Download a study of business investment in the us post-crisis.

A STUDY OF BUSINESS INVESTMENT IN THE US POST-CRISIS.
Hong Thi Anh Tran
B.Sc. Economics
2nd year
University College London
Explore Econ Undergraduate Research Conference
March 2017
Abstract: The paper aims to clarify some reasons behind fluctuations in the US business investment postcrisis; using the accelerator and the q-theory in which past output changes and the ratio of expected
marginal benefit over expected marginal cost are the dominant explanatory variables respectively. This
analysis supports the consensus that most deviations in investment from pre-crisis forecasts are well
explained by cumulative changes in the real output level. Business investment increased as soon as the
economy started to recover, though the recent “hollowing out” may be of another puzzle for ongoing
research. Other findings include a small role of the average Q – i.e. rising stock markets may not
sufficiently stimulate investment, the insignificant effect of cash flows, and the potential impact of
uncertainty on business investment decisions. These all suggest that policies which help improve future
output growth and policy stability; for example, increasing public expenditure spending, may create
favourable conditions for stronger business investment development.
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I. A summary on recent trends in the US business investment:
Private non-residential fixed investment (in short; business investment) measures spending by private
sectors on structure, equipment and intellectual property to facilitate as well as to expand production
capacity. This spending is divided among the improvement of existing assets, the replacement of obsolete
machinery, and the creation of new productive assets. While business investment, as a whole, has an
explicit contribution to aggregate demand; the third category is particularly vital for future productivity
growth and hence the supply-side development.
Despite recent strong outlooks on both output and employment prospects in the US economy, business
investment has remained below levels predicted by post-crisis trends (2007 Consensus Forecast). It has
flattened recently after the pickup during the initial phase of recovery. There is also a divergence between
different components with the share of structure falling against equipment while intellectual property has
been growing solidly since the early 2000s. Because structure depreciates relatively slowly compared to
equipment, the shift towards the latter may reflect an increasing proportion of business investment
actually spent on replacing obsolete capitals rather than on the effective net investment. Since a
significant share of the US gross private domestic investment is accounted for by non-residential
investment; its sluggishness has contributed the largest proportion to total private investment shortfall.
According to the OECD estimate, the US average annual capital stock growth stood at 1.5 percent during
2008-2014 (figure 2) – only a half of the pre-crisis rate. The risk of an insufficient level of net business
investment and a subsequent fall in capital per worker could put constraints on the supply side by
lowering potential output to as much as 0.5 percent per annum (OECD Economic Outlook 2015, Issue 1,
Box 1.3).
II. Literature review
Study of business investment is characterised by three main theories: the neoclassical, the accelerator
model, and the Tobin-q theories. Investment is defined as a flow concept, describing firms’ actions to
adjust the past level of capital stock to its current desired level. Specifically, total investment is defined as
a sum of net investment (𝑰𝒏𝒕 ) and capital replacement (𝑰𝒓𝒕 ). Net investment contributes directly to the
change in stock of capital and is expressed as the distributed lags on cumulative changes in the desired
capital stock K*:
𝑱
𝑰𝒏𝒕
= ∑ 𝜷𝒋 ∆𝑲∗𝒕−𝒋 (𝟏)
𝒋=𝟎
I tn
where βj represents the delivery lag distribution extending for J+1 periods. Estimates for β are expected to
be smaller than 1 to capture the time lags in the adjusting process. Capital depreciation rate is frequently
assumed to be geometrically constant at δ, thus replacement investment to repair old or obsolete capital
assets is:
𝑰𝒓𝒕 = 𝜹𝑲𝒕−𝟏 (𝟐)
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Since past capital stock is observable, the amount of investment depends on how firms estimate the
desired level (K*) consistent with their profit maximising behaviour. The Jorgenson’s neoclassical model
(1971) is one of the most frequently used specifications in empirical analysis as it provides a simple
expression for K* based on available data. Its main assumptions involve firms maximising the discounted
flows of profit over an infinite horizon and the production function being Cobb-Douglas, we can thereby
obtain the desired stock of capital (𝑲∗𝒕 ) as a function of output level (Yt) and the user cost of capital (Ct):
𝑲∗𝒕 = 𝜶𝒀𝒕 𝑪−𝝈
𝒕 (𝟑)
where α is the distribution parameter and σ represents the constant elasticity of substitution between
capital and variable inputs. Thus, total business investment under the Jorgenson’s neoclassical model is
defined as:
𝑱
𝑰𝒕 =
𝑰𝒏𝒕
+
𝑰𝒓𝒕
=
𝑱
∑ 𝜷𝒋 ∆𝑲∗𝒕−𝒋
𝒋=𝟎
+ 𝜹𝑲𝒕−𝟏 = ∑ 𝜶𝜷𝒋 ∆(𝒀𝒕−𝒋 𝑪−𝝈
𝒕−𝒋 ) + 𝜹𝑲𝒕−𝟏 (𝟒)
𝒋=𝟎
In the case of σ = 0, we have the pure accelerator model where net investment depends solely on
distributed lags of changes in output level. As implied by the name “accelerator” itself, an increase in
output would generate a higher level of business investment and vice versa. Under this specification,
fiscal and monetary policies that may affect business investment through reducing profit tax rates and
thus user cost of capital could be rendered ineffective.
