The Future of Nepal’s exports

My latest op-ed is about the future of Nepal’s exports. I will post an extended version of this article in next blog post. I am a bit optimistic about the future of Nepal’s exports industry.

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Future of Nepal’s exports

Everything ain’t good, but everything ain’t bad either

The prevailing perception among policymakers and analysts is that we are in an economic mess, the exports sector is doomed to fail, and imports will keep on imploding. With total merchandise exports and imports amounting to Rs 55.37 billion and Rs 343 billion, respectively, trade deficit has swelled to Rs 287.62 billion in the first eleven months of this fiscal year. The BOP deficit stands at around Rs 15 billion. The momentum of decline in exports and surge in imports looks unabated.

With no hopes of increasing exports, the idea of import-substituting policies, which is expected to at least curb imports and lower deficit, is seen as the policy of last resort. As I have argued before, these are convenient conclusions (see Convenient conclusions?, Republica, July 14) fostering misguided policy because if we can address non-economic constraints-- bandas, destructive activities of militant youth wings and combative labor unions, donation campaign, supply-side constrains, and power shortages--, then Nepal could see an increase in exports.

Curious why I remain optimistic than most of the pundits in Kathmandu? Allow me to explain. In South Asia, Nepal has one of the highest potentials for growth in the exports sectors. It could potentially export many new products with comparative advantage, if the right constraints on promotion, production, and accumulation of capabilities of key products are timely addressed.

A new study (As you sow so show shall you reap: From capabilities to opportunities) published by the Asian Development Bank (ADB) corroborates this view. It analyzes product spaces of 130 countries and ranks them on the basis of “Index of Opportunities”-- which is based on a country’s accumulated capabilities to undergo structural transformation and captures the potential for further product upgrading, growth, and development. A product space shows a graphical representation of all products exported in the world.

The good news is that the future of Nepal’s export industry and the economy’s potential to undergo structural transformation is not all that gloomy as has been portrayed by analysts. In the index, Nepal ranks second in South Asia with a score of 0.4729, following India which has a score of 0.8590. The higher the score, the better the potential to undergo structural transformation. Everything else remaining the same, a ten percent increase in the value of the index yields 0.31 percentage points of additional growth.

Among the 96 non-high income countries, Nepal ranks 33 while China and India rank second and third, respectively. For a country with this level of income per capita, Nepal’s standing is not bad at all. The exports sector is still capable of rebounding. There is no need to excessively fret about curbing imports just because exports are declining. With right policies in place, we can produce new products with high exports potentials and export with comparative advantage, thus inducing further structural transformation. With the existing state of capabilities, Nepal is expected to grow at an average annual rate of 5.49-6.69 percent over the period 2010-2030.

Ranking among 96 non-high income countries

Country	EXPY	EXPY-core	Diversification	Diversification-core	Share- core	Standardness	Open forest	Index of opportunities	South Asian countries rank	Non-high income countries (96) rank
India	0.6486	0.9328	0.9287	0.8611	0.6148	0.7917	0.8759	0.859	1	2
Nepal	0.4112	0.5926	0.4032	0.2214	0.3569	0.5219	0.2041	0.4729	2	33
Pakistan	0.3447	0.8006	0.48	0.1053	0.1421	0.4485	0.4379	0.4551	3	37
Sri Lanka	0.3259	0.8535	0.4279	0.1023	0.1546	0.4957	0.3657	0.4326	4	44
Bangladesh	0.2768	0.782	0.2386	0.0519	0.1387	0.2348	0.201	0.3576	5	73

The index is composed of seven indicators. Among the 96 non-high income countries, Nepal has a score of 0.4112 in the sophistication level of export basket, called “EXPY” and calculated as the weighted average of the sophistication of the products exported. Generally, countries with high EXPY tend to have high income per capita. A 10 percent increase in EXPY at the beginning of the period raises growth by about half a percentage point, according to a study by the ADB economist Jesus Felipe. For its given level of income, Nepal already has a pretty impressive level of sophistication of exports basket, although it needs enhancement if we want to enjoy the same level of sophistication as India and China have.

