The current talk in Republican circles is that one can achieve significant job growth by deregulating. One may want to question this idea on two fronts. First, regulation has been initially imposed not for the fun of killing jobs, but because it improves the well-being of people. There is a trade-off, and sometimes it is worth having a little fewer jobs if it means improving the life of a lot of people. Second, the job loss numbers from regulation are often more fantasy than reality.
This is not a new question. Take the case of Australia, as discussed by Bruce Chapman. He looks at estimate of job loss in Australian mining from the implementation of an emission trading scheme. These 23,510 lost jobs are not as large as they appear. First, there would be job gains elsewhere, in particular in alternative energies. Second, when compared to normal job flows in the mining sectors, this number is quite negligible. Third, once you look at a somewhat longer horizon, say, ten years, a job loss is virtually undetectable. I would add finally, measurement of jobs losses has high uncertainty, and any result commissioned by one party in the debate needs to be taken as an extreme value.
So, do not have too high hopes that a sudden deregulation will create a job boom, especially in a country that has remarkably little regulation to start with.
Showing posts with label regulation. Show all posts
Showing posts with label regulation. Show all posts
The imperfect market for re-insurance
The insurance market is thought to be rather competitive, at least for the most common risks. That is in part because insurance companies are willing to take risks thanks to re-insurance, where they can insure large event risks and to some degree over-exposure. But there are rather few actors on the re-insurance market. Is this bad, and does it have an impact on the insurance market?
Sabine Lemoyne de Forges, Ruben Bibas and Stéphane Hallegatte play with a model of re-insurance and find that there is a trade-off. The lack of competition leads to sub-optimal re-insurance provision, obviously. But is also allows the few players to take on larger risks, some of which may not have been insured otherwise. And, the larger the re-insurers, the more resilient the market can be. As a regulator, this means that means that you may to let the re-insurance companies grow larger than want is optimal in terms of competition.
Sabine Lemoyne de Forges, Ruben Bibas and Stéphane Hallegatte play with a model of re-insurance and find that there is a trade-off. The lack of competition leads to sub-optimal re-insurance provision, obviously. But is also allows the few players to take on larger risks, some of which may not have been insured otherwise. And, the larger the re-insurers, the more resilient the market can be. As a regulator, this means that means that you may to let the re-insurance companies grow larger than want is optimal in terms of competition.
Pollution has an impact on worker productivity
Pollution regulation is typically cast as a game between citizens and firms, the first suffering the consequences of pollution while the second are the origin of the pollution. In such a case, there is no incentive for firms to abate pollution, and the government has to mediate. But could a case be made that firms should be willing, individually or collectively, to reduce pollution. One way can be green labeling, which could increase the demand for their products. Another would be if firms realize pollution has an impact on their on productivity or on the labor supply.
Joshua Graff Zivin and Matthew Neidell take the worker productivity angle by using a dataset of dairy farm workers from a large farm in the Central Valley of California. In particular, they look how ozone levels impact the output of piece rate workers. At it is substantial. For example, a 10 ppb reduction of ozone increases productivity by 4.2%, noting that the standard deviation of ozone levels is 13 ppb. And if you object that some of the workers fall under minimum wage law and may not exert the right effort, be reassured, the authors took that into account. In addition, this impact happens even when the ozone level is well below the current national standards. Realizing this, industry should be more willing to accept the suggested tightening of pollution standards for ozone, and for nitrogen oxides and volatile organic chemicals that are the source of ground-level ozone.
Joshua Graff Zivin and Matthew Neidell take the worker productivity angle by using a dataset of dairy farm workers from a large farm in the Central Valley of California. In particular, they look how ozone levels impact the output of piece rate workers. At it is substantial. For example, a 10 ppb reduction of ozone increases productivity by 4.2%, noting that the standard deviation of ozone levels is 13 ppb. And if you object that some of the workers fall under minimum wage law and may not exert the right effort, be reassured, the authors took that into account. In addition, this impact happens even when the ozone level is well below the current national standards. Realizing this, industry should be more willing to accept the suggested tightening of pollution standards for ozone, and for nitrogen oxides and volatile organic chemicals that are the source of ground-level ozone.
