aside from a couple of specifications, the estimates of the first-order
autocorrelation coefficient, ρ, are relatively low. The estimates show that uncertainty related to
the survey-based measures, and GDP and S&P500 have a negative effect on the number of ‘All’
business, but the quantitative effects vary. Fuel price or Inflation uncertainty does not appear to
affect the number of ‘All’ businesses. Turning to the ‘Large’ businesses, we see that four of the
uncertainty measures appears to decrease the number of these businesses.
For the smaller businesses specifications in table 3.2, the
are in the 50% to 60%
range for the ‘Smaller’ group, and in the 70% range for the ‘Small’ group. The estimates of the
first-order autocorrelation coefficient, ρ, are uniformly low. The estimates show that uncertainty
related to both the survey-based measures, and GDP and Inflation have a negative effect on the
number of ‘Smaller’ business, but Fuel price and S&P500 uncertainty does not appear to affect
this group. Turning to the ‘Small’ group, the two survey-based measures, and GDP and Inflation
uncertainty have a negative effect on the number of businesses.
Based on the estimates in tables 3.1 and 3.2, uncertainty has a negative impact on the
number of businesses, and this effect appears to be more mixed across the various size classes.
The overall inferences we draw from the set of estimates presented in tables 2 and 3 are
that economic uncertainty dampens employment, and the effects appear to be concentrated in the
Smaller (<20 employees) and Small (<500 employees) groups. At broad brush, our findings
appear supportive of the results from the theoretical models we discussed in sections 2.1 and 2.2.
The effects on the number of businesses appears to be more mixed and need further
investigation.
Before closing this section we note that the estimated specifications include real GDP
growth, and model the dynamics of the included variables via lagged effects. Real GDP growth
is probably the single most important control variable in either employment or number of
businesses specifications. With increasing GDP growth, business opportunities are expected to
expand allowing for creation of more jobs as well as new businesses. The fact that we find
effects related to uncertainty even after controlling for GDP growth and the lagged dynamics,
makes our finding even more noteworthy.
<< Additional results and checks of robustness to be added >>
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
15
7. Discussion of results and conclusions
Our findings on the negative impact of economic uncertainty on employment in small
businesses as well as the number of small businesses are particularly revealing as these results
appear even after controlling for GDP growth and appropriate controls for lagged dynamics of
the included variables. The underlying theories we reviewed related to potential financingconstraints and the real-options channels. Given that the data we use from the U.S. Small
Business Administration is relatively aggregated, it is difficult to disentangle which of these two
channels may be playing the more dominant role. It is fair to assume that both channels are
important in determining the outcomes.
As was noted earlier, there exists a significant literature on financing-constraints. This
literature has noted important differences between smaller and larger businesses, and point to
smaller firms being relatively credit-constrained (e.g., Fazzari, Hubbard and Petersen, 1988;
Gertler and Gilchrist, 1994; Evans and Jovanovic, 1989; Lensink, Bo and Sterken, 2001; Ghosal
and Loungani, 2000; Himmelberg and Petersen, 1994; Winker, 1999; and Audretsch and Elston,
1997).
Viewing our estimation results and the above information on financing constraints
collectively, part of our results for the smaller business is undoubtedly emerging from the
financing-constraints channel. If this is true, then important policy implications emerge,
primarily in the form of initiatives and instruments designed to partly ease the credit-constraints
faced by smaller businesses.
To the question as to why governments might pay special attention to small businesses,
there are several responses. First, we noted earlier in the paper that a large fraction of
employment and businesses fall into the smaller categories. Second, a number of emerging
structural factors – such as those related to globalization and banking sector consolidation – are
likely to favor large businesses relative to the smaller ones. These considerations alone provide
important economic policy justification.
As with many governments worldwide, the U.S. has recently implemented policies and
programs to help small businesses bridge the capital and market gap and encouraged publicprivate partnerships to support small business and entrepreneurship by, for example: (a)
supporting more than $53 billion in SBA loan guarantees to more than 113,000 small businesses;
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
16
(b) awarding more than $221 billion in Federal contracts to small businesses (FY 2009 through
April 30, 2011); and (c) awarding more than $4.5 billion in research funding through the Small
Business Innovation and Research Program during FY 2009 and FY 2010.9
Such initiatives, along with appropriate lending policies, can help ease some of the
financing-constraints faced by smaller businesses.10 By doing so, and in the context of this paper,
such policies may also help alleviate some of the negative impact of uncertainty on smaller
businesses.
9
The National Economic Council (2011) and Sheets and Sockin (2012) provide extensive discussion on the
importance of small businesses and policy.
10
The papers by Audretsch and Elston (1997, 2002), for example, provide important insights in this dimension from
German policy initiatives.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
17
Appendix A: Selected empirical findings on the impact of uncertainty
The papers included below are not meant to be comprehensive, but display the range of variables used to measure
uncertainty (GDP, inflation, prices, energy prices, stock prices, among others), the specific statistical constructs to
capture uncertainty (unconditional variance, conditional variance derived from regression estimates, survey
measures), the level of aggregation of the studies (firm or industry level, economy-wide, or cross-country), and the
estimated quantitative and qualitative effects.
Table A.1. Selected papers examining the effects of uncertainty
Paper
Data
Estimation method
State variables
Uncertainty measure
Estimation results
Lensink, Robert, Paul van Steen and
Elmer Sterken. “Uncertainty and
Growth of the Firm,” Small Business
Economics, 2005, 381-391.
Survey of 1,097 Dutch firms in
1999.
Sales. Return on
Investment.
Logit model.
Conditional variance
over the conditional
mean.
Koetse, Mark J., Arno J. van der Vlist
and Henri L.F. de Groot. “The Impact
of Perceived Expectations and
Uncertainty on Firm Investment,”
Small Business Economics, 2006,
365-376.
Survey of 135 plant locations in
Netherlands in 1998.
Wages. Energy prices.
Output prices.
Tobit model.
Survey based.
Bo, Hong, and Elmer Sterken.
“Volatility of the interest rate, debt
and firm investment: Dutch
evidence,” Journal of Corporate
Finance, 2002, 179–193.
Driver, Ciaran, and Brendan Whelan.
“The Effect of Business Risk on
Manufacturing Investment,” Journal
of Economic Behavior and
Organization, 2001, 403-412.
Data for 41 Dutch listed firms
from 1984 to 1995.
Interest rate.
Uncertainty has a negative impact on
the size of investment, no matter what
the type of investment is used. Smaller
firms have a lower probability to invest
if uncertainty increases. In logit model,
for one unit increase in uncertainty, the
log odds of investment decreased by
0.878.
