6. Recommended indicators - methods

Based on the review presented in Sections 4.0-5.0, a set of indicators were identified which could feasibly be used to monitor social and economic impacts of forestry in Australia. Indicators were developed based on (a) identification of information needs, and (b) identification of cost effectiveness of potential data collection methods.

The initial set of proposed indicators was discussed in a workshop held in May 2008 with a group of researchers and forest industry representatives, at which the proposed indicators were discussed and prioritised. The prioritised set of indicators were then tested in two case study regions and revised before being presented in this report ³.

This section provides a detailed description of the methods used to calculate each of the recommended indicators, and how the indicator should be used, including the following:

Description of the indicator;
Data sources required to measure the indicator;
How often it can be measured;
Benchmarks the indicator can be compared to;
Cost, where:
- Low cost means the indicator could be measured at national and state scale for < $1,000 and at local scales for $1,000-$10,000 depending on the number of local areas to be included across Australia;
- Medium cost means the indicator could be measured for $10,000 to $50,000 depending on the number of businesses required to be surveyed and/or number of local areas to be included; and
- High cost means the indicator would cost > $50,000 to measure in most cases;
Forestry sectors which can be measured;
Scale of measurement, where:
- ‘Local’ means the indicator can be measured at or below the scale of the local government area;
- ‘Regional’ means the indicator can be measured at the State scale or for a large region within a State; and
- ‘National’ means the indicator is able to be measured at the national scale, providing a single figure for all of Australia;
Key questions answered;
Limitations of the indicator; and
Methods used to measure the indicator.

Indicators are presented in four groups:

Social and economic characteristics of the forest industry;
Impacts of the forest industry on the broader community;
Impacts of the forest industry on its workforce; and
Impacts of the forest industry on Indigenous people.

6.1 Characteristics of the forest industry: recommended indicators

The following characteristics of the forest industry should be monitored regularly over time to understand the social and economic characteristics of forestry in Australia:

Direct employment in the forest industry;
Proportion of land utilised by the forest industry;
Estimated value of production;
Estimated volume of production;
Efficiency of production (labour productivity); and
Consumption of wood and paper products.

This information about the industry forms the basis for other indicators, and provides the basic information on size and nature of the industry necessary to understandings it impacts.

Recommended methods for measuring each indicator are described on the following pages.

6.1.1 Direct employment in the forest industry

Description: This indicator measures the total number of people employed, by location, in the following sectors:

Forestry and logging;
Wood and paper product manufacturing;
Plantation forestry (hardwood, softwood); and
Native forestry.

Data source/s required:

ABS Census of Population and Housing data on employment in forestry and logging, and wood and paper product manufacturing;
National Forest Inventory (NFI) and National Plantation Inventory (NPI) data showing types of forestry occurring in different regions; and
Direct telephone survey of forest industry experts, forestry growers and processors to identify the proportion of employment in following forestry sectors: native forestry, softwood plantation, hardwood plantation.

How often can it be measured?

When based on Census of Population and Housing, every 5 years; or
It is feasible to undertake a brief annual survey of forestry firms to provide interim employment data between Censuses.

Benchmark/s: Absolute number and rate of change over time can be compared to the workforce for other industries, particularly other primary and manufacturing industries.

Cost: Low. While this indicator requires a survey of forestry growers and processors, this requires relatively low investment as the small number of questions required can be asked via phone, and there are a small number of growing and processing businesses in most regions. If contracting businesses were also surveyed, the cost would increase substantially.

Type/s of forestry: ABS forestry employment data separate the ‘forestry and logging’ and ‘wood and paper product manufacturing’ sectors. To identify the proportion of employment in the native forest and plantation sectors requires direct survey of forestry firms.

Scale/s of measurement: Local, regional, national.

Key questions answered: How many people are employed in the forest industry? How many depend on different sectors, eg plantations versus native forestry? How is total employment within the industry changing over time – is it growing or declining?

Limitations: This is a fairly broad measure which provides the basis for other indicators, such as dependence on forestry. A key limitation occurs at local scale, where it can be difficult to estimate the proportion of employment dependent on the native forest versus plantation sector with reasonable accuracy. It is recommended that care be taken when presenting data at local scale, and if there is uncertainty about where native forest and plantation sector employees of individual forestry businesses are located, these data should be presented at regional scale only.

This consultancy examined whether it is possible to identify the proportion of employment dependent on Managed Investment Schemes (MIS) within the plantation sector. This is only realistically possible within the ‘forestry and logging’ sector, as wood and paper product manufacturers typically cannot identify what proportion of their wood or fibre input derived from MIS versus non-MIS plantation. Within the forestry and logging sector, the extent of measurement is still limited, as:

it is difficult to identify what proportion of employment is dependent on MIS and non-MIS activities for businesses which undertake both types of activity; and
both MIS and non-MIS companies generate considerable employment in silvicultural contracting, and it is very difficult to identify the proportion of silvicultural contractors who are dependent on MIS versus non-MIS plantation-related activities.

It is therefore recommended that rather than attempt to regularly monitor the proportion of employment in MIS and non-MIS related plantation forestry, which would require an in-depth survey of all forestry growing, processing and contracting businesses at high cost, a better approach is to undertake irregular studies which identify these figures.

Methods: A key constraint of ABS data is that it does not separate native forestry and plantation sector employment. Direct phone survey of forest industry experts, major forestry growers and processors, can be used to obtain data enabling ABS data to be segmented into these sectors. This is done by:

Obtaining ABS Census of Population and Housing data on forestry employment (in ‘forestry and logging’ and ‘wood and paper product manufacturing’);
For each region, identifying what types of forestry and associated processing are undertaken using the Bureau of Rural Sciences’ National Plantation Inventory (NPI) and National Forest Inventory (NFI) data. If only one sector (eg native forestry, softwood plantations) exists in the region, it is possible to classify all employment in forestry and logging as falling within that sector. More care is needed when classifying processing employment, as wood and paper product manufacturers may source their wood and fibre input from areas a considerable distance from the processing plant, and therefore may not rely solely on the forest resource located within the region being examined. It is therefore recommended that a phone survey of forestry businesses be undertaken to ensure accuracy in estimation of employment in plantation versus native forest sectors for wood and paper product manufacturers;
Where more than one sector operates in a region, drawing up a list of forest growers and processors with the assistance of local experts, e.g. Private Forestry Development Committees, and also asking those experts to estimate business size and the sectors in which each business operates; and
Contacting forest growers and processors to survey them on (a) the sector/s in which they operate (native forests, softwood plantation, hardwood plantation), and (b) their total employment based on each sector.

Where a grower operates in more than one sector – for example, growing both plantations and native forest - the employment considered to be in each sector should be based on the percentage of total employee time spent in each sector. Where a processor operates in more than one sector, the employment considered to be in each sector should be based on the percentage of total wood input from each sector.

This method was tested in the two case study regions, and was successful in identifying employment in the native forest and plantation sectors to regional scale. It provided some data at the local scale, however the latter should be considered accurate to only within +/- 10%, due to difficulty identifying where employees of individual forestry businesses live in relation to the business office. Where data could not be obtained from individual businesses, local industry experts were a useful source of information on sector and business size, enabling more accurate classification of forestry employment into the different sectors.

6.1.2 Proportion of land utilised by the forest industry

Description: This indicator describes the proportion of land in a given area utilised by the forest industry, separated into native forest and plantation sectors.

Data source/s required:

Data on area of land (obtainable from ABS geographic areas);
Data on area of native forest used for different purposes e.g. commercial wood harvest, conservation (Bureau of Rural Sciences National Forest Inventory [NFI]);
Data on area of softwood and hardwood plantations (Bureau of Rural Sciences National Plantation Inventory [NPI]); and
Data on area of agricultural land.

How often can it be measured? This indicator can be measured at any point at which NFI and NPI data are updated.

Benchmark/s: Comparison of proportion of land used for native forestry and plantations in different regions.

Cost: Low

Type/s of forestry: It is important to ensure data are presented separately for native forests and plantations, as the differing nature of production in these sectors means it is not useful to combine the two when examining what proportion of land is used by forestry.

