Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - PERSISTDEBT (Debt and Persistence of Financial Shocks)

Teaser

In 2007-08, Europe and US were overwhelmed by a financial crisis, followed by a severe, persistent economic recession (some countries are still below their economic activity before the start of the crisis, and the Euro Area recovered their pre-crisis level just last year)...

Summary

In 2007-08, Europe and US were overwhelmed by a financial crisis, followed by a severe, persistent economic recession (some countries are still below their economic activity before the start of the crisis, and the Euro Area recovered their pre-crisis level just last year). Prior to the crisis, there was a debt and asset-price boom. Historical studies show that this is the common pattern: (i) Financial crises are followed by a strong contraction of aggregate output and employment (and credit) and take a longer time to recover (than non-financial recessions); (ii) The best predictor of financial crises is an ex-ante strong credit boom (accompanied by high asset prices).

There is growing finance-macro theory and new policy, but no micro evidence. Therefore, in this project I study: Why are the effects of debt and financial shocks strong and persistent? What are the channels of transmission (households, banks, firms, sovereigns)? As crises are not exogenous, what are the determinants? Can public policy (macroprudential, monetary) alleviate the negative effects? Are there costs or limitations of these policies?

To study these issues, we are constructing several micro datasets that are absolutely new in the literature:
• Comprehensive loan-level data for households, with borrower identifier, matched with household-level info (income, wealth, house variables, consumption, labor…; both administrative data and survey data).
• Security-time-bank level data (i.e., both securities and credit registers): each security a bank has in each period with rating, price, maturity, yield, coupons, whether a market price… and all loans.
• Real effects (of credit channels and policies): matched loan level data with administrative firm-level (and supervisory bank-level) data.

For identification of the above questions, we exploit the loan borrower-lender and security-bank level data, matched with household-firm-bank level info is crucial, including data on randomized house allocation. We can then aggregate up the data to quantify the real effects and exploit different shocks and policy changes.

Bank-level, firm-level, or aggregated macro data cannot identify (or even analyze) the channels and can provide wrong results. We also analyse the biases that aggregate data can have for the above academic and policy questions, and why micro data is better for answering those questions.

Work performed

The specific projects cover three parts:
1. Household credit
2. Securities and credit registers
3. Monetary and prudential policies, can other bank policies help?

On the first set of projects, as I will discuss below, there has been substantial progress on obtaining and cleaning the data. On the second and third, I already have papers that are forthcoming at top academic journals, see below.

In the existing academic literature, there are loan-level research papers (with borrower and lender identifier) for firms, but not for households. There is aggregated data on debt and real effects at county level for households, but this cannot identify credit availability, nor real effects as substantial heterogeneity in a county. Therefore, I am matching several household loan-level datasets with household-level info, in particular three different datasets:

Matched household credit register with household characteristics: We are starting to analyze the loan level data from Spanish credit register, including loan applications, volume, maturity, collateral, credit history and future defaults, with identifier of borrower and lender. We have matched this dataset with The Spanish Survey of Households Finance, there has been 5 waves since 2002, with household detailed information on not only income, wealth, debt…, but also consumption, saving, expectation on house prices, loan rejections. This project is with Olympia Bover and Gabriel Jimenez (both from Bank of Spain). We have the initial results in which we find that financial crises affect more some households via a reduction on the supply of credit, and now we are analyzing whether this mechanism has effects on real outcomes: consumption, employment, health, and other variables such as divorces. We plan to have the first draft this year, in 2017. I also have other projects with Bank of Spain data on the impact of prudential policies (including state-owned banks) on credit and on the real economy (my co-authors are, apart from Gabriel Jimenez, Jesus Saurina and Rafael Repullo).

Moreover, with Niels Johannesen (University of Copenhagen), we have constructed a matched dataset for each Danish household with their main bank, their total credit, and health outcomes, to study in detail how a financial crisis can truly real (real) effects. In Economics we argue that the real effects (compared to for example financial effects) are consumption and other related variables, but health effects stemming from a financial crisis can be argued that are more important (more real) than just a consumption reduction. The data is constructed and we have preliminary results. We believe we will have the first draft this year, 2017.

