A new way to measure national economic wealth

Economics is a dismal science. For a reason. The numbers which typically get touted as signs of economic health within a nation do not always correlate with citizens’ actual experience of their individual well-being. Much of this can be traced to the numbers used. Typically, a nation’s economic health is described through the crude measure of GDP, or Gross Domestic product. In other words, the production of goods and services within the borders of a country. If this is growing, the economy is said to be doing well. If it is declining, the economy is not doing well. So far, so good. What GDP suffers from is in its very name: it is a gross measure. It does not show anything other than total output. It does not take into account the distribution across society of production or of products and services. GDP can very well be growing, but with the gains accumulating only to a few citizens, while the rest of the population might see stagnant, or even declining standards of living. This has profound implications for the political and social health of a country. Throughout history, social inequality and economic injustice have gone hand in hand in creating conditions of instability and conflict. For policy makers and politicians, citizens and businessmen, the optimal state of a nation’s economy has been to see shared effort and work resulting in a fair share of the results. But too often, GDP alone is seen as the most important measure of whether political action in the economic sphere is providing positive or negative results. It is far past time for GDP to be retired from serious discussion as the sole measure of national economics. For the purposes of this essay, I’ll hereby only use GDP in its most useful social sense of per-capita GDP, not total overall GDP.

What should take the place of per-capita GDP? Because I am incredibly modest, I suggest something called the Lawrence A Pearlman Index. or PI, for short. This takes GDP, which is useful in a narrow sense, but combines it with the Gini coefficient of social inequality to provide a more holistic and complete picture of a country’s economic state. There could well be other measures included in a more complete equation, such as levels of health, education and other indicators of social welfare, but although GDP itself is very crude and simplistic, one should not go to the opposite extreme and try to create an alternative which is needlessly complex. The idea is simply to replace GDP as the de facto, basal, number of discussion in economic development terms. There is one additional factor which has an immense, and generally unspoken, effect of economic health which will be addressed later, but first things first.

The current gold standard for measuring a nation’s level of economic inequality is the Gini Index, in which a score of 0.0 would indicate a society in which all economic resources are divided completely equally to all citizens, and a score of 1.0 indicates a country in which one single citizen holds access to all resources and no-one else has anything. Obviously, it’s impossible to have score at either the extreme high end or low end; most of the more egalitarian North European nations have a score a score around .2-low.3, and most developing countries are less egalitarian and have scores somewhere around .4-.5.

Let’s look at some examples. A developed North American economy, represented by the United States. A developing South American country, represented by Brazil. A representative South-East Asian developing country, Thailand. A mature European economy, Holland. And finally China (All data in the tables below sourced from the World Bank’s website. Brazil’s Gini index number is from 2012, as there was no data from 2010 available). For the sake of making the GINI coefficients easier to work with, I’ve chosen to multiply them by 100, giving a positive integer.

A simple equation which will no doubt have some purist economists screaming in frustration (Because it’s using apples AND oranges to create a hybrid data set), is to simply divide GDP by the Gini coefficient. The result is in the third row below:

What does the modestly-named Pearlman Index (PI) tell us? It shows that although the US and the Netherlands have very similar per-capita GDP numbers, the Netherlands has a much more even distribution of that economic production, by a factor of roughly 50%. Anyone who has travelled in both the US and the Netherlands could confirm the general sense of a wider spread of economic income and activity across society as a whole in Holland, compared to the US with its greater spreads between the very wealthy and the very poor.

What about the other countries? A nice effect of the PI is that even with similar per-capita numbers, the differences in how economic activity actually spreads across a country are still easily seen at lower levels of economic development. Thailand and China have fairly similar GDP figures, a spread of less than ten percent, but combined with their different Gini numbers, the differences in the PI jump to nearly 20%. This has important implications for policy makers in developing countries. Focusing solely on GDP might well be a very narrow and, in the long run, counter-productive, goal. If the spread between per-capita GDP and the PI is large, it would seem to indicate that growth alone might not prevent potential social unrest in the future.

What about Brazil? It has truly staggeringly high inequality. Were it merely to have the same, still relatively high, levels of inequality as the US, its PI would be 267, rather than 208. That’s roughly a 25% increase in general, widely-spread economic growth and development. Food for thought.

The PI shown above is still a simple, crude device. As mentioned earlier, there is one more factor which has a large, and in some countries outsize, effect on a nation’s economy. That is corruption.

Raw output, as measured by GDP, matters. Social inequality, as measured by the Gini coefficient, matters, since it shows how economic activity and capital is actually distributed. And corruption, in its many forms, matters very much because it misallocates resources, diverts capital from being used efficiently, and in general acts as both a tax, and a brake, on economic activity.

How to construct the equation? All three elements – GDP, Gini coefficient, and corruption must be included, but care must be used. They all measure different things, after all. GDP and the Gini coefficient are easiest to reconcile, as demonstrated above. Corruption, however, is an animal of a different color. By its nature, it is difficult to find reliable data on how it affects economic development, since corruption relies on silence and thus is far from any official economic data set. The organization Transparency International has provided a valuable service with its Index of Perceived Corruption, yet as useful as their data is, it suffers from an inescapable problem of methodology.

Basically the Transparency International rankings are based on surveys. These might be rigorously researched, yet they do not actually measure any real, “hard”, data points as both the GDP figures and the Gini Index do. Nevertheless they are the only real source we have at present to account for the effect of corruption on an economy, imperfect though they might be. So let’s see what happens when we plug those numbers into the PI.

First of all, Transparency International rankings are relativistic, comparing all the nations of the Earth in which it was and is possible to conduct the survey. Since they are rankings based on nations vis-à-vis one another, we need to normalize them into some form of solid data that can be used together with GDP and Gini data. These are 175 in number. So we need to convert them from rankings…

…to “hard” (-ish) numbers. The easiest way to do this is to divide the ranking number by the number of countries in the total survey (175), and for the sake of legibility and simplicity, multiply by 100 to get something easy to use:

This converted TI number can now be used to divide the original GDP-and-Gini Pearlman Index.

Finally we have an index which measures economic activity overall, how well that economic activity is spread throughout society, and which takes account of corruption. The “New PI” number is simple to use, and rewards improvements in reducing both corruption and inequality on a logorithmic scale. Simply put, as corruption and/or inequality is reduced, the PI score would rise extremely rapidly. For this reason, I believe it might be useful for policy makers and concerned and informed citizens in general, as an additional means of comparing and evaluating their nations’ economic progress. As an American, it is rather unflattering to see my nation so far behind a modern, European country. But it gives a far more complete picture of overall economic development across society as a whole than merely comparing per-capita GDP, and it takes into account that economics and politics, social being and work, can and should be considered in whole, not part.