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Charles F. Manski - One of the best experts on this subject based on the ideXlab platform.
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communicating uncertainty in official Economic Statistics an appraisal fifty years after morgenstern
Journal of Economic Literature, 2015Co-Authors: Charles F. ManskiAbstract: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.
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Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern
Journal of Economic Literature, 2015Co-Authors: Charles F. ManskiAbstract: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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Communicating Uncertainty in Official Economic Statistics
National Bureau of Economic Research, 2014Co-Authors: Charles F. ManskiAbstract: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 non-sampling 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 non-response, and the Bureau of Labor Statistics seasonally adjusts employment Statistics.
Hugh Rockoff - One of the best experts on this subject based on the ideXlab platform.
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on the controversies behind the origins of the federal Economic Statistics
Journal of Economic Perspectives, 2019Co-Authors: Hugh RockoffAbstract:Our federal Economic Statistics originated in the Economic and political divisions in the United States and the bitter debates over Economic policy they engendered at the end of the 19th century and during the world wars and Great Depression. Workers were angry because they believed that they were being exploited by robber barons who were capturing all of the benefits of Economic growth, while employers were just as sure that the second industrial revolution had brought workers an unparalleled increase in real wages. Other debates centered on the effects of unrestricted immigration on wages and employment opportunities of native-born Americans, on the effects of tariffs on prices paid by consumers, on the effects of frequent financial panics on employment, and, during the world wars, on the effects of wage and price controls on the living standards of workers. Participants on all sides of these debates believed that nonpolitical and accurate Statistics constructed by experts would help to win support for the policies they favored. In most cases, the development of these Statistics was led by individuals, private organizations, and state governments, although the federal government eventually took over the role of producing these Statistics on a regular basis. Here I provide brief histories of the origins of US Statistics on prices, national income and product, and unemployment to illustrate this story.
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on the controversies behind the origins of the federal Economic Statistics
Research Papers in Economics, 2019Co-Authors: Hugh RockoffAbstract:Although attempts to measure trends in prices, output, and employment can be traced back for centuries, in the main the origins of the U.S. federal Statistics are to be found in bitter debates over Economic policy, ultimately debates over the distribution of income, at the end of the nineteenth century and during the world wars and Great Depression. Participants in those debates hoped that Statistics that were widely accepted as nonpolitical and accurate would prove that their grievances were just and provide support for the policies they advocated. Economists – including luminaries such as Irving Fisher, Wesley C. Mitchell, and Simon Kuznets – responded by developing the methodology for computing index numbers and estimates of national income. Initially, individuals and private organizations provided these Statistics, but by the end of WWII the federal government had taken over the role. Here I briefly describe the cases of prices, GDP, and unemployment.
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on the controversies behind the origins of the federal Economic Statistics
Social Science Research Network, 2019Co-Authors: Hugh RockoffAbstract:Although attempts to measure trends in prices, output, and employment can be traced back for centuries, in the main the origins of the U.S. federal Statistics are to be found in bitter debates over Economic policy, ultimately debates over the distribution of income, at the end of the nineteenth century and during the world wars and Great Depression. Participants in those debates hoped that Statistics that were widely accepted as nonpolitical and accurate would prove that their grievances were just and provide support for the policies they advocated. Economists – including luminaries such as Irving Fisher, Wesley C. Mitchell, and Simon Kuznets – responded by developing the methodology for computing index numbers and estimates of national income. Initially, individuals and private organizations provided these Statistics, but by the end of WWII the federal government had taken over the role. Here I briefly describe the cases of prices, GDP, and unemployment. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
William D Nordhaus - One of the best experts on this subject based on the ideXlab platform.
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a sharper image estimates of the precision of nighttime lights as a proxy for Economic Statistics
Journal of Economic Geography, 2015Co-Authors: William D Nordhaus, Xi ChenAbstract:Much aggregate social-science analysis relies upon the standard national income and product accounts as a source of Economic data. These are recognized to be defective in many poor countries, and are missing at the regional level for large parts of the world. Using updated luminosity (or nighttime lights) data, the present study examines whether such data contain useful information for estimating national and regional incomes and output. The bootstrap method is used for estimating the statistical precision of the estimates of the contribution of the lights proxy. We conclude that there may be substantial cross-sectional information in lights data for countries with low-quality statistical systems. However, lights data provide very little additional information for countries with high-quality data wherever standard data are available. The largest statistical concerns arise from uncertainties about the precision of standard national accounts data.
