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The productivity effects of stock option schemes: evidence from Finnish panel data

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Abstract

In this study we investigate the productivity effects of employee stock option schemes. We estimate Cobb-Douglas production functions by using new panel data for all Finnish publicly listed firms during 1992–2002. The data enable us to distinguish broad-based option plans, for which all employees are eligible, from those selectively allocated to particular employees. For both type of schemes, our baseline fixed effects estimators consistently find statistically insignificant associations with firm productivity. When endogeneity of production inputs and option-schemes are taken into account we continue to find no evidence of a link with firm productivity. Our main findings are consistent with hypotheses that predict negligible effects of option plans for enterprise performance, such as those based on free riding, psychological expectancy theory, accounting myopia, or rent-seeking. We consider reasons why our empirical findings on the impact of broad-based options differ from those found in earlier studies.

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Notes

  1. See Mäkinen (2001, 2007, 2008) and Jones et al. (2006) for studies of diverse dimensions of stock option schemes and CEO compensation in Finland.

  2. Our data contains all firms listed in the main list of the Helsinki Stock Exchange (HEX) for the period 1992–2006 and for the two minor lists, I-list and NM-list, for the period 1997–2002.

  3. In response to an anonymous referee’s comment we note that while there are certain industry specific patterns observed in our data, one should note that at the end of the observation period almost 74% of firms used stock options, so the phenomenon was common across many different industries.

  4. In public stock exchange reports, the firm typically reports whether the scheme is targeted only to managers, to managers and a selected group of key personnel or to the workforce more broadly. Our classification is thus different from Sesil et al. (2000, 2002), who use 50% threshold as a criterion for broad-based schemes. Our data do not include this information, but have the important advantage of being derived from publicly reported sources that must be externally verifiable, rather than from confidential surveys.

  5. See for example Ikäheimo et al. (2004) and Liljeblom and Pasternack (2006).

  6. We omit 8 firms or 15 firm-year observations due to their having fewer than 4 consecutive observations. To utilize all possible firm-level data we also collected, if possible, data on income statements and option schemes prior to a firm’s listing on the Helsinki Stock Exchange.

  7. One of a series of robustness checks we performed involved re-estimating many of the specifications to be discussed and reported in Tables 5, 6 and 7, but without excluding any outliers. Reassuringly, the key finding—that options did not affect productivity—was unaffected when this was done. However, if we adopted a rule of excluding firms who employed fewer than 18 persons, then we found mixed evidence on productivity effects.

  8. Finnish firms report only mean number of employees per year in financial statements. Unfortunately, our data do not contain information on firms’ labor costs.

  9. We kindly thank an anonymous referee for providing a program for the common factor restrictions.

  10. Since option plans typically are introduced publicly in early spring, a few weeks before the annual general meeting of shareholders, this seems to be a reasonable assumption. Therefore, a potential correlation is likely to be with a previous period rather than being contemporaneous. We also estimated an IV estimator (i.e. -ivreg2- with -cluster- in Stata), where an option program indicator was treated as an endogenous variable in a Cobb-Douglas production function. As excluded instruments we used the HEX general index, ICT sector dummy indicator and the share ownership of a largest shareholder. The instrumented option dummy indicator was still insignificant (p-value 0.84), when the F-test for the excluded instruments in the first-stage regression was highly significant (p-value 0.00) and the Hansen J statistic supported the validity of the (all) instruments (p-value 0.12).

  11. For more on dynamic GMM estimators and constructing an instrument matrix, see Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and Bond (1998, 2000).

  12. We use Stata/SE 10.1 in all estimations. We pooled the sectors in order to increase the number of observations, since GMM estimators can perform poorly in finite samples. However, in unreported regressions we estimated production functions separately for the three sectors; information technology (IT), service, and manufacturing. As expected, when comparing parameter estimates for production technology inputs, we find, in the fixed effects model context, that the labor coefficient was higher in the IT and service sectors than in manufacturing, whereas capital was clearly higher in the manufacturing sector compared to other two sectors. However, our key finding is preserved—we did not find statistically significant coefficients for our option program indicator variables opt, ssopt, bbsopt, dilu, diluss and dilubb in different model specifications. In the GMM context, however, the size of the instrument matrix can be large compared to the number of observations in the service and IT sectors, which seems to substantially bias estimation results.

  13. We do not report the differenced GMM estimation results in Tables 6 and 7, since our unreported results (available upon request) suggest that the individual series are highly persistent (but not an exact unit root). That being the case, the estimator is very likely to suffer from severe finite sample bias or inconsistency (e.g., Blundell and Bond 1998; Blundell et al. 2000).

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Acknowledgments

Earlier versions of this paper have benefited from comments by participants at the ASSA/ACES meeting in San Diego, January 2–5, 2004, the 12th IAFEP Conference in Halifax, July 8–10, 2004, the 16th EALE conference in Lisbon, September 9–11, 2004, the FPPE Industrial Organization Workshop in Helsinki, December 9–10, 2004, and the EALE/SOLE World Conference in San Francisco, June 2–5, 2005. We are grateful to comments from three anonymous referees as well as suggestions from Kari Hämäläinen, Kevin Hallock, Seppo Ikäheimo, Pekka Ilmakunnas, Uwe Jirjahn, Jeffrey Pliskin and Otto Toivanen. Also we acknowledge Mikael Katajamäki for his outstanding research assistance. Kalmi and Mäkinen gratefully acknowledge funding from the Academy of Finland, the Finnish Work Environment Fund, the Marcus Wallenberg Foundation and the Helsinki School of Economics Research Foundation. In addition, Mäkinen thanks the Yrjö Jahnsson Foundation and the Foundation of Kluuvi for financial support. Support from the Research Institute of the Finnish Economy (ETLA) is gratefully acknowledged. Jones’ work was supported in part by a Foundation for Economic Education Fellowship for which he is grateful.

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Authors and Affiliations

  1. Department of Economics, Hamilton College, Clinton, NY, 13323, USA

    Derek C. Jones

  2. Department of Economics, Helsinki School of Economics and HECER, PO Box 1210, 00101, Helsinki, Finland

    Panu Kalmi & Mikko Mäkinen

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  1. Derek C. Jones
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Correspondence to Mikko Mäkinen.

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Jones, D.C., Kalmi, P. & Mäkinen, M. The productivity effects of stock option schemes: evidence from Finnish panel data. J Prod Anal 33, 67–80 (2010). https://doi.org/10.1007/s11123-009-0146-6

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