Research

Working Papers

Decomposition of Consumer Sentiment and the Effects of Its Cyclical Component (Job Market Paper)

Conference Presentation: the 2024 SEA, the NTxEC 2024, the 2024 MEG, the 2024 WEAI, the 2024 CEA, the 2024 EEA, the 2024 WERI

Conventional empirical models occasionally incorporate sentiment to explain business cycle movements but rarely distinguish consumer optimism or pessimism from overall sentiment level. In this paper, I decompose consumer sentiment into trends and cycles using five filtering methods, with the cycles representing consumer over-optimism or pessimism. To mitigate discrepancies arising from specific statistical methods, I construct an average cycle based on a suite of five distinct commonly used measures. Using the average cyclical sentiment, I analyze the impact of sentiment on inflation expectations and macroeconomic variables. When using monthly U.S. data from 1978 to 2023, I find that cyclical sentiment significantly impacts inflation expectations, unemployment, and industrial production. However, the responses of core CPI inflation and CPI inflation to cyclical sentiment are ambiguous. I also incorporate several additional variables related to personal income, consumption, financial markets, and the labor market to examine their responses to cyclical sentiment. All these variables also exhibit short-lived but significant responses to cyclical sentiment movements.

Quantifying the Inflation Expectations Pass-Through over the Inflation Distribution (with Jose Barrales-Ruiz, Mikidadu Mohammed, Irina Panovska)

[Conference Presentation: the 2024 SEA]

This study investigates the impact of inflation expectations on the distribution of inflation in the US. We find that the effect of expectations varies depending on the distribution of inflation. Using a quantile vector autoregressive (QVAR) model, we find that when inflation is low, expectations play a limited role. However, when inflation rises above its median value, expectations exert a significant positive influence on the distribution of future inflation outcomes. We enhance the analysis by using kernel density estimation to examine the impact of inflation expectations shock on the entire distribution of inflation across various horizons. Our findings show that (i) the density of personal consumption expenditure (PCE) and core PCE inflation exhibits a nonlinear (bimodal or multimodal) distribution, and (ii) at higher horizons, the distribution of the responses becomes more dispersed.

Nonlinear Effects of Economic Variables on Disagreements about Inflation Expectations

I explore the dynamics of household disagreement about inflation expectations. Understanding how well inflation expectations are anchored is crucial for central bankers and policymakers in conducting monetary policy. However, the mean or median measures of household inflation expectations do not provide a complete picture; it is necessary to analyze measures of disagreement in inflation expectations (Reis, 2023). Using Local Projection, I analyze the impact of several exogenous shocks on households’ disagreement in inflation expectations. I use the University of Michigan’s Survey of Consumers monthly data from January 1985 to December 2023 to observe the response of disagreement in inflation expectations, measured by the interquartile range and standard deviation of one-year-ahead inflation expectations. I find that disagreement in inflation expectations responds significantly for a few months to various economic policy uncertainty shocks, sentiment shocks, and oil shocks. The nonlinear specification shows that the responses of disagreement in inflation expectations to these shocks depend on the level of inflation and on the state of the business cycle. Specifically, the responses of disagreement in inflation expectations are less pronounced when CPI inflation is in a high state compared to when it is low. The findings are consistent when analyzing the state of unemployment.

Estimated Output Gap in a Wage-Inflation Expectations Model (with Prajyna Barua Soni, Irina Panovska, Srikanth Ramamurthy)

[Conference Presentation: the SEM 2024, the SEA 2023, the CFE 2022]

We study how incorporating adaptive learning-based inflation expectations for price and wage inflation can improve the performance of Unobserved Components (UC) models both when it comes to inference about the output gap and when it comes to forecasting performance. We incorporate learning-based expectations and bivariate feedback in the learning dynamics for wage and price inflation, and we augment a conventional reduced-form UC model for output and unemployment with this bivariate learning process. Our model directly integrates the expectations dynamics of the Hybrid New Keynesian Philips Curve while also retaining the appealing statistical features of the UC framework, allowing us to extract information about the output gap. Three interesting sets of results stand out. First, while the perceived persistence of inflation fell during the early stages of the pandemic, it increased sharply and substantially during the period 2021Q2-2022Q2. Second, the estimated output gap started decreasing in mid-2019, decreased sharply during the early stages of the pandemic, and bounced back rapidly. Finally, including information about the output gap and about the price and wage inflation expectations process helps improve macroeconomic forecasts.

Does Physician Recruitment Impact Access and Health of Rural Residents? Evidence from the 2014 Recruitment of 6,000 Physicians in Bangladesh (with Redwan Bin Abdul Baten, Shahidul Islam, Ahmed Hossain)

This study evaluates the impact of a 2014 policy that increase physician supply in rural Bangladesh, utilizing data from the Household Income and Expenditure Survey (2005–2016). A Difference-in-Differences model compares healthcare access and outcomes for rural residents relative to their urban counterparts. Results indicate a 14-percentage-point rise in rural visits to government doctors and a 15-point decline in visits to private doctors. Reliance on informal care sources, such as pharmacy salesmen and traditional healers, decreased, while access to public medication improved. Although rural residents experienced higher transportation and medicine costs, overall healthcare costs declined due to greater reliance on public healthcare. Chronic conditions, including heart disease and arthritis, were more frequently reported, and average travel time to healthcare providers was reduced. These findings suggest that the intervention successfully enhanced healthcare access, utilization, and affordability for rural residents.