Tag Archives: welfare states

Why I am for the Swiss basic income proposal, but for somewhat unorthodox reasons

 

Swiss people will be the first to vote on the introduction of a nation-wide basic income scheme. While the details of such a scheme would be to be designed at a later stage, it is quite clear that such a move would be quite revolutionary. I myself quite literally sit on the fence, but I would arguably vote for a modest and well-designed version of a basic income scheme. Yet, I do not necessarily share the beliefs in the pros and cons often mentioned.

First and foremost, one has to be honest: there is a huge uncertainty proposing such a scheme. No country has ever done it on this scale. If you are risk averse that’s not good news. It’s honest news though. And perhaps, we should also give a premium on policy experiments (see below). Now let’s move to some arguments usually held against the basic income scheme.

A main argument is about work incentives. I think this cannot be easily discarded, but it shouldn’t be exaggerated either. If there is any type of graduation in the benefit, say in the form of a negative income tax, people taking up work ‘graduate’ into paying full tax load. At the margin, taking up work still will make sense (much more than with a standard social assistance scheme). The work-incentives literature is very complex and somewhat contradictory, but if anything it shows the disincentives are less than often feared. This is good news. That’s said there could be a major one-time reshuffling of the labour market which is hard to anticipate. Some jobs may simply prove to be too unattractive to do, once people have more choice and less financial pressure. Some of these jobs may also be jobs that would fade away due to technological progress anyways. In other cases, e.g. care personnel in social services society would need to re-evaluate its worth.

The perhaps even bigger argument against basic income schemes are the fiscal costs. That’s the aspect where a fiscally conservative country like Switzerland would truly break a barrier. There is even agreement on this point between advocates and critiques (gated link only, thanks NYT) of these schemes. The total costs seem enormous, even if, in part, they are offset by savings on other programs. Yet, more to the point, it would send a strong signal: you can still do progressive welfare state policy in an age of austerity. I don’t think it likely that one can finance such a scheme through yet another innovation such as a financial transaction tax (which, anyways would only really work if countries coordinated on such a tax). The main alternative would be a rise in VAT, in part eating up the benefit, and perhaps further increasing inflation. But the regressivity of the VAT raise would at least be compensated by the fact that the basic income scheme benefits the poor more than the rich.

Now to the alleged advantages. Most observers argue that such a system is very simple, very efficient, since it saves a lot of costs of targeting, monitoring conditions of standard social minimum programs etc. But is it true that a simple tax & transfer system is necessarily a good one? I am a bit more skeptical about the long run implications of such a system. While it is true that different forms of social protection and assistance have grown too far and sometimes even contradict each other, welfare states have to be necessarily multi-dimensional, complex institutions with several different programs.

The argument about simplicity seems to what unites what, in other respects, is a fairly heterogenous support group. Some conservatives would see a basic income scheme as a chance to put a tap on all other forms of spending: Why having an unemployment insurance, pension insurance with some redistributive qualities if you have a basic income?

There is a real-world example which exemplifies this danger. The closest relative to basic income schemes, social assistance/ minimum assistance programs are usually not very generous, one might even call them stingy (usually a country spends some 1-2% of GDP on social assistance, but up to 6-7% for pension or health). Some scholars call this the paradox of redistribution: only when the middle class benefits they agree on social policies. Does the middle class benefit from basic income? Not much, given that typically their tax allowance would eat up most (or all) of the basic income transfer. That could make them very skeptical about the scheme in the first place.

One of the key normative benefits of basic income is also one of its key political weakness: unconditionality. You may argue why unconditionality is normatively speaking a good thing:  why should rich people also get it? But on the plus side, these schemes are easy to implement, and do away with much of paternalistic welfare state policy and monitoring. Basic income schemes also take away a lot of stigma applying for the social minimum etc. But this feature is also what seems to make some conservatives quite hostile against these schemes: They want to see conditions (employment, health etc., having children) attached. These conditions often make little sense economically, and are sometimes, quite frankly inhumane (for instance, by asking lone mothers to work for a minimum wage). But very often, unconditional cash transfers are hard to sell to this ideology.

A final argument in favour of minimum income schemes is of the type ‘desperate times need desperate means’. Even some prominent economists nowadays come around on the question whether technology generates or destroys jobs. Not long ago, this position was still called a (lump-of-labour) fallacy. Yet, you don’t need to support basic income because you think technology makes labour redundant. Even if you don’t believe in a strong version of lumps of labour, the enfolding dilemma of a materially tremendously prosperous society with a very unequal distribution of income and work is enough to merit tools for insurance and redistribution.

