An alternative index for economic development

By Andres Gomez and Juan Tellez

One of the highlights of this year’s World Economic Forum was the urgency of a new economic measure, able to capture adequately the economic situation of a country. Gross Domestic Product (GDP) is not rising up to the challenges of the 21st century.

In Davos, Nobel Prize winner Joseph Stiglitz, IMF Head Christine Lagarde, as well as MIT Professor Erik Brynjolfsson, agreed on this issue and emphasized the value of finding a better measure of progress. Even though this is not the first time this concern has been raised, the increase of inequality, the worldwide crisis on immigration, the susceptibility of emerging markets to the volatility of commodity prices, the economic slow-down of China, the imminent collapse of Venezuela, and Brexit among others, have called the attention of academics and practitioners around the world to put urgently the discussion back again in the world agenda. Several reasons are brought up in this debate, but we specifically want to point out the necessity of a better measure of the economy that allows understanding the dynamics of growth and elucidates economic differences between places (countries, cities, regions).

There is a general trend to suggest alternative measures for economic development using statistical techniques that aggregate various indicators with the idea that adding more information is better. Some problems arise due to the strong assumptions that some of these methods require or the difficulty to interpret the final value of the index. An example in which a community has steered away from this trend is the recent health literature, in which the hemoglobin level, as a single indicator, has been preferred over the mix of many factors because this one alone is a global representation of a general health status and its interpretation is clear and straightforward. The fact is that averages are mathematical operations that eliminate information. They give the illusion of understanding and can miss relevant information about how the system works. What we need in economics is not an indicator that summarizes several statistics, but one that measures the main driver behind economic progress.

Evidence that an economy's diversity of know-how is a determinant of economic growth has started to accumulate. In particular, the notion of the division of labor, emphasized by Adam Smith, has started to be replaced by a notion stressing the division of information within a population. Crucially, this idea has not been limited to the empirical literature on economic development. It is an idea rooted in the latest advances in cultural anthropology and human evolutionary biology. These disciplines have established that the size and complexity of a society’s cultural repertoire are what have allowed them, and their individuals, to be best adapted to their environments.

When the bag of cultural know-how that a society has is large and complex, its individuals become smarter since more tools for problem solving become available. More possibilities for novel recombination of ideas emerge, innovation at a societal level is enabled, and the more opportunities for human flourishing are opened.

While anthropologists can only proxy the complexity of a culture’s know-how in ancient societies by counting the number of different tools they were able to manufacture, we now have much better access to the complete diversity of activities and know-how that workers, firms, and cities do and have. Know-how is relatively easy to track, and the effects of its diffusion easy to measure.

The Economic Complexity Index (ECI), developed at the Center for International Development at Harvard University, is a first step in quantifying the division of know-how in a society, and stands as a great alternative to GDP. It is a measure of the diversification of a country, which takes into account the complexity of the exported products. We believe an internationally organized program that investigates how to quantify the complexity of places should be installed. The number of industries, occupations, products and technologies a place has, and its impact on its economic progress should be carefully examined.

The ECI is easily constructed and has several interesting implications. It is interpreted as the underlying capabilities that a society possesses. In other words, it is the collective know-how that supports the economy of a country, city, or region. Besides, the index is the best growth predictor in the literature, better than standard education, institutional and political variables. Currently, ECI is to the health of economies what the hemoglobin level is to the health of human subjects.

bar graph explaining why Chiapas is poor without factoring in the ECI

Source: Constructed from Table 4a in D. Levy, R. Hausmann, M.A. Santos, L. Espinoza and M. Flores, “Why is Chiapas Poor?”, CID Working Paper No. 300, July 2015.

A recent CID study of Chiapas, a state in Mexico, showed that a worker’s average income in the rest of the country is approximately 1.69 times higher than the average income in Chiapas. Education, gender, ethnicity and being rural account for 31% of this income difference (see Graph Without ECI). When the ECI is included in the analysis, this percentage rises to 53% (see Graph with ECI). ECI is not only explaining a lot, it is the factor that explains the most. These results indicate that economic complexity acts through channels that are not addressed by other variables such as years of schooling, professional experience, or other demographics.

bar graph explaining why Chiapas is poor with ECI factored in

Source: Constructed from Table 4b in D. Levy, R. Hausmann, M.A. Santos, L. Espinoza and M. Flores, “Why is Chiapas Poor?”, CID Working Paper No. 300, July 2015.

Bottom line is that when it comes to the economic welfare of a society, and in a world that is increasingly connected, we need measures that capture collective properties. There are no perfect measures, and additional corrections to ECI, or perhaps complementary measures, should also be considered. For example, we hope similar efforts are put into developing measures that capture environmental degradation and intra-firm wage inequality.