Yearly Archives: 2006

84 – Estonia and Latvia

I recently spent two weeks visiting Estonia and Latvia. This discussion provides some history, some observations and some impressions about these fascinating countries.

Estonia and Latvia (together with Lithuania, known as the Baltic states) were the western-most parts of the former Soviet Union. They are small countries – Estonia is only slightly bigger than Switzerland and has a population of only 1.3 million – that have been pushed around by their various neighbours for centuries.

Prior to World War 1, Estonia had been ruled in sequence by Denmark, Sweden, Germany and Russia for eight centuries, while Latvia had been ruled by Germany, Poland, Sweden and Russia. Finally, in 1920 they were granted their independence, only to lose it again in 1940, when Stalinist Russia took control of the Baltic states, with Germany’s agreement. Russia set about terrifying the local people into submission, deporting to work camps in Russia, or just shooting, many thousands of intellectuals, artists and others.

In 1941, after only a year of Russian rule, Nazi Germany invaded, and set about massacring the Jewish communities in these countries. At one point, 25,000 Jews were killed in two days outside Riga (the capital of Latvia).

If that wasn’t bad enough, in 1944 the Russians re-invaded. It speaks volumes about how bad it had been under that one year of Russian rule (1940-1) that, with the prospect of Russia returning, hundreds of thousands of people fled to the west, including most of the remaining intellectuals. They felt that things would be much worse under Russia than under the Nazis!! The deportations and murders started up again and continued until Stalin died.

A small number of countries, including the USA and the UK, never accepted that Russian rule of the Baltic states was legitimate. Australia was also a strong supporter, apart from one year. Shamefully, in August 1974 Gough Whitlam’s government withdrew recognition of the Baltic states as separate counties. This decision was reversed in December 1975 by Malcolm Fraser’s new government.

The long history of internal and external pressure for Baltic independence finally bore fruit after the failed Moscow Coup of 1991. The last of the Russian troops left in 1994, and 10 years later the Baltic states joined NATO and the EU.

I have long had a negative attitude to the idea of patriotism. I thought, and still think, that American patriotism is a bad thing for the world. But two weeks in the Baltics gives one a much better understanding of how people can love their country. How could you fail to be passionate about your national identity and independence after what these people have been through? It is something that us cosy, safe Australians can hardly begin to understand. They each have their own languages, and their own distinct cultures, but they have only been independent countries for two brief periods totalling 35 years in the past 800 years. Between 1940 and 1949, Latvia lost 35 percent of its population to war, deportation, exile and mass murder.

We saw photos of how the shops had been almost completely empty towards the end of the USSR. A butcher’s shop with no meat. A 100-metre queue to buy bread. Since the mid 1990s, the economies of these countries have been transformed. The supermarkets are as well stocked as any I’ve ever seen, even if a lot of the food items seem unusual to us. Now the income level in Estonia has reached half the EU average, which is a phenomenal transformation, with Latvia only a little behind that.

One of the most striking things about travelling around in the countryside, which we did quite a bit of, is the low level of commercial agriculture. One sees a couple of cows here, a few sheep there, but remarkably few animals overall, and minimal evidence of cropping. Mostly what one sees is forest. There must be commercial agriculture somewhere, if only to provide the locally produced milk and vegetables, but we didn’t manage to stumble upon it, apart from some small plots of potatoes.

I found this lack of agricultural production quite extraordinary, especially since agriculture was a major activity before and during the Russian era. Explanations that we were given by Estonians included the following. (a) After the break up of the USSR, the directors of the communal farms took the equipment and sold it, leaving local farmers without tractors and other essential machinery; (b) Many of the workers on communal farms were people without a history in agriculture, and the communal farming system did not allow them to build up the skills to farm on their own; (c) many skilled farmers were deported. I’m also not sure what happened to property rights to land. I suspect that property rights are a key factor, but I wasn’t able to find out anything about that. Whatever the reasons are, much land that was formerly in productive agriculture has been abandoned and has reverted, or is reverting, to forest. Even the massive financial support that the EU provides to farmers is apparently not sufficient to reverse this.

Although Russian rule is finished, the ongoing influence of Russia is palpable. There was an explicit policy of Russification, and hundreds of thousands of Russians were moved into the Baltic states. Of the current populations, 25% of Estonians and 30% of Latvians are ethnically Russian. The Russian influence is even more pronounced in the cities: there are actually more ethnic Russians than ethnic Latvians in Riga, the biggest city in the Baltics. One hears Russian in the streets constantly, and public signage and advertising is in Russian as well as the national language.

