APPENDIX 1- FROM FARMING TO FORESTRY IN WAIROA.
ESTIMATE OF EMPLOYMENT AND HOUSEHOLD INCOME
CHANGES IN WAIROA DISTRICT ARISING FROM A
LAND-USE CHANGE FROM FARMING TO FORESTRY
Geoff Butcher
Butcher Partners
EXECUTIVE SUMMARY
- There has been continuing conversion of land from farming to forestry in
Wairoa during recent years (4 500 ha per year for the last two years), and it seems likely
that such conversion will continue.
- The Wairoa District Council wishes to obtain and disseminate more
information about the impacts on the District of these land-use changes. Impacts will
include changes in employment and income as well as changes in such areas as community
facilities, social structures and land ownership.
- This paper describes the development of a regional economic model to
estimate the impacts of these land-use changes on employment and income in the Wairoa
District. The model incorporates data from surveys in Wairoa and other areas, these
surveys providing detailed data on the mix and sources of inputs used in farm and forestry
businesses.
- This paper contains estimates only of the likely potential changes in
District income and employment in farming, forestry, related processing industries (such
as meat processing works and sawmills) and other support industries (e.g. retail trade).
It does not comment on associated changes in social impacts, nor does it assess whether
the potential changes in employment will be taken up by residents of Wairoa District, or
whether they will go to people outside the District.
- Changes in income and employment are both direct (a reduction in farm and
meat processing employment, and an increase in forestry and sawmill employment) and
indirect (a reduction in employment in industries and services supplying farms and
families who get income from farms, and an increase in industries and services supplying
inputs to forestry and to families who get income from forestry).
- To estimate potential net impacts, it has been assumed that 2 000 ha of
forestry will be established per year (in line with recent experience and current
expectations) until there is a total of 56,000 ha converted from farming to forestry. It
has also been assumed that every hectare going into forestry will reduce stock carried in
the District by 7.7 Stock Units (based on survey work by Agriculture New Zealand). For the
purposes of examining the impacts of wood processing in the region, one processing
scenario has been examined. In this scenario, it is assumed that approximately half of the
wood produced will be processed by sawmills and an MDF mill in the District.
- Using these assumptions and the survey information, it is estimated that
once 56,000 Ha is in rotation (2,000 ha undergoing each operation in the planting,
tending, and felling sequence), there will be an additional 525 jobs in forestry, logging
and log transport, 430 jobs in wood processing, and 350 jobs in other support industries
in the District. There will be an associated increase in gross household incomes of $22
million in forestry, $15.2 million in processing and $10.7 million in other support
industries in the District. However, note that the employment and income related to
forestry increases in a very irregular manner. There is growth for the first 10 years,
then no further growth for a further 18 years (year 28), when there is a huge jump in
income and employment once logging and processing begin.
- Farming declines steadily as land-use is changed at a constant rate.
After 28 years there will have been a loss of 147 jobs on farms (including shearing and
fencing), 28 jobs in meat processing, and 80 jobs elsewhere in the District. There will
have been a loss of $7.2 million in gross farm household income (including farm profits),
$0.8 million in meat processing household income and $2.6 million of household income
elsewhere in the District.
- The total effect of a conversion of 56 000 ha of land from farming to forestry is likely to be an increase of approximately 1 050 jobs and $37 million in household income in the District. This compares with current earned household income of approximately $70 million and 1991 census employment of 2880 people. The nett changes include:
- a loss of 147 jobs in farming, 28 jobs in meat processing and 80 jobs in associated support industries; and
- a gain of 525 jobs in forestry, logging and log transport, 430 jobs in wood processing and 350 jobs in associated support industries.
Total (including indirect) Wairoa District employment
impacts of conversion of 56 000 ha from farm to forestry (full time equivalent jobs)
| Forestry & Logging | Forest Processing | Farming | Meat Processing | Total | |
| Year 1 | 23 | 0 | (8.0) | (1.1) | 13.9 |
| Year 10 | 114 | 0 | (80) | (11) | 23 |
| Year 28 | 114 | 0 | (224) | (31) | (141) |
| Year 29+ Direct Total |
524 720 |
430 585 |
(147) (224) |
(28) (31) |
1050 |
Total (including indirect) Wairoa District gross household income
impacts of conversion of 56 000 ha from farm to forestry ($million)
|
Forestry & Logging |
Forest Processing |
Farming |
Meat Processing |
Total |
Year 1 |
0.6 |
0 |
(0.34) |
(0.03) |
0.23 |
Year 10 |
3.1 |
0 |
(3.4) |
(0.3) |
(0.6) |
Year 28 |
3.1 |
0 |
(9.7) |
(0.9) |
(7.5) |
Year 29+ Direct Total |
28.0 |
19.9 |
(9.7) |
(0.9) |
|
A1 INTRODUCTION
In recent years there has been extensive conversion of land from farming to forestry (4
500 ha per year in Wairoa District in the last two years), and the Wairoa District Council
wishes to have a better understanding of the likely effects of this conversion on
employment and household income within the District. The land-use change generates
employment in forestry and forest-related industries including planting, silviculture,
logging, log transport, sawmilling and, in the longer term as the log resource increases,
other wood processing. However, there is a loss of land available for farming, and this
reduces employment on-farm (management, fencing, shepherding, shearing etc.) and off-farm
(transport, meat processing). There may be further effects such as a loss of schools and
other community facilities and a change in community structure, but this will depend on
changes in employment, population and places of residence. There will almost certainly be
a change in land ownership, non-resident landowners (e.g. companies and forest
partnerships) being the norm in extensive forestry.
