RDP 2018-05: Do Interest Rates Affect Business Investment? Evidence from Australian Company-level Data 2. Data and Stylised Facts

2.1 Sample Construction

Our analysis focuses on a random sample of 100 non-financial non-resource companies that are publically listed on the Australian stock exchange. Balance sheet, profit and loss statement, and cash flow statement data are obtained from Morningstar. Equity market data, which are used in constructing the cost of equity and finance, are obtained from Bloomberg and the Center for Research in Security Prices. We hand collect information on company-specific interest rates based on the notes to listed companies' annual reports.

Australian listed companies are required to report how they manage their liquidity and interest rates risks in the notes to their annual reports. But, unlike balance sheet information, the notes on liquidity and interest rate risks are not reported in a standardised way across companies. The lack of consistency in how companies report the data may introduce measurement error. For instance, some companies report very detailed information on the interest rates that they pay on each of their debt instruments, other companies report only a weighted average interest rate, while some companies do not report interest rates at all. Also, companies differ in how they report their interest rate hedging activities. Some report the ultimate rate they face, while others report rates on the borrowing, as well as on the fixed- and floating-rate legs of their swaps.

To check for potential measurement error, we compare our hand-collected estimates to more conventional implied measures such as the ratio of interest payments to debt. Our estimates are broadly similar, which gives us confidence that we are capturing genuine variation in the cost of debt across companies. Moreover, the estimates were cross-checked by researchers that were not involved in the original data collection process.

The sample period covers the years 2004 to 2015. We do not restrict the sample to companies that survive for the entire period. Instead, we allow companies to both enter and exit the sample.[1] This minimises ‘survivorship’ bias, which occurs when delisted companies are excluded from the sample, leading to an unrepresentative sample of companies. This is potentially important as the characteristics of a company that determine its survival may also be associated with its investment and financing decisions (e.g. degree of risk aversion or governance structure).

As not all companies have debt (or report interest rates) in each year, we have interest rate data for 40–50 companies for each year of the sample period.

Overall, the sample appears representative of the universe of listed Australian non-financial non-resource companies. This is true across a wide range of metrics, from age to size to gearing (see Appendix A). Admittedly, the focus on listed companies means that the sample is skewed towards larger companies. But this is true of much of the existing research that explores the relationship between the cost of finance and investment. Moreover, the investment of listed companies could plausibly be less sensitive to the cost of finance than that of other businesses if they are less financially constrained, on average. This would imply that our results underestimate the effect of the cost of finance on aggregate investment.[2] Also, our estimates indicate that aggregate investment by non-financial non-resource listed companies is equivalent to around half of total capital expenditure by non-resource companies in Australia.[3] This suggests that our results are likely to be important for understanding developments in aggregate business investment.

2.2 The Cost of Debt

We measure the cost of debt as the weighted average borrowing rate paid by a company on their outstanding debt instruments at the end of their financial year.[4]

There is significant heterogeneity in the borrowing rates paid by companies. For instance, in 2015, a borrowing company could be paying an interest rate as high as 10 per cent (at the 90th percentile) or as low as 4 per cent (at the 10th percentile).

This distribution of company borrowing rates has also changed a lot over time. The median borrowing rate paid by companies in our sample has declined to its lowest level since 2004, alongside the cash rate and aggregate indicators of business lending rates (Figure 1). The 10th percentile rate has followed a similar pattern, while the 90th percentile rate remains around its sample average, though it has declined from its peak. This has resulted in the spread between the 90th percentile rate and the median rate, and between the 90th percentile rate and the 10th percentile rate, increasing by around 3 percentage points between 2004 and 2015. The increase in spreads occurred during the financial crisis and the European debt crisis, and has not been unwound. Similar patterns are evident for other deciles towards the top of the distribution.

Figure 1: Interest Rate on Debt
Figure 1: Interest Rate on Debt

Note: (a) Average bank borrowing rates; small business indicator is average rate on loans below $2 million; large business indicator is average rate on loans above $2 million

Sources: APRA; Authors' calculations; Company reports; RBA

The variation in borrowing rates across companies is likely to be related to variation in the perceived credit risk of each company.[5] Here, we examine how the borrowing rates vary with two key risk characteristics: company size and distance to default.

We find that large companies tend to pay lower interest rates on their debt relative to small companies (Figure 2). This is consistent with large companies being less likely to fail and hence being perceived to be less risky by creditors (e.g. Dunne, Roberts and Samuelson 1988; Kenney, La Cava and Rodgers 2016).

Figure 2: Median Interest Rates on Debt
By company size
Figure 2: Median Interest Rates on Debt

Notes: Size is based on total assets; small companies have assets below the median, medium companies are between the median and 75th percentile and large companies are above the 75th percentile

Sources: Authors' calculations; Company reports; Morningstar

Over recent years, the median borrowing rate for large companies (with assets greater than the 75th percentile) has declined substantially, to be at its lowest level in the sample period. The median rate paid by medium-sized companies (assets between the median and 75th percentile) has also declined, though to a lesser extent. In contrast, the median rate paid by small companies (with assets below the median) has been little changed since 2010 and remains near its decade-long average.[6] This suggests that declines in the cash rate in recent years have not necessarily flowed through to the interest rates faced by small companies.

