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Corporate liquidity in Italy and its increase in the long recession

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Abstract

We analyse the evolution over time and the determinants of corporate liquidity in Italy for the period 2002–2015 using a very large sample of firms (about 460 thousands per year on average) and including many that are small and unlisted. We document a substantial increase in cash holdings since 2011. This rise is shown to be mainly related to macro factors common to all firms. Among these macro factors, a strong correlation emerges with the lower opportunity cost of holding cash, as measured by the interest rate decline. We also assess the role of cash determinants at the firm level, relating them to different motives for holding cash, such as precautionary reasons, transaction costs, and the effects of information asymmetries in financial markets. Among firm-specific factors, the liquidity rise was initially linked primarily to the fall in investment and then to improved cash flows and enhanced deleveraging.

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Fig. 1

Source: Authors’ calculation based on Cerved Group data. Cash-to-asset ratio is computed as cash and liquid financial assets over total assets

Fig. 2

Source: Authors’ calculation based on Cerved Group data. Cash-to-asset ratio is computed as cash and liquid financial assets over total assets. Persistent firms are those present throughout all the sample years

Fig. 3
Fig. 4

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Notes

  1. It has been discussed at least since Keynes’ examination of in the 1930s.

  2. Sufi (2009) underlines that credit line-based liquidity may involve implicit costs since banks are usually allowed to restrict drawdowns under some circumstances. Since these circumstances tend to be related to declines in firm profitability, the liquidity source risks being dried up when it is needed the most.

  3. The liquidity provided through short term liabilities (e.g.: commercial papers and short term ABS) can suddenly decline and can be severely affected by demand shocks like the exit of typical investors such as money market funds (Chernenko and Sunderam 2014). Moreover, the liquidity that debt securities ‘buy’ as collateral is subject to market price fluctuations and haircuts: it can dramatically decrease the actual liquidity under fire sale dynamics. Acharya et al. (2007) show that cash stocks and debt capacity are not equivalent when there is uncertainty about future cash flows.

  4. A different type of agency cost, underlined by Jensen (1986), deals with the moral hazard problem of entrenched managers who would prefer retaining cash than increasing payouts to shareholders when the firm has poor investment opportunities. Harford et al. (2008) and Dittmar and Mahr-Smith (2007) provide empirical evidence in support of this theory.

  5. R&D expenditure tends to be funded more heavily by own funds since it can be usually pledged less than tangible capital. See Brown and Petersen (2011) for a study focusing on the relationship between cash holdings and R&D expenditure.

  6. Subramaniam et al. (2011) show that diversified firms have lower cash holdings than focused corporates. Ang and Smedema (2011) find some evidence that non financially constrained firms seem to adjust their cash holdings according to the probability of future recessions. Pinkowitz et al. (2016) find that multinational firms tend to hold more cash; this could be partly due to fiscal issues, such as repatriation taxes which foster keeping cash in subsidiaries abroad (Foley et al. 2007).

  7. Ferreira and Vilela (2004) find that firms hold more cash in countries with stronger investor protection schemes; Calcagnini et al. (2009) focus on employment protection rights, finding that in countries where they are stricter, cash holdings tend to be higher.

  8. Morellec et al. (2014) show that firms in more competitive industries tend to display a more accentuated cash hoarding behavior. Almeida et al. (2012) argue that a relatively liquid firm can be in a privileged position to acquire distressed firms in the same industry since they can access part of the target firm’s income that cannot be pledged outside the industry. Erel et al. (2015) study changes in liquidity position of merged firms before and after the acquisition, finding a significant decline in cash holdings and cash-flow sensitivity. Maksimovic et al. (2015) study whether some firms acted as liquidity providers to their suppliers during the crisis, without finding robust evidence.

  9. This is reminiscent of a preference for liquidity à la Hicks, i.e. to keep the hands unlocked as much as possible so as to be ready to act when necessary, without incurring disinvestment costs.

  10. We are able to eliminate this (spurious) effect through panel data techniques (see Sect. 4). While we highlight the link between investments and cash, the relationship between them was (more extensively) studied the other way round (Fazzari et al. 1988; Chirinko and Schaller 1995; Hubbard 1998). Among more recent contributions, Kahle and Stulz (2013) find weak evidence that firms holding more cash invested more. In a recent work based on listed firms in the euro area and in the UK, Mäkinen and Silvestrini (2016) find that cash reserves per se are not a significant determinant of investment, but they affect it positively by reducing the short net debt position.

