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I wrote this a few years ago privately, and now that I am no longer employed by the Australian Government, can post it up here. I’m doing so both because it’s interesting in itself, and because I may want to refer to it when discussing ‘additionality’ of all kinds of grants, taxes and subsidies in other posts. I should reiterate that this does not represent the views of any past, present or future employers, and given its age, it may not represent even my view any more.

TL;DR is: Usually yes, but it’s hard to think about let alone measure by how much.

Do Innovation Programs Actually Increase Innovation?

“Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.”

Paul Krugman’s famous quote highlights the centrality of productivity growth to a nation’s material welfare. Having accepted its underlying truth the only remaining question is, ‘from where will this productivity come?’ It is only in recent decades that economists have developed ‘endogenous growth theory’ to explain the innovation which underlies most productivity growth (Romer, 1990). The primary market failure that the resulting ‘innovation’ programs attempt to address is that innovation has ‘spill-over benefits’ that an innovator cannot capture. Wgeb they fail, the original innovator privately bears all the costs; when they are successful they will attract numerous imitators who benefit from their entrepreneurship while reducing their profits. The spill-overs from major advances can be very large and mostly accrue to consumers (Nordhaus, 2004). This will lead to an undersupply of innovation relative to the social optimum. Nonetheless private rewards do exist and a substantial amount of innovation will occur in the absence of any government intervention. This raises the question: how can a government induce additional innovation, without paying for ‘infra-marginal’ research that would have occurred in its absence? This essay will briefly investigate the theory and empirical evidence regarding the ‘additional effect’ (additionality) of three popular mechanisms for promoting research and development (R&D)

  • subsidising private sector R&D through subsidies, in particular tax concessions (e.g. in Australia, the R&D Tax Credit)
  • directly performing research through universities and public research organisations (e.g. CSIRO)
  • awarding funding for specific projects based on selection criteria (e.g. the Green Car Innovation Fund).

Their true impact is challenging to answer and yet crucial to assessing whether innovation programs provide value for money.

Crowding out, crowding in and cost-benefit analysis

Public R&D spending could reduce, or ‘crowd out’, private R&D spending through a variety of mechanisms, most notably,

  • public research organisations (PROs) pursuing the most fruitful lines of research, encouraging private firms to imitate them rather than innovate on their own
  • PROs and subsidies chasing an inelastic supply of researchers and other research inputs, driving up wages without expanding output
  • subsidies inducing private firms to spend less of their own money where the price elasticity of demand for R&D spending by a given firm is low
  • the government awarding money to ‘infra-marginal’ research projects that recipients would have conducted anyway, while ‘marginal’ projects go unfunded.

There are also ways public R&D could induce, or ‘crowd in’, private spending so long as it targets marginal rather than ‘infra-marginal’ spending, the elasticity of R&D demand is high, public R&D complements private R&D, or R&D has ‘economies of agglomeration’. The net effect of these will be revealed in the ‘additionality ratio’ (AR), or the amount of extra R&D induced for each dollar of public expenditure.1

For a program to provide any net benefit to the economy, equation 1 below would need to be positive. In Australia the average cost of public funds is estimated to be $1.20 and $1.30 for each dollar raised (Robson, 2005). The marginal cost is probably higher, but estimates differ widely depending on the tax that is increased. Estimates of the spill-overs from R&D also span a wide range and cannot be addressed here (PC Appendix E, 2007). Nonetheless, it is obvious from the equation that the AR will have a large impact on the admissibility of many programs under a cost benefit analysis.

Tax Concessions 

R&D tax concessions aim to increase innovation by lowering the price of R&D investments for firms. Such concessions exist in 20 of 34 OECD countries (OECD, 2010). To illustrate the effect, imagine a firm with the following demand for R&D,

R&D Demand = 100 – 5 * Price of R&D,

facing a constant R&D price of $10, of which $1 is tax (Figure 1). Initially $450 is spent on R&D, net of tax. Now suppose a new government tax concession for R&D expenditure lowers the price to $9, inducing the firm to lift R&D spending by $45, to $495. Ignoring other substitution effects, this costs the government $50 in tax revenue, resulting in an AR of 0.9. The same calculations reveal that a demand curve of Demand = 100 – 7 * Price leads to an AR of 2.1 while the equation Demand = 100 – 3 * Price generates an AR of 0.4. Under perfectly elastic supply, the AR is dependent on the 1) how much R&D the firm would engage in without any concession (less is better) and 2) the price elasticity of the firm’s demand for R&D (higher is better).

