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Department of the Prime Minister and CabinetEconomic Impact Assessment of automatic mutual recognition of occupational licensing - Final report

December 2020

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Executive summary

Over 124,000 licensees will benefit from reduced administrative costs from no longer being required to hold multiple licences on yearly basisThis will lead to an increase of $1.14 billion in GDP over the 10-year period of FY2021-2030

A further 44,000 licensees will benefit annually from optimising work allocation across jurisdictionsThis would generate an additional $462 million in GDP over the 10-year period of FY2021-2030.

Improved access to surge capacity across jurisdictions leads to higher capital productivityThere will be around $808 million in GDP growth over the 10-year period of FY2021-2030 as a result of increased capital productivity

Direct annual benefits to more than 168,000 licensees in the form of reduced administrative burden and improved labour mobility.

In addition, the changes could lead to other benefits not considered in this analysis which would all lead to higher economic benefits:• Potential efficiency gains from removing unnecessary regulatory requirements or

inconsistencies as part of implementing automatic mutual recognition

• Some workers may be able to obtain higher wages by providing services in other sectors and jurisdictions

• Increased labour mobility will help with economic recovery from COVID-19

An increase of at least $2.38 billion in GDP over the next 10 years

The economic impacts of automatic mutual recognition included in this analysis are expected to generate:

There are three economic impacts expected to flow as result of automatic mutual recognition.The relative contribution of each component is considered below:

This will lead to economic benefits for all States and Territories.

Source: PwC analysis (2020)

Contents

Executive summary

1 Introduction

2 Approach

3 Economic contribution of automatic mutual recognition

Appendix A Model assumptions 21

Appendix B CGE Overview 23

Appendix C Technical appendix for the economic impact 26

1 Introduction1.1 Context

On 17 August 2020, the Treasurer announced that the Council on Federal Financial Relations (CFFR) agreed to develop a framework for occupational licences to be automatically recognised across all Australian jurisdictions. CFFR is prioritising this work, with the ambition for widespread occupational mobility via automatic recognition of occupational licences to take effect from 1 July 2021, subject to the passage of legislation.

Automatic mutual recognition enables a worker licensed in one jurisdiction to undertake equivalent work in another jurisdiction, without applying for mutual recognition or a new licence or paying an additional fee (similar to the ‘driver’s licence’ approach). Each jurisdiction would continue to issue licences under existing jurisdictional categories and associated scopes of regulated work, and interstate workers would only be able to conduct work within the scope of their home licence. In essence this involves automating existing mutual recognition processes.

Box 1: Key features of a uniform scheme for automatic mutual recognition

A licensee would be able to work anywhere in Australia without having to apply or pay for a local licence when working in another jurisdiction (host state) within Australia, unless registrations are exempted

A person who holds a valid occupational licence for an activity in one state (home state) is taken to hold the equivalent occupational licence for the same activity in all states (second states) for the period of the licence

A licensee would need to operate within the scope of work and conditions of the home state licence

A person with adverse compliance and enforcement decisions will not be eligible for automatic mutual recognition and subject to disciplinary proceedings

Licensees choosing to work in other jurisdictions would still need to comply with all relevant local laws and regulators can take enforcement action consistent with local laws

Jurisdictions have discretion to specify a simple form of notification before a registered person can operate within their jurisdiction

A licensee would need to apply for a new home state licence, should the person move permanently into a state

Regulators may need to develop and agree on improved systems for information sharing to ensure effective operation of the scheme

Regulators will make available to each other enforcement action, suspension or cancellation of licences or where an individual has been prohibited from carrying-out an activity in a second jurisdiction

Regulators can prohibit an interstate licence holder from carrying out an activity in their state in the event of a serious breach of local laws by an interstate licence holder

Information needs to be provided to both the licensees and the customers about the arrangements in host jurisdictions and how to access and find appropriate licence holders

The existing mutual recognition arrangements will continue to be available

Source: Department of the Prime Minister and Cabinet (2020)

1.2 Scope

PricewaterhouseCoopers Australia (PwC) has been engaged to analyse the economic impacts of automatic mutual recognition to the Australian economy. The impacts on the flow and allocation of labour and capital have been modelled. The focus of the analysis has been to consider the productivity impacts associated with greater labour mobility across jurisdictions for short-term employment of occupational licensees.

Department of the Prime Minister and CabinetPwC 1

The analysis has drawn on data and information relating to occupational licences and combines this with insights from academic research on labour mobility and a number of assumptions to develop a view on the potential economic contribution of automatic mutual recognition.

The analysis estimates standard economic measures, which serve as indicators of changes in economic activity. It includes the impact of automatic mutual recognition on:

Gross domestic product (GDP) and gross state product (GSP) ($M): to measure the change in value of economic output

Household consumption ($M): to measure the changes to living standards and welfare of households

Employment (in full-time equivalents, FTEs): to estimate the indirect and induced employment across regions and industry sectors

1.3 Structure of this report

The structure of the remainder of the report is as follows:

Chapter 2 provides an overview of the approach used to model the economic impact

Chapter 3 details the economic contribution of automatic mutual recognition to Australia

Appendices A-C provide additional details on the analysis


2 Approach2.1 Overall approach

The impact analysis has been modelled using a multi-regional model that simulates the economic impact of automatic mutual recognition on the Australian economy each year (See Appendix B for an overview of the model).