Seat belts lead to safer driving
A classic example of the law of unintended consequences is how seat belt laws gave reasons to drive more dangerously, as car drivers feel more secure. This idea has been popularized by Sam Peltzman and several follow-up studies.
Yong-Kyun Bae puts some serious doubts in this results by pointing out that all these studies were based on aggregate data. Using individual data, which allows to exploit individual characteristics, as well as the circumstances of accidents. And once you control for these factors and exploit cross-state variations of how seat-belt laws became more or less stringent in the last decade, it appears more stringent laws make people drive more carefully. Indeed, pedestrians are getting safer. If this result stands, the challenge is to explain it: do tougher seat-belt laws signal stronger enforcement of other traffic laws? In particular, as Bae suggests, these laws may come in tandem with cell-phone and texting-while-driving laws.
Yong-Kyun Bae puts some serious doubts in this results by pointing out that all these studies were based on aggregate data. Using individual data, which allows to exploit individual characteristics, as well as the circumstances of accidents. And once you control for these factors and exploit cross-state variations of how seat-belt laws became more or less stringent in the last decade, it appears more stringent laws make people drive more carefully. Indeed, pedestrians are getting safer. If this result stands, the challenge is to explain it: do tougher seat-belt laws signal stronger enforcement of other traffic laws? In particular, as Bae suggests, these laws may come in tandem with cell-phone and texting-while-driving laws.
Why are Europe and the US so different in terms of regulation?
Europe and the United States have a different attitude towards many things, and one in particular is regulation. Think, for example, how Europe is adverse to genetically modified agricultural goods, while nobody really cares about that in the United States. Other examples abound, like the little checks there are in the American meat industry or the fact that helmets are not required for motorcycles in most US states. How can such drastic differences arise in countries that after all have a similar standard of living?
Johan F.M. Swinnen and Thijs Vandemoortele show that tiny differences in preferences can lead to large differences in regulation. To prove this, they develop a dynamic model with households, producers and political decisions on whether to allow a potentially objectionable technology. It implies that no regulation is imposed below some threshold level of preferences, and the technology is not allowed above that. This results is, I believe, mostly the consequence of the discrete nature of regulation here: either you allow or you do not. With intermediate levels of regulation, the story may be different. More interesting is the result that there is substantial hysteresis: once a decision is taken one way, it is very difficult to revert it even if preferences or the negative consequences of the technology change. This result is reinforced by the discreteness of regulation, but would most likely be present even without it. In other words, it is possible that tiny initial differences in preferences between countries can lead to large regulatory differences that cannot be overturned.
PS: As in much of this kind of literature, quadratic costs are imposed. I always wonder whether this functional form has implications on results, but nobody seems to care.
Johan F.M. Swinnen and Thijs Vandemoortele show that tiny differences in preferences can lead to large differences in regulation. To prove this, they develop a dynamic model with households, producers and political decisions on whether to allow a potentially objectionable technology. It implies that no regulation is imposed below some threshold level of preferences, and the technology is not allowed above that. This results is, I believe, mostly the consequence of the discrete nature of regulation here: either you allow or you do not. With intermediate levels of regulation, the story may be different. More interesting is the result that there is substantial hysteresis: once a decision is taken one way, it is very difficult to revert it even if preferences or the negative consequences of the technology change. This result is reinforced by the discreteness of regulation, but would most likely be present even without it. In other words, it is possible that tiny initial differences in preferences between countries can lead to large regulatory differences that cannot be overturned.
PS: As in much of this kind of literature, quadratic costs are imposed. I always wonder whether this functional form has implications on results, but nobody seems to care.
You want to restrict bankers' pay
There has been and there still is much outrage about the large bonus payments bankers get. What the public does not understand is that bonus pay is a very large part of total pay, and it is so to encourage bankers to perform really well. And they certainly put in the hours. For example, bonus pay has been criticized because there is most often no "malus," but given that base pay is relatively low, this should capture it. The main criticism is aimed at the disparity of these bonus payments with respect to the average pay of a worker. This is, however, not something that should be regulated at the level of bonus pay, but through redistribution with income taxes. In this regard, whether it is regular pay or bonus pay makes no difference. So, should then bonus pay in banking be left unregulated?