Note that if the government had perfect information about the firm’s demand for R&D it could have offered to pay a mere $2.5 to induce the same expansion, resulting in an AR of 18. This highlights an important principle in public policy design: ideally the government would pay firms just enough to make them willing to undertake the desired activity. Anything more is an unintended and costly transfer from taxpayers to recipients. Unfortunately, the government does not know how much to offer, so businesses can exploit this to earn ‘information rents’, a point that will be discussed in more detail later.

Econometric estimates of tax concession ARs use panel data and two kinds of exogenous shock 1) changes to concession rates or eligibility criteria over time 2) estimates of R&D elasticity of demand based on any research input price changes. They are complicated by the fact that elasticities grow the longer firms have to adjust. A review of 23 estimates across the US, Canada, Australia, France and Italy found a mean AR of 0.77 and median of 0.67 (PC Appendix M, 2007), indicating moderate levels of crowding out. Reinforcing the value of subsidising only marginal research, Bloom et al. (2001) found that programs which lower the cost of all business R&D have an average ratio of 0.83 while those which only subsidise expansions above typical or historical levels can be as high as 2.9.

Public Research Organisations (PROs)

Rather than lower R&D costs for private firms, government can employ researchers directly through PROs. Public research into promising new inventions might discourage private firms from working on the same problems, who might instead imitate PRO research. However government control of PROs allows them to focus on research the private sector is least likely to undertake. In light of the market failure described earlier, that would be research that cannot be patented and is easily imitated, with high research costs and little immediate commercial potential. So called ‘basic research’ is much more likely to be additional, and where it reveals new opportunities for commercial research could even be a complement rather than substitute for private sector R&D.

It has been suggested that PROs use up the limited supply of researchers and crowd out private R&D by driving its cost (Goolsby, 1998). However, higher wages will encourage more people to train as researchers, so this is probably only a problem in the short run. In a small country like Australia, this crowding out effect will also be reduced by the pool of foreign researchers who would relocate here as wages rose.

Guellec and Van Pottelsberghe (2001) performed an econometric analysis of different sources of R&D spending over time in a panel of 17 OECD countries, applying a range of controls. It is the largest evaluation of its kind. Across the sample they found that a 10% increase in government R&D funding reduced private R&D expenditure by 3%. The AR for non-university R&D was 0.62, while the AR for universities was 1, implying that university R&D was more distinct from, or complementary to, private R&D. When defence related R&D is excluded, non-university research also has an AR of approximately 1. Guellec and Van Pottelsberghe explain this by observing that defence R&D employs more engineers than scientists and the supply curve for engineers is less elastic. Another explanation could be that defence R&D has fewer knowledge or agglomeration spillovers for civilian R&D. This analysis reveals that local labour supply elasticities and the nature of research undertaken in PROs are key to determining their AR.

Research grants and prizes

The final way to fund R&D would be to offer grants to private firms. A serious concern in this case is a form of adverse selection: firms will most aggressively pursue grants for the large infra-marginal projects that they expect to be most profitable. This may be a successful strategy both because public servants are unable to assess how much the firm would have spent in the absence of the grant, and because the public sector would like to be seen to fund commercially successful projects (Klette et al., 2000). Finally, private organisations may be more willing to pay for the best negotiators, to assist them in seeking rents from the government. If a grant does not allow the recipient to escape some form of borrowing constraint, the firm may substitute public funding for their own dollar for dollar, resulting in an AR well below 1. Governments have used many approaches to avoid these problems, for instance setting criteria to target less profitable projects, requiring large co-investment and structuring grants as loans or equity investment to attract financially constrained firms.