Uncertainty has a larger influence on
decision making in small firms than in
large firms specifically for investment
in energy-saving technologies. In small
firms, input uncertainty and output
uncertainty have a differential impact
on both aggregate and energy-saving
investments.
Cross-effect of the interest rate
volatility and debt on investment is
positive. This effect is more important
for highly indebted firms than for lessindebted firms.
No strong effect of risk due to
convexities.
Risk did affect the timing of
investment for between a quarter and a
third of the sample. The greatest caution
in respect of timing was in the Hi-tech
sector which was also the sector with
the greatest damage from delay.
They find a U-shaped relationship
between market uncertainty and value
of investment. Also, they find an
inverted U-shaped relationship between
technological uncertainty and the value
of R&D capital.
Family firms’ investments are
significantly more sensitive to
uncertainty than nonfamily firms and
the greater sensitivity to uncertainty is
basically due to the greater opacity of
family firms and to their higher risk
aversion, rather than to the degree of
sunk fixed capital.
“Cautionary effects” of uncertainty are
large – going from the lower quartile to
the upper quartile of the uncertainty
distribution typically halves the first
year investment response to demand
shocks.
A negative relationship between
investment and price uncertainty only
exists in competitive industries.
One percentage increases in price
Conditional variance.
Panel Data, ARCH model, Fixed
effect estimation.
Disaggregated survey data of
Ireland in 1995.
Comparing the percentage of
different respondents in the
survey questions.
Future demand and
future price
Future unit input cost
Capacity
Delay risk
Subjective descriptions
Oriani, Raffaele, and Maurizio
Sobrero. “Uncertainty and the Market
Valuation of R&D within a Real
Options Logic,” Strategic
Management Journal, 2008, 343-361.
Data for 290 manufacturing
firms in UK from 1989 to 1998.
Bianco, Magda, Maria Elena
Bontempi, Roberto Golinelli and
Giuseppe Parigi. “Family Firms’
Investments, Uncertainty and
Opacity,” Small Business Economics,
2012, 1-24.
Data for 2,959 Italian private
companies from 1996 to 2007.
Bloom, Nick, Stephen Bond and John
Van Reenen. “Uncertainty and
Investment Dynamics,” Review of
Economic Studies, 2007, 391-415.
Data for 672 UK manufacturing
firms from 1972 to 1991.
Ghosal, Vivek, and Prakash
Loungani. “Product Market
Competition and the Impact of Price
Uncertainty on Investment: Some
Data for 254 US 4-digit SIC
manufacturing industries from
1958 to 1989.
Panel Data, Hedonic model
Industry output. Patents.
Absolute percentage
difference. Inverse of
the median age.
Sales.
Coefficient of variation.
Panel data, GMM
Panel data, GMM
Stock returns.
Std. deviation of daily
stock returns.
Product price.
Rolling regression
based conditional std.
18
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Evidence From US Manufacturing
Industries,” Journal of Industrial
Economics, 1996, 217-228.
Panel data, fixed effect – IVE.
deviation.
Fuss, Catherine, and Philip
Vermeulen. “Firms' Investment
Decisions in Response to Demand
and Price Uncertainty,” Applied
Economics, 2008, 2337-2351.
Survey of 279 firms from 19872000, and another survey of 319
firms from 1987-1999.
Expectations of future
demand and prices.
Bulan, Laarni. “Real Options,
Irreversible Investment and Firm
Uncertainty: New Evidence from U.S.
Firms,” Review of Financial
Economics, 2005, 255-279.
Data for 2,901 US firms from
1964 to 1999.
Ghosal, Vivek, and Prakash
Loungani. “The Differential Impact of
Uncertainty on Investment in Small
and Large Businesses,” Review of
Economics and Statistics, 2000, 338343.
Huizinga, John. “Inflation
Uncertainty, Relative Price
Uncertainty, and Investment in U.S.
Manufacturing,” Journal of Money,
Credit and Banking, 1993, 521-549.
Data for 330 US SIC 4-digit
manufacturing industries from
1958 to 1991.
Theil index.
Panel data, GMM.
Equity returns.
Standard deviation.
Panel data, 2SLS.
Profits.
Panel data, IVE.
Rolling regression
based conditional std.
deviation.
Data for 450 U.S. SIC 4-digit
manufacturing industries from
1954 to 1989.
Real wage. Output
price. Real materials
price.
Bivariate ARCH model.
Conditional std.
deviation.
Stein, Luke C.D., and Elizabeth C.
Stone. “The Effect of Uncertainty on
Investment: Evidence from Options,”
Stanford University Working Paper,
2010.
Data for 2,230 US
manufacturing firms from 1996
to 2009.
Stock price.
Folta, Timothy, and Jonathan P.
O’Brien. “Entry in the Presence of
Dueling Options,” Strategic
Management Journal, 2004, 121-138.
Data for 2,230 US
manufacturing firms from 1996
to 2009.
Expected volatility.
Panel data, 2SLS.
Panel data, 2SLS.
Data on 17,897 firms from 1980
to 1999.
Industry’s contribution
to GDP.
Square root of
conditional variance.
GARCH model.
Baker, Scott, Nick Bloom and Steven
J. Davis. “Has Economic Policy
Uncertainty Hampered the
Recovery?” Chicago Booth Paper,
No. 12-06, 2012.
Index of economic policy
uncertainty, news-based proxy,
government purchases data,
disagreement about future
indexes from 1985 to 2011. And
tax code expiration data is from
1991 to 2011.
Economic policy
uncertainty, news-based
proxy, government
purchases data,
disagreement about
future indexes, and
merge them into a new
proxy.
VAR model
Aggregating the above
Components to Obtain
an Index of Economic
Policy Uncertainty
uncertainty is estimated to cause the
ratio of gross industry investment (I/K)
decrease by 0.358 for most competitive
industries.
Demand uncertainty at the time of
planning depresses planned and
subsequent realized investment.
One standard deviation increases in
demand uncertainty is estimated to
reduce 6% of the average investment
ratio.
Increased industry uncertainty and firmspecific uncertainty display a
pronounced negative effect on firm
investment consistent with real options
behavior.
A one standard deviation increase in
industry uncertainty reduces a firm’s
investment-to-capital ratio by 6.4%. A
one standard deviation increase in firmspecific uncertainty decreases firm
investment by 19.3%.
Investment-uncertainty relationship is
negative and this negative impact is
greater in industries dominated by small
firms.