The approach to modelling involved separating direct impacts from indirect effects:

direct effects: relates to the impacts of automatic mutual recognition across jurisdictions, largely driven by the increased labour and capital productivity from:

– reduced administrative costs of holding multiple licences,– optimising work allocation across jurisdictions– improved disaster recovery with access to surge capacity across jurisdictions

indirect effects: relates to the flow on impacts throughout Australia including:

– lower costs of inputs for downstream industries, consumers and export customers– resultant changes to incentives to employ and invest

The direct impacts of automatic mutual recognition are derived from the data supplied by the Department of the Prime Minister and Cabinet with modelling being employed to estimate the total (direct and flow on) contribution of reduction in the regulatory requirements for labour mobility within jurisdictions for short-term employment that requires occupational licensing.

The direct impacts generated by the regulatory changes were translated into economy wide impacts by estimating ‘shocks’ to the economy, which form the basis for an input into the Victoria University Regional Model (VURM). These shocks will stimulate the model economy and provide estimates of the direct impacts of the investment, as well as the flow-on impacts to the rest of the economy.

The direct effects were estimated by PwC from data relating to the occupational licensees as well as PwC assumptions and various research sources.1 The indirect and total economic contribution of automatic mutual recognition draws on Australia modelling, applying the VURM.

Due to limitations of available data, the analysis has drawn upon previous studies and historical data in this field. Notably, we have relied primarily on the Productivity Commission’s 2015 Research Report on Mutual Recognition Schemes. The proportion of duplicate licences across sectors and jurisdictions is based on more recent data provided by state and territory officials to the department. As a result, in this analysis we have used the Productivity Commission data for the number of licences and have scaled the proportion of duplicate licences based on the more recent data. The new data suggests that over 10 per cent of the licences are duplicates as opposed to approximately 5 per cent which was reported by the 2015 Productivity Commission. Where appropriate, the specific data sources and assumptions have been adjusted to reflect updated estimates, and this has been described in more detail in Section 2.3.2

2.2 Modelling approach employed

The economic model used is a computable general equilibrium (CGE) model developed by the Centre of Policy Studies (CoPS) at Victoria University. It is a multi-regional CGE model of Australia’s eight regional economies — the six States and two Territories. Each region is modelled as an economy in its own right, with region-

1 These included the Productivity Commission report on Mutual Recognition Schemes, previous PwC work on regulatory impact statements and recent Australian and international literature on the impacts of occupational licensing and mutual recognition on labour mobility. These sources are referenced throughout the report, where relevant.

2 Productivity Commission (2015), Mutual Recognition Schemes research report


specific prices, region-specific consumers, region-specific industries, and so on. There are four key categories in the model: industries, households, governments and foreigners.

In recursive-dynamic mode, VURM produces sequences of annual solutions connected by dynamic relationships such as the inter-relationship between subsequent year’s physical capital accumulation. Policy analysis with VURM conducted in a recursive-dynamic mode involves the comparison of two alternative sequences of solutions, one generated without the policy change and the other with the policy change or ‘shock’ in place. The first sequence, called the base case projection, serves as a control path from which deviations are measured to assess the effects of the policy shock.

The model’s base case has been updated to adjust for the recent changes in the macroeconomic conditions and reflect the interruptions in GDP, population and employment growth as a result of the COVID-19 pandemic (See Appendix B for an overview of the model).

The model comprises:

a CGE core incorporating input-output production and consumption relationships

foreign accounts

the modelling of product and factor markets

a number of satellite modules providing more detail on the model’s government finance accounts, household income accounts, population and demography, and energy and greenhouse gas emissions.

Full electronic documentation for the VURM is available at http://www.copsmodels.com/elecpapr/g-254.htm.

2.3 Estimating the economic impacts

There are three separate impacts expected on the economy as a result of automatic mutual recognition. These impacts are described in detail in the following sections.

2.3.1 The reduced administrative costs from no longer being required to hold multiple licences will lead to increased productivity

Automatic mutual recognition will result in a reduction of administrative costs associated with the application and renewal of licences for those licensees who hold multiple licences – many of whom live close to State and Territory borders. Currently, additional effort is needed to apply for and renew a licence in more than one jurisdiction. This will no longer be needed – just like it is not necessary to apply and hold multiple driver’s licences to drive across borders and then return home. The cost of attaining multiple licences can be a significant barrier on geographic mobility3 and avoiding this impost will decrease costs, time and effort and this in turn will increase productivity across the economy.4 Over 124,000 licensees will benefit from this reduction in administrative costs from no longer being required to hold multiple licences.

To estimate the impact of this productivity gain, the model is shocked by the magnitude of costs of obtaining a separate licence in another jurisdiction. The assumption in the analysis is that the licensee maintains their licence in the state where they primarily operate and drops all mutually recognised licences in other jurisdictions. The reduction in administrative costs is estimated based on the proportion of the labour force who operate under a mutually recognised occupational licence across sectors and jurisdictions (i.e. the proportion of the occupational licences that are held only for the purpose of operating in a different jurisdiction). Due to data limitations, this analysis has drawn upon the 2015 Productivity Commission report to estimate the number of

3 Johnson and Kleiner (2020), Is Occupational Licensing a Barrier to Interstate Migration?, American Economic Journal: Economic Policy 2020, 12(3): 347-373



Approach

people impacted and the total costs of duplicate licences.5 Our analysis draws on the recent data on the proportion of mutually recognised licences across jurisdictions provided to the department by regulators across sectors and jurisdictions and uses the 2015 Productivity Commission report for the associated fees and renewal periods.6 Where appropriate we have adjusted these values for 2020.7

These costs have been captured across two broad categories (as demonstrated in Figure 1 below):

1 The fees associated with obtaining a new or renewed licence in another jurisdiction: The annual costs of obtaining a new licence is estimated based on the registration fee for the proportion of new licences that are mutually recognised across sectors and jurisdictions. The annual costs are multiplied by annual growth factors, to adjust for the increase in number of licensees and costs over the 10-year period of the analysis. Given that application and renewal fees are required to be determined on a cost recovery basis it would be reasonable to argue that any cancellation of a licence should be matched by an equivalent reduction in regulatory effort. That said, it may not be possible in early years for that reduction in regulatory effort to be instantly achieved as regulatory costs are often made up of a series of inter-dependent fixed and variable costs. To account for this and to take a conservative approach to the analysis we have estimated that automatic mutual recognition saves 50 per cent of the regulatory costs of obtaining a new licence.