Empirical estimates of the AR of grants, using a range of techniques, have varied widely, but in many cases produced estimates above 1 (PC Appendix M, 2007). One recent program evaluation in Australia (CIE, 2003) took two approaches, 1) surveys asking firms about additionality and commercial returns, 2) comparison with a control group of rejected applicants. Firm survey responses claimed a high AR but this was inconsistent with their reports that projects were highly profitability. Comparison with the control group indicated the grants had no impact, though the selection of the control group was heavily disputed during peer review. This is typical of the methodological difficulties in assessing grant programs and the conflicting indicators of their effect.


For a variety of reasons public R&D expenditure may increase total R&D by much more or much less than public outlays would suggest. As demonstrated above different approaches can have very varied impacts, and efficient use of public money requires that serious thought is given to the likely additionality of programs.

Read the rest of this entry »

Humans have successfully developed laws and social institutions that allow us to gradually improve our welfare over time. These include wealth redistribution among families, close friends and countries coupled with self-ownership and free exchange among billions of humans through markets. Other apparent keys are the incentive to innovate and the ability to accumulate new knowledge in journals and communities of experts.

Unfortunately animals don’t fit into this system. Animals are not able to use property, language, technology, trade and so on to achieve high states of wellbeing on their own. This is not going to change. The lot of animals is therefore up to humans; they will never be able to save themselves from poverty as we are doing for ourselves.

Currently, with a few exceptions, humans do not treat animals as worthy of concern. Farm animals through most of the world have few or no protections and are often treated very badly in order to minimise the resources humans need to sacrifice to raise them. Even the minority of people who care about the welfare of farm animals are generally unconcerned with the suffering of wild animals, no matter how bad life may be for them. Animals we have personal relationships with, like pets, get the best deal, but they are only a small share of all the animals that exist.

What might we hope that humans will do for animals?

One option would be increased regulation of the treatment animals in the same way that we now regulate the upbringing of children. While parents have a great deal of freedom in how they treat their children, they do not have free reign. They don’t ‘own’ children in the way that people currently own animals – rather they are considered to be ‘stewards’ of children. Greater wealth and education in the future might lead people to be willing to make the sacrifices to treat animals this way, just as increased wealth has made many parts of the world willing to dedicate a lot of resources to ensuring children are not mistreated.

A second approach, obvious only to an economist, would be for the government or another group to set financial incentives for treating animals well. People and businesses would be allowed to treat animals badly but they would have to pay a price if they wanted to do so, just as your employer would have to pay you to make you tolerate things you didn’t enjoy. Animal owners could also be rewarded for treating animals well. This would leave it up to the market to determine how animals should be treated once the appropriate incentives had been provided – incentives reflecting the importance society placed on the welfare of animals. One way of looking at this would be as the animal welfare equivalent of a ‘carbon tax’, where the suffering of animals was a social ill like pollution. An alternative perspective would be that the regulator was standing in as a negotiator on behalf of the animals who were themselves unable to negotiate ‘work’ contracts with their owners. These pseudo-contracts would replace the current system of slavery.

A third approach would be to take animals out of the picture altogether. If humans are able to continuously improve their lot in life with technology while non-human animals are not, then eventually human welfare will far exceed animal welfare. At that point it may just be best for humans to replace animals altogether. There are already plans to make farm animals obsolete by growing artificial meat in labs rather than on farms. Humans are also progressively displacing animals from the wilderness by clearing land for human settlement and farming. Humans might find that eventually the only animals they want to keep around are pets, which they enjoy treating well. This scenario would require humans or their descendants to continue to flourish and expand, which is possible but far from certain.

In the short run a greater appetite for direct regulation of animal welfare is the the most I really see happening. In the long term though I am hopeful that humans will end up living much better lives than they currently do, and find that they have nothing to gain by having suffering animals living on Earth.

A few months ago I wrote about how cleanliness was often an unhelpful addiction:

I am skeptical of cleaning, beyond that required to stay organised and avoid disease, for the same reason most people are nervous about drug habits. People differ enormously in how much cleanliness they expect. When someone catches the ‘cleanliness bug’, I doubt they are left any better off than someone with low expectations. They could easily be worse off if they have to incur the cost of cleaning just to maintain their original level of well-being. That is to say, I think cleaning exhibits strong dependency and addiction.