Furthermore, in “EXPY-core”, another measure of exports sophistication which looks at the core of the product space (machinery, chemical and metals), Nepal has a score of 0.5926. Note that even though the exports basket of Pakistan, Sri Lanka and Bangladesh are less sophisticated than Nepal’s, they nevertheless have high EXPY-core score, meaning that their exports sector has high-valued products that constitute a major portion of exports basket. No wonder they have higher exports revenue and, consequently, higher income per capita than Nepal’s. Nepal needs to improve on the exports of EXPY-core products.

In terms of exports diversification, Nepal’s score is 0.403, the second highest in South Asia. Diversification is measured by the number of products that a country exports with comparative advantage. Between 2001 and 2007, Nepal exported, on average, around 100 products with comparative advantage. Meanwhile, China and India exported 257 and 246 products, respectively. Looking closer at diversification of core products (“diversification-core”) only, Nepal has a score of 0.221 and exported less than 25 products with comparative advantage. In this category, China and India exported with comparative advantage 89 and 81 products, and had scores 0.9496 and 0.8611, respectively. Nepal’s score is not that discouraging given its income level.

The uniqueness of products exported by a country also matters in ensuring sustainability of exports sector and structural transformation. In terms of uniqueness of exports, termed “standardness”, Nepal has a score of 0.5219, while China and India have 0.7917 and 0.9352, respectively. Again, the standardness level of Nepal is better than that of countries with the same income level. Nepal’s good position in exports uniqueness and diversification is encouraging news for the listless, demoralized exports sector.

Policymakers and investors want to know the possibility of producing new products that could be exported with comparative advantage. “Open forest” analysis provides the answer. It basically shows how easy it is to produce “nearby” products than the ones that are “far away”. The capabilities to produce similar but slightly differentiated products (“nearby”) already exist in the economy and policies can be designed to facilitate this process. Nepal has a score of 0.2041, one of the lowest in South Asia, in “open forest” dimension of the index. It indicates the fact that the industrial sector needs revamping of its capabilities to produce goods that are sophisticated and are within close range of each others’ capital and resource requirements.

The exports industry is not done yet. There is no need to resort to widespread import curbing measures, especially of daily consumption goods. Exports can be increased if we could judiciously and decisively address non-economic constraints ailing the industrial sector and implement highly targeted and appropriate industrial and trade policies to augment capabilities. The product level analysis of sophistication of our exports basket reaffirms this conviction.

[Published in Republica, August 1, 2010, pp.6]

Ravallion and Alkire on Multidimensional Poverty Index (MPI)

Martin Ravallion on Multidimensional Poverty Index (MPI)-- via Duncan Green's blog. For introductory reading on MPI see this blog entry.

“Everyone agrees that poverty is not just about low consumption of market commodities by a household.  There are also important non-market goods, such as access to public services, and there are issues of distribution within the household. It is agreed that consumption or income poverty measures need to be supplemented by other measures to get a complete picture.

But does that mean we should add up the multiple dimensions of poverty into a single composite index? Or should we instead measure consumption poverty with the best data available, while also looking for the best data on other dimensions of poverty as appropriate to the country context?

The Oxford Poverty and Human Development Initiative (OPHI) has recently launched a Multidimensional Poverty Index (MPI), and calculated it for over 100 countries.   The MPI is a composite of indicators selected for consistency with the UNDP’s famous Human Development Index (HDI). The HDI uses aggregate country-level data, while the MPI uses household-level data, which is then aggregated to country level. The index has ten components; two represent health (malnutrition, and child mortality), two are educational achievements (years of schooling and school enrolment), and six aim to capture “living standards” (including both access to services and proxies for household wealth).  The three broad categories–health, education, and living standards–are weighted equally (one-third each) to form the composite index.  

One can debate the precise indicators chosen for the MPI by the Oxford team (who are clearly aware of the many data concerns). For example, the MPI’s six “living standard” indicators are likely to be correlated with consumption or income, but they are unlikely to be very responsive to economic fluctuations.  The MPI would probably not capture well the impacts on poor people of economic downturns (such as the Global Financial Crisis) or rapid upswings in macro-economic performance.

The precise indicators used in the MPI were not in fact chosen because they are the best available data on each dimension of poverty. Rather they were chosen because the methodology used by the MPI requires that the analyst has all the indicators for exactly the same sampled household. So they must all come from one survey. There is much better data available on virtually all of the components of the MPI, but these better data can’t be used in the MPI since they are only available from different surveys. This aspect of their methodology greatly constrains the exercise. If one chooses not to form the composite at household level but to look instead at the separate dimensions of poverty one is free to choose the best available data on each dimension of poverty.