This paper does not assess these community structure and land ownership changes,
considering only the likely impacts on employment and household income. The procedure used
was to generate a regional economic input-output model for the Wairoa District using the
GRIT technique (Hubbard & Brown 1981; Butcher 1985). The technique for estimating
national multipliers is described in section A1. The GRIT procedure for shifting from a
national to a regional model is described in section A2. This procedure produces a fairly
crude regional model, and more accurate results are obtained by undertaking surveys of the
industries of interest within the District. Hence surveys were undertaken in Wairoa of
farms, meat processing, and sawmills. Information from a recent survey of forestry in
Gisborne was also incorporated into the model (since it was felt that the two regions
would have similar industry structures), as was data from out-of-region MDF mills (as
representative of likely processing options).
The loss of farm employment occurs as soon as the land is planted in trees, and
continued planting means a continued reduction in farm-related employment. However, the
employment generated by continued planting of forests builds up over a period of 28 years
(a typical forest rotation), the majority of employment not coming until logging and
processing begin. For this reason, total impacts of land-use changes have been estimated
after one year, eight years (once silvicultural work has reached a plateau) and
immediately before and after logging begins. The estimates have been made under the
assumption that an area of 2 000 ha per year is converted from farming to forestry over
the next 28 years, after which planting is confined to areas logged in the previous year.
The cost advantages of processing in Wairoa (typically there is a 50% weight reduction in
processing) rather than transporting 100 km to an alternative processing centre means that
processing in Wairoa is a likely long-term option. Possible timing of processing plant
development is discussed in other parts of this overall study. The processing scenarios
developed in this paper are intended solely for illustrative purposes.
A2 ESTIMATION OF A REGIONAL TABLE
This section describes the method by which a simple Wairoa District inter-industry
table was generated, and refinements made to improve the accuracy of this table. The
section then outlines the way in which economic impacts are defined and estimated.
A2.1 GRIT technique
The GRIT technique (Generalised Regional Inter-industry Tables) for estimating regional
inter-industry tables was originally developed in Queensland. The underlying assumption is
that the technology used in a particular industry is the same for every region, which
means that that industry uses the same mix of inputs, no matter where in the country it is
located. However, different regions are more or less self-sufficient in the production of
inputs for that industry (for example, Wairoa does not produce resins used in a fibre
board plant), and this factor is taken into account by decreasing inputs from the chemical
industry (from the region) and increasing inputs from imports (of chemicals to the
region). Hence the final input structure of a regional table looks very different to the
national input mix.
The GRIT technique is a mechanistic procedure which has strong assumptions, and hence
can produce unreliable results. To remove the grossest errors when estimating impacts for
a particular industry, it is desirable to survey that particular industry in the region
and find out what inputs are actually used and where they come from.
A2.2 Surveys
In Wairoa, detailed surveys were undertaken of farms, meat processing works and
sawmills. Less detailed surveys were done of other business. Information from a recent
survey of forestry in Gisborne was also incorporated into the model (since it was felt
that the two regions would have similar industry structures) as was data from
out-of-region MDF mills.
Steps in regional table derivation
- Obtain national inter-industry table.
- Derive rough regional table using GRIT technique, and taking into account limited district self-sufficiency in some industries.
- Adjust rough table using data on industry inputs (type and source) gathered by survey.
- Estimate type II multipliers and use these to estimate indirect effects. (Note that the standard input - output nomenclature would refer to these as "indirect and induced effects", but the term "indirect" has been used in this paper as shorthand)
A2.3 Household income and spending
Household income includes all wages, salaries and drawings (including taxes) by
owner-operators. In the case of farming, gross household income has been defined to
include all capital surplus (profits). No allowance is made for forestry profits (i.e. log
royalties or profits to forest owners) being paid to local households, primarily because
the evidence suggests that much of the forests will be owned by outside interests.
Note that not all household income is spent within the region. Taxes go to central
government (and there is no direct link between taxes paid by a region and Government
spending in that region), and savings also do not lead directly to regional economic
activity. To take account of these factors, only disposable income is incorporated within
the model as having an effect on regional economic activity. Within farming, it is assumed
that depreciation is spent on replacement farm capital, and it is assumed that 50% of
capital surplus is spent on household consumption, with the balance being spent on
out-of-region investment (see section below for impacts of changing this assumption).
A2.4 Error margins
It is not possible to accurately derive error margins. However, previous research (e.g.
Butcher 1990) has shown that as long as the inputs for the industries under investigation
are reasonably accurate, variations in other industry structures will have little impact
on the final estimates of impacts. It is believed that the estimates of direct impacts are
probably accurate to within about 10-20%. The existing errors are due to inadequate
information from some respondents, and to lack of information about some industries.
The estimates of indirect effects are probably accurate to within 20-30%. The higher
error margin is due to the uncertainty of industry structures in non-surveyed industries.
