Productivity

Productivity is a measure of how efficiently an economy, industry or business turns inputs into output: the amount of goods and services produced per unit of labor, capital or other resources. The most common measure is labor productivity, defined as real output per hour worked or per worker. A broader measure, total factor productivity, captures the output growth that cannot be explained by more labor and capital.

Productivity matters because it sets the long-run limit on how fast wages, profits and living standards can rise without causing inflation. National statistics agencies publish official figures: the US Bureau of Labor Statistics (BLS) releases quarterly labor productivity for the nonfarm business sector, and the UK's Office for National Statistics, Eurostat and the OECD publish comparable series. In macroeconomics it is the main driver of potential output, and in business it is a core measure of competitiveness.

Imagine two cafés with the same number of staff. One serves 200 coffees in an eight-hour shift; the other, with a better layout and a faster espresso machine, serves 300. The second café is more productive: the same work produces more. It can pay its staff more, charge less, or both.

A whole country becomes richer in the same way. Population growth adds more workers, but lasting gains in what each person can buy come mainly from each hour of work producing more, thanks to better tools, skills, organization and ideas.

Labor productivity is calculated as:

Real output is usually measured as real gross value added or real GDP, adjusted for price changes. If a factory produces goods worth 10 million in a year using 200,000 hours of work, its labor productivity is $10{,}000{,}000 \div 200{,}000 = 50$ per hour. If next year it produces 10.6 million with the same hours, productivity has risen to 53 per hour, a 6 percent gain. Productivity growth is roughly equal to output growth minus growth in hours worked. International comparisons usually use output per hour rather than per worker, because average working hours differ widely: OECD data show workers in Mexico putting in far more hours a year than workers in Germany, so the two measures can rank countries quite differently.

Economists group the sources of productivity growth into a few broad categories:

• Capital deepening. Giving each worker more or better equipment, software and infrastructure, which comes from investment.
• Skills. Education and training that build human capital.
• Technology and ideas. Technological progress, research and development and the spread of new methods, captured in total factor productivity.
• Reallocation. Moving workers and capital from less productive firms and sectors to more productive ones, including through competition and the creative destruction of weaker firms.
• Scale and specialization. Economies of scale and the division of labor, which let firms and countries focus on what they do best.

Institutions also matter: secure property rights, functioning courts, open trade and efficient financial systems make it easier for productive firms to grow.

Productivity growth in most rich countries slowed after the early 1970s, picked up in the United States from the mid-1990s with the spread of information technology, then slowed again from the mid-2000s. In 1987 the economist Robert Solow remarked that computers could be seen everywhere except in the productivity statistics, an observation known as the Solow paradox. The United Kingdom has had especially weak growth since the 2008 financial crisis, often called the UK productivity puzzle. Proposed explanations include mismeasurement of digital services, slower spread of new technology from leading firms to the rest, weaker business investment, an ageing workforce and the idea that the biggest inventions are behind us. Some economists expect artificial intelligence to lift productivity growth; the size and timing of any effect is debated.

Productivity is easier to measure in manufacturing than in services such as healthcare, education and government, where output is hard to define, so some public-sector figures rely partly on inputs. Quality changes are difficult to capture: a phone that costs the same as last year's but does much more may be under-counted. Labor-intensive services tend to show slow measured productivity growth while their relative costs rise, a pattern known as Baumol's cost disease. Average productivity can also rise for the wrong reasons, for example when low-paid workers lose their jobs in a recession, so analysts look at productivity alongside employment and wages rather than in isolation.

• U.S. Bureau of Labor Statistics: Productivity