Scale/s of measurement: Local, regional, national.

Key questions answered: What area and proportion of land is used for native forestry? What area and proportion of land is used for plantation forestry? The latter can help answer concerns raised about the proportion of agricultural land being established to plantation forestry in some regions.

Limitations: In some cases, measuring the area of forestry as a proportion of total land area will not answer key questions asked by the community. For example, when examining expansion of plantation forestry, a key question asked is how much agricultural land in a region has been established to plantation over time. A measure based on total area of land may not provide an answer to this question, as the total area of land includes both agricultural land and other land tenures, such as publicly owned land and conservation reserves. When examining plantation forestry, the proportion of agricultural land established to plantation should be identified rather than the proportion of total land area, if possible. Estimates of agricultural land area vary across different data sources. It is recommended the estimates of the ABS Agricultural Census not be used, as they change over time depending on responses to the census, and the area of agricultural land excludes large-scale plantations, meaning it cannot be used to calculate proportion of land established to plantations.

Methods: This indicator is calculated quite simply, with the following equation used once data are obtained for a defined region:

area of native forest/plantation / area of land

6.1.3 Estimated value of forest industry production

Description: This indicator measures the gross value of production (GVP) of the forest industry. It can be measured at various stages in the chain of production to determine the value of:

log production (roundwood);
sawnwood;
wood based panels; and
paper and paperboard.

Where several steps of the value-adding chain are measured, it is possible to use this indicator to measure value-added at each stage of production^⁴.

Data source/s: ABARE Forest and Wood Product Statistics (FWPS) or direct survey of forest industry growing and processing businesses.

How often can it be measured?

When based on ABARE FWPS, annual (or even quarterly) measurement is possible; or
When based on survey, this indicator can be measured at any time.

Benchmark/s: Comparison to the value of production in other industries, and comparison of the rate of change to change in overall domestic product/gross state product over the same period.

Cost:

If based on ABARE FWPS – low; or
If based on direct survey – medium to high.

Type/s of forestry:

If using ABARE FWPS, it is not possible to distinguish between native forest and plantation sectors; or
If using a direct survey, it is possible to distinguish between native forest and plantation sectors.

Scale/s of measurement:

ABARE FWPS data enable this indicator to be measured at national and state scale; or
When using a direct survey, all scales are possible. However, the survey will typically be higher cost if aiming to produce small-scale data as well as large-scale data, because this requires a larger sample size. Confidentiality provisions may present reporting of data for local regions where fewer than three forestry processors operate.

Key questions answered: What is the dollar value of forest industry production? How is this changing over time? Changes in the dollar value of production can indicate potential for positive or negative impacts on regional economies, depending on the nature of the change.

Limitations:

It is important to carefully define at what points GVP will be measured, and to avoid double counting if GVP is calculated at multiple points in the chain of production;
The dollar value of production is not a measure of who receives the benefits of the industry – it provides no information about the distribution of the industry’s value and flow of benefits to different individuals and groups; and
The indicator does not provide information on the implications of changes in the value of production over time for local/regional/state/national communities, markets and economies.

Methods:

Using ABARE FWPS: ABARE FPWS data are presented in readily useable format, and all that is required is calculation of rates of change in the data over time, based on available FWPS data.

Using a direct survey of forestry businesses: The more expensive approach to this indicator is to directly survey forest industry growing and processing businesses involved in different parts of the forest industry. Because there are often a small number of forest growers and processors operating in a given area, and they operate businesses of widely varying size and nature, it is typically necessary to survey all growers and processors to obtain useful data. For example, a single region may have five sawmills, but one of these may process more wood than the other four combined, and they may use very different processing technology, resulting in widely differing volumes, and hence value, of production. It is necessary to survey all these businesses to obtain adequate data.

The survey needs to ask each business:

The volume of product produced over a defined period, for each product produced. In many cases, a business may produce several types of product;
The proportion of product derived from plantation and native forest sources. In some cases, products such as pulp or paper are produced using a combination of native forest and plantation sourced material. In these cases, the proportion of input from each sector must be identified to accurately identify the value of production derived from plantation and native forest sources; and
The value of the product, measured as gross payments received for that product.

6.1.4 Estimated volume of forest industry production

Description: This indicator measures of the volume of different products produced by the forest industry. Volume may be measured at the following stages:

log production (roundwood);
sawnwood;
wood based panels; and
paper and paperboard.

It is also possible to use a single measure of ‘gross roundwood equivalent’, which estimates total production as the equivalent of roundwood.

Data source/s: ABARE Forest and Wood Product Statistics (FWPS) or direct survey of forest industry growing and processing businesses.

How often can it be measured?

When based on ABARE FWPS, annual (or even quarterly) measurement is possible; or
When based on survey, this indicator can be measured at any time.

Benchmark/s: Comparison of rate of change over time to the rate of change in volume produced by other industries.

Cost:

If based on ABARE FWPS – low; or
If based on direct survey – medium to high.

Type/s of forestry:

If using ABARE FWPS, it is not possible to distinguish between native forest and plantation sectors; or
If using a direct survey, it is possible to distinguish between native forest and plantation sectors.

Scale/s of measurement:

ABARE FWPS data enable this indicator to be measured at national and state scale; or
When using a direct survey, all scales are possible. However, the survey will typically be higher cost if aiming to produce small-scale data as well as large-scale data, because this requires a larger sample size. Confidentiality provisions may present reporting of data at local scales where fewer than three forestry processors operate.

Key questions answered: What volumes are produced by the forest industry within different sectors and at different levels of production? How does this change over time? Changes in the volume of production can indicate potential for positive or negative impacts on regional economies, depending on the nature of the change. A drop in volume can indicate a likely fall in employment in the industry.

Limitations:

Volume of production can be measured at various stages in the chain of production. It is important to carefully define at what points volume will be measured.

This indicator provides an indication of change in volume of production but does not provide information on the implications of these changes for local/regional/state/ national communities, markets and economies. It can reasonable safely be assumed that a rapid decrease or increase in volume of production has employment implications for the industry, which is likely to have flow-on impacts on local and regional communities.

Methods:

Using ABARE FWPS: ABARE FPWS data are presented in a readily useable format, and all that is required is calculation of rates of change in the data over time based on the available FWPS data.

The survey needs to ask each business:

The volume of product produced over a defined period, for each product produced. In many cases, a business may produce several types of product; and
The proportion of product derived from plantation and native forest sources. In some cases, products such as pulp or paper are produced using a combination of native forest and plantation sourced material. In these cases, the proportion of input from each sector must be identified to accurately identify the value of production derived from plantation and native forest sources.

The survey should be combined with questions about value of production if measuring both Indicators 6.1.3 and 6.1.4.

6.1.5 Efficiency of production (labour productivity)

Description: This indicator measures the efficiency of production, based on volume of output produced per unit of labour input. Increasing efficiency of production over time is considered a sign of increasing productivity of an industry. The most feasible measure that can be undertaken cost effectively at regional and national scales for the forest industry is output produced per unit of employment.

Data source/s:

This indicator can be measured in two different ways:

Using ABS data on forest industry employment combined with ABARE FWPS data on volume of production, efficiency of labour can be calculated at State and National scale for the whole forest industry. Using these data sources, it is not possible to calculate efficiency of specific forestry sectors, and there is limited scope for assessing efficiency of production for different types of wood products. This measures therefore has limited use as the forest industry produces highly diverse products and, as this measure does not differentiate between them, it is not possible to identify if the productivity measured differs because of a real difference in productivity, or differences in the types of wood and paper products being manufactured in different regions; or
Using Indicator 6.1.1 together with data from direct survey of forestry businesses providing information on volume and value of production, more specific measures of labour productivity by wood and paper product type and sector are possible. These are more useful than the generic measure based on ABS and ABARE data.

How often can it be measured?

When based on ABARE FWPS, annual (or even quarterly) measurement is possible; or
When based on survey, this indicator can be measured at any time.

Benchmark/s: Efficiency of production over time can be compared to other industries, and to international benchmarks for the forest industry. Comparison to national and state averages can be made by comparing the percentage change in efficiency for the forest industry and for national/state economies over the same time period.