There is a deeper concern about the MPI, which holds even if the best data all came from just one survey. The index is essentially adding up “apples and oranges” without knowing their relative price. When one measures aggregate consumption from household-survey data for the purpose of measuring poverty, as in the World Bank’s “$1 a day” measures, one relies on economic theory, which says that (under certain conditions) market prices provide the correct weights for aggregation. We have no such theory for an index like the MPI. A decision has to be taken, and no consensus exists on how the multiple dimensions should be weighted to form the composite index.

On closer scrutiny, the embedded trade-offs (stemming from the weights chosen by the analyst) can be questioned, and may be unacceptable to many people.  In the context of the HDI, I pointed out 15 years ago that by aggregating GDP per capita with life expectancy the HDI implicitly put a value on an extra year of life, and I showed that this value rises from a very low level in poor countries to a remarkably high level in rich ones (4-5 times GDP per capita).   If it was made clearer to users, I expect that they would question this trade-off embedded in the HDI.

The MPI index faces the same problem. How can one contend (as the MPI does implicitly) that the death of a child is equivalent to having a dirt floor, cooking with wood, and not having a radio, TV, telephone, bike or car?  Or that attaining these material conditions is equivalent to an extra year of schooling (such that someone has at least 5 years) or to not having any malnourished family member?  These are highly questionable value judgments. Sometimes such judgments are needed in policy making at country level, but we would not want to have them buried in some aggregate index.  Rather, they should be brought out explicitly in the specific country and policy context, which will determine what trade off is considered appropriate; any given dimension of poverty will have higher priority in some countries and for some policy problems than others.

Poverty is indeed multidimensional.  But it is not obvious how a composite multidimensional poverty index such as the MPI contributes to better thinking about poverty, or better policies for fighting poverty.  Being multidimensional about poverty is not about adding up fundamentally different things in arbitrary ways. Rather it is about explicitly recognizing that there are important aspects of welfare that cannot be captured in a single index.”

Sabina Alkire, the one who came up with MPI, responded:

“As Martin Ravallion points out, we agree that poverty is multidimensional. The question is whether our efforts to incorporate multiple dimensions into the very definition of who is poor and the measurement of poverty “contributes to better thinking about poverty, and to better policies for fighting poverty.” Let me explain what I think the MPI adds.

The MPI measure has meaning in itself and can also be broken down immediately into its component parts.  Every time you see an MPI figure – for a person, an ethnic group, a state, or a country – you know that it also contains what could be thought of as a drop-down menu in two layers. The first layer shows incidence and intensity. The second breaks the MPI down by indicator and shows what poverty is made of.

If we know someone is income poor, we do not know if they are also illiterate or malnourished. If we know someone is multidimensionally poor, we can unpack the MPI to see how they are poor. That is one added value of our methodology.  That is why we call it a high resolution lens: you can zoom in and see more.

This feature could add value to the MDG indicators too. These show us the percentage of people who are malnourished, and the rate of child mortality and many other things, but not how the deprivations overlap. If 30% of people are malnourished and 30% of children are out of school, it would be useful to know if these deprivations affect the same families or different ones. With the MDG indicators we cannot see that; with the MPI, we can. Of course not for all MDG indicators, but it’s a start.

For example, the Somali have the highest multidimensional poverty of all ethnic groups in Kenya followed by the Masai. Looking at the MPI drop-down menu, we see that 96% of Masai are poor and 88% of the Somali. But poverty among the Somali is more intense: on average they are deprived in 67% of dimensions; the Masai in 62%. Zooming in further we note that the Somali are more deprived in education and child mortality, whereas malnutrition and standard of living indicators are worse among the Masai. So the MPI opens out into a wider field of information.

The other thing the MPI does is clean data of anomalies and focus on poor people. While indicators drawn from different surveys are tremendously useful for many purposes, they do not identify who is multidimensionally poor, so every MPI poor person experiences multiple deprivations. Consider a self-made millionaire who didn’t go to school. A MDG indicator includes this millionaire in the percentage of people who are uneducated. The MPI does not – if she’s not deprived in anything else, she’s not considered poor. In times of tighter fiscal resources we focus on people who are deprived in several things at the same time.