This is where the basic income scheme has received new tailwind. It’s a type of thinking outside the box. To some degree, European welfare states have been stagnant, self-devouring, or plainly in decline for the last 30 years. Going further back in time reveals that welfare states have always struggled with structural change in labour markets. The first big transition (from agriculture to industry) was the cradle of the welfare state; the the second (industry to service) already showed some of its limits;  the third (more automatization) will need a more inventive approach to policy. Given that labour markets will see sectoral change, perhaps at even increasing speed in the years to come, experimentation wouldn’t necessarily be a bad thing, it might be the only thing.

Given my own level of doubts, comments are greatly appreciated.

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Education levels for Trade Union Members over Time

Some years ago, I calculated the difference between the educational attainment of trade union members relative to non-members. I was interested, first, how ‘skilled’ trade union members are relative to those not in a union and, second, about the trend in time. I wonder whether trade unions have become ‘more’ or less representative of the work force, and how close they are to the low-wage sector.

Here is a graph using the Eurobarometer trendfile and some additional information from the World Value Surveys.

graph_education_levels_difference_unionmembers_nonmembers

The graph is quite insightful. Members seem to have higher educational attainment then non-members in most countries. It is hard to generalize a trend. Some countries see a relative increase (e.g.Belgium and Italy), some a decrease (France, perhaps West Germany).

There are, of course, some huge measurement issues, using this kind of comparative survey data. I cross-checked with aggregate union density data and found that the share of union members in some of the surveys differs quite a bit (especially for Portugal), but all in all the correlation is visible.

Graph_undenneu_allmean

 

 

Therefore, I wonder, has anyone tried to do something similar, or is there someone with more recent data? Comments, hints much appreciated.

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Five political lessons of the #PanamaPapers

1.) The size of the problem and why they cheat:
The Panama Papers are hard to quantify given the complexity and size of information. However, they reveal fascinating insights into the reasons for evading and avoiding taxation. This has deeper implications for the overall size of the problem. Some economists like Gabriel Zucman (@gabriel_zucman) estimate the size of informal/ illegal assets held in tax havens close to 10 percent of all global wealth. This is a ballpark estimate and hinges on many assumptions. For instance, as an economist Zucman is mainly concerned about the economic reasons of tax evasion, i.e. not to pay taxation. But there is a political story to it: Many prominent politicians, business people, celebrities need to conceal their true wealth levels. And they do so for other reasons than tax evasion (social pressure, political regulations/ corruption and higher morality standards applied to politicians). This leads to much more under-reporting, and so arguably the issue at stake is even larger.

2.) International Relations and who is to blame:
Panama and similar tax havens can only operate in the shadow of its big brother: the U.S., the UK etc (see a previous blog post). No tiny tax haven can survive and attract money without the legal backing of a large and sovereign state. Otherwise financial investors would fear the lack of legal security in these countries and would avoid them. (The exceptions are larger, sovereign tax havens such as Switzerland.) Hence, the problem is on the international level not only isolated in tax havens themselves. Larger, sovereign countries profit from smaller tax havens in a symbiotic relationship. After all, the money rarely stays in Panama or Guernsey Island, it flows back to the financial centers.

3.) What will the public think?
The real political problem goes far beyond the material damage in lost tax income. If people think other people don’t pay taxes they are also more likely to cheat! Unfair tax behavior leads to large scale erosion in the legitimacy to pay taxes. And tax compliance in recent years already took a serious hit in many countries. For instance, here is data on two different waves of the World Value Survey. The question asked is whether it is justifiable to cheat on paying taxes. The average over a group of 50 plus countries has considerably increased over the period, i.e. people become more cynical about paying taxes.
cheatingtaxation

4.) Populist response?
If both the current and past presidents of Argentina are involved in these practices, as the PanamaPapers imply, what will the Argentine public make of this? Beyond Argentina, what happens if people see their politicians cheating? Once the public thinks that all types of politicians, irrespective of their ideology, background or political program, are involved in illegal practices the reaction is likely populist, anti-elitist. Paradoxically, this could mean that anti-government ideologies piggy-bag on these scandals. Isn’t the real problem big, bureaucratic government trying to steal our money? A Leviathan so big and yet so incompetent in levying taxes deserves to be cheated on. Of course, such an argument is like saying I robbed a bank, because the security guards seemed so weak. Yet, the inconsistency is not easy to observe for everyone. Political cynicism might well play into the hands of those who produce the problem of tax evasion in the first place (The Trumps etc. of this world).

