In countries where private forest ownership is very important, knowledge of the behaviour of private forest owners is useful for the design and implementation of successful forest policies. This applies to Portugal where 86 % of the forest lands are private property. This paper presents a study carried out in a region of the Northern part of the country covered by a local forest owners’ association. Based on individual data about the members of this association concerning some of their characteristics (implementation of publicly subsidised afforestation projects, size of the forest holdings, number of forest holdings belonging to the same owner and distance between the permanent residence of the owner and his forest holdings), a multinomial logit model is estimated for the probabilities of participation on public incentive schemes to finance individual and grouped afforestation projects.non industrial private forest owners, afforestation projects, public incentives

Américo M. S. Carvalho Mendes

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
DOCUMENTOS DE TRABALHO 
 
WORKING PAPERS 
ECONOMIA 
 
ECONOMICS 
 
 
 
 
 
 
 
 
 
 
Nº 09/2007 
 
 
 
PARTICIPATION OF NON-INDUSTRIAL PRIVATE FOREST 
OWNERS IN NATIONAL FOREST PROGRAMMES: 
A DISCRETE CHOICE MODEL FOR NORTHERN PORTUGAL 
 
Américo Mendes 
Universidade Católica Portuguesa (Porto) 
 
 
 
 Participation of non-industrial private forest owners 
in National Forest Programmes: 
a discrete choice model for Northern Portugal
1 
 
 
Américo M. S. Carvalho Mendes 
 
Portuguese Catholic University 
Faculty of Economics and Management 
Rua Diogo Botelho, 1327 
4169-005 Porto - Portugal 
Tel. +351-2-6196294 / Fax +351-2-6196291 
amendes@porto.ucp.pt 
 
Abstract:   In  countries  where  private  forest  ownership  is  very  important, 
knowledge  of  the  behaviour  of  private  forest  owners  is  useful  for  the  design  and 
implementation of successful forest policies. This applies to Portugal where 86 % of 
the forest lands are private property. This paper presents a study carried out in a region 
of the Northern part of the country covered by a local forest owners’ association. 
Based on individual data about the members of this association concerning some of 
their characteristics (implementation of publicly subsidised afforestation projects, size 
of the forest holdings, number of forest holdings belonging to the same owner and 
distance between the permanent residence of the owner and his forest holdings), a 
multinomial logit model is estimated for the probabilities of participation on public 
incentive schemes to finance individual and grouped afforestation projects. 
 
Keywords:  non  industrial  private  forest  owners,  afforestation  projects,  public 
incentives 
 
 
Introduction 
 
  In  Portugal  85.7  %  of  forest  lands  are  under  private  management, the rest 
being almost entirely communal forests managed by the Forest Services. Except for 
one third of the eucalyptus plantations managed by the pulp and paper companies, 
almost all the remaining private forests belong to non industrial private forest owners 
(NIPFO). Therefore understanding the behaviour of this type of owners is important 
for forest policy design and implementation. 
  In this paper we take up the issue of participation of NIPFO in the public 
incentive  schemes  financing  afforestation  and  improvement  of  existing  stands
2  as 
described  in  Mendes  (1998).  The  regional  setting  for  this study is the area called 
Sousa Valley, located about 30 km eastwards from the city of Oporto. This zone has a 
                                                            
1 Paper presented at the Forest Policy Research Forum “The role of National Forest Programmes to 
ensure sustainable forest management”, 14-17 June 1999, European Forest Institute, Joensuu (Finland). 
2 To make the writing shorter, hereafter when we talk about “afforestation projects” we mean not only 
projects for afforestation, but also projects for improvement of existing stands.  total of 36249 ha of forests, mostly maritime pine and eucalyptus globulus, and more 
than 7200 forest owners, most of them with small forest holdings. 
 
