Climate activism, alarmism and effectiveness

In recent years, climate activism showed a regained interest in direct action and civil disobedience. One example is Extinction Rebellion. In their communication, these direct action groups often appeal to alarmism with doom scenarios about apocalyptic climate change. Direct actions could potentially be an effective form of climate activism, but the alarmism strategy is probably problematic. In this article I argue why alarmism is worrisome, and how alarmism can make direct action groups such as Extinction Rebellion ineffective or perhaps counterproductive.

The problems of alarmism

Alarmism is comparable to a badly adjusted accelerator pedal of a car. If you press it too little, the engine will stop. If you press the accelerator pedal slightly harder, the engine will rotate in an overspeed like a sports car from the starting blocks. This lack of control of an engine with a hypersensitive accelerator pedal can be dangerous. Like the hypersensitive engine, there are two possible reactions with alarmism: it creates a feeling of apathy resulting in inaction or a feeling of impatience resulting in overaction.

The alarmism risks for the general public: apathy, distrust and polarization

The first reaction of alarmism is a feeling of apathy. This can lead to fatalism, cynicism, paralysis, hopelessness, resignation and a lack of motivation to take action. Like an engine that stops, potential activists fall silent because of their false belief that it is too late, that the deadline is passed, that the climate change problem will not be solved and is impossible to solve.

This reaction is confirmed in psychological research: using terrifying messages and doom scenarios (“the current system will kill us all”) is anything but convincing (see, for example, “Yes! 50 Scientifically Proven Ways to be Persuasive” by Noah Goldstein, Steve Martin and Robert Cialdini). The alarmist strategy is irrational because it does not really encourage action, so that climate goals are not achieved.

As a consequence of this reaction, the general public might even experience a backfire effect: people who hear an uncomfortable alarming message of a problem that is beyond their control (such as the global political system that causes climate change) get even more convinced of the opposite. For example, they will deny climate change even more strongly. They put the head in the sand and become more convinced climate skeptics.

Apathy not only results in a lack of motivation to take action, but also in distrust. Twenty years ago, climate activists said that we only have ten years left to solve the climate crisis. Ten years later, the climate activists didn’t acknowledge that time is up and further actions are futile. Instead, they postponed the deadline: they gave us another ten years to win the battle. Ten years later, they still say “act now”, indicating that the battle is not yet lost. The alarmism of the activists decreases their credibility. This is comparable to an unreliable fuel meter. Suppose the fuel on the dashboard indicates too often that the fuel tank is empty while it is still full. In the beginning you will go to the gas station unnecessarily often. But after a while you will ignore the fuel meter and just continue driving. Until the fuel tank suddenly really runs out of gas. That happens to doom thinkers: if they sound too many false alarms, it leads to disbelief.

Another societal problem created by alarmism, is increased polarization. The reaction of the climate activists is very different from the non-activists: the activists become impatient and overzealous, proposing more radical direct actions and revolutions. This creates a polarization in society with two camps: the climate activists versus the climate skeptics. This polarization results in group think and ingroup-outgroup biases that can increase other biases such as confirmation bias, worsening judgments and decision making. A polarized society creates more mutual distrust, becomes less effective in making the right decisions and increases the risk of group discrimination and violence.

The alarmism risks for the climate activists: impatience, overzealousness and thoughtlessness

The second possible reaction of alarmism is a feeling of impatience. As a result, activists propose more and more drastic measures to save the climate. The impatience means the activists do not want to take enough time to study the effectiveness and costs and benefits of their actions and proposals. They thoughtlessly jump into a climate campaign, without taking the time to critically think about it and look for scientific evidence of its effectiveness.

One example is the disinvestment campaign. Some Extinction Rebellion climate activists took part in disinvestment actions, for example to demand that universities disinvest in the fossil fuel industry. However, the effectiveness of disinvestment actions is questionable. If the university does not invest in the fossil fuel industry, other people will invest in it (because demand in the share market is very elastic, so other investors will immediately take up the slack of a decrease in share price due to disinvestments). If the rates of return in that industry are lower than in other industries, the market mechanism will automatically push towards disinvestment in the less profitable fossil fuel industry, so disinvestment campaigns become superfluous. If the rates of return of that industry are higher than other industries, the university will lose money if it disinvests, and that means they have less money left to finance research and development of climate-neutral clean energy technologies. Those new technologies will do more good for the climate than a disinvestment (i.e. a decrease in harm when instead of the university a more careless investor reaps the rewards of investments in fossil fuels). Hence, a university disinvestment can even be counterproductive (because the other investors in the fossil fuel industry will not use their high profit rates for important climate and technology research). This critique on disinvestment is related to the idea of mission hedging (for a short presentation of this idea, see this video).

Jumping into a specific campaign without taking enough time for critical reflection, is dangerous. According to preliminary research about cost-effectiveness and cost-benefit analyses of interventions (see here, here, and specific examples in human health, education, animal welfare, climate mitigation options, individual climate actions), we can expect that most campaigns, actions, interventions or policy proposals are ineffective, weakly effective or sometimes counterproductive, and a minority is highly effective. Chances are low that the first climate campaign that you stumble upon is highly effective. If – because of impatience – you immediately jump into that specific campaign and invest time and resources in that campaign, there is a high risk for a sunk cost fallacy or escalation of commitment. If evidence shows that the campaign is less effective, you might be reluctant to let it go and do something different that is more effective, because you will not be inclined to accept that all the time and resources that you invested in that campaign were for nothing.

The lack of critical reflection also translates into the vagueness of many recommendations by impatient climate activists. They propose to fight the system, to overturn the government, to dismantle capitalism, to stop economic growth, but they do not make it clear what those recommendations mean, how to exactly achieve them and what the alternatives are. They basically target the wrong enemies, such as economic markets, neoliberalism, capitalism or economic growth. In their critique of the economic system, they do not follow two strategies: they do not base their critique on a scientific consensus among economists, and they do not offer and discuss empirical economic evidence or theoretical economic modelling that help to make the choice how to change the system into something better. This can be contrasted with effective altruists who use critical thinking and scientific evidence to do the most good. Effective altruists are consistently looking for new reliable evidence to update their beliefs and change their minds if necessary. In this sense, the effective altruist culture is very different from the alarmist climate activist culture.

Alarmist climate activists claim to follow the scientific consensus, but there is no consensus on their alarmist claims (e.g. that we are all going to die because of climate change or that we have no time left) nor on their policy proposals. There is no consensus among climate economists that a revolution and a destruction of capitalism is required or more effective to avoid climate change. On the contrary, there is a strong consensus that liberal market-compatible mechanisms (e.g. a cap-and-trade system of emission permits or a carbon tax), as well as investments in technological research are effective and required. A reform towards a green liberalism or green capitalism is probably more feasible than a revolution towards a green, just and efficient non-capitalist system. For example there is no good evidence that socialist or anticapitalist systems are better than capitalist systems in terms of human well-being. The same goes for sustainability: sustainable choices require good incentives and knowledge, and well-functioning free markets, corrected for market failures, have a price mechanism that generates the correct incentives for consumers and producers and integrates information about preferences and costs. Hence, such markets efficiently allocate resources to make sustainable decisions. Climate change is probably the biggest market failure, and a carbon price can correct the market.

Instead of improving market mechanisms of carbon pricing (as done by e.g. Carbon Market Watch), the antimarket, antiliberal and anticapitalist climate activists criticize those effective policy proposals. This is another example of a counterproductive climate action. The same goes for their criticism of economic growth. Long term technological growth is driven by technological innovations and knowledge. With sufficient investments in climate technology research and development, a decoupling between economic growth and greenhouse gas emissions is possible. The economy can grow, even when harmful economic activities decrease (think about slavery, whaling, horse manure in cities, banned toxics, acid rain, ozone depleting chemicals,…). A long term economic growth in value, knowledge and technology does not require fossil fuel use or greenhouse gas emissions. Fossil fuels are depletable, whereas knowledge is non-rivalrous (my consumption of knowledge does not impede your consumption of the same knowledge). We need economic growth to increase valuable knowledge and technologies that make the economy more sustainable.

Plus, we also have to take into account the welfare of everyone in the far future. Economic growth can drastically improve the living standards of humans and animals in the far future. This is one of the reasons to be worried about climate change: with climate change, the economy can shrink with say 20% (damage from climate change could cost 20% of global GDP). This is a lot of wasted money and wealth, that could be invested in scientific research to improve lives. If we take the longtermist perspective, we see that the number of lives in the far future (if we avoid complete extinction) is much bigger than the number of people alive today, so a difference in wealth of 20% means a lot because it affects a lot of people.

Economic growth creates positive sum games or win-win situations, whereas zero or negative growth returns us to zero sum games (win-lose games) that increases inequality, harm and rent seeking (taking wealth instead of producing wealth). Economic growth becomes even more important if we take non-human animals into account. Technology driven economic growth improves the lives of humans, so we can avoid human suffering. But with technologies such as cellular agriculture (clean meat that replaces animal meat) and human-on-a-chip (that replaces animal testing), we can also drastically reduce human caused animal suffering (livestock farming, animal experimentation,…). But by far the biggest realm of suffering could still exist for a long time, even if we eliminated all human diseases, violence, and animal abuse: wild animal suffering, including insect suffering. The size of far future wild animal suffering is probably orders of magnitude larger than current human and livestock animal suffering. If we value well-being in an impartial way, also the well-being of insects in the far future matters. Even if the well-being of one insect at one day in the far future matters only a little, because there will be so many insects born on so many days in the future, their total well-being becomes very important. The problem is: if we are poor, we will not be inclined to invest in research how to intervene in nature to improve wild animal welfare in the far future. However, if we are very rich, we can afford to spend a little bit of money on wild animal suffering research (e.g. research in welfare biology). The richer we are, the more likely we spend some money. As far future wild animal suffering is the most neglected area of suffering, any additional resources invested in improving wild animal welfare can do comparatively a lot more good than resources going to smaller and less neglected areas of suffering. Hence, economic growth increases the likelihood that we will do research and invest in technologies that tackle the largest and most neglected area of suffering. Once we invent those technologies, they can help huge numbers of wild animals for millions of years in the future. A difference between maximum sustainable economic growth and a 20% lower growth due to climate change or a zero or negative growth as proposed by some climate activists, could mean the difference of huge amounts of suffering of wild animals in the future. Hence, we should not underestimate the importance of economic growth (see also the arguments by Tyler Cowen why we should prioritize maximizing sustainable economic growth).

Not only do a lot of climate activists target the wrong enemies (capitalism, economic growth), but they propose risky alternatives. Doom thinking can lead to an overreaction with more harmful measures. People take too drastic means in a final attempt to save the world. An example is the call for mass civil disobedience and revolution by Extinction Rebellion, claiming that mass civil disobedience is the only option left to avoid a catastrophe. However, those activists do not take into account all the costs of such revolutionary direct actions. They do not think like an economist, considering both direct and indirect (opportunity) costs.

Mass civil disobedience has many risks and opportunity costs that are not fully taken into account by the activists. Some examples of costs:

  • time: the long occupations and jail time of activists preclude other use of time (such as time for scientific research, lobby work,…),
  • direct costs for the activists: fines and court costs from the lawsuits that cannot be spend on other effective climate measures, such as afforestation,
  • direct costs for governments for law enforcement, resulting in a misallocation of government resources (intelligence services,…) towards law breaking climate activists instead of more important issues (e.g. terrorism),
  • indirect costs for the broad population: loss of prosperity due to obstruction of economic activities.

Some examples of risks:

  • risk of loss of goodwill or sympathy among the wider population (the action style can induce antipathy),
  • risk of losing respect for our democratic legal system,
  • risk of uncontrollable evolutions due to weakened governments (as history demonstrates, getting governments on their knees through a revolution can create a power vacuum that is unmanageable and difficult to channel in safe ways),
  • risk of losing time and postponing more effective actions.

