This paper investigates the effect of terrorism financing and recruitment on attacks. I exploit a Sharia‐compliant institution in Pakistan, which induces unintended and quasi‐experimental variation in the funding of terrorist groups through their religious affiliation. The results indicate that higher terrorism financing, in a given location and period, generate more attacks in the same location and period. Financing exhibits a complementarity in producing attacks with terrorist recruitment, measured through data from Jihadist‐friendly online fora and machine learning. A higher supply of terror is responsible for the increase in attacks and is identified by studying groups with different affiliations operating in multiple cities. These findings are consistent with terrorist organizations facing financial frictions to their internal capital market.

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.

The benefits of new technologies accrue not only to high‐skilled labor but also to owners of capital in the form of higher capital incomes. This increases inequality. To make this argument, we develop a tractable theory that links technology to the distribution of income and wealth—and not just that of wages—and use it to study the distributional effects of automation. We isolate a new theoretical mechanism: automation increases inequality by raising returns to wealth. The flip side of such return movements is that automation can lead to stagnant wages and, therefore, stagnant incomes at the bottom of the distribution. We use a multiasset model extension to confront differing empirical trends in returns to productive and safe assets and show that the relevant return measures have increased over time. Automation can account for part of the observed trends in income and wealth inequality.

Rust (1997b) discovered a class of dynamic programs that can be solved in polynomial time with a randomized algorithm. For these dynamic programs, the optimal values of a polynomially large sample of states are sufficient statistics for the (near) optimal values everywhere, and the values of this random sample can be bootstrapped from the sample itself. However, I show that this class is limited, as it requires all but a vanishingly small fraction of state variables to behave arbitrarily similarly to i.i.d. uniform random variables.

We consider nonparametric estimation of a mixed discrete‐continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at rates that are equal to the derived lower bounds up to a log factor. Thus, Bayesian mixture of normals models can be used for (up to a log factor) optimal adaptive estimation of mixed discrete‐continuous distributions. The proposed model demonstrates excellent performance in simulations mimicking the first stage in the estimation of structural discrete choice models.

The operation of markets and of politics are in practice deeply intertwined. Political decisions set the rules of the game for market competition and, conversely, market competitors participate in and influence political decisions. We develop an integrated model to capture the circularity between the two domains. We show that a positive feedback loop emerges such that market power begets political power, and political power begets market power, but that this feedback loop is bounded. With too much market power, the balance between politics and markets itself becomes lopsided and this drives a wedge between the interests of a policymaker and the dominant firm. Although such a wedge would seem pro‐competitive, we show how it can exacerbate the static and dynamic inefficiency of market outcomes. More generally, our model demonstrates that intuitions about market competition can be upended when competition is intermediated by a strategic policymaker.

Sanctioned by its constitution, India is home to the world's most comprehensive affirmative action program, where historically discriminated groups are protected with vertical reservations implemented as “set asides,” and other disadvantaged groups are protected with horizontal reservations implemented as “minimum guarantees.” A mechanism mandated by the Supreme Court in 1995 suffers from important anomalies, triggering countless litigations in India. Foretelling a recent reform correcting the flawed mechanism, we propose the 2SMG mechanism that resolves all anomalies, and characterize it with desiderata reflecting laws of India. Subsequently rediscovered with a high court judgment and enforced in Gujarat, 2SMG is also endorsed by Saurav Yadav v. State of UP (2020), in a Supreme Court ruling that rescinded the flawed mechanism. While not explicitly enforced, 2SMG is indirectly enforced for an important subclass of applications in India, because no other mechanism satisfies the new mandates of the Supreme Court.

This comment includes a solution to a problem in Section 8 in Andrews (1991) and points out a method to generalize the mean‐squared error (MSE) bounds appearing in Andrews (1988) and Andrews (1991).