Looking forward to discussing the Matthijs and Blyth paper today. It’s one of the most important out there and a great way to bring the class to a close.

I want everyone to pitch in with their own ideas about the growth regime we find ourselves locked in and the sources of instability we are facing.


Simulation prep

1) Which parts of the loan document should you focus on for the simulation?

Only the parts that deal with fiscal policy. Control F the doc and you will find out.
3) When and how will we you assigned countries?

Here is the IMF executive board. I want an excel spreadsheet in my email by Weds at 5 on which name you will be representing. Failure to do so will result in me doing the assignments.
2) What will be the nature of the paper you have to write based on the simulation?

This is the template of the position paper you have to submit:

Do make it shorter though. I want exactly 750 words. No exceptions.

The rest of our sessions

April 18: IFIs and corruption

Persson, Anna, Bo Rothstein, and Jan Teorell. “Why anticorruption reforms fail—systemic corruption as a collective action problem.” Governance 26.3 (2013): 449-471.

Transparency International Report on the EIB

April 20: IFIs in the age of the anti-globalization backlash

Blyth, Mark, and Matthias Matthijs. “Black Swans, Lame Ducks, and the Mystery of IPE’s Missing Macro-Economy.” (2017). at

April 25. IMF board simulation

April 27. IMF board simulation

May 2: Wrap-up






Who stole the jobs: robots or globalisation?

Or less tendentiously, was the falling number of manufacturing jobs in rich countries caused by trade liberalisation or by automation and other productivity-enhancing technological change? For it is largely manufacturing jobs we are talking about. The US is special in that overall employment rates (for all jobs taken together) have fallen since the turn of the century, and for longer than that among men. As Jason Furman and his colleagues have documented extensively, the US faces an especially aggravating version of a more common problem in which manufacturing jobs have not only disappeared but failed to be replaced by anything at all. So are robots or trade to blame? The simple and largely true answer is: both. But there is still a question of their relative importance, and of what exactly the blame entails. All industrialised countries have been shedding labour in manufacturing for decades, a process that started before the wave of globalisation in the 1990s. It is clear that the balance of trade has little to do with it: the similarities between structural change in employment in perennial-surplus Germany and permanent-deficit US are much greater than the differences. But the growth in overall trade that accompanied the regional and global trade liberalisation during the three decades before the global financial crisis will have the effect of changing the employment and production structure of the opening economies — indeed that is part of the point of lowering trade barriers. Standard theory predicts that with more open trade, countries will specialise more intensively in production that makes most use of their relative endowment of labour, skill, capital and natural resources. Recent research by Adrian Wood measures to what extent this has indeed happened. As the table below shows (for more detail, look up the background paper) the share of manufacturing in global production and employment fell noticeably in the three decades from 1985. But different regions went through dramatically different changes. In particular, in most land-scarce regions (particularly prone to specialise in manufacturing, according to theory) the share of manufacturing in the economy expanded, while it shrank in all land-abundant ones. Wood suggests this shows that the dramatic changes in manufacturing employment can be laid at the door of economic globalisation. But the story is not as simple as that. Look where the biggest changes in employment shares happened. Among rich countries (OECD members), the manufacturing employment share fell just as much in land-rich and land-scarce economies. The output share increased in land-scarce ones — but in conjunction with the loss of manufacturing jobs, this is surely an effect of automation and technology. Meanwhile the two other regions with particularly large structural changes were the Soviet sphere, which in 1985 had an overgrown and inefficient manufacturing sector that collapsed under its own weight once the economy was liberalised, and China, whose liberalisation and trade integration surely contributed to its industrial revolution. What all this points to, then, is a process in which many poor countries went from a pre-industrial employment structure to an industrial one (but some stagnated, in particular in Africa), and in which all rich countries largely went from an industrial to a service-based employment structure. The poor country transition or lack of it no doubt owes a lot to the ability to enter the world trading system. In the rich country transition this looks more like what you would expect from the continued growth in manufacturing productivity, echoing what had earlier happened in agriculture. And conversely, there is direct evidence for the automation thesis. A new study by Daron Acemoglu and Pascual Restrepo tries to measure the effect on US jobs (and wages) of the increased use of industrial robots. An interview with Acemoglu about the findings puts the number of manufacturing jobs lost because of robots at 670,000 between 1990 and 2007. What lessons can we draw? First, that both trade and automation play a role. But second and more importantly, that the two cannot be neatly separated — automation-driven productivity growth and attendant job loss may be both an ultimately unavoidable part of economic change and be accelerated by trade liberalisation. Rich economies with highly-skilled labour forces are well placed to respond to greater trade by specialising in higher-value added products — just those where automation can do the most to increase productivity. Hence, for example, the success so far of the US car industry, which produces more vehicles than ever and exports finished cars to China. But third, that a protectionist trade policy may not do much good even if trade was part of what eroded certain jobs in the past. For if trade helped automation along the way, it is not as if restricting it will wind automation back. At most it may delay further automation, but that will come at a cost. In particular, it will make it harder to export manufacturing goods into a global market that uses the most cost-effective techniques. Trade sceptics who aim to protect manufacturing jobs should be alert to the distinction of protectionism and mercantilism. While the latter aims to boost exports, the former, by restricting imports, may well hold back exports, too.