Increased uncertainty about real wages
portends an immediate drop in capital
expenditures which is large in economic
terms; while increased uncertainty
about real output price does not signal
an immediate decline in capital
expenditures hut a sustained increase
may indicate a large reduction
eventually.
They find a negative and statistically
significant relationship between
uncertainty and investment. The
coefficient estimates are larger in
magnitude after addressing the
endogeneity of the uncertainty measure,
suggesting potential reverse causation
that biases the OLS estimates towards
zero.
They find the effect of uncertainty on
entry is non-monotonic and U-shaped.
And the turning points are influenced
by factors that should theoretically
influence options to grow and options to
defer.
Uncertainty has a potent effect on
entry even after controlling for firm
resource profiles, including the
relatedness to the target industry.
Historically high levels of policy
uncertainty in 2010 and 2011 mainly
reflect concerns about tax and monetary
policy and secondarily a broader range
of other policy-related concerns. Policyrelated concerns now account for a large
share of overall economic uncertainty.
A rise in policy uncertainty, similar in
magnitude to the actual change since
2006, is associated with substantially
lower levels of output and employment
over the following 36 months.
19
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Baker, Scott, Nicholas Bloom and
Steven Davis. “Measuring Economic
Policy Uncertainty,” University of
Chicago and Stanford University,
2012.
Constructing index data about
political uncertainty from 19852009.
VAR model
Newspaper coverage of
policy-related economic
uncertainty. Number of
federal tax code
provisions set to expire
in future years.
Disagreement among
economic forecasters.
Constructing an index
from three types of
underlying components.
Bloom, Nicholas. “The Impact of
Uncertainty Shocks,” Econometrica,
2009, 623-685.
Firm-level data from 2548 firms
U.S. firms from 1981 to 2000.
Panel data, VAR model,
SMM estimation
Driver, Ciaran, Paul Temple and
Giovanni Urga. “Profitability,
capacity, and uncertainty: a model of
UK manufacturing investment,”
Oxford Economic Papers, 2005, 120–
141.
Asteriou, Dimitrios and Simon Price.
“Uncertainty, investment and
economic growth: evidence from a
dynamic panel,” Review of
Development Economics, 2005, 277–
288.
Aggregate data of UK
manufacturing on two capital
assets (machinery and building)
from 1972 to 1999.
Bloom, Nick. “The Impact of
Uncertainty Shocks: Firm Level
Estimation and a 9/11 Simulation,”
CEP Discussion Paper, No. 718,
2006.
Data of 579 US manufacturing
firms from 1991 to 2000.
Greasley, David, and Jakob B.
Madsen. “Investment and
Uncertainty: Precipitating the Great
Depression in the United States,”
Economica, 2006, 393–412.
Data of investment information
in US from 1920 to 1938.
Kilian, Lutz. “Exogenous Oil Supply
Shocks: How Big Are They and How
Monthly production data for all
OPEC countries and for
GARCH model
Data of 59 industrial and
developing countries from 1966
to 1992.
Profits growth, stock
returns, TFP growth,
GDP forecasts.
Standard deviation.
Output growth.
Time-series conditional
volatility.
GDP per capita growth.
Conditional variance.
Panel data, PMG, MG, GARCH
Monthly share returns.
Standard deviations.
Panel data, time-varying
second moment model
Tobin’s q
Real stock prices.
Squared monthly
proportional change.
Oil production.
Policy related uncertainty played a role
in the slow growth and fitful recovery
of recent years, and they invite further
research into the effects of policyrelated uncertainty on economic
performance.
VAR estimates show that a policy
uncertainty shock equal in size to actual
increase in the index value from 2006 to
2011 foreshadows drops in private
investment of 16 percent within 3
quarters, industrial production drops of
4 percent after 16 months, and
aggregate employment reductions of 2.3
million within two years.
Uncertainty appears to dramatically
increase after major economic and
political shocks like the Cuban missile
crisis, the assassination of JFK, the
OPEC I oil-price shock, and the 9/11
terrorist attacks. The uncertainty
component alone generates a 1% drop
and rebound in employment and output
over the following 6 months, and a
milder long-run overshoot in these
economic shocks.
The GARCH model shows uncertainty
variable for the full sample are
estimated to be negatively significant at
the 5% level for machinery, and
negative but not significant for building.
Using PMG estimation, they find the
estimated coefficients were -0.360 for
the industrial and -0.053 for the
developing countries, which showed a
negative relationship between growth
and uncertainty in both cases. For the
effect of uncertainty on investment, the
PMG results revealed a significant
negative relationship for both subgroups, with higher negative
magnitudes for the industrial countries
(-3.280) than that of developing
countries (-0.081).
Uncertainty appears to vary strongly
over time, temporarily rising by up to
200% around major shocks like the
Cuban Missile crisis, the assassination
of JFK and 9/11.
It finds temporary impact of a second
moment shock is different from the
typically persistent impact of a first
moment shock. While the second
moment effect has its biggest drop in
month 1 and has completely rebounded
by month 5, a persistent first moment
shock will generate a drop in activity
lasting several quarters.
For investment, the results show that the
effects of heightened uncertainty
surrounding the expected marginal
profitability of capital, measured by
share price volatility, can explain
around 80% of the actual fall in the
business fixed investment ratio in 1930.
Thus, to a large extent, the investment
slump led the declines in income.
Using this approach and the new
exogenous oil supply shock measure, it
20
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Much Do They Matter for the U.S.
Economy?” Review of Economics
and Statistics. 2008, 216-240.
aggregate non-OPEC oil
production since 1973.
Exogenous variation.
OLS
Guo, Hui, and Kevin L. Kliesen. “Oil
Price Volatility and U.S.
Macroeconomic Activity,” Federal
Reserve Bank of St. Louis Review,
2005, 669-83.
Data of daily price of U.S. 1month futures and 12- month
futures contracts from 1983 to
2004.
Oil prices.
Realized variance
series.
Forecasting regression
Elder, John. “Another Perspective on
the Effects of Inflation Uncertainty,”
Journal of Money, Credit & Banking,
2004, 911-928.
Data of U.S. output growth rate,
inflation, CPI, commodity price
from 1966 to 2000.
Cross-section data,
MGARCH-M VAR model
Inflation
Conditional variance
Monthly neighborhood
returns
Conditional variance
Stilianos Fountas, Menelaos
Karanasos and Jinki Kim. “Inflation
Uncertainty, Output Growth
Uncertainty and Macroeconomic
Performance,” Oxford Bulletin of
Economics and Statistics. 2006, 319343.
Monthly data for the G7 on PI
and the IPI.