2 The time and effort required for a licensee to obtain another mutually recognised licence: it takes time and effort to complete the application process for a mutually recognised occupational licence. This time will no longer be required, and we have assumed that it can be productively applied to the relevant occupation. We have assumed that it takes an individual one and half hours of time and effort to obtain a separate licence in another jurisdiction in line with relevant regulatory impact statements including the Proposal for national licensing for electrical occupations (2012). For simple renewals this might be an overestimate of the time needed as all that may be needed is to pay the fee, but for other occupations the estimate of one and half hours would be a significant underestimate when we take into account continuing professional development or other requirements associated with renewal. In addition, there may still be a level of effort required to submit the short-form notification for automatic mutual recognition. We have converted the one and half hour time estimate to reflect a dollar cost – or opportunity cost – by using ABS Weekly Average Earnings plus an addition of an estimate for 1.75 in on-costs.8 This is in line with the framework set out in the Australian Government’s Regulatory Burden Measurement Framework.9


6 The new data suggests that over 10 per cent of occupational licences are duplicate licences across sectors and jurisdictions as opposed to the approximately 5 per cent that was previously reported by the 2015 Productivity Commission report.

7 The fees and the number of occupational licensees has been inflated to adjust for growth over the last five years. The number of occupational licences has increased proportionally with the increase in population and the costs of obtaining a new licence or renewal has been adjusted based on the growth in GDP. Source: Australian Bureau of Statistics (2020).

8 On average, for every hour saved per person in time and effort, there is approximately $46 million of savings across sectors and jurisdictions for the 10-year period of FY2021-2030.

9 Department of the Prime Minister and Cabinet, Office of Best Practice Regulation (2016), Regulatory Burden Measurement Framework, https://www.pmc.gov.au/sites/default/files/publications/rbm-framework-guidance-note.pdf


Approach

Figure 1 The approach to estimate the economic impacts of reduced administrative costs from no longer being required to hold multiple licences


The administrative savings shock to the CGE model is twofold:

A one-off productivity shock to the government sector (a proxy for all occupational licence regulators) by the magnitude of 0.077% based on 50 per cent costs of obtaining a new duplicate licence across sectors and jurisdictions. This productivity gain is applied as a saving of all primary factors by the Public Administration and Regulation industry.

A one-off productivity shock to all impacted industries based on the costs of renewing a duplicate licence and the time and effort required to obtain or renew a duplicate licence.10

2.3.2 Optimising work allocation across jurisdictions will increase labour productivity

There will be efficiency gains for a portion of the labour force who may now choose to spend a short period of their working year working in another jurisdiction under automatic mutual recognition.

Currently, any licensee who wants to operate in another jurisdiction is required to obtain the equivalent mutually recognised licence in the other jurisdiction. For a first-time mutual recognition application this takes time and effort and for short term work it may present a barrier for geographic labour mobility.11 This barrier won’t be large as the option to apply for a mutually recognised licence is well established in Australia. What we do expect though, is that for a small portion of occupational licensees and for a small portion of their time, the move to automatic mutual recognition will allow for a better allocation of work which in turn will optimise work across jurisdictions.

10 The magnitude of this shock is different across industries due to the different proportion of mutually recognised licences across each industry. For instance, the shock value for the building industry is 0.016%.

11 Productivity Commission (2014), Geographic Labour Mobility


Approach

As the majority of licences are granted at the jurisdictional level, it may limit the ability of workers to move between jurisdictions, affecting their capacity to take advantage of job opportunities in other places.12 Automatic mutual recognition is expected to support licensees to pursue short-term work best suited to their skills regardless of jurisdictional boundaries and this in turn is expected to lead to a slightly higher productivity per hour of work. In other words, this increased labour mobility would only be a short-term stint of the licensee’s working year rather than a permanent relocation to another jurisdiction. This would result in a one-off increase in labour productivity for that portion of the labour force who don’t currently hold a mutually recognised licence but would now be encouraged to work in another jurisdiction for a short period of time. That is, an increase in the portion of the licensees who operate in other jurisdictions beyond the existing portion who do so (i.e. on average five per cent across all sectors).13 An additional 44,000 licensees are expected to benefit from optimising work allocation across jurisdictions.

For the purposes of this analysis, it was necessary to form a view on the proportion of labour force who would take advantage the proposed changes, as well as the amount of time they would spend interstate.