Quiggin has a similar take on social expectation for housework and how we could alter our attitude to them to save ourselves the trouble:

That still leaves a number of inescapably physical and essentially crappy jobs, for which technology has yet to offer a solution. The obvious examples for me are cleaning (surfaces, baths, toilets etc) and ironing (not such a problem if, unlike me, you can do it while watching a video/TV). Something these tasks share, and which is true of a lot of crappy jobs, is that we do a lot more than is actually necessary. Social standards inherited from the days of cheap servant labour dictate much more cleanliness than is required for hygiene, and practices like ironing for which there is no need at all.

So, a final part of my idea of utopia would be the institution of social norms that frown on unnecessary crap-work. In my utopia, a freshly ironed shirt would attract the same kind of response that is now elicited by a fur coat or an ivory brooch – a mixture of anachronistic admiration with disapproval of the process by which it was produced, with the latter element predominating over time.

I am willing to do my part – or perhaps I should say not do my part – to push social norms in this direction!

ImageLast week I placed my first bets on the prediction market InTrade – the largest and most notable prediction market in the world. They were on behalf of a friend but I will soon start trading with my own money.

There was no fee for loading up my account or for trading – but there is a $5 account fee levied every month. You also lose the interest on your US dollars, but at the moment US dollars earn no interest so that’s not a problem. There is also a $20 fee for taking money out of the account. To minimise on these costs some friends and I are sharing an account. Unfortunately you can only add a maximum of $250 to your account within the first month.

The existence of these fees discourages people from signing up and taking advantage of incorrect standing odds. For a few weeks in 2008 a single cashed up trader managed to keep McCain the favourite to win the presidential election just by throwing more money at that market than new recruits could bring in to bet against. On the upside at least these fees do not distort people’s trading once they have signed up.

The website is quite easy to use, though it might confuse someone who was just used to gambling with ‘odds’ rather than trading shares in markets.

Given the fees involved I expect to lose money by trading, but see three other worthwhile benefits of participating. Firstly betting on events is fun as it gives you a stake in events you otherwise wouldn’t care about. Secondly, it is a humbling experience to realise how overconfident you are about most of your beliefs and be held accountable for your errors. Finally by providing liquidity to the market I am helping improve the accuracy of these markets and inform those who rely on the estimates they produce.

Anyone in New Zealand should sign up to iPredict which has lower fees and is subsidised as an academic experiment.

A few weeks ago I wrote about the importance of choosing the right counterfactual in policy analysis. I noted that quite often people choose ‘no change’ as their comparison for whatever they are considering. This is not a very interesting comparison. More relevant is a comparison to other plausibly good options, or at least what would be likely to happen if no conscious path were chosen.

This may seem a bit academic but in fact it can radically change a debate or how you approach a piece of research.

A major case in point has been identified by Scott Sumner in the debate over the effect of fiscal stimulus in the USA after the 2008 financial crisis.

Fifty elite economists were asked what I thought was a very simple question:

Because of the American Recovery and Reinvestment Act of 2009, the U.S. unemployment rate was lower at the end of 2010 than it would have been without the stimulus bill.

And only Pete Klenow got it right:

Agree. Caveat: how much was it offset by less aggressive (than otherwise) unconventional monetary policy?

Pete Klenow was the only one of 50 who seemed to understand the question. They were asking if fiscal policy lowered unemployment, i.e. boosted RGDP. But the standard model says that only occurs if it boosts AD. And that only occurs if NGDP rises. And the standard new Keynesian and monetarist and new classical models all agree that monetary policy drives NGDP. So it’s really asking if the 2009 fiscal stimulus in some way caused NGDP to evolve differently than otherwise, which is inescapably a question about how monetary policy would have evolved in the absence of the ARRA. And only one guy seemed to understand that.

The correct answer was; “What kind of question is that! How the hell can I answer that if you don’t tell me the monetary policy counterfactual.”

A simple explanation of this for non-economists woud be this.

