Differential Effect across Investment Types

Table2.B.3presents our main results from estimating regression equation2.2.7. The dependent variable is the log of investment of firmiin yeartin investment category c, where investment categories include the six investment types specified above: ad- vertising, IT, R&D, vehicles, machinery, and furniture & office equipment. The main regressor is an interaction term of the inverse of the depreciation rate of an invest- ment category as a measure of the time-to-payoff of an investment type as given by Table2.B.1and a time dummy variable that indicates the financial crisis (=1 in and after 2008). Standard errors are clustered at the firm level, allowing for autocorrelation across time and across investment categories within the firm.7 _{Column (1) implements}

the regression equation with just category and firm fixed effects. The coefficient on the interaction term is negative, implying that investments with a longer time-to-payoff fell more after the financial crisis than investments with a shorter time-to-payoff. The coefficient on the crisis dummy is also significant and strong, showing that most short

7_{Bertrand et al.(2004) point out that serially correlated outcomes in differences-in-differences estima-}

tions produce serially correlated residuals, and standard errors need to be adjusted accordingly. They recommend clustering errors at the group level, if the number of groups is large enough (e.g. 50). For our regression this would mean clustering at the investment category level, unfortunately our number of categories is too small for clustering (6 categories). We decided to correct for autocorrelation of the residuals by clustering at the firm level instead, which allows for arbitrary serial correlation over time as well as across investment categories within a firm.

run investment fell by 17 percentage points after the crisis. In column (2) we replace the crisis dummy with year fixed effects, which doesn’t change the coefficient on the interaction term.

It is possible that the demand shock rather than the credit squeeze drives our result. In column (3) we control for firm times year fixed effects. If demand shocks don’t have a differential effect across investment types, we manage to control for them in column (3). The magnitude of the effect increases somewhat and stays significant after introducing the firm year fixed effects. Note that in contrast to other papers on the effect of credit squeezes on investment this is likely to be a lower bound of the true estimate, because if demand shocks affect investment types differentially, they are likely to affect investments with a shorter time-to-payoff by more than investments with a longer time-to-payoff (the recession will, after all, finish at some point in the future). It is common that investment observations are often 0 and thus excluded from the analysis (in logs). Column (4) codes the 0’s as 1 euro and thus includes all those observations. The results are substantially stronger, suggesting our baseline analysis is very conservative.

Table2.B.4uses alternative measures for time-to-payoff instead of the inverse of the depreciation rate. For example, column (2) uses the rank of investment types according to depreciation rates, using the highest rank for investment with the longest time-to- payoff. Since the estimated depreciation rates in the literature vary within investment types and sometimes overlap, we regroup the investment categories in columns (2) to (5) of the table. For example, the depreciation rates of R&D and IT are not that different in the literature, so we group them together. Also machinery and furniture have quite similar depreciation rates, justifying a similar treatment. However, our finding is robust across all these alternative measures for time-to-payoff. The magnitudes of the effect differ, but this is because the different measures use different units. In column (6) we check whether our result is sensitive to the non-linearity implicit in measuring time-to-payoff as the inverse of the depreciation rate, and use the depreciation rate instead. The sign reverses, as a higher depreciation rate now meanslesstime to payoff, but the result remains again robust to this specification.

How can we interpret the economic significance of our effect? Our preferred specification in column (3) in Table2.B.3tells us that investment falls by 2 percentage points for a unit increase in the inverse depreciation rate. When we compare advertising, the investment category with the highest depreciation rate, to furniture and office equipment, the category with the lowest depreciation rate, the inverse depreciation rate increases by 8.3, so we need to multiply the regression coefficient by this value. This leads to our main result: Investment in office equipment gets reduced by 17 percentage points more than advertising expenditure. This is quite a sizable difference across investment categories.

Our theory suggests another way to interpret this result: as a tax on capital. Recall that

β1

lnp1τt 1q p1αq

Given that the investment gap between capital with the shortest and longest time-to- payoff (β1in the theory) is 17% and usingα1{3 (the capital share), this means that

the credit crunch is equivalent to an 11% tax on the long run investments relative to the shortest run one (τt 11expp0.172{3q 10.6%q.