So, the MPI – and the general methodology it uses that James Foster and I developed – adds value because of how it evaluates poverty. The method first determines the dimensions in which a person is deprived, and then ‘adds up’ that person’s deprivations using weights that reflect the relative importance of each deprivation. A person who is sufficiently ‘multiply deprived’ is considered poor. We measure multidimensional poverty as the incidence (or the percentage of the population that is poor) times the intensity (or the average percentage of deprivations poor people experience). Unlike the HDI, this construction does not add up achievement levels, which requires strong assumptions concerning the variables in question as Martin noted. Instead, we add up deprivations, which does not.

OK, now to the issue of weights. Income poverty aggregates within a country using actual or imputed prices (these are critical for fixing the income poverty standard across countries and time). Setting prices is not unproblematic in practice, particularly in Colombia where I am writing from. Indeed the Presidential address to the 2010 American Economic Association raised concerns such as the prices attributed to housing (Deaton 2010). Chen and Ravallion 2008 carefully review the robustness of their results to different pricing approaches.

As Martin observed, instead of using prices, the MPI sets weights as value judgements. Amartya Sen among others sees this feature as a strength not an embarrassment: “There is indeed great merit… in having public discussions on the kind of weights that may be used” (1997a).

In extensive writings, Sen presents several pertinent observations in favor of setting weights: first, prices may not exist for some aspects of poverty (morbidity, mortality, illiteracy) but giving zero weight to these does not seem right either. Second, setting precise weights may not be necessary: comparisons may be robust to a range of weights. Third, the weights trigger public debates which may be constructive as policy makers weight tradeoffs in practice anyway.

The key, Sen suggests, is to make the weights explicit: “It is not so much a question of holding a referendum on the values to be used, but the need to make sure that the weights – or ranges of weights – used remain open to criticism and chastisement, and nevertheless enjoy reasonable public acceptance” (1997b; see also Decancq and Lugo 2008).

Given this situation, Maria Emma Santos and I proceeded in a very practical way in the MPI. First, the weights are not buried; they are totally transparent (1/3 per dimension, and each indicator within a dimension equally weighted). If people disagree with these weights, they can propose improvements and also recalculate with different weights to check robustness. Second, the weights give some non-zero value to each dimension, which is a starting point. Third, the MPI fixes weights between countries to enable cross-national comparisons; alongside this we strongly encourage countries to develop national measures having richer dimensions, and indicators and weights that reflect their context as Mexico did and Colombia is doing. Fourth, we weight the three dimensions equally, this was corroborated by expert opinion (Chowdhury and Squire 2006), helps make it easy to understand (Atkinson et al. 2002) and at least for the HDI is quite robust (Foster McGillivray and Seth 2009). We do need to create robustness tests for MPI weights, and new methodologies of analysis to guide policy, and OPHI plan to work with other researchers on these. But the key thing is that the present MPI weights are transparent, and critical scrutiny of them is welcomed.

Finally, both previous blogs mentioned data constraints. Duncan criticised the MPI for including only three dimensions, “partly because it still relies on existing data sets.” Well, actually data constraints are the only reason only these three dimensions appear. We and the HDRO wish to include others without losing focus: indeed OPHI’s other research theme highlights the need to gather internationally comparable data on ‘Missing Dimensions’ of poverty – violence, informal work, disempowerment, and isolation/humiliation — given the importance these have in poor people’s lives. Our methodology is flexible enough to accommodate additional dimensions as they become available and we are eager to do so.

Finally, as Martin observed, our data must come from the same survey or from matched surveys. Yet multi-topic surveys have expanded rapidly, especially since the MDGs. The MPI is not perfect, but it uses these surveys to explore joint distribution – the deprivations that batter poor people’s lives at the same time. Such a multidimensional poverty measure complements existing tools. So though no measure is enough, we hope this work will enable others fight poverty and empower poor people more effectively.”

Global trade after the financial meltdown

A picture worth thousand words! The downfall of world merchandise trade during the crisis. Good news is that it is again picking up after hitting near-rock-bottom. Source: WTO

New measure of structural transformation: Index of Opportunities

Abdon, Felipe and Kumar have used Hidalgo et al. (2007) and Hausmann et al. (2007)’s concept of product space to come up with an “Index of Opportunities” (full paper here), which captures the potential for further upgrading production, economic growth, and development. It is based on a country’s accumulated capabilities (human and physical capital, legal system, institutions, etc.) to undergo structural transformation.