Theoretical model of the forest owners’ behaviour 
 
  Based on a data set with information about some characteristics of a sample of 
383 NIPFO of the Sousa Valley, the purpose of this paper is to investigate the factors 
influencing  the  probability  they  have  to  carry  out  an  afforestation  project  which 
applied for the public incentive schemes available for this purpose. The common set 
of observed characteristics of the individual forest owners will be summarised by a 
vector s.  
  The  set  of  mutually  exclusive  alternatives  available  to  an  individual  forest 
owner are the following: 
  - he can carry out an individual afforestation project only; 
  - he can join with his neighbours to carry out a grouped afforestation project; 
  - he can do both types of projects; 
  - he can just keep his forest lands as they are, without any project. 
  The set of observed attributes of each of these options will be summarised by a 
vector x. 
  Following  Domencich  and  McFadden  (1975),  we  will  assume  that  the 
individual behaviour of the forest owners is consistent with a well-defined stochastic 
utility  function  u U x s = ( , , ) e   where  e   is  a  random  vector  containing  all  the 
unobserved attributes of the alternatives and characteristics of the forest owners. 
  Assuming the forest owners are utility maximizers, each of them will choose 
the  alternative  i    if  this  is  the  one  which  yields  the  highest  utility  among  those 
available for choice which is denoted as follows, after dropping the random vector e  
to simplify the notation: 
 
(1) U x s U x s j i j n ki k kj k ( , ) ( , ) , ,..., > ¹ = 1  
 
where   k denotes the forest owner and  n denotes the number of alternative choices 
available to him, which is equal to four in our case. 
  Continuing with the approach presented in Domencich and McFadden (1975), 
let  Pki be the probability that individual  k chooses the alternative  i . For empirical 
work we need to come to an explicit functional form for these choice probabilities. To 
get there, the random utility function  U x s ( , ) can be decomposed into a stochastic 
term h( , ) x s  and a non-stochastic term V x s ( , ). With this decomposition, the choice 
probabilities Pki can be written as follows: 
 
(2) 
[ ]
[ ]
[ ]
[ ]
P P U x s U x s j i j n
P V x s x s V x s x s j i j n
P x s x s V x s V x s j i j n
F V x s V x s j i j n
ki ki k kj k
ki k ki k kj k kj k
kj k ki k ki k kj k
ki k kj k
= > ¹ = =
= + > + ¹ = =
= - < - ¹ = =
= - ¹ =
( , ) ( , ) , ,...,
( , ) ( , ) ( , ) ( , ) , ,...,
( , ) ( , ) ( , ) ( , ) , ,...,
( , ) ( , ) , ,...,
1
1
1
1
h h
h h
 
 
where F  is the cumulative joint distribution function of [ ] h h ( , ),..., ( , ) x s x s k k kn k 1 . Empirical model 
 
  A well known result proved by McFadden (1974) is that, if we assume the 
random variables h  have independent Weibull distributions, the choice probabilities 
have the following expression: 
 
(3) P
e
e
ki
V x s
V x s
j
n
ki k
kj k
=
= ∑
( , )
( , )
1
 
 
  This is known as the conditional logit model. For econometric estimation a 
linear approximation of the function V is usually taken. 
 
(4) V x s s x ki k i k ki ( , ) '
' = + b g  
 
  The data set available for this study does not contain a mixture of individual 
and choice specific information which means that the variables  xki are unobserved. 
Only some components of the  sk  vectors are observed, more precisely the following 
ones: 
  - size of the forest holdings measured in hectares (A); 
  - number of forest holdings (H);  
  - distance between the permanent residence of the forest owner and his main 
forest holding measured in kilometres (D). 
  The descriptive statistics for these variables are presented in table 1. 
 
Table 1: Descriptive Statistics of the individual characteristics of NIPFO 
 
Variables  Minimum  Maximum  Mean  Std. Deviation 
A  0.3  130  12.096  17.621 
H  1.0     7    1.413   0.879 
D  1.0  400  24.533  63.561 
 
  Because all the NIPFO in the sample belong to a local forest management 
association,  access  to  technical  advice  was  not  considered  as  a  regressor  in  the 
empirical model. 
  With  the  simplification  imposed  by  the  non  observability  of  the  choice 
attributes, the empirical model becomes what is called the multinomial logit. 
 
(4)  P
e
e
ki
s
s
j
n
i k
j k
=
= ∑
b
b
'
'
1
 
Results 
 
  The  maximum  likelihood  estimates  of  the  empirical  model  are  reported  in 
table 2. 
 Table 2: Estimated multinomial model for the probability of 
           a forest owner to carry out afforestation projects 
 
Variable  Coefficient  Standard error  t-ratio  p-value 
 
Characteristics in the numerator of   P 1 
Constant  -0.259476  0.243031  -1.068  0.2857 
A   0.014343  0.006902   2.078  0.0377 
H  -0.170948  0.149382  -1.144  0.2525 
D  -0.005319  0.002735  -1.945  0.0518 
Characteristics in the numerator of   P2 
Constant   0.237652  0.336045    0.707  0.4794 
A  -0.060826  0.023230  -2.618  0.0088 
H  -0.252187  0.234778  -1.074  0.2828 
D  -0.013074  0.006405  -2.041  0.0412 
Characteristics in the numerator of   P3 
Constant  -4.504956  0.708189  -6.361  0.0000 
A   0.025477  0.013960   1.825  0.0680 
H   0.372059  0.251898   1.477  0.1397 
D   0.001931  0.003705   0.521  0.6022 
 