Observations on inequality from Phil Mayfield (Thanks!)

Observations on the Economic Inequality Issue


Class made it abundantly clear that there is no surprise about the perception in the growth in economic inequality.


The various political campaigns around the world also indicate that people are responding to that growth in various ways. The populist wave is rooted in part in a desire of the losers to send a message to the winners in the global economic world.


The academic literature researching inequality is also growing.


We are now engaged in a search for an approach which can have the political pithiness of the Arthur Okun dictum that redistribution and growth are antithetical to each other and which was encapsulated in the Washington Consensus and thus embedded in the neoliberal prescriptions for fiscal and monetary policy.


The literature documents progress on some fronts.


  1. The recognition that the GINI Coefficient [GC] which underlies most media accounts of the growth in inequality has some serious deficiencies. The most important distinction is between ‘market inequality’ and ‘net inequality’.[1] This enables us to be more precise when comparing GINI Coefficient changes over time. This distinction has begun to seep into the media thus enabling a more catchy way of comparing countries or states. Talking about ‘redistribution’ in academic circles may be one thing. Talking about it politically is quite another. So Jeffrey Sachs was able to write a column for the Boston Globe in which he compares the GC on gross or market income with that on disposable income and thus avoiding the ‘hot political button’ of the term ‘redistribution’.[2]


I think the term ‘disposable income’ has several advantages over the term ‘net inequality’. More people instinctively know the meaning of ‘disposable income’ compared with ‘net’. Secondly, it permits more room to include or exclude some of the variables which have been discussed in the GC discussions.[3] Also the ongoing discussions in the US about redefining the poverty index, have focused more on the basket of goods needed for basic living thus establishing a more realistic poverty measure.[4]


  1. The research on mobility between economic quintiles is establishing more information on how growth policies are affecting people’s mobility between the income quintiles[5] (as reflected in market incomes and redistribution policies and as reflected in net incomes or disposable incomes). The studies indicate that income and wealth mobility is quite low, despite ‘common economic wisdom’.


Piketty’s case is that even if you exclude ‘rentier’ income there is a widening gap since the 1980s between the upper income and middle/lower income groups and that a major cause is the rise of ‘super managers’[6]. Piketty’s observation has also been studied in some other research. Walter Frick cites some research by David Card of Berkley and Nicholas Bloom of Stanford about the rise of the multi-nationals and the sorting effect.[7] This sorting effect essentially posits that as multinationals or other large corporations grow in size they have more purchasing power. This purchasing power enables these companies to invest more in people (with more talent), productivity (e.g., computers), patents (intellectual property) and lobbying (reducing competition or blocking negative legislation and rules). This in turn strengthens these ‘frontier firms’ in their competitive position thus completing the circle of sorting.[8] Depending upon the country, Card and Bloom posit that as much as 40% of the GINI Coefficient in a country can be explained by this phenomenon. In addition another study looked closely at pay within [UK] companies and concluded that “pay disparities between top-level jobs – those where

managerial skills and responsibility are most important–and bottom-level jobs are increasing in firm size. By contrast, pay differentials between jobs involving either no or only little managerial responsibility are invariant to firm size. Moreover, firms with higher within-firm pay inequality have better operating performance, higher Tobin’s Q, and higher equity returns”.[9]

[1] Forcefully stated in Jonathan D. Ostry, Andrew Berg, and Charalambos G. Tsanagnarides. “Redistribution, Inequality, and Growth”, IMF Staff Discussion Note February 2014.

[2] Jeffrey D. Sachs. ‘Facing up to Inequality’, Boston Globe October 2, 2016.

[3] See Ostry, p12.

[4] For example, the cost of shelter is not included in the current poverty index used in the US. The Supplemental Poverty Measure introduced in 2010 reflects a consensus amongst economists on a measure more in tune with reality and does include shelter costs.

[5] It should be noted that while there is much to be said from a media viewpoint for the division of family, household or personal income into upper, middle and lower classes (e.g., Thomas Piketty. ‘Capital in the 21st Century’, Harvard University Press, 2014, pp.250-255) but there is more precision when one divides income into quintiles.

[6] Ibid., p.303.

[7] Walter Frick. ‘Corporate Inequality is the Defining Fact of Business Today’, Harvard Business Review May 2016.

[8] Dan Andrews, Chiara Criscuolo and Peter N. Gal. ‘The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy’, OECD Productivity Working Papers 5, OECD Publishing, 2016, p 17.

[9] Holger M. Mueller, Paige P. Ouimet, Elena Simintzi. ‘Within-Firm Pay Inequality’, SSRN January 2016 revised January 2017.