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Does culture affect the performance of private equity buyouts?

Abstract

This paper uses GLOBE’s performance orientation (PO) dimension to investigate the relationship between national culture and private equity (PE) performance. We argue that high PO has negative performance implications as it provides the lowest potential for portfolio firms to benefit from a spillover of the distinct PE performance focus, and vice versa. To test this hypothesis, we analyze operating performance metrics of 946 deals from 26 European countries and exit channels of 5093 global deals from 67 countries. Consistent with the “spillover hypothesis”, we find that higher levels of PO are detrimental to efficiency improvements and increase the probability for unsuccessful exits. These findings hold after addressing endogeneity concerns, confounding and measurement error.

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Fig. 1
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Notes

  1. Firms in our sample grow on average 18.9% in low PO and 20.1% in high PO countries pre-PE. These firms have an average pre-PE EBIT margin of 5.1% in low PO countries and of 5.3% in high PO countries. All differences are economically small and statistically insignificant.

  2. Take asset turnover as an example (Table 5, Panel D, third column) and PO = 5.0 and pre-PE growth = 0.0 as the starting point. Our reference point calculates as − 0.339 × 5 − 3.872 × 0 + 0.599 × 0 × 5 = − 1.695. This number is not equal to our linear prediction because we disregard all other variables in this easy example (Fig. 1 considers the impact of all other control variables). It is, however, sufficient as a reference point to understand the impact of changes in PO and pre-PE growth on asset turnover because we keep all other variables constant. We first consider changes in pre-PE growth. For pre-PE growth = 0.2, we calculate − 0.339 × 5 − 3.872 × 0.2 + 0.599 × 0.2 × 5 = − 1.870, i.e. our linear prediction for asset turnover growth has a delta of − 1.870 − (− 1.695) = − 0.175. Higher pre-PE growth is, ceteris paribus, associated with a lower asset turnover growth (in this case 17.5 percentage points lower). Likewise, for pre-PE growth = − 0.2, we calculate a value of − 1.520, i.e. a delta of 0.175. Lower pre-PE growth is thus associated with higher asset turnover growth. We now consider changes in PO. For PO = 6 and pre-PE growth = 0, we calculate a value of − 0.339 × 6 + 0 + 0 = − 2.034, i.e. the delta to our initial reference point is − 2.034 − (− 1.695) = − 0.339. Higher PO is, c. p., associated with lower asset turnover growth. For PO = 6 and pre-PE growth = 0.2, we calculate − 0.339 × 6 − 3.872 × 0.2 + 0.599 × 0.2 × 6 = − 2.090, i.e. the delta to PO = 5 and pre-PE growth = 0.2 is − 2.090 − (− 1.870) = − 0.220. The impact of the increase in PO on asset turnover is less negative if pre-PE growth is large. For PO = 6 and pre-PE growth = − 0.2, we calculate − 0.339 × 6 − 3.872 × (− 0.2) + 0.599 × (− 0.2) × 6 = − 1.978, i.e. the delta to PO = 5 and pre-PE growth = − 0.2 is − 1.978 − (− 1.520) = − 0.458. The impact of the increase in PO on asset turnover is more negative if pre-PE growth is small. To summarize, for an increase from PO = 5 to PO = 6, we yield a delta of − 0.339 for pre-PE growth = 0, i.e. higher PO has a negative impact on performance. We yield a more negative value (− 0.458) for lower pre-PE growth (− 0.2) and a less negative value (− 0.220) for higher pre-PE growth (0.2). We observe these patterns and deltas in Fig. 1, which illustrates the link of Fig. 1 to Table 5.

  3. The following example of Andreß et al. (2013) illustrates the underlying idea of the fixed effects estimation technique. Imagine we want to estimate the effect of education on a person’s income with the simple model \(income_{it} = \beta_{0} + \beta_{1} \times education_{it} + u_{i} + \epsilon_{it}\), whereas u i is something typical for unit i, which is unobservable for us, such as a person’s ability. To control for u i , we could include a dummy variable for each unit of analysis. This approach is called Least Square Dummy Variables (LSDV) regression. However, including a dummy variable for each unit of analysis is not efficient. The alternative, which yields the same results as LSDV estimation, is the so called fixed effects transformation of the data. Instead of controlling for u i , we eliminate it from the regression function. This fixed effects transformation is effectively a time-demeaning of the panel data, i.e. we subtract the unit-specific mean of each variable from each observation. This transformation yields the new model \((income_{it} - \overline{{income_{i} }} ) = (\beta_{0} - \beta_{0} ) + \beta_{1} \times (education_{it} - \overline{{education_{i} }} ) + (u_{i} - u_{i} ) + (\epsilon_{it} - \overline{{\epsilon_{i} }} )\). Note that ui drops out of the model and that our estimates are now solely based on within-unit variation in the data.

  4. The coefficients of multinomial logit estimates can be interpreted as the change in multinomial log-odds. If we take the PO estimate of column (2) for receivership exits and increase PO by one unit, the multinomial log-odds for a receivership exit relative to a trade sale exit are expected to increase by 0.401 units. We can compute relative risk ratios by exponating the multinomial logit coefficient, i.e. eβ. For a one unit PO change, the relative risk ratio of receivership relative to a trade sale is expected to change by e0.401 = 1.493. The effect is even larger when referring to the PO coefficient estimates of model (1). Note that increasing PO by one unit is a big, yet possible, step, considering that we observe PO values between 5.095–6.564.

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Correspondence to Benjamin Hammer.

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We would like to thank two anonymous referees, Hue Hwa Au Yong (discussant), Aurélie Sannajust (discussant), and conference participants at the INFINITI Conference on International Finance 2017 (Valencia) and FMA European Conference 2017 (Lisbon). We further thank Robin Jung and Fabian Sommer who provided valuable research assistance.

Appendix

Appendix

See Tables 14, 15, 16, 17 and Figs. 3, 4.

Table 14 Private equity operating performance and exit channel literature overview
Table 15 Sample distribution benchmarking
Table 16 Correlations
Table 17 Cross-sectional analysis of PE operating performance (detailed)

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Hammer, B., Hinrichs, H. & Schwetzler, B. Does culture affect the performance of private equity buyouts?. J Bus Econ 88, 393–469 (2018). https://doi.org/10.1007/s11573-017-0886-0

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Keywords

  • Private equity
  • Leveraged buyouts
  • Exit channel
  • Operating performance
  • Culture

JEL Classification

  • G24
  • G34