5.) Football is the perfect micro cosmos to study problems of global political economy
As if further evidence was needed, FIFA’s involvement in the PanamaPapers shows how much one can learn from the world of football for the larger issues of regulating capitalism. FIFA does not need to avoid taxes, it needs to avoid transparency. Especially, when it needs to exchange bribes. Again, this implies that the issue is much larger than we think. In this sense studying FIFA is not only interesting for those who are fanatic about football.

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Book Review: The Second Machine Age

Throughout history, people have predicted the end of work, but so far it has never materialized. Is this time different? In their book The Second Machine Age Erik Brynjolfsson and Andrew McAfee argue it is. Machines already beat people regularly in chess and in the near future they will drive our cars, educate our students or do Amazon’s logistics. The authors call this the 2nd machine age. The first machine age, the industrial revolution, fundamentally transformed the world of the 19th century. In a similar way, the infinite re-combinations of computers and robots will transform our society.

Lao Tzu allegedly said “Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.” Indeed, the authors show that some predictions about the new age have proved remarkably wrong. Until recently, for instance, most experts believed that computers are bad at pattern recognition and good at routine tasks. Today it seems hard problems (computing a strategy for chess) are easy for computers, but easy tasks (found in the care or service sectors) are surprisingly difficult. In general however, the frontier of things machines can do is shifting rapidly. Automatically generated contents in newspapers or machines grading students’ essays are just two examples of what intelligent machines can do. The world seems at an inflection point. The authors invoke the famous Moore’s law that computer power doubles every year and find exponential growth confirmed in many dimensions of technological progress.

Skeptics wonder why this has not transformed into higher growth rates in the last years. Indeed, freely available content and cheap replication make it hard for many economists to see the profitability of it all. And yet, there is massive positive change even if its traces are more difficult to measure. First, the internet, Brynjolfsson and McAfee argue, has risen well-being by much more than growth in GDP; so much, in fact, that the 2nd machine age needs a different metric to think about social progress.  The authors estimate that intangible assets would add another 2 trillion to existing capital assets in the U.S. alone. Second, it will need time to make people realize the full potential, just like in the 1st machine age: it took several decades from the invention of the steam engine to using it in transportation and construction. Third, modern information technology lets people increasingly access to the world’s stock of knowledge. Options multiply. This is good news.

The bad news is, the benefits are not evenly spread. Sure, consumers benefit enormously, but the majority of employees will lose. Skill-based technological change puts a huge premium on college degrees, and creates job loss especially in routine blue- and white-collar occupations. Technical change can be relentless. Photo company Kodak, which in its heyday employed nearly 150,000 people, filed for bankruptcy in the same year Instagram, merely employing 15, was sold to Facebook for $1 billion.  Upswings in the economy create less and less new jobs, the share of labor in GDP is on decline in recent years, and even within this share, the spoils are distributed more and more unevenly. We experience the rise of what they (and others) call the superstar economy.

The dynamics of the superstar economy can be described in many ways: the Matthew effect (“For unto every one that hath shall be given, […] but from him that hath not shall be taken even that which he hath.”), a winner-takes-it-all society, or, more prosaically, the dominance of power laws. One J.K. Rowling sells a multiple of books of her closest competitors, those competitors sell a multiple of their closest rivals etc. These effects are hard to deny. Even if the authors refute simplistic fallacies, they are remarkably ambivalent about the possibility of what Keynes famously called technological unemployment. Granted, voluntary unemployment might be a society’s ultimate goal, freeing human potential from menial occupations, but this would require a very different social contract from the one we are seeing nowadays.

It is hard to predict in which domains the comparative advantage of humans will survive. Skills complementary to machines, e.g. engineering or data analysis, will probably be in higher demand. ‘Nerd is the new sexy’, as they say (though I suspect that it is, by and large, nerds who say that). To rather race with than against the machines the authors suggest a (laundry) list of short-term policy recommendations: teach children well, use technology, improve matching on the labor market, more infrastructure etc. In the long run, the authors recommend more controversial tools such as basic income schemes, or labeling products with high percentage of human content.

All in all, this is a very easy, sometimes gripping, even alarming read. The authors’ liquid writing style, in combination with a nice batch of anecdotal and systematic evidence attracts huge readership in and beyond academia. In some ways, the book is remarkable, especially given its provenance: M.I.T. It seems that the recent crisis has truly shaken mainstream economics and opened the cracks for heterodox thinking. Talking about technological unemployment seemed to be close to blasphemia only years ago. In this sense, the book is a welcome game changer that allows the public discourse to talk about really important issues of our time. The problem is, many economists tend to be ill equipped for these purposes.

