Why digital tech might not be the key to development for poor countries

Interesting essay by Dani Rodrik:

Any optimism about the scale of GVCs’ contribution must be tempered by three sobering facts. First, the expansion of GVCs seems to have ground to a halt in recent years. Second, developing-country participation in GVCs – and indeed in world trade in general – has remained quite limited, with the notable exception of certain Asian countries. Third, and perhaps most worrisome, the domestic employment consequences of recent trade and technological trends have been disappointing.

Upon closer inspection, GVCs and new technologies exhibit features that limit the upside to – and may even undermine – developing countries’ economic performance. One such feature is an overall bias in favor of skills and other capabilities. This bias reduces developing countries’ comparative advantage in traditionally labor-intensive manufacturing (and other) activities, and decreases their gains from trade.

Second, GVCs make it harder for low-income countries to use their labor-cost advantage to offset their technological disadvantage, by reducing their ability to substitute unskilled labor for other production inputs. These two features reinforce and compound each other. The evidence to date, on the employment and trade fronts, is that the disadvantages may have more than offset the advantages.

The usual response to these concerns is to stress the importance of building up complementary skills and capabilities. Developing countries must upgrade their educational systems and technical training, improve their business environment, and enhance their logistics and transport networks in order to make fuller use of new technologies, goes the oft-heard refrain.

And here’s the punchline:

But pointing out that developing countries need to advance on all those dimensions is neither news nor helpful development advice. It is akin to saying that development requires development. Trade and technology present an opportunity when they are able to leverage existing capabilities, and thereby provide a more direct and reliable path to development. When they demand complementary and costly investments, they are no longer a shortcut around manufacturing-led development.

Great essay.

Quote of the Day

“Data is neither a good or service. It’s intangible, like a service, but can easily be stored and delivered far from its original production point, like a good.” Michael Mandel

He goes on to make a useful observation about how our national statistics surveys may be missing something important:

Paradoxically, economic and regulatory policymakers around the world are not getting the data they need to understand the importance of data for the economy. Consider this: The Bureau of Economic Analysis, the U.S. agency which estimates economic growth, will tell you how much Americans increased their consumption of jewelry and watches in 2011, but offers no information about the growing use of mobile apps or online tax preparation programs. Eurostat, the European statistical agency, reports how much European businesses invested in buildings and equipment in 2010, but not how much those same businesses spent on consumer or business databases. And the World Trade Organization publishes figures on the flow of clothing from Asia to the United States, but no official agency tracks the very valuable flow of data back and forth across the Pacific.

The problem is that data-driven economic activities do not fit naturally into the traditional economic categories. Since the modern concept of economic growth was developed in the 1930s, economists have been systematically trained to think of the economy is being divided into two big categories: ‘Goods’ and ‘services’.

Goods are physical commodities, like clothes and steel beams, while services include everything else from healthcare to accounting to haircuts to restaurants. Goods are tangible and can be easily stored for future use, while services are intangible, and cannot be stockpiled for future use. In theory, a statistician could estimate the output of a country by counting the number of cars and the bushels of corns coming out of the country’s factories and farms, and by watching workers in the service sector and counting the number of haircuts performed and the number of meals served.

But data is neither a good or service…