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improved estimates of using luminosity as a proxy for Economic Statistics new results and estimates of precision
Research Papers in Economics, 2012Co-Authors: William D Nordhaus, Xi ChenAbstract:Previous work has analyzed whether luminosity data contain useful information for estimating Economic output and concluded that there was significant promise for regions with poor quality Economic Statistics. The present paper examines alternative measures of the precision of the estimates using bootstrap and prior estimates of the errors for both the luminosity quality and the national accounts quality. Based on the new results, we conclude: First, for countries with high quality systems, there is no reason to use luminosity data as a supplement to standard data in any context where standard data are available. Second, we find that there is no advantage at present of using lights data for time-series corrections for any purposes where standard data are available. Third, for countries with low quality statistical systems, the estimates suggest that there may be substantial information in the luminosity data for cross-sectional estimates of output. Fourth, the major concerns about the use of lights as a proxy involve uncertainties about the precision of standard national accounts data. Finally, we recommend that future work be concentrated on integrating luminosity data into the cross sectional estimates of national and regional output primarily for countries with poor quality statistical systems.
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using luminosity data as a proxy for Economic Statistics
Proceedings of the National Academy of Sciences of the United States of America, 2011Co-Authors: Xi Chen, William D NordhausAbstract:A pervasive issue in social and environmental research has been how to improve the quality of socioEconomic data in developing countries. Given the shortcomings of standard sources, the present study examines luminosity (measures of nighttime lights visible from space) as a proxy for standard measures of output (gross domestic product). We compare output and luminosity at the country level and at the 1° latitude × 1° longitude grid-cell level for the period 1992–2008. We find that luminosity has informational value for countries with low-quality statistical systems, particularly for those countries with no recent population or Economic censuses.
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the value of luminosity data as a proxy for Economic Statistics
National Bureau of Economic Research, 2010Co-Authors: Xi Chen, William D NordhausAbstract:One of the pervasive issues in social and environmental research has been to improve the quality of socioEconomic data in developing countries. Because of the shortcoming of standard data sources, the present study examines luminosity (measures of nighttime lights) as a proxy for standard measures of output. The paper compares output and luminosity at the country levels and at the 1° x 1° grid-cell levels for the period 1992-2008. The results are that luminosity has very little value added for countries with high-quality statistical systems. However, it may be useful for countries with the lowest statistical grades, particularly for war-torn countries with no recent population or Economic censuses. The results also indicate that luminosity has more value added for Economic density estimates than for time-series growth rates.
Xi Chen - One of the best experts on this subject based on the ideXlab platform.
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a sharper image estimates of the precision of nighttime lights as a proxy for Economic Statistics
Journal of Economic Geography, 2015Co-Authors: William D Nordhaus, Xi ChenAbstract:Much aggregate social-science analysis relies upon the standard national income and product accounts as a source of Economic data. These are recognized to be defective in many poor countries, and are missing at the regional level for large parts of the world. Using updated luminosity (or nighttime lights) data, the present study examines whether such data contain useful information for estimating national and regional incomes and output. The bootstrap method is used for estimating the statistical precision of the estimates of the contribution of the lights proxy. We conclude that there may be substantial cross-sectional information in lights data for countries with low-quality statistical systems. However, lights data provide very little additional information for countries with high-quality data wherever standard data are available. The largest statistical concerns arise from uncertainties about the precision of standard national accounts data.
