This article documents a new set of stylized facts on how pricing moments depend on product age and emphasizes how this heterogeneity is crucial for the amplification of nominal shocks to the real economy. Exploiting information from a unique panel containing billions of transactions in the US consumer goods sector, we show that our empirical findings are consistent with a narrative in which firms face demand uncertainty and learn through prices. Such a mechanism of active learning from prices can strongly influence an economy’s aggregate price level and can thus be important for assessing the degree of monetary non-neutrality. To quantify this, we build a general equilibrium menu cost model with active learning and exogenous entry that features heterogeneity in pricing moments over the life cycle of products. Under this setup, firms engage in active learning to deal with uncertainty on their demand curves. Firms choose prices not only to maximize static profits but also to create signals to obtain valuable information on their demand. In the calibrated version of our model, the cumulative real effects of a nominal shock are approximately three times as large compared to a standard price-setting model. The main intuition behind this result is that active learning weakens the selection effect. Price changes are mainly determined by forces of active learning and, hence, become more orthogonal to aggregate shocks, which reduces the aggregate price flexibility of the economy.

Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

Unlike in standard models, decision makers often narrowly bracket and make each decision in isolation. I develop a new approach, which I term narrow thinking, to systematically model narrow bracketing. The definition of narrow thinking is that different decisions are based on different, non-nested, information. As a result, the narrow thinker makes each decision with imperfect knowledge of other decisions and faces difficulties coordinating her multiple decisions. The narrow thinker effectively cares less about her other decisions when making each decision. The main application of narrow thinking is to provide a smooth model of mental accounting without requiring the decision maker to have explicit budgets. My approach generates unique predictions about how the degree of mental accounting depends on expenditure shares and cognitive limitations. It also illustrates how narrow bracketing and mental accounting can be explained by the same underlying friction.

In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through discrete variation under a non-periodicity condition on the hazard function of the error distribution. Furthermore, we show that the presence of continuous regressors may lead to stronger identification results. Our non-parametric estimator is shown to be consistent and asymptotically normal, and outperforms that of Lewbel and Linton (2002) in a simulation study. For the panel data setting, we provide the first systematic treatment of non-parametric identification and estimation of the truncated panel data model with fixed effects by extending our treatment of the cross-sectional case. We also consider various other extensions.

This paper examines the desirability of allowing a monopolist to determine the market price. The author finds that none of the regulatory mechanisms previously discussed in the "price versus quantities" literature strictly dominates unregulated, monopoly price-setting. Furthermore, despite suggestions by others, price-setting by a regulated monopolist whose profits coincide with society's net benefits is not always the most desirable means of control. Quantity-setting by such a monopolist may instead be the preferred choice. Combining both into one incentive-compatible mechanism provides the best regulatory scheme and one in which the regulator need not be informed about costs.

A buyer suggests prices to N sellers in a time period and buys from the seller who accepts the bid first. The number of bidding rounds is determined by how frequently the buyer can make an offer. We show that with no limit on the frequency and without discounting, the price path is either kept flat initially with large jumps at the end or increasing steadily over time. Which class of path occurs in equilibrium depends on the buyer's trade-off between committing to a price ceiling versus finely screening the sellers' costs. With discounting, limiting the number of rounds mitigates the delay caused by the reluctance to raise bids in the first class of equilibrium and therefore can benefit the buyer. This result suggests why, in reality, bargaining parties often take measures to make their offers rigid and consequently force themselves to make fewer offers.

We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we call profiled regret. We analyse the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.

We revisit the question of why shifts in aggregate demand drive business cycles. Our theory combines intertemporal substitution in production with rational confusion, or bounded rationality, in consumption and investment. The first element allows aggregate supply to respond to shifts in aggregate demand without nominal rigidity. The second introduces a “confidence multiplier,” that is, a positive feedback loop between real economic activity, consumer expectations of permanent income, and investor expectations of returns. This mechanism amplifies the business-cycle fluctuations triggered by demand shocks (but not necessarily those triggered by supply shocks); it helps investment to comove with consumption; and it allows front-loaded fiscal stimuli to crowd in private spending.

We study the problem of parameter inference in (possibly non-linear and non-smooth) econometric models when the data are measured with error. We allow for arbitrary correlation between the true variables and the measurement errors. To solve the identification problem, we require the existence of an auxiliary data-set that contains information about the conditional distribution of the true variables given the mismeasured variables. Our main assumption requires that the conditional distribution of the true variables given the mismeasured variables is the same in the primary and auxiliary data. Our methods allow the auxiliary data to be a validation sample, where the primary and validation data are from the same distribution, and more importantly, a stratified sample where the auxiliary data-set is not from the same distribution as the primary data. We also show how to combine the two data-sets to obtain a more efficient estimator of the parameter of interest. We establish the large sample properties of the sieve based estimators under verifiable conditions. In particular, we allow for the mismeasured variables to have unbounded supports without employing the tedious trimming scheme typically used in kernel based methods. We illustrate our methods by estimating a returns to schooling censored quantile regression using the CPS/SSR 1978 exact match files where the dependent variable is measured with error of arbitrary kind.

Many structural economics models are semiparametric ones in which the unknown nuisance functions are identified via non-parametric conditional moment restrictions with possibly non-nested or overlapping conditioning sets, and the finite dimensional parameters of interest are over-identified via unconditional moment restrictions involving the nuisance functions. In this article we characterize the semiparametric efficiency bound for this class of models. We show that semiparametric two-step optimally weighted GMM estimators achieve the efficiency bound, where the nuisance functions could be estimated via any consistent non-parametric methods in the first step. Regardless of whether the efficiency bound has a closed form expression or not, we provide easy-to-compute sieve-based optimal weight matrices that lead to asymptotically efficient two-step GMM estimators.