The latter concern is striking: Extinction Rebellion claims that we do not have time for other alternatives and mass civil disobedience is the only option left. However, the question is whether we have enough time to follow the four steps of Extinction Rebellion: first look for and gather enough activists for mass civil disobedience, second get the government on its knees, third install citizen assemblies that discuss and decide climate policies and finally implement those climate policies. It might take too much time to convince enough people to become climate activists that break the law. Extinction Rebellion is so impatient, that they claim we need climate neutrality by 2025 (that’s not what the IPCC and climatologists say), but they won’t reach that target if we first have to wait for those civil disobedience actions and citizen assemblies. Extinction Rebellion is not able to give reasonable arguments or empirical evidence that the strategy of mass civil disobedience will be quicker to solve climate change than other climate policy proposals. On the contrary, the proposals of Extinction Rebellion are more revolutionary and their demands are stricter than for example a carbon tax, so it seems less likely that those demands will find more political support in the shorter term. This can be contrasted with an effective altruist who not only takes into account the size of a problem or the impact of a solution, but also the chance of success (the political and economic feasibility) of that solution.

Alternatives: effective climate activism

The climate measure with probably the biggest impact, is support for organizations that lobby for more government investments in research and development of climate technologies (such as clean energy and carbon capture and storage). One of the best analyses that argue for public clean energy R&D as the most effective climate policy, was done by Let’s Fund (also covered in a Vox article). Another interesting analysis of effective climate actions in the effective altruism community was done by Founders Pledge. These analyses are much more thought-out than many of the analyses by alarmist climate activists.

Clean energies facilitate the next most important climate measure, widely supported by economists: carbon pricing. Market mechanisms such as a carbon tax or cap-and-trade become more politically feasible if we have cheap clean energy sources. Organizations such as Citizens Climate Lobby lobby for a carbon dividend (a carbon tax with revenues paid out as citizen dividends). These are more concrete, safe and effective policy measures than the vague proposals of many alarmist climate activists, such as revolution or dismantling capitalism.

Extinction Rebellion also promotes citizen assemblies to improve democratic decision making. But I think there are more promising (simple, clear, concrete) proposals based on economic analyses, such as approval voting, quadratic voting or a futarchy with prediction markets. When it comes to estimating the impacts of climate change, prediction markets and superforecasters are more reliable and accurate than alarmist climate activists, so we can invest more in such prediction markets and superforecasting.

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Some solutions to utilitarian problems

I have recently written a series of articles about problems in utilitarian ethics that are relevant for effective altruism.

A first article describes why I became a utilitarian and what kind of utilitarianism I support (i.e. preference, rule, variable critical level, variance normalized,…).

The second article deals with the problems of population ethics and argues for a variable critical level utilitarianism, a kind of critical level utilitarianism where everyone is free to choose his or her own critical level in each different situation. Total, average, critical level, negative and person affecting utilitarianisms are all different special cases of variable critical level utilitarianism. With variable critical level utilitarianism, we can avoid counter-intuitive problems in population ethics. This issue becomes crucial when we have to choose between avoiding actual suffering (e.g. of factory farmed animals today) versus increasing well-being in the long-term future (e.g. avoiding existential risks).

The next two articles deal with the problem of interpersonal comparison of well-being. The first discusses a general method of utility normalization, based on an analogy between measuring utilities and measuring temperatures. This applies to utility functions that have continuous inputs (perceptions or experiences). When inputs are discrete another method is possible that counts the amount of just-noticeable differences in utility. The utility function now looks like a multidimensional staircase where the steps can have different widths. With this method we can compare the utilities of for example insects with humans.

Finally, I deal with the more exotic problem of counting persons and conscious experiences. This problem becomes important when we deal with future conscious artificial intelligence and whole brain emulations, but it is also relevant when we discuss insect sentience or split-brain patients.

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About insect welfare and how to improve it

Recently, Rethink Priorities (a project of Rethink Charity) did an impressive review of our state of knowledge about invertebrate sentience. Considering the fact that 99,9998% of all animals are invertebrates[1], it is crucial to know whether they are sentient and can have positive and negative experiences.

Sentience implies two properties: having a consciousness (subjective experiences) and having a utility function (an internal goal function or reward system). A plant, a thermostat, an artificial intelligent machine or a robot are examples of things with a utility function but without consciousness. On the other hand, some experiences such as seeing a white wall or touching a table are conscious but neutral (no utility function): there is no reward or objective in seeing, touching, looking for or avoiding these things. Combining a consciousness with a utility function, we get valenced (positive or negative) mental states such as pain and pleasure.

Most of the invertebrates are very small roundworms and ringed worms, and it is not clear whether they are sentient. But as the Rethink Priority review shows, there is increasing evidence that arthropods are sentient and have valenced experiences. Arthropods are animals with an exoskeleton and a segmented body, such as insects, spiders and crustaceans. After reading their review, I have updated my personal guess about the probability of insects (in particular bees, fruit flies and ants) having valenced experiences to more than 50%.

Even if the probability of arthropods being sentient is considered much lower (e.g. less than 10%), the precautionary principle should be applied, because there are huge numbers of arthropods. At this moment, there are about 1018 terrestrial arthropods (mostly ants) and 1020 marine arthropods (mostly very small copepods as zooplankton). This can be compared with about 1010 humans. So if arthropods happen to be sentient and we erroneously believe they are not, we are neglecting huge amounts of welfare and suffering. When a lot is at stake, the precautionary principle is reasonable.

In this article I first present experimental results that are in my opinion the most convincing and amazing facts to demonstrate that insects (in particular bees, ants and flies) are sentient, i.e. they have a consciousness and a utility function. Next, I discuss the issue how to compare insect welfare with human welfare. Finally, I explore how we can avoid wild insect suffering (by bee protection) and cultured insect suffering (by avoiding the use of insects for food).

For more on insect suffering, see the work by Brian Tomasik here, here, here and here, and Wild Animal Initiative here and here.

Indications for a consciousness in insects

Although it is impossible to determine for sure whether an animal is conscious, clever experiments can indicate the presence of consciousness. We can use experiments to test for skills or behavioral responses that are never performed by humans who are believed to be in unconscious states, or that sometimes occur unconsciously in humans. For example there are unconscious learning processes[2], so learning does not necessarily require consciousness. Nociception is the ability to detect harmful stimuli, such as burning your finger. But pulling away your hand after touching a hot stove is an unconscious reflex. After a brief moment, you start to feel a burning pain in your finger. Pain is different from nociception, because pain involves a conscious state whereas nociception is an unconscious perception.

Integrated behavioral control system in the brain

Like the midbrain in vertebrates, insects also have a brain structure that integrates perceptual inputs (e.g. vision and touch) to create a neural simulation of the body position of an insect in space that allows for behavioral control. In other words: an insect can be conscious of its environment and its own body in that environment. This is demonstrated by looking at similarities between the functioning of brain structures between insects and vertebrates[3], and can be explained from an evolutionary perspective: those brain structures are an efficient solution to the basic problems of navigation.

Complex learning

Insects such as bees have complex learning skills that cannot be attributed to mere automatic reflexes or pre-programmed responses. They learn to use objects that they have never encountered before. For example bumblebees can learn to use small balls, rolling them to a target place to obtain a food reward. Bumblebees can learn this behavior from each other, and they can decide to use a ball with another color if that ball is closer to the target.[4]

Selective attention

A property of consciousness is selective attention: the ability to focus on one of several competing stimuli. This enables to ignore some stimuli and respond to the stimulus that is most relevant. Neural correlates of visual attention have been found in honeybees. When a bee has already seen something several times and it was not relevant for behavior, attention for that visual stimulus as well as activity in certain brain regions is reduced.[5]

Meta-cognition through uncertainty monitoring

Another indicator for consciousness is meta-cognition: being aware of one’s own mental states. One interesting mental state is the feeling of knowing something. There are experimental indications that insects such as bees and ants have an awareness of uncertainty. For example honeybees can learn to selectively avoid difficult choices that involve uncertainty.[6] They can learn to solve trials, a correct solution gives a reward (sugar), an incorrect solution gives a punishment (a bitter tasting chemical), but they can also decide to opt-out from the trial and receive neither reward nor punishment. When the difficulty of the trial increased, honeybees opted out more often because the risk of punishment increased. Using this option to opt out, honeybees improved their success-to-failure ratio. Hence, bees can perform rational behavior under uncertainty. In order to do that, bees need to monitor and evaluate their uncertainty. Also ants have the capacity for uncertainty monitoring.[7]

Indications for a utility function in insects

Pain can be distinguished from nociception because the former involves a consciousness. But merely having a consciousness is not enough for sentience, because conscious experiences and sensations can be neutral. For example, people with pain asymbolia or pain dissociation are able to consciously feel pain, but they do not have any negative evaluation or feeling of unpleasantness of that pain. For them, feeling a burned finger is like feeling a wet finger: the wetness of lukewarm water is neither positive nor negative. For positive and negative evaluations, a utility function is required. Some experiments indicate that insects have a utility function.

Making trade-offs

Animals can have a preference to avoid the unpleasantness of pain, but they can also prefer food (absence of hunger), safety (absence of predators) and so on. Sometimes they have to make trade-offs between different preferences. If they are able to make such trade-offs in a consistent way, they not only have a utility function, but their utility function is complex and integrated, involving several dimensions (preferences). Hence, being able to make trade-offs is an indicator for the presence of a utility function.

Fruit flies can make trade-offs. For example they choose to endure electric shocks in order to obtain access to alcohol. This is trading off a punishment for a drug.[8] They also trade-off safety for food: when fruit flies see an overhead shadow that resembles a predator, they disperse and hide to avoid potential predators. But if they are very hungry, they will continue eating from a food source.[9] Hence, fruit flies are able to weigh and evaluate benefits (food) and risks (predators), and that requires a unified utility function that measures both the preference for food and the preference for safety.

Communicating trade-offs

Even stronger evidence, is when insects are able to communicate their trade-offs to other animals. This is seen in the waggle dance of honeybees. When a bee has found a new food source, it faces a trade-off between abundance (how many flowers are there?), distance (how easy is it to get there?) and predation risk (how dangerous is it there?). This information is contained in the waggle dance to inform other bees.[10]

Self-administering drugs

Another behavior that is unlikely to be an unconscious process, is the self-administration of drugs. Insects like bees can get alcohol addictions, and they are able to actively look for alcohol.[11]

Sense of the future

Honeybees can not only make trade-offs between food and safety, but also between immediate versus deferred rewards. For example bees choose a larger droplet of sugar water received over five seconds above a smaller droplet received immediately.[12] These time trade-offs require self-control and a sense of the future.

Mood states

There is evidence that bees and fruit flies have mood states. The most impressive evidence is probably the pessimism bias of agitated honeybees. When humans are anxious, depressed or stressed, they often become more pessimistic in uncertain situations, which means they have increased expectations of negative outcomes. The same can be seen in honeybees who are agitated by shaking them, simulating a predator attack on a bee hive. These bees are learned to associate a reward (water with sweet sugar) with one type of odor and a punishment (water with bitter quinine) with another odor. When bees face a new, third type of odor, they can either expect a reward or a punishment, so they can choose to approach or withdraw from the water. This is a situation of uncertainty. The bees that were shaken became more pessimistic: they more often withdrew from the water, expecting a punishment. And these shaken bees had lower levels of happiness hormones such as dopamine and serotonine.[13] This cognitive bias is a measure of negative emotional states. In another experiment, honeybees became more aggressive when they were in social isolation, as if being in isolation increased their levels of frustration.[14]

Also fruit flies seem to experience mood states such as depression. Due to enduring, uncontrollable stress, they experience anhedonia, a loss of appetite.[15] Antidepressants that work in humans also work in these fruit flies.[16]

Next to anhedonia, learned helplessness is also associated with depression in humans and can be a marker for mood states in insects. Researchers compared fruit flies that were exposed to shaking in an inescapable maze versus flies that were shaken in an escapable maze. Both groups of flies were shaken equally hard. But when these flies entered another, escapable box several hours later, the flies who were shaken in the inescapable maze took much more time to escape from the box.[17] They were less willing to search for an escape route, as if they accepted their bad situation. They learned to be helpless, as if they had pessimistic judgments about their efforts to escape. Also honeybees show evidence of learned helplessness.[18]

Next to depression, fruit flies also appear to have anxiety states, with the physiological and behavioral responses that are similar to anxiety-like states in mammals such as rats.[19] Anxiolytics that work in humans also work in these fruit flies.

