The Impact of Economics and Quality of Life on Graduate Flows

1. The Impact of Economics and Quality of Life on Graduate Flows Marc Cowling Institute for Employment Studies

2. The broad issue • As the UK seeks to manage the transition to high value added, knowledge-based economy, the generation and transfer of knowledge is increasingly important for the UK as a whole, but also for regions and cities. The role of higher education institutions (HEIs) is a particularly interesting one, and is less well understood in the context of regional socio-economic development.

3. Our starting assumptions • This research starts with the assumption that it is of great long-term significance to regional and sub-regional socio-economic development that localities are able to retain the stock of human capital they have, develop it, and add to it from the annual additions of HEI graduates.

4. Where our research leads • Thus it is important to understand what factors attract students to study in a particular locality in the first instance and then establish what factors influence their subsequent employment decisions and what impact this has on cities.

5. Our approach • Using student destinations data, this research will consider how three broad factors influence human capital flows into and out of cities, namely; university quality; local labour markets, and; quality of life. It will then seek to establish what economic impact net flows of graduates has on cities, with a particular focus on innovative capacity.

6. More detailed approach • Three elements • Graduates (HESA graduate flows data) • University quality (RAE and teaching quality) • Socio-economics (at the city level, we aim to construct a quality of life index and cross-reference this against basic measures of economic performance)

7. Econometric approach • Using a simultaneous system of equations, to estimate the flows of students and graduates into and out of UK regions and cities • These transitions will be estimated as a function of quality of life, quality of HEI, economic and innovation variables and labour market indicators. • We use two alternative measures of innovative capacity, the proportion of the business stock in knowledge-intensive industry sectors, and the proportion of new entrepreneurial businesses in high-technology sectors. • Quality of HEI is also captured in terms of both teaching and research quality as we a priori expect that teaching quality might play a more prominent role in attracting students, and research quality in supporting innovative capacity.

8. Graduate flows • We also construct eight human capital flow variables to account for different impacts from graduates who have alternative patterns in terms of where they originated from, where they studied, and where they subsequently ended up. • This is important as previous research shows that (fully) locally retained graduates (lived, went to HEI, and got job locally) have quite different labour market experiences compared to in-migrants and even returning locals.

9. Mapping our graduate flows • The eight human capital graduate measures are loyalists (home, local HEI, local job), lost graduate (home, local HEI, away job), lost student (home, away HEI, away job), returners (home, away HEI, local job), incomers (away, local HEI, local job), passers through (away, local HEI, away job), poached graduates (away, away HEI, local job), and the default category of missed opportunities (away, away HEI, away job).

10. Modelling • For each of our human capital models (7 in total excluding the default category of missed opportunities), we can estimate the dynamic inter-relationships between human capital and innovative capacity. These can be estimated in pairs of the form: • Human Capital Measurei = α0i + α1i Innovative Capacity + α2i Quality of Life + α3i • Labour Market Indicators + α4i Geographic Indicator + α5i Economic Indicators + α6i HEI Quality Indicators + υi • Innovative Capacityi = β0i + β1i Human Capital Measure+ β2i Population Demographics + β3i Labour Market Indicators + β4i Geographic Indicator + β5i Economic Indicators + β6i HEI Quality Indicators + β7i Population + εi

11. Conceptual model Quality of Life Poor but High Quality of Life Rich and High Quality of Life Economic success (knowledge base) Poor and Low Quality of Life Rich but Low Quality of Life Source:

12. Value added? • we hope to shed more light on the factors that determine students and graduates locational decisions, and the extent to which this feeds through into local innovation systems and innovative capacity. The value added of our proposed research is fourfold: • we explore more fully the impact of quality of life and economics on these decisions • we focus on the city as a unit of analysis • we explore non-employment graduate outcomes • we explore how different types of graduates impact on innovative capacity