Publications

Structural transformation in most currently developing countries takes the form of a rapid rise in services but limited industrialization. In this paper, we propose a new methodology to structurally estimate productivity growth in service industries that circumvents the notorious difficulties in measuring quality improvements. In our theory, the expansion of the service sector is both a consequence—due to income effects—and a cause—due to productivity growth—of the development process. We estimate the model using Indian household data. We find that productivity growth in nontradable consumer services such as retail, restaurants, or residential real estate was an important driver of structural transformation and rising living standards between 1987 and 2011. However, the welfare gains were heavily skewed toward high-income urban dwellers.

This paper studies the optimal determination of deposit insurance when bank runs are possible. We show that the welfare impact of changes in the level of deposit insurance coverage can be generally expressed in terms of a small number of sufficient statistics, which include the level of losses in specific scenarios and the probability of bank failure. We characterize the wedges that determine the optimal ex ante regulation, which map to asset- and liability-side regulation. We demonstrate how to employ our framework in an application to the most recent change in coverage in the United States, which took place in 2008.

This paper points out some pitfalls in the use of two-way fixed effects (TWFE) regressions when outcome variables contain nonlinear or stochastic trend components.  When a policy change shifts trend paths of outcome variables conventional TWFE estimation can distort results and invalidate inference. A robust solution is proposed by identifying determinants of dynamic club membership based on the idea of relative convergence, which can be assessed empirically by the so-called ‘logt’ test (Phillips & Sul, 2007a). Club membership in each time period is estimated by recursive regression, transforming outcome variables to statistically stable, stationary status. Time varying club membership can then be used to identify the determinants of club memberships by running a panel logit or ordered logit regression. This approach is applied to study COVID-19 vaccination data across 50 states and the District of Columbia (DC). A new weekly database is created to track individual state and DC vaccination policies and mandates over the period from March 2021 to February 2022. Initially two convergent clubs are identified. Later evidence of the vaccination rates across states reveals a single convergent club. The primary determinant of this merger of sub-clubs is found to be federal-level vaccine mandates.

Australian housing markets experienced widespread and, in some cases, extraordinary growth in prices between 2020 and 2023. Using recently developed methodology that accounts for fundamental economic drivers, we assess the existence and degree of speculative behaviour as well as the timing of exuberance and downturns in these markets. Our findings indicate that speculative behaviour was indeed present in six of the eight capital cities at some time over the period studied. The sequence of events in this nation-wide housing bubble began in the Brisbane market and concluded in Melbourne, Canberra, and Hobart following the interest rate hike implemented by the Reserve Bank of Australia in May 2022. As of March 2023, the housing markets in Sydney, Canberra, and Hobart had broadly regained stable conditions, while Melbourne is more gradually returning to its normal state. In addition, over-corrections against fundamentals are evident in the housing markets of Brisbane, Adelaide, Darwin, and Perth. For regular updates on the housing markets, readers may visit the authors’ website at www.housing-fever.com.

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically endogenous market structure. We propose the use of Generalized Instrument Variables methods to identify those dynamic policy functions that are consistent with instrumental variable (IV) restrictions. Extending popular “two-step” methods, these policy functions then identify a set of structural parameters that are consistent with the dynamic model, the IV restrictions and the data. We provide computed illustrations to both single-agent and oligopoly examples. We also present a simple empirical analysis that, among other things, supports the counterfactual study of an environmental policy entailing an increase in sunk costs.

This article studies the optimal design of corporate taxes when firms have private information about future investment opportunities and face financial constraints. A government whose goal is to efficiently raise a given amount of revenue from its corporate sector should attempt to tax unconstrained firms, which value resources inside the firm less than financially constrained firms. We show that a corporate payout tax (a tax on dividends and share repurchases) can both separate constrained and unconstrained firms and raise revenue and is therefore optimal. Our quantitative analysis implies that a revenue-neutral switch from profit taxation to payout taxation would increase the overall value of existing firms and new entrants by 7%⁠. This switch could be implemented in the current US tax system by making retained earnings fully deductible.

A signal is privacy-preserving with respect to a collection of privacy sets, if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy-preserving signals for arbitrary state space and arbitrary privacy sets. A signal is privacy-preserving if and only if it is a garbling of a reordered quantile signal. These signals are equivalent to couplings, which in turn lead to a characterization of optimal privacy-preserving signals for a decision-maker. We demonstrate the applications of this characterization in the contexts of algorithmic fairness, price discrimination, and information design.

There is a growing debate about whether upzoning is an effective policy response to housing shortages and unaffordable housing. This paper provides empirical evidence to further inform debate by examining the various impacts of recently implemented zoning reforms on housing construction in Auckland, the largest metropolitan area in New Zealand. In 2016, the city upzoned approximately three quarters of its residential land to facilitate construction of more intensive housing. We use a quasi-experimental approach to analyze the short-run impacts of the reform on construction, allowing for potential shifts in construction from non-upzoned to upzoned areas (displacement effects) that would, if unaccounted for, lead to an overestimation of treatment effects. We find strong evidence that upzoning stimulated construction. Treatment effects remain statistically significant even under implausibly large displacement effects that would necessitate more than a four-fold increase in the trend rate of construction in control areas under the counterfactual of no-upzoning. Our findings support the argument that upzoning can stimulate housing supply and suggest that further work to identify factors that mediate the efficacy of upzoning in achieving wider objectives of the policy would assist policymakers in the design of zoning reforms in the future.