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ABSTRACT
This paper disentangles the effects of uncertainty in explaining the heterogeneity of firms’ investments. In particular, following Bloom [2007. “Uncertainty and the Dynamics of R&D.” American Economic Review 97 (2): 250–255], we test the role of uncertainty and liquidity constraints extending the model to include R&D, non-R&D intangibles, as well as physical capital. The analysis is performed on a large data set of Italian firms, covering both manufacturing and services sectors, as well as large and small firms. We show that non-convex adjustment costs affect different capital inputs in different ways, depending on their degree of firm-specificity. The results confirm the Bloom model: flow adjustment costs explain investment in R&D and, to a lesser extent, in non-R&D intangibles. However, it struggles to explain tangible investment plans because of the ambiguous effect of the stock adjustment costs.
Acknowledgements
I would like to thank the colleagues cited in funding (below) not only for their comments and encouragement, but also for allowing me to use our procedures and data. A preliminary version of this paper was presented at the 4th European Conference on Corporate R&D and Innovation: ‘Financing R&D and innovation for corporate growth in the EU: Strategies, drivers and barriers’ (CONCORDi-2013), European Commission, JRC-IPTS, Seville, Spain. My thanks also go to Nick Bloom, Bronwyn Hall and Werner Hölzl, to four anonymous reviewers, and to conference participants for their comments. The usual caveats apply. Elisa and Ernesto, thank you for being so marvellous.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. The physical capital model of Bloom, Bond, and Van Reenen (Citation2007, 399) includes an error correction term with a supposed positive parameter, so that firms with capital stock below their target level will eventually adjust upwards. The target capital stock is the stock a firm would have chosen as a function of real sales and the user cost of capital. In other terms, in the long run, it is the evolution of demand that drives investment, and not uncertainty and the gap between thresholds. Regardless of real options and irreversibility, uncertainty can only depress the expected long-term investment through its effect on the growth rate of demand (Bloom Citation2000).
2. Actually, the coefficient of the lagged-dependent variable times uncertainty for physical capital is not obvious, as emerged in a private conversation with Nick Bloom: the gap between actual and desired investment ratios depends on lagged-uncertainty (how reluctant firms are to make adjustment vis-à-vis their recent past), while companies suffer from current uncertainty; these two effects will be related, but the sign is not clear. In other words, the response to uncertainty can be spread out over time, imparting complex and persistent dynamics; investment could respond to both current and past demand shocks, so that firms with a recent history of positive demand shocks will be closer to their investment threshold and will be more inclined to invest.
3. Individual effects measure firm-specific irreversibility that can amplify the response of investment to uncertainty for a given capital good. Time effects account for panel cross-sectional correlation coming from individuals’ reactions to macroeconomic events, neighbourhood/industry effects, herd behaviour and social norms.
4. The disagreement over expected growth rates of individuals by groups, , is estimated by the standard deviation of
within groups of individuals.
5. Given preliminary results available upon request, the outcomes of the tests for partial optimality with our data always reject the null of investment plans optimality, regardless of the alternative specification of the Mincer–Zarnowitz regression (proposed since the seminal work of Mincer and Zarnowitz Citation1969). The discrepancy between plans and their actual realizations after one year is predictable at the time plans are made, and this suggests certain factors (such as bounded rationality or information asymmetries) that could negatively affect the planner's thinking. Consequently, investment plans and implementation do not bear the same information.
6. Note that the subjective uncertainty , being firm- and time-specific, condenses the individual and time-varying expectations of managers about future demand, and thus embodies those favourable features advocated in Manski (Citation2004). Moreover,
is close to the definition given by Bloom, Bond, and Van Reenen (Citation2007) of ‘uncertainty about demand and productivity conditions’. In line with the findings of Lahiri and Sheng (Citation2010), subjective uncertainty
and disagreement
markedly differ: our preliminary results – available upon request – from the comparison of these two uncertainty indicators call for a great care in interpreting the outcomes of disagreement measures of variability, as they are prone to be extremely poor proxies, especially in the presence of an unstable economic environment (a case that, unfortunately, is often the rule rather than the exception).