  Number of observations:                383 
  Log-likelihood function:      -403.8506 
  Restricted log likelihood:     -428.5671 
  Log-likelihood ratio statistic:  49.4330 
  Degrees of freedom:                           9 
  Significance level:                     0.0000 
 
  The  coefficients  of  the  number  of  forest  holdings  per  owner  are  not 
statistically significant by usual standards. The same happens with the coefficient of 
the distance, in the case where the forest owner implements individual and grouped 
projects.  In  spite  of  this  lack  of  statistical  significance  for  the  number  of  forest 
holdings, it is interesting to retain the signs of its coefficients: negative in the case of 
the probability of individual and grouped projects only and positive in the case of the 
probability of individual and grouped projects together. One way to interpret these 
results is as follows: 
  - in the case of individual projects the dispersion of the forest lands through 
more than one holding increases the transaction costs and other costs to implement a 
project, so that the more concentrated the forest lands are the more likely the forest 
owner is to carry out an individual project; 
  - the grouped projects are more likely to happen among small owners who 
rarely have more than one holding; 
  - the owners who are more likely to implement not only individual projects but 
also grouped projects are among those who have more than one holding where they 
can carry these different types of projects. 
  The coefficients of the size of the forest lands and the distance between the 
permanent residence and the main forest holding are statistically significant by usual standards,  with  the  exception  mentioned  before  for  the  distance,  in  the  case  of 
individual and grouped projects together. 
  The signs of these coefficients are intuitively plausible: 
  -  the  size  of  the  forest  lands  has  a  positive  effect  on  the  probability  of 
individual projects and individual and grouped projects together, and has a negative 
effect on the probability of grouped projects; 
  - the distance between the permanent residence and the main forest holding 
has a negative effect on the probability of individual and grouped projects. 
  The larger the size of the forest lands, the more likely a owner is to carry out a 
project  on  his  own.  Big owners  are  more  likely to have enough land to make an 
individual project viable on its own, without having to cope with the transaction costs 
of  setting  up  a  grouped  project  with  their  neighbours.  For  small  owners  grouped 
projects are the only alternative to carry out viable projects. 
  Proximity  to  the  forest  holdings  increases  the  probability  of  both  types  of 
projects. This proximity is sometimes associated with a farming activity for which 
forestry is still a useful complement. In this case and for other kinds of occupations 
proximity  to  the  forest  holdings  makes  the  owner  more  aware  about  what  can  or 
should be done to improve his forests. Also proximity reduces the travel costs of 
forest management.  
 
Model evaluation 
 
  As shown in table 2, the log-likelihood ratio test statistic has a value which 
clearly  rejects  the  null  hypothesis  that  all  the  coefficients  except  the  one  on  the 
constant term are equal to zero. So the result of this test shows that the estimated 
model is better to predict the probability of each type of project than if we had used 
only information about a “representative” forest owner (Chambers and Foster, 1983). 
The information about this “representative” forest owner is contained in the value of 
the coefficient of the constant term. Since the hypothesis that the coefficients of all the 
other regressors are equal to zero is rejected, this means that specific information on 
the  individual  characteristics  of  each  forest  owner  is  relevant  to  discriminate  the 
probability of choosing among the four alternatives available. 
  In spite of this result, the set of individual characteristics contained in the data 
set available for this study is to short to make the model a good predictor of the choice 
probabilities. In fact if we look at table 3 with the frequencies of actual and predicted 
outcomes, we can see that the estimated model predicts correctly 47,5 % of the actual 
outcomes, most of which corresponding to the case of no project. So more factors, 
besides the size, the number of holdings and the distance, are involved in the decision 
to  carry  out  a  project.  We  shouldn’t  forget,  however,  that  maximum  likelihood 
estimation is not a method conceived to maximise the goodness of fit. 
 