The idea is that “in the long run, a country's income is determined by the variety and sophistication of the products it makes and exports, and by the accumulation of new capabilities.” Hidalgo and Hausmann have shown that structural transformation occurs when countries grow sustainable by continually upgrading production structure, i.e. redeploying existing production structure to produce (upgraded) new products. It is related to “nearby goods”, “proximity”, and “open forests” concepts used in product space analysis. It is also used to see if coordination failures are binding constraints to growth while doing growth diagnostics of an economy. (An interesting idea that I have used to do growth diagnostics of the Nepalese economy).

Anyway, back to the new Index of Opportunities. It includes:

• Exports sophistication (EXPY--a weighted average of the income level of the products exported, where the latter is calculated as a weighted average of the GDP per capita of the countries that export a given product)
• Sophistication of the core commodities (machinery, chemicals and metals)
• Overall diversification (the number of products in which the country has acquired revealed comparative advantage)
• Diversification of core products (the number of core products in which the country has acquired revealed comparative advantage)
• Share of complex capabilities (the ratio of the number of core commodities with revealed comparative advantage to the total number of commodities with revealed comparative advantage)
• Standardness/uniqueness of the export basket (how many countries export the same product; this measure of uniqueness of the export basket has been called “standardness”)
• Open forest (measure of the potential for further structural change. This variable provides a measure of the (expected) value of the goods that a country could potentially export, i.e., the products that it currently does not export with revealed comparative advantage)

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“We estimate cross-country regressions of each of the seven indicators on the level of GDP per capita. Each indicator has two components that enter the construction of the Index. One is the actual value of the indicator, which captures the actual capabilities. The other one is the residual from the regression of the indicator on GDP per capita. This shows whether a country is a positive or a negative outlier given its income per capita. The residual obtained in each case is considered a “reward” or a “penalty”, respectively. A lower value of standardness is considered better. In this case, therefore, a negative residual corresponds to a reward and a positive residual to a penalty. We use highly disaggregated trade data covering 779 products for the years 2001-2007.

We rescale all seven indicators and the residuals such that they lie between 0 (minimum value) and 1 (maximum value). With all the seven indicators (and their residuals) scaled to lie between 0 and 1, and an increasing value corresponding to an improvement, we averaged the fourteen components to obtain the Index of Opportunities.”

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The result shows that China has the highest score, followed by India, Poland,Thailand, and Mexico. Nepal stands at number 33 with an index of 0.4729.

The authors use the index to predict average annual economic growth rate between 2010-2030. For instance, the average annual growth rate of Nepal between 1990-2007 was 4.33 percent. Based on the index, growth projection, average annual growth rate, for Nepal, between 2010-2030 is 5.49 to 6.61 percent. For the same timeframe, China’s is 10.34 and 4.15 to 5.12 percent and India’s is 6.47 and 5.78 to 7.07 percent. Their result is pretty close to the one done by Uri Dadush and Benn Stancil (2010) from Carnegie Endowment.

The conclusion is that countries (such as China, India, Poland, Thailand, Mexico, and Brazil) that have diversified and increased the level of sophistication of their export baskets have accumulated a significant number of capabilities, allowing them to perform well in the long run. For countries that have not done so yet, they will have hard time having structural transformation. The authors vouch for “soft” industrial policies advocated by Harrison and Rodriguez Clare (2010). [Soft industrial policies promote collaboration among government, industry, and cluster-level private organizations with an aim to directly increase productivity. It basically seeks to directly address coordination failures that keep productivity low in existing or promising sectors rather than engage in direct interventions that might distort prices. This is like facilitating the process that already looks promising but is not realizing its full potential, rather than instituting one all anew whose success is unclear.]

I am not sure how much impact this index will have but it does not add much new information than EXPY and PRODY analysis developed by Hausmann et al.. It just reaffirms the results that are already there (it reaffirmed the conclusion of the product space analysis and the projections done by Dadush and Stancil). Nevertheless, a series of interesting papers and one more index to look at export-led structural transformation. Also, read the papers I have linked to. The whole concept is amazing!

