The extent to which either supply or demand factors drive inflation has important implications for economic policy. I propose a framework to decompose inflation into supply- and demand-driven components. I generate two new data series, the supply and demand-driven contributions to personal consumption expenditures (PCE) inflation, which quantify the degree to which either demand or supply is driving inflation in a current month. The series show expected time-series patterns. The demand-driven contribution tends to decline during recessions, while the supply-driven contribution tends to follow food and energy prices. Monetary policy tightening acts to reduce the demand-driven contribution of inflation. Oil-supply shocks act to increase the supply driven contribution, but decrease the demand-driven contribution of inflation. The decompositions can be used to test theory or by policymakers and practitioners to track inflation drivers in real time.
This paper proposes a simple framework to help monitor and understand movements in PCE inflation in real time. The approach is to decompose inflation using simple categorical-level regressions or systems of equations. The estimates are then used to group categories into components of PCE inflation. I review some applications of the methodology, and show how it can help explain inflation dynamics over recent episodes. The methodology shows that inflation remained low in the mid-2010s primarily because of factors unrelated to aggregate economic conditions. I also apply the methodology to the Covid-19 pandemic. The decomposition reveals that a majority of elevated inflation in core PCE inflation in the 2021 2022 period was due to “Covid-sensitive” categories, that is, those categories where prices and quantities moved the most at the onset of the pandemic. Finally, I show how the methodology can be applied in a dynamic fashion, labeling categories as either supply- or demand-driven by month. This decomposition allows one to assess the extent to which supply and demand factors are impacting inflation.
What does it cost healthcare providers to collect payment in the complex U.S. health insurance system? We study this question using rich data on repeated interactions between a large sample of physicians and many different payers, and investigate the consequences when these costs are high. Payment uncertainty is high and variable, with 19% of Medicaid visits not reimbursed after the first claim submission. In such cases, physicians either forgo substantial revenue or incur costs to collect payment. Using physician movers and practices that span state boundaries, we find that providers respond to these costs by refusing to accept Medicaid patients in states with more severe billing hurdles. This supply margin is even more responsive to these costs than to reimbursement rates. Using these supply estimates, we calculate that the costs of billing Medicaid consume one-quarter of the average revenue from a Medicaid visit. We estimate a model of the billing process, and find that the variable costs of billing each visit account for 21 percentage points of this total cost. Analyzing healthcare prices without accounting for billing costs and payment uncertainty may substantially misrepresent differences between private payers and Medicaid.
We examine a model of consumer learning and price signaling where price and quality are optimally chosen by a monopolist. Through numerical solution and simulation of the model we find that price signaling causes the firm to raise its prices, lower its quality, and dampen the degree to which it passes on cost shocks to price. We identify two mechanisms through which signaling affects pass-through. The first is static: holding quality fixed, price signaling increases the curvature of demand relative to the case where quality is known, which ultimately acts to dampen how prices respond to changes in cost. The second is dynamic: a firm that engages in signaling recognizes that changing prices today affects consumer beliefs about the relationship between prices and quality in the future. We also find that signaling can lead to asymmetric pass-through. If the cost of adjusting quality is sufficiently high, then cost increases pass through to a greater extent than cost decreases.
This paper provides the first rigorous assessment of the homeownership experiences of subprime borrowers. We consider homeowners who used subprime mortgages to buy their homes, and estimate how often these borrowers end up in foreclosure. In order to evaluate these issues, we analyze homeownership experiences in Massachusetts over the 1989-2007 period using a competing risks, proportional hazard framework. We present two main findings. First, homeownerships that begin with a subprime purchase mortgage end up in foreclosure almost 20 percent of the time, or more than 6 times as often as experiences that begin with prime purchase mortgages. Second, house price appreciation plays a dominant role in generating foreclosures. In fact, we attribute most of the dramatic rise in Massachusetts foreclosures during 2006 and 2007 to the decline in house prices that began in the summer of 2005.