One area which is not related to the industry under examination but which nonetheless has
a significant impact on accuracy is household spending. Much of the downstream impact of
farming and forestry is driven by extra household spending and the extra wholesale and
retail trade activity that results. Unfortunately there is no comprehensive data on
household spending in the District (Statistics NZ will not release data on regional
household expenditure because of the high error margins in it), and the costs of getting
accurate data are prohibitive. Available data on the size of the wholesale and retail
trade sector in Wairoa relative to the rest of New Zealand suggest that households and
businesses probably do only about 60% of their purchasing within Wairoa District. Limited
survey data supported this assessment, business claiming to do about 50% of their
purchasing of goods in Wairoa, and farm families claiming to do about 65% of their
purchasing in Wairoa.
One area about which there is likely to be dispute is the proportion of capital surplus
that is likely to be spent on consumption and capital improvements in the District. Even
if it were all spent in the District (as opposed to the current assumption that only 50%
will be spent within the District), the effect would be to increase total farm-related
employment and income by only about 4%.
As an overall guide to accuracy, it is expected that total impacts estimated are
probably accurate to within 15-20%. However, note that as time passes and technology
changes, so will the impacts change, particularly the employment impacts. Hence, over the
longer term, error margins increase. However, changes in technology will affect both
farming and forestry, and one could expect that the relative scale of impacts will be
similar to what it is now.
A3 ECONOMIC IMPACT ANALYSIS
This section includes a brief description of an inter-industry table, and shows how
this can be transformed to provide direct input-output coefficients, which can then be
manipulated to generate inter-dependency coefficients showing the impacts of growth in one
industry on all other industries. Use of employment and household income data permits
estimation of the impacts of changes in industry output on total output, income and
employment.
A3.1 Inter-industry (input-output) transactions tables
An inter-industry study shows the structure of a nation's economy in a given year (e.g.
for the 1990/91 financial year). The information is presented as a transactions matrix
(see Table A3.1), each industry having an associated row and column. The row relating to
an industry shows the destinations of outputs of that industry, while the column describes
the sources of inputs used by that industry.
An inter-industry table requires the collection and arrangement of a vast body of
statistical data on production and consumption. The main data sources are the various
economic censuses that Statistics NZ carries out. The large amount of data required means
that the generation of an inter-industry table is an expensive and time-consuming
exercise, and it is carried out infrequently. In the past, an inter-industry table has
been prepared for every fifth year, the most recent official table relating to the 1990/91
year. However, the 1990/91 table is only an update of the 1986/87 table rather than a
completely new table based on survey data. The regional inter-industry table used in this
project is based on a 1990/91 national table developed by Butcher (1994) using a similar
methodology to that used by Statistics New Zealand. Butcher's table is preferred to
Statistics NZ's national table because it has a larger number of industries, a feature
which leads to reduced aggregation bias in the development of regional tables.
The 1986/87 table shows 184 separate industries, but these have been amalgamated to 80
industries for the 1990/91 table. The choice of industries was governed by availability of
data and decisions on the relative importance of industries for various purposes.
Table A3.1 Input-output table in transactions format ($millions)
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A3.2 Transformations
The transactions matrix (Table A3.1) can be transformed into the direct input-output
coefficients matrix (Table A3.2) by dividing each column element by the relevant column
total. Hence in Table A3.2 the left-hand column shows that 0.197 (19.7%) of all direct
inputs into primary industry come from primary industry, 10.0% come from secondary
industry, etcetera.
Table A3.2 Input-output table in coefficients format
INDUSTRIES |
FINAL DEMAND |
TOTAL |
|||||
| Primary | Manu. | Services | Hhold | Govt | Other | ||
| INDUSTRIES | |||||||
| Primary | 0.197 | 0.160 | 0.007 | 0.007 | 0 | 0.074 | 0.053 |
| Manufacturing | 0.100 | 0.225 | 0.103 | 0.192 | 0 | 0.383 | 0.177 |
| Services | 0.181 | 0.192 | 0.316 | 0.580 | 1.000 | 0.393 | 0.383 |
| PRIMARY INPUTS | |||||||
| Households | 0.128 | 0.134 | 0.227 | 0.000 | 0 | 0.002 | 0.118 |
| Other Factors | 0.354 | 0.167 | 0.294 | 0.092 | 0 | 0.043 | 0.185 |
| Imports | 0.039 | 0.121 | 0.054 | 0.129 | 0 | 0.105 | 0.084 |
| 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |
A3.3 Multipliers
Multipliers measure economic consequences in terms of output, income or employment
resulting from changes to final demand. Expansion of an industry has upstream and
downstream impacts. The upstream impacts occur as the expanding industry purchases more
inputs from various suppliers who thus have to increase their output. These industries in
turn have to increase their output, and hence have to increase their purchases of inputs,
creating expansion in their supplying industries. This expansion proceeds in an
ever-widening circle of ripple effects. However, the extent of the ripples is limited by
leakages from the system in the form of imports, savings and taxes. Downstream impacts
occur as increased output generates increased activity in processing industries. In some
industries (e.g. farming and forestry) the downstream impacts are greater than the
upstream impacts.
