Research

We use insurance claims data covering 28% of individuals with employer-sponsored health insurance in the United States to study the variation in health spending on the privately insured, examine the structure of insurer-hospital contracts, and analyze the variation in hospital prices across the nation. Health spending per privately insured beneficiary differs by a factor of three across geographic areas and has a very low correlation with Medicare spending. For the privately insured, half of the spending variation is driven by price variation across regions, and half is driven by quantity variation. Prices vary substantially across regions, across hospitals within regions, and even within hospitals. For example, even for a nearly homogeneous service such as lower-limb magnetic resonance imaging, about a fifth of the total case-level price variation occurs within a hospital in the cross section. Hospital market structure is strongly associated with price levels and contract structure. Prices at monopoly hospitals are 12% higher than those in markets with four or more rivals. Monopoly hospitals also have contracts that load more risk on insurers (e.g., they have more cases with prices set as a share of their charges). In concentrated insurer markets the opposite occurs—hospitals have lower prices and bear more financial risk. Examining the 366 mergers and acquisitions that occurred between 2007 and 2011, we find that prices increased by over 6% when the merging hospitals were geographically close (e.g., 5 miles or less apart), but not when the hospitals were geographically distant (e.g., over 25 miles apart). JEL Codes: I11, L10, L11.

We examined the growth in health spending on people with employer-sponsored private insurance in the period 2007–14. Our analysis relied on information from the Health Care Cost Institute data set, which includes insurance claims from Aetna, Humana, and UnitedHealthcare. In the study period private health spending per enrollee grew 16.9 percent, while growth in Medicare spending per fee-for-service beneficiary decreased 1.2 percent. There was substantial variation in private spending growth rates across hospital referral regions (HRRs): Spending in HRRs in the tenth percentile of private spending growth grew at 0.22 percent per year, while HRRs in the ninetieth percentile experienced 3.45 percent growth per year. The correlation between the growth in HRR-level private health spending and growth in fee-for-service Medicare spending in the study period was only 0.211. The low correlation across HRRs suggests that different factors may be driving the growth in spending on the two populations.

Evidence suggests that growth in providers’ prices drives growth in health care spending on the privately insured. However, existing work has not systematically differentiated between the growth rate of hospital prices and that of physician prices. We analyzed growth in both types of prices for inpatient and hospital-based outpatient services using actual negotiated prices paid by insurers.

We found that in the period 2007–14 hospital prices grew substantially faster than physician prices. For inpatient care, hospital prices grew 42 percent, while physician prices grew 18 percent. Similarly, for hospital-based outpatient care, hospital prices grew 25 percent, while physician prices grew 6 percent. A majority of the growth in payments for inpatient and hospital-based outpatient care was driven by growth in hospital prices, not physician prices.

Our work suggests that efforts to reduce health care spending should be primarily focused on addressing growth in hospital rather than physician prices. Policymakers should consider a range of options to address hospital price growth, including antitrust enforcement, administered pricing, the use of reference pricing, and incentivizing referring physicians to make more cost-efficient referrals.

This paper examines the impact of a government programme which facilitated the entry of for-profit surgical centres to compete against incumbent National Health Service hospitals in England. We examine the impact of competition from these surgical centres on the efficiency – measured by pre-surgery length of stay for hip and knee replacement patients – and case mix of incumbent public hospitals. We exploit the fact that the government chose the broad locations where these surgical centres (Independent Sector Treatment Centres or ISTCs) would be built based on local patient waiting times – not length of stay or clinical quality – to construct treatment and control groups that are comparable with respect to key outcome variables of interest. Using a difference-in-difference estimation strategy, we find that the government-facilitated entry of surgical centres led to shorter pre surgery length of stay at nearby public hospitals. However, these new entrants took on healthier patients and left incumbent hospitals treating patients who were sicker. This paper highlights a potential trade-off that policymakers face when they promote competition from private, for-profit firms in markets for the provision of public services.

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated randomization integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.

We quantify the distortionary effects of nexus tax laws on Amazon’s distribution network 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.

Big tech companies like Google, Amazon, and Facebook have amassed an unprecedented collection of individual data. The authors demonstrate why data ownership is insufficient to give consumers control over their data and how individual data is actually social data due to data externalities. The paper explores the effects of collecting individual data, terms of trade between consumers and digital platforms, the social dimension of data, and how data intermediaries change the level of aggregation and precision of information they provide. The authors find that the revenue-maximizing data policy is to collect consumer data but not forward it to producers.

Firms are increasingly turning to artificial intelligence to analyze marketplace trends and determine pricing strategies. This paper demonstrates how AI pricing algorithms may actually learn, with no human instruction, to set anti-competitive prices under standard economic conditions. The authors’ findings highlight the need for antitrust reform that recognizes the potential for algorithms to autonomously collude.