With several economists from Bank of England, I am analyzing the effects are of limits on mortgage debt for UK households depending on their income. Bank of England put restrictions on DTI (debt to income) and we have access to all mortgages in the UK at the household level, so we are analyzing this macroprudential tool. We are working on the datasets (household level and bank level datasets) and we plan to have the first draft next year, in 2018. The construction of the datasets can also allow us to answer other related questions, such as credit and household inequality, effects of monetary and prudential policies, and others.

Jose G. Montalvo has been working in the construction of the datasets of the core project of the proposal on the real effect of housing wealth on debt and the impact of debt on consumption, employment, health, etc. The process has been particularly slow and intense because in every step there have been some political hurdles to overcome. The basic data sources for this project are administrative data on the lotteries of public housing in the Basque Country. The reason for this choice is the wealth of information on lotteries’ participants and its demographic and economic conditions in this particular region. More than 250.000 participated in the lotter

Final results

We provide specific answers to understand the determinants and consequences of financial crises, and how public policy (e.g. prudential or monetary policy) can reduce the likelihood of crises, and conditioning on crises, the negative effects.

By exploiting randomized house allocation via lotteries: once financial crises or strong financial shocks arrive, we can analyse whether there is a (mortgage) debt overhang with negative effects on household-level consumption and employment, or, whether households with houses and debt try to work more as some other finance theories suggest. There are important results on health that we can identify, and as I was arguing above these could be key effects of strong financial shocks. Note that heterogeneity at the county level and non randomized data may provide misleading results, with misleading conclusions for public policy. We can also analyse whether supply or demand of mortgages and consumer lending is more important for the results, including LTV and DTI policy effects. Note that limits on household debt to income can have inequality effects, which we are investigating.

History shows that credit and asset-price booms are the key ex-ante correlates of financial crises, and there is theory on banks’ credit crunch by investing in securities in crises. Policy restrictions on security-trading by banks: Volcker rule in Dobb-Frank in U.S.; the Liikanen Report in Europe, and the Vickers Report in the UK. Therefore, I also have a set of projects analyzing securities registers, in addition to credit registers. We have already shown results that show an externality arising from fire sales in securities markets on credit supply to the real sector via the trading behaviour of banks. Moreover, we have new projects with the same data on whether banks manipulate and mask their risks to their supervisors (central banks). All these results are important on how to regulate and supervise banks.

Central banks have massively expanded their balance sheet since 2008, with main monetary rates around zero. However, the large injection of liquidity to banks may not have reached the real sector by means of expanded supply of credit. The potency of the bank lending channel of monetary policy may be limited if banks rebalance their portfolio towards securities holdings, e.g. to pursue liquidity hoarding or risk-shifting, as opposed to lending. For instance, in the words of Jeremy Stein (2013), Governor of the Federal Reserve Board: “A credit crunch may arise as other financial intermediaries (e.g., banks) withdraw capital from lending, so as to exploit the now-more-attractive returns to buying up fire-sold assets. Ultimately, it is the risk of this credit contraction, and its implications for economic activity more broadly, that may be the most compelling basis for regulatory intervention.”

To understand how monetary policy works via banks, including its possible limitations, and to test for the bank lending and risk-taking channels of monetary policy, it is thus crucial to analyse both the supply of bank credit to the real sector and securities trading by banks. Securities holdings by banks are a sizable fraction of their balance sheets, around 20% of assets in the US and Europe (e.g. in Germany and Italy), and several recent policy initiatives aim at limiting security trading by banks (Volker Rule in Dobb-Frank in the US, Likaanen Report in EU and Vickers’ report in the UK). A portfolio rebalance towards securities in crises may be the consequence of a credit demand problem, with few lending opportunities and with risky, highly leveraged borrowers. At the same time, the low level of bank capitalization can contribute to the impairment of the transmission of monetary policy to credit supply: banks, especially less capitalized ones, may e.g. decide to hoard liquid securities rather than issue relatively illiquid loans to SMEs.

Monetary policy may also have unintended consequences in terms of financi