We present an approach to analyse learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e. from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyse environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.
This paper studies the relation between volatility and informativeness in financial markets. We identify two channels (noise-reduction and equilibrium-learning) that determine the volatility-informativeness relation. When informativeness is sufficiently high (low), volatility and informativeness positively (negatively) comove in equilibrium. We identify conditions on primitives that guarantee that volatility and informativeness comove positively or negatively. We introduce the comovement score, a statistic that measures the distance of a given asset to the positive/negative comovement regions. Empirically, comovement scores (i) have trended downwards over the last decades, (ii) are positively related to value and idiosyncratic volatility and negatively to size and institutional ownership.
We argue that noisy aggregation of dispersed information provides a unified explanation for several prominent cross-sectional return anomalies such as returns to skewness, returns to disagreement and corporate credit spreads. We characterize asset returns with noisy information aggregation by means of a risk-neutral probability measure that features excess weight on tail risks, and link the latter to observable moments of earnings forecasts, in particular forecast dispersion and accuracy. We calibrate our model to match these moments and show that it accounts for a large fraction of the empirical return premia. We further develop asset pricing tools for noisy information aggregation models that do not impose strong parametric restrictions on economic primitives such as preferences, information, or return distributions.
This paper characterizes the optimal transaction tax in an equilibrium model of financial markets. If investors hold heterogeneous beliefs unrelated to their fundamental trading motives and the planner calculates welfare using any single belief, a positive tax is optimal, regardless of the magnitude of fundamental trading. Under some conditions, the optimal tax is independent of the planner's belief. The optimal tax can be implemented by adjusting its value until total volume equals fundamental volume. Knowledge of (i) the share of nonfundamental trading volume and (ii) the semielasticity of trading volume to tax changes is sufficient to quantify the optimal tax.
We compare contrarian to conformist advice, a contrarian expert being one whose preference bias is against the decision-maker’s prior optimal decision. Optimality of an expert depends on characteristics of prior information and learning. If either the expert is fully informed or fine information can be acquired cheaply, then for symmetric distributions F (of the state), a conformist (contrarian) is superior if F is single peaked bimodal. If only coarse information can be acquired, then a contrarian acquires more on average and hence is superior. If information is verifiable, a contrarian has less incentive to hide unfavorable evidence and again is superior.
Addressing public health externalities often requires community-level collective action. Due to social norms, each person’s sanitation investment decisions may depend on the decisions of neighbors. We report on a cluster randomized controlled trial conducted with 19,000 households in rural Bangladesh where we grouped neighboring households and introduced (either financial or social recognition) rewards with a joint liability component for the group, or asked each group member to make a private or public pledge to maintain a hygienic latrine. The group financial reward has the strongest impact in the short term (3 months), inducing a 7.5–12.5 percentage point increase in hygienic latrine ownership, but this effect dissipates in the medium term (15 months). In contrast, the public commitment induced a 4.2–6.3 percentage point increase in hygienic latrine ownership in the short term, but this effect persists in the medium term. Non-financial social recognition or a private pledge has no detectable effect on sanitation investments.
Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.
This chapter uses a college-by-graduate degree fixed effects estimator to evaluate the returns to 19 different graduate degrees for men and women. We find substantial variation across degrees, and evidence that OLS overestimates the returns to degrees with the highest average earnings and underestimates the returns to degrees with the lowest average earnings. Second, we decompose the impacts on earnings into effects on wage rates and effects on hours. For most degrees, the earnings gains come from increased wage rates, though hours play an important role in some degrees, such as medicine, especially for women. Third, we estimate the net present value and internal rate of return for each degree, which account for the time and monetary costs of degrees. Finally, we provide descriptive evidence that satisfaction gains are large for some degrees with smaller economic returns, such as education and humanities degrees, especially for men.
We quantify the distortionary effects of nexus tax laws on Amazon’s distribution net- work investments between 1999 and 2018. We highlight the role of two features of the expansion of Amazon’s network: densification of the network of distribution facilities and vertical integration into package sortation. Densification results in a reduction in the cost of shipping orders, but comes at the expense of higher facility operating costs in more expensive areas and lower scale economies of processing shipments. Nexus laws furthermore generate additional sales tax liabilities as the network grows. Combining data on household spending across online and offline retailers with detailed data on Amazon’s distribution network, we quantify these trade-offs through a static model of demand and a dynamic model of investment. Our results suggest that Amazon’s expansion led to significant shipping cost savings and facilitated the realization of aggregate economies of scale. We find that abolishing nexus tax laws in favor of a non-discriminatory tax policy would induce the company to decentralize its network, lowering its shipping costs. Non-discriminatory taxation would also entail lower revenue, however, as tax-inclusive prices would rise, resulting in a fall in proﬁt overall. This drop and the decline in consumer welfare from higher taxes together fall short of the increases in tax revenue and rival proﬁt, suggesting that the abolishment of nexus laws would lead to an increase in total welfare.
We analyze the consequences of noisy information aggregation for investment. Market imperfections create endogenous rents that cause overinvestment in upside risks and underinvestment in downside risks. In partial equilibrium, these inefficiencies are particularly severe if upside risks are coupled with easy scalability of investment. In general equilibrium, the shareholders' collective attempts to boost value of individual rms leads to a novel externality operating through price that amplifies investment distortions with downside risks but o sets distortions with upside risks.