Relief learning

Fruit flies can display relief learning. When the flies first experienced an electric shock and then smelled an odor after the shock, the flies learned that the odor predicted relief from the painful stimulus, so they started to like that odor (i.e. they approached it).[20]

Chronic pain

According to a recent study, fruit flies can experience chronic pain. When an injury damages a nerve in one leg of a fruit fly, the fly’s other legs had become more sensitive, even after the wound in the first leg healed.


Comparison of welfare

With the above evidence, we can estimate the likelihood that insects are sentient. So we can say that our confidence level for bees being sentient is say 60%. But even if we take the precautionary principle and assume they are sentient, we have to compare the welfare of a bee with the welfare of other animals such as humans. Interpersonal comparison of well-being is very difficult (I have made some attempts elsewhere).

Welfare has several dimensions, but when it comes to painful and pleasurable experiences, intensity and duration are two important dimensions (other dimensions are the certainty and the order of experiences, for example choosing between first pleasure and then pain or vice versa; see the felicific calculus). How can intensity and duration of for example pain be compared?

Concerning the intensity of a feeling, there are two extremal options. On the one side, we can equate minimum levels of perception (or minimum differences in utility) between individuals. There are indications that sense perception is discrete. For example a change of a subjective experience always requires a minimum (not infinitesimally small) amount of a change of an external stimulus. This is the just-noticeable difference or JND. Therefore, a painful sensation can be decomposed as a sum of just-noticeable differences in pain. The number of JNDs required to go from zero pain to the actual feeling of pain, can be considered as a measure of the level or intensity of the pain. More generally, the utility function can be represented as a multidimensional staircase with discrete steps in several directions. Moving from one situation (e.g. a reference point without pain and hunger) to another situation (e.g. with a certain level of pain and hunger), requires a number of steps of the utility function, and this number measures the overall preference of the new situation compared to the old situation.

Assume, as an example, that a bee is a sentient being and experiences burning pain from hot water. How bad are the burns for the bee? We can count the just-noticeable differences of pain from the burns. Going from zero burns to a just-noticeable burn decreases the utility of the bee with one unit. An extra burning sensation decreases the utility with another unit, and so on. Suppose the bee experiences 100 negative utility units, which means 100 just-noticeable differences of pain are exceeded. Now I want to compare this with my painful experience of hot water. Perhaps, hypothetically, when I put a fingertip (the size of a bee) in hot water, I also experience 100 negative utility units, as much as when the whole bee is submerged in hot water. So what the bee experiences is what I would experience when I burn my fingertip, and is much less painful than what I experience if I’m completely submerged in burning hot water. The underlying reason is the smaller size of the brain of a bee: a bee has fewer pain neurons and a smaller brain processing capacity to produce a feeling of pain. That means a bee would be less sensitive: relatively more external stimulus is required in order to generate a JND. If one JND of a bee is comparable to one JND for me, and if I know the number of JNDs of a bee, I can imagine how painful an experience is for a bee.

However, the second extremal option equates the maximum levels of experience between individuals. The brains of a sentient being are finite in size, so it is unlikely that they can generate infinite levels of pain and pleasure. If there exist a maximum level of pain, we can for example consider a bee who experiences maximum pain from burning in hot water. This can be equated to my maximum pain level when I feel burning hot water all over my body. If we take this comparison, a bee would be highly sensitive, like a human, and there will be huge amounts of insect suffering in the world.

To make it more quantitative: suppose there are 1018 insects and 1010 humans. Suppose an average insect has a brain with 105 to 106 neurons (bees have relatively large brains for insects, with roughly 1 million (106) neurons). A human has roughly 1011 neurons. Suppose that the number of JNDs of an experience with a given perceptual input is proportional to brain size, measured as the number of neurons (other options for brain size are the number of neuronal connections or synapses). Hence, a human is more than 105 times as sensitive as an insect. Suppose all humans and insects experience a similar situation, such as dying (from disease, injuries, coldness, starvation or predation). According to the first approach, equating the minimum levels of utility, this experience generates more than 105 times as much discomfort in a human than in an insect. So we have to discount the experiences of insects with a factor 105 or 106. As there are 108 more insects than humans, and their suffering counts 105 to 106 less, total insect suffering from dying is 100 to 1000 times higher than total human suffering from dying. However, if we take the second approach, equating the maximum levels of utility, insect experiences are not discounted and total insect suffering is 108 times higher than human suffering from dying. Furthermore, the rate of dying of insects is much higher, because their lifespans are much shorter (ranging from a few days to a few years). If an average insect lives for a few weeks, it’s mortality rate is 1000 times higher than the human mortality rate, which means insect suffering from dying can be 106 or 1011 times higher than human suffering.

Matters could be even worse for insect suffering when we account for the subjective duration of an experience. Insects such as flies have faster brain processing speeds. Consider vision: humans can see at most 60 flashes of light per second. Showing flashes at a higher frequency results in seeing a continuous light. The flicker fusion rate measures how fast a light has to be switched on and off before one sees it as a continuous light. A fly has a flicker fusion rate four times higher than a human, which means a fly can see 250 images or flashes per second. This explains why it is so difficult to swat a fly: a fly sees everything in slow motion, four times slower than we do.

Perhaps not only vision, but also conscious experiences have a maximum frequency. What is the smallest time interval that we can experience? Suppose an experience of pain is turned on and off. Suppose at this moment you do not feel pain, a second later you feel pain, another second later the pain is gone. That means every second you can have a different conscious experience. But what if we increase the frequency? At this moment you do not feel pain, a millisecond later there is a pinprick. Another millisecond later the needle is removed, and so on. Now you might feel a slight, continuous pain instead of different pain pulses, which means you cannot consciously distinguish milliseconds.

Suppose the flicker fusion rate of your consciousness is 60 experiences per second, as with vision. This is as if you have an internal clock that has a moving hand rotating full circle in 60 steps per second. Every position of the moving hand corresponds with a different conscious state. You can have at most 60 different conscious experiences per second. But some insects may have faster internal clocks. In one real second, they can have 250 different conscious experiences. If you experience pain for one second, you actually have 60 conscious states of pain. But if insects can feel pain and if they feel pain for one second at a higher brain speed, that corresponds with 250 conscious states of pain. It is as if you would experience 4 seconds of pain.

Perhaps the tiny brains of insects indicate that the intensity of their pain experience is lower than the intensity of pain experienced by animals with larger brains. But if their brains are faster, they experience pain in slow motion, meaning that a second of pain appears to last longer for insects. That means one second of pain for a human should be discounted compared to one second for an insect: one second of insect pain counts a few times more than one second of human pain.

One further complication is our sensitivity for time intervals. We have a decreasing marginal sensitivity for time: the longer the time interval, the less important an extra second becomes. The difference in the preference for 0 seconds of extreme pain above 1 seconds might be bigger than the difference in the preferences for 1000 versus 1001 seconds of extreme pain. In the latter case, the one extra second is less important (you probably won’t notice the extra second). The rate of decreasing marginal sensitivity for time intervals might also depend on brain complexity and size. The bigger the brain, the more memory capacity it has and the more previous seconds are remembered and taken into account. This means that insects with smaller brains could have an almost constant marginal sensitivity for time: no matter how much time they experienced pain, an extra second of pain remains equally bad for them. On the other hand, it is possible that insects are not capable to perceive and judge long time intervals, which means they don’t have a preference between 100 seconds of pain and 1000 seconds of pain.

Avoiding insect suffering: prioritization

Humans are harming insects, by accidentally killing them (when running around, driving cars,…), intentionally killing them (using insects for food and clothing, using insecticides, insect traps, …) or indirectly killing them (by competing for resources, natural habitat destruction, pollution…). So one could argue we need less cars, less agriculture, less pollution, less concrete, less insecticides, less walking on the grass,… But things are not so simple when it comes to wild animal suffering.

First, a lot of insects are parasites or predators that kill other insects. So if we (accidentally, intentionally or indirectly) kill some insects, especially predators, we might save the lives of many other insects. Or stated differently: saving one ladybird might mean killing hundreds of aphids. Second, insects in the wild can have net-negative lives, i.e. short lives with more negative than positive experiences. These are lives not worth living. This is due to their reproductive strategy: a fertile adult insect can lay thousands of eggs. If the insect population does not explode at an extreme exponential rate, it is logically required that almost all of the newborn insects will have to die prematurely. The ways of dying are often extremely negative experiences: coldness, starvation, predation, parasitism,…. If an insect is killed, it prevents the birth of many insects with net-negative lives. So, if most insects face very short lives anyway and die horrible deaths anyway, it is far from clear whether killing insects increases overall future insect suffering. We need much more scientific research to estimate the overall effect of killing insects on global welfare.

Prioritization research involves looking for the most effective methods and interventions to improve insect welfare and reduce insect suffering. For example if we come to the conclusion that we should minimize killing insects, we can look for effective means to reduce the killing of insects. One interesting opportunity is the use of non-lethal methods for insect pest control. There are also many methods to avoid and remove insects in your house, for example catching flies with a transparent glass and releasing them outside.

If killing insects is inevitable, we should look for humane killing methods. Switching to more humane insecticides in agriculture could be a very effective way to minimize suffering. Using natural predators to combat insect pests on the other hand might be as bad as using inhumane insecticides.

In our prioritization research we should not only consider harm to insects caused by humans. Harm caused by nature (e.g. by other animals) counts equally. We can investigate safe and effective methods to intervene in wild nature to improve insect welfare. For example new technologies such as gene editing and gene drives could help reduce insect suffering by controlling insect populations in order to limit predation, parasitism, starvation and other causes of suffering.

Avoiding wild insect suffering: bee protection

Our prioritization research should not only consider different methods or interventions, but also consider which populations or species to target. Probably the clearest case can be made for bee protection. One three levels, bees are special.

First, as we have seen above, of all the studied insects, bees (the clade of antophyla) show probably the most scientific evidence for having consciousness and sentience. If they are not conscious, then other insects are probably not conscious either.

Second, unlike most other insects, bees have no negative externalities. A lot of insects are predators or parasites, which means they harm other animals. Herbivorous insects do not kill others, but they can be a pest in agriculture. Beas are the best: they do not harm other animals (except in self-defense) and they do not destroy food (e.g. crops) for other animals.

Third, bees have huge positive externalities: they improve crop yields by pollination. So bees even help to provide food for other animals (humans, birds) who like to eat fruits.

The bad news is: bees face difficult times. The colony collapse disorder kills many bees. Some neonicotinoid insecticides can make the bees more vulnerable to diseases and parasites, so an effective intervention is to replace those insecticides. However, those insecticides are not the only culprit of the colony collapse disorder. Pests such as pathogens and parasites are the biggest threat. Effective solutions for these threats are not yet known, so research is important.

Other ways to help bees, especially in western Europe, is to combat the Asian hornet. This is an invasive exotic species, and European bees do not recognize this insect as a predator. As a consequence, Asian hornets kill many bees (as well as many other insects). Eradicating a nest of Asian hornets could save many bees and other insects. This measure also finds support amongst environmentalists who are against invasive exotic species.