  1. In theory there are two ways of stimulating the economy in a downturn – fiscal policy (higher government spending) and monetary policy (lowering interest rates, which is to say printing money). What matters to the economy is primarily the combined effect of both fiscal and monetary policy.
  2. In 2009 the United States Congress passed a huge fiscal stimulus.
  3. Had they not done so, the US Federal Reserve would have noticed that the economy was doing worse and chosen to do more monetary stimulus. This might have included unusual practices like ‘asset purchases’ to get money into the economy even if interest rates had been stuck at zero.
  4. Almost all analysis of ‘the impact of fiscal stimulus’ assumes that without that stimulus monetary policy would otherwise have stayed the same.
  5. That is an irrelevant issue.

If you use the correct counterfactual you are left with a different research question. Rather than just estimating the effect of fiscal you also need some idea of what the reserve bank would have done, and how effective it would have been. Considering this will probably make fiscal policy look less effective.

Some methods for estimating the effect of fiscal stimulus will compare to the actual counterfactual, while others will not. No surprise then when they disagree!

Even though this is something hundreds of the smartest people in the world research and it is of huge consequence almost everyone in newspapers and general policy circles is asking the wrong question. A sobering thought.

John Quiggin suggests that we could feed everyone a high-meat diet and reduce climate change to boot by shifting from livestock to chickens:

I’ve previously argued that we can feed the world if we make the right choices. More precisely, our current food system produces more per person than is needed for adequate nutrition, and can continue do so in future if the right policy choices are made. The key problem is distribution, not production.

But the meat consumption data leads me to a more surprising conclusion.  Using current technology and with no additional diversion of food grain, the world could produce enough meet to give everyone an intake comparable to that of the average person in the Netherlands [fn1].

Each kg of grain-fed beef requires about 8kg of grain, compared to 2kg for chicken, and the trade-off similar when cattle are pastured on land that could be used for grain. So, 5kg of beef could be replaced by 20 kg of chicken.

The other main user of grain (apart from human consumption) is ethanol production which now takes something like 140 million tonnes a year. Fed to chickens that would produce around 70 million tonnes or 10kg per person per year.

That would give an average of 62kg [meat consumption] per person per year, not far below the Dutch average. To fill the remaining gap, I’ll call on the usual suspects, reductions in inefficiency and waste.

But a large part of my reason for doing exercises like this one is to consider the feasibility of a better world, even if it might be considered utopian at present.

This may all be correct, but far from being an unachievable utopian vision it sends a shiver down my spine. Brian Tomasik has crunched some numbers and estimated that the direct animal suffering caused by each kg of chicken meat produced is probably an order of magnitude greater than the suffering per kg of beef produced. This is because chickens are much smaller than cows and because their lives on factory farms are worse, being confined to tiny cages as they are.

If we were looking to paint utopian food scenarios, I could do better than envisage an explosion in the number of broiler chickens. We could see a shift towards vegetarianism, which the article implicitly observes requires fewer resources than meat-based diets. We could learn to grow meat or other meat substitutes the same way we grow plants, removing the need for all the suffering and inefficiency of incarcerating actual animals. Or at least we could develop the conscience not to torture chickens in this way in order to save small amounts of money.

In Australia the best term deposit rate I can find is 5.5%. This is a lot higher than you would be getting on US dollars (~0%) or the Euro (~2%) at the moment. With an inflation rate of 2.5% that looks like a real annual return of 3%, but a peculiarity in our income tax system means that tax is charged not just on the real 3% return as you might expect. Rather tax is charged on the full ‘nominal’ 5.5%.

As a result the real return I earn after tax is under 1.4%. If I were to jump up to the next income tax bracket I would be earning about 0.8%. In a year with inflation on the higher end of the Australian target, the real return would be roughly nothing. The interest I would earn on cash savings are a tiny fraction of the interest I would be charged on cash borrowings – about 15%.

The household saving rate in Australia has gone from 0% to 10% of national income since the 2008 financial crisis and a lot of those funds have gone into bank accounts. I can’t imagine the desire to earn interest has anything to do with it. Read the rest of this entry »

ImageI had noticed the ‘collider’ sampling bias before but never thought about how common it must be:

Sampling error? Omitted variable bias? Bah, that’s for first-year grad students. What I find really interesting is there are some fairly basic principles for how analysis can get really screwy but which can’t be fixed by adding more control variables, increasing your sample size, or fiddling with assumptions about the distribution of the dependent variable. I’m thinking about really scary sources of model specification problems. Or actually, not model specification in of itself, but data collection. Your typical social science graduate curriculum talks a lot about getting standard error right but on a day to day basis most of our work goes into getting the data into the proper form and this is also where most problems come from.

