On Jan. 1, 2013, the Bush era tax cuts are scheduled to end, and tax rates will return to what they were before those cuts were put in place. Moreover, mandated cuts in federal spending would go into effect. While the combination of higher revenues and lower government expenses would effectively raise revenues and reduce the growth rate of the national debt, the worry is that higher tax rates would encourage businesses to shed workers, raising the unemployment rate and pushing the U.S. economy back into recession. Despite the political jockeying for position currently going on in Washington, virtually no one wants to go over the fiscal cliff. Predictive analytics may help.
Using Predictive Analysis in Economic Modeling
Predictive analysis is a hot topic in advanced analytics technologies, especially with the rise of interest in big data. Predicting the future is useful, as it can be an aid (but only an aid) to decision making. Now, predictive analysis may be used to analyze actual data (such as in a trend analysis comparing a current situation to historical data) or to investigate proposed policy actions (such as an increase in tax revenues) to predict what impacts they might have.
The Congressional Budget Office (CBO) has long used two economic models to analyze the medium- and long-term impacts of federal tax-and-spend policies. The Solow-type growth model estimates the impact of proposed policy changes, such as the example given on tax increases and labor supply changes at a given point in time. In contrast, the life-cycle model estimates the impact on the supply of labor that depends on people's expectations on how they expect their after-tax compensation to change over time.
Despite the promotion of analytics as a useful tool, all models have their limits.
• Models are only as good as their assumptions. That may seem trite, but economies (like businesses) are not static, and the value of some parameters may change or may have more significance than anticipated. Virtual reality is not physical reality, so surprises are not only possible but likely, especially over long time frames.
• Models can only deal with endogenous conditions, which means those that they presume respond to the changes made; they cannot respond to exogenous conditions, which arise outside the ability of the model to predict but can nevertheless affect the model's results. One example would be the spillover effects of, say, a European recession on U.S. economic output.
Why Use Models?
While models have limitations, a well-crafted economic model is much better than a manual analysis, which cannot consider the impact of many variables and is often the product of wishful thinking rather than objective analysis. For example, a simpler (although still quite complex) business model that I built for a division of a Fortune 500 company many years ago enabled senior management to make decisions over a period of years that increased revenues, changed pricing policies, cut costs and increased the return on capital investments. The model sometimes provided results that seemed at first to be counter-intuitive, but that improved overall understanding of what could and could not be done.
The output of economic models is not as easy to accept as business models, which may give results (such as increased revenues) that everyone finds acceptable. Instead, the outputs from every policy action may have some positive benefit, but also have an unpalatable side effect. Those tradeoffs are likely to be painful to someone. However, the models can serve as a common point of reference and discussion.
Now, there may not be better economic models than those used by the CBO, but the CBO has the imprimatur of the Congress itself and is a non-partisan organization. The CBO is generally considered to be well-run and independent, although in a political domain, there will always be critics.
Next page: How Can Models Help?