During the USSR era, the locals were required to learn Russian, and many ethnic Russians would not converse in anything other than Russian. Now the tables have turned, especially in Estonia. The remaining Russians have become a disadvantaged minority, with big social problems in some areas. Only Estonian language is accepted for official purposes, so older Russians are struggling to pick up a difficult language that they managed to avoid for decades. (Estonian is related only to Finnish, and not to any other European language.)

Related to this, it was striking that the poor people we saw begging on the street were mostly old, and I think they were mostly Russian. It was very sad to see so many old ladies begging – something I have not seen anywhere else. My daughter Rosie was constantly stopping us so that she could give a few coins to some desperately sad looking old lady.

Another strong social trend is that, since they joined the EU, many of the young people of these countries have moved to western Europe, especially the skilled young people. This has resulted in a major shortage of labour and skills, and predictably wages have risen.

One of the most visible of the Russian legacies is the preponderance of big, ugly, grey, concrete, rectangular apartment blocks. Even in the country, we kept coming across two or three story apartment blocks in the middle of nowhere, built to house immigrant Russians. Now most of them are desperately run down, and many are abandoned. We were also struck with how run down many of the buildings are generally. Even most private houses seem terribly neglected on the outside. The part of Riga where we stayed, in particular, is just awful – dirty and dilapidated to an extent I’ve only seen in developing countries before.

Despite all the problems, economic growth has been rapid and relentless. I expect it will all look radically different in 10 or 15 years. It will be fascinating to come back and have a look.

David Pannell, The University of Western Australia

83 – “Water Resource Policy” workshop

Deb Peterson and I have organised a pre-conference workshop on water resource policy for the coming conference of the International Association of Agricultural Economists. It will be on 12 August 2006. Here is an ad for the workshop.

IAAE 2006 Preconference Workshop

Water Resource Policy

Water resources are high on the resource policy agenda throughout the world. There is a wide range of policy problems that governments continue to struggle with. This workshop brings together an outstanding program of international speakers to address some of the key policy issues in water resources around the world. Themes to be addressed include: water trading and prices; water and China (a joint session with the China workshop); water quality and environmental issues; and institutional arrangements. The workshop will include discussions by a panel of water experts. The workshop should be of value to policy makers and major water-resource users, as well as to economic researchers.

Date: Saturday 12 August 2006

Venue: Gold Coast Convention & Exhibition Centre, Gold Coast Hwy, Broadbeach.

Registration: register online or download printable registration form at



Welcome and Introduction
Deborah Peterson (Productivity Commission)
Water Trading and Prices
Chair: Donna Brennan (REAP Research)
Groundwater Irrigation in North India: Institutions and Markets
J.V. Meenakshi (IFPRI, HarvestPlus)
Questions and Discussion
Water Trading and Brokerage Mechanisms for Developing Countries
Mark Rosegrant (IFPRI)
Questions and Discussion
Water Trading in New Zealand – Theory, Practice and Potential
Irene Parminter (Ministry of Agriculture and Forestry, New Zealand)
Questions and Discussion
Water and China
Chair: Neil Byron (Productivity Commission)
Evolution of China’s Water Management—Overview
Jinxia Wang (Chinese Academy of Sciences)
Is China Exploiting Its Groundwater Resources? An Analysis of the Demand for Water
Richard Howitt (UC Davis)
Improving Allocative Water Efficiency in the Yellow River Basin, Northern China: A Study of Water Trade and the Return to Water Rights
Anna Heaney (ABARE)
Qin Fu (Chinese Academy of Agric. Sciences)
Zhanyi Gao (Ministry Water Resources, China)
Noel Gollehon (US Department of Agriculture)
General Discussion
Remarks from Malcolm Thompson for the premier sponsor, National Water Commission
Water Quality and Environmental Issues
Chair: Malcolm Thompson (NWC)
Environmental flows
John Quiggin (U Queensland)
Questions and Discussion
Salinity and Drainage Management in Irrigated Agriculture
Keith Knapp (UC Riverside)
Questions and Discussion
Protecting Water Quality
Dave Sunding (UC Berkeley)
Questions and Discussion
Institutional arrangements
Chair: David Pannell (Uni of Western Australia)
Economic aspects of infrastructure provision for rural water supply
Steve Beare (ABARE)
Questions and Discussion
Panel Discussion
Chair: Peter Cullen
David Zilberman (UC Berkeley)
Wendy Craik (CEO of Murray-Darling Basin Commission)
Wilfrid Legg (OECD)
Siwa Msangi (IFPRI)
General Discussion
Rapporteur: Mike Young (CSIRO Land and Water)

