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Uncertainty and the employment dynamics of small and large businesses

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Abstract

We examine the impact of uncertainty on employment dynamics. Alternative measures of uncertainty are constructed based on the survey of professional forecasters, and regression-based forecasting models for GDP growth, inflation, S&P500 stock price index, and fuel prices. Our results indicate that greater uncertainty has a negative impact on growth of employment, and the effects are primarily felt by the relatively smaller businesses; the impact on Large Businesses are generally non-existent or weaker. Our results suggest that to truly understand the effects of uncertainty on employment dynamics, we need to focus on the relatively smaller and entrepreneurial businesses. We discuss implications for the framing of economic policy.

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Notes

  1. Dixit and Pindyck (1995) present an excellent non-technical exposition of the option value approach. Additional results on the effects of uncertainty via the real-options channel are noted in Abel et al. (1996).

  2. These are presented in Figs. 1 and 3 for the base cases, and Tables 1 and 2 for the general cases.

  3. The class of models considered above require an extensive set of complex mathematical and computational assumptions, and it is very hard to obtain closed-firm solutions; hence, these papers resort to numerical simulations to shed light on the quantitative importance of the effects.

  4. Lensink, Bo and Sterken (2001) provide a lucid discussion of credit market conditions in the general context of investment behavior, including the roles played by uncertainty and sunk costs.

  5. Issues related to uncertainty arising from industry- or-firm-specific demand, cost, regulatory and other aspects can be addressed using micro-datasets. As we have stated earlier, this is not the focus of our more economy-wide analysis as the data from the U.S. Small Business Administration are aggregate.

  6. In a complementary literature, some differentiate between uncertainty v. disagreement. Compare two contexts: experts agree that there is a lot of uncertainty on the future growth rate; experts are confident about their estimation of future growth, but these estimations are very heterogeneous. Gollier and Zeckhauser (2005) demonstrate the effect of disagreement on the aggregate risk premium. While distinguishing between the effects of uncertainty v. disagreement on firms’ decision variables is a useful exercise, we do not explore the implications of this in the current paper.

  7. It is assumed that the expected value of the error term in Eq. (1) is zero.

  8. As the theoretical solution for these models are well established (e.g., Charles et al. 1960; Kennan 1979; Hendry et al. 1983; Jorgenson 1986), we do not repeat the details and model structure here. The optimization models refer to a representative firm and then applied to more or less disaggregated data.

  9. Since we are interested in the short-term effects of uncertainty on employment and that the underlying data on employment and GDP contain trends, we measure both of these variables in logarithmic first-differences (rate of growth), denoted by \({\text{E}}\mathop {\text{M}}\limits^{ \cdot } {\text{P}}_{t}\) and \({\text{G}}\mathop {\text{D}}\limits^{ \cdot } {\text{P}}_{t}\).

  10. We enter the uncertainty measure in natural logarithms as the mean values of the uncertainty variables vary enormously in size across the different measures (see Table 1). Using the actual values of the uncertainty variables in the estimated regressions resulted in large numerical differences in the estimates due to pure scaling effects. Entering the uncertainty variables in natural logarithms resulted in no differences in inferences (related to the Small versus Large Business differences) compared to entering them in levels.

  11. As noted earlier, our six uncertainty measures are constructed over a longer time period. We use their values over 1988–2011 in estimating specification (7).

  12. As we note in Sect. 7.3, our experiments using longer lag lengths did not provide additional insights into the effects of uncertainty.

  13. There is a significant literature which notes the similarities and differences between Small Business and entrepreneurship in dimensions related to growth and innovation. See, for example, ACCA (2010), Carland et al. (1984), di Giovanni et al. (2010) and Katz and Green (2010).

  14. Studies on industry dynamics and firm-churning data show that most business churning occurs at the smaller firm end: for example, Audretsch (1995), Sutton (1997) and Caves (1998). To this extent, purely as an accounting matter, one can argue that if we observe effects, it will more likely be in the smaller business category. But this is not a direct theoretical prediction.

  15. The literature has noted important differences between smaller and larger businesses, and point to smaller firms being relatively credit-constrained. E.g., Audretsch and Elston (1997), Fazzari et al. (1988), Gertler and Gilchrist (1994), Evans and Jovanovic (1989), Lensink et al. (2001), Ghosal and Loungani (2000), Himmelberg and Petersen (1994) and Winker (1999).

  16. The National Economic Council (2011) and Sheets and Sockin (2012) provide extensive discussion on the importance of Small Businesses and policy.

  17. The papers by Audretsch and Elston (1997, 2002), for example, provide important insights in this dimension from German policy initiatives.

  18. There is strong evidence that financing constraints are tighter for more innovative and R&D-intensive firms (e.g., Himmelberg and Petersen 1994; Guiso 1998; and Carpenter and Petersen 2002). With firm or industry data, one could use R&D intensity or high-tech industries as a moderator of the uncertainty–employment relationship (in addition to firm size which we consider). We thank an anonymous referee for pointing this out.

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Acknowledgments

We are extremely grateful to two anonymous referees and the editor for insightful and constructive suggestions that have significantly improved the paper. We are indebted to Christian Gollier and Pierre-André Chiappori for helpful comments on an earlier version of this paper, and to the seminar participants at Mizuho Research Institute (Tokyo), the Development Bank of Japan (Tokyo) and the Center for Economic Studies (Munich) for feedback. Part of this work was conducted when Vivek Ghosal was a Visiting Scholar at the Research Department of the International Monetary Fund (Washington, DC); I benefited from valuable discussions with Prakash Loungani, and helpful suggestions by Sangyup Sam Choi and Akito Matsumoto.

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  1. School of Economics, Georgia Institute of Technology, Atlanta, GA, USA

    Vivek Ghosal

  2. European Business School, Wiesbaden, Germany

    Vivek Ghosal

  3. CESifo, Munich, Germany

    Vivek Ghosal

  4. Whitman School of Management, Syracuse University, Syracuse, NY, USA

    Yang Ye

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Appendix: Selected empirical findings on the impact of uncertainty

Appendix: Selected empirical findings on the impact of uncertainty

The papers included below are not meant to be a comprehensive review of the studies in this area, but to 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, industry, and macroeconomic) and the estimated quantitative and qualitative effects (Table 8).

Table 8 Selected papers examining the effects of uncertainty
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Ghosal, V., Ye, Y. Uncertainty and the employment dynamics of small and large businesses. Small Bus Econ 44, 529–558 (2015). https://doi.org/10.1007/s11187-014-9614-0

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