We also have to be careful not to take ineffective or counterproductive measures to protect bees and other insects. One example of an ineffective measure is the ban on GMOs in Europe. Bt-crops are GMOs that produce an insecticide (Bt) that is normally found in soil bacteria. According to a meta-analysis, Bt-crops are not harmful for honeybees.[21] Another meta-analysis shows that fields with Bt-crops have higher biodiversity levels of nontarget invertebrates (beetles, butterflies, spiders,…) compared to non-GMO fields where Bt-insecticide is sprayed (including organic fields, because Bt-insecticides are allowed in organic farming).[22] Spraying of Bt-insecticides not only kills the pest insects but also many other nontarget insects. Bt-crops can reduce the spraying of Bt-insecticides, and hence reduce the overall killing of insects. Also organic farming could be a counterproductive measure for bee protection: organic farming allows the use of insecticides that are harmful for bees, and the lower crop yields in organic farming means that more agricultural land is required. Hence, land that could serve as flower meadow for bees is sacrificed. We have to be careful however: as mentioned above, reducing insect killing or increasing natural habitat might increase insect suffering. So we first need more scientific research to estimate the overall effects of interventions.

The same goes for the most obvious animal rights issue related to bees: the consumption of honey. The production of honey is in many ways harmful to bees: they are often killed accidentally and in many cases also intentionally by the bee keepers (culling less productive hives, making the bees vulnerable to diseases by taking away their nutritious honey, clipping the queen bees’ wings,…). However, boycotting honey most likely means a replacement by other sweeteners, and most of those sweeteners come from agruculture that harms wild insects. Sugar involves the accidental and intentional killing of insects (using insecticides, machines,…). Perhaps very sweet stevia or some artificial sweeteners such as aspartame are better for the insects because they involve less agriculture. But a cookie or breakfast cereals with a strong sweetener contains less volume of sugar and hence more weight in grains and fats, which means more agriculture. On the other hand, more agriculture means less natural habitat and hence less suffering of insects in wild nature. The situation is very complex; we really don’t know the overall effects of honey. The situation is comparable to fishing: there is direct harm to used animal, the captured fish, but all the indirect effects on aggregate welfare of aquatic animals are unknown. As a rule of thumb one could use a provisional deontological principle and abstain from the consumption of fish and honey, avoiding direct harm associated with the use of animals. The idea is: if the presence of the body (of the bee or fish) is required to obtain your objective (consuming honey or fish), and if that animal is harmed (i.e. treated against his/her will), that harm counts more than all the unknown indirect effects. In the meantime, much more research about those indirect effects is required. We should not underestimate the value of information about indirect consequences of our choices. If the picture about indirect effects become clear, we might come to the conclusion that fishing or honey production are overall not negative for aggregate welfare. The known indirect effects gain more weight in the overall evaluation.


Avoiding cultured insect suffering: insects for food

Another important measure, is the decrease of insect farming. Insects are used for food and clothing (silk). An example is cochineal, a scale insect that produces a red dye carmine that is used as a colorant in food. It takes almost 100.000 insects to produce one kilogram of dye. Replacing cochineal dye with synthetic dyes is very feasible, because synthetic dyes are about four times less expensive.

Another worrying trend is the rise of insect meat consumption. Worms and crickets are used for insect burgers and sausages. The problem is that a lot of insects are required for insect meat. If one insect sausage requires more than 100 insects, whereas one beef sausage requires less than one thousandth of a cow, the number of animals used and killed for insect meat could be almost a million times higher than beef meat. Even discounting for brain size or pain sensitivity, insect meat can involve a lot of suffering.

Insect meat is often promoted as a more sustainable option than livestock meat. Chicken meat has the lowest environmental footprint of all livestock meats (lower than pork and beef), and the footprint of insect meat is about half that of chicken meat. However, the environmental impact (in terms of agricultural land use and greenhouse gas emissions) of insect meat is still 10%-50% higher than plant-based protein sources.[23] Eating insects is usually less efficient than eating plants. In other words, insect meat not only requires the intentional direct killing of insects for meat, but also more indirect and accidental killing of insects in agriculture to produce insect feed, compared to vegan alternatives. As insect meat is on the rise in Western countries but still far from being established, it is still possible to halt insect meat. Hence, campaigning against insect meat is very feasible.

[1] Bar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences, 115(25), 6506-6511.

[2] Kuldas, S., Ismail, H. N., Hashim, S., & Bakar, Z. A. (2013). Unconscious learning processes: Mental integration of verbal and pictorial instructional materials. SpringerPlus, 2(1), 105.

[3] Barron, A. B., & Klein, C. (2016). What insects can tell us about the origins of consciousness. Proceedings of the National Academy of Sciences, 113(18), 4900-4908.

[4] Loukola, O. J., Perry, C. J., Coscos, L., & Chittka, L. (2017). Bumblebees show cognitive flexibility by improving on an observed complex behavior. Science, 355(6327), 833-836.

[5] Paulk, A. C., Stacey, J. A., Pearson, T. W., Taylor, G. J., Moore, R. J., Srinivasan, M. V., & Van Swinderen, B. (2014). Selective attention in the honeybee optic lobes precedes behavioral choices. Proceedings of the National Academy of Sciences, 111(13), 5006-5011.

[6] Perry, C. J., & Barron, A. B. (2013). Honey bees selectively avoid difficult choices. Proceedings of the National Academy of Sciences, 110(47), 19155-19159.

[7] Czaczkes, T. J., & Heinze, J. (2015). Ants adjust their pheromone deposition to a changing environment and their probability of making errors. Proceedings of the Royal Society B: Biological Sciences, 282(1810), 20150679.

[8] Kaun, K. R., Devineni, A. V., & Heberlein, U. (2012). Drosophila melanogaster as a model to study drug addiction. Human genetics, 131(6), 959-975.

[9] Gibson, W. T., Gonzalez, C. R., Fernandez, C., Ramasamy, L., Tabachnik, T., Du, R. R., … & Anderson, D. J. (2015). Behavioral responses to a repetitive visual threat stimulus express a persistent state of defensive arousal in Drosophila. Current Biology, 25(11), 1401-1415.

[10] Abbott, K. R., & Dukas, R. (2009). Honeybees consider flower danger in their waggle dance. Animal Behaviour, 78(3), 633-635.

[11] Maze, I. S., Wright, G. A., & Mustard, J. A. (2006). Acute ethanol ingestion produces dose-dependent effects on motor behavior in the honey bee (Apis mellifera). Journal of insect physiology, 52(11-12), 1243-1253.

[12] Cheng, K., Peña, J., Porter, M. A., & Irwin, J. D. (2002). Self-control in honeybees. Psychonomic bulletin & review, 9(2), 259-263.

[13] Bateson, M., Desire, S., Gartside, S. E., & Wright, G. A. (2011). Agitated honeybees exhibit pessimistic cognitive biases. Current Biology, 21, 1070e1073.

[14] Breed, M. D. (1983). Correlations between aggressiveness and corpora allata volume, social isolation, age and dietary protein in worker honeybees. Insectes Sociaux, 30(4), 482-495.

[15] Abelaira, H. M., Reus, G. Z., & Quevedo, J. (2013). Animal models as tools to study the pathophysiology of depression. Brazilian Journal of Psychiatry, 35, S112-S120.

[16] Ries, A. S., Hermanns, T., Poeck, B., & Strauss, R. (2017). Serotonin modulates a depression-like state in Drosophila responsive to lithium treatment. Nature communications, 8, 15738.

[17] Brown, G. E., Mitchell, A. L., Peercy, A. M., & Robertson, C. L. (1996). Learned helplessness in Drosophila melanogaster?. Psychological reports, 78(3), 962-962.

[18] Dinges, C. W., Varnon, C. A., Cota, L. D., Slykerman, S., & Abramson, C. I. (2017). Studies of learned helplessness in honey bees (Apis mellifera ligustica). Journal of Experimental Psychology: Animal Learning and Cognition, 43(2), 147.

[19] Mohammad, F., Aryal, S., Ho, J., Stewart, J. C., Norman, N. A., Tan, T. L., … & Claridge-Chang, A. (2016). Ancient anxiety pathways influence Drosophila defense behaviors. Current Biology, 26(7), 981-986.

[20] Yarali, A., Niewalda, T., Chen, Y. C., Tanimoto, H., Duerrnagel, S., & Gerber, B. (2008). ‘Pain relief’learning in fruit flies. Animal Behaviour, 76(4), 1173-1185.

[21] Duan, J. J., Marvier, M., Huesing, J., Dively, G., & Huang, Z. Y. (2008). A meta-analysis of effects of Bt crops on honey bees (Hymenoptera: Apidae). PloS one, 3(1), e1415.

[22] Marvier, M., McCreedy, C., Regetz, J., & Kareiva, P. (2007). A meta-analysis of effects of Bt cotton and maize on nontarget invertebrates. Science, 316(5830), 1475-1477.

[23] Smetana, S., Mathys, A., Knoch, A., & Heinz, V. (2015). Meat alternatives: life cycle assessment of most known meat substitutes. The International Journal of Life Cycle Assessment, 20(9), 1254-1267.

Van Diepen J. e.a. (2018). Eiwit-transitie Vlaanderen. Studie naar de status en het potentieel van (hoog-) technologische oplossingen om vleeseiwitten te vervangen in het dagelijks dieet. Blonk Consultants. Gouda, Nederland.

Broekema R. & van Paassen M. (2017). Milieueffecten van vlees en vleesvervangers. Blonk Consultants. Gouda, Nederland.

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On the interpersonal comparison of well-being, part 2

In a previous article I discussed the possibility of comparing the levels of well-being between different individuals. This is a crucial problem if we want to compare levels of suffering and happiness between different animals such as humans, insects, fish or birds. I demonstrated a general method to put everyone’s utilities (evaluations of well-being) on the same scale. But I ended that article with an open question about the existence of an objective utility measure. Here I discuss a hypothetical possibility for the such an objective measure of well-being that can always be compared with other individuals.

The discreteness of subjective experiences

The crucial idea for an objective measure of well-being, is the finiteness and discreteness of our subjective experiences. From a neurobiological point of view, we can expect that our experiences are fundamentally discrete. Experiences such as pain are generated by a brain, and a brain consists of a discrete, countable number of neurons (about 100 billion in a human brain), which consist of a discrete number of atoms following the laws of quantummechanics where fundamental properties are quantized. Furthermore, each neuron has a discrete number of connections with other neurons (about 1000), and a neuron has a threshold potential, a critical level of polarization of the neuron membrane that initiates an action potential (a firing of the neuron). This means that a neuron fires a discrete number of times per second (about 200 times per second). As a result, a brain can process a discrete, finite amount of information bits per second (about 20 million billion bits per second for the human brain).

The discreteness of brain information processing is confirmed in experimental psychological, in particular psychophysics: the quantitative investigation of the relation between objective external stimuli and subjective internal experiences or conscious perceptions and sensations. Our perceptions appear to be discrete, with a so called just-noticeable difference or JND: the minimum amount an objective stimulus must be changed before the corresponding subjective experience changes. For example, I can increase room temperature from 20°C to 20,1°C and I don’t notice the difference, but when I enter a room at 21°C, I notice the temperature increase.

Our brains are also finite, which means they cannot generate infinite experiences. Hence, there must also be a maximum noticeable difference or MND. For example, above a certain temperature, say 1000°C, I’m no longer able to feel any differences. A thousand or a billion degrees feels the same.

In summary: there is plenty of evidence that our subjective experiences are finite and discrete in nature. This discrete nature of our experiences is crucial for an objective, interpersonal comparison of experiences. Without this discreteness, we have to rely on the general method described in the previous article to interpersonally compare well-being.

The staircase model of experiences

If a stimulus (for example room temperature) is increased, our perception (for example the sensation of heat) increases with many small discrete steps, just like a staircase.The staircase has two important properties. First, and most importantly in this context, each step has a minimum height. No step is infinitesimally high. In other words, the staircase does not look like an inclined plane or slide. Second, the staircase has a minimum and a maximum level. The minimum level is the ground floor, the value 0. And when you reach the maximum level, there is no further step going up.

When there are several stimuli, the staircase can have multiple directions or dimensions. Each stimulus corresponds with a direction. The stimuli include pinpricks, room temperatures, taste sensations, and all other possible perceptions, including the flow of time. So we have a just-noticeable difference for a time interval. We may experience a difference between seconds, but not between milliseconds. There is a maximum frequency, and changes of stimuli at a rate above this frequency are not noticed. Due to the finite neuronal firing rate, we do not experience changes faster than a fraction of a second. If your brain is faster in information processing, you can experience more in one second. For example the visual perception part of a fly’s brain is four times faster than ours, so when a fly looks at something for one second, it is comparable to us looking at it for four seconds. It is as if the fly sees everything in slow motion.