7. Overall, the share of missing data in SIM covered by CADS information on financial variables is about 47%, while the share covered by NA information on deflators is about 29%.
8. Besides the full sample analysis presented in this section, Appendix 3 – based on SIM surveys for 2010 and 2011 – also reports additional information on certain firms’ characteristics and their R&D spending, and on the sources of R&D financing which are useful during the empirical modelling phase.
9. Given that investment inaction followed by periods of intensive adjustment of capital stock may indicate a central role of non-convex (irreversibility) and of fixed adjustment costs that lead to lumpy investment (see, e.g. Doms and Dunne Citation1998; Barnett and Sakellaris Citation1998; Cooper, Haltiwanger, and Power Citation1999; Cooper and Haltiwanger Citation2006), Appendix 4 analyses the features of volatility, persistence and co-movements over time of investments at both macro- and micro-economic levels.
10. Note also that the shares of observations with zero investments in tangibles (i.e. machinery and buildings) are quite close to those reported in Bloom, Bond, and Van Reenen (Citation2007) for the UK.
11. The source of USA estimates is in Bloom (Citation2007, note 5, p. 252).
12. For an extended analysis of the cyclical features of investments in Italy, see Appendix 4.
13. In fact, the initial conditions regressor , being made of actual investments and actual sales, differs from the genuine lagged-dependent variable of expectations and plans-scaled models
and
, respectively. The variable
represents a ‘true’ lagged-dependent variable in the accounting-data model only, and as such must be instrumented.
14. Note that with our rather large sample sizes, the use of one-step GMM estimators or Windmeijer's finite-sample corrections to the asymptotic variance of the GMM estimators would deliver qualitatively similar results.
15. Note also that, being in the 20-35% range, these p-values are not too high to evoke a lack of power induced by too many over-identification restrictions; on this point see e.g. Bontempi and Mammi (Citation2015).
16. Define, in the plans-scaled model, the change in R&D spending Ir
over the lagged level of sales Y due to a change in the sales growth rate g as . The corresponding
parameter in expectations and accounting-data models is related to
by means of the following formula:
. Therefore, given a positive estimate of
, the precondition for a positive estimate of
as well is that:
.
17. Note that qualitatively similar results are obtained if we measure uncertainty with disagreement rather than with the subjective min–max range.
18. In order to investigate this point further, we estimated the expectations model using both subjective uncertainty and – alternatively – disagreement in two sub-samples (before and after 2008) characterized by different degrees of stability in the economic environment. Results (available upon request) are quite clear cut: while the estimation results of the model using subjective uncertainty show a remarkable stability across the sub-samples, those deriving from the model using disagreement show profound parameter breaks in the estimates of delay and caution effects β4 and β5. The cyclical instability of the latter model emphasizes the problems occurring when the economic environment is unstable.
19. What is discussed here also holds true qualitatively for the unreported OLS estimates of the plans-scaled and the accounting-data models.
20. In particular, for non-R&D intangibles we have the extreme case in which the delay effect only comes about as a result of the interaction with uncertainty, as the estimate of β1 is not significant while that of β4 is both significant and very large.
21. The SIM sample is stratified by firm size (number of employees), branch of activity and regional location. Thanks to the country-wide coverage of Bank of Italy's branches and their continuous interaction with the local productive and financial system, the SIM achieves high response rates, ranging from 70% to 80%. Each time a survey was run, no respondents are replaced by other firm in the same branch and size class. Estimates of the distribution of investments by branch of activity deducted from SIM are similar to those obtained from official sources, such as the NA and ISTAT's Survey of Enterprises (see Bank of Italy Citation2005). Updated and very detailed descriptions on SIM sample design, response behaviour, data quality checks in each year are available at the Bank of Italy web site: http://www.bancaditalia.it/statistiche/indcamp/indimpser/boll_stat.