The amount of downstream economic activity that the multiplier measures depends on the
categories of effect that are taken into consideration. These can be either the initial
effect, the first-round effects (i.e. purchases of direct inputs), the industrial support
effects (i.e. the expansion of other firms to meet the increased demands of the initial
input-supplying firms) or the consumption-induced effects (i.e. the expansion of industry
to meet the increased demand from households as income increases). While conceptually
there could be further impacts arising from increased profits and increased payments of
taxes, these are usually ignored since it is presumed that investment and Government
spending decisions are unlikely to be affected by normal levels of expansion.
The multipliers can be calculated by performing matrix algebra on the direct coefficients matrix and generating the "Leontieff Inverse Matrix", named after the originator of the technique.
A3.4 Leontieff inverse matrix
The upper-left quadrant of the direct coefficients matrix (generally
termed the A matrix) can be manipulated to generate the Leontieff Open Inverse Matrix
(I-A)-1, which identifies the direct and indirect effects of output changes. A
row i column j element of a Leontieff Open Inverse matrix shows the direct and indirect
increase in output of industry i arising from a one-unit initial increase in output of
industry j. Summing all elements of a column j gives the total direct and indirect
increase in output across all sectors arising from a one-unit initial increase in output
of industry j. When the upper-left quadrant is expanded to include household inputs and
household consumption, manipulation yields the Leontieff Closed Inverse Matrix (I-A*)-1.
The elements in the Closed Inverse identify the direct, indirect and induced increase in
output of industry i arising from an initial one-unit increase in output of industry j.
A3.5 Type I and Type II multipliers
A multiplier is the ratio of the total effect to the direct effect.
Typically, two types of multiplier are calculated. A >Type I= multiplier takes into account only
the direct and indirect impacts, while a >Type II= multiplier also takes into account
the induced impacts triggered by increased household consumption. A Type I output
multiplier for an industry is calculated by adding up the elements of the relevant column
vector of the Leontieff Open Inverse matrix. The effect on income can be calculated by
multiplying the Leontieff column vector by a vector representing income:output ratios for
each industry. Similarly, the effect on employment can be calculated by multiplying the
Leontieff column vector by a vector representing employment:output ratios for each
industry. Type II multipliers are calculated by carrying out the same operations on a
Leontieff Closed Inverse matrix. In this report, only Type II multipliers and impacts are
reported, since there is general acceptance that increased household spending will
generate substantial changes in regional economic activity. Multipliers are usually
calculated for output, income and employment. In the current economic climate, the latter
is often of most interest.
A3.6 Limitations
There are a number of limitations to the accuracy of simple multiplier
analysis, and these are described in some detail in Butcher (1985, pp.6-7). The greatest
limitation is at a macro-economic level. Multipliers are based on the assumption that the
only restriction on output is a lack of aggregate demand. However, there may be other
limitations, and Government may wish to restrict aggregate demand (through monetary or
fiscal policy) to meet other objectives such as inflation or balance of payments
objectives (although the last has become largely irrelevant with a free foreign exchange
market). In such circumstances, expansion of one industry may inhibit the potential
expansion of other industries, and hence the multipliers may be overstatements of the
increases in activity that will actually occur. The analysis also makes assumptions of
linear production functions which imply constant returns to scale and to levels of
capacity utilisation.
It should also be realised that some supplying industries have relatively inflexible outputs. For example, the construction of a new meat processing works will probably not generate additional on-farm employment because it is unlikely that farm production of meat will increase (although there may be a change in where it is slaughtered). Even if meat production does increase, there is likely to be a decline in other farm production.
A4 IMPACTS OF SILVICULTURE, LOGGING AND PROCESSING
The estimates of employment opportunities in forest roading, planting
and silviculture, logging, and log transport are based on 1994 survey data from Gisborne
forestry contractors. The information on their expenditure patterns was adjusted to fit
into the Wairoa economic model and to recognise the very limited manufacturing,
wholesaling/retailing and business services base of Wairoa. The additional spending that
does take place in Wairoa will be concentrated primarily on repairs and maintenance and on
household expenditure. Estimates of employment and income in log cartage are based on
industry average figures and likely distances from forest to port in the context of
Wairoa. No estimate has been made of the impacts of forest management and nurseries,
primarily because it seems probable that management services will continue to be located
in Gisborne and Napier, which are only 1.5 hours drive away, while existing nurseries are
quite capable of supplying seedlings to the Wairoa District.
Employment is given in FTEs, or Full Time Equivalent jobs. Results are
given on both a per hectare basis and for one planting scenario (of the many possible).
This scenario assumes that planting will continue at 2 000 ha per year until there is a
total of 56 000 ha converted from farming to forestry. This level of planting is
reasonably consistent with recent history and current proposals, but may differ greatly
from final outcomes.
A4.1 Employment and income opportunities in planting and
silviculture
The cost of establishing and tending a forest depends on the current
state of the land and the proposed end-use of the logs. The majority of new forest being
planted is extensively pruned and thinned, the objective being to produce high-value
pruned sawlogs, with residue and poor-quality logs used for MDF (Medium Density
Fibreboard) or similar. The costs of planting and pruning are approximately $1 700 per ha
(three prunings and one thinning, with scrub clearance on some land and a fourth pruning
in some forests), of which 75-80% is labour. The labour involved is estimated at 10.5 days
per ha. Costs and times are based on survey data from silvicultural gangs and from
managers of forests in the region. The $1 700 and 10.5 man-days is spread over the first
ten years of the tree's life. From year 10 (after forest establishment begins), there is a
constant level of planting and silvicultural work, with every year some 2,000 ha being
treated.