In the same way economists for long have defined away the problem of inequality as none of their business, job scarcity has, by and large, been a temporary or government-related issue. In some sense, this has changed. The new rise of automatization has created a lot of scholarly interest, much of which is referenced in the book: David Autor, Daron Acemoglu, Joseph Stiglitz, Lawrence Summers to name but a few. The diagnosis coming from this research seems sound – as far as I can judge – even if somewhat partial. Most examples come from technology enclaves around M.I.T. and Stanford. It is a bit alarming that many of the aforementioned authors even use the same case studies, which questions the generalizability of these findings. A bit of long-term perspective would also sometimes be consoling: perhaps the largest transformation of labor markets so far was not the industrial, but the agricultural revolution, which essentially made everyone work for endless hours on the fields, only to barely survive on a highly unbalanced diet of mono-crops.

Whereas the diagnostic part of the book is fascinating, the sections about policy recommendations are disappointing. They basically don’t go beyond the litany of ideas found in any econ 101 textbook. Looking at some policy recommendations in more detail fully reveals a kind of helplessness usually only seen in a rabbit shortly before being gobbled up by a snake. When it comes to schooling the authors first criticize the tendency of educational institutions of being too lax and not making students work enough before they praise Montessori schools for letting pupils decide themselves what they are best at.

In more general, the book shows the kind of lop-sided thinking which cares much more about allocation than distribution. However, the main issue has and always will be a problem of distribution. Taking away unemployment benefit systems and job agencies, takes away (legally defined) unemployment, but it does not take away underemployment, inequality and poverty. In this sense, the recommendations found in the book are fairly limited. If social scientists like Karl Polanyi are right, the first industrial revolution generated a new kind of welfare model. Back then higher growth did also not turn automatically into benefits for everyone, and perhaps would have never, if not for better organization and more solidarity among workers. If contemporaries of the industrial revolution had recommended something akin to things Brynjolfsson and McAfee suggest, we would still have Speenhamland (British 18th century poor laws) instead of the modern welfare state. In this sense, the new revolution, assuming that there is one, will only benefit everyone, if accompanied by fundamental changes in the social contract.

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Why Top Universities Feed on Income Inequality

Since my time serving as director of a PhD school I stumbled more and more into the issue of higher education. One problem that strikes me as odd is trying to convert university education into a business in adverse contexts. For instance, how do you make a living on students’ fees in a country like Hungary, where average purchasing power is not high? Moreover, and that’s the real issue I want to talk about, in such countries the average is pretty average: there is relatively little dispersion and hence income inequality is low. With little income inequality it is hard to recruit students from well-off families that would be willing and capable of paying high tuition fees.
This is why Thomas Piketty in his famous book is convinced that the increasing income inequality driven by top (wage) incomes in the US is correlated with increasing tuition fees of Ivy League universities. Both processes arguably feed each other: higher income inquality (and higher incomes it is fair to say) allow top universities to charge higher fees, and graduates afterwards need to earn more to justify higher investments. I wondered whether this argument also has some currency beyond the US. Specifically, it made me thinking what actually ‘explains’ how many highly ranked universities a country has.
As in one of my previous posts, I used the Times Higher Education ranking for the global top 400 universities. Graph_no_highed_per_country shows the number of higher education institutions per country. I needed to log the numbers, since otherwise the US would be well off the chart. It is easy to see that large countries have more, though some tiny countries like Hong Kong or Singapore do perform, relatively speaking, very well. What’s more interesting is that Anglo-saxon countries are so dominant. Why?

Well there may be many reasons. I ran a little and very naïve regression, with very few observations, so it is clearly not a definite proof of what is going on. Nonetheless results are interesting. I use the logged number of institutions as dependent variable, and control for population size, economic prosperity (GDP per capita), spending on education (as % of GDP), and income inequality (measured as GINI coefficient by OECD). It is somewhat tedious to look at regression tables so I spare you the details. It suffices to say that population size and economic prosperity are both important explanatory factors. Spending on education is, by the way, negative which might imply that country spending more do spend more on mass education than top universities. More interesting to me is the relationship with income inequality. graph_inequality_topuni shows the partial relationship between income inequality and the number of top universities. Indeed it is positive (and statistically significant, if not with the highest power of test one could imagine). Moreover the effect is sizeable, if the GINI coefficient increases by some 4% points (one standard deviation from the mean, or roughly from Germany to the UK) the number of top universities more than doubles. This means that if income inequality rises, so does the number of top universities!
Let me hasten to add that this is not a causal claim. You cannot simply increase inequality and get more or better (top) universities. But it might show the limits of building institutions for the elites, in countries that are anti-elitist. And once more, it shows the futility of using higher education rankings, if you want to know how good the average university in a country is. It rather seems that is hard for countries to have both many top universities and good mass education. But why does seemingly everyone nowadays automatically favour the former over the latter?

