Category: Climate Change

I am an economist by training, experience and by inclination so the answer seems an obvious yes. Welfare economics generally looks at exactly these sorts of questions. However, the debate and current policy mix for Climate Change does not appear how I would expect it to, especially given the lack of agreement on carbon pricing which has dropped out of the discussion.

Assigning monetary value?

A common mistake that economists make is, because a given model works for some aspects of human behaviour, they assume it can always be used and possibly generalised to all human behaviours, often by adding some sort of arbitrary assumption to make it fit. The consensus in neoclassical modelling is for a marginalist approach with rational agents.

Take gift giving, it is economically inefficient but still takes place. The neoclassical approach is to assume that cash giving has an unexplained stigma or that buying your lover a thoughtful gift acts as signal that you love them. Of course, these fail to resonate with our actual experience of gift giving and receiving, and so fail the common-sense approach.

This is a classic logic error – there are many (infinite?) ways to model and explain behaviour. Just because you have one that you like does not mean it is the only one or that it must be superior to the others. If to make your model fit the external reality you are required to add counterintuitive assumptions then this can often sign to rethink your entire approach.

What other approaches are there?

The environment is a moral not an economic question

This idea was sparked reading Michael Sandel’s “What Money Can’t Buy”.
As a political philosopher, he is highly critical of the current trend towards adding commercial thinking and monetary values into our lives, using moral arguments of unfairness and degradation of values. There are many examples: paying to avoid queuing, the rise of corporate boxes at venues, paying to get a nicer prison cell and, in this category, he also places the environment.

Another famous example of a counterproductive effect of replacing a moral code with a monetary incentive is a school adding a fee for late pick-up of children after school. Parents were much happier paying a price for lateness than infringe a moral obligation to pick up the children on time and the incidence increased. Related to this, when people are paid for blood donations, the amounts and quality of blood donated both fall.

When Sandel in 1997 wrote a piece in the New York Times arguing against carbon trading, he was inundated with “scathing letters – mostly from economists” who assumed that he simply did not understand that their model, that trading would always be good, was obviously true. 20 years later some economists may be more sympathetic to the idea that not everything should be analysed in this way. http://www.nytimes.com/2011/04/22/opinion/22krugman.html?_r=1&hp

A different view?

Do the efforts to “stigmatize” excessive carbon usage and “promoting virtuous attitudes” work? Moral codes in society are very powerful. But as a prevention mechanism, they do not always work. They may not be as universally shared as their advocates like to assume; If you want to reduce teen pregnancy the approach of teaching that sex before marriage is immoral and the promotion of abstinence have been shown to be highly ineffective. The attempt to change people’s behaviour on carbon consumption by stigmatizing it appears to me to have the same flaw. People may feel a little guilty but will not actually change their behaviour, whilst the moralisers can enjoy the feeling of superiority, getting us nowhere.

I was once memorably informed that my willingness to experiment with models and analysis that put a monetary value on human life was “sociopathic”. I did wonder at the time if they knew or cared how counterproductive it is to alienate sympathetic non-believers if your objective is to influence behaviour.

Lexicographic vs marginal preferences

Amartya Sen is a wonderful writer on justice, development and social choice who developed ideas in welfare economics beyond thinking purely about Pareto optimality. I was recently reminded of this concept in Marc Lavoie’s “Introduction to Post-Keynesian Economics”. It was striking to me that I had forgotten about it which implies it is not commonly used.

Faced with needs, the idea is that people do not make marginal decisions across every item but rather make choices only within categories. There is a hierarchy of needs, and only when the essential ones are obtained, then the next category can be bought. Therefore, substitution of goods only happens within categories and not between them.

For the environment, people that think that climate change is a compelling moral issue then discussing the price is bizarre and inappropriate. It is a moral need and comes in a very important category. For people who have consumption desires they find more pressing such as housing, clothing and food, then the morality of climate change is in a lower category and so not highly valued. This helps explain the observation that when asked how much they value the environment people’s answers tend to be extremely high numbers or extremely low ones. There is no smooth substitution along an indifference curve. It also explains why people express that they care enormously about the environment when the economy is doing well and it is not mentioned during a recession.