The direct employment and household income generated by this is of the order of 95 FTEs (10.5 days/ha x 2000 ha / 220 days work / year) and $2.5 million (10.5 days/ha x 2000 ha x $120/day) gross household income. In addition to this there are indirect effects, primarily in areas such as repairs and maintenance and retail trade. The regional economic model suggests that for every 100 direct jobs created in the District in planting and silviculture there are a further 15 indirect jobs generated in the District, and for every $100 of direct household income there is a further $15 of indirect income. Hence from year 8 on there will be an additional 110 jobs and $2.9 million of gross household income available in the Wairoa District.
Table A4.1 Employment impact of planting and silviculture
| Year | Operation | Days/ha | % of forest affected | Effective time (days/ha) | Direct FTEs per 2000 ha | Total FTEs per 2000 ha |
| 1 | Clearing Planting Releasing |
3 0.8 0.5 |
15 100 100 |
1.8 |
16.4 |
19 |
| 4 | Pruning 1 Thinning 1 |
2.1 1.0 |
100 100 |
3.1 |
28.2 |
32 |
| 6 | Pruning 2 | 2.2 | 100 | 2.2 | 20.0 | 23 |
| 8 | Pruning 3 Thinning 2 |
2.4 0.5 |
100 35 |
2.6 | 23.6 | 27 |
| 10 | Pruning 4 | 2.4 |
35 |
0.8 |
7.3 |
8 |
| Total Direct | 10.5 | 95 | 110 | |||
| Total Direct & Indirect | 12.1 | 110 | ||||
A4.2 Employment and income opportunities in logging
Approximate production at maturity is estimated at 700 m3/ha. The costs of
logging are expected to be approximately $20/m3 on average. Productivity in
logging is approximately 25m3/person/day or 5,000 m3/person/year
(considerably lower than on the easier country in some other areas). Wages (including
management and administration) form from 35-50% of logging costs, depending on whether the
logging is ground-based or hauler-based. From year 28, production logging of 2000 ha (1
400 000 tonnes) per year will generate directly approximately 280 jobs and household
income of $12.7 million. The regional economic model suggests that for every 100 direct
jobs created in the District in logging there will be a further 35 indirect jobs generated
in the District, and for every $100 of direct household income there is a further $22 of
indirect income. Hence from year 28 on there will be an additional 380 jobs and $15.5
million of gross household income available in the Wairoa District.
A4.3 Employment and income opportunities in log transport
Transport costs are approximately 15 cents per tonne-km, and average transport distance
is likely to be approximately 100 km (from forest to port at Napier or Gisborne). Hence
freight costs will average $15 per tonne or $21 million annually for 2000 ha logged per
annum. Using total road transport industry average figures for income and employment, it
could be estimated that log transport should generate directly 163 jobs and $6.9 million
of gross household income. However, the true employment opportunities are likely to be
somewhat lower, log freight having different characteristics from general freight, and
drivers working long hours. Limited survey data suggests that transport of 1 400 000
tonnes of logs would generate directly 135 FTEs (1.4 million tonnes / 28 tonnes/trip / 2
trips/day / 240 days/year x 1.3 factor to allow for administration staff and mechanics =
135) and $6.3 million of gross household income. The regional economic model suggests that
for every 100 direct jobs created in the District in transport, there will be a further 48
indirect jobs generated in the District, and for every $100 of direct household income
there will be a further $33 of indirect income. Hence from year 28 on there will be an
additional 200 jobs and $8.4 million of gross household income available in the Wairoa
District from log transport.
A4.4 Employment and income opportunities in forest road construction
Discussions with forest managers in the region suggest that there will be 1 km of
tracks ($5000 / km) for every 30 ha planted, while at logging these will be upgraded to
feeder roads ($25 000 / km) and there will be 1 km of arterial road ($50 000 / km) for
every 100 ha of forest logged. Hence in years 1-28 there will be expenditure of $0.33
million, and in years 29-56 there will be expenditure of $2.7 million. Direct employment
impacts of this are estimated at 1.7 FTEs in years 1-28 and 14.3 FTEs in the next 28
years. Direct gross household income impacts are estimated at $70 000 in years 1-28 and
$0.54 million in years 29-56. The regional economic model suggests that for every 100
direct jobs created in the District in road construction, there will be a further 125
indirect jobs generated in the District, and for every $100 of direct household income
there is a further $114 of indirect income. Hence in years 1-28 there will be in total an
additional 4 jobs and $0.14 million of gross household income in Wairoa District from
forest road construction, and in years 29-56 an additional 32 jobs and $1.2 million of
gross household income associated with forest road construction. Subsequent loggings will
involve road reinstatement only, at a much lower cost.