David Pannell, The University of Western Australia

82 – Seeking more effective NRM policies

Two of Australia’s main national programs for natural resource management are in their latter stages: the National Action Plan for Salinity and Water Quality, and the Natural Heritage Trust. These programs have been at the centre of a major national experiment in delivery of national and state funds through regional bodies. Discussions are currently occurring on what their successor program(s) should look like. It is timely, then, to consider the performance of the current arrangements and the potential for improvements to them.

The Australian Government is, indeed, already doing this. In late 2005, they commissioned a series of 8 reviews, conducted by external consultants, addressing issues such as salinity outcomes from regional investment, and the national investment stream of the Natural Heritage Trust (see under Books and Reports). Simultaneously, a three-person panel toured the country, reviewing the regional arrangements, and reported to government.

Of course, such a large experiment was destined not to succeed on all fronts. Weaknesses I see with the current arrangements include the following:

  • In a number of states, the regional bodies are still maturing.
  • Even for the relatively mature regional bodies, the task that they have been set under these programs is extremely challenging. It appears that the difficulty in designing and implementing cost-effective, outcome-oriented regional plans for natural resource management was not fully recognised by governments, at least initially.
  • The programs are too impatient, especially in relation to salinity. Ministers prefer the programs to focus expenditures on “on-ground works”. However, determining which on-ground works offer the best value for money requires detailed analysis, and the timeframes for planning effectively discourage such analysis. Further, in many cases, farmers lack options for salinity management that are economically viable on sufficient scale to address the problem. Their development in ongoing R&D will take time.
  • In many regional plans, the link between funded actions and the stated target outcomes is weak. The plans tend to be much too optimistic about what can be achieved by extension/education or by small, temporary incentive payments.
  • The use of science and economics in many plans is inadequate. There is reliance on community preferences without adequate assessment of their scientific or economic realism.

Notwithstanding these and other problems, it is clear that the government will continue the regional delivery system in a more-or-less similar form. How, then, can we enhance the performance of the arrangements? Here are some suggestions.

1. Develop an agreed set of investment principles to be applied by all regional bodies.

2. Require regional bodies to demonstrate consistency with those principles, through use of a sound investment framework. The framework should be focused at the asset level, and should result in much tighter targeting of investments. For example, I suggest that the regional bodies overall are currently trying to do about 10 times too much in their salinity plans.

3. Strengthen the accreditation process for regional plans and investment strategies in relation to their use of science. This includes social science – prioritisation processes for existing plans generally neglect what we know from research about the achievement of practice change.

4. Adopt a more rigorous approach to setting resource-condition targets and management-action targets. Current targets are often a compromise between a limited use of science and what the community might be prepared to accept. The current approach of selecting aspirational targets is counterproductive as it causes a disconnect between actions and outcomes. Targets are needed, but should be based on what is achievable by realistic actions.

5. Develop a more rigorous evidence-based approach to determining the relative allocation of funding to different regions. The current allocation is not consistent with needs and opportunities.

6. Introduce a subprogram for major investments that are funded separately from the regional process. In many cases, the most cost-effective investments for dryland salinity would be targeted, major projects to protect ‘iconic’ assets. The regions have mostly chosen not to undertake such major projects, and in any case they should be prioritised at the state or national levels rather than the regional level.

7. Introduce a subprogram for provision of appropriate technical and other support to regions. This needs to be done in collaboration with the States, who hold most of the technical information. Support is needed in the following areas:

  • Guidelines on the implications of latest research.
  • Guidelines on the investment framework to be used, and support in applying it.
  • Guidelines on the appropriate circumstances to use different policy tools (extension, MBIs, R&D, engineering, etc.). Use of inappropriate tools is a major weakness in the current program. (See here for my framework that gives guidance on this.)
  • Processes to ensure the provision of appropriate technical support (models and technical advice) and provision of consistent, high-quality data on key variables (e.g. groundwater salinity, depth to groundwater) at usable scales of resolution. The current strategy of leaving these issues to individual regions results in duplication of effort, inconsistency, and failure to use the best information.
  • Development of improved salinity management technologies and systems.

