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

Key macro indicators such as output, productivity, and inflation are based on a complex system across multiple statistical agencies using different samples and levels of aggregation. 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 data and other sources. The price and quantity data are integrated at a high level of aggregation. This paper explores alternative methods for reengineering key national output and price indices using item-level data. Such reengineering offers the promise of greatly improved key economic indicators along many dimensions.

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

International Journal for Population Data Science ◽

10.23889/ijpds.v3i5.1055 ◽

2018 ◽

Vol 3 (5) ◽

Author(s):

Gabe Ehrlich ◽

John Haltiwanger ◽

Ron Jarmin ◽

David Johnson

Keyword(s):

Price Change ◽

Census Bureau ◽

Output Growth ◽

Alternative Methods ◽

Producer Price ◽

Productivity Data ◽

Level Data ◽

National Output ◽

High Level ◽

Bureau Of Labor Statistics

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.

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Harnessing Agricultural Potentials for Economic Growth in North Carolina

Sustainable Agriculture Research ◽

10.5539/sar.v2n2p142 ◽

2013 ◽

Vol 2 (2) ◽

pp. 142 ◽

Cited By ~ 1

Author(s):

Janaranjana Herath ◽

David Hill

Keyword(s):

Economic Growth ◽

North Carolina ◽

Work Force ◽

Census Bureau ◽

Agricultural Activities ◽

Income Levels ◽

Rural Counties ◽

Level Data ◽

Education And Employment ◽

Bureau Of Labor Statistics

Agriculture in North Carolina contributes to 19 percent of the state’s income and employs over 20 percent of the work force. Agricultural activities are significant in rural counties and nearly 30 percent of the total population of North Carolina lives in 85 rural counties. Individuals in these rural counties have less income, education, and employment opportunities eventually in high poverty and unemployment rates. The objective of this study is to examine the potential use of agriculture in economic growth of North Carolina using county level data. Data were gathered from U.S. Bureau of Labor Statistics, U.S. Department of Agriculture, and U.S. Census Bureau for the period of 2000 to 2010. A system of simultaneous equations is used for the analysis. Results highlight that increasing income increases agricultural activities and vise versa. Thus, the counties with high household income levels are more capable of incorporating agriculture in economic growth while the counties with significant agricultural activities are more competent of improving income levels. Overall, results conclude the importance of secured satisfactory level of income through agriculture to enhance economic growth.

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U.S. consumer price index in the second half of XX century: calculation strategy and ways of its changes

Statistics and Economics ◽

10.21686/2500-3925-2019-6-67-76 ◽

2019 ◽

Vol 16 (6) ◽

pp. 67-76

Author(s):

M. A. Kozlova

Keyword(s):

Price Index ◽

Consumer Price Index ◽

Index Number ◽

Goods And Services ◽

Level Of Aggregation ◽

High Level ◽

Bureau Of Labor Statistics ◽

Alternative Solutions ◽

Data Generalization ◽

Index Calculation

The purpose of this research is a detection of U.S. consumer price index development and change ways emerged in the second half of XX century. Consumer price index is considered as a practically evaluable index number.Materials and methods. This research is based on the methodology documents of U.S. Bureau of Labor Statistics and its theoretical and practical papers published in Monthly Labor Review. The basic method is historical and descriptive techniques.Results. Data generalization for U.S. consumer price index across five revisions is realized in structure of the calculation method, adapted by ROSSTAT for the national consumer price index. Firstly the dynamic of number of cities, included in consumer price survey and changes of its sample is analyzed. Secondly the principles of point of purchase sampling is in focus. Thirdly the set of goods and services and dynamics of its structure are considered. Fourthly there is a generalization of pricing procedure principles that is frequency according to the type of cities and feature of goods and services. Fifthly the source and limits of data collecting for weights which needed for consumer price index calculation on the high level of aggregation. And sixthly there is description of mean price and price index calculation.Conclusion. The main ways of development and transformation in U.S. consumer price index are defined. It may be considered as alternative solutions in consumer price index of other countries. The main ways are the increase of city and goods sampling, extension of probability use, formation of good classification, equal temporal interval of weight renovation and creation of price index system.

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Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern

Journal of Economic Literature ◽

10.1257/jel.53.3.631 ◽

2015 ◽

Vol 53 (3) ◽

pp. 631-653 ◽

Cited By ~ 29

Author(s):

Charles F. Manski

Keyword(s):

Sampling Error ◽

The United States ◽

Census Bureau ◽

Statistical Uncertainty ◽

The Public ◽

Point Estimates ◽

Economic Statistics ◽

Labor Statistics ◽

Statistical Agencies ◽

Bureau Of Labor Statistics

Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics. (JEL B22, C82, E23)

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