𝑱
𝑰𝒕 =
𝑰𝒏𝒕
+
𝑰𝒓𝒕
= ∑ 𝜶𝜷𝒋 ∆𝒀𝒕−𝒋 + 𝜹𝑲𝒕−𝟏 (𝟓)
𝒋=𝟎
The traditional methods described above, however, do not successfully incorporate technological
development and rational expectation formation in analysing business investment decisions. The idea of
explaining investment using firm forward-looking behaviour was initially introduced by Keynes (1936).
Though emphasising the influence of “future” expected demand, Keynes assumed long-term expectations
as given and hence considered investment as exogenous in his short-run analysis. This insight was
subsequently revitalised and elaborated by William Breinard and James Tobin (1968) to provide a neat
micro-founding solution. “q” measures the expected marginal benefit from an additional unit of capital
stock divided by its marginal cost, thus firms will adjust their investment until the optimal level
associating with q=1 or marginal benefit equals marginal cost. Because the above marginal “q” is
unobservable, additional conditions have been added to apply other observable indicators into the theory.
Fumio Hayashi (1982, 1985) established those formal conditions as: 1) product and factor markets are
competitive, 2) production and adjustment cost technologies are linear homogenous, 3) capital is
homogenous, and 4) investment decisions are largely separate from other real and financial decisions.
Under such restrictions; the average Q, defined as the ratio of the financial market’s valuation of the firm
to the replacement cost of its existing capital stock, is an appropriate substitution for the marginal q.
Hence, the traditional q-theory with perfect competition and perfect capital market takes the form:
𝑱
𝑰𝒕
= 𝑰𝑲𝒕 = 𝝍 + ∑ 𝜷𝒋 𝑸𝑨𝒕−𝒋 (𝟔)
𝑲𝒕−𝟏
𝒋=𝟎
where QA is the average Q. Although the utilisation of average Q to replace the unobservable marginal q
is subject to much criticism, the general framework of the q-theory has some particular advantages. It
avoids the problem of Lucas critique in which an extrapolation of past events could fail to form
predictions about future structural changes. In addition, the q-theory is an adaptation to the revolution of
rational expectation in which forward-looking expectations are entirely captured in q. However, the
disappointing empirical performance and ongoing debates among academics are evidence for q not being
the sole determinant in business investment decisions. Details are beyond the scope of this paper;
however, a thorough discussion is provided in Chirinko’s paper (1993).
A closer examination of the q-theory with non-constant return and imperfect competition leads to the
incorporation of accelerator mechanism in which, conditional on q, the change in investment is related to
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the level of output. Recent developments in investment analysis also allow for other factors such as
liquidity to solve for the problem of imperfect capital market as well as uncertainty.
III. Data and empirical testing
1. Data
The paper uses quarterly data from 1999:1 to 2015:4. Both series for real output and real business
investment are from the National Income and Product Accounts (NIPA). Capital stock and real aftertaxed corporate profits are from the Bureau of Economic Analysis (BEA)’s fixed assets and income
tables. Computed q values are from Smithers&Co online publication which utilises data from Measures of
Stock Market Value and Returns for the Non-financial Corporate Sector 1900-2002 by Stephen Wright
and Federal Reserve Z1 Table B.103& R.103 to Q3 2016. Finally, the paper uses uncertainty data from
policyuncertainty.com as suggested in the OECD Economic Outlook, Issue 2, 2015.