(Failing to) Read Keynesian economics correctly…

Read today's piece by Niall Ferguson, who frets about  warlike spending during no-war time (but forgets about the depression economy), in FT:

When Franklin Roosevelt became president in 1933, the deficit was already running at 4.7 per cent of GDP. It rose to a peak of 5.6 per cent in 1934. The federal debt burden [in the United States] rose only slightly – from 40 to 45 per cent of GDP – prior to the outbreak of the second world war. It was the war that saw the US (and all the other combatants) embark on fiscal expansions of the sort we have seen since 2007. So what we are witnessing today has less to do with the 1930s than with the 1940s: it is world war finance without the war.

Those economists, like New York Times columnist Paul Krugman, who liken confidence to an imaginary “fairy” have failed to learn from decades of economic research on expectations. They also seem not to have noticed that the big academic winners of this crisis have been the proponents of behavioural finance, in which the ups and downs of human psychology are the key.

The evidence is very clear from surveys on both sides of the Atlantic. People are nervous of world war-sized deficits when there isn’t a war to justify them. According to a recent poll published in the FT, 45 per cent of Americans “think it likely that their government will be unable to meet its financial commitments within 10 years”. Surveys of business and consumer confidence paint a similar picture of mounting anxiety.

The remedy for such fears must be the kind of policy regime-change Prof Sargent identified 30 years ago, and which the Thatcher and Reagan governments successfully implemented. Then, as today, the choice was not between stimulus and austerity. It was between policies that boost private-sector confidence and those that kill it.

Brad DeLong has a piece that tackles most of the concerns raised by Ferguson:

Here we have the crux: Greece, Ireland, Spain, Portugal and Italy need to be austere. But Germany, Britain, America and Japan do not. With their debts valued by the market at heights I had never thought to see in my lifetime, the best thing they can do to relieve the global depression is to engage in co-ordinated global expansion. Expansionary fiscal, monetary and banking policy, are all called for on a titanic scale. But, the members of the pain caucus say, how will we know when we have reached the limits of expansion? How will we know when we need to stop because the next hundred billion tranche of debt will permanently and irreversibly crack market confidence in dollar or sterling or Deutschmark or yen assets? Will this shrink rather than increase the supply of high-quality financial assets the world market today so wants, and send us spiralling down? Economists had asserted before 1829 that what we call “depressions” were impossible because excess supply of one commodity could be matched by excess demand for 

another: that if there were unemployed cobblers then there were desperate consumers looking for more seamstresses, and thus that the economy’s problems were never of a shortage of demand but of structural adjustment. But once Mill had pointed out that these economists had forgotten about the financial sector, the way forward was clear – if you could cure the excess demand in the financial sector. Monetarist dogma says the key excess demand in that sector is always for money – and so you can always cure depression by bringing the money supply up. The doctrine of the British economist Sir John Hicks says the key financial excess demand is for bonds, and you can cure the depression by either getting the government to borrow and spend or by raising business confidence so the private sector issues more bonds.

Followers of the US economist Hyman Minsky say the monetarists and the Hicksians (usually called Keynesians, much to the distress of many who actually knew Keynes) are sometimes right but definitely wrong when the chips are as down as they are now. Then the key financial excess demand is for high-quality assets: safe financial places in which you can park your wealth and still be confident it will be there when you return. After a panic, Minsky argued, boosting the money stock would fail.Cash is a high-quality asset, true, but even big proportional boosts to the economy’s cash supply are small potatoes in the total stock of assets and would not do much to satisfy the key financial excess demand. Trying to boost investment would not work either, for there was no excess demand for the risky claims to future wealth that are private bonds. The right cure, his followers argued, was the government as “lender of last resort”: increase the supply of safe assets that the private sector can hold by every means possible: printing cash, creating reserve deposits, printing up high-quality government bonds and then swapping them out into the private market in return for risky assets.

We don’t need one of expansionary monetary and fiscal and banking policy, we need all of them – until further government action begins to crack the status of the US Treasury bond as a safe asset, and further government bond issues reduce the supply of safe high-quality assets in the world economy. Has that day come? No. The US dollar is the world’s reserve currency, the US Treasury bond is the world’s reserve asset. The US has exorbitant privileges that give it freedom of action that others such as Argentina and Greece do not have. Will that day come soon? Probably not.

