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We show that collateral constraints restrict firm entry and postentry growth, using French administrative data and cross-sectional variation in local house-price appreciation as shocks to collateral values. We control for local demand shocks by comparing treated homeowners to controls in the same region that do not experience collateral shocks: renters and homeowners with an outstanding mortgage, who (in France) cannot take out a second mortgage. In both comparisons, an increase in collateral value leads to a higher probability of becoming an entrepreneur. Conditional on entry, treated entrepreneurs use more debt, start larger firms, and remain larger in the long run.

The risk and return trade-off, the cornerstone of modern asset pricing theory, is often of the wrong sign. Our explanation is that high-beta assets are prone to speculative overpricing. When investors disagree about the stock market's prospects, high-beta assets are more sensitive to this aggregate disagreement, experience greater divergence of opinion about their payoffs, and are overpriced due to short-sales constraints. When aggregate disagreement is low, the Security Market Line is upward-sloping due to risk-sharing. When it is high, expected returns can actually decrease with beta. We confirm our theory using a measure of disagreement about stock market earnings.

What is the impact of real estate prices on corporate investment? In the presence of financing frictions, firms use pledgeable assets as collateral to finance new projects. Through this collateral channel, shocks to the value of real estate can have a large impact on aggregate investment. To compute the sensitivity of investment to collateral value, we use local variations in real estate prices as shocks to the collateral value of firms that own real estate. Over the 1993-2007 period, the representative US corporation invests $0.06 out of each $1 of collateral.

This paper empirically documents the performance and behavior of family firms listed on the French stock exchange between 1994 and 2000. On the French stock market, approximately one third of the firms are widely held, whereas the remaining two thirds are family firms. We find that, in the cross-section, family firms largely outperform widely held corporations. This result holds for founder-controlled firms, professionally managed family firms, but more surprisingly also for firms run by descendants of the founder. We offer explanations for the good performance of family firms. First, we present evidence of a more efficient use of labor in heir-managed firms. These firms pay lower wages, even allowing for skill and age structure. We also find that descendants smooth out industry shocks and manage to honor implicit labor contracts. Second, we present evidence consistent with outside CEOs in family firms making a more parsimonious use of capital. They employ more unskilled, cheap labor, use less capital, pay lower interest rates on debt and initiate more profitable acquisitions.

Banks' balance sheet exposure to fluctuations in interest rates strongly forecasts excess Treasury bond returns. This result is consistent with optimal risk management, a banking counterpart to the household Euler equation. In equilibrium, the bond risk premium compensates banks for bearing fluctuations in interest rates. When banks' exposure to interest rate risk increases, the price of this risk simultaneously rises. We present a collection of empirical observations that support this view, but also discuss several challenges to this interpretation.

We evaluate the effect of downside insurance on self-employment. We exploit a largescale reform of French unemployment benefits that insured unemployed workers starting businesses. The reform significantly increased firm creation without decreasing the quality of new entrants. Firms started postreform were initially smaller, but their employment growth, productivity, and survival rates are similar to those prereform. New entrepreneurs' characteristics and expectations are also similar. Finally, jobs created by new entrants crowd out employment in incumbent firms almost one-for-one, but have a higher productivity than incumbents. These results highlight the benefits of encouraging experimentation by lowering barriers to entry.

We propose a method to estimate the effect of firm policies (e.g., bankruptcy laws) on allocative efficiency using (quasi-)experimental evidence. Our approach takes general equilibrium effects into account and requires neither a structural estimation nor a precise assumption on how the experiment affects firms. Our aggregation formula relies on treatment effects of the policy on the distribution of output-to-capital ratios, which are easily estimated. We show this method is valid for a large class of commonly used models in macrofinance. We apply it to the French banking deregulation episode of the mid-1980s and find an increase in aggregate TFP of 5 percent.

We show that retail trading activity has a positive effect on the volatility of stock returns, which suggests that retail investors behave as noise traders. To identify this effect, we use a reform of the French stock market that raises the relative cost of speculative trading for retail investors. The daily return volatility of the stocks affected by the reform falls by 20 basis points (a quarter of the sample standard deviation of the return volatility) relative to other stocks. For affected stocks, we also find a significant decrease in the magnitude of return reversals and the price impact of trades.

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.

We model an organization as a two-agent hierarchy: an informed Decision Maker in charge of selecting projects and a (possibly) uninformed Implementer in charge of their execution. Both have intrinsic preferences over projects. This paper models the costs and benefits of divergence between their preferences, that is, dissent within the organization. Dissent is useful to (1) foster the use of objective (and sometimes private) information in decision making and (2) give credibility to the Decision Maker's choices. However, dissent comes at the cost of hurting the Implementer's intrinsic motivation, thereby impairing organizational efficiency. We show that dissent can be optimal, in particular, when information is useful and uncertainty is high. Moreover, dissent remains an optimal organizational form even when Implementers can choose their employer or when Decision Makers have real authority over hiring decisions.