We can also consider a more explicit measure of default risk. Distance to default (D2D) is a forward-looking market-based measure of default risk calculated using equity prices and the book value of liabilities.[7] A decline in D2D indicates deteriorating financial health and greater default risk. We classify companies with D2Ds below the 25th percentile as ‘more risky’ companies. All other companies are classified as ‘less risky’.

Similar to the trends based on company size, the median interest rate for the more risky companies is currently around its sample average, while, for less risky companies, it has declined substantially to be at its lowest level since 2004 (Figure 3). As a result, the spread between the median interest rate for more and less risky companies is elevated.

Figure 3: Median Interest Rates on Debt
By riskiness

Note: (a) More risky companies are those with distance to default below the 25th percentile

Sources: Authors' calculations; Company reports; Morningstar; RBA

In part, the high level of the spread is likely to reflect an upward repricing of default risk following the financial crisis. It could also reflect the fact that the relative riskiness of the two groups has changed over recent years, with more risky companies becoming even more risky compared to other companies (Figure 3). Median D2Ds declined for both groups during the financial crisis; however, while the median D2D for less risky companies has trended higher since the crisis, the median D2D of more risky companies has remained at a low level.

2.3 The Cost of Equity

The focus in this paper is mainly on the cost of debt and its relationship with investment. However, for completeness, it is worth also considering what has happened to the cost of equity.

We consider two measures of the cost of equity, both of which are real, rather than nominal, measures. The first is analysts' forecasts for sectoral earning yields in one year's time (total earnings before interest and tax divided by market capitalisation; Figure 4). This is an ex ante measure of the required rate of return on equity, which makes it superior, at least conceptually, to ex post observed earnings yields used in previous studies such as La Cava (2005).

Figure 4: Sectoral Earning Yields
Analyst expectations of yields in one year's time
Figure 4: Sectoral Earning Yields

Note: Shading denotes range of yields

Source: Bloomberg

While there is a degree of heterogeneity, the costs of equity for all sectors have followed broadly similar paths. The cost of equity increased sharply around the financial crisis and remained high for several years, before returning to pre-crisis levels.

The second measure of the cost of equity is a company-specific rate estimated using a capital asset pricing model (CAPM). The CAPM states that:

where Inline Equation is the cost of equity for company i at time t, Inline Equation is the risk-free interest rate, Inline Equation is the market rate of return and βi indicates the degree of co-movement between returns on the company's equity and the market portfolio (the degree of systematic risk).

To estimate Equation (1) we need an observed ex post measure of the cost of equity. For this we use the share market return over a given period. We then assume this ex post return is an unbiased proxy for the ex ante cost of equity. This allows us to construct estimates of βi by running a regression at a monthly frequency for the full sample period (or the maximum possible sub-sample if the company was listed or delisted during the period). We take the return on the ASX 200 to be the market rate of return, and calculate both the return on the ASX 200 and on the individual company using accumulation indices that account for dividends, share issuance and share splits. Our risk-free rate is the average real cash rate expected over the next 10 years. The estimated β tend to be similar if we use short-term nominal rates instead.[8]

Many listed companies use the CAPM approach to estimate their cost of equity (Lane and Rosewall 2015). However, the cost of equity estimates are prone to model misspecification in the CAPM relationship. For example, the estimated β parameter is somewhat sensitive to the sample choice.

Figure 5: Company-specific Cost of Equity
Percentiles of distribution of individual company costs
Figure 5: Company-specific Cost of Equity

Note: Estimated using a company-specific CAPM model

The median of the company-specific cost of equity measures follows a similar path to the sectoral measures (Figure 5). Unsurprisingly, there is greater heterogeneity in the cost of equity based on the company-specific measure compared to the sectoral measure. The cost of equity for the 10th percentile remained relatively low during the crisis and has trended downwards over recent years. In contrast, the cost of equity for the 90th percentile increased by more during the crisis and remains at an elevated level.[9]

2.4 The Cost of Finance

For completeness, we also consider a more comprehensive measure of the ‘cost of finance’. This captures the fact that some companies rely on debt, some rely on equity and some rely on a combination of both. The cost of finance is measured as the weighted average of the costs of debt and equity. The weights on debt (D) and equity (E) are their respective shares in the total book value of the company. Specifically, the cost of finance is given by:

where Inline Equation is the cost of finance for company i at time t, Inline Equation is the cost of debt, (1 – τt) accounts for the tax deductibility of interest payments, Inline Equation is expected inflation and Inline Equation is the cost of equity.

There is again a significant degree of heterogeneity across companies in the cost of finance, reflecting heterogeneity in both of the components. This additional detail is lost when focusing on aggregate measures, such as indicator lending rates.

