Planning for a high tech/biotech research park

Abstract

This paper explores the use of scientific approaches when planning for the development of a new high tech/biotech research park. The emphasis is placed on those methods that can provide useful information to various stakeholders about potential economic impacts of a planned high tech/biotech research park in specific urban neighbourhoods. While considerable literature has addressed the overall role of high tech/biotech research parks for regional growth, the potential impacts on disadvantaged urban neighbourhoods are less known. We argue that the geographic scale of analysis is a critical factor. We use a new data set developed by IMPLAN which reports data at the zip code/postal code scale. Using this data set we can conduct input‐output (I/O) analysis of research park impacts and contributions at the micro (one postal code) scale, at the metropolitan scale, at the state scale, and finally at the US scale. We also recognize that different I/O metrics ‘tell different stories’. In our analyses, we use employment, occupation, and wage data. Further, we report findings by the minimum required educational attainment for appropriate occupations. Resumen. Este artículo explora el uso de enfoques científicos al planificar el proyecto de un nuevo parque de investigación científica y tecnológica. Se pone énfasis en aquellos métodos que pueden proporcionar información útil a varias partes interesadas sobre los posibles impactos económicos de un parque de investigación tecnológica y biotecnológica en sectores urbanos específicos. A pesar de los numerosos estudios sobre el papel genérico de los parques de investigación científica y tecnológica en el crecimiento regional, no se conoce tanto sobre sus impactos potenciales en los distritos urbanos más desfavorecidos.Aducimos que la escala geográfica del análisis es un factor crítico. Utilizamos un nuevo conjunto de datos desarrollado por IMPLAN que produce informes con datos a escala de código postal. Utilizando este conjunto de datos podemos realizar un análisis input‐output (I/O) de los impactos y contribuciones a micro escala (un código postal), a escala metropolitana, a escala estatal y finalmente a escala de los EE.UU. Reconocemos asimismo que medir el I/O de manera distinta nos “cuenta historias diferentes”. En nuestros análisis utilizamos datos del empleo, la ocupación y salariales. Además, mostramos los resultados respecto del nivel educativo mínimo requerido para cada ocupación.

An analysis of the impact of federally‐funded investments in science, research and technology across regions and education groups in Arizona

“…Indeed, our results show that the wage per worker for the labour with a ‘high school only’ degree is higher than that for the ‘BS/BA Degree’ holders with induced jobs. This result is consistent across the various spatial units we have studied and confirms the conclusions of Lim et al () on a new high tech/biotech research park in Tucson. If careful attention to the heterogeneity present in the groups of workers were to become more systematic in policy evaluation and if such non‐linear effects are persistent, it would provide policy‐makers with a tool to reduce wage inequality that is novel, growth‐enhancing and that would complement progressive taxation (Piketty and Saez ; Atkinson ).…”

“…This paper extends previous contributions on Arizona (Gibson et al ; Lim et al ) by relying on the most recent IO regional data and using the true values of federal investments in science as a way to positively shock the local economy. County‐level federal investments in science, research and technology are transformed into expanded sales for the related industrial sectors.…”

“…In light of the growing literature focusing on the skill bias of technology and how it has increased the returns to education and wage inequality (Acemoglu ; Autor et al ; Atkinson et al ; Stiglitz ), this paper's contribution is to highlight that the wages created by the R&D investments studied here do not decrease linearly with education level. In the case of a high tech/biotech research park in Tucson, Lim et al () find a similar result and highlight that it is primarily the newly created direct wages that contribute to it. Indirect and induced wages, on the other hand, experience a distribution across the various educational groups that is more in line with traditional expectations.…”

“…He finds that more than one‐third of the estimated direct jobs at the two proposed parks could be available to a workforce with a fairly low educational attainment and some on‐site training. More recently, Lim et al () estimate the occupational distribution of jobs based on a proposed University of Arizona Bioscience Park. They further extend their analysis to workers with minimum educational attainment and rely on IMPLAN ® 's occupational matrix for each economic activity to have the most detailed level of analysis possible.…”

“…An evidence of this type of social return appears in the IO study by Gibson et al () where the authors demonstrate that 30 per cent of the newly created jobs in high‐tech manufacturing sectors in 2006 in Pima county, where Tucson is located, were available for the group with ‘high school or less education’ while another 30 per cent were available for the group with ‘high school and some college or specialized training’. Another empirical IO study (Lim et al ) finds that the higher share of the direct jobs (46%) created in Arizona in 2009 was available for the group with ‘high school or less education’ and another 21 per cent was available for the group with ‘high school and some college or specialized training’. For both studies, the authors use IMPLAN ® 's (MIG, Hudson, WI) occupational matrix for each economic activity.…”