Shifting Lending from SOEs to SMEs

The issue of constrained access to finance for SMEs continues to plague our small business sector, and it is often blamed on the narrow approach to SME lending by banks. The lack of cashflow-based lending, the heavy focus on high collateral requirements, inability to assess risk in SMEs, are the usual suspects. These are all very valid, and probably the most crucial factors. However, in recent conversations with bankers and financial sector leaders I’ve realised that there are at least two other factors we need to consider, which often don’t get highlighted. One is the high operational expenses of Sri Lankan banks (cost to income ratio). On this, I don’t have enough information and it’s something I would be taking a closer look at. There is some limited analysis in this paper by two University of Colombo academics, but it limitedly looks at the factors influencing the efficiency of Sri Lankan banks and does not actually quantify the efficiency levels.

The second is the concentration of banking in the state banking sector and the role that might play in constraining credit to SMEs. The eight state banks in Sri Lanka account for close to half of the assets of the total banking sector. And the larger share of lending by these banks tends to be to state-owned enterprises (SOEs) . The largest commercial bank, Bank of Ceylon, lent a staggering 38% of its total portfolio to SOEs in 2013 and the second largest bank, People’s Bank, lent 28% of its total portfolio to SOEs. And many of these are loss-making entities, as this latest study by Advocata Institute has shown. No doubt this would crowd out the available funds for lending to enterprises. We must shift lending by these state banks from SOEs to SMEs.

Which Institutions Have The Most Sci & Eng Researchers in Sri Lanka?

For some innovation eco-system work I have been doing, I came across some startling numbers that give us a sense of the narrow pool of researchers in science and engineering subjects. According to data available on the new Sri Lanka Innovation Dashboard, an initiative by COSTI, University of Moratuwa leads the way as the leading institution with science and engineering researchers, University Peradeniya is a distant second, and the other universities trail behind.

Meanwhile, public R&D institutions (PRIs) like the NERD-Centre (located in the Ekala Industrial Zone) ranks quite high. The Industrial Technology Institute, which is a key focal point for private sector industries seeking S&T solutions to their problems, ranks surprisingly low. Meanwhile, I need to find out by SLINTEC (Sri Lanka Institute of Nanotechnology) doesn’t come up in the top 10, given there success at attracting many nanotech scientists into their PPP-structured outfit.

What I’m probably most surprised about, though, is that there are only 4 universities in this top 10, even though universities probably get much more funding, overall, than these other institutions.

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Innovation as Interaction

In the course of some innovation policy work that I’ve been doing over the past year, I’ve noticed that the public policy conversations around innovation are dominated by a focus on knowledge creation, so stuff like R&D spend, putting money into government labs, research institutes, etc. Thercsm_Casti-interactions_52b6f2d9e5e is much less focus on the interactions between knowledge creators and industry actors/entrepreneurs. For the latter, we need to consider things like firm level adoption of technology, firm level readiness, access to technology from within or outside the country, technology commercialisation, technology transfer/transmission, and the overall ability for the country to be innovative, that goes beyond just knowledge creation and innovation inputs and outputs. This conceptual issue around innovation policy is important to consider, and has implications for how government’s support innovation. We seriously need to shift the debate beyond the narrow focus on knowledge creation alone, and towards fostering dynamic interaction among different players in the innovation eco0system.

“GDP” Was Never Meant to Last This Long


I’m almost surprised it’s taken this long. “GDP” was never intended to be the end all and be all measure of the health of economies. Yet, over 85 years since it(or a version of it) was first used, it continues to dominate the mainstream economic discourse around prosperity, growth, and development.

The most recent critique of it came in the recent edition of The Economist, which captured the challenge nicely:

GDP is often wildly inaccurate: Nigeria’s GDP was bumped up by 89% in 2014, after number-crunchers adjusted their methods. Guesswork prevails: the size of the paid-sex market in Britain is assumed to expand in line with the male population; charges at lap-dancing clubs are a proxy for prices. Revisions are common, and in big, rich countries, bar America, tend to be upwards. Since less attention is paid to revised figures, this adds to an often exaggerated impression that America is doing far better than Europe. It also means that policymakers take decisions based on faulty data.