But before talking math, let’s contemplate a recent overheard confession that, “Turns out those funny looking toe shoes are pretty comfortable.” As someone who feels naked without footwear that involves both socks and laces I had never given much thought to this and to the extent that I had, I assumed wearing these things was a costly signal of geekiness. But on reflection it makes perfect sense. After all if something as ridiculous looking as toe shoes were not comfortable then nobody would wear them. Conversely, four inch heels are very uncomfortable (or so I am given to understand) but many women wear them because they’re attractive. So we can imagine a negative association between how attractive shoes are and how good they feel. Indeed, this describes my own collection of incredibly comfortable but informal Chucks, fairly comfortable and decent-looking dress shoes, and a second pair of dress shoes that are uncomfortable but fancy. One interpretation of this (and bear with me as I briefly sound like a critical studies type person) would be something along the lines of a sadistic gaze wherein the perceived attractiveness of a shoe is directly derived from the discomfort we imagine it imposing on its wearer. I don’t doubt that people have made this argument but I don’t buy it as a general argument because I can imagine shoes that are both hideous and uncomfortable — say Crocs made of gravel and epoxy. There is no ontological reason why we can’t have shoes that are both hideous and uncomfortable but rather there is a practical reason in that nobody wears shoes that are terrible in every way and so such shoes don’t make it unto the market. That is, there is a big difference between the covariance of traits for all conceivable shoes versus covariance of traits among those shoes that actually get bought and worn.

I took the 2010 wave of the General Social Survey  and pulled all 395 Republicans and GOP-leaning independents (PARTYID==4/6). For these people I compared their attitudes on marijuana (GRASS) and government redistribution of wealth (EQWLTH, which I cut to a binary with responses 1/4). Among Republicans who oppose wealth distribution, 37% favor legalizing marijuana, as opposed to 38% among those who favor wealth redistribution. This difference of one percentage point is not even remotely statistically significant (chi2 0.08, 1 df).

OK, now wait a minute you may be saying, he promised us negative relationships but this is no trend at all. True, but let’s contrast it with the same analysis for the whole sample, regardless of party. In general, 42% of those who oppose redistribution favor legalized marijuana against 53% of those who favor redistribution. This relationship is strongly statistically significant (chi2 14.50, 1 df). So among the general population there is a positive association between marijuana legalization and wealth redistribution. Among Republicans this effect is perfectly counterbalanced by conditioning on a collider. People presumably join the GOP because they agree with it on at least some issues. Republicans who oppose both weed and redistribution we can call movement conservatives, those who oppose weed but favor redistribution we can call social conservative populists, those who favor weed but oppose redistribution we can call libertarians, and those who favor both we can call people who should probably change their party registration. This case illustrates how conditioning on a collider doesn’t necessarily result in a net negative relationship but rather can partially or complete suppress an underlying general trend.

A common folk explanation for the triumph of capitalism over communism goes along these lines:

Communism has some lovely notions about sharing wealth between people in proportion to their needs and ideally we would indeed live that way. But people are not motivated to work under such egalitarian conditions. Humans are somewhat pro-social and do make some sacrifices for others, especially close friends and family. But that just isn’t enough to keep people working hard and productiviely in big, anonymous, industrial economies year in, year out. The economic system has to go with the grain of human nature and appeal to people’s greed by offering private rewards for work hard and risk-taking. That is why market economies have become rich and centrally planned ones have stagnated. Communism was a triumph of idealism over the realities of human nature.

If this really is the reason capitalism has been so successful, I’m afraid the future doesn’t look so good for capitalism.

In that caricature, capitalism is only the best economic system given the constraints imposed by human nature. Human nature has turned out to be harder to mould than 19th century idealists had hoped, but it will not remain fixed in that way forever. Over thousands of years evolution can and will change human nature, leaving us free to choose from a broader range of social structures.