8. A stronger partnership with the states is needed. The states have essential roles to play in the following areas: legal/regulatory approaches (e.g. the need to purchase water rights to plant perennials in water resource catchments, as discussed in the National Water Initiative); development of improved technologies, such as more profitable (more adoptable) farming practices for salinity management; on-ground works on public lands (e.g. pumping in nature reserves); research to provide improved data for subsequent planning.

Overall, I believe that measures such as these are much needed. If they can be introduced, there is great scope for strengthening the cost-effectiveness and achievement of outcomes in Australia’s main NRM policy programs.

David Pannell, The University of Western Australia

Further Reading

Pannell, D.J. (2005). Salinity: new knowledge with big implications, A transcript from ABC Radio National, from the Ockham’s Razor progam. Full paper (19K)

Pannell, D.J. (2006). Public benefits, private benefits, and the choice of policy tool for land-use change,

Pannell, D.J. and Ewing, M.A. (2006). Managing secondary dryland salinity: Options and challenges, Agricultural Water Management 80(1/2/3): 41-56. Full paper (66K)

Ridley AM and Pannell DJ (2005). SIF3: An investment framework for managing dryland salinity in Australia. SEA Working paper 1901. CRC for Plant-based Management of Dryland Salinity, University of Western Australia, Perth. Full paper (126K pdf) 2-page summary SIF3 project page

81 – The cult of the asterisk

Researchers in many fields have an essential reliance on statistics, but researchers often apply them mechanically, and sometimes they misrepresent what the results really mean. In particular, statistical “significance”, while a useful concept, can be a poor indicator of the importance of a variable in an economic or management sense.

Statistical analysis is a standard and essential tool of researchers in most scientific disciplines. Statistical methods can be things of beauty and power. They allow us to make rigorous statements about the probabilities of certain ideas being true, based on the evidence embedded within a set of data. Unfortunately, statistics are often applied in a mechanical way, and this can lead to problems.

For example, in the early days of statistics, someone decided that it would be reasonable to choose 5% as the cut off point for uncertainty about the idea being tested. If the statistics showed that there was less than 5% probability of being in error when concluding, for example, that there was a positive relationship between fertilizer input and crop yield, then we would accept that there probably is a relationship. If the probability of being in error was more than 5%, we would conclude that the idea was not true. (Strictly speaking, we would not reject the idea that it was not true, although in practice, this usually is taken as evidence that it is not true.)

Of course, the 5% cut-off is just an arbitrary choice. Why not 10%, or 1%, or 3.3%? Recognising this arbitrariness, researchers often put asterisks next to their statistical results to indicate just how low the cut-off can be set and still conclude that the result is “significant”: e.g. one asterisk for 10%, two asterisks for 5%, three asterisks for 1%. The more asterisks, the better.

While that strategy avoids some of the arbitrariness in the general approach, it doesn’t get away from another problem: that this approach to testing the truth of an idea is unbalanced in the way it deals with different sorts of potential errors.

To illustrate, return to the example of testing for a relationship between fertilizer input and crop yield. In the standard approach to statistics, we start by assuming that there is no relationship (that the slope of the relationship is zero) and test whether this appears wrong. A zero slope is established as the point of comparison.

[From this point, the way that standard statistics proceeds can be a bit hard to get your mind around. I’ll warn you that the next paragraph might be a bit of a brain twister. I can’t make it any simpler, because it is trying to represent the way statistics actually operates.]

We then ask ourselves the following: assuming that the slope actually is zero, what is the probability that a non-zero slope as big as the one we observe in the data set would occur just by chance, as a result of random fluctuations. The bigger the observed slope, the less likely it is that it could have occurred just by chance, and therefore, the more likely it is that the slope really is non-zero. If the probability of getting the observed slope by sheer chance is less than 5%, we reject the starting assumption that the slope is zero.

This implies that, if we looked at lots of examples where the slope really was zero, we would mistakenly reject the idea of a zero slope 5% of the time (and we’d correctly accept that there is a zero slope the other 95% of the time). Clearly, this approach is conservative in avoiding the error of concluding that there is a slope when there isn’t one. (This is the so-called Type-I error that we are taught in statistics.)

On the other hand, if there actually is a positive slope, the approach has a tendency to lead you to a conclusion that there isn’t one (a Type-II error). If the slope isn’t big enough, we conclude that there is no slope, rather than concluding that there is a low slope. There is, in a sense, a bias towards accepting that there is no slope.

The approach effectively gives a high weight to avoiding Type-I errors, but pays little or no attention to Type-II errors. But who’s to say that Type-I errors are much more important than Type-II. In reality, Type-II could easily be more important in an economic sense.

