Tag Archives: GDP

What should rule the world after GDP?

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[amazon_link id=”0691166528″ target=”_blank” ]The Little Big Number: how GDP came to rule the world and what to do about it[/amazon_link] by Dirk Philipsen is out next month. As a GDP-afficionado, I was eager to read it and found it an enjoyable read and generally thought-provoking, although not agreeing with the author on all points.

[amazon_image id=”0691166528″ link=”true” target=”_blank” size=”medium” ]The Little Big Number: How GDP Came to Rule the World and What to Do about It[/amazon_image]

My main disagreement was more about tone and exaggeration in what is a rather emotional book. There are for example assertions like: “It is safe to say our ancestors, for some 200,000 years prior to the agricultural revolution, engaged in labour only to the very extent to which it helped them survive.” Really? No cave paintings, ancient jewellry, religion? Was Stonehenge essential for survival? Or, because of our “fixation with the accumulation of things”, trying to capture the reality of late 18th century life “by saying that people were poor would represent a fundamental misread.” So were they not less well-nourished than us with more illnesses and shorter lives and many children dying in infancy? Did women (and even men) not spend hours in domestic drudgery? I don’t hesitate to call people in the 18th century poor on this basis; it’s nothing to do with a passion for accumulating cars or handbags. I don’t want more than one washing machine but wouldn’t be without the one – just like Hans Rosling’s mother.

Many readers will like the polemical tone of [amazon_link id=”B00V943S3W” target=”_blank” ]The Little Big Number[/amazon_link]; it’s obvious I didn’t. Apart from that, this is an interesting book looking at the history of GDP, its inadequacies as a measure of social welfare, and the environmental consequences of seeking continuing economic growth. It covers some of the same ground as my own [amazon_link id=”0691156794″ target=”_blank” ]GDP: A Brief but Affectionate History[/amazon_link], with additional detail. The book identifies the turn to growth rather than levels of national income as a policy aim in the 1950s. Philipsen attributes this to American optimism as the victor in World War 2. I wonder if it isn’t more related to the dawning Cold War, and the need to demonstrate over and over again that the American system was superior to the Soviet one? Geoff Tily (pdf) pinpoints an OECD document of 1961 as the first official reference to targetting growth, so quite a while after the end of the Second World War. (And as ever for anyone who hasn’t read it, I highly recommend Francis Spufford’s [amazon_link id=”0571225241″ target=”_blank” ]Red Plenty.[/amazon_link])

The Soviet bloc used Net Material Product as their definition of ‘the economy’. The statistics were highly suspect, even those calculated as a best effort by the CIA; it wasn’t really until the mid-1980s, with glasnost, that the true, sustained weakness of growth in the planned economies became evident. There is a nice anecdote in the book: “Once Soviet archives opened to historical research in the years after 1991, we learned that American GDP figures of the Soviet national economy had been far more accurate than estimates provided by the Soviet Union’s own economic planners, who found it near-impossible to come up with reliable data for their centralized planned economy. What did Soviet planners do? They spied on American economists calculating Soviet GDP, and then incorporated what they learned from their American colleagues into their own planning.” Of course, the Americans were spying on the Soviets to get the basic data. Who knows what the figures meant? But the queues and shortages and poor quality of goods were all real.

The second half of the book looks at the ‘Beyond GDP’ debate, although oddly asserting that nobody paid much attention to the issues between Robert Kennedy’s assassination and Congressional hearings in 2001. This is a little US-centric; the global environmental movement kept the candle burning for alternatives all through that period. Philipsen concurs with the kinds of indicator like the Global Progress Index that show progress coming to a complete halt in the 1970s. This always seems absurd to me: even if that was a real turning point in terms of costs to the environment, which gets a heavy weight in the alternative index, there has been a lot of welfare-enhancing innovation and straightforward growth since the 1970s. It’s not just the invention of tamoxifen or the internet, but the fact that more westerners live in houses with phones, indoor toilets and central heating. Sure, there’s a trade-off with the environment but is that really no progress? Nor is Philipsen interested in the issues about defining either market output or social welfare for the growing category of digital goods that are often free and have strong public good characteristics.

As for what to do about it, the book advocates ditching GDP completely, and having a national dialogue about economic goals based on the principles of sustainability, equity, democratic accountability and economic viability. It isn’t clear how this prescription fits with the several ‘dashboard’ initiatives under way now, which are described here. The dashboard approach is attractive, as is public consultation. However, it isn’t yet clear which dashboard is best or what should go in it – it’s easy to end up with a laundry list of good things, and no analytical framework for assessing outcomes or trade-offs. So the real need now is for the hard grind of the kind that Kuznets, Stone and Meade and their many colleagues sustained through the 1930s and 40s in creating the national accounts to make a GDP-plus set of social accounts practical.

I still think dropping GDP altogether would be a mistake – hence ‘affectionate’. How would a government run fiscal policy or a central bank monetary policy without a nominal GDP figure and some of the national accounts detail? The national accounts statistics as a whole also contain a lot of the material that could furnish a meaningful dashboard, so again it would be a waste of an intellectual asset to ditch all of that.

