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Drivers of Economic Growth

In the previous section, researchers looked at the performance of the Tampa Bay region in comparison to other MSAs on various indicators of inclusive economic growth. The trend graphs illustrate the direction in which the region is moving in terms of competitive position as well as actual values across the outcomes. At this juncture, a couple of questions arise:

1. What can be done so that the region performs better?
2. Are there any policy initiatives that might be taken to improve the competitive position of the Tampa Bay region in coming years?

To answer these questions, econometric models were built to identify the drivers of inclusive economic growth.

The independent variables used for the analysis are possible drivers of the economic growth. These variables fall into five different categories: economic vitality, talent, infrastructure, civic quality and innovation. These possible driver variables have been identified after many interviews with the business leaders from the greater Tampa Bay region. A total of 19 variables were considered for the analysis. The annual data for these variables for the MSAs from 2008 through 2017 was collected from federal sources such as the U.S. Census Bureau, the U.S. Bureau of Labor Statistics and the U.S. Bureau of Economic Analysis.

The data for the four Tampa Bay MSAs (Tampa-St. Petersburg-Clearwater, Homosassa Springs, Lakeland-Winter Haven and North Port-Sarasota-Bradenton) was aggregated to derive the values for the Tampa Bay region and the data for Raleigh and Durham was aggregated to derive the values for the Raleigh-Durham region. In summary, this report uses data for six outcome variables (for the economic mobility outcome variable, a different strategy was used as described below) and 22 possible economic drivers for 20 regions for 10 years. The data was adjusted for the cost of living and inflation.

Panel data methods were used to create models for each outcome. For each of the outcomes, multiple drivers were identified. One prime driver for each outcome was identified based on the strength of potential causal explanation.

The outcome variable of economic mobility has data that compares the outcome over two time intervals. Thus, this data cannot be included in the panel data model as there is only one number for each MSA which reflects opportunities for economic mobility over a long time period (around 40 years). To identify drivers for economic mobility, this report adopts an innovative approach.

The driver for economic mobility is identified by using a regression analysis where the outcome variable (or y variable) value for each MSA is derived from the Opportunity Atlas, and the values of each of potential drivers (or x variables) are derived by taking the average values of each variable over the time period.

Drivers of Inclusive Economic Growth

The significant drivers for each of the indicators of inclusive economic growth are given in the tables below. The sign indicates the direction of impact. The plus (+) sign indicates the impact in the positive direction which implies that as the value of the driver variable increases, the value of the outcome variable of interest increases. Similarly, the minus (-) sign indicates the impact in the negative direction.

The report identifies one prime driver for each of the outcome economic indicator as an actionable driver. The choice was made based on the strength of causal explanation. The prime drivers, which are highlighted in yellow, can be used for policy initiatives.

It is important to remember that, depending on the indicator, a move in a negative direction could be positive (for instance, seeing the unemployment rate go down is positive).