Cost:

If based on ABARE FWPS and ABS forestry employment data – low; or
If based on direct survey – medium to high.

Type/s of forestry:

If using ABARE FWPS and ABS forestry employment data, it is not possible to distinguish between native forest and plantation sectors, or different types of wood and paper products; or
If using a direct survey, it is possible to distinguish between native forest and plantation sectors, and different types of wood and paper products.

Scale/s of measurement:

ABARE FWPS enables this indicator to be monitored at national and state scale; or
When using a direct survey, all scales are possible. However, it is recommended that this indicator be reported at regional scale to ensure that a large enough sample of businesses are included to ensure the reporting reflects overall trends in the forest industry, rather than trends for a single forestry business.

Key questions answered: How efficiently does the forest industry utilise labour to produce outputs? How is efficiency of labour changing over time? An increase in efficiency per labour unit usually indicates investment in technology or other changes in business practices are enabling more efficient production of wood-based products.

Limitations: Increasing productivity may have multiple impacts, which are not easily identified based on the indicator alone. For example:

If less employment is required to generate output, this may result in job losses unless there is a corresponding increase in output; and
Profits may grow if productivity increases, but this depends on how and why productivity increased – for example, an increase in efficiency per unit of labour may have resulted from investment in technological advances, with the same overall costs incurred but less labour used.

Therefore changes in the indicator may have multiple implications for social and economic impacts on human communities, which are difficult to identify based on the indicator alone.

Methods: Using data on (a) volume of outputs produced, (b) employment utilised, whether from ABARE/ABS or direct survey, this indicator is calculated as:

Efficiency of production of labour = units of output produced / units of labour

It is essential that the output produced and units of labour be consistent – in other words, that there is certainty that for the output being examined, the labour data is specific enough to identify the employment required to produce that specific type of output.

Productivity should be measured for individual wood products, rather than generically across all types of wood and paper products produced, to ensure the productivity reflects efficiency of labour rather than differences in types of products and level of value adding occurring across different regions.

6.1.6 Consumption of wood and paper products

Description: This indicator measures consumption rates, per capita, for different wood and paper products such as sawnwood, wood-based panels, and paper-based products.

Data source/s: ABARE Forest and Wood Products Statistics, ABS Estimated Resident Population

How often can it be measured? This indicator can be reported annually based on ABARE FWPS.

Benchmark/s: Consumption rates can be compared to rates of change in other countries and regions, if measured in the same way.

Cost: Low.

Type/s of forestry: Currently available data does not distinguish between consumption of plantation and native forest-based products.

Scale/s of measurement: National. It is not currently possible to measure consumption at other scales using available data. Collection of data enabling measurement at smaller scales would be a high-cost option not suitable for regular monitoring.

Key questions answered: What is the per capita demand for wood and paper products in Australia?

Limitations:

It can be difficult to estimate total consumption of wood and paper products when many end products include embedded wood and paper combined with other materials; and
The data provide an indication of change in consumer demand, but do not provide information on the reasons for changes, or their implications for the forest industry or consumers of wood and paper products.

Methods: Calculated as:

Volume consumed of specified product / Population

6.2 Impacts of the forest industry on the broader community: recommended indicators

The following indicators should be monitored regularly over time to help understand the impacts of the forest industry on the broader community:

Dependence on the forest industry (% employment);
Social characteristics of forestry-dependent regions;
Location of forest industry employment;
Impact of plantation forestry on rural population; and
Values, uses and perceptions about forestry activities.

This information answers some key questions about impacts of the industry, and provides detailed information that helps identify where further information about impacts, gathered via more in-depth studies, may be needed. For example, these indicators may show that the social characteristics of forestry regions are changing in different ways to non-forestry regions over time, indicating a need to undertake studies that examine why this is the case.

Recommended methods for measuring each indicator are described on the following pages.

6.2.1 Dependence on the forest industry (% employment)

Description: This indicator measures the percentage of the workforce in a given area that is dependent on the forest industry. It is measured as the proportion of the employed labour force employed in the forest industry. This indicator identifies which areas are the most highly dependent on forestry, enabling identification of communities likely to be most impacted by any changes to the forest industry.

Data source/s:

Estimate of employment in forest industry (from Indicator 6.1.1); and
ABS Census of Population and Housing labour force data.

It is important to ensure that both Indicator 6.1.1 and labour force data are based on the same Census count method. It is recommended that the ‘place of usual residence’ count method be used, as a large proportion of the wages/salaries earned by forest industry workers will be spent in the locality in which they live, rather than that in which they work^⁵.

How often can it be measured?

When based on ABS forestry employment data, this indicator can be measured every 5 years; or
It may be possible to update the indicator in the interim based on (a) direct survey of the forest industry and (b) using labour force estimates derived for the ABS Labour Force Survey^⁶ and, at local scales, by the Labour Market Strategies Group (LMSG) Small Area Labour Market data, which estimates the labour force to a small-area scale on a quarterly basis⁷.

Benchmark/s: Comparison to other regions provides an indication of relative level of dependence on the forest industry.

Cost: Low.

Type/s of forestry: If using Indicator 6.1.1 data, dependence can be identified for all types of forestry. If unadjusted ABS data are used, then it is not possible to distinguish different types of forestry employment beyond different stages in the chain of production (forestry and logging, and wood and paper product manufacturing).

Scale/s of measurement: Local, regional, national.

Key questions answered: Which communities/regions depend on the forest industry for employment, and are therefore most likely to be impacted by any changes to the forest industry? How dependent are they on the forest industry?

Limitations:

It can be difficult to define the threshold at which a community should be said to be ‘highly’ dependent on the forest industry. It is therefore recommended that levels of dependence be evaluated based on examining relative levels of dependence across different regions. Further study is needed to better understand the implications of different levels of dependence on forestry employment for a region.

Methods: This indicator is measured by calculating, for a defined region:

Number of people employed in forest industry (or in a defined forest industry sector) / Total employed labour force

As noted in the ‘data sources’ section, both figures should be based on the same ‘count’ method, with figures based either on where all types of workers live, or where they work, as a person’s place of work and home residence are sometimes different.

6.2.2 Social characteristics of forestry-dependent regions

Description: This indicator monitors the nature and rate of change in key social characteristics of forestry dependent regions. Each characteristic, or sub-indicator, is chosen as it is believed to be related to the ability of the community living in that region to adapt to change. This is important as a community’s ability to adapt to change affects how it is impacted if a change occurs to an industry such as the forest industry.

It is commonly hypothesised that the ability of a community to adapt to change may be enhanced if the people living and working in that community have:

an average or above average household income;
an lower than average dependency ratio, such that there is not a high proportion of the population dependent on working age people;
a low unemployment rate;
high education levels;
high economic diversity; and
a population that is stable or growing, rather than declining in size.

Therefore the characteristics recommended to be profiled are:

Total population;
Unemployment rate;
Educational qualifications – the proportion of the population aged over 15 with (a) no post-school qualifications, (b) certificate/diploma, (c) bachelor degree or higher;
Median household income;
Median age;
Economic diversity, measured as the proportion of employment dependent on the top three employing industries; and
Dependency ratio: the ratio of working age population (age 15-64) to child (0-14) and retirement age (65+) population.

Data source/s: ABS Census of Population and Housing (CPH). These data should be based on the ‘place of usual residence’ count method.

How often can it be measured?

Social characteristics can be profiled every five years when an ABS CPH is undertaken. It is not possible to measure most of the individual characteristics at more regular intervals, except for unemployment, which is estimated quarterly to a local area scale by the LMSG Small Area Labour Markets data series, and at larger scale by the ABS Labour Force Survey.

Benchmark/s: Characteristics of forestry dependent regions can be compared to averages for all Australian regions, to identify if forest-dependent regions have different characteristics to other regions.

Cost: Low

Type/s of forestry: The use of ABS data does not allow different types of forestry to be distinguished. However, if the types of forestry operating in a given region are known based on Indicator 6.1.1, it may be possible to compare regions which are dependent on different types of forestry.

