Prices,Markups, and Trade Reform

This paper examines how prices, markups, and marginal costs respond to trade liberalization. We develop a framework to estimate markups from production data with multi‐product firms. This approach does not require assumptions on the market structure or demand curves faced by firms, nor assumptions on how firms allocate their inputs across products. We exploit quantity and price information to disentangle markups from quantity‐based productivity, and then compute marginal costs by dividing observed prices by the estimated markups. We use India's trade liberalization episode to examine how firms adjust these performance measures. Not surprisingly, we find that trade liberalization lowers factory‐gate prices and that output tariff declines have the expected pro‐competitive effects. However, the price declines are small relative to the declines in marginal costs, which fall predominantly because of the input tariff liberalization. The reason for this incomplete cost pass‐through to prices is that firms offset their reductions in marginal costs by raising markups. Our results demonstrate substantial heterogeneity and variability in markups across firms and time and suggest that producers benefited relative to consumers, at least immediately after the reforms.     

The Value of Free Water: Analyzing South Africa's Free Basic Water Policy

This paper analyzes South Africa's Free Basic Water Policy, under which households receive a free water allowance equal to the World Health Organization's recommended minimum. I estimate residential water demand, evaluate the welfare effects of free water, and provide optimal price schedules derived from a social planner's problem. I use a data set of monthly metered billing data for 60,000 households for 2002–2009 from a particularly disadvantaged suburb of Pretoria, with rich price variation across 20 different nonlinear tariff schedules. I find that the free allowance acts as a lump‐sum subsidy, without large effects on water consumption. However, it is possible to reallocate the current subsidy to form an optimal tariff without a free allowance, which would increase welfare while leaving the water provider's profit unchanged. This optimal tariff would also reduce the number of households consuming low quantities of water, a desirable policy goal according to the WHO.     

Insurer Competition in Health Care Markets

The impact of insurer competition on welfare, negotiated provider prices, and premiums in the U.S. private health care industry is theoretically ambiguous. Reduced competition may increase the premiums charged by insurers and their payments made to hospitals. However, it may also strengthen insurers' bargaining leverage when negotiating with hospitals, thereby generating offsetting cost decreases. To understand and measure this trade‐off, we estimate a model of employer‐insurer and hospital‐insurer bargaining over premiums and reimbursements, household demand for insurance, and individual demand for hospitals using detailed California admissions, claims, and enrollment data. We simulate the removal of both large and small insurers from consumers' choice sets. Although consumer welfare decreases and premiums typically increase, we find that premiums can fall upon the removal of a small insurer if an employer imposes effective premium constraints through negotiations with the remaining insurers. We also document substantial heterogeneity in hospital price adjustments upon the removal of an insurer, with renegotiated price increases and decreases of as much as 10% across markets.     

A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.     

Sales Force and Competition in Financial Product Markets: The Case of Mexico's Social Security Privatization

This paper examines how sales force impacts competition and equilibrium prices in the context of a privatized pension market. We use detailed administrative data on fund manager choices and worker characteristics at the inception of Mexico's privatized social security system, where fund managers had to set prices (management fees) at the national level, but could select sales force levels by local geographic areas. We develop and estimate a model of fund manager choice where sales force can increase or decrease customer price sensitivity. We find exposure to sales force lowered price sensitivity, leading to inelastic demand and high equilibrium fees. We simulate oft proposed policy solutions: a supply‐side policy with a competitive government player and a demand‐side policy that increases price elasticity. We find that demand‐side policies are necessary to foster competition in social safety net markets with large segments of inelastic consumers.     

