Abstract
We combine survey and administrative data for about 13,000 New Zealand firms from 2005 to 2013 to study intangible investment and firm performance. We find that firm size and moderate competition is associated with higher intangible investment, while firm age is associated with lower intangible investment. Examining firm performance, we find that higher investment is associated with higher labour and capital input, higher revenue, and higher firm-reported employee and customer satisfaction, but not with higher productivity or profitability. The evidence suggests that intangible investment is associated with growth and ‘soft’ performance objectives, but not with productivity or profitability.
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Notes
By analogy, the building fires to which the most fire engines are sent are also the ones in which the largest amount of property damage occurs. It is likely that, holding constant the initial intensity of the fire, sending more engines reduces the amount of damage. But that relationship is obscured by the ‘reverse causality’ running from fire damage to number of engines.
The batch of questions also asks about acquiring of machinery and equipment; acquiring of other knowledge (e.g., licenses, patents, or other intellectual property); and marketing the introduction of new goods or services. We exclude the first as it is a measure of tangible investment, and exclude the latter two as firms may see them as innovation-output indicators, rather than measures of intangible investment.
In 2005 the question only asks whether the activities were done to support innovation, meaning there is a systematic difference in our intangible measures between 2005 and the other years. Including year fixed effects in our later regression analysis helps to deal with this issue.
This question comes from the main ‘business operations’ module, and so asks whether R&D occurred in the previous year rather than in the previous 2 years. The question does not ask whether it is done to support innovation, though presumably fostering innovation is an inherent goal of R&D.
We assume the information in these answers is too messy and better dropped. This sets 12% of index values to be missing, though the majority (72%) of these changes come from the 2005 BOS, where non-innovating firms were steered away from the question on intangible investments.
In practice we only use the primary principal component, but present details on the second component for completion. In addition we use tetrachoric correlations between the underlying indicators, which estimate the correlation between two indicator variables, assuming that some normally-distributed latent variable underlies them.
This question was not asked in 2005; our expenditure measures are missing for this year.
The question is slightly rephrased for clarity, but the substance and key words are unchanged.
We also use the alternate firm identifiers developed in Fabling (2011) to fix broken firm identifiers.
The large ratio values of three and above in panel B are driven by firms with very low average index values, which blow up the proportion when used as the denominator.
Appendix Table 5 presents summary statistics of variables appearing in any of the regression tables in this paper.
Average marginal effects are very similar when estimating fractional logit models in columns (1)–(2) and logit models in columns (4)–(6). We show OLS results because of the ease of interpretation and because the estimator is tractable enough to include industry-year interactions.
Equation (2) with MFP as the dependent variable is closely related to a model where the stock of intangible assets is added as a factor of production in the production function (Griliches, 1979). We adopt the approach of first constructing MFP as a residual from the production function, and then regressing this residual on the intangible assets because we have a much larger sample of firms with production data than those for which we have the intangibles data. Thus the other parameters of the production function (e.g. capital and labour elasticities) can be estimated very precisely on this large sample, whereas if we estimated the production function only on the smaller intangibles-data sample the production function would be much less well estimated.
We also ran output-weighted regressions to estimate the association for the average unit of output, rather than the average firm. The results do not change qualitatively.
We also ran regressions where the dependent variable is an indicator for a larger than one and a larger than 15 percentage point increase in MFP. Results are similar, with positive but economically small estimates. Average marginal effects from the logit estimator are also similar.
Note that the very youngest firms (< 2years) cannot be included in this regression because we are looking at productivity as a function of intangible investment 2 years previous.
OLS estimates in columns (5) and (6) are similar to the average marginal effects from logit estimates. We exclude firms with negative or zero profitability in these regressions, both in Table 6 and Table 7, because we use the log transformation in columns (1) and (3). We similarly exclude firms with negative or zero profit in Appendix Table 7, and firms with negative or zero labour productivity in Table 6 and 7 when modelling labour productivity.
In conditional quantile regressions we cluster standard errors at the firm level using the package that was created by Machado et al. (2015).
We drop industry-specific year effects for empirical tractability, but leave in both year and industry fixed effects.
In addition to the models that we report herein, we also explored whether any individual forms of intangible investment or categories of such investment as used by Corrado et al. (2012) have positive associations with productivity. We found none.
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Acknowledgements
This research is partially funded by the Productivity Hub under the Productivity Partnership programme, and by Queensland University of Technology. We would like to thank Lawrence J. White and an anonymous referee for valuable feedback. We also thank participants at an internal Motu seminar, as well as participants at a Productivity Commission of New Zealand workshop for helpful comments.
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Disclaimer: The results in this paper are not official statistics, they have been created for research purposes from the Integrated Data Infrastructure (IDI) managed by Statistics New Zealand. The opinions, findings, recommendations and conclusions expressed in this paper are those of the authors, not Statistics New Zealand or Motu Economy and Public Policy Research.
Access to the anonymised data used in this study was provided by Statistics New Zealand in accordance with security and confidentiality provisions of the Statistics Act 1975. Only people authorised by the Statistics Act 1975 are allowed to see data about a particular person, household, business, or organisation; and the results in this paper have been confidentialised to protect these groups from identification. Careful consideration has been given to the privacy, security, and confidentiality issues that are associated with using administrative and survey data in the IDI. Further detail can be found in the privacy impact assessment for the IDI available from www.stats.govt.nz.
The results are based in part on tax data supplied by Inland Revenue to Statistics New Zealand under the Tax Administration Act 1994. This tax data must be used only for statistical purposes, and no individual information may be published or disclosed in any other form, or provided to Inland Revenue for administrative or regulatory purposes. Any person who has had access to the unit-record data has certified that they have been shown, have read, and have understood Sect. 81 of the Tax Administration Act 1994, which relates to secrecy. Any discussion of data limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data’s ability to support Inland Revenue’s core operational requirements.
Appendix
Appendix
See Fig. 5, Tables 11, 12, 13, 14, 15, 16, 17 and 18.
Mean and spread of intangibles principal component, by industry. Notes: Appendix Fig. 5 presents, as dots, the mean intangibles principal component for all firm-years by industry over the period 2005–2013. The bands show all values that fall within one standard deviation of the mean for each industry. Full industry descriptions are given in Appendix Table 13
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Chappell, N., Jaffe, A. Intangible Investment and Firm Performance. Rev Ind Organ 52, 509–559 (2018). https://doi.org/10.1007/s11151-018-9629-9
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DOI: https://doi.org/10.1007/s11151-018-9629-9