Scale/s of measurement: Local, regional. National scale data provide a useful average with which to compare characteristics of forestry dependent regions.

Key questions answered: Do forest-dependent regions differ to other regions in terms of characteristics such as their rate of population growth, the average level of education of the adult population, age distribution, and household income?

Limitations: While these indicators will identify if forestry-dependent regions are different to other regions, they do not provide an indication of whether the differences are related to the activities or presence of the forest industry in that region.

Methods: The calculation of each subindicator is described below:

Total population: Using ABS CPH population data, all that is required is calculation of the average annual rate of change in total population over time;
Unemployment rate: Unemployment data are readily available and require no additional calculation;
Educational qualifications: This is calculated as the proportion of the population aged over 15 with (a) no post-school qualifications, (b) certificate/diploma, (c) bachelor degree or higher;
Household income: Household income data are readily available and require no additional calculation;
Median age: Median age data are readily available and require no additional calculation;
Economic diversity: This sub-indicator is calculated by firstly identifying the top three employing industries for the region being studied, using ABS Census of Population and Housing data on employment by industry. The sum of people employed in the top three employing industries for a region is then divided by the sum of the total labour force to identify the proportion of the employed labour force dependent on the top three employing industries; and
Dependency ratio: The dependency ratio is calculated as the ratio of working age population (number of people aged 15-64) to the sum of the number of people aged 0-14 years and over 65 years of age.

6.2.3 Location of forest industry employment

Description: This indicator measures the proportion of forest industry employees based in small, medium and large towns. It is a useful measure of where impacts of changes in the forest industry are likely to occur, and provides answers to commonly asked questions about where forest industry employees are located. For example, some have questioned whether establishment of plantations on agricultural land changes job availability in small and regional towns, believing that forest industry workers are typically located in different sized towns to the agricultural industry workers who were previously employed on land established to plantation (Schirmer et al. 2008).

Data source/s:

ABS CPH urban centre/locality (UC/L) data: total labour force by UC/L, and employment in the forest industry by UC/L.

An ‘urban centre/locality’ refers to a town with 200 or more residents, as calculated by the ABS using a specific methodology for defining the boundaries of urbanised areas. A locality is defined by the ABS as a settlement with 200-999 residents, and an urban centre has 1,000 or more residents.

How often can it be measured? This indicator can be measured very five years when an ABS Census of Population and Housing is undertaken.

Benchmark/s: Comparison of location of forestry employment to distribution of the total labour force across different sized UC/Ls, or to the labour force of comparison industries such as agriculture or manufacturing.

Cost: Low.

Type/s of forestry:

ABS forestry employment data by UC/L differentiate employment into the ‘forestry and logging’ and ‘wood and paper product manufacturing’ sectors. It is more difficult to accurately estimate forestry employment in the native forest and plantation sectors for individual UC/Ls, as the method used for adjusting forestry data described in Indicator 6.1.1 is less reliable for small scales, and data gathered from forestry firms does not provide detailed information on what specific towns their employees live in. A relatively expensive survey of forestry firms would be required to specifically identify plantation and native forest employment by UC/L, as firms would need to provide detailed data on home address of individual employees, cross-referenced by the forestry sector in which that employee works.

Therefore this indicator cannot currently be measured separately for plantation and native forest employment, unless a high-cost survey of forestry businesses is undertaken.

Scale/s of measurement: Individual urban centre/locality.

Key questions answered:

What size towns are forest industry employees typically based in?
Is a greater proportion of forest industry employment located in large regional towns compared to other rural industries? and
Where are the impacts (negative and positive) of forest industry-based employment experienced?

Limitations: Care is needed to identify appropriate thresholds for defining ‘small towns’ versus ‘medium’ and ‘large’ urban centres/localities.

Methods: This indicator is calculated by calculating the distribution of forest industry employment by town size, and comparing it to distribution of agricultural industry employment and of the total labour force.

The town size classes recommended are:

Rural land and localities with < 200 population;
200-499 population;
5,00-999 population;
1,000-1,999 population;
2,000-2,999 population;
3,000-4,999 population;
5,000-9,999 population; and
> 10,000 population.

This range of town sizes is recommended as many of the towns in the case study regions in which indicators were tested had a population of less than 5,000, and so it was considered useful to ensure several categories of town size were included. Rural and regional areas in which the forest industry is typically located have very few urban centres with over 10,000 residents, and so it is not recommended that large towns be further differentiated beyond the ‘> 10,000 population’ category.

6.2.4 Impact of plantation forestry on rural population

Description: This indicator measures the rate of change in rural population over time in areas experiencing plantation expansion, compared to the average for rural areas. It can answer questions commonly asked about whether plantation expansion has a negative impact on the number of people living in rural areas.

Data source/s: ABS Census of Population and Housing population data (requiring data on total population of a region and population of the UC/Ls within that region), and Bureau of Rural Sciences National Plantation Inventory data on area of plantations over time.

How often can it be measured? This indicator can be profiled every five years when an ABS Census of Population and Housing is undertaken. It is not possible to profile this indicator between censuses.

Benchmark/s: The indicator can compare rates of rural population change in areas experiencing plantation expansion to the average for all rural areas.

Cost: Medium.

Type/s of forestry: Plantation forestry only. This indicator is specifically designed to answer questions about expansion of plantation forestry, and does not examine other types of forestry.

Scale/s of measurement: Local. This indicator is only meaningful at the local scale, as across a larger region it is likely plantation expansion will vary considerably and hence the indicator does not necessarily provide an indicator relevant to plantation forestry. At the local scale it is possible to identify areas experiencing high and low rates of plantation expansion and compare rural population change in these areas. However, the indicator needs to be measured for a large number of local areas with differing rates of plantation expansion to provide useful answers on impacts of plantation expansion on population change. The number of local regions able to be compared in the case studies conducted for this consultancy was relatively low, and provided data of limited usefulness.

Key questions answered: Is expansion of plantation estate associated with changes in total population of rural and regional areas?

Limitations: Many factors influence change in rural population. It is important to compare rural population change in plantation areas to an appropriate range of averages to ensure that change is not inappropriately identified as being due to expansion of plantations. If possible, analysis should include more in-depth identification of the different factors that may affect population levels in forest dependent areas, to gain a more holistic understanding of population trends.

Methods: This indicator is calculated by:

Identifying the rate of first rotation plantation establishment in a defined region over a defined period of time (using National Plantation Inventory data);
Calculating rate of change in rural population over the same period of time. Rural population is defined the number of people living on rural properties or in localities with less than 200 residents, and is calculated by, for a defined region, subtracting the total UC/L population from the total regional population; and
Comparing rates of change in rural population in areas experiencing differing levels of plantation expansion.

6.2.5 Values, attitudes, uses and perceptions of forestry activities

Description: This indicator refers to the measurement of values and attitudes towards forestry, uses of forests, and other perceptions about forest-related activities. The goal is to understand the perceptions and understandings of forestry held by different groups, and what they value about forests. A wide range of different indicators related to values, attitudes and uses can be measured. These indicators are presented as a single group as they have one thing in common: they involve measuring subjective perceptions.

Data source/s: Direct survey of the general population, repeated over time to enable comparisons. The number of people to be sampled would vary considerably depending on the scale at which results need to be interpreted. A survey can ask multiple questions relating to (a) the values held regarding different types of forests, (b) uses of different types of forests, (c) perceptions and awareness of forest industry activities.

How often can it be measured? This indicator can be measured at any time.

Benchmark/s: Comparisons to be made over time and between different groups included in the survey.

Cost: Medium-high, depending on size of sample and number of questions asked.

Type/s of forestry: All types of forestry can be distinguished when using a direct survey to collect data.

Scale/s of measurement: All scales possible; higher cost if aiming to produce small-scale data as well as large-scale data.

Key questions answered: What do people value most about forests? How are these values changing over time? How are forest uses changing over time? Is the general public’s understanding of forestry practices the same as that of forest managers? These questions help inform decisions about managing forests to achieve valued outcomes, and can also inform design of communication about forestry.