The Weber-Fechner law says that the steps of the staircase can have different widths. In particular: the width of the steps increase when you are higher on the staircase. For example, I can notice a difference between 20°C and 21°C, but when I’m in a sauna, I do not notice the difference between 90°C and 91°C, even though the absolute temperature difference is the same. However, I can notice a difference between say 90°C and 100°C. So the step at 90°C is much wider than the step at 20°C. Around 20°C, the staircase of my heat perception is much steeper.

You can say that evolutionary pressures determined the steepness of the staircase. It is useful to experience a difference between 20°C and 21°C, because these temperatures are experienced in daily life, whereas it becomes less useful to be able to experience the difference between 90°C and 91°C. It would require too much brain capacity and information processing energy to be as highly sensitive at all temperatures as we are at 20°C. So we became most sensitive in the stimulus ranges that we encountered mostly in our evolutionary history. If I burn my hand, it doesn’t matter if the water is 90°C or 100°C, so I don’t need to be able to feel that temperature difference. But when I go for a swim, it matters if the water is 10°C or 20°C.

However, our sensitivity, i.e. the steepness of the staircase and the widths of the steps, can change according to circumstances. For example, when I hold my hand in warm water for a while, my heat sensation becomes adapted to that temperature, and that means I become more sensitive when I put my hand in colder water. Habituation and drowsiness can decrease sensitivity and make the staircase less steep. Also some drugs such as analgesics and anesthetics can make the staircase flatter: a much stronger external stimulus is required before the threshold for pain is reached. When you are in an unconscious state such as a coma or deep sleep, the steps are basically infinitely wide and the staircase is completely flat. When you are slowly waking up after sleep, the staircase becomes steeper again. Expectations, anticipations, attention or focus, and some stimulant drugs can also increase the sensitivity and the steepness of the staircase. For example when you expect a temperature increase, you may notice an increase in temperature sooner.

Due to these circumstantial factors, it is difficult to precisely measure the width of the steps, i.e. the just-noticeable differences (that is why the JND is measured with a detection rate of more than 50%, i.e. when you notice the difference more than 50% of the times). But the crucial point is that the height of the steps is indeterminate, and therefore we can assume that all the steps have the same height. If I can as barely feel the difference between water at 50°C and 51°C as I can feel the difference between 90°C and 100°C, the pain increase when water temperature is raised from 50°C to 51°C is equal to the pain increase when water temperature raises from 90°C and 100°C.

The experiential (hedonic) utility function

A crucial notion of well-being is the utility function. One sentient being can be defined by having one integrated utility function that allows for trade-offs between different costs and benefits, i.e. different positive and negative experiences. Here I focus on the experiential or hedonic utility function, i.e. a function of how good or bad an experience is, where all possible subjective perceptions are the inputs of the function. This utility function is used in hedonistic utilitarianism that aims at maximizing the sum of everyone’s (selfish) happiness minus suffering. For example I have perceptions of room temperature, fear, joint pain, taste pleasure, musical enjoyment, income,… My utility function is integrated, because I can trade-off these perceptions: for example going to a colder, less comfortable room to get a delicious ice cream. Or working less to have more leisure time to listen to music, at a cost of decreasing my income.

The experiential utility function can also include expectations of future experiences. For example I may experience fear of getting toothache when I eat sweet ice creams, and if this fear is worse than my enjoyment of the ice cream, I decide not to eat the ice cream.

The whole input space of the utility function can be divided in three parts: the inputs that generate a positive, a zero and a negative utility. For example, when I’m very rich but have pain from a disease, my overall utility or well-being can be zero. But when a friend comes to visit me, my utility can become positive. The set of inputs that generate a zero utility cuts the input space in two regions. However, this zero utility set can also shift, for example due to adaptive preferences. When I win the lottery and become very happy, after a while I can adapt to my new situation and the richness no longer makes me happy. When I encounter a richer person, I may become jealous and suffer from frustration that I do not have even more money. Or when I have a disability, I can adapt and learn to live with it, such that feelings of discomfort decrease.

Also the steepness of the utility function can change due to circumstances (expectations, drugs, alertness, habituation…). For example consider the pain from pinpricks. I may be able to feel a difference between 2 and 3 needles in my arm, but with some drugs, it is possible that adding an extra needle doesn’t bother me so much anymore (even if I’m still able to feel the difference if the extra needle). In that case, the utility function in that dimension becomes very flat. There are people with pain asymbolia or pain dissociation, who can feel pain, but who do not have any negative evaluation or feeling of unpleasantness of that pain. For them, their utility function in the pain dimension is flat. On the other hand, when you become more and more sensitive to pain from pinpricks, and when you negatively evaluate that pain, your utility function becomes steep. Suppose you are being tortured and you have 100 needles in your arm. The torturer adds a few extra needles, but you won’t feel extra pain. Only after 10 needles have been added, you start to feel a pain increase and your utility level decreases with 1 utility unit due to the extra pain. Your just-noticeable difference in this case is 10 needles, i.e. the width of the pain steps can be measured as 10 input units (10 needles). But the torturer gives you a drug such that you become ten times more sensitive. You can start to feel pain from the slightest tough of an extra needle. You can now feel a difference in pain between 100 and 101 needles. Adding one extra needle becomes as painful as adding 10 needles used to be. So your utility function decreases with one utility unit when only one needle is added. This means your utility function becomes ten times as steep.


Comparing experiential utilities

There are two crucial properties of the inputs of a utility function that allows us to compare utilities of different sentient beings. First, all the inputs are measured on the positive real line segment, i.e. they have positive values. Second, the inputs are discrete and finite.

Concerning the first property, consider for example your income, your fever when you are ill, your desire for food, your stress level, your number of itchy mosquito bites, your number of friends, the number of times you laugh when watching a comedy, your amount of leisure time,… None of these values can be a negative number. These values can be inputs of your utility function, and there is a unique corner point where all the values are zero. In that case, you have zero income, zero friends, zero pain from cold, zero pain from heat, zero suffering from disease, zero enjoyment from food, zero hunger, zero minutes of relaxation with music, zero minutes of stressful work. This corner point is comparable to non-experience or non-existence, so we can give it a utility of zero. Every sentient being has such a unique corner point with utility zero, with zero positive and zero negative experiences. So the corner point always belongs to the zero utility set. Starting from this corner point, we can derive the zero utility set as an indifference curve by trading of inputs. For example increase the fever of a disease and then increase the intensity of laughter with a friend visiting you in the hospital, such that the positive experience of laughter exactly cancels out the negative experience of fever. Increase pleasure that cancels out pain. Increase income that cancels out the loss of leisure time. Decrease income to obtain perfect health, as in the notion of equivalent income for health.

With a corner point of utility zero, and a well-defined utility function that allows to derive an indifference curve starting at this corner point, we can interpersonally compare zero well-being. But we can not yet compare different positive or negative levels of well-being. For this, we need the second crucial property of the inputs: the finiteness and discreteness. We have seen that the inputs of a utility function are discrete, and that means that the levels that the utility function can take are discrete as well. The utility function becomes like a multidimensional staircase. Consider a room with walls in the north-south and west-east directions. In this room, there is a two-dimensional staircase. The south-western corner of the room is at the ground floor level (i.e. zero utility). Moving north corresponds with increased happiness, so the stairs go up. Moving east corresponds with increased suffering, so the stairs go down to the basement. Moving in a north-eastern direction keeps you at the ground floor level. The stairs cannot only go up or down, but might also have different steepness levels. Moving from south to north might be steep, with high steps, moving from west to east might be relatively flat, with small steps.

Furthermore, because of the finiteness of our brains, the inputs (stimuli) as well as the utility function itself are always finite: they cannot take a value of plus or minus infinity. The utility function has horizontal asymptotes. That means our multidemensional staircase has a highest and a lowest level. Also, the finiteness of the inputs imply that the steps cannot go infinitesimally small (low or flat). That is crucial, because now we can go looking for the smallest step in this multidimensional staircase: the step with the least height. There is a point and a direction, such that if you are at that point and move in that direction, then you rise the least, because that step has the smallest height. You can define its height to be one utility unit. So now we have our utility scale that allows for interpersonal comparisons.

For each sentient being, there is such a smallest difference in experienced utility, a smallest step. That step can be defined to have a height of one utility unit. That means that next to the zero level we can also interpersonally compare utility units or utility differences. Each sentient being has its own utility function that can be represented as a multidimensional staircase in a multidimensional room. Some stairs go up, some stairs go down. Two persons have two rooms that each contain one staircase, and the challenge is to derive whether a point in the first room is at the same height as another point in the second room. This can be done: every staircase has a unique ground floor (level zero), and the corner of the room is at this level. And the height of the smallest step of each staircase is the same for each room. With these two properties, we can compare the heights between different rooms.


The full (preference) utility function

For most sentient beings, their utility function has only subjective perceptions or experiences as inputs. However, some sentient beings with the capacity for abstract, rational thought, can also include non-experiential aspects in their utility function. For example you might have a preference for the truth, even if it hurts when you know the truth. You might have a preference that your spouse doesn’t cheat on you, even if you will never know about the adultery and never experience bad emotions. You might also have an altruistic preference that other people are happy, even if you will never know those other people or will never see their happiness. You might have a preference for fairness and justice, even when you are not the victim of injustice. You might even have a preference for your own non-existence.

The full utility function takes into account our hedonic experiences plus all other things that we want, all our other preferences. So our complete utility function can be very complex and broad. It contains literally everything that we value and prefer, even if we don’t experience it. This full utility or preference utility is used in preference utilitarianism.  The non-experiential aspects might raise or lower the full utility function. This property is useful in population ethical theories such as (variable) critical level utilitarianism where the experiential utility function gets subtracted with a constant.

The interpersonal comparison of utility can easily be generalized to the full utilities, because we can make trade-offs between a perceptual input and a non-perceptual input: how much are you willing to pay (experience a lower income) or suffer to know the truth? How much are you willing to be tortured in order for there to be world peace after you die? How much are you willing to sacrifice yourself in order to conserve nature or beautifull works of art after you (and all other sentient beings) die? The point is that we can compare the minimum difference in experiential utility with a difference in non-experiential utility, even if the utility of the non-experiential preferences would be continuous instead of discrete, and even if we do not subjectivelly feel those more abstract non-experiential preferences like we feel pain from a needle.

Comparing full utilities

There are some issues with comparing full utilities. First is the issue of multiple simultaneous utilities. It is possible that the brains of one sentient being generate two or more separate utility functions at the same time, as if that person has a multiple personality disorder. Think for example of a split-brain patient whose right hemisphere has different preferences than his left hemisphere. Or a sentient being who can make a trade-off between food and safety (e.g. moving to an unsafe area to obtain food and avoid a feeling of hunger), and between mating and pain relief (e.g. choosing for painful mating), but not between food and pain relief. Each utility function counts as a separate person.

Second is the issue of counting different utilities through time. The evaluation of time introduces two problems. First is the question how much we experience in a time interval, and second is the question how we value the different experiences in a time interval.

We can directly experience the flow of time, but not time intervals (except indirectly through memory, but then the time interval is already in the no longer experienced past). However, as mentioned above, our experiences have a maximum frequency due to the finite information processing rate of our brains. This means your utility function is evaluated a finite number of times per second. After your just-noticeable time difference, you reevaluate your utility function. Someone with a faster brain has more experiences and more evaluations of its utility function in a time interval. In interpersonal comparisons, every separate evaluation of the utility function counts. It is as if each instantaneous experience counts as an experience of a separate person. In other words, for an interpersonal comparison of well-being, the well-being experienced in a second for a sentient being whose brains are twice as fast as yours, counts twice as much as your well-being experienced in that second.