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Why Large Welfare States Should Use Different University Rankings

Rankings are very fashionable among politicians and policy experts. Universities are no exception to this. Yet they are very controversial, and there are endless fights about a ‘fair’ methodology. Recently, the German political science association even recommended its member institutions not to participate in the most important German ranking any longer

Indeed, loosers of these rankings often complain and maybe sometimes for good reasons. One option is not to produce rankings, but another option is to produce other rankings. I, for once, would love to see an international ranking of the average university in a country. I think this would make a big difference. The reason is that many countries, especially in Europe, but maybe also in Asia have social and political preferences for redistribution and the equalization of standards of living. These countries carefully avoid too much social and regional heterogeneity. Money flows from rich to poor individuals, from strong to weak regions. Under these circumstances, we should not expect universities to reach the top of international rankings. A fairer measure would be how good a university on average would do, compared to an average university in another country.

Currently there is not enough data to do this. But we can do some simple exercises with the available rankings. I choose the Times Higher Education Ranking for 2012-3 with detailed info for the top 200 and less detailed info for the next 200. From this I compute the country average of those universities listed. This gives a different view on who is top and who is not.

We all know that US universities dominate the top. But how do they do on average? If we use the detailed info for the top 200 we see that the US is not top any more but third (See table 1). China and Singapore rank 1 and 2 respectively. This seems exaggerated, and indeed it is. The problem is, we do not get information about the weakest universities, since they do not appear in the ranking. Stats people call this selection bias.

There is no way to avoid the problem, but we can at least extend the list to the top 400 universities (see table 2). If we look at the averages for the top 400 we see that the US is now fifth and Singapore is top (after all there are not that many universities in Singapore). China goes down the table. The Netherlands is now second. These are mock results. I am not saying that these are the real country rankings. But they illustrate the idea.

In more general we see that many European countries improve once we look at the averages of a larger number of universities. Countries that redistribute less go down in the ranking. Japan, Australia are extreme cases. There are important exceptions to this rule, such as Switzerland or Israel, not the most benevolent welfare state, but shooting up in the rankings. And yet, for countries in which the whole polity is built around avoiding excess inequality, it would be wise to focus on averages and not the champions.

 

 

Table 1: Countries Ranked by Average University’s Position in Top200

location avg. university rank country rank
China

49

1

Singapore

58

2

United States

86

3

Republic of Korea

90

4

Australia

93

5

Canada

93

6

Sweden

97

7

Japan

99

8

Switzerland

100

9

Netherlands

100

10

Hong Kong

102

11

Finland

109

12

United Kingdom

111

13

South Africa

113

14

France

116

15

Germany

122

16

Belgium

127

17

Denmark

132

18

Taiwan

134

19

Republic of Ireland

149

20

Brazil

158

21

New Zealand

161

22

Austria

162

23

Israel

163

24

 

 

 

 

Table 2 Countries Ranked by Average University’s Position in Top400

location avg. university rank country rank
Singapore

58

1

Netherlands

109

2

Switzerland

128

3

Republic of Korea

135

4

United States

155

5

Israel

163

6

Hong Kong

166

7

Sweden

182

8

United Kingdom

183

9

France

188

10

Canada

191

11

Germany

204

12

Belgium

207

13

Brazil

211

14

Denmark

212

15

Russian Federation

226

16

China

228

17

Japan

230

18

Australia

232

19

South Africa

248

20

Turkey

253

21

Norway

260

22

Iceland

263

23

Republic of Irel

265

24

Austria

269

25

Finland

280

26

New Zealand

282

27

Taiwan

291

28

India

292

29

Spain

299

30

Italy

315

31

Czech Republic

326

32

Greece

326

32

Iran

326

32

Saudi Arabia

326

32

Colombia

376

36

Estonia

376

36

Mexico

376

36

Poland

376

36

Portugal

376

36

Thailand

376

36

 

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