Regional needs: forecasting and targeting university actions

www.laurea.fi

Director of Regional Services Dr Teemu Ylikoski

Regional needs: Forecasting and targeting university actions

2

Regional Services & the UAS field

Improving the depth and width of regional cooperation

Partnership management

Systematic cooperation

Quality management

and feedback

3

PERSONNEL

NETWORK

PROJECTS

NEEDS

PresentationPro

finding adaptable solutions

coordination

one-stop shopping

common goals

Regional Services

4

A win-win-win equation

Regional cooperation

needs to benefit all

parties

Regional partner Laurea

Student Authenticity; Employability

Competitive position in the

educational field

Improved processes; new

skills

5

Needs and capabilities

L

R

S

Capabilities: L = Learning; R = Research; S = Services

uni

S

I

C

region

Needs: S = Skills;

I = Innovation; C = Community

Modified from Goddard & Chatterton 1999 Goddard, J. B., & Chatterton, P. (1999). Regional Development Agencies and the knowledge economy: harnessing the potential of universities. Environment and Planning C, 17, 685-700.

What are the capabilities needed

for assessing: -  The region’s needs

in terms of skills gaps, innovation

needs, community interests?

-  The Uni’s ability to produce learning,

research and services to match the above needs?

Mod

e 1

to 2

D

irec

t re

lati

onsh

ips

to r

egio

nal n

ets

Regi

onal

fun

ding

mec

hani

sms

6

Capabilities in data acquisition: Forecasting regional development needs in the Uusimaa region

Country level soc-dem data

Matching Proxy for regional needs

Total students / projects / etc

required

Education to occupation

match: FNBE

workforce demand

forecasts by industry: GIER

Workforce structural

forecasts by industry: GIER

Employment forecast: STAT

Labor market exit forecast:

GIER*

Regional socio-dem trends: STAT

Legend: GIER = Government Institute for Economic Research (VATT) STAT = Statistics Finland FNBE = Finnish National Board of Education (OPH) *future of forecast uncertain as of 9/14

Extrapolation to regional

level

Loosely based on the ”Mitenna” forecasting model (FNBE)

7

Data acquisition: other perspectives

"   Towards a dynamic, future-oriented view "   Aggregate country level data is updated infrequently

and lags 12mths or more "   Stiff trends do not allow for agile responses "   In terms of emergent regional needs, primary data

collection may be necessary "   Primary data collection: considerations

"   Due to the diversity of the population (regional clusters) and actor size dispersion (SMEs), a potential survey faces severe complexities

"   For certain selected subgroups, other approaches may be available (regional forums, industry panels, associations), however, this will likely result in non-standardized data

8

Capabilities in forecasting system deployment

"   Data interfaces: multiple, non-standard data sources; non-standard update procedures, non-standard update frequencies. Manual entry likely needed.

"   User interfaces: an open, online platform with low entry threshold likely needed – ensuring low barriers for regional public sector, private sector, third sector "   User adoption rate has a positive return loop to data

quality "   Privacy issues should be avoidable by focusing on an

aggregated, semi-public level in the raw data

9

An opportunity to learn

Best practices benchmarking various approaches

comparison of current options

New approaches compensating for missing data

dynamic capabilities; towards a real-time view

www.laurea.fi

[email protected] | twitter:@teemuy

thank you