Limitations: It is important to carefully choose and design questions; only a relatively small set of questions can be asked on a single survey, and both the questions and the survey sample need to be designed to be readily replicable over time.

Methods: Direct survey of a statistically significant sample of the general population of defined regions. Sample sizes would vary considerably depending on the region/s being examined, the number of regions for which a statistically significant sample was required, and the number of groups whose views are to be compared.

Recommended topics to be included in a regularly repeated survey are (see Section 7.2 for further detail):

Acceptability of different forest practices;
Values and beliefs about the environment/natural resource management;
Perceptions about the nature forestry activities;
Information dissemination and communication – where and how do people obtain information about forestry and interact with the forest industry; and
Socio-demographic characteristics of respondents.

6.3 Impacts of the forest industry on its workforce: recommended indicators

The following indicators should be monitored regularly over time to help understand the impacts of the forest industry on forestry workers:

Income earned by forestry workers;
Physical health – reported injury rates;
Self-rated physical and mental health;
Self-rated wellbeing;
Age distribution;
Gender;
Attachment to place;
Cultural and family attachment to forestry;
Working hours; and
Educational qualifications.

This information answers some key questions about impacts of the industry, and provides detailed information that helps identify where further information about impacts, gathered via more in-depth studies, may be needed. For example, these indicators may show that the forestry workforce is ageing rapidly in some regions and not others, or that forest worker wellbeing is better in some regions than others, indicating a need to undertake studies that examine why this is the case and can be used to develop strategies to address ageing or low well-being.

Recommended methods for measuring each indicator are described on the following pages.

6.3.1 Income earned by forestry workers

Description: This indicator measures the average income earned by forestry workers. It is possible to identify and compare income earned in different forestry sectors, and to compare these to average income earned across the whole labour force.

Data source/s: Data can be collected in two ways:

ABS Census of Population and Housing data – this requires a special data request from the ABS, which does not publicly publish data on forest worker income; or
Direct survey of forestry workers, in which they are asked their income.

How often can it be measured?

ABS Census of Population and Housing data – every five years; or
Direct survey of forestry workers – at any point in time.

Benchmark/s: Comparison of forest industry worker’s incomes can be made with income earned by the labour force working in the same region.

Cost:

ABS data – low-medium cost, depending on extent to which forestry sectors are separated; or
Survey – medium-high cost.

Type/s of forestry:

ABS Census of Population and Housing data – it is possible to separate the forestry and logging and wood and paper product manufacturing sectors, but not to separate plantation and native forest sectors; or
Direct survey of forestry workers – it is possible to identify income separately for all types of forestry sector.

Scale/s of measurement: Regional, national.

In the case study regions, this indicator was tested at local scale, but the data produced could not be easily interpreted due to the small number of workers in many local areas. It was not possible to identify if variance in income across different local areas was due to real differences in income paid by forestry businesses, or simply the natural variance expected with a small sample of workers. It is therefore recommended this indicator be measured only at the regional and national scale.

Key questions answered: Do forestry workers earn an adequate income? How does the income of forestry workers compare with others?

Limitations:

While this indicator can identify if forestry workers earn a similar income to the general labour force in the same region, it does not provide information on how income influences wellbeing.

Methods:

ABS data: Comparison of forest industry workers income to the general labour force, for different income categories (e.g. nil income, $0-399, $400-599, $600-799 etc, based on weekly individual income). The comparison made should be of the proportion of the workforce falling into each category rather than the total number of workers, to enable appropriate comparison over time. Note that the ABS changed the categories into which they classify income between the 2001 and 2006 Censuses. This limits the ability to identify change in income over time, as the income categories for 2001 and those for 2006 have ranges which do not overlap in some cases; or
Direct survey of a statistically significant sample of workers employed in different parts of the forest industry: This is only needed if there is a desire to identify income of forestry workers employed in the native forest versus plantation sectors, as ABS data enable identification of income of forestry workers working in forestry and logging versus wood and paper product manufacturing.

6.3.2 Physical health – reported injury rates

Description: This indicator identifies the rate of reported injuries per 1,000 forest industry workers over a 12 month period, based on worker’s compensation statistics.

Data source/s: The Australian Safety and Compensation Council’s National Workers' Compensation Statistics database (http://nosi.ascc.gov.au/). Annual data are available from1997/98 onwards.

How often can it be measured? This indicator can be monitored annually using data from the Australian Safety and Compensation Council’s National Workers’ Compensation Statistics database.

Benchmark/s: Forest industry data can be compared to the Australian average rate of injuries per 1,000 workers and to benchmarks for appropriate comparison industries. For example, injury rates for wood and paper products manufacturing can be compared to injury rates in the manufacturing sector as a whole.

Cost: Low.

Type/s of forestry: It is possible to separate injuries occurring in the forestry and logging sector from those occurring in wood and paper processing. It is not possible to distinguish other forestry sectors e.g. native forest versus plantations.

Scale/s of measurement: National. The database does not contain data for regional or local scales.

Key questions answered: Are forestry workers more or less likely to be injured than those working in other Australian industries?

Limitations: The database does not include unreported or uncompensated injuries, and so represents a subset of all health and safety issues in the industry.

Methods: Obtain data on injury rate per 1,000 workers from National Workers’ Compensation Statistics Database. Note that the injury rate is calculated as:

= Total number of injuries / Total number of workers x 1000

An alternative way of collecting data on workplace injuries is via survey of forestry workers, as discussed in Indicator 6.3.3.

6.3.3 Self-rated health (physical and mental)

Description: This indicator identifies the self-rated health of forestry workers as measured through a direct survey in which workers are asked to indicate the extent to which they have experienced symptoms such as difficulty sleeping, depression, stress or anxiety, and physical injury whiles working, as well as the level of work-related risk perceived to result from the physical conditions in the workplace, hours worked, equipment used, noise and stress.

Data source/s: Data collected through direct survey of forestry workers.

How often can it be measured? A direct survey of forestry workers can be undertaken at any point in time.

Benchmark/s: Questions can be designed so responses of forestry workers can be compared to national benchmarks on rates of workplace injury, mental illness, and physical health. Ideally, the benchmark should be compared on the basis of socioeconomic status (gender and age) and location.

Cost: Medium-high depending on the sample surveyed. If it is necessary to compare many small regions or different forest industry sectors, a larger number of forestry workers will need to be surveyed than if the goal is simply to identify general trends across a single large region or the whole industry, incurring higher cost.

Type/s of forestry: All types of forestry can be distinguished when using a direct survey to collect data. However, if the goal is to compare health and wellbeing across many different forestry sectors, a larger survey sample size will be needed compared to gathering data for the whole forest industry.

Scale/s of measurement: All scales are possible, although costs will increase if many small regions need to be compared, as this requires high sample sizes.

Key questions answered: How healthy are forestry workers compared to the general population? Do people in the general population from the same location, of similar gender and age to forestry workers, experience similar health problems to those in the forest industry?

Limitations: While it may be possible to identify how healthy forestry workers are compared to the general population, for health issues other than direct injury incurred at work it is difficult to identify whether working in the forest industry is the factor causing differences in the health of industry workers and the general population. Many confounding factors may exist, including that people living in rural locations or with particular socio-demographic characteristics may be more susceptible to particular health problems than those living in urbanised areas.

Methods: Direct survey of a statistically significant sample of workers employed in different parts of the forest industry. Care is needed to identify an appropriate sample if the goal is to compare different forestry sectors and/or different geographic regions.

6.3.4 Self-rated wellbeing

Description: This indicator measures the average wellbeing of forestry workers, as self-rated by forestry workers.

Data source/s: Data to be collected through direct survey of forestry workers.

How often can it be measured? A direct survey of forestry workers can be undertaken at any point in time.

Benchmark/s: Comparison can be made to results of regular national surveys of wellbeing if the survey uses a comparable scale to that used in the comparison survey (e.g. the Australian National Unity Wellbeing Index^⁸). Note that the ABS does not undertake regular surveys of health and wellbeing, having last undertaken a ‘one-off’ survey of mental health and well-being in 1997.