Inflation
Output growth
Bivariate GARCH model, VAR
model
Conditional variance
Ghosal, Vivek. “Demand Uncertainty
and Capital-Labor Ration: Evidence
from U.S. manufacturing Sector,” The
Review of Economics and Statistics,
1991, 157-161.
Ghosal, Vivek. “Does uncertainty
influence the number of firms in an
industry?” Economics Letters, 1996,
229-236.
Jonathan, O'Brien, Timothy Folta.
"Sunk costs, uncertainty and market
exit: A real options perspective."
Industrial & Corporate Change.
2009, 807-833.
Date for 125 U.S.
manufacturing industries from
1968 to 1977.
GARCH model
Standard deviation
Li, Yong. “Duration analysis of
venture capital staging: A real options
perspective,” Journal of Business
Venturing, 2008, 497–512.
46,976 portfolio company-round
pairs in U.S. for 1975–2005,
involving 3737 venture capital
firms and 15,786 portfolio
companies.
Market price volatility;
The stage of
development at the time
of financing.
Fluctuations of
shipments
Standard deviation
OLS
Date for 196 U.S. industries
from 1973-1986.
IV Estimation
Data for 16,447 firms operating
in 405 industries from 1985 to
2003.
GARCH model
Industry-specific price
Standard deviation of
residuals
Volatility of stock
returns
Conditional variance for
market uncertainty;
Use the three stages:
Seed/Startup, Early, and
Expansion/Late stages.
(The first two represent
finds statistically significant evidence of
a sharp drop in real GDP growth five
quarters after an exogenous oil supply
shock and of a spike in CPI inflation
three quarters after the shock. And it is
shown that exogenous oil supply shocks
made remarkably little difference
overall for the evolution of U.S. real
GDP growth and CPI inflation since the
1970s.
A volatility measure constructed using
daily crude oil futures prices has a
negative and significant effect on future
GDP growth over the period 1984-2004.
Moreover, the effect becomes more
significant after oil price changes are
also included in the regression to
control for the symmetric effect.
Uncertainty about inflation has
significantly reduced real economic
activity over the post-1982 period, with
the effect concentrated after a twomonth lag.
Using MGARCH-M VAR model,
they find that one standard deviation
increase in inflation uncertainty has
tended to reduce real economic activity
over three months by about 22 basis
points in the post-1982 period.
First, inflation does cause negative
welfare effect. Secondly, in some
countries, e.g. Canada and the UK,
more inflation uncertainty provides an
incentive to Central Banks to surprise
the public by raising inflation
unexpectedly. Thirdly, business cycle
variability and the rate of economic
growth are related. More variability in
the business cycle leads to more output
growth.
There is a significant negative
relationship between demand
uncertainty and the capital-labor ratio,
and that an increase in firm size
counteracts this negative influence.
Price uncertainty has a statistically
significant and quantitatively large
negative impact on the number of firms
in an industry.
Uncertainty dissuades firms from
exiting an industry, but only when the
sunk costs of entering and exiting that
industry are sizeable. Moreover, sunk
costs can be influenced by the
technological intensity of an industry,
by the extent to which a firm competes
on the basis of innovation, and by the
firm’s diversification strategy.
Market uncertainty encourages venture
capital firms to delay investing at each
round of financing, whereas
competition, project-specific
uncertainty and agency concerns
prompt venture capital firms to invest
sooner.
21
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Podoynitsyna, Ksenia, Michael Song,
Hans van der Bij and Mathieu
Weggeman. “Improving new
technology venture performance
under direct and indirect network
externality conditions,” Journal of
Business Venturing, 2013 195–210.
A sample of 385 NTVs drawn
from the VentureOne 2001
database and the 1995–2000
Inc. 500 list.
Li, Yong, Joseph T. Mahoney “When
are venture capital projects initiated?”
Journal of Business Venturing, 2011,
239–254.
Date of venture capital
investment of 22,164 ventures
made between 1980 and 2007 in
the U.S.
high uncertainty and list
one is low uncertainty.)
of development at the
time of financing as an
indicator of the level of
project-specific
uncertainty.
Five main uncertainty
management strategies:
(Avoidance, Imitation,
Control, Cooperation,
Real options)
OLS multiple regression
Use scales to measure
each of the five main
uncertainty
management strategies:
(e.g. for avoidance
strategy, they ask firms
to select from the three
performance: introduce
their product with low
uncertainty, Postpone a
market entry if the
market is too uncertain,
Grow from small scale
to large scale when
entering a new market.)
Volatility of market
returns.
The standard error of
the regression.
Accelerated-failure-time models
Li, Dan. “Multilateral R&D alliances
by new ventures,” Journal of
Business Venturing, 2013, 241–260.
Freel, Mark S.. “Perceived
Environmental Uncertainty and
Innovation in Small Firms,” Small
Business Economics, 2005, 49–64.
They show that real options reasoning
does not always perform better under
conditions of higher uncertainty, such as
uncertainty due to direct network
externalities.
173 new ventures involved in
multilateral R&D alliances in
high-technology industries
during 1990–2005, and 173
matching ventures which are
not.
Heckman two-stage regressions.
Firm-level survey data of UK
SMEs in 1996, 1998, 2000.
Discriminant functions
Mean monthly stock
price volatility.
Standard deviation.
Supply, finance,
competitors, trade
Asking the firms to
report their feeling of
uncertainty on a fivepoint scale.
Venture capitalists tend to defer new
investment projects in target industries
with substantial market volatility. This
delay effect of market volatility is
reduced if the target industry
experiences high sales growth or if
competition among venture capitalists is
intense in the target industry.
An inverted U-shaped relationship
between market uncertainty and a new
venture's likelihood of forming
multilateral R&D alliances.
Firms engaged in novel product
innovation appear to perceive a less
hostile, competitive environment.
Also, higher levels of innovation in
manufacturing firms are associated with
higher perceptions of supplier
uncertainty, whilst, higher levels of
innovation in service firms are
associated with higher perceptions of
human resource uncertainty.
22
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
References
Abel, Andrew. “Optimal Investment under Uncertainty,” American Economic Review, 1983,
228–233.
Appelbaum, Elie, and Eliakim Katz. “Measures of Risk Aversion and Comparative Statics of
Industry Equilibrium,” American Economic Review, 1986, 524–529.
Association of Chartered Certified Accountants (ACCA). “Small Business: A Global Agenda,”
ACCA Accountants for business Report, 2010.
Asteriou, Dimitrios, and Simon Price. “Uncertainty, Investment and Economic Growth:
Evidence from a Dynamic Panel,” Review of Development Economics, 2005, 277–288.