But we also have to make a distinction between the remembering self and the experiencing self. The experiencing self lives in the present moment and evaluates well-being or utility after each just noticeable time period. The remembering self evaluates utility for a past episode, based on memory of the experiences in that episode. The utility function of the remembering self can be different from the (sum of) utility functions at each of the experienced instantaneous moments, i.e. for each of the experiencing selves during a time interval. Suppose for example you can choose between two painful episodes. The first has a longer time interval with a steady pain slowly decreasing to zero. The second has a shorter time interval, with a very brief high peak of pain and an abrupt ending where pain suddenly drops to zero. Combining your experiencing selves, they choose the second episode, because there are fewer moments of experienced pain. But the remembering self uses a peak-end rule: in the second painful episode, the pain at its peak and the pain at the end are both higher than in the first episode, so the second episode is considered worse. Also, the remembering self evaluates life satisfaction, whereas the experiencing self evaluates momentaneous happiness, which is different from life satisfaction.

When comparing well-being between persons, we have to make sure that we either compare the utilities evaluated by their remembering selves, or the utilities evaluated by their experiencing selves, but not the evaluation of one person’s remembering self with the other person’s experiencing self.

An example

Assume, as an example, that a caterpillar is a sentient being and is on fire. How bad are the burns for the caterpillar? According to the discrete input model discussed above, we can measure the just-noticeable differences of pain from the burns. Going from zero burns to a just-noticeable burn decreases the utility with one unit. An extra burning sensation decreases the utility with another unit, and so on. Suppose the caterpillar on fire experiences 100 negative utility units, which means 100 just-noticeable differences of pain are exceeded. Now I want to compare this with me being on fire. Perhaps, hypothetically, when my little finger is on fire, I also experience 100 negative utility units, as much as when the whole caterpillar is on fire (because my little finger is as big as the caterpillar’s body). The surface area of my whole body is say 1000 times the surface area of my little finger, but that does not yet mean that my whole body being on fire is 1000 times worse than my finger being on fire. Due to the Weber-Fechner law, the more I am on fire, the less extra pain I experience when an extra square centimeter of skin is on fire. That means when my body is fully on fire, I experience say only 10.000 negative utility units instead of 100.000. In other words, for a caterpillar, being completely on fire feels the same as my little finger being on fire feels to me. When I’m completely on fire, it feels 100 times worse than what the caterpillar can experience.

But we also need to take into account the brain processing speed. Perhaps my brain is ten times faster, which means ten times more pain evaluations per second. In that case, the caterpillar being on fire for ten seconds feels like my little finger being on fire for one second. That does not yet mean that my finger being on fire for one second is 10 times worse than a caterpillar being on fire for one second, because I might have different preferences for time intervals. We have to consider the utility function of my remembering self and the remembering self of the caterpillar. Perhaps my remembering self doesn’t care that much about the number of experiences per second or the length of a painful episode. That means the length of time gets discounted. If my remembering self mainly cares about the peak pain experience, then the length of time is less important and one second of having a finger on fire is say only twice as bad as a caterpillar being on fire for one second. When my whole body is on fire for one second, it becomes 200 times as bad as what the caterpillar experiences.

What if experiences are not discrete?

Suppose that our assumption of discreteness of inputs of the utility function is invalid: the inputs as well as the utility function are continuous, which means that the utility function can take any real value and there is no smallest utility difference (minimal step height). In that case, we can no longer compare the differences in levels of utility, and we have to revert to the method described in the previous article, namely normalization (e.g. dividing your utility level by the spread or standard deviation of all possible utility levels that you can get for all possible choices that we can make). This can have drastically different results when comparing well-being and suffering.

In the above example with the caterpillar, I argued that my pain could be 100 times worse than the pain of the caterpillar. Now suppose that pain experiences were continuous instead of discrete. With a continuous input model, we have to use the utility normalization procedure. Suppose there are only two possible situations for the caterpillar: either the caterpillar dies by fire, or lives by avoiding the flames. Suppose the first situation corresponds with 100 negative utility units, the second with 0. The spread of utilities between those two situations is also 100, so dividing the utility levels by the spread gives a normalized utility of minus 1 for being on fire. Now we do the same for me: there are two possible situations: being completely on fire, with 10.000 negative utility units, and not being on fire, with utility 0. Dividing by the spread (10.000) gives a normalized utility of minus 1 when I’m on fire. In that case, being on fire for the caterpillar is as bad as being completely on fire for me, and 1000 times worse than my little finger being on fire. If insects can suffer, and if suffering is continuous instead of discrete, insect suffering might vastly trump human suffering.

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Mogen rupsen dan wel het milieu vervuilen?

Opinie over eikenprocessierupsen en milieu-ethiek, verschenen in De Morgen (06-07-2019)


Nu dachten we goed te doen voor het milieu door zomereiken te planten in onze parken, tuinen en lanen. In tegenstelling tot de Amerikaanse eik trekt de zomereik meer insectensoorten aan; goed voor de biodiversiteit. Maar daardoor krijgen we meer last van de eikenprocessierups. Was het hoogmoed, dat we de neveneffecten van ons ingrijpen in de complexe natuur onderschatten? Niet enkel onze gezondheid, maar ook onze morele waarden worden getroffen door giftige rupsenhaartjes.
We vinden een gezond milieu belangrijk. Maar dat rondslingeren van giftige piepkleine brandharen die jarenlang problemen kunnen veroorzaken, is niets anders dan serieuze milieuvervuiling. Als een fabriek zoiets deed, hadden milieuactivisten massaal geprotesteerd. Mensen zijn niet de enige milieuvervuilers. Maar hoe pakken we deze rupsenvervuiling aan?
Milieuactivisten verkiezen een preventief beleid. Dus in dit geval de oorzaak aanpakken, door preventief de eiken te kappen? Nee, dat is louter symptoombestrijding, zeggen ze. Volgens sommigen is de echte oorzaak de mens. Niet omdat wij de eiken hebben aangeplant, maar omdat wij het klimaat hebben veranderd, wat die rupsen graag hebben. Eigen schuld, jeukende bult. De mate van misantropie valt op bij veel activisten die vinden dat we de rupsen nu maar moeten tolereren. Die misantrope houding is riskant, want het kan de geloofwaardigheid van de milieubeweging helpen ondergraven. Zeggen dat die rupsen nuttig zijn in de natuur, helpt ook niet, want dan zijn meer rupsen misschien nog beter. Of is het huidig aantal rupsen toevallig optimaal?
Het verschil tussen symptoom- versus oorzaakbestrijding is irrelevant, want het is niet helder te bepalen. Symptomen kunnen op zich oorzaken zijn van problemen. En er zijn vele parallelle oorzaken van een probleem, die elk op hun beurt dieperliggende oorzaken hebben. De haartjes, de rupsen, de eiken, de klimaatverandering, het economisch systeem, de mens, de oerknal,… Welke deelverzameling van al die oorzaken zijn dan de ‘echte’ oorzaken om te bestrijden? Waar het wel om gaat, is het verschil tussen kosteneffectievere versus minder effectieve maatregelen. Denk zoals een econoom: hoe bereiken we onze doelen zo goedkoop mogelijk? De afgelopen klimaatverandering is dan wel een oorzaak van de huidige rupsenoverlast, maar het klimaat op korte termijn terugschroeven is allesbehalve kosteneffectief.
Denk ook zoals een ethicus: wat zijn onze doelen of waarden precies? De processierupsendiscussie toont aan dat we het niet goed weten. Processierupsen komen minder voor op beschaduwde eiken in bossen, dus laten we de vrijstaande eiken vervangen door andere bomen? Nee, dat is tegen de belangen van eiken, zeggen bomenliefhebbers. Dan zijn processierupsen een dubbel kwaad: ze schenden zowel ons belang om niet ziek te worden als het eikenbelang om niet kaalgevreten te worden.
We moeten belangen afwegen. Maar we mogen geen fictieve belangen meetellen. Een eik zelf, of een stadspark, heeft geen bewust besef van belangen, en dus geen voorkeur voor boomsoort, doodsoorzaak of natuurlijk evenwicht. Onze voorkeur voor een eik in plaats van een berk is louter onze eigen esthetische voorkeur. Wij kunnen een eik waardevol vinden, maar de eik zelf vindt niets. We mogen dus niet onterechte, fictieve belangen toeschrijven aan eiken, stadsparken of natuurgebieden. De belangen die we moeten afwegen, zijn onze esthetische voorkeur voor een bepaalde boomsoort, versus ons belang (en dat van onze honden en andere dieren) om niet ziek te worden van giftige rupsenhaartjes.
Het wordt complexer: er komen meer aanwijzingen dat insecten welzijnsgevoelig zijn en dus welzijnsbelangen hebben. De rupsen vergiftigen of verbranden? Nee, dat is te drastisch volgens dierenliefhebbers: we gaan toch ook geen mensen in brand steken, zelfs al zouden ze even hardnekkig het milieu vervuilen? Tegelijk horen we dan het voorstel om natuurlijke rupsenbestrijding in te zetten, zoals koolmezen. Dat is alsof we reusachtige vliegende dinosaurussen zouden inzetten om menselijke milieuvervuilers te bejagen. De populatie koolmezen neemt trouwens ook toe, en ze doden net als insectengif ook onschuldige rupsen. En ze stammen af van dinosaurussen.
De huidige kennis is te beperkt om te weten of het welzijnsverlies veroorzaakt door rupsenverdelging erger is dan de gezondheidsproblemen van rupsenhaartjes bij mensen, honden en wilde dieren. Er moet meer wetenschappelijk onderzoek komen naar het welzijn van dieren in de natuur, en hoe we veilig en doeltreffend kunnen ingrijpen om ieders welzijn te bevorderen. Hoe we het rupsenprobleem nu oplossen, laat ik in het midden, maar we moeten dringend onze waarden en opvattingen over mens en natuur helder krijgen, en we mogen geen denkbeeldige belangen meerekenen.

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The large income benefits of migration

This is an English summary of my master’s thesis in policy economics (University of Leuven). The thesis can be downloaded here.

Injustice, inefficiency and harm of migration restrictions

Unequal pay for equal work, a welfare loss of billions of euros, and thousands of deaths. Injustice, inefficiency and harm are the three consequences of a migration restriction or closed borders policy.

Migration restrictions create a global wage gap, with real wages (for the same work, the same skills and the same employer education level) about 4 times higher in rich countries than in low and middle income countries (Clemens, Montenegro & Pritchett, 2008). This factor 4 is the place premium that measures the increase in income of a foreign worker from a poor country who is going to work in a rich country. This place premium in wages is one of the biggest price disruptions on the international market. The hourly wage in a poor region is 75% lower than in a rich region. This percentage can be compared with the gender pay gap: in Belgium the average hourly wage of a woman is about 8% lower than a man (Van Hove & De Vos, 2017). The pay gap based on origin is almost 10 times larger than the pay gap based on gender.

This injustice is the result of a global labor market that is not in equilibrium. Productive workers are prevented from going to work where labor productivity is highest (Borjas, 2015). In poorer countries there is a labor surplus (or capital shortage), in richer countries there is a labor shortage (or capital surplus).

This disequilibrium in the global labor market also leads to a loss of productivity worth billions of euros. If workers cannot migrate to places with the highest labor productivity and entrepreneurs cannot migrate to places with the best entrepreneurial climate, then win-win situations are hampered. Stopping migrant families at the border prevents mutually beneficial transactions between employees and employers, between producers and consumers, between buyers and sellers, between tenants and landlords. Just like freer international movement of goods and capital, freer movement of workers will improve the efficiency of the world economy. A freer migration can increase real global income (according to purchasing power) or gross world product (GWP) by 10% to 100% (Clemens, 2011). For comparison: the potential global income growth of freer migration is an order of magnitude higher than the income growth from further liberalizing international goods and capital markets. The removal of barriers to goods and capital flows can increase global income by only a few percent (Clemens, 2011).