Cost: Medium-high depending on sample size required. A higher sample size would be required if there is a need to compare wellbeing of workers operating in different forest industry sectors; or a need to compare wellbeing of workers in many different local regions.

Type/s of forestry: All types of forestry can be distinguished when using a direct survey to collect data. However, a larger sample is needed to compare several sectors to each other, as this requires a statistically significant sample of workers from each forestry sector to be compared.

Scale/s of measurement: All scales possible; higher cost if aiming to produce small-scale data as well as large-scale data.

Key questions answered: How happy are forest industry workers? How do they rate their well-being? This provides direct answers to the question of the wellbeing of workers, for which other characteristics, such as income, are commonly used as a proxy.

Limitations: This method can provide considerable detail, but is relatively costly compared to some other indicators. It is also difficult to identify the extent to which the level of wellbeing reported by forestry workers is related to or influenced by their work in the forest industry.

6.3.5 Age of forest industry workers

Description: This indicator monitors the age distribution of forestry workers to identify if the forestry workforce as a whole is ageing or becoming younger over time. An ageing workforce may indicate problems with recruitment of new workers into the industry; a declining average age of the workforce may indicate a need for increased skills training provision for inexperienced workers.

Data source/s: Data can be collected in two ways:

ABS Census of Population and Housing data – this requires a special data request from the ABS, which does not publicly publish data on forest worker age; or
Direct survey of forestry workers in which they are asked their age. This requires a representative, statistically significant sample to ensure that the ages identified are representative of the total population of forestry workers.

How often can it be measured?

ABS Census of Population and Housing data – every five years; or
Direct survey of forestry workers – at any point in time.

Benchmark/s: Comparison of forest industry worker’s age can be made with the age distribution of the labour force working in the same region.

Cost:

ABS data – low-medium cost; or
Survey – medium-high cost. It is important to survey a statistically significant, representative sample to ensure age distribution accurately reflects that of the forest industry as a whole.

Type/s of forestry:

ABS Census of Population and Housing data – it is possible to separate the forestry and logging and wood and paper product manufacturing sectors, but not to separate plantation and native forest sectors; or
Direct survey of forestry workers – it is possible to identify age distribution separately for all forestry sectors.

Scale/s of measurement: Regional, national.

In the case study regions, this indicator was tested at local scale, but the data produced could not be easily interpreted due to the small sample of workers available in individual local government areas. It was not possible to identify if variance in age distribution across different local government areas was due to differences in the forest industry across these areas, or simply the natural variance expected with a small sample of workers. It is therefore recommended this indicator be measured only at the regional and national scale.

Key questions answered: Is the forestry workforce older or younger on average than the general labour force? Is the forestry workforce ageing more rapidly than average? If yes, this may indicate unsustainable replacement of workforce, and a potential for increasing skills shortages.

Limitations: It can be difficult to interpret this indicator – is an ageing workforce necessarily negative? What should be considered a problematic rate of change in average age of the workforce? Further work should be undertaken to improve interpretation of this indicator.

Methods:

ABS data: Comparison of the proportion of forest industry workers and proportion of the general labour force falling into the following age categories: 15-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65 years and over. The comparison made should be of the proportion of the workforce falling into each category, rather than the total number of workers, to enable appropriate comparison over time; or
Direct survey of a statistically significant sample of workers employed in different parts of the forest industry: This is only needed if there is a desire to identify income of forestry workers employed in the native forest versus plantation sectors, as ABS data enable identification of the age distribution of forestry workers working in forestry and logging versus wood and paper product manufacturing.

6.3.6 Gender of forest industry workers

Description: This indicator monitors the proportion of forestry workers who are male and female, compared to the labour force as a whole, and how the proportion of men and women is changing over time.

Data source/s: Data can be collected in two ways:

ABS Census of Population and Housing data; or
Direct survey of forestry workers in which they are asked their gender. Direct survey is only useful if there is a need to distinguish between gender distribution in the native forest versus plantation sectors, as ABS data can be used to identify gender distribution across other forest industry sectors.

How often can it be measured?

ABS Census of Population and Housing data – every five years; or
Direct survey of forestry workers – at any point in time.

Benchmark/s: Comparison of forest industry worker’s gender can be made with gender distribution of the labour force working in the same region, or of the labour force working in relevant comparison industries.

Cost:

ABS data – low cost; or
Survey – medium-high. It is important to survey a statistically significant, representative sample to ensure age distribution accurately reflects that of the forest industry as a whole.

Type/s of forestry:

ABS Census of Population and Housing data – it is possible to separate the forestry and logging and wood and paper product manufacturing sectors, but not to separate plantation and native forest sectors,; or
Direct survey of forestry workers – it is possible to identify age distribution separately for all forestry sectors.

Scale/s of measurement: Regional, national. In the case study regions, this indicator was tested at local scale, but the data produced could not be easily interpreted due to the small sample of workers available in individual local government areas. It was not possible to identify if variance in gender distribution across different local government areas was due to differences in the forest industry which have resulted in differing gender profiles of the workforce, or simply the natural variance expected with a small sample of workers. It is therefore recommended this indicator be measured only at the regional and national scale.

Key questions answered: Does the forestry workforce have a similar proportion of male and female workers as the overall labour force?

Limitations: It can be difficult to interpret this indicator – is a higher proportion of men than women necessarily negative? To what extent should dominance of the forestry workforce by male workers be considered problematic? Some studies have indicated that women may find it difficult to enter or maintain work in the forest industry (Buchy 2001), but few in-depth studies have been undertaken to examine why the forest industry typically has a higher proportion of male workers than the average, and whether there are any barriers to women entering employment in the industry. Further work should be undertaken that can assist in interpreting the meaning of this indicator.

Methods:

ABS data: Comparison of the proportion of forest industry workers and proportion of the general labour force who are male and female. The comparison made should be of the proportion of the workforce falling into each category rather than the total number of workers to enable appropriate comparison over time; or
Direct survey of a statistically significant sample of workers employed in different parts of the forest industry. This is only needed if there is a desire to identify gender of forestry workers employed in the native forest versus plantation sectors, as ABS data enable identification of gender of forestry workers working in forestry and logging versus wood and paper product manufacturing.

6.3.7 Attachment to place

Description: This indicator identifies the level of attachment forestry workers have to the local area in which they are currently living. Attachment to place may influence the vulnerability of workers to change, dependency of workers on particular types or locations of employment, and worker’s flexibility and willingness to move for employment. It can therefore affect unemployment rates, as well as provide some indication of the stability of the industry in an area.

Data source/s: Direct survey of forestry workforce

How often can it be measured? A direct survey of forestry workers can be undertaken at any point in time.

Benchmark/s: The data from this indicator can be monitored over time to identify of level of attachment to place is changing. As there are no sets of regularly collected data on attachment to place for the labour force as a whole, comparison to the general population or other industries must rely on opportunistic comparison to the results of one-off studies if and when they are undertaken.

Cost: Medium-high cost.

Scale/s of measurement: All scales possible; higher cost if aiming to produce small-scale data as well as large-scale data.

Key questions answered: To what extent do forestry workers identify themselves as having attachment to their local region and community, as evidenced by their length of residence, views about their local area as a desirable place to live, and expectations of continued residence.

Limitations: Care is needed to design questions which adequately reflect attachment to place. Many measures assume attachment to place is related to length of residence and an individual’s stated views about the desirability of the area and community in which they live. It may also be useful to explore the extent to which an individual would change jobs or industry of employment in order to stay living in a particular location, or other aspects of attachment to place.

Methods: Direct survey of a statistically significant sample of workers employed in different parts of the forest industry.

6.3.8 Cultural and family attachment to forestry

Description: This indicator identifies the cultural and family attachment workers have to the forest industry, based on identifying the extent to which a forestry worker indicates their family and friends work in the forest industry, have an individual or family history of working in the industry, and the reliance of their social networks on the industry. These factors influence flexibility and willingness to work in other industries if there is a downturn in forestry-based employment. It can therefore affect unemployment rates and wellbeing. Long term attachment to the forest industry may also influence community identity.