John Thanassoulis does not think so. He argues that as bank compete for top bankers and try to shift the risk on them, they end up paying them too much and all in bonuses. This is optimal for the bank as it lowers its costs right when things get critical. But as a consequence, the bank gets too much into risky activities, as competition for bankers drives bonuses up higher than socially optimal, especially if there is a contagion risk of default for other banks. So you want a regulator to limit bonuses, but in a flexible way, or the benefit of having bonuses in the first place gets eroded. Indeed, it is the top brass that sets the bank level risk, whereas other employees all the way down to secretaries (who also get bonuses) are less influential, even collectively, on the aggregate risk. Thus the idea is not to cap bonuses individually, but at the bank level as a proportion of the balance sheet (which is what matters in terms of default). The pay structure would then presumably be readjusted by the bank, relying more on bonuses where it matters the most. Taxing bonuses has no risk impact, though, except for reducing bankers' pay.
Another possibility could be the dynamic incentive accounts I mentioned before.
John Thanassoulis does not think so. He argues that as bank compete for top bankers and try to shift the risk on them, they end up paying them too much and all in bonuses. This is optimal for the bank as it lowers its costs right when things get critical. But as a consequence, the bank gets too much into risky activities, as competition for bankers drives bonuses up higher than socially optimal, especially if there is a contagion risk of default for other banks. So you want a regulator to limit bonuses, but in a flexible way, or the benefit of having bonuses in the first place gets eroded. Indeed, it is the top brass that sets the bank level risk, whereas other employees all the way down to secretaries (who also get bonuses) are less influential, even collectively, on the aggregate risk. Thus the idea is not to cap bonuses individually, but at the bank level as a proportion of the balance sheet (which is what matters in terms of default). The pay structure would then presumably be readjusted by the bank, relying more on bonuses where it matters the most. Taxing bonuses has no risk impact, though, except for reducing bankers' pay.
Another possibility could be the dynamic incentive accounts I mentioned before.
Privatization-nationalization cycles
The past two decades have seen an impressive wave of privatizations all around the world, especially in utilities and resources. This trend has recently been reversed though, with several large nationalization waves, in particular Latin America. This kind of cycle is not new, as especially the gas industry has gone through several waves each way during the last century. Why all this back and forth?
Roberto Chang, Constantino Hevia and Norman Loayza observe that nationalizations typically happen when the price of the output of reference is high and inequality of wages is also high. The opposite is the case for privatizations. They can explain this with a model of a benevolent government that maximizes a social welfare function represented by the average utility of workers. Under nationalization, all workers are paid the same and exert little effort. Under privatization, firms can discriminate workers, who then put more heart at work, creating wage differentials. When prices for the commodity increase, this generates larger rents for the most productive, and inequality increases.
The story is then of a inequality-efficiency trade-off for the government. In the naturalized state, inequality is low, but so is efficiency. If prices are low, it is more important to increase efficiency, and the firm is privatized. But as it becomes more efficient and discriminates its workers, inequality becomes more important, and the firm is nationalized back. And the cycle continues, with an average of 12 years of privatization and 25 years for nationalization. While this is a very stylized story, after all the model assume an economy with a single sector that has no impact on world prices, it is still a compelling story.
Roberto Chang, Constantino Hevia and Norman Loayza observe that nationalizations typically happen when the price of the output of reference is high and inequality of wages is also high. The opposite is the case for privatizations. They can explain this with a model of a benevolent government that maximizes a social welfare function represented by the average utility of workers. Under nationalization, all workers are paid the same and exert little effort. Under privatization, firms can discriminate workers, who then put more heart at work, creating wage differentials. When prices for the commodity increase, this generates larger rents for the most productive, and inequality increases.
The story is then of a inequality-efficiency trade-off for the government. In the naturalized state, inequality is low, but so is efficiency. If prices are low, it is more important to increase efficiency, and the firm is privatized. But as it becomes more efficient and discriminates its workers, inequality becomes more important, and the firm is nationalized back. And the cycle continues, with an average of 12 years of privatization and 25 years for nationalization. While this is a very stylized story, after all the model assume an economy with a single sector that has no impact on world prices, it is still a compelling story.
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