Finally, limiting migration and closing national borders is directly harmful. There is direct damage because migrants, asylum seekers and refugees often take unnecessarily high risks of entering a country. At least 1000 people die every year in their attempt to reach Europe via the Mediterranean (UNITED, 2018).

The income changes of migration

The income effects for different population groups were studied on the basis of a literature review (see references). The literature studies show coherent results. The migration surplus per migrant measures the expected increase of the world income (the sum of everyone’s income) when one extra migrant from a poor region is admitted to the labor market of a rich region. This migration surplus is almost 30,000 euros per year per migrant.

The migrants are the biggest winners, because they can see their real wage multiplied by a factor of 4.  But the native population in the recipient countries can also benefit from immigration. Extra migrants means: extra customers and extra tenants, but also extra entrepreneurs and of course extra workers who can, for example, mitigate the public finance problems associated with an aging population in rich countries. All this contributes to the local economy in a recipient country. As a result, about one third of the migration surplus of € 30,000 per year per migrant, is to the benefit of the native population, in particular capital owners (employers, shareholders, real estate owners).

The average real wages of native workers are almost unaffected by immigration: studies indicate small income changes, sometimes a bit positive, sometimes a bit negative. If mainly low-skilled workers immigrate, then the wages of native low-skilled workers and the already present migrants may fall. For the low-skilled workers in the rich region, this is a decrease of around 1000 euros per year per new migrant. This cost is distributed over the entire population of low-skilled native workers and can be more than offset by the increase of 10,000 euros per year for the rest of the native population. With a progressive tax and income redistribution, the net wages of (low-skilled) native workers can rise.

The migrants receive roughly two thirds of the migration surplus (i.e. € 20,000 per year per migrant), but a bit less than half of their share is send to their relatives who are left behind in the poor region. Due to these remittances, the population in the countries of origin can also benefit from emigration. Especially workers in those poor countries can see an income increase.

The result of the literature overview can be summarized in the figure below. The width and height of the bars correspond to the population size and the real incomes of the various population groups, respectively. The dotted lines represent the situation after migration. The biggest winners are the migrants (green bar) who see their income increase sharply. The remaining workers in the countries of origin (yellow bar) see their income rise because there is less labor surplus in those countries during emigration and because the local population receives money transfers (remittances) from the migrants. The capital owners in the recipient countries (blue bar) see their capital income increase because their capital becomes more productive with extra workers. The average wages of workers (red bars) remain approximately constant. The highly educated can get slightly higher incomes, the lower educated slightly lower.

Income effects of migration

Explanations of the income effects

On the basis of a global general equilibrium model, explanations were given for the absence of significant decreases in the average wages of native workers due to immigration. The two most important explanations for this wage inelasticity of native workers are:
1) the complementarity of migrant and native labor in the short run, because immigrants are more mobile and can therefore ‘grease the wheels of labor market’ in the rich region by migrating to places where their productivity is highest (Borjas, 2001), and
2) the additional capital investments in the long run , because they can greatly increase productivity and employment in the rich region.

Policy implications

Economics professor Bryan Caplan and supporters of open borders (Caplan, 2015; Matthews, 2014; Open Borders, 2019) argue that migration restrictions are comparable to stopping job applicants and workers at the gates of companies, or stopping customers at the doors of shops. This restriction of freedom is not only harmful to the applicant, worker or customer, but also to the employer and shopkeeper.

There is a lot of arbitrariness in the policy of closed borders: why should borders be closed to migrant workers while they are open to flows of capital and goods and to tourists? Why should borders be closed between countries or country unions (such as the EU), while they are open between municipalities, provinces, regions and states?

Migration restrictions lead to an unjust pay gap between poor and rich regions, a global loss of wealth of billions of euros (almost half of total income), an aggravation of the aging problem in rich countries, a disadvantage for both native people and migrants, a huge death toll of migrants, a restriction on freedom of consumers and producers, and an undesirable arbitrariness. If we look at the economic literature on migration, we see a clear consensus among economists that a policy of freer migration offers more advantages than disadvantages (New American Economy, 2017; Open Borders, 2019c).

Opening up national borders for labor migration (Caplan, 2015; Matthews, 2014; Open Borders, 2019a) is the most far reaching but logical recommendation of this study. Open borders between countries are an extension of open borders between municipalities: just as one can move from one municipality to another (subject to registration in the new municipality), one could move from one country to another for living, working and shopping. In the short run, when completely opening borders is not politically feasible, one could gradually liberalize migration and increase the immigration rate.


Peer reviewed academic articles

Aleksynska, M., & Tritah, A. (2015). The heterogeneity of immigrants, host countries’ income and productivity: a channel accounting approach. Economic Inquiry, 53(1), 150-172.

Alesina, A., Harnoss, J., & Rapoport, H. (2016). Birthplace diversity and economic prosperity. Journal of Economic Growth, 21(2), 101-138.

Angrist, J. D., & Kugler, A. D. (2003). Protective or counter‐productive? Labour market institutions and the effect of immigration on EU natives. The Economic Journal, 113(488), F302-F331.

Aubry, A., Burzyński, M., & Docquier, F. (2016). The welfare impact of global migration in OECD countries. Journal of International Economics, 101, 1-21.

Aydemir, A., & Borjas, G. J. (2007). Cross-country variation in the impact of international migration: Canada, Mexico, and the United States. Journal of the European Economic Association, 5(4), 663-708.

Bargain, O., Orsini, K. & Peichi, A. (2011). Labor Supply Elasticities in Europe and the US. IZA Discussion Paper No. 5820.

Bauer, T. & Zimmermann, K. (1999). Assessment of Possible Migration Pressure and its Labour Market Impact Following EU Enlargement to Central and Eastern Europe. IZA Research Report No. 3.

Blau, F. D., & Kahn, L. M. (2015). Immigration and the Distribution of Incomes. In Handbook of the economics of international migration (Vol. 1, pp. 793-843). North-Holland.

Borjas, G. J., & Ramey, V. A. (1995). Foreign competition, market power, and wage inequality. The quarterly journal of economics, 110(4), 1075-1110.

Borjas, G. J., Freeman, R. B., Katz, L. F., DiNardo, J., & Abowd, J. M. (1997). How much do immigration and trade affect labor market outcomes?. Brookings papers on economic activity, 1997(1), 1-90.

Borjas, G.J. (1995). The Economic Benefits of Immigration. Journal of Economic Perspectives, 9(2), 3-22.

Borjas, G. J. (2001). Does immigration grease the wheels of the labor market?. Brookings papers on economic activity, 2001(1), 69-119.

Borjas, G. J. (2003). The Labor Demand Curve is Downward Sloping: Reexamining the Impact of Immigration on the Labor Market. Quarterly Journal of Economics, 118(4), 1335-1374.

Borjas, G. J. (2006). Native internal migration and the labor market impact of immigration. Journal of Human resources, 41(2), 221-258.

Borjas, G. J. (2008). Labor outflows and labor inflows in Puerto Rico. Journal of Human Capital, 2(1), 32-68.

Borjas, G. J. (2013). The analytics of the wage effect of immigration. IZA Journal of Migration, 2(1), 22.

Borjas, G. J. (2015). Immigration and globalization: A review essay. Journal of Economic Literature, 53(4), 961-74.

Bratsberg, B., Raaum, O., Røed, M., & Schøne, P. (2010). Immigration Wage Impacts by Origin (No. 1030). Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.

Card, D. (2001). Immigrant inflows, native outflows, and the local labor market impacts of higher immigration. Journal of Labor Economics, 19(1), 22-64.

Card, D. (2009). Immigration and inequality. American Economic Review, 99(2), 1-21.

Clemens, M. (2011). Economics and Emigration: Trillion-Dollar Bills on the Sidewalk? Journal of Economic Perspectives, 25(3), 83–106.

Clemens, M. A. (2013). Why do programmers earn more in Houston than Hyderabad? Evidence from randomized processing of US visas. American Economic Review, 103(3), 198-202.

Cortes, P. (2008). The effect of low-skilled immigration on US prices: evidence from CPI data. Journal of political Economy, 116(3), 381-422.

D’Amuri, F., & Peri, G. (2014). Immigration, jobs, and employment protection: evidence from Europe before and during the great recession. Journal of the European Economic Association, 12(2), 432-464.

Di Giovanni, J., Levchenko, A. A., & Ortega, F. (2015). A global view of cross-border migration. Journal of the European Economic Association, 13(1), 168-202.

Docquier, F., Özden, Ç., & Peri, G. (2011). The wage effects of immigration and emigration. The World Bank.

Docquier, F., Ozden, Ç., & Peri, G. (2013). The labour market effects of immigration and emigration in OECD countries. The Economic Journal, 124(579), 1106-1145.

Docquier, F., Machado, J., and Sekkat, K. (2015). Efficiency Gains from Liberalizing Labor Mobility. Scandinavian Journal of Economics 00(0), 1–44.

Dustmann, C., Frattini, T., & Preston, I. P. (2012). The effect of immigration along the distribution of wages. Review of Economic Studies, 80(1), 145-173.

Dustmann, C., Frattini, T., & Glitz, A. (2007). The impact of migration: a review of the economic evidence. Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London, and EPolicy LTD, November, 1-113.

Edo, A. (2018). The impact of immigration on the labor market. Journal of Economic Surveys. doi:10.1111/joes.12300.

Elsner, B. (2013). Emigration and wages: The EU enlargement experiment. Journal of International Economics, 91(1), 154-163.

Felbermayr, G. J., Hiller, S., & Sala, D. (2010). Does immigration boost per capita income?. Economics Letters, 107(2), 177-179.

Foged, M., & Peri, G. (2016). Immigrants’ effect on native workers: New analysis on longitudinal data. American Economic Journal: Applied Economics, 8(2), 1-34.

Friedberg, R. M., & Hunt, J. (1995). The impact of immigrants on host country wages, employment and growth. Journal of Economic perspectives, 9(2), 23-44.

Gagnon, J. (2011). Stay With US? The Impact of Emigration on Wages in Honduras, OECD Development Centre Working Paper No. 300.

Gibson, J., McKenzie, D., Rohorua, H., & Stillman, S. (2017). The long-term impacts of international migration: Evidence from a lottery. The World Bank Economic Review, 32(1), 127-147.

Glitz, A. (2012). The labor market impact of immigration: A quasi-experiment exploiting immigrant location rules in Germany. Journal of Labor Economics, 30(1), 175-213.

González, L., & Ortega, F. (2008). How do very open economies absorb large immigration flows. Recent Evidence from Spanish Regions. Economic Reports, 06-08.

Hall, R., Jones, C.I. (1999). Why Do Some Countries Produce So Much Output per Worker Than Others? Quarterly Journal of Economics, 114(1), 83–116.

Hamilton, B. and Whalley, J. (1984). Efficiency and Distributional Implications of Global Restrictions on Labour Mobility. Journal of Development Economics, 14, 61–75.

Hanson, G. H. (2005). Emigration, labor supply, and earnings in Mexico (No. w11412). National Bureau of Economic Research.

Hanson, G. H. (2009). The Economic Consequences of the International Migration of Labor. Annual Review of Economics, 1(1), 179–207.

Hendricks, L., & Schoellman, T. (2017). Human capital and development accounting: New evidence from wage gains at migration. The Quarterly Journal of Economics, 133(2), 665-700.

Iranzo, S., & Peri, G. (2009). Migration and trade: Theory with an application to the Eastern–Western European integration. Journal of International Economics, 79(1), 1-19.

Iregui, A. M. (2005). Efficiency Gains from the Elimination of Global Restrictions on Labour Mobility. In G. J. Borjas and J. Crisp (eds.), Poverty, International Migration and Asylum, Palgrave Macmillan, New York, pp. 211-238.

Jaeger, D. A. (1996). Skill Differences and the Effect of Immigrants on the Wages of Natives. US Bureau of Labor Statistics Working Paper, 273. Revised in 2007.

Jaumotte, M. F., Koloskova, K., & Saxena, M. S. C. (2016). Impact of migration on income levels in advanced economies. International Monetary Fund.