Audretsch, David. Innovation and Industry Evolution. Cambridge: MIT Press, 1995.
Audretsch, David, and Julie Elston. “Does Firm Size Matter? Evidence on the Impact of
Liquidity Constraints on Firm Investment Behavior in Germany,” International Journal of
Industrial Organization, 2002, 1–17.
Audretsch, David, and Julie Elston. “Financing the German Mittelstand,” Small Business
Economics, 1997, 97-110.
Baker, Scott, Nicholas Bloom and Steven Davis. “Has Economic Policy Uncertainty Hampered
the Recovery?” Chicago Booth Paper, No. 12-06, 2012.
Baker, Scott, Nicholas Bloom and Steven Davis. “Measuring Economic Policy Uncertainty,”
University of Chicago and Stanford University, 2012.
Bianco, Magda, Maria Elena Bontempi, Roberto Golinelli and Giuseppe Parigi. “Family Firms’
Investments, Uncertainty and Opacity,” Small Business Economics, 2012, 1-24.
Bloom, Nicholas. “The Impact of Uncertainty Shocks,” Econometrica, 2009, 623-685.
Bloom, Nicholas. “The Impact of Uncertainty Shocks: Firm Level Estimation and a 9/11
Simulation,” CEP Discussion Paper, No. 718, 2006.
Bloom, Nick, Stephen Bond and John van Reenen. “Uncertainty and Investment Dynamics,”
Review of Economic Studies, 2007, 391-415.
Bo, Hong, and Elmer Sterken. “Volatility of the Interest Rate, Debt and Firm Investment: Dutch
Evidence,” Journal of Corporate Finance, 2002, 179–193.
Box, G.E.P., and , G.M. Jenkins. Time Series Analysis, Forecasting and Control. San
Francisco: Holden Day, 1970.
23
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Bulan, Laarni. “Real Options, Irreversible Investment and Firm Uncertainty: New Evidence from
U.S. Firms,” Review of Financial Economics, 2005, 255-279.
Caballero, Ricardo, and Robert Pindyck. “Investment, Uncertainty and Industry Evolution,”
International Economic Review, 1996, 641-662.
Caves, Richard. “Industrial Organization and New Findings on the Turnover and Mobility of Firms,”
Journal of Economic Literature, 1998, 1947-1982.
Crain, Nicole, and W. Mark Crain. “The Impact of Regulatory Costs on Small Firms,” Small
Business Administration, 2010.
Delli Gatti, Domenico, Mauro Gallegati, Gianfranco Giulioni and Antonio Palestrini. “Financial
Fragility, Patterns of Firms’ Entry and Exit and Aggregate Dynamics,” Journal of Economic
Behavior and Organization 51, 2003, 79-97.
Di Giovanni, J., Levchenko, A. A. and Rancière, R. “Power Laws in Firm Size and Openness to
Trade: Measurement and Implications,” IMF Working Paper No. 10/109.
Driver, Ciaran, Paul Temple and Giovanni Urga. “Profitability, Capacity, and Uncertainty: A
Model of UK Manufacturing Investment,” Oxford Economic Papers, 2005, 120–141.
Driver, Ciaran, and Brendan Whelan. “The Effect of Business Risk on Manufacturing Investment,”
Journal of Economic Behavior and Organization 44, 2001, 403-412.
Evans, David and Boyan Jovanovic. “An Estimated Model of Entrepreneurial Choice under Liquidity
Constraints,” Journal of Political Economy, 1989, 808-827.
Fazzari, Steven, Glenn Hubbard and Bruce Petersen, “Financing Constraints and Corporate
Investment.” Brookings Papers on Economic Activity, 1988, 141-195.
Folta, Timothy, and Jonathan O’Brien. "Entry in the Presence of Dueling Options," Strategic
Management Journal, 2004, 121-138.
Folta, Timothy. “Governance and Uncertainty: The Trade-off Between Administrative Control
and Commitment,” Strategic Management Journal, 1998, 1007-1028.
Freel, Mark S.. “Perceived Environmental Uncertainty and Innovation in Small Firms,” Small
Business Economics, 2005, 49–64.
Fuss, Catherine, and Philip Vermeulen. “Firms’ Investment Decisions in Response to Demand
and Price Uncertainty,” Applied Economics, 2008, 2337-2351.
Gertler, Mark, and Simon Gilchrist. “Monetary Policy, Business Cycles, and the Behavior of Small
Manufacturing Firms,” Quarterly Journal of Economics, 1994, 309-340.
24
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Ghosal, Vivek, and Prakash Loungani. “The Differential Impact of Uncertainty on Investment in
Small and Large Businesses,” Review of Economics and Statistics, 2000, 338-343.
Ghosal, Vivek, and Prakash Loungani. “Product Market Competition and the Impact of Price
Uncertainty on Investment: Some Evidence From US Manufacturing Industries,” Journal of
Industrial Economics, 1996, 217-228.
Ghosal, Vivek. “The Effects of Uncertainty and Sunk Costs on Firms’ Decision-Making:
Evidence from Net Entry, Industry Structure and Investment Dynamics.” In The Economics of
Imperfect Markets, Giorgio Calcagnini and Enrico Saltari (Eds.), Contributions to Economics,
Springer: New York, 2009, Chapter 9, 167 – 182.
Ghosal, Vivek. “Does Uncertainty Influence the Number of Firms in an Industry?” Economics
Letters 50, 1996, 229 – 237.
Ghosal, Vivek. “Demand Uncertainty and the Capital-Labor Ratio: Evidence from the U.S.
Manufacturing Sector,” Review of Economics and Statistics 73, 1991, 157 – 162.
Greasley, David, and Jakob Madsen. “Investment and Uncertainty: Precipitating the Great
Depression in the United States,” Economica, 2006, 393–412.
Greenwald, Bruce, and Joseph Stiglitz. “Macroeconomic Models with Equity and Credit
Rationing,” in Hubbard, R. Glenn. ed., Asymmetric Information, Corporate Finance, and
Investment. Chicago: University of Chicago Press, 1990, 15-42.
Guo, Hui, and Kevin Kliesen. “Oil Price Volatility and U.S. Macroeconomic Activity,” Federal
Reserve Bank of St. Louis Review, 2005, 669-83.
Hartman, Richard. “Factor Demand with Output Price Uncertainty,” American Economic Review,
1976, 675–681.
Himmelberg, Charles, and Bruce Petersen. “R & D and Internal Finance: A Panel Study of Small
Firms in High-Tech Industries,” Review of Economics and Statistics, 1994, 38-51.