2. Empirical testing
Although there have been many unsolved issues in the literature of business investment determination,
most academics agree that the quantity variable – output or sales - is the dominant factor in business
investment decision while the user cost only has a modest effect. Thus the paper will start with the
accelerator model using specification from Oliner (1995) in which investment is described by (5) but also
scaled by the lagged capital stock to address the concern of nonstability:
𝟏𝟐
𝚫𝒀𝒕−𝒋
𝑰𝒕
𝝍
= 𝑰𝑲𝒕 = 𝜹 +
+ ∑ 𝝎𝒋
+ 𝒖𝒕 (𝟕)
𝑲𝒕−𝟏
𝑲𝒕−𝟏
𝑲𝒕−𝟏
𝒋=𝟏
Since investment is also a component of aggregate demand, there is a positively reverse relationship
between the dependent and explanatory variables, thus endogeneity results in an upward-biased OLS
estimate of the coefficient on the contemporaneous output change. The common answer for this problem
is to use instrumental variables, but obtaining valid instruments is a difficult task. Based on Bennett’s
proposal (1989) of using fiscal policy variables as an instrument to control for simultaneity between
investment and current output level, the IMF study (WEO April 2015) has used data of fiscal
consolidation episodes across advanced economies to analyse the output-investment relationship. Because
fiscal consolidation is enacted based on governments’ objectives and independent of domestic investment,
this proxy is an appropriate instrument. From this report, an increase of 1 percent in contemporaneous
output growth has a statistically and economically significant effect of increasing investment growth by
2.45 percent. In the case of the US, it is thus highly expected that output also plays a dominant role in
explaining investment fluctuation. Other economists such as Christopher Sims (1980) argue for regressing
current endogenous variables on their own lags and other exogenous variables in the system. However,
this type of instrument may perform poorly since output has frequently been characterised as a random
walk, thus the correlation between its first difference and the lagged values may be weak (OECD
Economic Studies No.16. Spring 1991).
From an ad-hoc justification, table 2 of time-series analysis of investment-capital ratio on quarterly basis
shows that even the upward-biased OLS estimate on the contemporaneous output change is statistically
insignificant and can be rejected by the F-test at 5% significance level (p-value = 0.476). Hence, the paper
chooses to follow the typical approach of dropping the contemporaneous change in output without
significantly underestimating the accelerator effect. A choice of twelve period lags is also consistent with
the conventional empirical option. Table 3 reports the regression of the accelerator model with a total
cumulative effect estimated at 2.912. All coefficients on lagged values are statistically significant,
illustrating the long-lasting effect of output changes on investment. Predicted values from the model
follow real data closely while residuals fluctuate around zero on average. These results support the
general consensus that recent outputs are crucial in explaining business investment behaviours. According
to the neoclassical model, a consistent decrease in real user costs of capital recently is expected to
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stimulate more investment spending. Thus the persistent shortfall in investment growth compared to the
pre-crisis trend indirectly proves the small or muted effect of that capital cost variable.
A simple regression of investment-capital ratio on the current average-Q yields a small but statistically
significant coefficient estimate of 0.04. However, it is also reasonable to argue for a dynamic q-theory
model which includes lagged values up to two periods, according to the conventional option.
𝟐
𝑰𝒕
= 𝑰𝑲𝒕 = 𝝍 + ∑ 𝜷𝒋 𝑸𝑨𝒕−𝒋 (𝟖)
𝑲𝒕−𝟏
𝒋=𝟎
This modification clearly reduces the coefficient on contemporaneous q to be insignificant while
estimates on lagged coefficient are more important. Although strong stock market performance and
consistently high levels of q near one have not translated into improving investment demand, this is not
unusual from historical analysis perspective. The stronger impact of lagged average Q on investment may
signal a future possibility of a pickup in investment if stock market valuation remains buoyant over an
extend period of time (IMF WEO April 2015).
