Category Archives: Main Page

Big data meets humanitarian response

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Some time ago I co-authored a report (for the UN Foundation and Vodafone Foundation) with Patrick Meier on the use of mobiles in emergencies and disasters. Patrick has just released a whole book on this subject, going much wider than the original report, [amazon_link id=”1482248395″ target=”_blank” ]Digital Humanitarians: How Big Data is Changing the Face of Humanitarian Response[/amazon_link].

The technology has already moved on considerably – the Big Data phenomenon, for one thing. Importantly, there’s a chapter covering verification techniques; while we found in the original work that crowd-sourced data (as it wasn’t yet called when we first wrote about this) was often more accurate than ‘official’ information, the more verification the better. There’s also a chapter on digital activism – the book’s website sets out all the chapters with brief summaries.

Digital Humanitarians looks like it has lots of examples and it certainly covers some very important and timely questions. Patrick blogs at iRevolution and his latest post talks about the book.

[amazon_image id=”1482248395″ link=”true” target=”_blank” size=”medium” ]Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response[/amazon_image]

Historicism and its enemies

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By the time I had to head back to the station yesterday, I’d almost finished reading the manuscript I’m reviewing, so I borrowed Karl Popper’s [amazon_link id=”0415278465″ target=”_blank” ]The Poverty of Historicism[/amazon_link] from the shelves of my University of Manchester office mate John Salter (as he teaches political economy, and theories of justice, he has a fine and tempting collection of classics).

I’m not very far into it yet, but it’s striking that Popper excludes economics from his pronouncements about methodology in the social sciences. Eg, “I am convinced that such historicist doctrines of method are at bottom responsible for the unsatisfactory state of the theoretical social sciences (other than economic theory).” No doubt all will be revealed, but it’s surprising to read this at a time when economics is widely criticised.

[amazon_image id=”0415065690″ link=”true” target=”_blank” size=”medium” ]The Poverty of Historicism[/amazon_image]

Next book?

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I’ve almost finished reading Richard Flanagan’s [amazon_link id=”0701189053″ target=”_blank” ]The Narrow Road to the Deep North[/amazon_link] – a worthy Booker winner. (Not about economics, of course!) I need a short read for the train tomorrow and think it’s going to be [amazon_link id=”026202859X” target=”_blank” ]Understanding Global Crises: An Emerging Paradigm[/amazon_link] by Assaf Razin.

[amazon_image id=”0701189053″ link=”true” target=”_blank” size=”medium” ]The Narrow Road to the Deep North[/amazon_image]    [amazon_image id=”026202859X” link=”true” target=”_blank” size=”medium” ]Understanding Global Crises: An Emerging Paradigm[/amazon_image]

Nanny state or government Mad Men?

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[amazon_link id=”0691164371″ target=”_blank” ]Government Paternalism: Nanny State or Helpful Friend[/amazon_link] by Julian Le Grand and Bill New has landed on my desk and it looks a very interesting assessment of the trade-off between good ‘outcomes’ from nudge policies and the infantilization of individual choice – a useful counterbalance to the series of books from Cass Sunstein advocating nudging. (Gilles St Paul has a counter-nudge book too, [amazon_link id=”0691128170″ target=”_blank” ]The Tyranny of Utility: Behavioural Science and the Rise of Paternalism[/amazon_link].)

Although recognising the power of the argument that governments (and others) can’t avoid ‘nudging’ because the status quo is a choice architecture anyway, I lean towards being very uneasy about the enthusiasm for policymakers using behavioural techniques (familiar to ad men and Mad Men) to manipulate behaviour. So I’m looking forward to this new book.

[amazon_image id=”0691164371″ link=”true” target=”_blank” size=”medium” ]Government Paternalism: Nanny State or Helpful Friend?[/amazon_image]

Mastering ‘Metrics

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I had thought Joshua Angrist and Jörn-Steffen Pischke had reached the pinnacle of accomplishment when it came to econometrics texts with their [amazon_link id=”0691120358″ target=”_blank” ]Mostly Harmless Econometrics[/amazon_link]. That’s a fabulous, clear, practical manual – it’s so good that my eldest son, a recently-minted economist, has perma-borrowed my copy. What was particularly good about that book is its clarity about the importance of thinking through your null and alternative hypotheses – it’s one of my bugbears in life that people so rarely are clear about their counterfactual.

[amazon_image id=”0691120358″ link=”true” target=”_blank” size=”medium” ]Mostly Harmless Econometrics: An Empiricist’s Companion[/amazon_image]

Yesterday I read – devoured – almost all of [amazon_link id=”0691152845″ target=”_blank” ]Mastering ‘Metrics: The Path From Cause to Effect[/amazon_link], their follow-up textbook, covering more basic material at a level suitable for students meeting it for the first time – but also for practising economists who learned their econometrics long ago. Econometrics is one of the unsung fields of economics where there has been a stupendous amount of progress during the past 20 years, due to a mixture of more data, faster computers, better software – and improved econometric methodology. A lot of this methodological advance happened after I did my PhD (macro – yes, really! – and econometrics), so although I’ve picked up a lot of the material this book covers, I found it an incredibly illuminating read.

[amazon_image id=”0691152845″ link=”true” target=”_blank” size=”medium” ]Mastering ‘Metrics: The Path from Cause to Effect[/amazon_image]

The perspective the book takes is how to answer questions about causality, and it presents five approaches: randomised trials, regression, IV/2SLS, regression discontinuity design, and differences in differences. Each chapter sets out an empirical question which is used to take the reader step by step through the methodology. The chapters mainly use verbal explanation, with a minimum of equations, and each has a more technical but still extremely clear appendix for students or practitioners needing that material. There are plenty of practical tips, for example, on how to interpret the size of coefficients, how to sense check results, how to check whether there might be omitted variables bias and what sign/size it might be. I love it that they say, your software programme will calculate this complicated standard error for you, no need to memorize the extremely complicated formula (I speak bitterly as one who wrote Fortran programmes to calculate the damn things, 30 years ago). Each of the examples reveals why simple, compelling data correlations of the kind discussed constantly in the world of policy can be completely misleading – or not.

Another nice feature is that each chapter ends with a couple of pages on the pioneers of statistical methods, explaining their contribution and the kinds of empirical problem they were innovating to be able to address.

It isn’t a perfect book. There’s a Kung Fu theme which is meant to make it more approachable and fun, but grated with me a bit. Still, it may be as close to perfection as you can get in this world to an introductory econometrics text. Ideal for students, ideal for older economists who privately admit they could do with brushing up their econometric knowledge a bit, as they look at the figures generated by their software packages. I’ll find this a very useful book, not least when it comes to reading other economists’ papers. It explains how to set about delivering on the huge promise of economics as a careful, empirical science, although of course there is no substitute for thinking carefully about the context in which any given set of data has been generated, and what causal influences could have given rise to it.

Update: Dimitrios Diamantaras pointed me via Twitter to Francis Diebold’s dyspeptic review of Mostly Harmless Econometrics – I think the tone of this is harsh and seems to be mainly concerned with the title; but it is worth noting that the two Angrist and Pischke books indeed do not cover time series econometrics.