The good news was that the gross domestic product report came in higher than expected at 4.9% for the third quarter. The bad news was that too much of the GDP growth came from government spending. The old joke: Economists, to prove they have a sense of humor, provide forecasts with three numbers past the decimal point.
When the government releases the GDP number once a month at 8:30 in the morning, there is a flurry of excitement (as there is with the other economic releases) by the talking heads on TV — as if they all understood what precisely the number meant. In actuality, few have any notion of how the number is created and how dirty the data is. Data that fails to correctly measure what it claims to because of incompetence, carelessness or, at times, the near impossibility of obtaining accurate information is considered dirty data.
GDP is the total market value of all goods and services produced in a given time period (normally a year) in a defined geographical area. It is measured both by expenditures and by income. The expenditure approach adds “consumption” to “government spending” to “investment,” while netting out imports and exports, to arrive at the GDP number. The income approach calculates the sum of the income earned by all of the factors of production, including wages paid to labor, returns to capital such as interest, corporate and other business profits, and rents on land, minus property and sales taxes and depreciation of assets.
In theory, the GDP number should be the same, whether it is calculated by the expenditure or income approach, but there is always a big difference, called “statistical discrepancy,” because the underlying data is incomplete and inaccurate.
“Nominal” GDP values expenditures or income at the prices actually paid in current dollars, while “real” GDP attempts to net out inflation to make year-to-year comparisons easier to understand.
The total GDP for a country is partially a function of its population. For instance, China has the second-largest GDP after the U.S., but China has roughly four times as many people as the U.S. So, to determine GDP per capita, economists divide the total GDP by the population, which gives more useful but still highly imperfect information about relative well-being between countries. In poorer countries, prices of many goods and services are less than in richer countries because of lower wage costs and other factors, so economists have devised an arbitrary way to adjust for differences in local prices, which they call an international dollar. This device makes it easier to do comparisons of real incomes and living standards between countries. The World Bank, the International Monetary Fund and the U.S. government all provide measures of comparative living standards by countries on what they call a purchasing power basis, or PPP.
Simon Kuznets, a well-known economist in the 1930s at the National Bureau of Economic Research, proposed the idea of the GDP measure in 1937, which was adopted by most countries at the Bretton Woods Conference in 1944 and finally by the U.S. in 1991. From the beginning, there have been many criticisms of GDP as a measure. It does not account for relative health or happiness, nor for various sizes of the “underground” economy or volunteer activities. The output of women who stay at home and work hard maintaining the house and caring for the children is not included in GDP, but the wages paid to an outside housekeeper or nanny doing the same work are included.
Remittances by foreign workers and profits earned by overseas companies are treated differently from place to place. Much of business-to-business activity, particularly between foreign countries, is ignored or treated inconsistently.
Differences between the values of various types of expenditures are largely ignored. An investment in an advanced semiconductor chip factory that hires highly skilled workers or in a farm machinery company that produces products that greatly enhance productivity are clearly valuable additions to GDP. By contrast, the addition of government workers engaged in income redistribution schemes or regulatory activities that negatively affect output and productivity are treated as adding to GDP when, in fact, they are wealth-reducing activities.
As the size of government grows relative to the overall economy, more wealth-reducing activities occur, as contrasted with wealth-enhancing activities. This is why it is possible to have a “full employment” economy that is actually stagnating and resulting in lower inflation-adjusted real incomes.
Over the years, various economists have proposed improvements for measuring GDP or estimates of inflation (which are notoriously bad). Several economists, notably Mark Skousen, have advocated using a measure called gross output to be more widely adopted as an addition to the GDP numbers.
Over the decades, I became reasonably knowledgeable of how much government data was collected — including international trade data. In sum, advanced statistical techniques are often applied to dirty data, giving the illusion of precision (e.g., the Centers for Disease Control and Prevention and the Food and Drug Administration). What we have learned, particularly since COVID-19, is that many serious economic and health-affecting decisions are made using incomplete and dirty data by a broad spectrum of government agencies.
Being a skeptic about any number the government gives you may not only save your pocketbook but also your life.
• Richard W. Rahn is chairman of the Institute for Global Economic Growth and MCon LLC.
https://www.washingtontimes.com/news/2023/oct/30/dirty-data-scam-government-produced-gdp-growth/
© Copyright 2023 The Washington Times, LLC.