Overall, the model assumes two productivity shocks with the magnitude of 0.001 per cent and 0.01 per cent across impacted industries. These values are based on the following approach: (as demonstrated in Figure 2 below):

On average 17.5 per cent of workers across the relevant industries operate with an occupational licence: This value has been used as a proxy for the average proportion of workers with occupational licences across all sectors and jurisdictions, instead of using specific averages for each group. This is done because the reported values appear to be incomplete for some sectors and jurisdictions. The 17.5 per cent is based on Productivity Commission’s estimate that in most jurisdictions, 15 to 20 per cent of employed persons work in an occupation that required them to be licensed in 2011.14 This is approximately equivalent of 1.2 million licences across different sectors and jurisdictions. This is a conservative approach as we have excluded a number of industries less likely to include occupational licencing from the total number of employees.15

There will be a 1 to 3.5 per cent increase in the portion of individuals who are willing to operate in other jurisdictions at some capacity under the low and high scenarios respectively: On average five per cent of the total licensees already operate across jurisdictions under the current settings. Under automatic mutual recognition, there will be an additional licensed individual who would chase higher productivity work. To estimate this proportion, the analysis uses a US study as a proxy, in which individuals working in state-specific occupations moved between states at a 7 percent lower rate compared to members of quasi-national licensed occupations.16,17 Given automatic mutual recognition would in effect transform the state-specific occupations to a quasi-national licensed occupation, this provides a proxy for this analysis.

12 Johnson and Kleiner (2020), Is Occupational Licensing a Barrier to Interstate Migration?, American Economic Journal: Economic Policy 2020, 12(3): 347-373



15 The 17.5 per cent is only estimated across 14 of the 19 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries. The five excluded industries are Wholesale Trade, Retail Trade, Accommodation and Food Services, Scientific and Technical Services and Administrative and Support Services. This is under the assumption than an occupational licensee that operates in one of these excluded industries is accounted for as part their profession industry.

16 Janna E. Johnson and Morris M. Kleiner (2020), Is Occupational Licensing a Barrier to Interstate Migration?, American Economic Journal: Economic Policy 2020, 12(3): 347-373

17 There are a number of other studies which indicate the 7 per cent is a conservative estimate of the impact of occupational licensing on interstate migration. Some of these findings are noted below. According to a study done by the White House (The White House (2015), Occupational Licensing: A Framework for Policymakers), the interstate migration rates for workers in the more licensed occupations are lower by 14 percent of the average migration rate compared to those in the less licensed occupations. Another study (Mulholland, Sean E. Young, Andrew T (2016), Occupational Licensing and Interstate Migration) finds that US states with a 10 per cent lower relative number of occupational licensees experience a 6.5 per cent higher in-migration rate for individuals without a college education.


Approach

Nonetheless, we take a conservative approach given the differences between Australia and United States. If licences are accepted nationally we assume that over the 10-year period of the analysis, only 1 or 3.5 per cent of licensed workers would operate at some capacity in at least another jurisdictions. Under these assumptions, up to 44,000 licensees could benefit from automatic mutual recognition across sectors and jurisdictions and use a portion of small portion of their time to work in another jurisdiction.18

This group of licensees is expected to spend no more than 20 per cent of their year working in another jurisdiction: As per Productivity Commission’s report, trade across jurisdictions accounts for at least 20 per cent of each Australian jurisdiction’s GSP. This value has been used as a proxy for how long a licensee will operate in a different state.19

During the period these licensees work in another jurisdictions, they will have a 4 or 8 per cent productivity gain under the low and high scenarios respectively: This increase will come from their ability to chase higher value work and increase their per hour productivity. These estimates are in line with recent US studies on job-to-job productivity increase by moving across jurisdictions.20 For the purposes of this impact, the model is not assuming any productivity gain from the rest of the labour force and the only source of increased productivity is from the increased productivity of this group for the 20% of their working time which they spend in another jurisdiction. In addition, these workers may experience additional productivity gain for the rest of the 80 per cent of the time where they operate in their own state. However, this secondary productivity gain is not included in the analysis.

Figure 2 The approach to estimate the economic impacts of optimising work allocations across jurisdictions


2.3.3 Improved access to surge capacity across jurisdictions leads to higher capital productivity

Automatic mutual recognition is expected to also support the country to recover faster from natural disasters or events that require surge labour capacity. The way this has been modelled in this report is to estimate a

18 Data extracted from PwC’s Geospatial Economic Modelling (2020).


20 Richard A Weisser (2019), The price of mobility, Review of Regional Research vol. 39, (1) 25-64


Approach

reduction in capital 'downtime' as automatic mutual recognition is expected to remove the short-term labour rigidity and support mobility to allow an impact region to return quicker to business as usual.

Under the current circumstances, there are challenges which maybe limit geographic labour mobility for contract or short-term work in instances such as natural disaster recovery.21 The facilitation of labour mobility and access to surge capacity when required as a result of automatic mutual recognition would decrease this total downtime, as skilled resource availability continues to be a critical factor influencing larger scale natural disasters.22 Australian jurisdictions have previously allowed a temporary form of automatic mutual recognition in emergency situations, such as bushfires or cyclones, when the number of local professionals has been insufficient to promptly provide emergency response and reconnect services (such as reconnecting power to homes). This is different to the proposed changes as this process is not permanent and relies on jurisdictions invoking a special permission or exemption.23

To estimate this, we analyse the costs of a natural disaster arising from the infrastructure downtime. While we would wish it to be otherwise, it is a reoccurring fact that Australia is often impacted by cyclones, fire, floods and other disasters. Our analysis has interpreted these phenomena as part of living in Australia and therefore if automatic mutual recognition can reduce the downtime associated with recovery, the effects of this can be simulated as an increase in the productivity of capital. We assume that a destructive natural disaster occurs every three years in line with the frequency of recent disasters including the 2019-2020 bushfire season and recent cyclones including Debbie (2017), Ita (2014) and Oswald (2013).24

To capture the costs of infrastructure downtime in the event of a natural disaster the following approach has been taken (as demonstrated in Figure 3 below):

