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The Role of Innovation and Management Practices in Determining Firm Productivity

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Abstract

In this paper, we compare the impacts of management practices and innovation on productivity, using data from a unique firm-level survey covering 30 countries in Eastern Europe and Central Asia in the period 2011–2014. We estimate a structural model linking productivity to innovation activities and management practices. Results suggest that both returns to innovation and returns to management practices are important drivers of productivity. However, productivity in lower-income economies is affected to a larger extent by management practices than by innovation, while the opposite holds in higher-income economies. These results imply that firms operating in less favourable business environments can reap large productivity gains by improving the quality of management practices, before engaging in innovation through imitating and adapting foreign technologies.

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Fig. 1

Source: Authors’ representation of the model

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Notes

  1. Unless stated otherwise, the analysis includes the following countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FYR Macedonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan.

  2. Arundel et al. (2013) do a similar exercise for Australia only.

  3. BEEPS covers topics such as infrastructure, competition, sales and supplies, labour, innovation, land and permits, crime, finance, employment and business–government relations. It uses stratified random sampling to select eligible firms to participate in the survey. Strata are defined by sector (typically manufacturing, retail and other services), size (5–19, 20–99 and 100 + employees) and regions within a country. See http://ebrd-beeps.com for further details.

  4. Russia was the first country in which BEEPS V was implemented. The number of firms with at least 50 employees was not as high as expected, so the threshold was lowered to 20 employees in subsequent countries. The findings are robust to excluding Russia from the sample (see “Changes in the Sample” section).

  5. As indicated in Table 1, data availability varies. We provide information on the differences between firms that report sales and capital per employee and those that do not in Online Appendix OD.

  6. As shown in “Changes in the Sample” section, our results are robust to excluding one country at a time.

  7. The cleaning process is described in more detail in Online Appendix OA.

  8. Online Appendix OB provides more details on the questions and the ratings. The questions on management practices came at the end of a long face-to-face interview. This resulted in an unusually large number of people responding “don’t know” or refusing to answer. Observations with a response rate excluding “don’t know” or refusal below 62.5 per cent prior to recoding were excluded.

  9. We follow an established way of calculating index numbers—see Bresnahan et al. (2002).

  10. The correlation coefficient of the quality of management practices and cleaned product innovation is 0.164 (self-reported 0.175), with cleaned process innovation it is 0.161 (self-reported 0.167), and with marketing innovation it is 0.149 in the full sample, all statistically significant at p = 0.000.

  11. Note that the way the questionnaire is set up, innovation occurs within the three-year period preceding the survey, while the productivity and management quality data refer to the last complete fiscal year, which is typically the last year of the 3-year period that innovation variables refer to.

  12. X2, \(X_{3}^{1}\) and \(X_{3}^{2}\) also contain indicators for “don’t know” values of the number of years of manager’s sector experience, having an internationally recognised certification and percentage of employees with a completed university degree, respectively.

  13. We found none of the variables in X4 to be statistically significant if also included in (1) or (3).

  14. It should be noted that the nature of our data prevents us from improving our identification strategy further and to claim that the relationships we find are causal.

  15. The results are robust to using the same number of observations in all three stages. We have favoured using the maximum possible number of observations in each equation to increase the efficiency of the estimation.

  16. Tables 3 and 4 show that as we include more exogenous variables to identify the endogenous variables in our model, the sample size decreases slightly from 2842 observations for the estimation of R&D and management practices to 2139 observations for the estimation of the productivity equation. This is primarily due to the unavailability of data on sales and employment.

  17. Table OC.1 in Online Appendix reports the coefficient estimates of the structural equations.

  18. See Table OC.2 in Online Appendix.

  19. When estimating the innovation equations without including R&D, the total marginal effect of management practices was even higher than in Table 3. Part of the marginal effect of managerial practices is now carried by R&D but its estimate is not sufficiently precise to be significantly different from zero.