Individual Heterogeneity and Average Welfare

Individual heterogeneity is an important source of variation in demand. Allowing for general heterogeneity is needed for correct welfare comparisons. We consider general heterogeneous demand where preferences and linear budget sets are statistically independent. Only the marginal distribution of demand for each price and income is identified from cross‐section data where only one price and income is observed for each individual. Thus, objects that depend on varying price and/or income for an individual are not generally identified, including average exact consumer surplus. We use bounds on income effects to derive relatively simple bounds on the average surplus, including for discrete/continuous choice. We also sketch an approach to bounding surplus that does not use income effect bounds. We apply the results to gasoline demand. We find tight bounds for average surplus in this application, but wider bounds for average deadweight loss.     

Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis

Using an exhaustive data set on claims held by trade creditors (suppliers) on failed trade debtors (customers), we quantify the importance of trade credit chains for the propagation of corporate bankruptcy. We show that trade creditors experience significant trade credit losses due to trade debtor failures and that creditors' bankruptcy risks increase in the size of incurred losses. By exploring the roles of financial constraints and creditor‐debtor dependences, we infer that the trade credit failure propagation mechanism is driven by both credit losses and demand shrinkage. Finally, we show that the documented propagation mechanism constitutes a significant part of the overall bankruptcy frequency, suggesting that it has measurable implications for the aggregate level.     

Parametric Inference and Dynamic State Recovery From Option Panels

We develop a new parametric estimation procedure for option panels observed with error. We exploit asymptotic approximations assuming an ever increasing set of option prices in the moneyness (cross‐sectional) dimension, but with a fixed time span. We develop consistent estimators for the parameters and the dynamic realization of the state vector governing the option price dynamics. The estimators converge stably to a mixed‐Gaussian law and we develop feasible estimators for the limiting variance. We also provide semiparametric tests for the option price dynamics based on the distance between the spot volatility extracted from the options and one constructed nonparametrically from high‐frequency data on the underlying asset. Furthermore, we develop new tests for the day‐by‐day model fit over specific regions of the volatility surface and for the stability of the risk‐neutral dynamics over time. A comprehensive Monte Carlo study indicates that the inference procedures work well in empirically realistic settings. In an empirical application to S&P 500 index options, guided by the new diagnostic tests, we extend existing asset pricing models by allowing for a flexible dynamic relation between volatility and priced jump tail risk. Importantly, we document that the priced jump tail risk typically responds in a more pronounced and persistent manner than volatility to large negative market shocks.     

Poverty and Self‐Control

We argue that poverty can perpetuate itself by undermining the capacity for self‐control. In line with a distinguished psychological literature, we consider modes of self‐control that involve the self‐imposed use of contingent punishments and rewards. We study settings in which consumers with quasi‐hyperbolic preferences confront an otherwise standard intertemporal allocation problem with credit constraints. Our main result demonstrates that low initial assets can limit self‐control, trapping people in poverty, while individuals with high initial assets can accumulate indefinitely. Thus, even temporary policies that initiate accumulation among the poor may be effective. We examine implications concerning the effect of access to credit on saving, the demand for commitment devices, the design of financial accounts to promote accumulation, and the variation of the marginal propensity to consume across income from different sources. We also explore the nature of optimal self‐control, demonstrating that it has a simple and behaviorally plausible structure that is immune to self‐renegotiation.     

Grouped Patterns of Heterogeneity in Panel Data

This paper introduces time‐varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a “grouped fixed‐effects” estimator that minimizes a least squares criterion with respect to all possible groupings of the cross‐sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.     

The Economics of Density: Evidence From the Berlin Wall

This paper develops a quantitative model of internal city structure that features agglomeration and dispersion forces and an arbitrary number of heterogeneous city blocks. The model remains tractable and amenable to empirical analysis because of stochastic shocks to commuting decisions, which yield a gravity equation for commuting flows. To structurally estimate agglomeration and dispersion forces, we use data on thousands of city blocks in Berlin for 1936, 1986, and 2006 and exogenous variation from the city's division and reunification. We estimate substantial and highly localized production and residential externalities. We show that the model with the estimated agglomeration parameters can account both qualitatively and quantitatively for the observed changes in city structure. We show how our quantitative framework can be used to undertake counterfactuals for changes in the organization of economic activity within cities in response, for example, to changes in the transport network.     