Hopenhayn, Hugo. “Entry, Exit and Firm Dynamics in Long Run Equilibrium,” Econometrica, 1992,
1127-1150.
Huizinga, John. “Inflation Uncertainty, Relative Price Uncertainty, and Investment in U.S.
Manufacturing,” Journal of Money, Credit and Banking, 1993, 521-549.
Kilian, Lutz. “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They
Matter for the U.S. Economy?” Review of Economics and Statistics. 2008, 216-240.
Koetse, Mark J., Arno J. van der Vlist and Henri L. F. de Groot, “The Impact of Perceived
Expectations and Uncertainty on Firm Investment,” Small Business Economics, 2006, 365-376.
25
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Lensink, Robert, Hong Bo and Elmer Sterken Investment. Capital Market Imperfections, and
Uncertainty: Theory and Empirical Results. Edgar Elgar, 2001.
Lensink, Robert, Paul van Steen and Elmer Sterken. “Uncertainty and Growth of the Firm,”
Small Business Economics, 2005, 381-391.
Li, Dan. “Multilateral R&D Alliances by New Ventures,” Journal of Business Venturing, 2013,
241–260.
Li, Yong. “Duration Analysis of Venture Capital Staging: A Real Options Perspective,” Journal
of Business Venturing, 2008, 497–512.
Li, Yong, Joseph T. Mahoney “When are venture capital projects initiated?” Journal of Business
Venturing, 2011, 239–254.
Marcellino, Massimiliano, James Stock and Mark Watson. “Macroeconomic Forecasting in
the Euro Area: Country Specific versus Euro Wide Information,” European Economic
Review, 2003, 1-18.
Meese, Richard, and John Geweke. “A Comparison of Autoregressive Univariate Forecasting
Procedures for Macroeconomic Time Series,” Journal of Business and Economic
Statistics, 1984, 191-200.
National Economic Council. “The Small Business Agenda: Growing America’s Small
Businesses to Win the Future,” 2011.
Oriani, Raffaele, and Maurizio Sobrero. “Uncertainty and the Market Valuation of R&D within a
Real Options Logic,” Strategic Management Journal, 2008, 343-361.
Pakes, Ariel, and Richard Ericson. “Empirical Implications of Alternate Models of Firm Dynamics,”
Journal of Economic Theory, 1998, 1-45.
Podoynitsyna, Ksenia, Michael Song, Hans van der Bij and Mathieu Weggeman. “Improving
New Technology Venture Performance under Direct and Indirect Network Externality
Conditions,” Journal of Business Venturing, 2013 195–210.
Saemundsson, Rognvaldur, and Asa Lindholm Dahlstrand. “How Business Opportunities
Constrain Young Technology-Based Firms from Growing into Medium-Sized Firms,” Small
Business Economics, 2005, 113-129.
Schmitt, John, and Nathan Lane “An International Comparison of Small Business Employment,”
Center for Economic and Policy Research (CEPR) Reports and Issue Briefs, 2009.
Sheets, Nathan, and Robert Sockin. “Does Size Really Matter?: The Evolving Role of Small
Firms in the U.S. Economy,” Empirical & Thematic Perspectives, Citi Research, 2012.
26
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Stein, Luke, and Elizabeth Stone. “The Effect of Uncertainty on Investment: Evidence from
Options,” Stanford University Working Paper, 2010.
Sutton, John. “Gibrat’s Legacy,” Journal of Economic Literature, 1997(a), 40-59.
U.S. Small Business Administration. Advocacy: the voice of small business in government.
“Frequently Asked Questions about Small Business Finance,”
http://www.sba.gov/sites/default/files/files/Finance%20FAQ%2082511%20FINAL%20for%20w
eb.pdf
Winker, Peter. “Causes and Effects of Financing Constraints at the Firm Level,” Small Business
Economics, 1999, 169-181.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
27
Figures 1.1-1.4: Annual percentage change in employment by size class
Figure 1.1. Percentage change in total employment
0.06
0.04
0.02
0
-0.02 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.04
-0.06
Figure 1.2. Percentage change in employment in large (≥500 employees) businesses
0.06
0.04
0.02
0
-0.02 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.04
-0.06
Figure 1.3. Percentage change in employment in small (<500 employees) businesses
0.04
0.02
0
-0.02 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.04
-0.06
Figure 1.4. Percentage change in employment in small (<20 employees) businesses
0.04
0.02
0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.02
-0.04
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
28
Figures 2.1-2.4: Annual percentage change in number of businesses by size class
Figure 2.1. Percentage change in total number of businesses
0.04
0.02
0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.02
-0.04
Figure 2.2. Percentage change in number of large (≥500 employees) businesses
0.08
0.06
0.04
0.02
0
-0.02 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.04
-0.06
Figure 2.3. Percentage change in number of small (<500 employees) businesses
0.04
0.02
0
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.02
-0.04
Figure 2.4. Percentage change in number of small (<20 employees) businesses
0.03
0.02
0.01
0
-0.01 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
-0.02
-0.03
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
29
Tables
Table 1. Summary Statistics
Variable
By Size Class
Employment
EMP
Size: All
EMP
Size: Large w ≥500 employees
EMP
Size: Small w <20 employees
EMP
Size: Small w <500 employees
Number of Businesses
FIRMS
Size: All
FIRMS
Size: Large w ≥500 employees
FIRMS
Size: Small w <20 employees
FIRMS
Size: Small w <500 employees
Mean
Std. Dev.
106.540
10.665
51.999
6.797
20.134
1.048
54.563
3.900
5.539
0.347
0.016
0.002
4.956
0.305
5.523
0.345
30
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
Estimation tables.
Employment specifications: Tables 2.1 and 2.2
Number of Businesses specifications: Tables 3.1 and 3.2
Notes to tables:
1. p-values (two-tailed test), based on efficient standard errors, are reported in parentheses. An * denotes significance at least at the 10% level. All specifications
are estimated using data over 1988-2010.
2. The first-order autocorrelation coefficient is denoted by ρ.
3. The variable definitions are as follows. (As noted in section 5, the uncertainty terms are measured in logarithms.)
= annual percentage change in employment.
= annual percentage change in number of businesses.
= annual percentage change in real GDP.
Survey forecast uncertainty measures
= survey forecast variance of gdp
= survey forecast variance of industrial (manufacturing) production
Estimated uncertainty measures
= gdp uncertainty.
= inflation uncertainty.
= S&P 500 uncertainty.
= fuel price uncertainty.
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
31
Table 2.1. Employment Specifications.