Long before ‘natural selection‘ has much impact I expect that ‘human directed selection’ will take off. Initially children will be chosen for things like beauty, intelligence and health, but eventually our personalities will also become a parental or social choice. It will then be within our power to take the pro-social behaviour that humans currently display to only a small in-group of close friends and family, and direct it towards larger groups of our choosing. Communism could get a second run, only this time it wouldn’t have to work against a human nature that evolved to serve our hunter-gatherer ancestors!

Communist communities whose members are selected to cooperate selflessly among themselves could turn out to be more productive and gradually out-compete individualistic or capitalist communities. These communities might resemble hyper-social super-organisms like ant or bee colonies.

The competitive dynamics of such a scenario are a challenge to imagine. There would be lots of ways such cooperation could be undermined but it might also be possible to sustain. Excluding and punishing free-riders within the community will be an option for people as it is for insects.

Such communities might still choose to use markets and prices to solve the economic calculation problem but then redistribute what they produce in a very egalitarian way. Or future technologies might allow them to dispense with markets altogether.

Though I am personally quite an individualist and enjoy the classically liberal way of life, I am not so horrified by the thought of human or post-human societies being very different in the future. The members of such a future ‘communist’ society would not necessarily share my individualistic preferences and so might not suffer to live as slaves to giant communities as humans today do. The desirability of this scenario was discussed by Peter Singer and Tyler Cowen a few years ago:

Cowen: Let’s try some philosophical questions. You’re a philosopher, and I’ve been very influenced by your writings on personal obligation. Apart from the practical issue that we can give some money and have it do good, there’s a deeper philosophical question of how far those obligations extend, to give money to other people. Is it a nice thing we could do, or are we actually morally required to do so? What I see in your book is a tendency to say something like “people, whether we like it or not, will be more committed to their own life projects than to giving money to others and we need to work within that constraint”. I think we would both agree with that, but when we get to the deeper human nature, or do you feel it represents a human imperfection? If we could somehow question of “do we in fact like that fact?”, is that a fact you’re comfortable with about human nature? If we could imagine an alternative world, where people were, say, only 30% as committed to their personal projects as are the people we know, say the world is more like, in some ways, an ant colony, people are committed to the greater good of the species. Would that be a positive change in human nature or a negative change?

Singer: Of course, if you have the image of an ant colony everyone’s going to say “that’s horrible, that’s negative”, but I think that’s a pejorative image for what you’re really asking …

Cowen: No, no, I don’t mean a colony in a negative sense. People would cooperate more, ants aren’t very bright, we would do an ant colony much better than the ants do. …

Singer: But we’d also be thinking differently, right? What people don’t like about ant colonies is ants don’t think for themselves. What I would like is a society in which people thought for themselves and voluntarily decided that one of the most satisfying and fulfilling things they could do would be to put more of their effort and more of their energy into helping people elsewhere in need. If that’s the question you’re asking, then yes, I think it would be a better world if people were readier to make those concerns their own projects.

Here are the podcasts I’m tracking at the moment, with a link to a notable recent episode.

EconTalk – Lengthy interviews with experts on economics and social science. e.g. Acemoglu on Why Nations Fail

In Our Time – Panel discussions on random topics from history and science. e.g. Voltaire’s Candide

Planet Money – News coverage of economics that presents complex ideas quite simply. e.g. Cage Match: Coin Vs. Bill

Surprisingly Free – Interviews with experts on the internet and new technologies in general. e.g. Naomi Cahn on the digital afterlife

This American Life – Stories from Americans on random topics from current affairs to childhood memories. The best show of its kind. e.g. Retraction

More or Less – Entertaining investigations into numbers that appear in the media from Tim Harford. e.g. Austerity, border queues and bank holidays

Savage Love – Relationship and sex advice show. Equal parts entertaining, useful and disgusting. e.g. Savage Love Episode 287

While we are talking about  it, I also use BeyondPod to keep the episodes on my phone up to date! Podcasts are good for commuting and exercising, if you’re not yet hooked on them.