A related problem is that, just because a variable is statistically significant (at 5% or any other level), it does not necessarily follow that the variable is important, in the sense of having a major influence on the issue. Even if variable X has little effect on variable Y, its effect might be statistically significant, if the relationship is very tight, meaning that there is little random scatter in the data, or if the data set is large enough. Statistical significance indicates that the relationship is real, not that it is important.

Various writers have pointed this out. For example, Dillon (1977) puts it beautifully:

“[Through] tests of statistical significance (the “cult of the asterisk”) involving mechanical application of arbitrary probabilities of accepting a false hypothesis, traditional procedures … have aimed at protecting the researcher from “scientific error”. In doing so, these procedures have led to a far greater error of research-resource waste. The farmer’s problem is not whether or not there is a 5 per cent or less chance that a crop-fertilizer response function exists. His problem is how much fertilizer to use. Even if the estimated function is only statistically significant at the 50 per cent level, it may still be exceedingly profitable … for the farmer to base his decisions on the estimated function.”

“In the mechanical fashion in which they are usually applied, significance levels have no economic relevance, except by chance, to farmer decisions about best operating conditions. (Dillon, 1977, p. 164).”

He was referring to the use of statistics in the analysis of agricultural experiments. Unfortunately, the problem is just as serious in economics. McCloskey and Ziliak (1996) went through all of the statistical papers published in the American Economic Review during the 1980s to check how many researchers were relying solely on statistical significance as their measure of real-world importance.

The answer was, most of them. Even in what is arguably the highest prestige economics journal, only about 30% of articles considered more than statistical significance as being decisive in drawing conclusions about the real world, or made any distinction between statistical significance and substantive importance.

That was in the 1980s, but little has changed. The cult of the asterisk is alive and well. I find it particularly disappointing that it affects economics so deeply. One would have thought that, given their disciplinary interests in decision making, economists would have known better.

Traditional statistics is an important tool, but it can be useful to supplement tests of statistical significance by also calculating other indicators of the importance of the variables. For example, in Abadi et al. (2005) we used “importance” indicators, representing how much difference the variables make to the predicted results. Essentially, our importance indicators answered the following question: if we varied a variable over the range that is present in the data, how much difference does it make to the model’s output? Predictably, we found that not all statistically significant variables were important, and not all of the important variables were statistically significant.

David Pannell, The University of Western Australia

Further Reading

Abadi Ghadim, A.K., Pannell, D.J. and Burton, M.P. (2005). Risk, uncertainty and learning in adoption of a crop innovation, Agricultural Economics 33: 1-9.

Dillon, J.L. (1977). The Analysis of Response in Crop and Livestock Production, Pergamon, Oxford.

McCloskey, D. N. and Ziliak, S. T. (1996), “The standard error of regressions”, Journal of Economic Literature 34, 97-114.

80 – Public benefits, private benefits: the final framework

This is the seventh and final instalment of a series that examines a simple framework for choosing environmental policy instruments, as outlined in PD#73. The framework is based on levels of public and private net benefits of changing land management, and a set of simple rules. This time we pull together refinements developed for each part of the framework over the past five Pannell Discussions, and present a revised version of the overall framework.

In PD#73 I showed how a set of simple and reasonable rules can lead to a useful map of efficient policy instruments. The context is an environmental manager considering prospective projects to change land use in particular ways on particular pieces of private land. The map shows that the choice of instruments depends crucially on the levels of public and private net benefits from those projects. A particular project to change land use in particular ways on particular pieces of land would be represented by a dot somewhere on Figure 1. Depending on where the various dots lie, different types of policy response are recommended.

In the past five Pannell Discussions we have looked in more detail at the individual areas of the map, and refined its recommendations. This article pulls together those refinements to present a revised overall framework.

The refined map shown in Figure 1 is based on environmental managers requiring a benefit:cost ratio (BCR) of at least 1.0 in order to invest in incentives or extension.

Figure 1. Efficient policy mechanisms for encouraging land use on private land, refined according to PD#75, PD#76 and PD#78, assuming managers require BCR > 1.