As discussed in the previous session, one suggestion for improving the performance of the q-theory is to
incorporate additional variables such as liquidity to account for an imperfect capital market via the
following specification (Chirinko 1993):
𝑰𝒕
𝒍𝒕
= 𝑰𝑲𝒕 = 𝝅𝟎 + 𝝅𝟏 𝒒𝟏 + 𝝅𝟐
+ 𝒖𝒕 (𝟗)
𝑲𝒕−𝟏
𝑲𝒕−𝟏
where ℓt is a liquidity variable specified as a flow. However, the coefficient estimate is unexpectedly
negative and statistically insignificant, suggesting liquidity may not be a crucial constraint on US business
investment post crisis. One reason could be the relatively well-developed financial markets in the US
which provide a substantial source of external funding, thus reducing the constraint of internal finance on
firm investment decisions. In addition, it could be attributed to the tendency among corporations to use
profits for boosting dividends or share buybacks, which reached the substantial average of 37% and 54%
of total earning respectively from 2003 through 2012 (William Lazonick, 2014). Hence, strong profit
earnings during the last few years may unsurprisingly have only a small effect, if any, on investment
demand.
Other directions in research agenda have made some improvement in using determinants such as policy
and financial uncertainty to explain investment behaviour. However, the appropriate measure of economic
uncertainty is far from clear and the key challenge remains in separating potential endogeneity in
explanatory variables. On the one hand, rising uncertainty levels could affect firm expectations of future
sales and inhibit investment project approvals. On the other hand, the relatively volatile characteristic of
investment may generate even a stronger uncertainty in the economy. One widely used measure is the
stock-market-based volatility formulated in Bloom et.al (2007). His study found some initial evidence
that higher uncertainty reduces the response of investment to demand shocks as well as monetary and
fiscal policy. Klein (2012), on the other hand, shows that uncertainty tends to depress capital investment,
hiring and advertising but encourage research and development spending. Indeed, an incorporation of the
newly developed “Economic Policy Uncertainty Index” (Baker et.al 2013) into the traditional q-model
also produces a small negative but statistically significant coefficient estimates as expected. It also
slightly reduces the gap in the model residuals, hence improving the empirical performance of the qtheory.
IV. Conclusion:
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This paper supports the consensus among economists that most deviations in investment from its precrisis forecasts are well explained by the conventional accelerator mechanism. This success may be partly
attributed to firms forming expectations by extrapolating past output changes as an indication for future
sales. The contemporaneous average Q has a small but statistically significant impact on influencing
investment decision as estimated in most empirical analysis though effects have increased as further
lagged values are added. The inclusion of uncertainty generally reduces the variation of Q-model’s
predictions from actual data. However, the task of obtaining more accurate measures remains as a
challenge for future studies. Nevertheless, these results have suggested a rather optimistic view of policy
implication than by merely observing data. US business investment may recover more strongly if policies
to maintain sufficiently high and stable output growth are successfully implemented, stock markets
remain buoyant, and expansion in government’s infrastructure expenditure could efficiently spur private
investment incentives.
The research presented so far has not included other potential sources of sluggish investment arising from
structural changes, such as the shift to more intangible assets or ICT-related capitals, as well as external
factors such as opportunities of outsourcing investment to foreign countries. Since actual investment level
has consistently remained below pre-crisis forecasts despite the strong recovery in the US economy, the
emphasis on thinking about those alternative determinants may provide some solutions for the remaining
puzzle.
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Figure 1: Weak net investment and average annual capital growth
Figure 2: Weak net investment and average annual capital growth
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Figure 3:
Source: OECD Economic Outlook 97.
Figure 4:
Source: Policyuncertainty.com.
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Figure 5: Comparison of models and real data on the investment-capital ratio (1999-2015)
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Figure 6: Ratio of residuals over actual investment-capital ratio (1999-2015)
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Table 1: Summary of results from mainstream models in which “kinver” is the inverse of the lagged
capital stock, “yk(.)” are ratios of corresponding lagged output changes to lagged capital stock, and L1.q,
L2.q are the first lag and the second lag of “q” respectively.
Table 2: (IMF WEO April 2015) Investment - Output Relationship: Instrumental Variables Estimation
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