A central question surrounding the current subprime crisis is whether the securitization process reduced the incentives of financial intermediaries to carefully screen borrowers. We examine this issue empirically using data on securitized subprime mortgage loan contracts in the United States. We exploit a specific rule of thumb in the lending market to generate exogenous variation in the ease of securitization and compare the composition and performance of lenders' portfolios around the ad hoc threshold. Conditional on being securitized, the portfolio with greater ease of securitization defaults by around 10%–25% more than a similar risk profile group with a lesser ease of securitization. We conduct additional analyses to rule out differential selection by market participants around the threshold and lenders employing an optimal screening cutoff unrelated to securitization as alternative explanations. The results are confined to loans where intermediaries' screening effort may be relevant and soft information about borrowers determines their creditworthiness. Our findings suggest that existing securitization practices did adversely affect the screening incentives of subprime lenders.

Statistical default models, widely used to assess default risk, fail to account for a change in the relations between different variables resulting from an underlying change in agent behavior. We demonstrate this phenomenon using data on securitized subprime mortgages issued in the period 1997–2006. As the level of securitization increases, lenders have an incentive to originate loans that rate high based on characteristics that are reported to investors, even if other unreported variables imply a lower borrower quality. Consistent with this behavior, we find that over time lenders set interest rates only on the basis of variables that are reported to investors, ignoring other credit-relevant information. As a result, among borrowers with similar reported characteristics, over time the set that receives loans becomes worse along the unreported information dimension. This change in lender behavior alters the data generating process by transforming the mapping from observables to loan defaults. To illustrate this effect, we show that the interest rate on a loan becomes a worse predictor of default as securitization increases. Moreover, a statistical default model estimated in a low securitization period breaks down in a high securitization period in a systematic manner: it underpredicts defaults among borrowers for whom soft information is more valuable. Regulations that rely on such models to assess default risk could, therefore, be undermined by the actions of market participants.

This paper exploits intertemporal variations in employment protection across countries and finds that rigidities in labor markets are an important determinant of firms' capital structure decisions. Over the 1985–2007 period, we find that reforms increasing employment protection are associated with a 187 basis point reduction in leverage. We interpret this finding to suggest that employment protection increases operating leverage, crowding out financial leverage. This result does not appear to be due to pretreatment differences between treated and control firms, omitted variables, unobserved changes in regional economic conditions, and reverse causality. Heterogeneous treatment effects are consistent with our economic intuition.

Recent decades have witnessed several waves of buyout activity. We find leveraged buyouts (LBOs) to be a significant concern for bondholders by showing that a) intra-industry credit spreads increase upon an LBO announcement, b) yields on bonds without event risk covenants are, on average, 21 basis points higher than those on same-firm bonds with such covenants, and c) structural models calibrated to historical LBO events imply an impact of 18–21 basis points on 10-year credit spreads. The impact is strongest in expansion periods and for bonds with maturities of 10–20 years.

We present evidence that cultural proximity (shared codes, beliefs, ethnicity) between lenders and borrowers increases the quantity of credit and reduces default. We identify in-group lending using dyadic data on religion and caste for officers and borrowers from an Indian bank, and a rotation policy that induces exogenous matching between them. Having an in-group officer increases credit access and loan size dispersion, reduces collateral requirements, and induces better repayment even after the in-group officer leaves. We consider a range of explanations and suggest that the findings are most easily explained by cultural proximity serving to mitigate information frictions in lending. (JEL D82, D83, G21, G28, O16, Z12, Z13)

The likelihood that a bank loan will default is of interest to both regulators and investors. Under the Basel II regulatory guidelines, a bank must hold capital in proportion to the riskiness of its assets. The probability of default is a pri mary determinant of the riskiness of a loan. Investors, in turn, price a loan in the secondary market based on its expected cash flow, which again depends on the default probability. How should market participants assess the default probability on a pool of bank loans? It is natural to consider historical data on loan condi tions and default rates, and to estimate a statisti cal model that can be used to predict defaults going forward. Such statistical models have been widely used across the financial markets, to enhance market liquidity and impose capital requirements on financial institutions. The accuracy of predictions from statistical models was especially poor in the subprime mortgage market in the period from August 2007 onwards.1 We argue that one cause for this failure was that these models relied entirely on hard information variables and ignored changes in the incentives of lenders to collect soft infor

We study the wealth accumulation of Indian state politicians using public disclosures required of all candidates. The annual asset growth of winners is 3–5 percent higher than that of runners-up, a difference that holds also in a set of close elections. The relative asset growth of winners is greater in more corrupt states and for those holding ministerial positions. These results are consistent with a rent-seeking explanation for the relatively high rate of growth in winners’ assets.

ABSTRACT The importance of skilled labor and the inalienability of human capital expose firms to the risk of losing talent at critical times. Using Swedish microdata, we document that firms lose workers with the highest cognitive and noncognitive skills as they approach bankruptcy. In a quasi‐experiment, we confirm that financial distress drives these results: following a negative export shock caused by exogenous currency movements, talent abandons the firm, but only if the exporter is highly leveraged. Consistent with talent dependence being associated with higher labor costs of financial distress, firms that rely more on talent have more conservative capital structures.

We provide microeconomic evidence on ethnic frictions and market efficiency, using dyadic data on managers and borrowers from a large Indian bank. We conjecture that, if exposure to religion-based communal violence intensifies intergroup animosity, riot exposure will lead to lending decisions that are more sensitive to a borrower’s religion. We find that riot-exposed Hindu branch managers lend relatively less to Muslim borrowers and that these loans are less likely to default, consistent with riot exposure exacerbating taste-based discrimination. This bias is persistent across a bank officer’s tenure, suggesting that the economic costs of ethnic conflict are long-lasting, potentially spanning across generations.