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improved estimates of using luminosity as a proxy for Economic Statistics new results and estimates of precision
Research Papers in Economics, 2012Co-Authors: William D Nordhaus, Xi ChenAbstract:Previous work has analyzed whether luminosity data contain useful information for estimating Economic output and concluded that there was significant promise for regions with poor quality Economic Statistics. The present paper examines alternative measures of the precision of the estimates using bootstrap and prior estimates of the errors for both the luminosity quality and the national accounts quality. Based on the new results, we conclude: First, for countries with high quality systems, there is no reason to use luminosity data as a supplement to standard data in any context where standard data are available. Second, we find that there is no advantage at present of using lights data for time-series corrections for any purposes where standard data are available. Third, for countries with low quality statistical systems, the estimates suggest that there may be substantial information in the luminosity data for cross-sectional estimates of output. Fourth, the major concerns about the use of lights as a proxy involve uncertainties about the precision of standard national accounts data. Finally, we recommend that future work be concentrated on integrating luminosity data into the cross sectional estimates of national and regional output primarily for countries with poor quality statistical systems.
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using luminosity data as a proxy for Economic Statistics
Proceedings of the National Academy of Sciences of the United States of America, 2011Co-Authors: Xi Chen, William D NordhausAbstract:A pervasive issue in social and environmental research has been how to improve the quality of socioEconomic data in developing countries. Given the shortcomings of standard sources, the present study examines luminosity (measures of nighttime lights visible from space) as a proxy for standard measures of output (gross domestic product). We compare output and luminosity at the country level and at the 1° latitude × 1° longitude grid-cell level for the period 1992–2008. We find that luminosity has informational value for countries with low-quality statistical systems, particularly for those countries with no recent population or Economic censuses.
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the value of luminosity data as a proxy for Economic Statistics
National Bureau of Economic Research, 2010Co-Authors: Xi Chen, William D NordhausAbstract:One of the pervasive issues in social and environmental research has been to improve the quality of socioEconomic data in developing countries. Because of the shortcoming of standard data sources, the present study examines luminosity (measures of nighttime lights) as a proxy for standard measures of output. The paper compares output and luminosity at the country levels and at the 1° x 1° grid-cell levels for the period 1992-2008. The results are that luminosity has very little value added for countries with high-quality statistical systems. However, it may be useful for countries with the lowest statistical grades, particularly for war-torn countries with no recent population or Economic censuses. The results also indicate that luminosity has more value added for Economic density estimates than for time-series growth rates.
Jack E Triplett - One of the best experts on this subject based on the ideXlab platform.
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the solow productivity paradox what do computers do to productivity
Canadian Journal of Economics, 1999Co-Authors: Jack E TriplettAbstract:(1) You don't see computers 'everywhere,' in a meaningful Economic sense. Computers and information processing equipment are a relatively small share of GDP and of the capital stock. (2) You only thinkyou see computers everywhere. Government hedonic price indexes for computers fall 'too fast,' according to this position, and therefore measured real computer output growth is also 'too fast.' (3) You may not see computers everywhere, but in the industrial sectors where you most see them, output is poorly measured. Examples are finance and insurance, which are heavy users of information technology and where even the concept of output is poorly specified. (4) Whether or not you see computers everywhere, some of 'what they do is not counted in Economic Statistics. Examples are consumption on the job, convenience, better user-interface, and so forth. (5) You don 't see computers in the productivity Statistics yet, but wait a bit and you will. This is the analogy with the diffusion of electricity; the idea that the productivity implications of a new technology are only visible witlh a long lag. (6) You see computers everywhere but in the productivity Statistics because computers are not as productive as you think. Here, there are many anecdotes, such as failed computer system design projects, but there are also assertions from computer science that computer and software design has taken a wrong turn. (7) There is no paradox. some economists are counting innovations and new products on an arithmetic scale when they should count on a logarithmic scale.
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Economic Statistics the new economy and the productivity slowdown
Business Economics, 1999Co-Authors: Jack E TriplettAbstract:From 1949-73, the Bureau of Labor Sta tistics (BLS) estimates thatnonfarm multifac tor productivity grew at 1.9 percent per year. After 1973, the comparable number is 0.2 percent, in spite of the economy's substantial investment in computing equipment, the growth of the information economy, and the many innovations that have come to be known as the "new economy. " Many economists believe that productivity must be growing more rapidly than the government numbers suggest. This article reviews two reasons— one wrong and one more plausible—for be lieving that inadequate measurement of out put in our Economic Statistics may be hiding essential developments in our economy.