2.5 Corporate Investment

We consider two different estimates of corporate investment:

  1. The net change in the fixed capital stock (‘net investment’);
  2. Cash spent on property, plant and equipment (‘gross investment’).

There are a few differences between these two estimates. The first estimate is an accrual-based measure of real net investment. It is an accrual-based measure because it recognises investment at the time the expense is incurred and not when the cash is spent. It is a real measure because the underlying capital stock is measured at replacement cost, using a method that accounts for changes in the price of capital goods (similar to Mills, Morling and Tease (1994) and La Cava (2005)). And it is a net measure because it excludes depreciation (i.e. spending to replace depreciated capital is not considered investment).[10]

The second estimate is a cash-based measure of nominal gross investment. It is a cash-based measure because it recognises investment at the time the cash is paid. It is a nominal measure because it does not allow for changes in capital goods prices (although the model specification will soak up aggregate changes in the relative price of capital goods). And it is a gross measure because it includes total spending on investment (i.e. does not exclude depreciation) and is therefore more aligned with aggregate investment indicators, such the ABS capital expenditure survey.

In both cases we focus on investment in tangible assets, such as property, plant and equipment, rather than investment in intangible assets, such as the accumulation of goodwill or investment in R&D. While a number of recent papers have demonstrated the importance of accounting for investment in intangibles when estimating investment regressions (e.g. Peters and Taylor 2016), limitations in our company balance sheet data make this difficult.

We scale both measures of investment by the previous period's capital stock, measured at the (real) replacement cost. Importantly, we trim the top and bottom 5 per cent of observations to remove extreme observations. This is fairly common practice in the investment literature using micro data (e.g. Bond and Van Reenen 2007). Given the initial small sample size, the results are a bit sensitive to the choice of trim. But the results are qualitatively similar if we, for example, trim the top and bottom 1 or 10 per cent.

Focusing on the median company, both gross and net investment follow similar paths and have been consistently lower after the financial crisis than before the crisis (Figure 6). This is consistent with aggregate measures of investment, and also with the overall cost of finance remaining elevated despite lower borrowing rates.[11]

Figure 6: Cost of Debt, Cost of Finance and Investment
Median company
Figure 6: Cost of Debt, Cost of Finance and Investment

Notes: (a) Collected borrowing rates
          (b) Property, plant and equipment

Sources: Authors' calculations; Company reports; Morningstar


Specifically, companies are required to be listed on the stock exchange in 2007/08. This date was chosen to ensure we captured fluctuations in investment and interest rates around the time of the financial crisis. Within the 2007/08 listed company population, 100 companies were selected using a random number generator. [1]

The investment of listed companies may be more sensitive to interest rates than that of other firms if unlisted companies are totally excluded from the debt market (i.e. cannot borrow). But, in this case, the cost of equity should be important and we control for this in the analysis. [2]

There are differences in the way these two things are measured. For example, the ABS capital expenditure survey excludes land and second-hand asset purchases from capital spending. These are included in the company-level estimates. [3]

This is a nominal interest rate, whereas theory would suggest the real interest rate should be the more relevant determinant of investment. The results are robust to using a more complete real cost of debt measure that accounts for taxes and expected inflation. However, we do not use this in the main specification as the calculated real rates are negative for some observations. As we use log interest rates in the model these observations have to be discarded, which is not ideal given the already small sample. [4]

As well as the characteristics of the borrower, the characteristics of the loan are also likely to influence measured interest rates. Such characteristics might include: the term of the borrowing; the type of borrowing (e.g. bank loans and corporate bonds); the extent of collateralisation; or whether the company has existing relationships its lenders. While some companies do report this information, it is too sparse to be useful for analysis. [5]

We classify companies with assets below the median as ‘small’ to imperfectly account for the roughly log-normal distribution of company assets – the company with median assets (around $40–50 million) is much closer in size to the company at the 25th percentile of the asset distribution (around $10 million) than it is to the company at the 75th percentile (around $200–250 million). [6]

The median D2D for companies in our sample is broadly consistent with the median D2D for all non-financial listed companies. The D2D measure is the difference between the expected value of assets and the expected value of liabilities at a chosen horizon (one year in this paper) and is measured in standard deviations of asset value growth. For more information, see Robson (2015). [7]

We construct this measure using the term structure model laid out in Hambur and Finlay (2018). [8]

For some sectors, such as utilities and consumer discretionary, the median company-specific cost of equity follows the sectoral cost of equity quite closely. For others, such as consumer staples, it does not. [9]

A problem with the net investment measure is that it can be affected by mergers, acquisitions and sales of business divisions. Ideally, we would abstract from these transactions as they do not really reflect the creation or destruction of capital in the economy and could be driven by very different factors to capital spending. While we do not directly observe these transactions, we control for some of them by deleting outliers from the distribution. [10]

A fall in the median gearing ratio amongst the sample could help to account for the fact that the median cost of finance has remained elevated despite declines in the median cost of debt. [11]