The total costs of a natural disaster are estimated using the proportion of people who have been impacted for the period that the infrastructure was down by using ‘average infrastructure value per person’ as a proxy of the costs of this impact. The average infrastructure value per person is the impacted state’s annual value of infrastructure based on its expected return on investment divided by the total estimated resident population of the affected state. The average infrastructure value is estimated based on the average annual infrastructure investment provided by the ABS25, and Benefit-Cost Ratio of two26. This value is multiplied by the magnitude of the impacted individuals for the downtime period to estimate these costs. For instance, if Cyclone Debbie impacts 5 per cent of the Queensland population for a six-month period, the costs of the down time would be the 2017 Queensland’s average infrastructure value per person for the impacted proportion.27

For the purposes of this analysis, we assume the increased labour mobility can decrease total downtime costs by 10 per cent. This provides a conservative magnitude of this impact given recent studies have indicated there is up to 50 per cent shortage of skilled workers within the construction industry after a natural disaster.28 For instance, increased labour mobility would decrease a three-month infrastructure downtime by

21 National Occupational Licensing Authority (2013), Submission to the Productivity Commission in Relation to Geographic Labour Mobility Issues Inquiry.

22 Chang, and Wilkinson (2012), Managing resources in disaster recovery projects


24 Australian Institute for Disaster Resilience (2020), Australian Disaster Resilience Knowledge Hub

25 Australian Bureau of Statistics (2020), Australian National Accounts: National Income, Expenditure and Product

26 The Benefit-Cost Ratio is based on PwC’s previous engagements.

27 Queensland Government (2018), The Cyclone Debbie Review, BBC (2017), The damage caused by Cyclone Debbie in Australia.

28 Chang, Wilkinson (2017), Effects of a major disaster on skills shortages in the construction industry: Lessons learnt from New Zealand, Engineering, Construction and Architectural Management. Vol 24 No 1


Approach

approximately nine days. This is a conservative approach, yet demonstrates the magnitude of these proposed changes on ongoing capital productivity.

Figure 3 The approach to estimate the economic impacts of improved access to surge capacity across jurisdictions


The capital productivity shock is applied to the model as a one-off capital productivity shock to all industries with the magnitude of 0.0067%.

2.3.4 There are a series of other elements not included in this analysis which is expected to lead to higher net economic benefit

These include:

Potential efficiency gains for regulators if the changes reduce inter-jurisdictional inconsistencies: This could support long-term regulatory performance through reassessing some of the overlapping regulatory issues across jurisdictions.29 This may also support increased alignment across jurisdictions which may lead to additional cost-savings. For the purposes of this analysis, this is not included and the only economic impact to the regulators is from the removal of multiple licences.

Potential for the changes to better support post Covid-19 recovery investment and projects: In the recovery period, automatic mutual recognition can facilitate labour mobility - particularly because of the change in demand for some trades in different jurisdictions and increased government focus on infrastructure projects. The improved labour mobility and adaptability of the workforce will be more important than before, with workers wanting to go where the jobs are and infrastructure projects requiring timely availability of skilled workers. For the purposes of this analysis, we have not included the impact of this in our analysis, however, recent studies indicate that it is expected to have significant impacts.30

29 Carroll (2014), Managing Regulatory Competition: The Implications of Mutual Recognition, International Journal of Applied International Business, Vol 1, Issue 3.

30 In response to COVID-19 activation of emergency automatic recognition has been demonstrated internationally across the healthcare system. In the United States, some of the more impacted states such as Washington have activated its emergency volunteer health practitioners for surging demand on the state’s health care system allowing interstate volunteers as long as they are licensed. Similarly, South Carolina, Texas, and Maryland have issued temporary licences to doctors and nurses holding out‐of‐state licences. See: National Conference of State Legislatures (2020), COVID-19: Occupational


Approach

Potential for more competitive markets for occupational work: A plethora of research explores the impact of occupational licensing and job-to-job interstate migration on wage premiums across sectors and jurisdictions.31 In this analysis, we do not consider any movement that is induced by wage differential across jurisdictions, however, other studies indicate many workers may relocate for the best economic opportunities available to them32 and receive higher wages for increased productivity33 which would have higher economic impact.

2.3.5 CGE modelling

The economic contribution of automatic mutual recognition has been reported in terms of its direct and total (direct and flow-on) contribution. Figure 4 displays this distinction, depicting how data provided by the Department of the Prime Minister and Cabinet were used to derive the direct contribution before flowing through the CGE model to determine the total economic contribution to the Australian economy.

Figure 4 Economic impacts flow of automatic mutual recognition into the CGE model


The CGE model procures estimates of standard economic measures, which serve as indicators of changes in economic activity. These are defined as follows:

Gross domestic product (GDP) and gross state product (GSP) - The measure of economic value used in this report is ‘value added’, which is the gross value of production less the cost of intermediate inputs

Licensing During Public Emergencies, CATO Institute (2020), States Lead The Way in Coronavirus Crisis With Emergency Removal of Occupational Licensing Obstacles. In addition, OECD’s 2020 paper on the contribution of migrant doctors and nurses to tackling COVID-19 crisis in OECD countries highlights the significant contribution of mobilised health workers with foreign credentials in response to the global health worker shortage.

31 See Gittleman and Kleiner (2015). Wage Effects of Unionization and Occupational Licensing Coverage in the United States. International Labor Relations Review, Hernandez-Murillo et al. (2011), Patterns of Interstate Migration in the United States from the Survey of Income and Program Participation, Kleiner and Kluger (2010), The Prevalence and Effects of Occupational Licensing, British Journal of Industrial Relations

32 Borjas (2001), Does Immigration Grease the Wheels of the Labour Market?, OECD - Brookings Papers on Economic Activity

33 Duranton and Puga (2003), Micro-foundations of Urban Agglomeration of Economies. National Bureau of Economic Research


Approach

used in production. Value added represents the returns to primary factor inputs – capital, labour and land. The sum of value added for all Australian industries is approximately equal to gross domestic product (GDP). At the state level, the equivalent measure of economic value is referred to as gross state product (GSP).