Data source/s: Direct survey of forestry workforce How often can it be measured? A direct survey of forestry workers can be undertaken at any point in time.

Benchmark/s: The data from this indicator can be monitored over time to identify if the level of attachment to the forest industry is changing. As there are no sets of regularly collected data on attachment to industry of employment for the labour force as a whole, comparison to the general population or other industries will rely on opportunistic comparison to the results of one-off studies if and when they are undertaken.

Cost: Medium-high cost.

Scale/s of measurement: All scales possible; higher cost if aiming to produce small-scale data as well as large-scale data.

Key questions answered:

How many forestry workers have immediate/extended family or friends who work in forest industry or forest industry-related jobs?
How long have forest industry worker’s families worked in the industry? and
To what extent are a forest industry worker’s social networks dependent on the forest industry?

Limitations: This indicator assumes that involvement of friends and family in the forest industry, history of working in the industry, and depth of industry-dependent social networks are related to level of attachment to the industry. It assumes this is an indicator of likely reluctance to change jobs to take up work outside the industry, and of higher vulnerability to changes in the industry; however, this assumption requires testing in more in-depth studies.

Methods: Direct survey of a statistically significant sample of workers employed in different parts of the forest industry.

6.3.9 Hours worked by forest workers

Description: This indicator monitors the average hours worked per week by forestry workers, and how average hours worked is changing over time. This can identify if forestry workers on average work more or less hours than the labour force average. Working hours are considered a key indicator of wellbeing, with excessively high work hours often associated with decreased wellbeing. It is more difficult to assess wellbeing for workers who work less than full-time, as some may do this by choice, while others may have an unfulfilled preference to work longer hours.

Data source/s: Data can be collected in two ways:

ABS Census of Population and Housing data – this requires a special data request from the ABS, which does not publicly publish data on hours worked by forest industry workers; or
Direct survey of forestry workers in which they are asked about their working hours. This requires a representative, statistically significant sample to ensure that the working hours identified are representative of the total population of forestry workers.

How often can it be measured?

ABS Census of Population and Housing data – every five years; or
Direct survey of forestry workers – at any point in time.

Benchmark/s: Working hours within the forest industry can be compared to working hours for the labour force working in the same region.

Cost:

ABS data – low-medium cost; or
Survey – medium-high cost. It is important to survey a statistically significant, representative sample to ensure age distribution accurately reflects that of the forest industry as a whole.

Type/s of forestry:

ABS Census of Population and Housing data – it is possible to separate the forestry and logging and wood and paper product manufacturing sectors, but not to separate plantation and native forest sectors; or
Direct survey of forestry workers – it is possible to identify hours worked separately for all forestry sectors.

Scale/s of measurement: Regional, national. In the case study regions, this indicator was tested at local scale, but the data produced could not be easily interpreted due to the small sample of workers available in individual local government areas. It was not possible to identify if variance in hours worked across different local government areas was due to differences in the forest industry which have resulted in differing working hours for workers, or simply the natural variance expected with a small sample of workers. It is therefore recommended this indicator be measured only at the regional and national scale.

Key questions answered: Do forest industry workers work longer or shorter hours than the average? Working longer hours is generally interpreted as an indicator of a reduction in quality of working conditions.

Limitations: It can be difficult to identify whether a greater or lesser amount of hours worked means the industry’s working conditions are better or worse than others. The indicator is only a partial indication of overall working conditions and wellbeing.

Methods:

ABS data: Comparison of the proportion of forest industry workers and proportion of the general labour force falling into the following categories of hours worked per week: nil hours, 1-15 hours, 16-24 hours, 25-34 hours, 35-39 hours, 40 hours, 41-48 hours, 49 hours and over. The comparison made should be of the proportion of the workforce falling into each category rather than the total number of workers, to enable appropriate comparison over time; or
Direct survey of a statistically significant sample of workers employed in different parts of the forest industry. This is only needed if there is a desire to identify hours worked by forestry workers employed in the native forest versus plantation sectors, as ABS data enable identification of the working hours of forestry workers working in forestry and logging versus wood and paper product manufacturing.

6.3.10 Educational qualifications of forest industry workers

Description: This indicator identifies the proportion of forest industry workers with different levels of formal educational attainment. It helps in identifying the extent of formal skills training achieved by industry workers, and how this is changing over time.

Data source/s: Data can be collected in two ways:

ABS CPH data – this requires a special data request from the ABS, which does not publicly publish data on educational attainment of forest industry workers; or
Direct survey of forestry workers in which they are asked to indicate their level of educational attainment, and or the ways they have learned the skills they use in their work. This requires a representative, statistically significant sample to ensure that the results identified are representative of the total population of forestry workers.

A third possible method is to ask forestry employers about the qualifications of the staff who work for their business, rather than directly surveying individual forestry workers. However, in many cases employers do not maintain records on the educational attainment of workers. A recent survey of forestry businesses in Tasmania asked this question of employers; too few employers were able to answer questions about the educational attainment of their staff to enable analysis of the responses (Schirmer 2008).

How often can it be measured?

ABS Census of Population and Housing data – every five years; or
Direct survey of forestry workers – at any point in time.

Benchmark/s: Comparison of forest industry worker’s educational attainment can be made with the educational attainment of the labour force working in the same region.

Cost:

ABS data – low-medium cost; or
Survey – medium-high cost.

Type/s of forestry:

ABS Census of Population and Housing data – it is possible to separate the forestry and logging and wood and paper product manufacturing sectors, but not to separate plantation and native forest sectors; or
Direct survey of forestry workers – it is possible to identify educational attainment separately for all forestry sectors.

Scale/s of measurement: Regional, national. In the case study regions, this indicator was tested at local scale, but the data produced could not be easily interpreted due to the small sample of workers available in individual local government areas. It was not possible to identify if variance in educational attainment across different local government areas was due to differences in the forest industry which have resulted in differences in education level of workers, or simply the natural variance expected with a small sample of workers. It is therefore recommended this indicator be measured only at the regional and national scale.

Key questions answered:

How does the level of education attained by forestry workers compare to other industries? Low levels of education can indicate potential literacy and industry development challenges, and predict difficulty adapting to changing skills needs and technology;
Are different levels of educational attainment correlated with particular types of employment within the forestry sector? and
When direct survey is used: how do forest industry workers learn their skills and how does this influence the type of employment they are involved in within the forestry sector?

Limitations: It can be difficult to interpret this indicator – is a low level of formal educational attainment necessarily negative? What should be considered the appropriate level of education for different types of forestry workers? Further work should be undertaken that can assist in interpreting the meaning of this indicator. Formal education is not necessarily a good indicator of the level of skills and experience a person has in their employment, and should always be considered to represent only a part of the set of skills a person has.

Methods:

ABS data: Comparison of the proportion of forest industry workers and proportion of the general labour force with the following level of educational attainment: no post high-school qualification, certificate/diploma, or bachelor degree or other postgraduate qualification. The comparison made should be of the proportion of the workforce falling into each category rather than the total number of workers to enable appropriate comparison over time; or
Direct survey of a statistically significant sample of workers employed in different parts of the forest industry: This is only needed if there is a desire to identify educational attainment of forestry workers employed in the native forest versus plantation sectors, or if there is a desire to obtain more detailed information on skills attainment than is possible from ABS data.

6.4 Impacts of the forest industry on Indigenous people: recommended indicators

The following indicators should be monitored regularly over time to help understand the impacts of the forest industry on Indigenous people:

Indigenous employment in the forest industry – quantity;
Indigenous employment in the forest industry – quality; and
Area of forest owned or accessed by Indigenous people.

This information answers some key questions about impacts of the industry on Indigenous people, although the indicators provided information on only a limited number of issues related to Indigenous access to forests. See Section 7.1 for discussion of other, in-depth studies needed to better understand impacts of the forest industry on Indigenous people.

Recommended methods for measuring each indicator are described on the following pages.

6.4.1 Indigenous employment in the forest industry – quantity

Description: This indicator identifies the proportion of forest industry workers who identify as Indigenous (Aboriginal or Torres Strait Islander).