A4.5 Source of forestry labour
There is a major uncertainty as to the proportion of jobs which will actually accrue to
residents of the Wairoa District. A survey of those employing forestry contractors (for
silviculture and logging) revealed that only 20-25% of the forestry employment in the
District is going to local work gangs, the rest going to external gangs. The problems
appear to be that Wairoa lacks sufficient skilled people (planting and pruning are no
longer regarded as jobs for unskilled labour) and managers of contracting gangs to take
advantage of available opportunities. Unless this situation is rectified, the majority of
forestry employment will go to people who live outside the District. The figures reported
here relate to potential employment arising from forestry developments in Wairoa District.
No attempt is made to estimate what proportion of these jobs will finally come to Wairoa.
Discussions with meat companies and sawmillers in the District reveal that very little
of their outward freight is carried by local carriers. The reason appears to be, at least
in part, that the opportunities for using a truck for alternative routes and products are
better if trucks are located in larger centres. However, this is less likely to be a
factor with specialised vehicles such as logging trucks, and it seems more likely that
logging trucks could be located in the District.
A4.6 Employment and income opportunities in wood processing
Once felling of 2000 ha per year starts in year 28 (under the scenario being used here
for illustrative purposes), the annual wood volume will be some 1.4 million m3
/ year. Once these large volumes of wood come on stream, it is almost inevitable that
serious consideration will be given to processing more wood in the District because of the
resulting economies of freight (two tonnes of raw wood is converted to approximately one
tonne of processed wood). Production of this quantity of wood could support a range of
processing plants. To demonstrate the potential impacts of processing, it is assumed that
approximately half of the 1.4 million tonnes will be processed in the region, 550 000 m3
/ year going to large modern sawmills and 400 000 tonnes (150 000 tonnes of small size
pieces and low-quality trees and 250 000 tonnes of sawmill residues) going to a
large-scale MDF (Medium Density Fibreboard) mill.
A4.6.1 Sawmills
The estimates of the impacts of sawmills are based on information from major
sawmillers, and incorporate some expectations of developments in labour productivity.
Small-scale sawmills, such as are at present operating in the District, directly employ
around 3.5 persons per 1000 m3 of sawn timber ("sawmilling" being taken to include sawing, kiln drying and treatment)
and generate direct household incomes of the order of $80 000 per 1000 m3 of
sawn timber. Indirect effects increase these impacts and total impacts are estimated at
4.2 jobs and $100 000 of household income per 1000 m3. Larger mills generate in
total only 0.95 jobs and $31 000 of household income per 1,000 m3 of sawn
timber, and this number is likely to shrink even further in future. For the purposes of
this comparison between farming and forestry impacts, it seems appropriate to base sawmill
impact estimates on current large-scale technology.
The total impact on Wairoa District of 550 000 m3 of logs
being sawn each year (300 000 m3 of sawn wood at a 55% conversion factor) would
be the direct generation of 190 jobs and $6.6 million of household income. The regional
economic model suggests that for every 100 direct jobs created in the District in
sawmilling there will be a further 50 indirect jobs generated in the District, and for
every $100 of direct household income there will be a further $40 of indirect income.
Hence from year 28 on there could be an additional 280 jobs and $9.3 million of gross
household income available in the Wairoa District as a result of sawmilling.
A4.6.2 MDF Mills
MDF mills employ approximately 220 persons for 100 000 m3 of
output (although there are significant economies of scale, and doubling the output
increases direct employment by only 10%). The associated direct household income is
estimated at $7.8 million. Indirect effects increase these figures by over one-quarter,
and total District impacts of a 100 000 m3-output MDF mill in Wairoa are
estimated at 280 jobs and $9.8 million. However, from this total one must deduct the
impact of the reduced transport of logs, which travel a short distance to the MDF plant
rather than a long distance to alternative destinations. The approximate total effect of a
reduction of 100 000 tonnes of product transported (100 000 tonnes of MDF rather than 200
000 tonnes of logs) is a loss of 15 jobs and $0.6 million in household income. Hence the
total impact of a 100 000 m3 output MDF plant in Wairoa District is estimated
to be 265 jobs and $9.2 million in gross household income.
A 200 000 m3 mill would generate greater impacts. Direct
impacts would be 240 jobs and $8.6 million in household income while total impacts would
be 300 jobs and $10.6 million in household income.
A4.7 Total forest-related employment and income opportunities in
Wairoa
As is shown in the tables below, development of forestry generates a
limited number of jobs in the initial years of forest establishment and management, but a
very large number once harvesting begins. To put these numbers into context it should be
noted that total employment in Wairoa District at the 1991 census was approximately 2800.
Table A4.2 Total Wairoa District employment impacts of forestry
development, based on 2000 ha per year for 28 years (Full-time equivalent jobs).
| Roads | Plant, prune, thin | Logging | Log freight | Processing | Total | |
| Year 1 | 4 | 19 | 0 | 0 | 0 | 23 |
| Years 10-28 | 4 | 110 | 0 | 0 | 0 | 114 |
| Year 29+ Direct Total |
32 |
110 |
380 |
200 |
585 |
1,305 |
Table A4.3 Total Wairoa District gross household income impacts of forestry, based on
2000 ha per year for 28 years ($million).
| Roads | Plant, prune, thin | Logging | Log freight | Processing | Total | |
| Year 1 | 0.14 |
0.5 |
0 |
0 |
0 |
0.6 |
| Years 10-28 | 0.14 |
2.9 |
0 |
0 |
0 |
3.1 |
| Year 29+ Direct Total |
1.2 |
2.9 |
15.5 |
8.4 |
19.9 |
47.9 |
A4.8 Employment Opportunities in Wairoa
The rate of forest planting in the future is expected to be similar to that of recent
years. As is shown in the above tables, forestry employment steadily picks up in the first
few years until it reaches a plateau in about year 8 (once there is no further
silvicultural work being done on the oldest trees). Employment continues at this level
until harvest at year 28 when employment rockets with logging, transport and processing.