Employment - Estimates of indirect and induced employment (in FTE terms) are made by the CGE model. Indirect employment impacts refer to changes in employment across regions and industry sectors that are a result of the purchase of goods and services and the flow on impacts. Induced employment impacts refer to the number of jobs generated as a result of wages expenditure – for example, the increased demand for goods and services by households. In CGE models such as VURM, positive consequences for labour occur in the long-run as higher real wages rather than increase in employment. The overall level of employment is determined by broad macroeconomic considerations and policy settings. Changes such as automatic mutual recognition may have short-term consequences for employment, however in the long-term there are mechanisms in the model that push employment back to base case.

Household consumption (welfare) - Change in household consumption expenditure is reported as an output of the CGE model. This is a proxy measure of the effect on living standards, or welfare, of households.

The CGE outputs are largely driven by production rather than consumption. That is, under the model’s assumptions the economic impact from a licensee working in another state is captured in the state which the work has been conducted, as opposed to where the licensee is from. However, the model also captures the interstate use of inputs and flows of payments between states.


Approach

3 Economic contribution of automatic mutual recognition

Automatic mutual recognition leads to increased productivity in both labour and capital. This economic contribution is estimated below. All contributions to the economy are reported in current real dollars.

These contributions have been reported as undiscounted amounts for annual totals but have been discounted for reporting the cumulative totals over the entire 10-year analysis period. Figure 5 provides an overview of the economic contribution.

Figure 5 The economic impact of automatic mutual recognition on the Australian economy

Over 124,000 licensees will benefit from reduced administrative costs from no longer being required to hold multiple licences on yearly basisThis will lead to an increase of $1.14 billion in GDP over the 10-year period of FY2021-2030

A further 44,000 licensees will benefit annually from optimising work allocation across jurisdictionsThis would generate an additional $462 million in GDP over the 10-year period of FY2021-2030.

Improved access to surge capacity across jurisdictions leads to higher capital productivityThere will be around $808 million in GDP growth over the 10-year period of FY2021-2030 as a result of increased capital productivity

Direct annual benefits to more than 168,000 licensees in the form of reduced administrative burden and improved labour mobility.

In addition, the changes could lead to other benefits not considered in this analysis which would all lead to higher economic benefits:• Potential efficiency gains from removing unnecessary regulatory requirements or

inconsistencies as part of implementing automatic mutual recognition

• Some workers may be able to obtain higher wages by providing services in other sectors and jurisdictions

• Increased labour mobility will help with economic recovery from COVID-19

An increase of at least $2.38 billion in GDP over the next 10 years

The economic impacts of automatic mutual recognition included in this analysis are expected to generate:

There are three economic impacts expected to flow as result of automatic mutual recognition.The relative contribution of each component is considered below:

This will lead to economic benefits for all States and Territories.


Approach

Note: NPV values are calculated with a 3 per cent discount rate.


3.1 Combined economic impact of automatic mutual recognition34

Automatic mutual recognition will directly benefit more than 168,000 licensees in form of reduced administrative burden and improved labour mobility. These impacts lead to an increase of at least $2.38 billion in GDP and $1.10 billion in household consumption over the next 10 years.

These economic impacts are described in more details below.

Figure 6 Key economic outputs for combined economic impact of automatic mutual recognition

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

0.004

0.008

0.012

0.016

0.02

Changes in key economic outputs from base case (FY2021-2030)

real GDP real consumptionreal investment real exports

Year

Cha

nge

(%)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

50100150200250300350400

Increase in GDP from base case ($M)

Year

GD

P ($

M)

Source: PwC analysis using the VURM model (2020)

34 Note: The estimated numbers reported throughout this report are based on the relative contribution of these three impacts when ran as one integrated shock. If each of these shocks are ran in isolation, they would lead to a higher impact over the 10-year period: (i) $1.44 billion contribution from reduced administrative costs, (ii) $599 million from optimising work allocation across jurisdictions, and (iii) $1.05 billion from improved surge capacity across jurisdictions. The combined GDP growth of $2.38 billion (as reported in this analysis) is smaller compared to the sum of these three individual impacts ($3.09 billion) indicating these shocks are not additive. This is because there are short-term employment gains in the disaster recovery scenario that do not occur when combined with the other scenarios. The short-term employment gains resulting from the disaster recovery shock is to an extent offset by the increase in extra employment available as part of the labour productivity shock of the first and the second shock.


Approach

Box 2: Summary of the jurisdiction-specific effects

Automatic mutual recognition will lead to higher economic impact across all jurisdictions

The analysis has been conducted at a national level; however, the results indicate higher economic impact across all jurisdictions. The growth in Gross State Product (GSP) is demonstrated below:

Jurisdiction Growth in GSP by 2030 (%)

1. New South Wales 0.013

2. Victoria 0.015

3. Queensland 0.015

4. South Australia 0.014

5. Western Australia 0.019

6. Tasmania 0.016

7. Northern Territory 0.013

8. Australian Capital Territory 0.006

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

-0.005

0

0.005

0.01

0.015

0.02

0.025

Growth in GSP from the base case between FY2021-2030

NSW VIC QLD SA WA TAS NT ACT

Year

Cha

nge

(%)

As can be seen, the majority of jurisdictions clump together. Western Australia is slightly higher as Western Australia has a higher portion of export-oriented activity and the proposed changes are expected to grow exports. Australian Capital Territory has a higher proportion of government services and given the expected reduction in administrative burden this flows through to a slightly lower level of government services.