Accounting for a country’s production and income was first thought of in the 1930s, responding to information gaps that became apparent during the ‘Great Depression’. Subsequently, it rose to importance during World War II, for planning needs amidst constrained resources, and thereafter in the 1950s and 60s when stimulating economic growth was top of the agenda. The man tasked with developing the measure was American Professor Simon Kuznetsk (of Kuznetsk curve fame). It’s ironic that the man who gave birth to this measure of an economy’s output – which is now being bashed for not capturing dimensions like growth inequality and so on – is also the man who became famous for developing the ‘Kuznet’s Curve’ hypothesis of economic inequality.

As this article from 2014 notes, “GDP has become the king of all statistics. It’s kept by every country in the world”, but also goes on to say “…it’s a good 1950s number. The question is, is it a good 2014 number?” The point being that our current measurement of GDP may not be the most contemporary way to measure the prosperity of nations. It’s simply a measure of what was produced and consumed.

The most mainstream critique of GDP came this year at the World Economic Forum in Davos, when IMF chief Christine Lagarde, Nobel prize-winning economist Joseph Stieglitz, and MIT professor Erik Brynjolfsson agreed that GDP is a poor way of assessing the health of our economies and we urgently need to find a new measure. The Forum even created a separate page on their site that captures the new debate on going ‘beyond GDP’.

For too long we have been pre-occupied with rates of growth, rather than quality of growth. In an economy, we can have 7% growth with just one or two sector growing very very rapidly. (Indeed thats what happened in the post-war period in Sri Lanka – growth was driven by a handful of domestic non-tradable sub-sectors like banking, construction, retail and wholesale trade, and hotels and restaurants.). We can also have rapid GDP growth with just a handful of people contributing to creating that growth or benefiting from that growth – essentially, growth with inclusivity. Yet, we are still to come up with a measure to capture ‘economic inclusive growth’. A measure that is comparable across countries, is fairly all-encompassing, and easily understandable.

Another reason why existing measures of GDP may prove inaccurate, moving forward, is because of changes in technology, how societies operate, how we work, how things are produced, where value is generated and captured, and where things are made. All of these dimensions are being disrupted by technology, as I argued here. The Fourth industrial Revolution will make it harder to distinguish between products and services, between sectors. There will be biotech services embedded in agriculture and healthcare services embedded in pharmaceutical manufactures. One part of value may be added in a country, another part in the ‘cloud’. How do you measure GDP now? Central Banks and national statistics agencies will have to complete re-think it.


As The Economist article captured it,

“The services to consumers provided by Google and Facebook are free, so are excluded from GDP. When paid-for goods, such as maps and music recordings, become free digital services they too drop out of GDP. The convenience of online shopping and banking is a boon to consumers. But if it means less investment in buildings, it detracts from GDP.”

 But as the article acknowledged, we can’t just dump GDP as a measure. It’s too widely used, has ‘enduring appeal’ as a summary statistic that quickly tells us how an economy is doing. So, instead, “statisticians should improve how GDP data are collected and presented.”. The article calls for using 1) more firm-level data; 2) a new measure that captures services, informality, unpaid work, etc better (and overall captures well-being better); and 3) capture changes in ‘wealth’ better – ranging from private wealth to public wealth in the form of various asset classes.
All these are interesting suggestions. But the challenge lies in getting governments to recognise that GDP needs a re-look, and getting statistics agencies to get round to developing something new. GDP, in the way it began nearly a century ago and has only incrementally changed ever since, was never meant to last this long. It’s time to make it better.

Economists, lets get real (and less dogmatic)

This has got to be one of the best interviews on the discipline of economics that I’ve read in a while, and its with none other than Dani Rodrik – a trailblazer in empirical economics and author of the new book ‘Economics Rules: The Rights and Wrongs of the Dismal Science‘.

My favourite bit from the interview is about how economists need to get over dogma, getting real the usefulness of theories, and recognising that its all happening in an evolving space and is highly context dependent,

Too often economists debate a policy question as if one or the other theory has to be universally correct. Is the Keynesian or the Classical model right? In fact, which model works better depends on setting and context. Only empirical diagnostics can help us know which works better at any given time — and that is more of a craft than a science, certainly when it is done in real time. If we economists understood this, it would make us more humble, less dogmatic, and more syncretic.

