Reliable local area economic data is no longer hard to come by

With something like 200 councils using profile.id ^® over the past 15 years or so, a frequently asked question at council briefing and training sessions has been, “Can you extend profile.id ^® to include economic data?” These requests grew in number as Local Government’s role in economic development has grown. Quite simply, this was the inspiration to develop economy.id® – and now to extend it with an Impact Assessment model. However, getting reliable economic data for sub-State economies is a significant challenge and this blog discusses how we discovered a solution by partnering with economic brains trust, NIEIR.

The challenges

We started by evaluating what economic data was available in the Census. Having used Census Journey to Work data many (many) years ago for my Geography honours thesis, I was aware that Census Journey to Work destination data provided a good basis for profiling an economy at the local area level. The data provides in aggregate, a rough the number of jobs by industry sector for any local government area. However, while providing a reasonably accurate ‘shape’ of the economy (dominant and emerging industry sectors), it does not provide an adequate measure of economic activity at the local area level. For example, we know that Census counts of employment are notoriously inaccurate, with up to a 20% undercount due to the automatic exclusion of those persons who are employed but fail to state a location, or who can’t be coded to their exact workplace address. While Census data is an excellent resource for detailed worker characteristics (and is used in economy.id®), using it as the main source of broad employment numbers is problematic.

Reliable primary economic data sets exist only at the national, state and regional level at best. Therefore the only way to get a realistic measure of jobs, output, turnover etc. at the local area level is to undertake economic modelling. The most significant challenge with local area economic modelling is to ensure that the process reflects the unique economic characteristics of the local area. For example, one of the traps is to apply national and state-level productivity propensities at the local level, which we now know is simply not accurate because it assumes a that the economic characteristics of all local areas are the same.

The need for local nuance

At .id we have been looking for a solution to this problem for quite some time. Enter Peter Brain from the National Institute for Economic and Industry Research (NIEIR). While Peter is famous for predicting major economic crises – namely the Asian Economic Crisis of 1997 and more recently the Global Financial Crisis – even more importantly, NIEIR’s reputation among the council officers familiar with their work preceded them and attracted .id’s interest in a partnership in developing economy.id ^®.

NIEIR are recognised as industry leaders in the development and provision of robust economic modelling at the smallest credible geographic unit (Local Government Area). For over 10 years NIEIR have been producing the annual benchmark State of Regions Report commissioned by the Australian Local Government Association (ALGA).

NIEIR modelling draws on many data sources to offer the most nuanced data possible at the local level. The NIEIR dataset is the result of a process of economic micro-simulation modelling – it is an amalgam of many different existing data sources (between 6 and 10 depending on the region and time period) which are synthesised to produce a series of estimates of the size and value of each industry. All the modelled data uses a breakdown of 19 ANZSIC industry divisions (e.g. Manufacturing) into 49 sub-groupings (e.g. Food Product Manufacturing) providing a highly detailed picture of which industries are contributing to the local economy.

Importantly, the NIEIR model is updated on an annual basis (with quarterly breakdown for some characteristics). This means the impact of global, national and local economic changes can be clearly seen on each industry sector at the local level.

We established that this modelling was superior to any other models we evaluated for the following reasons:

• Uses micro-simulation modelling (bringing together multiple data sources to simulate a realistic view of the local economy) Modelled annually ensuring the model is regularly updated to reflect global, national and local factors.
• Does not rely purely on Census counts of employment (Census data is a fantastic resource but is known to undercount employment by 20% because it excludes anyone who does not state their workplace address).
• Uses Centrelink, DEEWR labour market statistics, ATO data to provide a more accurate estimate of CURRENT employment.
• Estimates Hours Worked and converts this into a measure of Full-Time Equivalent Employment to ensure that underemployment cannot be hidden.
• Does not assume that the productivity of an industry sector is the same across all LGAs in the State, but uses ATO data to ensure local differences are taken into account.
• Uses locally derived inputs such as commercial building approvals by floor space, and Dun and Bradstreet datasets on business start-ups and exits to capture local industrial growth and decline.
• Makes manual adjustments are made to refine the model based on local knowledge such as arrival of new employers, closing industries, large building construction etc (clients can input into this process)
• Mining areas are treated differently to take into account fly-in-fly-out and other considerations.

Please contact us for a detailed paper on the NIEIR model.

Measuring the impact of economic activity

The NIEIR model has enabled us to develop an Impact Assessment Module to economy.id which enables you to measure and analyse the impact of changes to each industry on the rest of the local and regional economy. This is a powerful tool for developing local economic development strategies. You can read more about it in Lailani’s case study blog here.

Future developments

We are currently developing a Tourism Module for the large number of councils for which Tourism is an important part of their economies.

We are also developing Employment Forecasts and a Local Area Economic Typology module the enables councils to clearly identify the performance of their economy against like economies and identify how economic performance can be improved.

Supporting local government

While local government has always had a role in the development of local economies, I know that economic development is a relatively new discipline in local government. Local government economic development officers and strategic planners need clarity and confidence to make informed decisions. .id is keen to contribute what we can to making more informed decisions in this important field.With economy.id the search for local area economic data is over! The challenge is met – economy.id ^® provides the evidence base, enabling council officers to spend their time on the policy work, decision making and strategic planning

To see the full list of economy.id clients, please refer to .id’s client list. Also on our website, you will find more information on economy.id. To arrange a presentation at your council, please email info@id.com.au.

Acknowledgement

Thanks to Glenn Capuano, Lailani Burra and Peter Brain for providing much of the material for this blog.