This sort of heterodox critique of neoclassical marginalism is compelling, but the next step of suggesting a policy is lacking. Lavoie says that “post-Keynesians have never really developed their views on consumer choice in any systematic way” and only have “insights”.

Where next?

I am left thinking that at pure moral non-market approach to dealing with Climate Change is not effective. A lexicographic approach gives a useful description that fits observed behaviour but does not give me any useful approaches to designing policy. So, I return to welfare economics and thinking in terms of cost-benefit analysis and externalities.

Since I discussed models in my last post, I could not resist saying something quickly about what they are for. This is a very important topic and one I will frequently return to.

But for now, I will just give you a brilliant short story from the most inventive and thought-provoking author I know.

Jorge Luis Borges. “On Exactitude in Science” or “Del rigor en la ciencia”.

If this blog were the BBC, in an effort for impartiality, I would give equal time to 2 ideas:

1. Climate change is completely true
   Agreed by all reputable scientists and means drastic action needs to be taken immediately
2. Climate change is a hoax
   Devised by the Chinese to limit the US economy and we need do nothing

However, I’m not bound by any such desire for manufactured impartiality so would like to ignore climate denial theories. I want effective action to be taken and I’m interested in why the first argument is failing to find broad enough support against those who want to dismantle the Paris Agreement.

The Science

I think I can reasonably simplify the scientific arguments to this:

1. There is an empirical relationship between carbon and temperature
2. There is a causal theory relating the two, the greenhouse effect
3. There is no better theory, in fact no other credible theory

The criticisms for each item can be summarised like this:

1. Poor and incomplete data
2. The underlying system is dynamic and complex
3. The models are not very precise with large amounts of uncertainty.

The responses I have seen to these criticism from climate change scientists are:

1. We are collecting more data all the time. We are cleaning up the old data sets.
2. We are building more complex models which incorporate more variables
3. We are working to improve our accuracy with better models to reduce the level of uncertainty.

Coming from a macroeconomics background, the criticisms wouldn’t bother me much. We face them all the time. The responses from the climate change scientists do bother me however. If this reflects their research programmes, I fear they are heading in the wrong direction, sharing many of the methodological problems we see in macro and likely making the same mistakes.

My view

My preferred responses to the criticism would be

1. Yes, the data is poor and incomplete. Not much we can do about that. We will not clean up data to pretend it is better than it is.
2. Yes, it is dynamic and complex. Attempting to making models more complex would mean data-mining the limited data sets to produce models which are pretty but have no validity. Simple models of complex systems often work much better. Read the Borges short story (https://appliedmacro.com/2017/05/11/models/) for an elegant refutation of the idea that seeking perfection in models leads to good outcomes.
3. Accurate forecasts are not possible because we do not have ‘out of sample’ data and associated feedback. There is little new data to use so we cannot properly test hypotheses in the way we can with weather forecasting.

The good news is that we do not need accurate forecasts because:

1. If our model provides an unbiased best estimate and uncertainty is two-sided, then the level of uncertainly around it does not affect the policy response. Essentially the policy response will be versus the expected increase in temperature.

2. We are providing conditional, not unconditional forecasts.
   To take an analogy, I am thinking of running the London marathon next year. Please estimate how long it will take me to run it i) in running kit ii) wearing a gorilla costume. I would strongly expect that your confidence in both of your answers is very low. However, I bet you are very confident that ii) will take longer than i).

Financial markets can be a very good training ground for learning about practical model building and their methodical dangers. Most of us have been seduced by beautiful models with great back tests with desirable correlations and high Sharpe ratios. But then trying to use them to make money, we find they had no predictive qualities at all. After enough painful experiences, we learn to be highly suspicious of any model that fits the data too well – it is the obvious symptom of data-mining. The models that work in practice are the ones that are intuitive, simple and accept that the world is a messy complicated place.

What climate change scientists and macro-economists can actually do have a lot of similarities. We do not know what the temperature will be on October 23rd 2087 but we have a good guess it will be higher if there is more carbon in the atmosphere.