We estimate a model of foreclosure using a data set that includes every residential mortgage, purchase-and-sale, and foreclosure transaction in Massachusetts from 1989 to 2008. We address the identification issues related to the estimation of the effects of house prices on residential foreclosures. We then use the model to study the dramatic increase in foreclosures that occurred in Massachusetts between 2005 and 2008 and conclude that the foreclosure crisis was primarily driven by the severe decline in housing prices that began in the latter part of 2005, not by a relaxation of underwriting standards on which much of the prevailing literature has focused. We argue that relaxed underwriting standards did severely aggravate the crisis by creating a class of homeowners who were particularly vulnerable to the decline in prices. But, as we show in our counterfactual analysis, that emergence alone, in the absence of price collapse, would not have resulted in the substantial foreclosure boom that was experienced.
Using a new dataset on household purchases of personal computers (PCs), we document positive correlations between buyers’ incomes and the prices they pay for seemingly identical PCs. These results suggest that firms may be successful at separating the market and charging different prices to consumers with different levels of willingness to pay. We consider the implications of this kind of market separation for price and quality measurement via a theoretical model based on Mussa and Rosen (1978). The model suggests that, in markets like these, standard methods that do not account for this heterogeneity can understate inflation in a cost-of-living context. Consistent with the model, our empirical work shows that controlling for income yields indexes that show slower price declines than seen in standard indexes. This understatement of the cost-of-living measure likely mitigates the unrelated upward biases found in recent studies by Bils (2009), Erickson and Pakes (2010), Broda and Weinstein (2010).
Published Articles (Refereed Journals and Volumes)
We propose a new approach to estimating central bank preferences, including the implicit inflation target, that requires no priors on the underlying macroeconomic structure nor observation of monetary policy actions. Our approach entails directly estimating the central bank’s objective function from the sentiment expressed by policymakers in their internal meetings. We apply the approach to the objective function of the U.S. Federal Open Market Committee (FOMC). The results challenge two key aspects of conventional wisdom regarding FOMC preferences. First, the FOMC had an implicit inflation target of approximately 1½ percent on average over our baseline 2000 – 2011 sample period, significantly below the commonly-assumed value of 2. Second, the FOMC’s loss depends strongly on output growth and stock market performance and less so on their perception of current economic slack.
Using price quote data that underpin the official U.K. consumer price index (CPI), we analyze the effects of the unexpected passing of the Brexit referendum to the dynamics of price adjustments. The sizable depreciation of the British pound that immediately followed Brexit works as a quasi-experiment, enabling us to study the transmission of a large common marginal cost shock to inflation as well as the distribution of prices within granular product categories. A large portion of the inflationary effect is attributable to the size of price adjustments, implying that a time-dependent price-setting model can match the response of aggregate inflation reasonably well. The state-dependent model fares better in capturing the endogenous selection of price changes at the lower end of the price distribution, however, it misses on the magnitude of the adjustment conditional on selection.
We use variation in banks’ loan exposure to industries adversely affected by the oil price declines of 2014 to explore how they respond to a net worth shock. Using granular data obtained under the Fed’s stress testing programs we show that exposed banks tightened credit on corporate lending and on mortgages that they would ultimately hold on their balance sheet. However, they expanded credit for mortgages to be securitized, particularly those that are government-backed. Thus, banks re-balance their portfolio so as to lower their average risk weight, rather than scaling back the size of their balance sheet, as looking at on-balance-sheet corporate or residential lending alone would suggest. These results show the importance of taking a cross-balance sheet perspective when examining bank behavior. In addition, in terms of the ultimate “credit channel” to firms and households, we show precisely how borrowers substitute to alternative financing when banks they initially borrow from tighten credit. In showing that there was ultimately a minimal impact on borrowers’ overall funding, we provide a benchmark for crisis-period studies, which typically find a powerful credit channel effect.
This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results highlight the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing “off-the-shelf” models. Lastly, we provide two applications to the economic research on sentiment. First, we show that daily news sentiment is predictive of movements of survey-based measures of consumer sentiment. Second, motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks, finding that positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.
This paper measures the costs and types of administrative inputs in health care. We use data on labor and nonlabor inputs by industry and categorize them as administrative or not. We find that nonlabor inputs are a critical part of administrative spending, over and above labor inputs. Trends in nonlabor administrative input spending have differed dramatically from that of labor input spending for hospitals over the last 20 years. Hospitals have substituted away from office workers and toward externally purchased inputs. The share of managers and technical workers in administration has grown. The technology of health care administration is changing.