  11. Other papers focusing on the bank lending channel during the crisis are Ivashina and Scharfstein (2010) and Campello et al. (2012). The latter show that cash and credit lines, though naturally linked through a substitution relationship, also display some degree of complementarity, in so far as greater cash increases the likelihood of having/renewing a credit line. For a survey of the empirical literature on the use of bank credit lines see Demiroglu and James (2011).

  12. By including country dummies, Ferreira and Vilela (2004) find that Italian firms tend to hold more cash in comparison, e.g., to Spain and Portugal. However, the identification of country dummies prevents the inclusion of fixed effects at the firm level to control for time-invariant unobserved firm heterogeneity, which is found to be relevant by Ozkan and Ozkan (2004). See also Sect. 4.

  13. Differently from Calcagnini et al. (2009), we find a negative effect on leverage, which is however found in several other studies (e.g., Ferreira and Vilela 2004; Ozkan and Ozkan 2004; Opler et al. 1999; Bates et al. 2009).

  14. Cash represents overall more than 80% of our measure of liquidity, with a minimum of 77% in 2014 and a maximum of 83% in 2012. Financial securities are basically entirely concentrated into the highest 10% of the firms’ distribution.

  15. Performing a simple regression of the average cash ratio on a time trend and a constant, we find a statistically significant positive trend coefficient of 0.14%.

  16. In the different panels of Fig. 5 we split the sample according to quintiles of a variable of interest (e.g.: size) in each year and plot the evolution of the average cash ratio for each group.

  17. Again, this result is not mechanically driven by simultaneity, as it is when we compute quintiles based on 1-year lagged net working capital.

  18. In 2008 these variables were subject to a monetary re-evaluation which artificially created a jump in the series. In order to curb this disturbance, we replaced the 2008 data with the average of the changes in tangible and intangible assets in the year before and the year after. In all the regressions we include a variable resulting from the interaction of investment and year 2008 to control for this issue.

  19. For each year we classify a firm as being in: (1) the no-investment class if the ratio between the annual change in the value of tangible and intangible assets and total assets is negative; (2) the low-investment class if the ratio is below the median of firms with a strictly positive investment; (3) the high investment class if the ratio is above the median of firms with a strictly positive investment.

  20. We consider three years instead of ten as in Bates et al. (2009) to prevent the loss of many observations and to allow for greater cyclical variability. Unlike the baseline model used by Bates et al. (2009), but also as done in Opler et al. (1999), we measure volatility at the firm level instead of at the industry level, since what ultimately characterizes the idiosyncratic uncertainty is the firm’s own cash-flow volatility.

  21. The descriptive evidence (not reported but available from the authors upon request) suggests that the latter channel seems to fit better for Italian firms.

  22. There are also other two variables commonly considered in this stream of literature that we cannot account for: (1) the market-to-book assets ratio, which is actually not defined for most of the firms in the dataset since they are not listed; (2) the acquisition to assets ratio, which in our context should likely play a minor role, if any.

  23. In Sect. 4.2 the vector X is augmented with other variables available in the dataset.

  24. Not reported, we also add the interaction between Inv and a year dummy for 2003 and 2008 to further control for level shifts in the dataset in those years.

  25. Leverage is also winsorized at the 99th percentile when values are either higher or negative. Since leverage is computed as the share of financial debts over financial debts and net equity, firms having a negative net equity have a leverage higher than 1. While this is still meaningful as a measure of high indebtedness, when negative net equity is close to the value of financial debts, leverage rises to unrealistically high levels. When net equity is even larger (in absolute terms) than financial debts, leverage becomes negative: if no correction is applied, these firms would be wrongly analysed as having less leverage than firms with zero financial debts; for the same reason it would also be wrong to normalize negative values of leverage to zero: they represent a totally different situation with respect to no indebtedness. Therefore we winsorize negative values of leverage to the 99th per cent level of non-negative values in each year.

  26. The FMB estimation is performed through the Stata routine developed by Hoechle (2011).

  27. Moreover, in order to check whether a random effects model could be used, we also perform a robust Hausman test based on Mundlak (1978) as suggested in Wooldridge (2010). The null hypothesis for coefficients on \( \bar{X}_{i} \) jointly equal to zero is strongly rejected, thus suggesting that we keep the FE estimator.

  28. The fall in the magnitude of the volatility coefficient when we move from the OLS or the FMB to the FE estimates might suggest that there are actually some time-invariant firm-specific unobserved factors positively correlated with volatility that tend to inflate its coefficient in columns (1) and (2).

  29. See for example Carruth et al. (2000) and Bloom et al. (2007). In particular, for the Italian context see Guiso and Parigi (1999) and Busetti et al. (2016).