The model inputs are drawn from national averages and while this flows through to ACT, a more granular consideration of the administrative changes would most likely smooth this impact across all jurisdictions. That is, this impact is unlikely to only be felt in ACT. ACT would more likely be grouped with the pack above and the benefits enjoyed by more populous states such as NSW and VIC would be slightly lower than the model suggests.

Source: PwC analysis using the VURM model (2020)


Approach

3.2 The reduced administrative costs from no longer being required to hold multiple licences

Over 124,000 licensees will benefit from reduced administrative costs from no longer being required to hold multiple licences. This shock leads to a relative increase of $1.14 billion in GDP over the 10-year period of FY2021-2030. This saving comes from the reduced administrative costs by shocking (i) the government sector by half the magnitude of the multiple licence costs,35 and (ii) proportionally shocking different industries based on the size of their mutually recognised licences by the magnitude of costs of renewing the licence and the time and effort to obtain and renew it. The increase in productivity from reduced administrative costs leads to cost savings across the economy, indicating more investment is profitable, and exports are more competitive.36

3.3 Optimising work allocation across jurisdictions

In addition to the existing 124,000 licensees, over 44,000 licensees would benefit from this optimised work allocation across jurisdictions. This shock leads to a relative increase of $462 million in GDP over the 10-year period of FY2021-2030. The economy gets larger because it can use the saved resources to produce more. Relative to the previous shock, investment performs more strongly than exports. This is mainly the result of the savings in this shock coming as labour savings, whereas a portion of the previous shock was the savings by the regulator. Under the smaller shock of 0.001, there is an increase of $62 million over the 10-year period of the analysis.

3.4 Improved access to surge capacity across jurisdictions

There will be a relative increase of around $808 million in GDP over the 10-year period of the analysis as a result of increased capital productivity. This is under the assumption that there will be on average at least one significantly destructive natural disaster in Australia every third year. For this capital shock, GDP increases more initially than it does longer term, whereas for the other shocks, GDP increases gradually. In this scenario the investment falls as less capital is required for current output due to the capital savings productivity shock. This is different to the other scenarios where investment increases. Appendix C provides additional information on the economic impacts of each of the shocks on the economy.

35 This shock is applied as an all primary factor shock, meaning that both labour and capital are saved.

36 By way of sensitivity, if we were to only use the 2015 Productivity Commission report, as opposed to the more recent data provided by the States and Territories, there would be a lower proportion of duplicated licences (approximately 5 per cent instead of 10 per cent of licences across sectors and jurisdictions). This would still lead to significant benefits for over 62,000 licensees with an increase of $520 million in GDP over the 10-year period of FY2021-2030. This suggests there are still significant gains to be had from the reduction of the administrative costs from no longer being required to hold multiple licences.


Appendices

Appendix A Model assumptions 19

Appendix B CGE Overview 21

Appendix C Technical appendix for the economic impact 24


Appendix A Model assumptionsTable A1 sets out the key assumptions used in the quantification of the benefits. More detailed discussions about the assumptions.

Table A1 General model assumptions

Assumption Value Discussion/ explanation

The time period of the analysis 10 years The analysis has been conducted across a 10-year period.

Start year 2021 The year that the key policy decisions in relation to automatic mutual recognition are assumed to be implemented

End year 2030 The 10th year of the analysis.

Discount rate 3%

Average weekly wages $1,304.7 Based on ABS (2020) - Table 6302.0: Average Weekly Earnings, Australia (Dollars)

Wage on-costs 1.75 Based on OBPR’s Regulatory Burden Measurement Framework



Appendix B CGE OverviewComputable General Equilibrium (CGE) modelling is an economic modelling technique used to evaluate the direct and indirect impacts of policy reforms, environmental impacts, and other economy-wide changes. This appendix outlines what CGE models are; what they can and cannot do; and the CGE model that we propose to use in this engagement.

1 What are CGE models?

CGE models are detailed representations of the Australian economy, combining a real-world database (sourced from ABS input–output tables) with economic theory. These models are used to estimate the impact of external changes on the real economy. Based on the input – output National Accounting framework, they focus on the productive economy by looking at the way that different industries demand labour, capital and intermediate inputs subject to economic capacity constraints. Prices in the model are market clearing by default for all goods and services (although this assumption can be relaxed). The models include numerous industries, regions, and labour types, as well as several types of final demand (consumption, investment, state and federal governments, and exports).

CGE models are used to examine the economy-wide impacts of reform. By including each industry’s demand for intermediate inputs, inter-state trade connections, and labour–capital intensity, the degree to which one industry impacts on another can be estimated. This not only includes the productive output of all industries, but also the income flows associated payments to labour, capital and governments (through taxation). In this way CGE models are used to examine three changes associated with an external impact to the economy (called a ‘shock’):

The degree to which the shock impacts directly on the industries targeted by the change

The degree to which other industries are indirectly impacted by their connections to the directly targeted industry

The degree to which economy-wide aggregates (such as gross state or national product, household consumption or real wages) are impacted, through the aggregation and interaction of all the industries in the economy.

Due to the detail in CGE models, they are able to report on a large number of industries, regional, and macroeconomic results. At the industry level, CGE models can report changes in activity level, employment, capital utilisation, wages, and prices, amongst others. At the regional level, they can report domestic and international trade flows, final demands, and population movements. At the macroeconomic level, they can be used to examine:

gross domestic product (GDP)

final demand

trade balances

government accounts

various price aggregates (e.g. CPI and the GDP deflator).

