Introduction
The Good Food Institute is an American commercial startup focused on producing plant-based meat using alternative proteins. The overall goal of such a project is to eliminate the use of natural animal proteins and synthesize proteins that vegetarians and vegans can consume (GFI, n.d.). The choice of New Mexico was driven by an interest in exploring cities where sustainability, liberalism, tolerance, and inclusivity are not prioritized by communities, such as California cities with abundant startups (Florida et al., 2020). The three largest urban agglomerations, Albuquerque, Santa Fe, and Las Cruces, were selected in New Mexico. The analysis was based on data from the US Bureau of Labor Statistics.
Description of Criteria
The criteria of interest included five data points, namely Civilian Labor Force, Employment, Unemployment Rate, Financial Activities 12-month ∆(%), and Leisure and Hospitality 12-month ∆(%) — the choice of each was driven by the desire to assess the economic health of the city. First, the Civilian Labor Force measures the availability of skilled workers in the civilian sector — the higher the Civilian Labor Force, the larger the labor force in the region, and therefore the greater the opportunities to hire talent. Second, Employment characterizes the demand for labor in the city — the higher this indicator, the more dynamic the business sector, and the easier it is for companies to find employees and conduct operational processes.
The third is the Unemployment Rate, which determines the stability of the regional economy. The lower this indicator is, the more stable the city’s economy is and the less staff turnover there is. It may be interesting for a startup to consider regions with higher Unemployment Rates, as this may indicate a more flexible labor market.
Fourth, the Financial Activities 12-month ∆(%) criterion evaluated the percentage change in financial activities in the city, which shows the city’s economic stability. Specifically, a positive percentage change indicated increased opportunities for emerging businesses. Finally, the last criterion for analysis was Leisure and Hospitality 12 months ∆(%), which characterized the percentage change in the leisure and hospitality industry. Since ‘The Good Food Institute’ conducts activities in the entertainment industry, changes to this criterion are essential. If the changes are positive, it indicates greater potency for the startup as the sector grows.
Analysis of the Cities According to the Criteria
As shown in Figure 1, which illustrates the analysis results, each city had different successes and failures across the five criteria. Conventional column formatting was used to rank, with higher values highlighted in green, average values in yellow, and minimum values in red. For Civilian Labor Force and Employment, Albuquerque had the highest result; for Unemployment Rate, Santa Fe had the best result; Santa Fe and Las Cruces were the leaders in Financial Activities 12-month ∆(%), and Santa Fe had the highest result in Leisure and Hospitality 12-month ∆(%).
Separately, it is worth noting that the highest unemployment rate in Santa Fe may not be as favorable to The Good Food Institute, as higher unemployment rates in a region make the labor market less flexible and fewer candidates available. Albuquerque, which has an average unemployment rate, is the most favorable choice for this criterion. Albuquerque may be the leader according to the analysis, since more criteria align with the potential for strong startup development.
Conclusion
Conducting such an analysis makes practical sense for a startup, as it provides an informed, unbiased basis for decision-making. Choosing any of the three cities solely on personal preferences and the manager’s opinion can be dangerous; it should be based on real, objective data. On the contrary, statistical analysis is more dependable and produces better results. One extension of this analysis could be to assign each criterion a weight to account for its relative power.
References
Florida, R., Adler, P., King, K., & Mellander, C. (2020). The city as startup machine: The urban underpinnings of modern entrepreneurship. In M. N. Iftikhar, J. B. Justice, & D. B. Audretsch (Eds.), Urban Studies and entrepreneurship (pp. 19-30). Springer Cham.
GFI. (n.d.). Reimagining protein. GFI.
US BLS. (n.d.-a). Southwest information office — Albuquerque. US Bureau of Labor Statistics.
US BLS. (n.d.-b). Southwest information office — Santa Fe. US Bureau of Labor Statistics.
US BLS. (n.d.-c). Southwest information office — Las Cruces.US Bureau of Labor Statistics.