The effects of expected and unexpected volatility on long-run growth: evidence from 18 developed economies.

1. Introduction

Although there is disagreement about the magnitude, many economists agree that business cycles have negative consequences for welfare in the short run by causing output to deviate from potential. As a result, most policymakers regard reducing volatility as a desirable goal. However, there is disagreement about the long-run consequences of business cycles.

Some models suggest that business cycle volatility should reduce long-run growth, in these models, increased volatility increases risk, reduces investment, and slows the growth rate of output. In addition, volatility may reduce the diffusion rate of new technology, which might reduce the long-run growth rate. Hence, business cycles have negative short-run consequences (by causing output to deviate from the trend) and negative long-run consequences (by slowing the long-run growth rate). If these models are accurate, then the welfare consequences of business cycles are more severe than previously thought.

An alternative view of the growth-volatility relationship suggests that there may be a long-run benefit to business cycles. In these models, increased volatility stimulates inventive activity, which increases the long-run growth rate. Reduced volatility will be beneficial in the short run, but if reduced volatility decreases the long-run growth rate, then there are costs to stabilization. As a result, policymakers would face a trade-off between business cycle volatility and long-run growth. In addition, it is possible that the long-run costs of stabilization policy might exceed the short-run benefits.

This article sheds new light on the growth-volatility relationship. First, this article focuses on two types of volatility: expected volatility and unexpected volatility. This allows a more thorough test of the two general hypotheses linking growth to volatility. Second, this article empirically examines the relationship between growth and volatility for 18 industrialized nations over a 110-year period. Therefore, the analysis avoids the problem of short time span of data.

This article proceeds as follows. Section 2 clarifies the relationship between the competing hypotheses as well as between expected and unexpected volatility. Section 3 analyzes the growth-volatility relationship without making the distinction between expected and unexpected volatility. Section 4 uses generalized method of moments (GMM) and ordinary least squares (OLS) along with panel data to estimate the effects of expected and unexpected volatility on growth. Section 5 concludes with suggestions for further research.

2. The Growth-Volatility Relationship

The view that business cycle volatility reduces the long-run growth rate focuses on risk. Bernanke (1983), Ramey and Ramey (1991), and Pindyck (1991) argue that volatility creates risk about future demand and that firms are unlikely to invest in new plants and equipment if they are unsure about the demand for their product. The greater the volatility in output, the more uncertain future demand becomes; the greater the risk, and the less likely firms are to invest. This negative relationship between volatility and investment might lead to a negative relationship between growth and volatility: Increased volatility decreases investment, which then slows the growth rates of the capital stock and output. The effect would be especially pronounced if new technology is embodied in new capital goods. Ramey and Ramey (1995) and Macri and Sinha (2000) have found results consistent with this view of the growth-volatility relationship using aggregate data. This view emphasizes the risks associated with unexpected changes in output: When firms cannot accurately forecast the demand for their goods, they reduce capital expenditures, which reduces growth.

The view that business cycles might increase long-run growth focuses on the opportunity cost of productivity-enhancing activities (PEAs). Bean (1990) and Saint-Paul (1993) view firms as solving an intertemporal profit-maximizing problem in which producing goods provides profits today but engaging in PEAs produces profits only in the distant future. In this case, a countercyclical opportunity cost would exist if the profits from PEA are relatively stable over the business cycle but the profits from producing output are temporarily high during expansions and temporarily low during recessions. Under these conditions, the profitability of PEA relative to production falls during expansions and rises during recessions. This would lead firms to increase PEA during recessions and might lead to a positive relationship between growth and volatility.

The opportunity cost view seems most closely tied to expected volatility: When firms can accurately forecast the demand for their goods, they can plan PEA for the downturns when the opportunity cost is low. This opportunity-cost effect has found empirical support in several articles. Bean (1990) found that human capital accumulation is countercyclical, Hall (1991) argued that organizational capital accumulates more quickly during recessions, and Saint-Paul (1993) found evidence that negative aggregate demand shocks stimulate productivity growth. In addition, Kormendi and Meguire (1985), Grier and Tullock (1989), and Caporale and McKiernan (1996, 1998) have all found empirical support for the view that increased volatility stimulates growth, which is consistent with the opportunity-cost effect.

The literature emphasizes two different types of volatility: expected and unexpected volatility. Therefore, the theoretical models emphasizing risk and the opportunity-cost effect are not mutually exclusive, and it is possible that firms respond to both increased risk from business cycles and the fluctuation in the opportunity cost of PEAs over the business cycle. The more interesting question, then, is which effect is stronger empirically.

Cooper and Haltiwanger (1993) and Cooper, Haltiwanger, and Power (1999) examine issues related to the opportunity-cost effect. Cooper and Haltiwanger (1993) develop a deterministic model in which machine replacement occurs toward the end of economic downturns due to the low opportunity cost. In addition, they show that machine replacement in the automobile industry coincided with seasonal downturns (which are relatively regular and predictable) in production. Cooper, Haltiwanger, and Power (1999) generalize the model to allow for a stochastic environment and show that the timing of machine replacement depends on the underlying stochastic process and the specification of the adjustment costs. Specifically, in an environment with persistent shocks and fixed adjustment costs, machine replacement is procyclical rather than countercyclical.

Neither Cooper and Haltiwanger (1993) nor Cooper, Haltiwanger, and Power (1999) refers directly to the opportunity-cost literature. However, these articles do study the relationship between output fluctuations and one form of PEA (machine replacement). This is in the spirit of the opportunity-cost literature. Most importantly, the studies demonstrate that the relationship between fluctuations in output and PEAs depends on the forecastability of output. In particular, forecasted downturns allow firms to replace capital goods, which is essential for the diffusion of new technology, while unexpected downturns will not necessarily allow for this type of activity. Therefore, Cooper and Haltiwanger (1993) and Cooper, Haltiwanger, and Power (1999) provide additional motivation for examining the relationship between growth and volatility at the aggregate level.

This article improves on the existing literature by distinguishing between expected and unexpected volatility and estimating the relationship between growth and each type of volatility. However, this is not the only way to build on the existing literature. Policy shocks might increase growth and increase volatility or decrease growth and decrease volatility regardless of whether the shocks are expected or not. For example, the onset of major wars might increase volatility and decrease growth regardless of whether the war was expected or not. Similarly, changes in international openness might increase growth and increase volatility regardless of whether the change was expected or not. Therefore, the relationship between expected volatility and growth implied by the opportunity-cost literature and the relationship between unexpected volatility and growth implied by the literature focusing on uncertainty are not the only possible relationships between growth and volatility. Even with this caveat in mind, the results here suggest that expected and unexpected volatility have different relationships with growth.

3. Growth and Volatility: Preliminary Findings

The length of the data set (120 years) used by Caporale and McKiernan (1998) is desirable since one would like as many business cycles as possible in the data set. This article uses a data set of similar time span, but unlike Caporale and McKiernan (1998), who focus on just the United States, it extends the analysis to additional developed countries.

Maddison (1995) published annual gross domestic product data for developed economies from 1870 to 1994. This data set allows one to study the growth-volatility relationship for eighteen developed nations with a long time span of low-frequency data. (1) Therefore, one can test the nature of the relationship between growth and volatility and be relatively confident that the estimates reflect long-run rather than short-run relationships. In addition, one can also get a sense of whether the Caporale and McKiernan (1998) results are typical for developed countries.