  30. Another possibility is the agency motive highlighted by Jensen (1986): entrenched managers would rather retain cash than increase payouts to shareholders when investment opportunities are low.

  31. It has to be taken into account that in our specification Liquid is a stock measured at the end of time t, while the Intang variable is the change in intangible assets between t − 1 and t and (over sales at t − 1), thus being already occurred when the dependent variable is measured.

  32. The partial effect is computed by taking the derivative with respect to Inv: βInv + 2 βInv_sq Inv.

  33. The OLS regression for model (6), which by construction estimates θ with an upward bias, returns \( \hat{\theta } \) = 0.720. Even in this case, the hypothesis of unit root is largely rejected.

  34. The classification is based on the amount of sales per year: firms are classified as small if sales are below €10 million.

  35. Following Arellano and Bover (1995) and Hayakawa (2009), we do orthogonal deviations instead of first differencing, taking into account that the panel is unbalanced.

  36. Details are provided in Table 4. Time dummies are removed because of multicollinearity, given that they are unit-invariant just like the macro factors.

  37. The main changes involve the attenuation in the size effect and the intensification of the volatility effect.

  38. A priori the effect of the bank lending yield could also be negative if firms trade their internal liquidity for external financing from banks for any given amount of resources, i.e.: when borrowing from banks grows more costly, more internal liquidity is used. Whether this negative relationship or the positive one descried in the text dominates is an empirical matter: we find that the positive one slightly prevails but the small magnitude of the net effect suggests that the two channels might almost compensate for each other.

  39. We consider model 2 that includes all covariates at the firm level, but results are basically confirmed under the baseline model with fewer firm variables.

  40. In a fully balanced panel this effect would be zero.

  41. The change in the fixed effect is greater in 2015 partly because the sample size is smaller in the last year of the dataset because not all the data are readily available.

  42. We obtain this linear combination by using as weights the coefficients of a regression of the time dummy coefficient on the four macro variables and a constant, plus a residual term. The coefficients (standard errors) are: GDP growth 0.070 (0.023), T-bill − 0.749 (0.705), market volatility − 0.013 (3.282), and bank lending 0.309 (0.512).

  43. For the sake of space, the decomposition of the implied effects for the rise in cash-ratio is not reported, but it is available from the authors upon request.

  44. Firm turnover has also played a role: new entrant firms initially have a higher liquidity level than outgoing firms for reasons related to firm life cycle (firms exiting the market may experience liquidity shortages in the last part of their life cycle, while newborn firms may initially have liquidity to undertake the necessary investments for future years).

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Acknowledgements

The authors would like to thank two anonymous referees, the participants at the AISRe conference and the Mo.Fi.R. seminar held in Ancona on 2016, the participants at the “International Conference on Small Businesses, Banks, Innovation and Growth” held in Urbino in 12–13 October 2017, Paolo Sestito, Antonio De Socio, Tiziano Ropele (Bank of Italy), Claudia Pigini and Elizabeth J. Casabianca (Polytechnic University of Marche), for helpful suggestions. Research assistance by Massimo Marcozzi is gratefully acknowledged. All remaining errors are ours.

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  1. Local Economic Research and Analysis Division, Bank of Italy, Ancona Regional Branch, Piazza Kennedy 9, 60122, Ancona, Italy

    Davide Dottori & Giacinto Micucci

  2. Mo.Fi.R. (Money and Finance Research Group), Ancona, Italy

    Davide Dottori

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  1. Davide Dottori
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Correspondence to Davide Dottori.

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Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10, 11 and Figs. 5, 6.

Table 4 Dataset description
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Table 5 Aggregate, average and median cash and leverage (percent)
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Table 6 Cash ratio by firm size (percent)
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Table 7 Summary of descriptive statistics (1)
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Table 8 Robustness checks
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Table 9 Dynamic models on the medium and big firms subsample
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Table 10 Decomposition of the rise in cash ratio from 2011 to 2015
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Table 11 Allowing for slope changes since 2011 (1)
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Fig. 5
figure 5

Source: Authors’ computations on Cerved Group data. Cash ratio is computed as cash and liquid financial assets over total assets.

Average cash ratio by several firm features (percent).

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Fig. 6
figure 6

Source: OLS and FE estimation described in Table 1. (1) The year 2002 is used as reference

Year dummy coefficients (1) (point estimate).

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Dottori, D., Micucci, G. Corporate liquidity in Italy and its increase in the long recession. Econ Polit 35, 981–1014 (2018). https://doi.org/10.1007/s40888-018-0117-3

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