Read the full interview here :

Cities and productivity


There’s a new McKinsey Global Institute commentary piece titled ‘Inclusive cities are productive cities’ that argues that inclusiveness (including openness to migrants) is important to maximise the productivity gains of urbanisation. This has important ideas for Sri Lanka’s own urbanisation process via the Western Region Megapolis Project. In a previous article on this blog, I highlighted that the earlier urbanisation exercise was re-writing the DNA of entire communities, and we need to better manage the fallout and maximise the gains.

Some of the article’s commentary on openness to migration may appear more relevant to the European case right now, but it is quite relevant to Sri Lanka as well. Migration isn’t only about foreign nationals, but also about internal rural to urban migration, which is inevitable if the Western Region super-agglomeration takes off.

The opening paragraph captures it nicely,

Cities are productivity engines. They create productivity by enhancing the number and frequency of interactions. Higher population density equals higher frequency of interactions, and the more interactions there are, the more you can figure out what you’re good at and what you’re not. Then, we stop doing what’s not good, and we become better at the good. That’s specialization. That’s productivity. Doing that with as many people as you can creates the opportunity for growth.

During the January Sri Lanka Economic Forum organised by the Open Society Foundations and the Harvard Centre for International Development (CID), one of the CID’s scholars highlighted new research by them that showed that workers who moved to urban settings have higher returns to their skills and training than before they moved. This was largely due to the more dynamic interactions that are possible in urban contexts.  Further work by CID showed that mobility of workers helps diffusion of industries and overall economic diversification.

Leveraging on Location with Logistics


I tend to be far too alliteration happy sometimes, and it’s probably a good thing I didn’t title my speech as I did this blog post. But it was exactly my message to the industry leaders of the Women in Logistics and Transport forum during a keynote address at their Annual General Meeting last week  – that leveraging on location will be a big game-changer for economic growth in Sri Lanka, and the key to that is the logistics and transport industry. I argued that,

[…] overwhelmingly, the investors we meet say that Sri Lanka’s location and access to big markets like India, Pakistan, and eventually China, is a key attraction in their decision to invest here reiterates the case for focussing on our trade, investment and logistics policies.

I highlighted some recent growth and industry statistics; and spoke about the role logistics and transport will play in truly achieving regional hub status and becoming a serious player in commerce in the region; the need to focus more strongly on the aviation sector (and air connectivity) to boost passenger and cargo flows; the need to attract more women into the L&T sector (currently at an abysmal 2.9% of total employment in the sector).

In the speech I mentioned results of a new survey conducted by us at the Chamber EIU which demonstrated that prospective investors consider Sri Lanka’s geographical location and access to regional markets are key attractions in investing here. See the one-pager on the results here.

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The slides of the keynote are below. For the full text of the speech, browse the embedded doc below or click here.

And here’s some press coverage around it:

Dailymirror‘Country location key to spur growth: economist’ 

Daily FT‘Logistics and transport to shape new Sri Lankan economy’

The Island‘Women representation in logistics as low as 2%’


Can a robot create IP and own it?


Sounds like a silly question right? But a new paper by WIPO (World Intellectual Property Organisation) on ‘Robotics, innovation and intellectual property’ argues that it is one of the fundamental questions thrown up by the emergence of robotics innovation. As the paper notes,

A question that cannot yet be considered settled law in any nation, but for which IP practitioners around the globe may soon face, is whether IP can be created by a robot, and if so, who owns IP created by a robot?

While there’s a lot of work out there on the Intellectual Property (IP) issues related to biotech, nanotech, pharma, etc., this is probably the first report that looks at robotics innovation and IP. I met one of the co-authors of the paper, Sacha, during a recent visit to WIPO last month, and found him to be an extremely interesting economist looking at innovation – there aren’t many of them around yet.

 In other insights contained in the report, something that jumps out strongly is the extent to which East Asian countries are leading the robotics space. Just look at the Top 10 patent filers in robotics globally – ALL are from East Asia – predominantly China. If you exclude China, still, 8 out of 10 institutions are from East Asia (the other two are German). Strikingly, of those 8, 6 are from South Korea.

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