Kennan, J. (2013). Open Borders. Review of Economic Dynamics, 16, L1–L13.

Klein, P. and Ventura, G. (2007). TFP Differences and the Aggregate Effects of Labor Mobility in the Long Run, B.E. Journal of Macroeconomics 7, article 10.

Klein, P. and Ventura, G. (2009). Productivity Differences and the Dynamic Effects of Labor Movements. Journal of Monetary Economics, 56, 1059–1073.

Leeson, G. (2012). Migration as a policy response to population ageing. International Risk Governance Council – Public Sector Governance of Emerging Risks.

León‐Ledesma, M. & Piracha, M. (2004). International Migration and the Role of Remittances in Eastern Europe. International Migration, 42, 65-83.

Lewis, E. G. (2011). Immigrant-native substitutability: The role of language ability (No. w17609). National Bureau of Economic Research.

Longhi, S., Nijkamp, P., & Poot, J. (2005). A meta‐analytic assessment of the effect of immigration on wages. Journal of economic surveys, 19(3), 451-477.

Manacorda, M., Manning, A., & Wadsworth, J. (2012). The impact of immigration on the structure of wages: theory and evidence from Britain. Journal of the European economic association, 10(1), 120-151.

Mishra, P. (2007a). Emigration and Wages in Source Countries: Evidence from Mexico, Journal of Development Economics, 82, 180-199.

Mishra, P. (2007b). Emigration and Brain-Drain: Evidence from the Caribbean, The B.E. Journals in Economic Analysis & Policy, 7(1) Article 24.

Mishra, P. (2014). Emigration and wages in source countries: A survey of the empirical literature. International Handbook on Migration and Economic Development, Cheltenham: Edward Elgar, 241-266.

Monras, J. (2018). Immigration and wage dynamics: Evidence from the mexican peso crisis.

Moses, J. W. & Letnes, B. (2004a). The Economic Costs to International Labor Restrictions: Revisiting the Empirical Discussion. World Development, 32(10), 1609–1626.

Münz, R., Straubhaar, T., Vadean, F., & Vadean, N. (2006). The costs and benefits of European immigration. HWWI Policy Reports 3, Hamburg Institute of International Economics (HWWI).

Okkerse, L. (2008). How to measure labour market effects of immigration: A review. Journal of Economic Surveys, 22(1), 1-30.

Orrenius, P. M., & Zavodny, M. (2007). Does immigration affect wages? A look at occupation-level evidence. Labour Economics, 14(5), 757-773.

Ortega, F. & Peri, G. (2013). Migration, Trade & Income, IZA Discussion Paper 7325.

Ottaviano, G. I., & Peri, G. (2008). Immigration and national wages: Clarifying the theory and the empirics (No. w14188). National Bureau of Economic Research.

Ottaviano, G. I., & Peri, G. (2012). Rethinking the effect of immigration on wages. Journal of the European economic association, 10(1), 152-197.

Ratha, D., De, S., Ju Kim, E., Plaza, S., Schuettler, K., Seshan, G. & Yameogo, N.D. (2018), Migration and Remittances, Recent Developments and Outlook. Migration and Development Brief 29. World Bank Group.

Walmsley, T. L. & Winters, L. A. (2005). Relaxing the Restrictions on the Temporary Movement of Natural Persons: A Simulation Analysis. Journal of Economic Integration, 20, 688–726.


Altonji, J. G., & Card, D. (1991). The effects of immigration on the labor market outcomes of less-skilled natives. In Immigration, trade, and the labor market (pp. 201-234). University of Chicago Press.

Bouton, L., Paul, S., & Tiongson, E. R. (2011). The impact of emigration on source country wages: evidence from the Republic of Moldova. The World Bank.

Clemens, M. A., Montenegro, C. E., & Pritchett, L. (2008). The place premium: wage differences for identical workers across the US border. The World Bank.

Iregui, A. M. (2005). Efficiency Gains from the Elimination of Global Restrictions on Labour Mobility. In G. J. Borjas and J. Crisp (eds.), Poverty, International Migration and Asylum, Palgrave Macmillan, New York, pp. 211-238.

Moses, J. W., & Letnes, B. (2004b). If people were money: Estimating the gain and scope of free migration. In G. J. Borjas & J. Crisp (Eds.), Poverty, international migration and asylum. London: Palgrave, pp. 188-210.

National Academies of Sciences, Engineering, and Medicine. (2017). The economic and fiscal consequences of immigration. National Academies Press.

Posner, E. & Weyl, G. (2018). Radical Markets. Uprooting Capitalism and Democracy for a Just Society. Princeton University Press.

Pritchett, L. (2006). Let Their People Come: Breaking the Gridlock on Global Labor Mobility. Center for Global Development, Washington DC.

Van Hove, H. & De Vos, D. (2017). De loonkloof tussen vrouwen en mannen in België. Instituut voor de gelijkheid van vrouwen en mannen, Brussel.

World Bank (2005) Global Economic Prospects 2006: Economic Implications of Remittances and Migration, Washington, D.C.: World Bank.

World Bank (2018). Moving for Prosperity. Global Migration and Labor Markets. World Bank Group, Policy Research Report, Washington.

Internet and media

Bulman, M. (2017). Brexit: People voted to leave EU because they feared immigration, major survey finds. Independent, 28-06-2017.

Caplan, B. (2015). The case for open borders, Time, 7-10-2015

Matthews, D. (2014). The case for open borders, Vox, 15-12-2014

New American Economy (2017). An Open Letter from 1,470 Economists on Immigration.

Open Borders (2019a).

Open Borders (2019b).

Open Borders (2019c).

Peri, G. (2013). The economic benefits of immigration, Berkeley Review of Latin American Studies, University of California, Berkeley

UNITED (2018). The Fatal Policies of Fortress Europe, list of deaths.

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Economics and ethics for a rational politics

People often ask me why I started a master’s study in policy economics after finishing two PhD’s in natural sciences and in moral philosophy. I think that economics, ethics and science are three important areas of research for a rational politics. Rationality means accurateness in beliefs, effectiveness in means and consistency in ends. Science deals with the first kind of rationality (epistemic rationality), economics deals with the second kind (instrumental rationality) and ethics deals with the third (axiological rationality).

During the recent Belgian and European elections, I realized how much our political parties are driven by irrationalities. Interestingly, we can roughly say that conservative right-wing political parties are too irrational about ends, whereas progressive left-wing political parties are too irrational about means. Therefore, the combination of good economics and ethics is crucial to improve the rationality of our political decision making.

What are ethics and economics?

Ethics is the study about the best (optimal) choices of ends (such as moral values), economics is the study about the best (optimal) choices of means (such as technologies). Both disciplines face two fundamental questions:

  • Procedural (about strategy): how to make the best choices of ends/means?
  • Substantive (about content): what are the best ends/means?

The first fundamental ethical question: how to make the best choices of ends?

To make good choices of ends, we need to avoid unwanted arbitrariness. Unwanted arbitrariness means making a choice arbitrarily (i.e. without following a rule) whereby the consequences are unwanted by at least one individual (i.e. they cannot be consistently preferred by everyone). This can be translated to the most fundamental principle in ethics: if you make a choice, you have to be able to give a justifying rule of which you can consistently want that everyone follows it in all possible situations.

A good procedure to make the best choices of ends for society, is a rational democracy or futarchy, where political parties and representatives represent moral values such that people can vote on values, but discriminatory and antidemocratic parties and candidates should be excluded from the elections. Democratic voting procedures can be improved further by quadratic voting and approval voting mechanisms. Such democracy is a direct consequence of the principle to avoid unwanted arbitrariness: in non-democratic systems, there is always an arbitrary group of individuals who are excluded from political decision making or who have less voting power than others, and those excluded people cannot want that exclusion. For example a dictator arbitrarily excludes the rest of the population and a patriarchy arbitrarily excludes women from voting.

However, when it comes to procedural choices of ends, conservative right-wing parties often have irrational strategies that include discrimination. This can be seen at the extreme right, with its ethnocentrism, racism, sexism and other kinds of discrimination. These extreme right-wing parties are often antidemocratic. But also on the center-right we often see unwanted arbitrariness in party positions.

The second fundamental ethical question: what are the best ends?

In recent years, a lot of psychological research has been done about moral foundations (Jonathan Haidt) and the values of progressives and conservatives. Basically, we all value ends (moral foundations) such as well-being, happiness, honesty, care, safety, protection, fairness and justice. Progressive left-winged political parties also prioritize environmental sustainability (intergenerational justice), freedom (liberty) and diversity. These values are legitimate, because they do not necessarily contain unwanted arbitrariness.

But conservative right-wing political parties often include irrational ends such as group loyalty, (religious) authority, tradition and purity. These ends are irrational, because they contain unwanted arbitrariness. There are many groups, religions and traditions so why choosing this particular group, religion or tradition over another? Consider nationalism: why should this nation be more important than another? And why should loyalty or patriotism be exclusively towards people in your nation and not in your street, town, province, state or continent? With group loyalty, one arbitrarily picks a group and exclude other individuals from this group. With religious authority, one arbitrarily picks a leader or text book from an arbitrarily picked religion. With respect for tradition, one arbitrarily picks a practice that was common in an arbitrarily picked time period. With purity, one can arbitrarily pick one of the many interpretations of this ambiguous concept and declare that an arbitrarily chosen practice (such as gay sex) is impure. With tradition, one can arbitrarily pick a point in history and a certain region and declare that the common practice in those days counts as the real tradition that should be preserved. There are no universal rules that dictate which practices counts as traditional or impure. Tradition and purity are often related to symbolic values, and one can argue indefinitely about symbolisms.

The first fundamental economics question: how to make the best choices of means?

Economics includes the study of mechanisms that allow us (individuals, consumers, producers, governments,…) to make the optimal choices of means. These mechanisms can be market mechanisms. For example, in rational democracy or futarchy, we can implement prediction markets such that people can bet on beliefs. This market mechanism allows us to improve the accurateness of beliefs (the quality of valuable information). We can also increase the set of means, through technological research. Market mechanisms such as patent systems and pay-for-performance impact funds (e.g. the Health Impact Fund) and other economic mechanisms (e.g. government subsidies) create incentives to advance technological research and development (see for example this analysis about the importance of funding R&D for clean energy to avoid climate change).

However, when it comes to procedural choices of means, progressive left-wing parties often have irrational strategies or procedures. One clear example is extreme left communism, where centralized decision making procedures and the absence of price mechanisms and private property rights (private ownership of production means) creates the wrong incentives. This destroys productive capital and decreases social welfare. Left-wing parties often have a resistance against corrected market mechanisms (corrected for market failures such as externalities, asymmetric information,…), although such market mechanisms, through the price mechanism, can improve the quality of valuable information about costs and benefits (such that better decisions can be made and more preferences can be satisfied at lower costs) and the alignment of incentives (such that individual preferences become aligned with social welfare and people automatically choose what is best for social welfare).


The second fundamental economics question: what are the best means?

Next to the question how to make optimal choices of means, economists also study what those optimal means are. This requires cost-benefit and cost-effectiveness analyses. All the benefits (for everyone involved) and all the costs (including opportunity costs, sacrificed time, prices of technologies,…) should be compared.

Because right-wing political parties often have irrational ends such as ambiguous moral values, it is difficult to say whether they choose the best means to reach those vague and ambiguous ends. Left-wing parties on the other hand have clearer ends, so the effectiveness of the chosen means can be studied. But now it becomes clear that left-wing political parties often choose irrational means, i.e. ineffective measures that can even backfire. Examples include the support for organic farming, fair trade, taxes that create huge deadweight losses (economic inefficiencies), and the resistance against free trade, globalization, nuclear energy,…


Ethics studies how to make the best choices about ends and what those optimal ends are. In this area, conservative, right-wing political parties are often irrational: their ends or moral values often contain unwanted arbitrariness. Economics studies how to make the best choices about means and what those optimal means are. In this area, progressive left-wing political parties are often irrational: their chosen means are often ineffective or sometimes even counterproductive.

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