2025 E-Insights Report

Section 3: Talent Pipeline

The future success of Tampa Bay’s economy is intrinsically tied to the strength and stability of its talent pipeline. This section explores the diverse factors that influence talent availability including job opportunities, salary trends, migration patterns, and the search behaviors of job seekers. By leveraging real-time data from Google Trends and Adzuna, this analysis provides an in-depth understanding of the region’s workforce dynamics and economic vitality.

Salary and Job Search Trends

Salaries are a critical variable influencing talent availability and retention. Insights into average salaries for sectors such as engineering, health care and information technology are sourced from Adzuna, a job aggregation platform. These figures highlight Tampa Bay’s competitiveness in attracting skilled professionals across industries. Google job search trends also provide a realtime snapshot of workforce behaviors shed light on regional employment preferences and emerging workforce patterns.

Indicators of Talent and Migration

Tampa Bay’s talent pipeline is assessed through indicators including college persistence rates, first-time-in-college student enrollment, and job market growth across industries like health care, telecommunications and professional services. The Indeed Job Postings Index and Net Job Flows provide useful insights for job availability and workforce migration, while international migration patterns reveal the region’s attractiveness on a global scale.

Google Trends

Some talent indicators analyzed in this section relied on data from Google Trends. For our research, obtaining real-time data was crucial to accurately capture the evolving dynamics and trends across various regions.

Google Trends Methodology

Google Trends serves as a powerful tool to understand public interest in specific search queries across different regions. The platform assigns an index value (0-100) to each search term, representing its relative popularity within a specific timeframe and geography. For our analysis, we utilized Google Trends data to rank the popularity of job-related search terms across multiple MSAs from 2019 to 2024.

Due to Google Trends’ limitation of comparing only five MSAs at a time, we adopted a weighted averaging methodology to ensure comprehensive coverage of all MSAs. In this approach, we selected one MSA (Charlotte) as the constant anchor city across all rounds of comparisons. For every year, we conducted multiple rounds of comparisons, where the average score for each MSA in each round was normalized relative to the anchor city’s average score. This normalization involved calculating a scaling factor (100 divided by the anchor city’s average score) for each round and applying it to the scores of all MSAs in that round. Once normalized, the scores across all rounds were combined to produce a unified ranking of MSAs for each year. The rankings represent the relative popularity of job-related searches, allowing us to identify key trends and hotspots effectively. This methodology enabled us to account for Google Trends’ constraints while ensuring an accurate and holistic analysis of public interest in full-time and part-time job searches.

Our goal was to observe and track a specific theme across 20 MSAs selected for the study. We analyzed the themes with their corresponding search terms as follows: Full-time Jobs as “Full time jobs,” Part-time Work as “Part time jobs,” Remote Jobs as “Remote jobs,” and Business Activity as “How to start business.” The index values collected for these themes are limited to the Tampa-St Petersburg-Clearwater MSA, due to unavailability of data for other Tampa Bay MSAs.