  20. Since the dependent variable is the natural logarithm of labour productivity, the discrete impact of binary variables is computed as \({\text{e}}^{\text{marginal effect}} - 1\).

  21. This estimation does not correct for the endogeneity of capacity utilisation and capital intensity in labour productivity (see, for example, Olley and Pakes 1996).

  22. Because there are only few high-(low-) income economies in our sample, we group them with upper- (lower-) middle-income economies. We decided to use 2007 as a cut-off for two reasons: (1) innovation variables refer to the period of three years before the interview took place, which in the case of Russia means 2008–2011, and (2) existing evidence suggests that management practices evolve slowly over time due to informational barriers (see, for example, Bloom and Van Reenen 2010; Bloom et al. 2013; Acemoğlu et al. 2007), so the assumption that the management practices as reported at the time of the interview are similar to those the firms had in 2007 is acceptable (although not perfect). The results are broadly robust to using GNI per capita in more recent years instead.

  23. As expected, the average percentages of firms with above median quality of management practices as well as firms that have introduced a technological innovation in the last 3 years are higher in higher-income countries than in lower-income countries: 53.4 versus 42.8 per cent for management practices and 32.3 versus 22.5 per cent for technological innovation. There is sufficient variation: the standard deviation of management practices is virtually the same in both groups (0.50) and the standard deviations for technological innovation also do not differ much: 0.47 versus 0.42.

  24. We refrain from reporting the control variables and focus on the marginal effects of interest, namely innovation, R&D and management practices. The complete results are available on request.

  25. See Table OC.3 in Online Appendix OC for the estimated differences in the means of latent variables in the subsamples used in this section.

  26. We alternatively include labour productivity three fiscal years ago in order to correct the productivity figures from a firm specific time-invariant effect. The inclusion also results in a reduced sample size, which is still slightly bigger than when including capital per worker. However, the impact on the coefficients of interest is remarkable. Neither innovation nor management practices are significantly associated with labour productivity any longer. Lagged labour productivity is statistically and economically significant; a one per cent increase in lagged labour productivity is associated with a 0.7 per cent increase in labour productivity in the last fiscal year. (Persistence in productivity is a stylised fact as reported by Syverson 2011.) As reported earlier, management practices are rather stable over time, and if innovation is also persistent, management quality and innovation effects are captured by the lagged labour productivity.

  27. The results are available on request.

  28. See McKenzie and Woodruff (2014) for a review of evaluations of business training programmes in developing countries.

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Acknowledgements

This paper builds on the authors’ work on the 2014 EBRD Transition Report. We thank an anonymous referee for offering very constructive suggestions. We would like to thank Erik Berglöf, Werner Bönte, Ralph de Haas, Sergei Guriev, Jacques Mairesse, Alexander Plekhanov, Alessandro Sterlacchini, John Van Reenen and Adalbert Winkler for helpful discussions as well as the participants at the 8th MEIDE conference, the 6th Asia–Pacific Innovation conference, UCL SSEES “Achievements and Challenges for the Emerging Economies of Central Europe” Economics and Business Conference, and seminars at the EBRD, KOFF, OECD, SSB Norway, Université de Lille, Universidad de Navarra, Bergische Universität Wuppertal and University of Piacenza for their comments and suggestions. The views expressed in this paper are our own and do not necessarily represent those of the institutions of affiliation.

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Authors and Affiliations

  1. OECD, Paris, France

    Wiebke Bartz-Zuccala

  2. UNU-MERIT and Maastricht University, Maastricht, Netherlands

    Pierre Mohnen

  3. European Bank for Reconstruction and Development, London, UK

    Helena Schweiger

Authors
  1. Wiebke Bartz-Zuccala
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  2. Pierre Mohnen
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  3. Helena Schweiger
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Correspondence to Helena Schweiger.

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Bartz-Zuccala, W., Mohnen, P. & Schweiger, H. The Role of Innovation and Management Practices in Determining Firm Productivity. Comp Econ Stud 60, 502–530 (2018). https://doi.org/10.1057/s41294-018-0075-3

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