Please Note: This document is being distributed for purposes of discussion only. No part of this document has been reviewed by KUCR. All aspects of the research design and procedures are subject to the approval of KUCR.
5/5/2004
To establish the current size and past growth of bioscience-related employment, the research team will examine survey data (described below) as well as a variety of official statistical series—such as County Business Patterns, Occupational Employment Survey, Bureau of Census Public Use Micro Sample (PUMS), ES202 payroll data—to measure current employment and salary levels and past growth in bioscience-related employment.[1] Past trends will be used in conjunction with other data to develop a range of possible 10-year growth scenarios in bioscience employment for the Lawrence/Douglas County area. The study will also consider a scenario that adds 3000 additional new bioscience-related jobs in the area over ten years.
Data on the current size of bioscience-related employment will be used with impact multipliers to generate estimates of the current economic impact of the industry, or in particular, the amount of additional income and employment produced in the county as a result of multiplier effects stemming from this employment. Further, the growth scenarios for bioscience employment will be used to generate estimates of future economic impacts of the industry.
The main approach used in the analysis will assume that new bioscience employment reflects a net addition to local job growth
Specifically the report will estimate the impact of each scenario on Douglas County over a ten year period with respect to[2]:
· Output, income, and employment by business sector (defined as NAICS code aggregates)
· Total local government taxes and expenditures
· Total employment broken out by ultimate source (local unemployed persons, new KU graduates, in-commuters, in-migrants), as well as induced turnover of local employed persons receiving better jobs
· Families and population
· Household units
· Housing starts
· Business construction.
In this model some effects are steady over time (mainly from construction of new housing and infrastructure), while other effects build up steadily as the workforce and population grow. The net results will be displayed in terms of snapshots at various years, such as at the 1st, 5th, and 10th years.
Using data from various sources, PRI will identify all known bioscience firms and their suppliers in the area. PRI will interview managers in approximately ten identified firms willing to be interviewed. The Chamber and KUCR will assist in obtaining interviews. Interviews will cover topics such as:
Transcripts will be made of the interviews to allow accurate summarization.
PRI will also work with KUCR to gather data on local bio-science activities within KU. This will generally be broken out between grant-funded activities, versus activities as part of KU’s base and educational budgets. The data should include total expenditures, equipment and supplies, and employment broken out by occupation and skill level.
Research activities will include these steps:
· Define the “bioscience” sector by NAICS codes
· Identify existing bioscience-related firms in the area
· Design and implement an interview-survey of existing bioscience firms
· Identify defunct bioscience firms in the area over the last 5-10 years
· Gather data on defunct firms
· Gather KU data
· Analyze ES 202 data
· Analyze available official statistics
· Analyze national prospects in bioscience-related business over 5-10 years
· Informally analyze local capability to capture shares of nation growth in bioscience-related business
· Acquire and install an IMPLAN input-output model of Douglas County
· Modify the model to break out a biotech sector
· Expand and modify the model to include features developed below.
· Analyze national data to determine distribution of jobs in Biotech industries by occupation and wage
· Build a model of origins of local workers, including:
o Analyze PUMS data to estimate shares of jobs held by in-commuters (by occupation)
o Analyze PUMS data to determine available workers by job category
o Analyze KU data to determine new graduates available by job category
o Analyze PUMS data to determine migration in-migration patterns of new workers
· Analyze PUMS data to create ratios for family size, workers per household, and housing demand
· Solve the model and estimate results
[1] The definition of the “biotech” sector will be determined in consultation with the clients during the course of the research, based on considerations that include Lawrence conditions, data availability, and provisions in HB 2647.
[2] For reasons of data availability the unit of analysis will generally be impacts on Douglas County as a whole. Some of the effects will be modeled based on three counties included in the Douglas County PUMS data set.