Data source/s: ABS Census of Population and Housing. Identifying Indigenous people who work in the forest industry requires specific data order from the ABS, as these data are not produced as part of publicly released information by the ABS.

An alternative method of gathering data would be via direct survey of forestry workers, or of forestry businesses. A large sample of forestry workers would need to be surveyed to accurately identify the proportion of Indigenous employment, due to the low proportion of workers who are Indigenous. Care would also be needed to ensure the survey reached forestry workers who are Indigenous. A recent survey of forestry businesses asked them to identify the number of Indigenous workers they employed. Many businesses could not answer this question, as they do not ask workers if they are Indigenous (Schirmer 2008). ABS data, while having important limitations, are therefore the best measure currently available.

How often can it be measured? This indicator can be measured every five years, when the ABS Census of Population and Housing is undertaken.

Benchmark/s: Comparison of the proportion of Indigenous and non-Indigenous worker in the forest industry to the proportions in the overall labour force, and comparison of rates of change over time.

Cost: Low-medium cost.

Type/s of forestry: ABS data provide information on employment in the forestry and logging, and wood and paper product manufacturing sectors. It is not possible to identify Indigenous employment in the native forest versus plantation sectors using ABS data.

Scale/s of measurement: Regional, national. At smaller scales, the number of Indigenous employees is small and data cannot be considered accurate, both because of potential limitations of the data set (see ‘limitations’), and because of randomisation of data by the ABS, which limits usefulness of data involving very small numbers.

Key questions answered: How many Indigenous people are employed in the forest industry? What proportion of forest industry employees are Indigenous? These data provide information needed to monitor the outcomes of the National Indigenous Forestry Strategy.

Limitations: ABS data may underestimate total Indigenous employment in forestry, as the CPH requires self identification of Indigenous status. This is a significant issue, and it is difficult to identify the extent to which Indigenous employment will be under- estimated as a result of Census respondents deciding not to self-identify as Indigenous.

Methods: The proportion of Indigenous workers is calculated as:

Number of Indigenous workers / Total number of workers

6.4.2 Indigenous employment in the forest industry – type

Description: This indicator identifies the proportion of Indigenous (Aboriginal or Torres Strait Islander) forestry workers employed in different occupations. This information helps identify whether Indigenous workers are represented equally across all types of forest industry occupation, or tend to be employed in particular occupations.

Data source/s: ABS Census of Population and Housing. Identifying Indigenous forestry workers by occupation requires specific data order from the ABS, as these data are not produced as part of publicly released information by the ABS.

An alternative method of gathering data would be via direct survey of forestry workers, or of forestry businesses. A large sample of forestry workers would need to be surveyed to accurately identify the proportion of Indigenous employment by occupation, due to the low proportion of workers who are Indigenous. Care would also be needed to ensure the survey reached forestry workers who are Indigenous. A recent survey of forestry businesses asked them to identify the number of Indigenous workers they employed. Many businesses could not answer this question, as they do not ask workers if they are Indigenous (Schirmer 2008). ABS data, while having important limitations, are currently the best measure available.

How often can it be measured? This indicator can be measured every five years, when the ABS Census of Population and Housing is undertaken.

Benchmark/s: Comparison of the proportion of Indigenous and non-Indigenous forestry workers employed in different occupations within the forest industry.

Cost: Low-medium cost.

Scale/s of measurement: Regional, national. At smaller scales, the number of Indigenous employees in some occupations is small and data cannot be considered accurate, both because of potential limitations of the data set (see ‘limitations’), and because of randomisation of data by the ABS, which limits accuracy of data involving very small numbers.

Key questions answered: Do Indigenous workers have the same types of jobs as non-Indigenous workers in the forest industries? If there are differences in occupation, what are they?

Limitations: ABS data may underestimate total Indigenous employment in forestry, as the CPH requires self identification of Indigenous status. This is a significant issue, and it is difficult to identify the extent to which Indigenous employment will be under-estimated as a result of Census respondents deciding not to self-identify as Indigenous.

Methods: Data on Indigenous employment in the forest industry and total employment in the forest industry by occupation are compared to identify whether there is a similar distribution of occupations across Indigenous and non-Indigenous workers. The occupations compared are:

Managers;
Professionals;
Technicians and trades workers;
Community and personal service workers;
Clerical and administrative workers;
Sales workers;
Machinery operators and drivers; and
Labourers.

For definitions of these occupations, see ABS (2006).

6.4.3 Area of forest owned or accessed by Indigenous people

Description: This indicator identifies the area of forest owned or accessed by Indigenous people, based on (a) area of forested land under Indigenous ownership and (b) area of forest on the Register of the National Estate for Indigenous values. These figures represent a subset of the forests that are important to Indigenous people in Australia, but provide some indication of how forest access and ownership by Indigenous people in Australia is changing over time.

Data source/s: Bureau of Rural Sciences National Forest Inventory.

Benchmark/s: Change over time can be analysed to identify if the area of forest owned or accessed by Indigenous people is increasing or decreasing.

Cost: Low cost.

Type/s of forestry: Native forest and plantation forests can be separated. Scale/s of measurement: Regional, national.

Key questions answered: What areas of forest are owned and/or used by Indigenous communities?

Limitations: This indicator provides information on specific types of Indigenous access to and ownership of forests in Australia. Many more forests would be regularly accessed and used by Indigenous people, or have cultural and spiritual significance. This indicator should be understood as a limited representation of Indigenous peoples’ interests and interaction with Australia’s forests. The indicator also provides no information on the types of interactions and uses of forests by Indigenous people.

Methods: Data analysed and reported in Australia’s State of the Forests report are utilised for this indicator.

3 Results of the case studies are presented in two separate reports (Schirmer et al. 2008a,b).
4 Many measures of value of the forest industry are possible, including measures of the dollar value of goods produced, value added through the chain of production, expenditure, and levels of different type of investment. The measure of gross value of production is recommended here as it is relatively easy to measure and to compare across industries.
5 Census data are reported based on three Census count methods: place of usual residence (data are reported based on where a person indicates they usually live), location on Census night (data are reported based on where a person was physically located on Census night), and place of employment (data are reported based on where a person works).
6 See http://www.abs.gov.au/websitedbs/c311215.nsf/0/BF6068ABC64802DECA256BD500169F18?Open for further information on the ABS Labour Force Survey.
7 See http://www.workplace.gov.au/workplace/Publications/ResearchStats/LabourMarketAnalysis/SmallAreaLabourMarkets/ for further information on the Small Area Labour Markets data series.
8 For more information, see http://www.australianunity.com.au/wellbeingindex/ The Australian Unity Wellbeing Index has been undertaken since 2001 on a regular basis and is expected to continue into the future.

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6. Recommended indicators - methods

6.1 Characteristics of the forest industry: recommended indicators

6.1.1 Direct employment in the forest industry

6.1.2 Proportion of land utilised by the forest industry

6.1.3 Estimated value of forest industry production

6.1.4 Estimated volume of forest industry production

6.1.5 Efficiency of production (labour productivity)

6.1.6 Consumption of wood and paper products

6.2 Impacts of the forest industry on the broader community: recommended indicators

6.2.1 Dependence on the forest industry (% employment)

6.2.2 Social characteristics of forestry-dependent regions

6.2.3 Location of forest industry employment

6.2.4 Impact of plantation forestry on rural population

6.2.5 Values, attitudes, uses and perceptions of forestry activities

6.3 Impacts of the forest industry on its workforce: recommended indicators

6.3.1 Income earned by forestry workers

6.3.2 Physical health – reported injury rates

6.3.3 Self-rated health (physical and mental)

6.3.4 Self-rated wellbeing

6.3.5 Age of forest industry workers

6.3.6 Gender of forest industry workers

6.3.7 Attachment to place

6.3.8 Cultural and family attachment to forestry

6.3.9 Hours worked by forest workers

6.3.10 Educational qualifications of forest industry workers

6.4 Impacts of the forest industry on Indigenous people: recommended indicators

6.4.1 Indigenous employment in the forest industry – quantity

6.4.2 Indigenous employment in the forest industry – type

6.4.3 Area of forest owned or accessed by Indigenous people

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