On the face of it one might expect that opportunities for employment in Wairoa have
already got to the first plateau and will not increase until large-scale harvesting begins
in about 20 years (logs harvested in earlier years are already committed to existing
processing plants in other centres). While it is true that there is likely to be little
growth of employment in forestry in the District, it is not necessarily true as
regards employment in forestry of people from the District.
As has been mentioned earlier, there is a great deal of work being done in District forests by people from outside the District (although indications from the survey of forestry companies are that this is changing). The District residents have a natural locational advantage in obtaining employment in the District, but they will need to ascertain why they have not yet been able to more fully utilise this natural advantage. Having identified the reasons, they will then need to develop and adopt a strategy for addressing these reasons.
A5 IMPACTS OF FARMING AND MEAT PROCESSING
Data on on-farm employment and farming purchase patterns (what inputs farmers purchase
and where they purchase them from) was obtained from a survey of farmers in the District.
Data on meat processing employment and purchase patterns was obtained from AFFCO Wairoa
works. All this information was incorporated into the District economy model, and total
farming and processing impacts on the District were estimated. The impacts were then
converted to a per thousand Stock Unit basis, and these impacts were applied to the
estimated reduction in Stock Units flowing from conversion of land from farming to
forestry. The estimates were calculated on the basis of the amount (2000 ha per year) and
carrying capacity (7.7SU/ha ) of land going into forestry.
A5.1 Employment and income impacts of farming.
The farms in the sample were split into two groups, large and small. The survey
expenditure, income and employment data were calculated for each group, and then a
weighted average was calculated, the weightings being based on the total area on each sort
of farm.
On average, direct employment on farms (including working owners, employees and
contract workers such as fencers and shearers is 0.34 FTEs per 1000 SUs (Full Time
Equivalent jobs per 1000 Stock Units), and gross household income which includes wages to
permanent and contract workers and cash surplus of farms, is $16 800 per 1000 SUs. Of
this, approximately $9500 is spent on household consumption. There are significant
indirect income and employment effects, primarily in the area of agricultural contracting,
transport, retailing, and local government services. The regional economic model suggests
that for every 100 direct jobs created in the District on farm (including contract work
such as shearing) there will be a further 55 indirect jobs generated in the District, and
for every $100 of direct gross household income there will be a further $33 of indirect
income. Hence the total impact of a 1000 LSU reduction is the loss of 0.52 FTEs and $22
400 of gross household income.
A5.2 Employment and income impacts of meat processing
The AFFCO plant in Wairoa has expanded output dramatically since the closure of
Weddel's Kaiti plant in Gisborne, and the Wairoa plant is now running at full capacity.
Since the break-even cost of the plant is considerably below full capacity, there would
have to be a very large reduction in slaughtering to cause plant closure - much greater
than the decline estimated in the scenario under examination in this paper.
AFFCO split their costs into fixed costs (those independent of throughput) and variable
costs (those which vary directly with numbers slaughtered). In looking at the impact of a
decline in slaughter arising from land-use changes, marginal cost data has been used.
However, total cost data has also been used to estimate the impact on the District of
complete closure of the AFFCO works.
Fixed costs in the AFFCO works are high, and a 10-30% reduction in throughput would
have no significant impact on these. The major elements of these fixed costs are
administration, a significant proportion of maintenance, property costs and depreciation.
Variable costs are primarily wages, processing materials, packaging, freight, energy and
water, and a proportion of maintenance.
For every 1000 lamb equivalents processed (a lamb equivalent is 1.08 sheep or 0.13
cattle), direct marginal employment and income are 0.22 FTEs and $6100. Indirect effects
arise primarily in the electricity, water, and wholesale and retail trade industries. The
regional economic model suggests that for every 100 direct jobs lost in the District as a
result of a marginal decline in meat processing there will be a further 13 indirect jobs
lost in the District, and for every $100 of direct household income there will be a
further $15 of indirect income. Hence total Wairoa District impacts of an increase or
decrease in throughput at AFFCO are estimated to be 0.25 FTEs and $7000 of household
income per 1000 lamb equivalents.
The effects of AFFCO closure (compared to a throughput of 1.6 million lamb equivalents)
would be the direct loss of 381 FTEs and $12.9 million in gross household income. Total
effects on the Wairoa District would be the loss of 440 FTEs and $15 million of gross
household income.