22. High-technology industries are Aerospace, Computer, Electronics, Pharmaceutical; Medium-High technology industries are Scientific Instruments, Motor vehicles, Electric machinery, Chemicals, Other transport equipment, Non-electric machinery; Medium-Low technology industries are Rubber and plastic products, Shipbuilding, Manufacturing n.e.c., Non-ferrous metal, Non-metallic mineral products, Basic metals and Fabricated metal products, Coke and refined petroleum products; Low technology industries are Pulp and paper, Textile-clothing, Food, beverages and tobacco, Wood.
23. According to the European Commission (6 May 2003) ‘Recommendation 2003/361/EC: SME Definition’ the category of SMEs is made up of enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding 50 million euro, and/or an annual balance sheet total not exceeding 43 million euro.
24. The acquisition includes (a) preventive and proactive maintenance and the share of the corrective maintenance, invoiced by the suppliers, that could be capitalized by law and (b) production and repair of own capital goods made by the firm and capitalized.
25. The expected inflation is available for total tangible investment and for software expenses. For buildings and machinery (software and R&D) is estimated by
, where
is the total investment price inflation on goods f and m (nr and r), and
is the j-type investment price inflation rate for j = f, m, nr, r.
26. For an international comparison of accounting principles on intangibles, see Stolowy and Jeny-Cazavan (Citation2001).
27. An intangible asset should be recognized at cost if and only if it is identifiable, it is probable that specifically attributable economic benefits will flow from the assets, and its cost can be measured reliably.
28. On average, expensed R&D and patent-fees represent a mere 7% of good r. Operative and recurrent advertising is excluded from good r.
29. Actually, individual sales’ deflators are obtained by applying the SIM growth rate for year t to the previous-year NA deflator level of the sector to which the firm belongs. We use NA sector deflator levels when SIM growth rates are not available.
30. This is available in SIM data set only for two years (1993 and 2005). In the 2005 survey, the question about the min–max range was substituted for by a more complex one on the firms’ subjective probability distribution. For this, the min–max range data for 2005 are obtained as in Bianco et al. (Citation2013) See Guiso and Parigi (Citation1999) for the use of uncertainty based on the subjective probability distribution of respondents in 1993.
31. The same picture with industries ordered according to the percentage of sales over advertising shows shipbuilding, motor vehicles, rubber and plastic, petroleum, aerospace at the higher positions. Pharmaceutical, ADV/R&D/Com, scientific instruments display low percentages like the low-technology industries (food, paper, textile and wood).
32. In the 2010 and 2011 surveys, there was a question on whether the R&D spending would have been of the same or more or less amount without receiving public funding. The main part of the companies declared the same or a higher amount; from 2010 to 2011 the percentage of firms declaring a lower amount increased by 6 percentage points.
33. The firms which more frequently used public finance did not apply for new loans from banks or other financial intermediaries because they were convinced that their application would be rejected.
34. Some further descriptive analysis (not reported but available upon request) shows that a high percentage of firms collaborating with Italian universities was asked by the creditors for early repayment of loans granted in the past.
35. Details about the techniques we used and the full set of results are available upon request.
36. It is worth noting that such micro variability of intangibles is not merely due to the presence of many cases of individual zero expenditure in R&D, as the R&D variability would have further increased (and not decreased) if we computed it by excluding all these zeroes. This outcome suggests that the presence of spikes explains the large R&D micro variability, and that these spikes do not only occur when the expenditure was zero in the previous year. Conversely, the exclusion of zero R&D observations drops invariant observations that, if left in the sample, would deflate the estimate of variance.
37. Oppositely to what happens for variability, the exclusion from the sample of the observations with zero R&D increases persistence. The presence of zeroes negatively contributes to the estimate of the micro R&D persistence, as the negative contribution to covariance estimates (due to positive spikes occurring when the expenditure in t−1 was zero, i.e. below the average) is larger than the negative contribution to variance estimates (due to a number of zero expenditures).