During the Great Recession, policymakers in the US and Europe sought to stimulate the economy by providing banks with lower-cost capital and liquidity. One objective of these actions was to stimulate aggregate demand. The idea was the banks would respond to the lower cost of borrowing by providing more credit to households and firms, who would in turn increase their borrowing, spending, and investment. For instance, when introducing the Financial Stability Plan, US Treasury Secretary Geithner argued that, "the capital will come with conditions to help ensure that every dollar of assistance is used to generate a level of lending greater than what would have been possible in the absence of government support".
Now that the Great Recession has passed, economists are engaged in a vigorous and important debate about the effectiveness of the policies used during this period (e.g. Sufi et al. 2015). In a new working paper, we contribute to this debate by digging into rich data on millions of credit cards to consider whether reducing banks’ cost of funds effectively targets those households that would respond by consuming more (Agarwal et al. 2015a).
At a conceptual level, the impact of a decrease in bank borrowing costs on borrowing and spending depends on two factors:
- First, banks need to respond to lower borrowing costs by expanding their supply of credit – what we call banks’ marginal propensity to lend;
- Second, consumers need to respond to an increased supply of credit by increasing their borrowing and spending – what we call consumers’ marginal propensity to borrow.
The impact of these policies depends on the level and the correlation of these forces. Importantly, if banks do not want to lend to consumers who want to borrow, the impact of credit expansions in stimulating economic activity will be limited.
Our study seeks to shed light on this question by estimating heterogeneous marginal propensities to borrow and marginal propensities to lend in the US credit card market during the Great Recession. To do this, we use rich panel data on all credit cards issued by the eight largest US banks. These data include account-level information on contract terms, utilisation, payments, and costs at the monthly level for more than 400 million credit card accounts between January 2008 and December 2014. We describe these data in detail in Agarwal et al. (2015b).
Our research design exploits the fact that banks sometimes set credit limits as discontinuous functions of consumers' FICO (a credit assessment company) credit scores. For example, in Figure 1, a bank grants a $2,500 credit limit to applicants with a FICO score below 720 and a $5,000 credit limit to applicants with a FICO score of 720 or above. We identify a total of 743 credit limit discontinuities at different points in the FICO score distribution. We show that other borrower and contract characteristics trend smoothly through these cutoffs, allowing us to use a regression discontinuity strategy to identify the causal impact of providing extra credit at prevailing interest rates.
Using this regression discontinuity design, we estimate substantial heterogeneity in the MPB across the FICO score distribution. As shown in Figure 2, for the lowest FICO score group (≤ 660), a $1 increase in credit limits raises borrowing volumes on the treated credit card by 58 cents at 12 months after origination. For the highest FICO score group (> 740), we estimate a 23% effect on the treated card that is entirely explained by a shifting of borrowing across credit cards, with an increase in credit limits having no effect on total borrowing. These estimates suggest that bank-mediated stimulus will only raise aggregate borrowing if credit expansions are passed through to low FICO score households.
We next consider how banks pass through credit expansions to different households. Directly estimating a bank's marginal propensity to lend out of a change in its cost of funds is difficult, because changes in banks' cost of funds are typically correlated with unobserved factors that also affect lending. Our approach is to build a simple model of optimal credit limits that characterises a bank's marginal propensity to lend with a small number of parameters we can estimate using our credit limit quasi-experiments. This approach requires that bank lending responds optimally on average to a change in the cost of funds and that we can measure the incentives faced by banks. We think both assumptions are reasonable in our setting. Credit card lending is highly sophisticated and our estimates of bank incentives are fairly precise. Indeed, we show that observed credit limits are quite close to the optimal credit limits predicted by our model.
In our model, banks set credit limits at the level where the marginal revenue from a further increase in credit limits equals the marginal cost of that increase. A decrease in the cost of funds – e.g. due to an easing of monetary policy, a reduction in capital requirements, or a market intervention that reduces financial frictions – reduces the cost of extending a given unit of credit and corresponds to a downward shift in the marginal cost curve. As shown in Figure 3 such a reduction has a larger effect on credit limits when marginal revenue and marginal cost curves are relatively flat (Panel A) than when these curves are relatively steep (Panel B).
What are the economic forces that determine the slope of marginal costs? One factor is the degree of adverse selection. With adverse selection, higher credit limits are disproportionately taken up by consumers with higher probabilities of default. These higher default rates raise the marginal cost of lending, thereby generating upward-sloping marginal costs (Mahoney and Weyl 2013). Higher credit limits can also raise marginal costs holding the distribution of marginal borrowers fixed. For example, if higher debt levels have a causal effect on the probability of default – as they do in the strategic bankruptcy model of Fay, Hurst and White (2002) – then higher credit limits, which increase debt levels, will also raise default rates. As before, this raises the marginal cost of lending, generating upward sloping marginal costs.