Sign Restrictions,Structural Vector Autoregressions,and Useful Prior Information

This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.     

The Political Economy of Moral Conflict: An Empirical Study of Learning and Law Enforcement Under Prohibition

The U.S. Prohibition experience shows a remarkable policy reversal. In only 14 years, a drastic shift in public opinion required two constitutional amendments. I develop and estimate a model of endogenous law enforcement, determined by beliefs about the Prohibition‐crime nexus and alcohol‐related moral views. In turn, the policy outcomes shape subsequent learning about Prohibition enforcement costs. I estimate the model through maximum likelihood on Prohibition Era city‐level data on police enforcement, crime, and alcohol‐related legislation. The model can account for the variation in public opinion changes, and the heterogeneous responses of law enforcement and violence across cities. Results show that a 15% increase in the homicide rate can be attributed to Prohibition enforcement. The subsequent learning‐driven adjustment of local law enforcement allowed for the alcohol market to rebound to 60% of its pre‐Prohibition size. I conclude with counterfactual exercises exploring the welfare implications of policy learning, prior beliefs, preference polarization, and alternative political environments. Results illustrate the importance of incorporating the endogenous nature of law enforcement into our understanding of policy failure and policy success.     

Nonparametric Stochastic Discount Factor Decomposition

Stochastic discount factor (SDF) processes in dynamic economies admit a permanent‐transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to analyze the permanent‐transitory decomposition of SDF processes. Specifically, we show how to estimate nonparametrically the solution to the Perron–Frobenius eigenfunction problem of Hansen and Scheinkman, 2009. Our empirical framework allows researchers to (i) construct time series of the estimated permanent and transitory components and (ii) estimate the yield and the change of measure which characterize pricing over long investment horizons. We also introduce nonparametric estimators of the continuation value function in a class of models with recursive preferences by reinterpreting the value function recursion as a nonlinear Perron–Frobenius problem. We establish consistency and convergence rates of the eigenfunction estimators and asymptotic normality of the eigenvalue estimator and estimators of related functionals. As an application, we study an economy where the representative agent is endowed with recursive preferences, allowing for general (nonlinear) consumption and earnings growth dynamics.     

Forecasting With Model Uncertainty: Representations and Risk Reduction

We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use the parameter estimates for forecasting. We investigate the distributional properties of a number of different schemes for model choice and parameter estimation, including: in‐sample model selection using the Akaike information criterion; out‐of‐sample model selection; and splitting the data into subsamples for model selection and parameter estimation. Using a weak‐predictor local asymptotic scheme, we provide a representation result that facilitates comparison of the distributional properties of the procedures and their associated forecast risks. This representation isolates the source of inefficiency in some of these procedures. We develop a simulation procedure that improves the accuracy of the out‐of‐sample and split‐sample methods uniformly over the local parameter space. We also examine how bootstrap aggregation (bagging) affects the local asymptotic risk of the estimators and their associated forecasts. Numerically, we find that for many values of the local parameter, the out‐of‐sample and split‐sample schemes perform poorly if implemented in the conventional way. But they perform well, if implemented in conjunction with our risk‐reduction method or bagging.     

Identifying Equilibrium Models of Labor Market Sorting

We assess the empirical content of equilibrium models of labor market sorting based on unobserved (to economists) characteristics. In particular, we show theoretically that all parameters of the classic model of sorting based on absolute advantage in Becker, 1973 with search frictions can be nonparametrically identified using only matched employer–employee data on wages and labor market transitions. In particular, these data are sufficient to nonparametrically estimate the output of any individual worker with any given firm. Our identification proof is constructive and we provide computational algorithms that implement our identification strategy given the limitations of the available data sets. Finally, we add on‐the‐job search to the model, extend the identification strategy, and apply it to a large German matched employer–employee data set to describe detailed patterns of sorting and properties of the production function.     