Dependent variable:
Size Group: Large (≥500 Employee) Businesses
Size Group: All Businesses
Const
S1
-0.0269*
(0.001)
-0.2362
(0.111)
0.0339
(0.706)
0.5673*
(0.007)
0.7491*
(0.002)
0.0002
(0.936)
-0.0025
(0.112)
-
S2
-0.0272*
(0.001)
-0.3491*
(0.080)
-0.0058
(0.949)
0.5104*
(0.001)
0.8640*
(0.001)
-
S3
-0.0270*
(0.081)
-0.3651*
(0.038)
0.0596
(0.525)
0.6953*
(0.001)
0.8069*
(0.002)
-
S4
-0.0477*
(0.022)
-0.4119*
(0.049)
-0.0288
(0.736)
0.5847*
(0.001)
0.9379*
(0.001)
-
S5
-0.0359*
(0.001)
-0.2578*
(0.091)
0.1467
(0.141)
0.6067*
(0.001)
0.7471*
(0.001)
-
S6
-0.0516*
(0.001)
-0.5174*
(0.009)
0.1003
(0.201)
0.4495*
(0.001)
0.9808*
(0.001)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0016
(0.225)
-0.0017
(0.259)
-
S1
-0.0189*
(0.058)
0.1224
(0.317)
0.0271
(0.834)
0.6167*
(0.008)
0.6277*
(0.001)
0.0010
(0.795)
-0.0008
(0.678)
-
-
-
-
-
-
-
-
-
-
0.0029*
(0.079)
-0.0031*
(0.054)
-
-
-
-
-
-
-
0.0005
(0.728)
-0.0026*
(0.027)
-
-
-
-
-
-
-
-
-
-
0.792
0.198
-
ρ
S2
-0.0197*
(0.001)
0.1853
(0.107)
0.0020
(0.987)
0.5778*
(0.001)
0.5719*
(0.003)
-
S3
-0.0148
(0.518)
-0.0435
(0.798)
0.0899
(0.556)
0.7624*
(0.003)
0.6561*
(0.006)
-
S4
-0.0570*
(0.038)
0.0928
(0.394)
0.0414
(0.725)
0.5902*
(0.001)
0.5653*
(0.007)
-
S5
-0.0264*
(0.006)
0.1329
(0.129)
0.0668
(0.577)
0.6264*
(0.001)
0.5774*
(0.001)
-
S6
-0.0436*
(0.001)
-0.0807
(0.524
0.1345)
(0.188)
0.5152*
(0.001)
0.7329*
(0.001)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0019
(0.235)
0.0009
(0.635)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0042
(0.137)
-0.0034
(0.155)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.812
0.822
0.806
0.816
-0.0040*
(0.001)
-0.0016
(0.140)
0.878
0.0001
(0.903)
-0.0014
(0.160)
-
-
-
-0.0004
(0.474)
-0.0019*
(0.029)
-
-0.0015
(0.470)
-0.0020*
(0.081)
-
0.732
0.746
0.786
0.753
0.746
-0.0031*
(0.026)
-0.0017
(0.147)
0.782
0.222
0.177
0.258
0.099
-0.117
0.175
0.115
0.142
0.203
0.147
0.030
-
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
32
Table 2.2. Employment Specifications
Dependent variable:
Size Group: Small (<500 Employee) Businesses
Const
S1
-0.0361*
(0.001)
-0.4275*
(0.006)
-0.1217
(0.406)
0.4205*
(0.047)
0.8137*
(0.001)
-0.0019
(0.532)
-0.0038*
(0.013)
-
-0.0554*
(0.001)
-0.4867*
(0.006)
-0.0417
(0.716)
0.5535*
(0.003)
0.7747*
(0.002)
-
-0.0686*
(0.021)
-0.6164*
(0.008)
-0.2012
(0.227)
0.5509*
(0.001)
0.9617*
(0.002)
-
-0.0382*
(0.001)
-0.3832*
(0.045)
0.0554
(0.751)
0.5868*
(0.001)
0.7351*
(0.003)
-
-0.0483*
(0.001)
-0.5633*
(0.003)
-0.1006
(0.410)
0.4293*
(0.001)
0.9505*
(0.001)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0028*
(0.016)
-0.0036*
(0.008)
-
S1
-0.0192*
(0.001)
0.0942
(0.593)
-0.2843*
(0.084)
0.2969*
(0.006)
0.1232
(0.314)
-0.0028*
(0.062)
-0.0031*
(0.004)
-
-
-
-
-
-
-
-
-
-
0.0004
(0.804)
-0.0037*
(0.011)
-
-
-
-
-
-
-
-0.0002
(0.932)
-0.0035*
(0.029)
-
-
-
-
-
-
-
-
-
-
0.738
0.084
-
ρ
S2
-0.0344*
(0.001)
-0.6403*
(0.001)
-0.2457
(0.110)
0.4117*
(0.002)
0.9656*
(0.001)
-
S3
S4
Size Group: Small (<20 Employee) Businesses
S5
S6
S2
-0.0137*
(0.001)
0.0228
(0.883)
-0.3003
(0.106)
0.3898*
(0.001)
0.1082
(0.318)
-
S3
S4
S5
S6
-0.0353*
(0.001)
0.0212
(0.915)
-0.1759
(0.277)
0.3849*
(0.001)
0.1238
(0.379)
-
-0.0222
(0.277)
0.1123
(0.604)
-0.2511
(0.279)
0.4481*
(0.001)
0.1231
(0.351)
-
-0.0155*
(0.007)
0.1421
(0.527)
-0.1418
(0.557)
0.4523*
(0.001)
0.0931
(0.501)
-
-0.0220*
(0.012)
0.0135
(0.941)
-0.2754*
(0.013)
0.3781*
(0.001)
0.1812
(0.105)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0025*
(0.001)
-0.0017*
(0.027)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0014
(0.237)
-0.0016*
(0.078)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.815
0.756
0.741
0.737
-0.0038*
(0.004)
-0.0008
(0.618)
0.785
-0.0004
(0.457)
-0.0008
(0.358)
-
-
-
-0.0001
(0.824)
-0.0022*
(0.038)
-
-0.0005
(0.688)
-0.0007
(0.610)
-
0.745
0.798
0.718
0.622
0.648
-0.0027*
(0.003)
-0.0001
(0.899)
0.754
0.064
0.237
0.257
0.110
0.140
0.001
0.089
0.106
0.053
0.090
0.095
-
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
33
Table 3.1. Number of Businesses Specifications.
