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Essays on Price Dispersion and Policy Analysis

  • Author(s): Sheremirov, Viacheslav
  • Advisor(s): Gorodnichenko, Yuriy
  • et al.

A pivotal question in macroeconomics is how output, employment, and price level react to

monetary, 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.

In macroeconomic models, the level of price dispersion, which is typically approximated

using its relationship with inflation, is a central determinant of welfare, the cost of business

cycles, the optimal rate of inflation, and the trade-off between inflation and output stability.

While the comovement of price dispersion and inflation implied by standard models is positive,

in this dissertation I show that it is actually negative in the data. Chapter 1 shows that sales

play a pivotal role: i) if sales are removed from the data, the comovement of price dispersion

and inflation turns positive; ii) models in which price dispersion is due to price rigidity cannot

quantitatively match the comovement even for regular prices; iii) the Calvo model with sales

can quantitatively match both the negative comovement found in the data and the positive

comovement for regular prices. Finally, I show that models that fail to match the degree of

comovement in the data can significantly mismeasure welfare and its determinants.

Chapter 2 focuses on price-setting practices in online markets examined through the lens of

a novel dataset on price listings and the number of clicks from the Google Shopping Platform.

This unique dataset contains information on price quotes and the number of clicks at the daily

frequency 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,

price 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,

which is typical in the literature on online prices. It concludes that many internet prices that

do not change often obtain very few clicks. The key findings are the following: First, despite

the 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.

In Chapter 3, I use a multi-country dataset on disaggregated military spending to document

the effect of government expenditure by sector on aggregate output. The data obtained

from multiple sources including UN, NATO, and the Stockholm International Peace Research

Institute (SIPRI) allow to systematically break down total military expenditure into that on

durables versus nondurables and services for 69 countries within 1950-1997 period. I show

that the spending multiplier is larger when government spends on durables rather than on

nondurables or services, which could be due to differences in price flexibility, intertemporal

elasticity of substitution, or some other sectoral factors. Although the estimates suffer from

the 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

and services. I use the dataset to estimate the standard spending multiplier as a litmus test,

which results in a conventional fiscal multiplier of the size of about 1 ranging from 0.6 to 1.3

in different samples of countries.

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