Assumptions within the CGE model can also be controlled to reflect the nature of the scenario being examined. Short-run policies (approximately five years) can be examined, as can longer run policies (estimating the impact of policy in 20+ years). Further, the models can be tailored to reflect certain characteristics of the economy unique to the modelling: the nature of government balances, drivers of government spending, and household consumption.


2 What the results of CGE models mean

CGE models are principally used to look at the impacts of policy changes on real economic variables, such as employment or the productive capital stock. They are used to estimate the relative expansion or contraction of industries or regions relative to one another. The models themselves are built on a strong and well researched academic foundation: including a variety of price responses and substitution effects.

However, CGE results should not be interpreted as a prediction of exactly where the economy will be at a certain point in the future. CGE models are based on the economic concept of the general equilibrium: the point at which the markets for all goods and services clear. As a result, CGE models do not incorporate the range of disequilibrium impacts seen in the short run in the real world. As a result, CGE model results can be thought of as the medium-to long-run impact that would result on the baseline level of output in the economy, abstracting from nominal and short-run disequilibrium effects.

CGE models only show the impact of the policy under investigation. They are not a broader tool for economic forecasting. As a result, any given CGE simulation will likely omit a range of external influences that are not directly relevant to the policy under investigation. Consequently, CGE results represent the change in the baseline level in the economic variables under investigation, solely attributable to the policy in question.

The interpretation and relevance of CGE results can be seen in the context of an example; in this case the construction of a new hospital in Victoria. A CGE model would describe the number of jobs created by the hospital, the degree to which it inflated the local wage and bid workers away from other industries, and the likely impact on gross state product. However, it would not reflect disequilibrium properties in the short run (e.g. the time required to train new labour to work in the hospital, financing issues associated with acquiring a capital (such as X-ray machines)). Further the results would be ‘all other things equal’: they would not reflect an unforeseen decline in labour supply that emerged five years down the line unless the modeller inserted this change). In this way, CGE models present an overarching ‘big picture’ impact of a change, once it has resolved itself in the economy and become part of the economic baseline.

CGE models do not include financial markets. It is argued that financial markets have no long-run persistent impacts on the real economy, only having real impacts in the short run. These short-run, disequilibrium states are not included in CGE results. Long-run impacts resulting from financial markets — such as changes in consumer preferences resulting from stocks of wealth — must be inserted into the modelling externally. Further, CGE models are built around the ABS National Accounts Input–Output framework (as mentioned above), which does not include financial data. To include them would upset the balances in the national accounting.

3 The VURM model

The Victoria University Regional Model (VURM) is a multi-regional, dynamic CGE model. It distinguishes up to eight Australian regions (six States and two Territories) and up to commodities/industries. The model recognises:

domestic producers classified by industry and domestic region

investors similarly classified

up to eight region-specific household sectors

an aggregate foreign purchaser of the domestic economy's exports

flows of greenhouse gas emissions and energy usage by fuel and user

up to eight state and territory governments


CGE Overview

the Commonwealth Government

The model contains explicit representations of intra-regional, inter-regional and international trade flows based on regional input-output data developed at the Centre of Policy Studies (CoPS) and includes detailed data on state and Federal governments' budgets. As each region is modelled as a mini-economy, VURM is ideally suited to determining the impact of region-specific economic shocks. Second round effects are captured via the model's input-output linkages and account for economy-wide and international constraints. Outputs from the model include projections of:

GDP and aggregate national employment

sectoral output, value-added and employment by region

export earnings, import expenditure and the balance of trade

greenhouse gas emissions by fuel, fuel user and region of fuel use

energy usage by fuel, energy user and region of energy use

State and Territory revenues and expenditures

regional gross products and employment

regional international export earnings, international import expenditures and

international balance of payments.

4 The VURM baseline adjustment for COVID-19

The VURM model base case has been recently updated using an indicative baseline provided by CoPS. It includes medium term macroeconomic conditions which are dominated by COVID-19 considerations as well as longer term trends for industry and technology. The base case has a downturn in GDP overall and associated interruptions to population and employment growth. The fall in GDP is consistent with the recent Commonwealth Budget's forecast of a fall in GDP of 3.75 per cent but for technical modelling reasons is spread over 3 years. The base case also includes particularly sharp falls in exports of higher education (80 per cent) and tourism (75 per cent). In the longer term the macroeconomy recovers to grow as normal and most parts of the economy expand, although the growth of some export commodities such as coal and LNG is restricted by anticipated supply and/or demand conditions.


CGE Overview

Appendix C Technical appendix for the economic impactThe following figures demonstrate the following for each of the economic shocks:

the change in key economic outputs (real) compared to the base case in 2020 (%)

the increase in GDP over the 10-year period ($M)

1 The reduced administrative costs of multiple licences

Figure C1 - Key economic variables for the reduction in administrative costs (%)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

0.005

0.01

0.015

Changes in key economic outputs from base case between FY2021-2030


Year

Cha

nge

(%)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

50

100

150

200

250

Increase in GDP between FY2021-2030 ($M)

Year

GD

P ($

M)

Source: PwC analysis using VURM model (2020)


CGE Overview

2 Optimising work allocation across jurisdictions

Figure C2 - Key economic variables for the optimisation of work allocation across jurisdictions

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

0.005

0.01



Year

Cha

nge

(%)

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

20

40

60

80

100


Year

GD

P ($

M)



CGE Overview

3 Improved disaster recovery with access to surge capacity across jurisdictions

Figure C3 - Key economic variables for the improved disaster recovery

2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02



2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

50

100

150

200

250


Year

GD

P ($

M)



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