A valuable approach to forecasting regional growth

Regional growth forecasting can be challenging, with unexpected impacts making accurate forecasting even more difficult. For transportation planners this data is vital, but it often has to be taken with a grain of salt. What valuable approach could planners take to improve the accuracy of demand and revenue forecasting?

Regional transportation planning is inextricably tied to views about how the region will grow. While regional forecasts are generally available, the challenges of producing forecasts for regional growth are considerable, especially over the longer term. This means that transportation planners are often required to evaluate growth forecasts critically, or even to generate their own.

What are the determinants of regional growth? The field of regional economics would point to a list of factors that are associated with faster economic growth, including industrial mix (does the local economy have industries that are expanding), the level of human capital (i.e. education) in the workforce, amenities (accessibility, weather, culture and performing arts) and a high level of in-migration. These various factors are highly interrelated, and tend to reinforce each other in effect.

In an important study in the 1990s, the economists Olivier Blanchard and Lawrence Katz studied the growth of states in the US postwar period. They found that each state tended to have its own innate growth rate, with some states growing consistently faster than the average and some consistently slower.  They also found that, within these trends, states were continually affected by “shocks” (recessions, decline of an industry, temporary boom due to in-migration), which would permanently affect the level of employment before eventually returning to the original trend.

Key to the return to the growth trend for a regional economy is adjustment through labor mobility: in good times prices of labor and real estate rise, dampening growth; in bad times a region’s prices fall, encouraging firms to move in and hire new workers, which then attracts in-migration.

These basic findings are important to consider when modeling regional growth. At the very least, they imply that simple trend extrapolation of past growth may be inherently problematic, as it will ignore the process by which regional economies adjust to shocks through changes in prices.

For example, the chart above shows employment growth trends between 2001 and 2008 for Canada (consistently on the rise) and the Detroit-Dearborn-Livonia metropolitan region in Michigan (consistently declining). Simply extrapolating the line for either may seem simplistic but is, often, the approach used by many forecasters.

What actually occurred in Detroit was quite different than suggested by the trends between 2001 and 2008. After years of decline that had begun in the 1960s, and as suggested by regional economic theory, prices (wages and real estate) declined enough that firm in-migration eventually began to outweigh out-migration, and employment growth has now been positive for the last seven years.

While simple extrapolation of trends for Canada would have been a reasonable guess, it would have been very wrong in the case of Detroit.

Although regional growth forecasting is challenging, and unforeseen shocks will always conspire to make accurate forecasting even harder, it is also true that some approaches are better than others.

One approach increasingly used by Steer Davies Gleave is to model regional growth in population and employment as a system, with prices included as well. These models have the benefit of representing the regional growth process, particularly the roles played by prices. Combined with extensive qualitative assessments of a region’s competitive position, this approach is proving to be a valuable addition to the planning toolkit.