We examine the impact of Medicare Part D on mortality for the population over the age of 65. We identify the effects of the reform using variation in drug coverage across counties before the reform was implemented. Studying mortality rates immediately before and after the reform, we find that cardiovascular-related mortality drops significantly in those counties most affected by Part D. Estimates suggest that up to 26,000 more individuals were alive in mid-2007 because of the Part D implementation in 2006.
The administrative costs of providing health insurance in the US are very high, but their determinants are poorly understood. We advance the nascent literature in this field by developing new measures of billing complexity for physician care across insurers and over time, and by estimating them using a large sample of detailed insurance “remittance data” for the period 2013–15. We found dramatic variation across different types of insurance. Fee-for-service Medicaid is the most challenging type of insurer to bill, with a claim denial rate that is 17.8 percentage points higher than that for fee-for-service Medicare. The denial rate for Medicaid managed care was 6 percentage points higher than that for fee-for-service Medicare, while the rate for private insurance appeared similar to that of Medicare Advantage. Based on conservative assumptions, we estimated that the health care sector deals with $11 billion in challenged revenue annually, but this number could be as high as $54 billion. These costs have significant implications for analyses of health insurance reforms.
We study the impact of competition among physicians on service provision and patients’ health outcomes for the U.S. commercial market. We focus on cardiologists treating patients with a first-time heart attack treated in the emergency room. Physician concentration has a small, but statistically significant effect on service utilization. Cardiologists in more concentrated markets perform more intensive procedures, particularly, diagnostic procedures—services in which the procedure choice is more discretionary. Higher concentration leads to fewer readmissions but no effect on mortality. These findings suggest that changes in organizational structure, such as a merger of physician groups, not only influence the negotiated prices of services, but also service provision.
Medical-care expenditures have been rising rapidly, accounting for almost one-fifth of GDP in 2009. In this study, we assess the sources of the rising medical-care expenditures in the commercial sector. We employ a novel framework for decomposing expenditure growth into four components at the disease level: service price growth, service utilization growth, treated disease prevalence growth, and demographic shift. The decomposition shows that growth in prices and treated prevalence are the primary drivers of medical-care expenditure growth over the 2003 to 2007 period. There was no growth in service utilization at the aggregate level over this period. Price and utilization growth were especially large for the treatment of malignant neoplasms. For many conditions, treated prevalence has shifted towards preventive treatment and away from treatment for late-stage illnesses.
We provide guidelines to researchers measuring health expenditures by disease and compare these methodologies’ implied inflation estimates. A convenience sample of commercially-insured individuals over the 2003 to 2007 period from Truven Health. Population weights are applied, based on age, sex and region, to make the sample of over 4 million enrollees representative of the entire commercially-insured population. Different methods are used to allocate medical care expenditures to distinct condition categories. We compare the different methods based on their estimates of disease-price inflation. Across a variety of methods, the compound annual growth rate stays within the range 3.1 to 3.9 percentage points. Inflation at the disease category level is more sensitive to the selected methodology. The selected allocation method impacts aggregate inflation rates, but considering the variety of methods applied, the differences appear small. Future research is necessary to better understand these differences in other population samples and to connect disease expenditures to measures of quality.
We examine how the confluence of competition and upstream innovation influences downstream firms’ profit-maximizing strategies. We focus on personal computers (PCs) and using two novel data sets describe the dramatic fall in both price (27 percent at an annual rate) and sales of a computer over its product cycle. Further, we document that computers are typically sold for only 4 months before being replaced by a higher-quality product. To explain these facts, we develop and calibrate a vintage-capital model that combines a competitive market structure with an exogenous rapid rate of innovation.
The medical-care sector often experiences changes in medical protocols and technologies that cause shifts in treatments. However, the commonly used medical- care price indexes reported by the BLS hold the mix of medical services fixed. In contrast, episode expenditure indexes, advocated by many health economists, track the full cost of disease treatment, even as treatments shift across service categories (e.g., inpatient to outpatient hospital). In our data, we find that these two conceptually different measures of price growth show similar aggregate rates of inflation. Although aggregate trends are similar, we observe differences when looking at specific disease categories that have implications for the productivity of disease treatment.
