This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

« Back to Results

Using Micro Data to Understand Macro Aggregates

Paper Session

Saturday, Jan. 5, 2019 2:30 PM - 4:30 PM

Atlanta Marriott Marquis, A602
Hosted By: American Economic Association
  • Chair: Stephen James Redding, Princeton University

Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities

Gabriel Ehrlich
,
University of Michigan
John Haltiwanger
,
University of Maryland
Ron Jarmin
,
U.S. Census Bureau
David Johnson
,
University of Michigan
Matthew Shapiro
,
University of Michigan

Abstract

Key macro indicators such as output, productivity and inflation are based on a complex system of collection from different samples and different levels of aggregation across multiple statistical agencies. The Census Bureau collects nominal sales, the Bureau of Labor Statistics collects prices, and the Bureau of Economic Analysis constructs nominal and real GDP using these and other data sources. The price and quantity data are integrated at a high level of aggregation (product and industry classes). A similar mismatch of price and nominal variables pervades the productivity data, which use industry-level producer price indexes as deflators. This paper explores alternative methods for re-engineering key national output and price indices using transactions-level data. Such re-engineering offers the promise of greatly improved macroeconomic data along many dimensions. First, price and quantity would be based on the same observations. Second, the granularity of data could be greatly increased on many dimensions. Third, time series could be constructed at a higher frequency and on a more timely basis. Fourth, the use of transactions-level data opens the door to new methods for tracking product turnover and other sources of product quality change that may be biasing the key national indicators. Implementing such a new architecture for measuring economic activity and price change poses considerable challenges. This paper explores these challenges, along with a re-engineered approach’s implications for the biases in the traditional approaches to measuring output growth, productivity growth, and inflation.

Measuring Productivity: Lessons from Researcher Designed Surveys

David Atkin
,
Massachusetts Institute of Technology
Amit Khandelwal
,
Columbia University
Adam Osman
,
University of Illinois-Urbana-Champaign

Abstract

This paper explores the relationship between revenue and quantity productivity estimated using standard methods from typical firm-level surveys. As is well known, standard measures are confounded by quality differences, markups, and the fact that even single-industry firms produce many products. We draw on detailed firm-product-level data that was designed to account for these potential confounds to estimate TFPQ and TFPR. We further compare firm-level estimates with direct measures of firm productivity collected in a lab setting. We use our findings to provide guidance to researchers using standard datasets to infer productivity.

Aggregation and the Gravity Equation

Stephen James Redding
,
Princeton University
David Weinstein
,
Columbia University

Abstract

In a wide class of economic models, the impact of country participation in the global economy on welfare is summarized by import price indexes. Using data from the World Integrated Trade Solution (WITS) database, we provide new evidence on the evolution of import price indexes across countries, industries and over time. We use the new exact price index (the unified price index) for the CES demand system from Redding and Weinstein (2016), which allows for the non-conventional forces of variety, demand/quality and heterogeneity across goods, as well as the conventional forces of price. We show that our model-consistent import price indexes diverge substantially from standard statistical measures of import price indexes, highlighting larger contributions of trade to welfare than conventionally measured.
JEL Classifications
  • O4 - Economic Growth and Aggregate Productivity
  • F6 - Economic Impacts of Globalization