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We provide evidence that domestic outsourcing increases young-worker entry into the formal sector. Leveraging a pair of 1993–1994 Brazilian reforms that reduced the relative cost of outsourcing security guards, a triple-differences design shows that the reforms increased formal guard employment by 4% and hiring from unemployment or informality by 7%, while reallocating formal employment from older to younger workers and leaving demographic-adjusted wages unchanged. Census data corroborate the rise in formality, driven by the youngest cohorts. The compositional shift mirrors a general pattern in Brazil’s matched employer–employee records: conditional on total firm size, employers with greater occupation-specific scale hire younger workers, paid less at entry and more likely to be entering formal employment for the first time. The evidence is most consistent with contract firms supplying at scale the capabilities needed to hire productive workers from outside the formal sector—a demand-side channel for increasing formal sector employment.

Evidence that domestic outsourcing lowers pay comes largely from on-site transfers, in which workers move to a contractor but keep the same jobs. Displacement is rarely observed: whether workers lose their jobs, where they go, how earnings evolve. In Brazil’s 1993–1994 pro-outsourcing reforms, which differentially affected security guards, such transfers were rare; firms instead used occupational layoffs, shedding their guards while keeping other workers. Displaced guards’ employment recovered within five years, but many changed occupations and wages stayed about 12% lower. Lifetime losses average 1.2 to 1.5 years of pre-layoff earnings, concentrated among workers from high-wage firms, reflecting lost premia.

Health insurance lowers the out-of-pocket price of healthcare, and it is well-established that this leads to higher utilization of care. This manifestation of “moral hazard” is typ ically viewed as a social cost of insurance. Within a standard model, this paper shows that a consumer’s ability to change her behavior in response to insurance can also play a central role in the ability of insurance to protect her from risk. We provide a theoretical characterization of this channel and quantify its importance empirically. Under stan dard parameterizations and estimates in the literature, we find that insurance-induced healthcare utilization can account for more than half of the total value of risk protection derived from insurance. Preventing consumers from changing their behavior would lower healthcare spending, but also result in a major loss of risk protection, on-net reducing social welfare in some cases. Our results suggest that under-utilization of healthcare may thus be an equally important threat to welfare as over-utilization.

The optimal mechanism for selling a divisible good under convex production costs can be arbitrarily complex, yet firms overwhelmingly use two-part tariffs: a fixed fee plus a constant markup over production cost. We quantify the profit this simplicity sacrifices. For regular value distributions, a two-part tariff guarantees a fraction of the optimal profit that depends only on a lower bound m on the elasticity of the marginal cost, independent of the value distribution. The guarantee approaches 1 as m grows, and is impossible without such a bound. Beyond regularity, no two-part tariff guarantees a constant fraction, but a menu of K + 1 tariffs with a common markup does when the ironed virtual value has K ironing intervals; moreover, the menu size must scale with K. With multiple product lines, the better of separate sales and a single access fee granting purchases at production cost achieves a constant fraction of the optimal profit. These results provide a theoretical foundation for the ubiquity of simple cost-based pricing.

The Credit Surface along the leverage dimension gives the bond spread as a function of the loan-to-value ratio. Empirically, we show that uncertainty shocks typically increase spreads and steepen the credit surface, profoundly affecting the supply of credit. Theoretically, we derive necessary and sufficient conditions for the convexity of the credit surface, and for changes in the anticipated distribution of collateral prices that lead to steepening of the credit surface. Finally, we show that the credit surface itself fully reveals the entire distribution of collateral prices, thus providing a new and vivid language with which to describe uncertainty and stochastic orders. Credit surface steepening itself is a new stochastic order that may better capture our intuitive notion of more uncertainty.

This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data stream and are well suited to streaming data or to settings with storage/privacy constraints. For semiparametric monotone index models, the averaged trajectory of the second phase lead to estimators that are automatically orthogonalized and satisfy the laws of the iterated logarithms, and policy functionals are updated along the same trajectory at negligible additional cost. The averaged trajectories satisfy functional central limit theorems, which yield fast online inference via random scaling and bypass the explicit variance estimation that complicates inference for semiparametric models. Applied to a fixed large sample, our online algorithm achieves substantial computational gains over corresponding offline procedures without sacrificing statistical performance. Monte Carlo experiments show adequate behavior. Our methods are applied to 19 million traffic-stop online records from the North Carolina State Patrol (Pierson et al. 2020) and to the international trade data of Helpman et al. (2008) with over 300 regressors. We also compare our estimator to its parametric benchmark in both empirical illustrations.

With uncertainty about persistence, we show that forecasts necessarily become more persistent and over-react at long horizons. For these reasons, correctly specified and Bayesian forecasts may under-react at short horizons and over-react at long horizons. These results provide a unified explanation for several asset pricing and forecasting puzzles, including: (i) the excess responsiveness of long-horizon rates to short rates, (ii) the dominance of apparent term premia for long-term rates, (iii) the ex post predictability of bond yields, (iv) the excess volatility of long-horizon forward prices, (v) the excess persistence of long-horizon forecasts, and (vi) the over-reaction of long-horizon forecasts.

Empirical models of multi-product demand rely on low-dimensional product representations to capture substitution patterns, increasingly using proxies built from unstructured data. When proxies are imperfect, standard workflows yield biased counterfactuals and invalid inference. We develop a practical toolkit to address these issues. Our methods apply to market-level and/or individual data, require minimal additional computation, provide simple standard-error formulas, and accommodate proxies from fine-tuned models. Further, we propose diagnostics to assess proxy quality. Our methods yield meaningful improvements in predicting substitution in empirically calibrated simulations and in an application where we assess counterfactual prediction performance against a ground truth.