In broad terms, the framework indicates the use of:

  • positive incentives if the public net benefits of land-use change are high, and the private net benefits are not too negative;
  • extension if the public net benefits of land-use change are high, and the private net benefits are moderate;
  • no action if private net benefits are positive and public net benefits are not sufficiently high;
  • no action if private net benefits are greater than public net costs;
  • negative incentives if private net benefits are less than public net costs;
  • no action if public net benefits and private net benefits are both negative;
  • technology development if private net benefits are negative and public net benefits are not sufficiently high to warrant incentives;

Figure 1 is broadly similar to the original map in PD#73, with the main difference being in the extension area, which is more targeted to projects with higher public net benefits or lower but still positive private net benefits.

Figure 2 shows a comparable diagram based on a required BCR of at least 2.0, which is probably a more reasonable guide to investment than Figure 1, given that program resources are limited and there are more worthwhile projects available than the program can afford to fund. (Also, we might need a BCR of at least 2 to outweigh the overhead costs of running the program.) This more targeted strategy shows that, broadly speaking, the higher priority projects are those where private net benefits are closer to zero, and/or public net benefits are more extremely positive or negative.

Figure 2. Efficient policy mechanisms for encouraging land use on private land, refined according to PD#75, PD#76 and PD#78, assuming managers require BCR > 2.

A much smaller number of projects would qualify for incentives or extension in the more targeted approach of Figure 2. For example, over 35% of the area of Figure 1 is occupied by incentives or extension, whereas in Figure 2, they occupy less than 15%. If we allow for the reality that most projects involve negative private net benefits, the proportion qualifying as high-priority targets for intervention is lower again.

As noted in PD#78, most agricultural land probably falls into the technology development area. For most land, the best available environmental projects involve negative private net benefits and positive, but not extremely high, public net benefits. This highlights the important role of technology development. It has been relatively neglected in current programs.

Finally some general observations about the framework. The recommendations in Figures 1 and 2 (like all of the recommendations in the framework) depend on the landholders having reasonably accurate perceptions about the private net benefits of adoption. If this is not true, there may be roles for extension, positive incentives or negative incentives in other parts of Figures 1 and 2.

It is notable that the choice of policy response depends at least as much on the level of private net benefits from the land-use change as on the public net benefits. Indeed, in the more targeted version in Figure 2, results are even more sensitive to private than to public net benefits. This is an important finding as many environmental managers focus predominantly on the public benefits, but pay little attention to the estimation of private net benefits. As a consequence, they are under-informed about the landholders’ likely responses to any proposed changes in land use, which is one of the key factors that should influence the choice of policy response.

This begs the question, how should environmental managers estimate the costs and benefits? A glib answer is, “as best they can”. In the case of public net benefits, the framework does not require environmental managers to do things that they should not already be doing. Somehow they are choosing which environmental projects are of highest priority, so there must be some assessment of the environmental benefits, even if only implicitly. It is unrealistic to expect that projects could be ranked according to their environmental benefits with any great precision, but even relatively qualitative ratings could be applied within this framework.

To estimate private net benefits, one option is to invest in some good quality economic modelling. Another is to look at what farmers are currently doing. If they are choosing not to adopt a practice that has been around for a while and with which they are familiar, this provides a strong indication of their assessment of its private net benefits (including issues beyond just short-term financial returns). A third option is to run a conservation auction, in which landholders reveal their willingness to act in response to a subsidy level chosen by them.

It is important to recognise that both categories of net benefits depend on several elements. The public net benefits are not simply the value of the environmental assets involved, and the private net benefits are not simply the profits from the new land use. Indeed, the private net benefits of a project (i.e. a specific set of land-use changes) would depend on:

  • the financial returns from the new land uses;
  • the financial returns from the land uses that are replaced (the “opportunity costs”);
  • any change in risks faced as a result of the change;
  • indirect impacts on other aspects of the farm system or on the farmer’s lifestyle;
  • the farmer’s own interest in the environmental outcomes.

The public net benefits would depend on:

  • the value or importance of the environmental assets that are affected by the changes;
  • the degree of degradation that the assets were facing or had already suffered;
  • the extent to which that degradation can be prevented or alleviated by the changes;
  • any lags in the response of the biological or physical system to the land-use changes.

Overall, the framework highlights the importance of targeting funds in environmental programs to selected areas, based on the levels of public and private net benefits. Currently, environmental managers do pay some attention to the level of public benefits when selecting their investments, but in my experience few pay adequate attention to the level of private net benefits, which, perhaps surprisingly, turns out to be even more important as a driver of policy decisions.

David Pannell, The University of Western Australia

Further Reading

A consolidated paper on the public benefits, private benefits framework (combines all seven related Pannell Discussions): Public benefits, private benefits, and the choice of policy tool for land-use change