Liran Einav

Determinants of international tourism: a three-dimensional panel data analysis

International tourism is a fast growing industry generating half a trillion dollars in annual revenues and accounting for almost 10% of total international trade, and almost half of total trade in services. Yet, it has so far failed to receive the attention it deserves from mainstream economics. This paper attempts to provide an initial understanding of the determinants of international tourism. This paper claims that international tourism, as other forms of trade in services, is driven by unique factors of production, and may be better dealt with in a single industry study rather than in a general equilibrium trade model. In order to understand these determinants the world is viewed as a market of differentiated products, and a discrete choice estimation technique is applied to a large three-dimensional data set of tourist flows. It is shown that a relatively simple estimation technique, combined with a rich data set, can deliver reasonable substitution patterns. It is found, among other things, that political risk is very important for tourism, and that exchange rates matter mainly for tourism to developed countries. These have exchange rate elasticity of about one.

The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities

This paper investigates the effects of mandatory seat belt laws on driver behavior and traffic fatalities. Using a unique panel data set on seat belt usage in all U.S. jurisdictions, we analyze how such laws, by influencing seat belt use, affect the incidence of traffic fatalities. Allowing for the endogeneity of seat belt usage, we find that such usage decreases overall traffic fatalities. The magnitude of this effect, however, is significantly smaller than the estimate used by the National Highway Traffic Safety Administration. In addition, we do not find significant support for the compensating-behavior theory, which suggests that seat belt use also has an indirect adverse effect on fatalities by encouraging careless driving. Finally, we identify factors, especially the type of enforcement used, that make seat belt laws more effective in increasing seat belt usage.

Economics in the age of big data.

Background Economic science has evolved over several decades toward greater emphasis on empirical work. The data revolution of the past decade is likely to have a further and profound effect on economic research. Increasingly, economists make use of newly available large-scale administrative data or private sector data that often are obtained through collaborations with private firms, giving rise to new opportunities and challenges. The rising use of non–publicly available data in economic research. Here we show the percentage of papers published in the American Economic Review (AER) that obtained an exemption from the AER’s data availability policy, as a share of all papers published by the AER that relied on any form of data (excluding simulations and laboratory experiments). Notes and comments, as well as AER Papers and Proceedings issues, are not included in the analysis. We obtained a record of exemptions directly from the AER administrative staff and coded each exemption manually to reflect public sector versus private data. Our check of nonexempt papers suggests that the AER records may possibly understate the percentage of papers that actually obtained exemptions. The asterisk indicates that data run from when the AER started collecting these data (December 2005 issue) to the September 2014 issue. To make full use of the data, we define year 2006 to cover October 2005 through September 2006, year 2007 to cover October 2006 through September 2007, and so on. Advances These new data are affecting economic research along several dimensions. Many fields have shifted from a reliance on relatively small-sample government surveys to administrative data with universal or near-universal population coverage. This shift is transformative, as it allows researchers to rigorously examine variation in wages, health, productivity, education, and other measures across different subpopulations; construct consistent long-run statistical indices; generate new quasi-experimental research designs; and track diverse outcomes from natural and controlled experiments. Perhaps even more notable is the expansion of private sector data on economic activity. These data, sometimes available from public sources but other times obtained through data-sharing agreements with private firms, can help to create more granular and real-time measurement of aggregate economic statistics. The data also offer researchers a look inside the “black box” of firms and markets by providing meaningful statistics on economic behavior such as search and information gathering, communication, decision-making, and microlevel transactions. Collaborations with data-oriented firms also create new opportunities to conduct and evaluate randomized experiments. Economic theory plays an important role in the analysis of large data sets with complex structure. It can be difficult to organize and study this type of data (or even to decide which variables to construct) without a simplifying conceptual framework, which is where economic models become useful. Better data also allow for sharper tests of existing models and tests of theories that had previously been difficult to assess. Outlook The advent of big data is already allowing for better measurement of economic effects and outcomes and is enabling novel research designs across a range of topics. Over time, these data are likely to affect the types of questions economists pose, by allowing for more focus on population variation and the analysis of a broader range of economic activities and interactions. We also expect economists to increasingly adopt the large-data statistical methods that have been developed in neighboring fields and that often may complement traditional econometric techniques. These data opportunities also raise some important challenges. Perhaps the primary one is developing methods for researchers to access and explore data in ways that respect privacy and confidentiality concerns. This is a major issue in working with both government administrative data and private sector firms. Other challenges include developing the appropriate data management and programming capabilities, as well as designing creative and scalable approaches to summarize, describe, and analyze large-scale and relatively unstructured data sets. These challenges notwithstanding, the next few decades are likely to be a very exciting time for economic research. The quality and quantity of data on economic activity are expanding rapidly. Empirical research increasingly relies on newly available large-scale administrative data or private sector data that often is obtained through collaboration with private firms. Here we highlight some challenges in accessing and using these new data. We also discuss how new data sets may change the statistical methods used by economists and the types of questions posed in empirical research.