We use the same quasi-exogenous variation in credit limits to estimate the slope of marginal costs, allowing us to quantify the effect of asymmetric information and other factors on the marginal propensity to lend without untangling their relative importance. We find that the (positive) slope of the marginal cost curve is largest for the lowest FICO score borrowers, driven by steeply upward sloping marginal chargeoffs for these households. We also find that the (negative) slope of the marginal revenue curve is steeper for these households, since marginal fee revenue, which is particularly important for lending to low FICO score borrowers, is decreasing in credit limits. Taken together, these estimates imply that a 1 percentage point reduction in the cost of funds increases optimal credit limits by $239 for borrowers with FICO scores below 660, compared with $1,211 for borrowers with FICO scores above 740.
The result, as shown in Figure 4, is that marginal propensity to lend and the marginal propensity to borrow are strongly negatively correlated, with banks’ marginal propensity to lend lowest exactly for those households with the highest marginal propensity to borrow. We find that correctly accounting for this negative correlation reduces the estimated effect of a decrease in the cost of funds on total borrowing after 12 months by 49% relative to a calculation that estimates this effect as the product of the average marginal propensity to lend and the average marginal propensity to borrow in our data.
We view our paper as making a number of contributions. It builds on a literature that has estimated marginal propensities to consume and marginal propensities to borrow using shocks to income and liquidity (Gross and Souleles 2002, Parker et al. 2006). Relative to this literature, we estimate marginal propensities to borrow during 2008 to 2012, which is the relevant time period for examining the effects of policy responses to the Great Recession. Our research design and large sample size also allow us to estimate marginal propensity to borrow heterogeneity with more precision than the prior literature.
It is also the first paper to combine estimates of consumers' marginal propensities to borrow with estimates of banks' marginal propensities to lend. Estimating both objects jointly is important because many policies that aim to target consumers with high marginal propensities to borrow are intermediated by banks. We show that the interaction between marginal propensities to borrow and marginal propensities to lend across different types of consumers is key to understanding the effectiveness of these policies.
Our work also relates to a literature that has identified declining household borrowing volumes as a proximate cause of the Great Recession (e.g. Mian and Sufi 2010, 2012, and many others). Within this literature, there is considerable debate over the relative importance of credit supply and credit demand in explaining the reduction in borrowing. Our estimates suggest that both explanations have merit, with credit supply being the limiting factor at the bottom of the FICO score distribution and credit demand being the limiting factor at higher FICO scores.
Our findings are subject to a number of caveats. First, while we identify one important reason why policies to reduce banks' cost of funds were relatively ineffective at raising household borrowing during the Great Recession, other forces also played a role. For instance, stress tests and higher capital requirements may have increased the cost of lending, particularly to low FICO score borrowers, and thus might have offset the policies we consider that were designed to reduce banks' cost of funds.
Second, we do not assess the desirability of stimulating household borrowing from a macroeconomic stability or welfare perspective. For example, while extending credit to low FICO households might lead to more borrowing and consumption in the short run, we do not evaluate the consequences of the resulting increase in leverage.
Finally, our results do not capture general equilibrium effects that might arise from the increased spending of low FICO score households and are not informative about the effectiveness of monetary policy through other channels, such as a redistribution from savers to borrowers, or in its role in preventing a collapse of the banking sector.
Agarwal, S, S Chomsisengphet, N Mahoney, and J Stroebel (2015a), “Do Banks Pass Through Credit Expansions to Consumers Who Want to Borrow?”, Working Paper.
Agarwal, S, S Chomsisengphet, N Mahoney, and J Stroebel (2015b), “Regulating Consumer Financial Products: Evidence from Credit Cards”, Quarterly Journal of Economics 130.
Gross, D B, and N S Souleles (2002), “Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data”, Quarterly Journal of Economics 117(1), 149–185.
Johnson, D S, J A Parker, and N S Souleles (2006), “Household Expenditure and the Income Tax Rebates of 2001”, American Economic Review 96(5): 1589–1610.
Mahoney, N, and E Glen Weyl (2013), “Imperfect Competition in Selection Markets”, Working Paper.
Mian, A, and A Sufi (2010), “Household Leverage and the Recession of 2007–09”, IMF Economic Review 58(1): 74–117.
Mian, A, and A Sufi (2012), “What Explains High Unemployment? The Aggregate Demand Channel”, NBER Working Paper.
Sufi, A (2015), “Out of Many, One? Household Debt, Redistribution and Monetary Policy during the Economic Slump”, Andrew Crockett Memorial Lecture, BIS.