This study examines the impact of major health insurance reform on payments made in the health care sector. We study the prices of services paid to physicians in the privately insured market during the Massachusetts health care reform. The reform increased the number of insured individuals as well as introduced an online marketplace where insurers compete. We estimate that, over the reform period, physician payments increased at least 11 percentage points relative to control areas. Payment increases began around the time legislation passed the House and Senate—the period in which their was a high probability of the bill eventually becoming law. This result is
consistent with fixed-duration payment contracts being negotiated in anticipation of future demand and competition.
We study the degree to which greater physician concentration leads to higher service prices charged by physicians in the commercially insured medical-care market. Using a database of physicians throughout the United States, we construct physician-firm concentration measures base “fixed-travel-time HHI” (FTHHI). We link these concentration measures to health insurance claims. We find that physicians in more concentrated markets charge higher service prices—a physician in the 90th percentile of market concentration will charge 14 to 30 percent higher fees than a physician in the 10th percentile. Our estimates imply that physician consolidation has caused about an 8 percent increase in fees on average over the last 20 years, and substantially higher increases in concentrated markets.
This study introduces a new framework for measuring and analyzing medical-care expenditures applied to the study of commercial medical-care markets. The framework focuses on expenditures at the disease level that are decomposed between price and utilization. These measures show that a particular MSA may have high overall prices, but may actually have low medical-care spending per episode due to low utilization. Prices within an MSA appear to be quite homogeneous, implying that regional factors explain a large degree of price variation. However, within an MSA there is a large degree of heterogeneity in utilization patterns between disease categories. This implies that most MSAs do not have systematically high or low utilization for all disease categories. We find evidence of a negative correlation between price and utilization across MSAs for many diseases, so it appears that the greater expenditures from higher prices are partly offset by lower utilization.
This study provides empirical evidence documenting how price dispersion moves with the business cycle in the airline industry. Performing a fixed-effects panel analysis on 17 years of data covering two business cycles, we find that price dispersion is highly pro-cyclical. This effect is especially pronounced for legacy carriers relative to low-cost carriers. We show that our empirical result is consistent with firms implementing second-degree price-discrimination tactics.
Strategic alliances are arrangements in which firms combine efforts and resources to jointly pursue a business objective while remaining separate entities. An example of such a practice is airline codesharing, in which allied carriers engage in the cooperative marketing of certain flights. We empirically test for the presence of competitive motives behind such alliances by identifying an incumbent airline’s use of codesharing in response to the threat of future entry by a competitor. Using within-flight segment, fixed-effects regressions on panel data from 1998-2010, we estimate the impact of exogenous threats of entry on an airline’s decision whether to codeshare with a partner on a specific segment. Estimates show that when an incumbent carrier’s segment is threatened by a low-cost competitor it is approximately 25% more likely than average to be codeshared with its partner. Further tests show that this effect depends strongly upon the level of market share that the airline has on the segment in question. We interpret this as evidence of a strategic alliance being used to preemptively act in anticipation of future competition.
We analyze the effects of competition on price dispersion in the airline industry, using panel data from 1993:Q1 through 2006:Q3. Competition has a negative effect on price dispersion, in line with the text-book treatmetn of price discrimination. This effect is pronounced for routes wtih consumers characterized by relatively heterogeneous elasticities of demand. On routes wtih a homogeneous customer base, the effects of competition on price dispersion are smaller. Our results contrast with those of Borenstein and Rose, who found that price dispersion increases with competition. We reconcile the different results by showing that the cross-sectional estimator suffers from omitted-variable bias.
It has become customary to estimate the New Keynesian Phillips Curve (NKPC) with GMM using a large instrument set that includes lags of variables that are ad hoc to the model. Researchers have also conventionally used real unit labor cost (RULC) as the proxy for real marginal cost, even though it is difficult to support its significance. This paper introduces a new proxy for the real marginal cost term as well as a new instrument set, both of which are based on the micro foundations of the vertical chain of production. I find that the new proxy, based on input prices as opposed to wages, provides a more robust and significant fit to the model. Instruments that are based on the vertical chain of production appear to be both more valid and relevant towards the model. This paper was revised in July 2006.