For many years during my undergraduate degree I was living on a scholarship alone and so learned to be a very frugal person. As computers and mobile phones got cheaper, I would always take advantage of that to get cheaper rather than better models when upgrading. Last year for instance I bought a basic smartphone for $100 and a netbook for $250. This year on the sage advice of Luke Muehlhauser I changed my approach and splurged on a MacBook and higher end Android phone. Having experienced both I realised that buying the cheap electronics was a false economy and that if I had thought about the decision properly I would have worked that out much earlier.

The reason is simple.

A high quality laptop cost me $1100 while a comparable low quality one would have cost me $500. I use my laptop an average of about 2 hours a day, and expect it to last around two years. Over its lifetime then I should expect to use it about 1400 hours. A high end laptop then costs $0.42 an hour over a low end one. I estimate that the MacBook’s design and reliability boost my productivity by at least 10%. Do I value a 10% productivity boost at $0.42 an hour? Given my wages and the importance I place on getting things done – definitely. And then there is the pleasure and serenity I get from using a well designed product on top of that.

Likewise, a good phone cost $200 more and I use my phone about half an hour a day and also expect it to last for two years so it comes to about 55c extra each hour of use. While I don’t use the phone as much, it is particularly valuable to be able to do what you need to do on your mobile quickly, for example when you are trying to find an event, some piece of information or a person you a meeting. The faster processor and better software on the expensive phone allow me to perform most tasks almost twice as quickly as on the cheap phone. This is certainly worth the cost.

My instinct without doing the numbers was that ‘to be frugal is a virtue’, but in order to save my money I was inadvertently a spendthrift with my time. In future I will divide the price of durable items like laptops into hourly costs as I have done above in order to make it easier to work out the best decision.

The power of exponential growth seems to make a compelling case for effective altruists to delay their donations. An average 5% return on investment (ROI) would turn one dollar into ten in 50 years time. If saving a life costs $2000 now and similar opportunities will exist in the future it would cost just $200 to save a life in 2062 – a relative bargain! Sadly things aren’t so simple. Whether we really should delay depends on specifics of the activities we are funding and difficult predictions about the future. Here I’ll summarise the most important uncertainties as a roadmap for future posts.

Our goal can be summarised as choosing the time t which maximises

(1 + Return on investment)t × Cost effectiveness of donation

× Probability of donation actually being madet.

Unless you are a multimillionaire, the relevant expected ROI is the highest one available without regard to risk. Giving $2m will do about twice as much good for the world as $1m, so to maximise your expected impact you should just maximise your expected donation. Note that if your favourite charity would be able to use money now to attract donations at a rate faster than you expect your investments could return profits then donating would have to be better.

The second and more challenging issue is how cost effective your donation will be in the future relative to now. If you thought basic health would be the optimal cause this would involve anticipating things like

  • the extent of poverty
  • the cost of delivering health services
  • how much other donors will be funding the low hanging fruit.

The last point is especially relevant for those like me thinking of funding existential risk reduction because a few billion from governments or philanthropists could make a big impact on the value of further funding in that area.

In evaluating cost effectiveness we must factor in that any good charity will have impacts that propagate through time and so offer its own ROI. For instance, combatting contagious diseases now rather than in 2062 should lead to fewer people becoming infected in the meantime and so result in a richer and healthier population in 2062. Similarly, spending on existential risk reduction draws attention, money and researchers to that issue. Giving now leaves your donations more time to have this snowball effect during the window of greatest extinction risk.

On the other hand delaying leaves you more time to identify cost-effective targets for donations. Personally, I am investing rather than giving mostly because I expect groups like 80,000 Hours to give me a much better idea of how to best reduce existential risk within the next decade.

Finally you must assess the risk of your donation never being made, for example due to a catastrophe which eliminates your savings. If you can’t bind yourself through a trust fund, you must also worry about changes to you or your life which result in you deciding not to give.

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Robert WiblinHi! I am a young Australian man ostensibly interested in the truth and maximising the total number of preferences that are ever satisfied, weighted by their intensity. I also enjoy reading and writing about the topics listed above. If you share my interests, friend me on , , or or subscribe to my RSS feed .

All opinions expressed here are at most mine alone, and have nothing to do with any past, present, future or far future employers.

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