Dependent variable:
Size Group: Large (≥500 Employee) Businesses
Size Group: All Businesses
Const
S1
-0.0080
(0.110)
0.6509*
(0.001)
-0.3361
(0.115)
0.2958*
(0.001)
-0.0838
(0.384)
-0.0027*
(0.052)
-0.001
(0.846)
-
S2
-0.0052
(0.127)
0.5933*
(0.002)
-0.3399
(0.158)
0.3834*
(0.001)
-0.1614*
(0.085)
-
S3
-0.0131
(0.229)
0.6646*
(0.003)
-0.2262
(0.260)
0.3013*
(0.001)
-0.1077
(0.228)
-
S4
-0.0147
(0.474)
0.6629*
(0.011)
-0.0519
(0.853)
0.3755*
(0.001)
-0.2485*
(0.025)
-
S5
-0.0068
(0.127)
0.7189*
(0.001)
-0.2926*
(0.091)
0.3614*
(0.001)
-0.1501*
(0.065)
-
S6
-0.0059
(0.230)
0.6887*
(0.001)
-0.3688*
(0.075)
0.3713*
(0.001)
-0.1300
(0.176)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0019*
(0.031)
-0.0006
(0.409)
-
S1
0.0092
(0.335)
-0.5427*
(0.004)
-0.5929*
(0.003)
0.0102
(0.961)
1.1061*
(0.002)
0.0067*
(0.073)
-0.0027
(0.569)
-
-
-
-
-
-
-
-
-
-
-0.0023*
(0.016)
0.0010
(0.352)
-
-
-
-
-
-
-
-0.0017
(0.216)
0.0005
(0.562)
-
-
-
-
-
-
-
-
-
-
0.729
-0.101
-
ρ
S2
-0.0028
(0.661)
-0.5486*
(0.009)
-0.5323*
(0.019)
-0.1991
(0.241)
1.2435*
(0.001)
-
S3
0.0637*
(0.068)
-0.7865*
(0.001)
-0.8287*
(0.001)
0.1012
(0.679)
1.4109*
(0.001)
-
S4
-0.0031
(0.949)
-0.6529*
(0.003)
-0.6838*
(0.001)
-0.1944
(0.272)
1.3490*
(0.001)
-
S5
-0.0147
(0.146)
-0.6209*
(0.001)
-0.6059*
(0.001)
-0.1520
(0.431)
1.1398*
(0.001)
-
S6
-0.0416*
(0.010)
-0.6841*
(0.005)
-0.6216*
(0.001)
-0.3017*
(0.067)
1.1185*
(0.001)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0032
(0.160)
-0.0046
(0.161)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0093*
(0.008)
-0.0021
(0.552)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.755
0.747
0.706
0.715
-0.0011
(0.107)
0.0002
(0.805)
0.707
0.0001
(0.971)
-0.0035*
(0.025)
-
-
-
-0.0008*
(0.085)
-0.0002
(0.736)
-
0.0018
(0.672)
-0.0021
(0.528)
-
0.446
0.470
0.556
0.400
0.462
-0.0059*
(0.028)
-0.0037
(0.254)
0.555
-0.177
-0.130
-0.021
-0.106
-0.174
0.241
0.323
0.135
0.248
0.151
0.100
-
Ghosal and Ye. Uncertainty and Small Businesses. (Work-in-progress.)
34
Table 3.2. Number of Businesses Specifications.
Dependent variable:
Size Group: Small (<500 Employee) Businesses
S1
Const
S2
-0.0079
(0.112)
0.6645*
(0.001)
-0.3385
(0.110)
0.2939*
(0.001)
-0.0881
(0.351)
-0.0027*
(0.053)
-0.0002
(0.854)
-
S4
S5
S6
S1
S2
-0.0051
(0.128)
0.6055*
(0.001)
-0.3412
(0.154)
0.3813*
(0.001)
-0.1651*
(0.077)
-
-0.0127
(0.235)
0.6781*
(0.002)
-0.2287
(0.249)
0.2988*
(0.001)
-0.1108
(0.208)
-
-0.0137
(0.495)
0.6789*
(0.008)
-0.0721
(0.794)
0.3736*
(0.001)
-0.2449*
(0.025)
-
-0.0067
(0.127)
0.7356*
(0.001)
-0.3007*
(0.074)
0.3575*
(0.001)
-0.1529*
(0.056)
-
-0.0057
(0.244)
0.7051*
(0.001)
-0.3720*
(0.070)
0.3685*
(0.001)
-0.1345
(0.158)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0019*
(0.033)
-0.0006
(0.401)
-
-0.0075
(0.175)
0.4901*
(0.016)
-0.1020
(0.661)
0.2633*
(0.003)
-0.0859
(0.431)
-0.0031*
(0.047)
0.0004
(0.716)
-
-
-
-
-
-
-
-
-
-
-0.0022*
(0.016)
0.0010
(0.340)
-
-
-
-
-
-
-
-0.0016
(0.241)
0.005)
(0.576)
-
-
-
-
-
-
-
-
-
-
-
-
ρ
S3
Size Group: Small (<20 Employee) Businesses
S3
S4
S5
S6
-0.0046
(0.248)
0.4257*
(0.051)
-0.0946
(0.717)
0.3679*
(0.004)
-0.1891*
(0.068)
-
-0.0153
(0.278)
0.4556*
(0.065)
0.0178
(0.939)
0.2646*
(0.007)
-0.1100
(0.318)
-
-0.0282
(0.263)
0.4202
(0.112)
0.2864
(0.368)
0.3507*
(0.004)
-0.3252*
(0.037)
-
-0.0078
(0.195)
0.4713*
(0.029)
-0.0006
(0.997)
0.3804*
(0.004)
-0.1895*
(0.044)
-
-0.0060
(0.361)
0.5053*
(0.016)
-0.1483
(0.605)
0.3606*
(0.004)
-0.1455
(0.161)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0019*
(0.067)
-0.0004
(0.692)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0027*
(0.014)
0.0012
(0.359)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.0005
(0.413)
-0.0007
(0.244)
-
-
-
-0.0008*
(0.081)
-0.0001
(0.762)
-
-0.0029*
(0.094)
0.0004
(0.705)
-
-
-
-
-
-
-
-
0.581
0.583
0.609
0.584
0.542
-0.0009
(0.375)
-0.0001
(0.966)
0.518
-0.087
-0.142
-0.101
0.116
-0.056
-0.095
0.732
0.757
0.750
0.706
0.718
-0.0011
(0.105)
0.0003
(0.781)
0.711
-0.102
-0.178
-0.132
-0.026
-0.107
-0.178
-