However, the answer to the underlying question, are we going to move ‘beyond GDP’ is: yes.

Lies, damned lies, statistics, and GDP

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On the train to Manchester this morning I finished a terrific book I should really have read long ago. I’m very glad I finally have. It’s Morten Jerven’s [amazon_link id=”080147860X” target=”_blank” ]Poor Numbers: how we are misled by African development statistics and what to do about it.[/amazon_link] The title made me think it was only relevant to African statistics, when in fact anybody interested in GDP and national accounts should read it.

[amazon_image id=”080147860X” link=”true” target=”_blank” size=”medium” ]Poor Numbers: How We are Misled by African Development Statistics and What to Do About it (Cornell Studies in Political Economy)[/amazon_image]

The book is short and non-technical, but includes a number of important arguments and examples. Here are the conclusions I take from it:

1. Statistics are the ‘facts’ “states collect to get knowledge about their own economic or social conditions.” Having reliable statistics is a marker of an effective state – “the ability to collect information and taxes are closely related” – and the statistics chosen reflect the power structures and political priorities of states. African states are not effective, their statistics are not reliable. (But this also made me reflect that there is a lot happening in the developed economies for which we have no statistics – and no ability of the state to understand or influence change.)

2. African GDP statistics in the key online databases used by economists – the World Bank, the Penn World Tables, the Maddison database – are inconsistent because of different interpretations of the underlyaing national data, different base years, different price indices. The sources even rank African countries differently in terms of GDP per capita. Econometric work will get different results depending which is used.” Jerven argues that economists need to have a much more detailed understanding of both the data they download and the specifics of individual countries’ circumstances to be able to interpret the numbers.

3. The underlying national level data are unreliable because of a lack of resources and statistical capacity. Surveys are rarely carried out, there is much guesswork, base year changes happen too infrequently, there is political influence.

4. 2 and 3 together mean little reliance can be placed on the standard cross-country regressions using the standard data sets. “These problems undermine any general conclusions drawn about what stimulates or hinders economic development in Africa.’

5. The standard national accounts concepts don’t apply well to developing economies with a large informal sector. The distinction between production and consumption or working and not-working is not as clear. (And may be becoming less clear in developed economies too, as technology blurs these boundaries and working patterns change.)

The book argues that the standard outline of African growth – a dismal 1970s, a better outcome post- structural adjustment remedies, and a recent acceleration in growth is largely ‘illusory’. The recent uplift in particular comes from the World Bank/IMF splicing recent rebased GDP figures onto an earlier series, as Jerven describes it. He argues that more data needs to be collected, in regular surveys, to enable both good statistics and an effective state knowing what is happening in the economy and to its tax base. He also argues strongly for greater transparency by national statistical offices but especially by the international agencies such as the World Bank and IMF, whose say-so determines the methods used to create the statistics and the world’s interpretation of what is happening in each economy.

“Accounting for the national economy is fundamental for government accountability. Without reliable macro data, political transparency is hard to imagine. …. Numbers are too important to be ignored and the problems surrounding the production and dissemination of numbers too serious to be dismissed.”

So don’t make my initial mistake of thinking this is a bit of a specialist book. It’s a fascinating and important read.

The joy of GDP – and beyond

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Late last week I attended a special IARIW-OECD conference on the future of the national accounts, and don’t tune out – it was fascinating. I’ll write up my own talk soon: one of my two main themes was that whatever approach one takes to measuring economic and social progress, there needs to be a more explicit social welfare framework informing the measurements. It’s often said that GDP is not a measure of welfare but of activity, and yet we freight it with value judgements and use it as an indicator of living standards.

All the alternatives have the explicit aim of measuring welfare but end up usually as ad hoc lists because the analytic framework is only implicit. A recent example is the Social Progress Index published recently (and Michael Porter explains its rationale here), which has 54 indicators in 12 categories and makes a point of excluding economic measures such as employment and income, which seems odd to me given that surely we want to understand the trade-offs. Anyway, the indicators included are all Good Things, but then so are the categories in the OECD’s Better Life Index and the Human Development Index. How should we choose?

Anyway, clarity about the relationship between the economy and nature on the one hand, and the economy and non-efficiency, social indicators on the other, was on the mind of many participants at the conference. The conference papers are well worth a browse.

GDP is certainly a surprisingly popular subject at the moment. Apart from my own [amazon_link id=”0691169853″ target=”_blank” ]GDP: A Brief but Affectionate History[/amazon_link], there was Zachary Karabell’s [amazon_link id=”1451651228″ target=”_blank” ]The Leading Indicators[/amazon_link] (which I reviewed for the New York Times) and Lorenzo Fioramonti’s [amazon_link id=”B00EKYOQ8O” target=”_blank” ]Gross Domestic Problem[/amazon_link]. These were all published around the same time. In June there will be Dirk Philipsen’s [amazon_link id=”0691166528″ target=”_blank” ]Little Big Number: How GDP Came to Rule the World and What to Do About It[/amazon_link], which I’m half way through and is in the Fioramonti vein. At the conference Quentin Dufour pointed me to a French book (published in English in 2002) by Alain Desrosières, [amazon_link id=”067400969X” target=”_blank” ]The politics of large numbers: a history of statistical reasoning[/amazon_link].