A5.3 Total farm-related impacts of land-use changes
Using the approximation of 2000 ha of land going into forestry for 28 years and
assuming a decline in carrying capacity of 7.7 SUs per ha, there would be a loss of 15 400
stock units per year and an estimated decline of 11,000 lamb equivalents per year being
slaughtered. John King (MAF Policy, Napier) has also estimated that a 1000 LSU reduction
in carrying capacity will be associated with a decline of 290 lamb equivalents being
slaughtered in Wairoa (this figure embodying assumptions about the final destination of
store stock and the percentage of stock going to slaughter which are slaughtered in the
region). Farming-related employment in Wairoa District would steadily decline by 9.1 jobs
per year and household income would decline by $0.37 million per year. After 28 years the
total losses would be 255 FTEs and $10.6 million of gross household income.
Table A5.1 Total Wairoa District employment impacts of farming reduction, based on
2000 ha per year and 7.7 su/ ha for 28 years. (Full time equivalent jobs)
| Farming | Meat processing | Total | |
| Year 1 Direct Total |
(5.2) (8.0) |
(1.0) (1.1) |
(9.1) |
| Year 10 Direct Total |
(52) (80) |
(10) (11) |
(91) |
| Year 28+ Direct Total |
(147) (224) |
(28) (31) |
(255) |
Table A5.2 Total Wairoa District gross household income impacts of farming
reduction based on 2000 ha per year and 7.7 su/ ha for 28 years. ($million).
| Farming | Meat processing | Total | |
| Year 1 Direct Total |
(0.26) (0.34) |
(0.02) (0.03) |
(0.37) |
| Year 10 Direct Total |
(2.5) (3.4) |
(0.2) (0.3) |
(3.7) |
| Year 29 + Direct Total |
(7.2) (9.7) |
(0.8) (0.9) |
(10.6) |
A6 COMBINED IMPACTS OF LAND-USE CHANGES
Using the approximation of 2000 ha of land going into forestry for 28 years, and the
above assumptions about declines in stock capacity, the total potential employment and
household income effects in Wairoa are as shown below. There is a steady increase in
available employment and income over the first 10 years of the planting cycle, as planting
and silviculture build to a peak, but for the next 18 years there is a slow decline in
total employment as forestry employment remains constant (older trees have no
silvicultural work done on them), and farm employment continues to decline as farmland
continues to be converted to forestry. By the end of the forest expansion and before the
onset of harvesting, available employment has fallen by approximately 140 jobs relative to
the position before the start of the planting cycle, and household income has fallen by
$7.5 million. Once harvesting begins there is a dramatic increase in employment and income
opportunities. The total potential impact of conversion of a total of 56 000 ha of land in
the District from farming to forestry, and processing of half that wood within the region,
is an overall increase of 1050 jobs and $37 million in gross household income.
To put all these figures into context, it is noted that total employment in Wairoa
District is currently of the order of 2800 FTEs, and earned household income is of the
order of $70 million. (Note that this figure excludes all transfers from Government, and
the increase in earnings estimated above ignores the reduction in transfer payments as
people shift off benefits and into the workforce.)
Table A6.1 Total Wairoa District employment impacts of conversion of 2,000 ha per
year (56,000 ha in total) from farming to forestry (Full time equivalent jobs).
| Forestry & logging | Forest processing | Farming | Meat processing | Total | |
| Year 1 | 23 | 0 | (8.0) | (1.1) | 13.9 |
| Year 10 | 114 | 0 | (80) | (11) | 23 |
| Year 28 | 114 | 0 | (224) | (31) | (141) |
| Year 29+, Direct Total |
524 722 |
420 585 |
(147) (224) |
(28) (31) |
|
Table A6.2 Total Wairoa District gross household income impacts of conversion of
2000 ha per year (56,000 ha in total) from farming to forestry ($ million).
| Forestry & Logging | Forest Processing | Farming | Meat Processing | Total | |
| Year 1 | 0.6 | 0 | (0.34) | (0.03) | 0.23 |
| Year 10 | 3.1 | 0 | (3.4) | (0.3) | (0.6) |
| Year 28 | 3.1 | 0 | (9.7) | (0.9) | (7.5) |
| Year 29+, Direct Total |
28.0 |
19.9 |
(9.7) |
(0.9) |
37.3 |
A7. DEVELOPMENTS IN
WAIROA
To date there has probably been a decline in farm-related employment in
the District much along the lines of the figures given in earlier sections. In contrast,
the increase in forestry-related employment has been much less than the potential
increase. As was earlier outlined, Wairoa has been able to develop only about a quarter of
the available employment opportunities.
The figures displayed in earlier sections show the potential impacts of
changes in land-use. There has already been significant planting in the District, and
limited logging is already taking place. Within the next 5-10 years there will be a
significant increase in logging, and this will generate additional employment
opportunities. It is unlikely that there will be wood available for large-scale processing
within the next 10 years, but beyond that period it is quite possible that large-scale
processing will begin within the District. Wairoa needs to ensure that training is
available for local residents so that they are in a position to take advantage of the
available employment opportunities. The District Council should also consider reviewing
the location options for processors and comparing Wairoa with alternatives, with a view to
ensuring both that no unnecessary impediments to location in Wairoa exist and that
potential processors are fully aware of any advantages of locating in Wairoa.
Contact for Enquiries
Rural Affairs Coordinator
Sector Performance Policy
MAF Policy
Ministry of Agriculture and Forestry
PO Box 2526
Wellington
NEW ZEALAND
Phone: +64 4 894 0675
Fax: +64 4 4 894 0745
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