Economy (copy) (copy) (copy) (copy)

Increasing evidence suggests the measuring sticks we use gauge the economy are outdated and don't capture what is really happening. Reforming them should be a top national priority. File/AP/Mark Lennihan.

When the government told us last week that its preliminary estimate for the growth of gross domestic product from July through September was 1.9%, the data were widely dissected and commented on for what they told us about the direction of the economy and its political implications.

Some looked at the high employment and consumption figures as proof that President Trump’s tax cuts are delivering benefits; some looked at the investment side as proof that the president’s trade policies have cost jobs. Some federal benefits are indexed to economic growth, so people could see how much — or little — they may rise next year.

But there is increasing evidence that our measuring sticks used to guide all sorts of federal, state and local government policies are outdated and do not capture what is really happening in the economy. Reforming them should be a top national priority.

South Carolina and the nation may be richer than we think. Or, in some cases, poorer. We just don’t know.

Today we measure the income of the nation and each state by its GDP and its derivative, per capita GDP, and we measure the extent of poverty by a yardstick known as the Federal Poverty Level as estimated by the Orshansky method, a creation of the 1960s War on Poverty.

The Financial Times of London recently reported that correcting long-known shortcomings of GDP show the economy has been growing at a rate nearly 1% faster than measured by the current GDP methodology. Over time, that adds considerably to national income and per capita GDP, leading to the conclusion that we may be richer than we think.

Among the shortcomings of the present methodology, developed when the main measure of the economy’s output was industrial production, is that it does not capture the contributions of services to the economy, overlooks uncompensated contributions like child rearing, scores polluting activities as a positive contribution and overlooks the impact of technological innovation. These are all correctable faults.

GDP says nothing about the real distribution of incomes in an economy. That raises the question of measuring the extent of poverty in the nation and how to address it.

Get a weekly recap of South Carolina opinion and analysis from The Post and Courier in your inbox on Monday evenings.

The 1960s Orshansky method’s estimates of how much money is required for basic food, clothing, and shelter are based on spending patterns now more than 50 years out of date. It fails to take into account supplemental income support such as food assistance, subsidized housing and refundable tax credits, but also ignores income taxes and the costs of health care. It does not recognize major differences in the cost of living around the nation. In the 1990s, the National Academy of Sciences recommended that the program use modern budgets and local costs of  living. Nothing has been done to meet this recommendation.

Conservatives think a recalculation will show poverty has declined to maybe 5% of the population compared to the current estimate of 12.3% in 2017, which is the latest available data. It is higher in rural areas than in cities.

Liberals believe that the added costs of living on modern budgets compared to 60 years ago will show that poverty has increased, especially in cities.

Oddly enough, both may be right, with rural poverty declining and urban poverty increasing under a more up-to-date methodology. We just don’t know the answer, but federal funds are handed out to states and cities based on 1960s measures of poverty that all agree are giving a us false picture.

The hopeful point here is that both conservatives and liberals agree the poverty measure gives misleading answers. They should find common ground in fixing the poverty standard, and give full support to an official revision of the GDP methodology. We need good data to know where we are going.