Is GDP the Best Measurement of Economic Growth?

Numbers are only representations of ideal states and are in and of themselves subjective to what they measure. A paper by Stow & Stow (2013) discusses some of the fallacies of relying too heavily on Gross Domestic Product (GDP) without considering the deeper meaning of the numbers. Fallacies of judgment can occur when governments adjust their economy to improve upon GDP but don’t look at actual economic activity.

GDP is calculated by adding =C+I+G+NX. Any improvement in consumption (C), Investment (I), Government Spending (G) and Net Exports (NX) would result in an improvement in overall GDP. The numbers could be misleading in the long run and lead to poor policies decisions.

When consumers spend more money they are not necessarily improving total wealth of the nation even though GDP rises. They are simply spending their money, dwindling their savings, buying now instead of investing later, and taking on debt. They may be encouraging organizational profits but not exclusively the wealth of the nation as an entire economic system. 

A similar fallacy can be found in government spending where an increase in expenditures can raise GDP numbers that don’t actually reflect national growth. Spending more today has obvious costs in terms of debt, flexibility, and confidence that are not calculated into the factor. Spending should be in areas that improve overall wealth or reduce liabilities. 

The paper is solid in the sense that numbers are only just numbers and relying on them too heavily can lead to policy mistakes that can be costly down the road. Overreliance on a single number encourages greater government spending and interventionism that can be self-perpetuating as politicians seek to justify new and expanded budgets at the detriment longer term sustainability. Using a battery of different numbers can help provide a greater context more data points to understanding true growth and development. 

Strow, B. & Strow, C. (2013). Gross Actual Product: Why GDP Fosters Increased

Government Spending and Should Be Replaced. The Journal of Private Enterprise, 29(1)

Successful Economic Forecasting with the Bayesian Method

Gupta and Kabundi (2010) started with an interesting question on which macroeconomic models are most likely to predict economic growth and success. Decision-makers that have tools are better able to make current decisions that are likely to foster greater growth in the future. The researcher used emerging markets of South Africa but these same models may apply to economic hubs and the factors that predict their success. 

Models are simply explanations that attempt to predict activities within the environment. Some models are more successful than others. Success is determined through a process of validity where multiple researchers over a period of time analyze the same phenomenon over and over in multiple ways to determine if the model makes sense. 

Common data points in measuring economic development include per capita growth rate, consumer price index (CPI), inflation, the money market rate, and the growth rate of nominal effective exchange rates. These data points often work their way into various models in an effort to create and develop some predictability. 

Bayesian VAR (BVARs) are based upon the Bayesian Method which is a subjective probability analysis used in a number of different fields. It is a rational decision making regression analysis for updating beliefs. In economics, the methods use monthly, yearly and other time based measurements to help determine the vector and trajectory of actions. It provides a method of blending new information with prior beliefs. 

BVAR models incorporate a greater amount of data than a number of other common models. The authors found that the BVARs have more predictability and would be beneficial for evaluating economic growth. Administrators that consider these models may find an additional tool for understanding and managing economic hubs. 

Gupta, R. & Kabundi, A. (2010). Forecasting macroeconomic variables in small open economy: a comparison between small-and large-scale models. Journal of Forecasting, 29 (2).