RDP 2018-05: Do Interest Rates Affect Business Investment? Evidence from Australian Company-level Data 1. Introduction

‘… it is well known that to get the user cost to appear at all in the investment equation, one has to display more than the usual amount of econometric ingenuity, resorting most of the time to choosing a specification that simply forces the effect to be there.’ (Blanchard 1986, p 153)

Modern macroeconomic textbooks typically suggest that there is an inverse relationship between interest rates and business investment (e.g. Mankiw 2007; Blanchard 2017). In the textbook description, this inverse relationship is essential to understanding how changes in monetary policy affect the economy.

Despite its theoretical importance, as the quote above suggests, empirical evidence for this inverse relationship is difficult to establish. In surveys, businesses themselves argue that their investment decisions are not affected by changes in the cost of borrowing. This is because these businesses have very high and sticky hurdle rates of return (Sharpe and Suarez 2014; Lane and Rosewall 2015). More generally, modelling business investment is inherently difficult, particularly with aggregate time-series data (e.g. Chirinko 1993; Caballero 1999; Hassett and Hubbard 2002; Cockerell and Pennings 2007).

Investment is a heterogeneous activity. At any point in time, some businesses are investing a lot while others are not investing at all. As such, empirical studies that exploit this heterogeneity at a company level typically have more success in modelling business investment (e.g. La Cava 2005; Gilchrist and Zakrajšek 2007). Company-level data can also help to ameliorate some of the endogeneity that arises in time-series analysis. For instance, central banks tend to raise interest rates in periods of strong economic activity when investment is high (and vice versa). This reverse causality makes it harder to identify a negative correlation between interest rates and investment using aggregate data.

But even in micro data studies it can be hard to pin down the causal effect of interest rates on investment. There are several sources of potential endogeneity. First, omitted variables may drive the correlation between the cost of debt and investment. For example, risky companies are likely to pay relatively high interest rates. But risky companies may be more likely to invest than other companies if they have better growth prospects. This would lead to a positive correlation between company-level interest rates and investment. Second, there may be sample selection bias. The cost of debt is only observed for companies that have taken on debt. Hence the sample is limited to indebted companies. The decision to apply for (and be granted) debt may be correlated with the decision to invest; as such, companies with debt may invest more (or less) than other companies. The sensitivity of investment to interest rates may also differ for companies with and without debt.

An additional challenge in identifying the effect of interest rates on investment is that company-specific information on interest rates is not readily available in standardised company reports. Instead, research typically relies on proxies for the implied interest rate facing a company. These proxies include the ratio of interest expenses to the stock of debt (La Cava 2005), corporate bond rates (Gilchrist and Zakrajšek 2007) or business borrowing indicator rates more generally.

A key contribution of this paper is to provide direct company-specific estimates of the cost of debt. These data are hand collected from the notes to company annual reports. Australian companies are required to report how they manage risks, including liquidity and interest rate risks. As part of this reporting, most Australian companies provide information on the interest rate payable on each debt instrument that they hold.

Our interest rate estimates are an improvement on the existing literature in at least three respects. First, unlike aggregate time-series indicators such as business indicator rates, our estimates capture the cross-sectional heterogeneity in rates faced by companies (and how this heterogeneity changes over time). Second, our estimates are closer to the theoretical concept of the marginal cost of debt than are more conventional measures such as the ratio of interest paid to the stock of debt. This ratio can be heavily influenced by companies choosing to pay down debt during the year. For example, a company that chooses to make excess repayments in a year may be wrongly classified as facing a high interest rate. Our measure reflects rates at the end of the financial year and so should be less affected by such distortions. Third, our company-specific estimates are broader than those used in previous research as they cover not only corporate bond issuance but intermediated bank debt too.

An additional contribution of this paper is to demonstrate the importance of controlling for sample selection when studying investment using company-level data. The existing company-level research typically relies on estimates of interest rates that are calculated on existing holdings of debt, thereby restricting the sample to indebted companies. We explicitly address this sample selection issue through multiple imputation techniques. To the best of our knowledge, this has not been done before in the business investment literature.

Exploiting our novel dataset, three main observations emerge:

  1. There is a large degree of heterogeneity in the interest rates paid by Australian companies. Since 2004, the gap between the top and bottom deciles has been around 5 percentage points, on average. Moreover, since the global financial crisis, the spread between the rates paid by companies at the top and bottom of the distribution has widened, with rates for the top decile of companies (the ‘riskier’ companies) having remained high in recent years despite falls in the cash rate and in aggregate indicators of business lending rates.
  2. There is a strong and robust inverse relationship between the marginal cost of finance and investment for companies with debt. A 1 per cent decrease in the interest rate paid on debt is associated with the investment rate (investment divided by the previous period's capital stock) rising by ¼–½ percentage points, on average.
  3. By controlling for sample selection through multiple imputation techniques, we find some evidence that companies with debt are significantly more sensitive to interest rates than those without debt. This puts into question the external validity of papers that focus solely on companies with debt, and suggests that the elasticities of investment with respect to interest rates estimated in these papers should not be generalised to the full universe of firms.

Taken together, our results suggest that, in contrast to the Blanchard (1986) quote, you do not need econometric ingenuity but, rather, good data to identify the link between interest rates and business investment. The results also provide a partial explanation for why, up until recently, non-mining investment has been relatively weak: despite the fact that policy rates and indicators of aggregate lending rates are low, interest rates for up to a fifth of companies remain reasonably high.

Our results do not establish why there is a link between interest rates and investment because it is difficult to identify a plausibly exogenous source of interest rate variation. The negative relationship between interest rates and investment does not appear to reflect changes in monetary policy, at least not directly, as we control for this in the analysis. The relationship is also not due to variation in company risk, as we control for this too. Instead, we believe it is due to credit supply effects; a relaxation of lending standards leads to lower interest rates (for a given company profile) and boosts investment.