Table 3: Frequencies of actual and predicted outcomes 
Predicted 
Actual  0  1  2  3  Total 
0  170    8  0  1  179 
1  104  12  0  0  116 
2    81    0  0  0    81 
3     6    1  0  0     7 
Total  361  21  0  1  383   Another important test to make about this kind of model has to do with the 
property  of  independence  of  irrelevant  alternatives  (IIA).  This  means  that  in  the 
multinomial  logit  model  the  relative  probabilities  between  two  alternatives  are 
independent of the remaining alternatives. So adding new alternatives or withdrawing 
some of the remaining ones does not affect the relative probabilities. 
 
(5)   P P
e
e
ki kj
s
s
i k
j k
/
'
' =
b
b  
 
  One way to check the validity of this property is to carry out a Hausman’s test. 
This test consists in comparing the values  $ b  of the coefficients estimated with the 
whole sample with the values 
~
b  estimated with a truncated sample resulting from the 
elimination of the observations corresponding to some of the choices available to the 
individuals. Under the null hypothesis that the IIA property holds, there will be no 
significant difference between the two estimates. Therefore, under the null hypothesis, 
~ $ b b -  will be a consistent estimator of zero and the statistic 
 
(6)  ( ) [ ] ( ) S Cov Cov = - - -
- ~ $ (
~
) ($)
~ $
'
b b b b b b
1
 
 
is asymptotically chi-square with degrees of freedom equal to rank of the estimated 
covariance matrices of the coefficients. 
  The truncated sample we used is the one resulting from the elimination of the 
observations corresponding to choices for grouped projects alone (choice coded with 
number 2) or together with individual projects (choice coded with number 3). The 
value of the Hausman’s test statistic for this case is  031818 . . So the null hypothesis 
cannot be rejected at less than 1,5 % significance level. 
 
Conclusions and forest policy implications 
 
  Individual characteristics of the NIPFO, in particular the size of their forest 
lands and the proximity to their forest holdings, proved to be statistically significant 
variables to predict the probability they have to implement afforestation projects. 
  Considering the “size effect”, in a region of small scale forestry like the one 
chosen for this study, there is some demand for grouped afforestation forests by small 
NIPFO, public incentives being a big help to overcome the heavy transaction costs of 
this kind of operations. NIPFO with larger forest domains are have likely to skip these 
costs and carry out afforestation projects on their own. So some intensive extension 
work is needed to help NIPFO of different sizes to cooperate in joint projects able to 
improve forest management in a wider scale than the one resulting from these more 
spontaneous choices. 
  This extension work is also important if we take into account the “distance 
effect”  which  reduces  the  propensity  for  afforestation  projects  by  NIPFO.  Forest 
extensionists can help to make the connections between local NIPFO interested in 
grouped afforestation projects and neighbouring NIPFO who live far away from their 
holdings.   This  extension  work  is  an  important  mission  for  local  forest  management 
associations, like the one created some years ago in the region of this study and in 
other parts of Northern Portugal where small scale forestry is predominant. Because of 
the “public goods” nature of most of this work public financial support for these 
associations is crucial at these early stages of their lives. 
 
References 
 
Chambers, Robert G. & William E. Foster. 1983. Participation in the Farmer-Owned 
Reserve Program: A Discrete Choice Model. American Journal of Agricultural 
Economics. Vol. 65, N.º 1, pp. 120-124.  
 
Domencich,  Thomas  A.  &  Daniel  McFadden.  1975.  Urban  Travel  Demand:  A 
behavioral analysis. North Holland, Amsterdam. 
 
McFadden, Daniel. 1974. Conditional Logit Analysis of Qualitative Choice Behavior. 
In P. Zaremka (ed.), Frontiers in Econometrics. Academic Press, New York, 
pp. 105-142. 
 
Mendes, Américo M. S. Carvalho. 1998. Forest Policy in Portugal: main issues at 
stake. In Ilpo Tikkanen & Brita Pajari (eds.), Future Forest Policies in Europe - 
Balacing  Economic  and  Ecological  Demands,  EFI  Proceedings  N.º  22. 
European Forest Institute, Joensuu, pp. 351-390. 
 
 

Conditional Logit Analysis of Qualitative Choice Behavior. In

Forest Policy in Portugal: main issues at stake.

Participation in the Farmer-Owned Reserve Program: A Discrete Choice Model.

Urban Travel Demand: A behavioral analysis. North Holland,

Participation of non-industrial private forest owners in National Forest Programmes: a discrete choice model for Northern Portugal

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Participation of non-industrial private forest owners in National Forest Programmes: a discrete choice model for Northern Portugal

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