Female Labor Supply,Human Capital,and Welfare Reform

We estimate a dynamic model of employment, human capital accumulation—including education, and savings for women in the United Kingdom, exploiting tax and benefit reforms, and use it to analyze the effects of welfare policy. We find substantial elasticities for labor supply and particularly for lone mothers. Returns to experience, which are important in determining the longer‐term effects of policy, increase with education, but experience mainly accumulates when in full‐time employment. Tax credits are welfare improving in the U.K., increase lone‐mother labor supply and marginally reduce educational attainment, but the employment effects do not extend beyond the period of eligibility. Marginal increases in tax credits improve welfare more than equally costly increases in income support or tax cuts.     

Examining the Impact of Pre‐Implementation Expectations on Post‐Implementation Use of Enterprise Systems: A Longitudinal Study

This article builds upon the technology acceptance model and theories of technology sensemaking to explore pre‐enterprise system adoption expectations and post‐enterprise system adoption outcomes in a longitudinal setting. Building on the exploitation and exploration paradigm, we propose that task productivity and task innovation expectations are the key drivers of users’ pre‐adoption enterprise system usage intention. Further, we argue that the enterprise system facilitates generation of a common knowledge base that may encourage a more integrated organizational culture and promote shared understanding among employees. Considering the distinction between mandatory and voluntary contexts, we propose that user acceptance of the enterprise system at the pre‐ and post‐adoption stages will mediate these relationships in a mandatory context. The results show that the influence of pre‐adoption expectations regarding task productivity and task innovation on intention to use an enterprise system is mediated by user acceptance of the enterprise system. Intention to use an enterprise system is positively related to actual use. At the post‐adoption stage, the influence of actual use on shared understanding is mediated by user acceptance of an enterprise system and enterprise system use has a direct negative impact on task efficiency in the initial period after implementation. Overall, the results highlight that user acceptance at both pre‐ and post‐adoption stages are critical factors when usage is mandatory. These findings suggest a number of important implications for research and for managerial action.     

Consumption Dynamics During Recessions

Are there times when durable spending is less responsive to economic stimulus? We argue that aggregate durable expenditures respond more sluggishly to economic shocks during recessions because microeconomic frictions lead to declines in the frequency of households' durable adjustment. We show this by first using indirect inference to estimate a heterogeneous agent incomplete markets model with fixed costs of durable adjustment to match consumption dynamics in PSID microdata. We then show that aggregating this model delivers an extremely procyclical Impulse Response Function (IRF) of durable spending to aggregate shocks. For example, the response of durable spending to an income shock in 1999 is estimated to be almost twice as large as if it occurred in 2009. This procyclical IRF holds in response to standard business cycle shocks as well as in response to various policy shocks, and it is robust to general equilibrium. After estimating this robust theoretical implication of micro frictions, we provide additional direct empirical evidence for its importance using both cross‐sectional and time‐series data.     

Nonparametric Welfare Analysis for Discrete Choice

We consider empirical measurement of equivalent variation (EV) and compensating variation (CV) resulting from price change of a discrete good using individual‐level data when there is unobserved heterogeneity in preferences. We show that for binary and unordered multinomial choice, the marginal distributions of EV and CV can be expressed as simple closed‐form functionals of conditional choice probabilities under essentially unrestricted preference distributions. These results hold even when the distribution and dimension of unobserved heterogeneity are neither known nor identified, and utilities are neither quasilinear nor parametrically specified. The welfare distributions take simple forms that are easy to compute in applications. In particular, average EV for a price rise equals the change in average Marshallian consumer surplus and is smaller than average CV for a normal good. These nonparametric point‐identification results fail for ordered choice if the unit price is identical for all alternatives, thereby providing a connection to Hausman–Newey's (2014) partial identification results for the limiting case of continuous choice.