Essays on Price Dispersion and Policy Analysis

A pivotal question in macroeconomics is how output, employment, and price level react to/nmonetary, fiscal, and productivity shocks, both in business-cycle models and in the data. Sticky prices are often considered as one of the key amplification and propagation mechanisms for such shocks. However, there is still a widespread debate how sticky prices are and why they are sticky. This dissertation sheds a new light on this question. Chapter 1 relies on a relatively understudied measure of price stickiness--cross-sectional dispersion of prices--to distinguish between different models of price rigidity, while Chapter 2 measures price stickiness in online markets. With e-commerce becoming a significantly larger sector of the economy, this is one of the first attempts to understand pricing in online markets from data comparable to those used for brick-and-mortar stores. Since different business-cycle models make conflicting predictions about effects of demand shocks, in Chapter 3 I approach this question empirically by estimating the size of fiscal multipliers from military spending data. Such empirical estimates may help researchers and policymakers to distinguish between various models./nIn macroeconomic models, the level of price dispersion, which is typically approximated/nusing its relationship with inflation, is a central determinant of welfare, the cost of business/ncycles, the optimal rate of inflation, and the trade-off between inflation and output stability./nWhile the comovement of price dispersion and inflation implied by standard models is positive,/nin this dissertation I show that it is actually negative in the data. Chapter 1 shows that sales/nplay a pivotal role: i) if sales are removed from the data, the comovement of price dispersion/nand inflation turns positive; ii) models in which price dispersion is due to price rigidity cannot/nquantitatively match the comovement even for regular prices; iii) the Calvo model with sales/ncan quantitatively match both the negative comovement found in the data and the positive/ncomovement for regular prices. Finally, I show that models that fail to match the degree of/ncomovement in the data can significantly mismeasure welfare and its determinants./nChapter 2 focuses on price-setting practices in online markets examined through the lens of/na novel dataset on price listings and the number of clicks from the Google Shopping Platform./nThis unique dataset contains information on price quotes and the number of clicks at the daily/nfrequency for a broad variety of consumer goods and sellers in the US and UK over the period of nearly two years. This chapter provides estimates of the frequency of price adjustment,/nprice synchronization across sellers and goods, as well as the distribution of the sizes of price changes. It compares the estimates for the case when information on quantity margin is observed--as in the scanner data from brick-and-mortar stores--with the case when it is not,/nwhich is typical in the literature on online prices. It concludes that many internet prices that/ndo not change often obtain very few clicks. The key findings are the following: First, despite/nthe cost of price change being negligible, prices appear relatively sticky. Second, if the quantity margin is accounted for, prices are much more flexible. It remains a question why low-demand sellers do not adjust their prices often, yet maintain costly price listings on the platform. Third, in spite of low costs of monitoring competitors' prices and high benefits from doing so--since search costs for consumers are low too--there is little price synchronization across sellers. Fourth, the distribution of the sizes of price changes is characterized by a non-trivial mass around zero, which is inconsistent with the state-dependent models with fixed menu costs, but favors time-dependent models of price adjustment. Hence, online prices change infrequently, by a large amount, and are not synchronized across sellers./nIn Chapter 3, I use a multi-country dataset on disaggregated military spending to document/nthe effect of government expenditure by sector on aggregate output. The data obtained/nfrom multiple sources including UN, NATO, and the Stockholm International Peace Research/nInstitute (SIPRI) allow to systematically break down total military expenditure into that on/ndurables versus nondurables and services for 69 countries within 1950-1997 period. I show/nthat the spending multiplier is larger when government spends on durables rather than on/nnondurables or services, which could be due to differences in price flexibility, intertemporal/nelasticity of substitution, or some other sectoral factors. Although the estimates suffer from/nthe lack of precision, the finding is robust across data sources and groups of countries. Quantitatively, the durables multiplier could be up to four times as high as that for nondurables/nand services. I use the dataset to estimate the standard spending multiplier as a litmus test,/nwhich results in a conventional fiscal multiplier of the size of about 1 ranging from 0.6 to 1.3/nin different samples of countries.

Advisor: Gorodnichenko, Yuriy