The Data Revolution and Economic Analysis

Many believe that “big data” will transform business, government, and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe economic activity. They can also enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics.

Contract Pricing in Consumer Credit Markets

We analyze subprime consumer lending and the role played by down payment requirements in screening high-risk borrowers and limiting defaults. To do this, we develop an empirical model of the demand for financed purchases that incorporates both adverse selection and repayment incentives. We estimate the model using detailed transaction-level data on subprime auto loans. We show how different elements of loan contracts affect the quality of the borrower pool and subsequent loan performance. We also evaluate the returns to credit scoring that allows sellers to customize financing terms to individual applicants. Our approach shows how standard econometric tools for analyzing demand and supply under imperfect competition extend to settings in which firms care about the identity of their customers and their postpurchase behavior.

OPTIMAL MANDATES AND THE WELFARE COST OF ASYMMETRIC INFORMATION: EVIDENCE FROM THE U.K. ANNUITY MARKET

Much of the extensive empirical literature on insurance markets has focused on whether adverse selection can be detected. Once detected, however, there has been little attempt to quantify its welfare cost or to assess whether and what potential government interventions may reduce these costs. To do so, we develop a model of annuity contract choice and estimate it using data from the U.K. annuity market. The model allows for private information about mortality risk as well as heterogeneity in preferences over different contract options. We focus on the choice of length of guarantee among individuals who are required to buy annuities. The results suggest that asymmetric information along the guarantee margin reduces welfare relative to a first-best symmetric information benchmark by about £127 million per year or about 2 percent of annuitized wealth. We also find that by requiring that individuals choose the longest guarantee period allowed, mandates could achieve the first-best allocation. However, we estimate that other mandated guarantee lengths would have detrimental effects on welfare. Since determining the optimal mandate is empirically difficult, our findings suggest that achieving welfare gains through mandatory social insurance may be harder in practice than simple theory may suggest.

A Theory of Endogenous Commitment

Commitment is typically modelled by assigning to one of the players the ability to take an initial binding action. The weakness of this approach is that the fundamental question of who has the opportunity to commit cannot be addressed, as it is assumed. This paper presents a framework in which commitment power arises endogenously from the fundamentals of the model. We construct a finite dynamic game in which players are given the option to change their minds as often as they wish, but pay a switching cost if they do so. We show that for games with two players and two actions there is a unique subgame-perfect equilibrium with a simple structure. This equilibrium is independent of the order and timing of moves and is robust to other protocol specifications. Moreover, despite the perfect information nature of the model and the costly switches, strategic delays may arise in equilibrium. The flexibility of the model allows us to apply it to various environments. In particular, we study an entry deterrence situation. Its equilibrium is intuitive and illustrative of how commitment power is endogenously determined. Copyright 2008, Wiley-Blackwell.

The impact of credit scoring on consumer lending

We study the adoption of automated credit scoring at a large auto finance company and the changes it enabled in lending practices. Credit scoring appears to have increased profits by roughly a thousand dollars per loan. We identify two distinct benefits of risk classification: the ability to screen high-risk borrowers and the ability to target more generous loans to lower-risk borrowers. We show that these had effects of similar magnitude. We also document that credit scoring compressed profitability across dealerships, and provide evidence consistent with the view that credit scoring may have substituted for varying qualities of local information.

MORAL HAZARD IN HEALTH INSURANCE: DO DYNAMIC INCENTIVES MATTER?

Using data from employer-provided health insurance and Medicare Part D, we investigate whether health care utilization responds to the dynamic incentives created by the nonlinear nature of health insurance contracts. We exploit the fact that because annual coverage usually resets every January, individuals who join a plan later in the year face the same initial (“spot”) price of health care but a higher expected end-of-year (“future”) price. We find a statistically significant response of initial utilization to the future price, rejecting the null that individuals respond only to the spot price. We discuss implications for analysis of moral hazard in health insurance.

Multilateral Bargaining with Concession Costs

This paper presents a new non-cooperative approach to multilateral bargaining. We consider a demand game with the following additional ingredients: (i) There is an exogenous deadline, by which bargaining has to end; (ii) Prior to the deadline, players may sequentially change their demands as often as they like; (iii) Changing ones demand is costly, and this cost increases as the deadline gets closer. The game has a unique subgame perfect equilibrium prediction in which agreement is reached immediately and switching costs are avoided. Moreover, this equilibrium is invariant to the particular order and timing in which players make demands. This is important, as multilateral bargaining models are sometimes too sensitive to these particular details. In our context, players with higher concession costs obtain higher shares of the pie; their increased bargaining power stems from their ability to credibly commit to a demand earlier. We discuss how the setup and assumptions are a reasonable description for certain real bargaining situations.