[amazon_image id=”B00WAM16BS” link=”true” target=”_blank” size=”medium” ]GDP: A Brief but Affectionate History[/amazon_image]  [amazon_image id=”1451651228″ link=”true” target=”_blank” size=”medium” ]The Leading Indicators: A Short History of the Numbers That Rule Our World[/amazon_image]  [amazon_image id=”B00EKYOQ8O” link=”true” target=”_blank” size=”medium” ]Gross Domestic Problem: The Politics Behind the World’s Most Powerful Number (Economic Controversies) by Lorenzo Fioramonti published by Zed Books (2013)[/amazon_image]  [amazon_image id=”B00V943S3W” link=”true” target=”_blank” size=”medium” ]The Little Big Number: How GDP Came to Rule the World and What to Do about It[/amazon_image]  [amazon_image id=”067400969X” link=”true” target=”_blank” size=”medium” ]The Politics of Large Numbers: A History of Statistical Reasoning[/amazon_image]

The wave of publication is surely a sign that something is shifting? The last big wave of books about measuring the economy dates to the early years of national income accounting, including Richard Stone’s [amazon_link id=”0751201863″ target=”_blank” ]The Role of Measurement in Economics[/amazon_link] and J.R. Hicks’ [amazon_link id=”B0006DLA5A” target=”_blank” ]The Social Framework[/amazon_link].

A challenge to techno-euphoria

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After plucking it off the shelf for yesterday’s post on the ebb and flow of economic power in the long sweep of history (or – what I did on my holidays), Angus Maddison’s [amazon_link id=”9264022619″ target=”_blank” ]The World Economy: A Millennial Perspective [/amazon_link] (read it online here) absorbed me. He identifies three forces driving long term growth: conquest and settlement; trade (specialisation and the division of labour); and technological innovation. On the last of these, he writes:

“It is clear that technological progress has slowed down. It was a good deal faster from 1913 to 1973 than it has been since. The slowdown in the last quarter century [ie. to 1999] is one of the reasons for the deceleration of world economic growth. ‘New economy’ pundits find the notion of decelerating technological progress unacceptable and cite anecdotal or microeconomic evidence to argue otherwise. However, the impact of their technological revolution has not been apparent in the macroeconomic statistics until very recently, and I do not share their euphoric expectations.”

I would really challenge the implication here that macroeconomic statistics are facts and microeconomic evidence just anecdote. SInce Maddison wrote this, we have had the early 2000s boom and then the financial crisis and its aftermath. It will be a while before the macro data can make sense of it all.

It’s quite clear though that there are some innovations that have not improved productivity or welfare – see Thomas Philippon’s marvellous paper Has the US Finance Industry Become Less Efficient? (Answ: Yes) The Maddison challenge is a good one to those of us who do think there is important technological innovation occurring – just as when Solow made his famous comment about computers, there is a question about why it doesn’t show in macro data. One answer might be that GDP data don’t capture the welfare gain due to new technologies (see my [amazon_link id=”B00M0H5PGU” target=”_blank” ]GDP[/amazon_link] for more). Another might be that the technologies are doing more for growth outside the OECD countries – think mobiles in Africa, South Asia or Latin America. But if Maddison is right, the interesting question then is why this wave of technology uniquely has not translated into faster growth and social welfare?

Not all numbers are equal

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I’ve started reading Tony Atkinson’s new book, I[amazon_link id=”0674504763″ target=”_blank” ]nequality: What can be done?[/amazon_link] and already think it a much better book than the famous [amazon_link id=”067443000X” target=”_blank” ]Capital in the 21st Century[/amazon_link] (which for me was marred by the half-baked r and g business – see for example the Jaume Ventura slides here – as well as the lack of any practical policy suggestions).

[amazon_image id=”0674504763″ link=”true” target=”_blank” size=”medium” ]Inequality[/amazon_image]

Although not far into [amazon_link id=”0674504763″ target=”_blank” ]Inequality[/amazon_link], I completely and utterly agree with the following, in a chapter describing carefully the sources and character of the data (something else on which Piketty is actually rather weak – hence the challenge much reported this week from a graduate student at MIT):

“In seeking to draw lessons from the statistics on inequality, we have to be confident in the quality of the data we are using. This is why I begin this chapter by describing and evaluation the sources of evidence on which scholars of inequality can draw. Such scrutiny is essential. All too often economists race ahead, drawing conclusions from figures that happen to be there, without asking why the data are suitable.”

Serendipitously, while reading this I’ve also been thinking about a keynote I’m giving soon at an OECD conference on [amazon_link id=”0691156794″ target=”_blank” ]GDP[/amazon_link] and the national accounts statistics in a couple of weeks’ time. All the thousands of studies and political claims resting on GDP growth figures are based on shifting sands, and we economists need to think far more carefully about what they take to be evidence for strong claims. There are some powerful examples in a paper presented by Samuel Williamson and Enrico Berkes recently at the Economic History Society conference.