But trust me, we will know when the time comes to stop expansion. Financial markets will tell us. And not by whispering in a still, small voice.

Brad DeLong has more to say about Ferguson's weak arguments bashing Keynesian economics:

Could we please have some acknowledgement of the fact that the reason the debt-to-GDP ratio did not rise across the 1930s was because GDP rose, not because debt didn't rise? Debt more than doubled from $22.5 billion to $49.0 billion between June 30, 1933 and June 30, 1941. But nominal GDP rose from $56 billion in 1933 to $127 billion in 1941.

And could we please have some acknowledgement that our 9.4% of GDP deficit in fiscal 2010 pales in comparison to the 30.8% of GDP deficit of 1943, or the 23.3% and 22.0% deficits of 1944 and 1945?

Niall Ferguson should not do this. The Financial Times should not enable Niall Ferguson to do this.

Krugman chips in:

If you were ignorant of basic facts about the Depression — or if you didn’t know that movements in a ratio can reflect changes in the denominator as well as the numerator — you might think that it’s possible to summarize fiscal policy by looking at the federal debt-GDP ratio, which looks like this from 1929-41:

Clearly, then, Herbert Hoover was a wild deficit spender, while FDR was much more cautious. Right?

OK, we know that’s wrong. Here’s what nominal debt, the numerator in the debt ratio, looks like:

So Hoover ran up very little debt — only about 6 percent of 1929 GDP. FDR, on the other hand, ran up a lot of debt, about 47 percent of 1933 GDP. But Hoover presided over a shrinking, deflationary economy, while FDR presided over a rapidly growing (from a low base) economy with rising prices.

Multidimensional Poverty Index (MPI) -- Nepal edition

The UNDP and OPHI have come up with a new measure of poverty— Multidimensional Poverty Index (MPI) — that will be used (rather update) the existing Human Poverty Index (HPI) calculated annually by the UNDP in its flagship report HDRs. Here is the full paper and methodology used in calculating MPI.

The MPI assesses a range of critical factors or “deprivations” at the household level: from education to health outcomes to assets and services.  The index ranges from zero to one, with low value meaning low MPI. It ranks countries based on MPI. The MPI value reflects both the incidence (percentage of people who are poor) and intensity (the average number of depravations each household faces) of poverty. Education, health and living standard are the three main indicators. Education is composed of two sub-indicators: years of schooling and child enrolment. Health is composed of two sub-indicators: mortality (any age) and nutrition. Living standard is composed of six sub-indicators: electricity, sanitation, drinking water, floor, cooking fuel, and asset ownership.

The MPI uses 10 indicators to measure three critical dimensions of poverty at the household level: education, health and living standard in 104 developing countries. These directly measured deprivations in health and educational outcomes as well as key services such as water, sanitation, and electricity reveal not only how many people are poor but also the composition of their poverty. The MPI also reflects the intensity of poverty – the sum of weighted deprivations that each household faces at the same time. A person who is deprived in 70% of the indicators is clearly worse off than someone who is deprived in 40% of the indicators.

The measure reveals the nature and extent of poverty at different levels: from household up to regional, national and international levels. The multidimensional approach to assessing poverty has been adapted for national use in Mexico, and is now being considered by Chile and Colombia.

OPHI researchers analyzed data from 104 countries with a combined population of 5.2 billion or 78 per cent of the world’s total. About 1.7 billion people in the countries covered – a third of their entire population – live in multidimensional poverty, according to the MPI. This exceeds the 1.3 billion people, in those same countries, estimated to live on $1.25 a day or less, the more commonly accepted measure of “extreme poverty”.

The MPI also captures distinct and broader aspects of poverty. For example, in Ethiopia 90 per cent of people are “MPI poor” compared to the 39 per cent who are classified as living in “extreme poverty” under income terms alone. Conversely, 89 per cent of Tanzanians are extreme income-poor, compared to 65 per cent who are MPI poor. The MPI captures deprivations directly – in health and educational outcomes and key services, such as water, sanitation and electricity. In some countries these resources are provided free or at low cost; in others they are out of reach even for many working people with an income.

Half of the world’s poor as measured by the MPI live in South Asia (51 per cent or 844 million people) and one quarter in Africa (28 per cent or 458 million).

 

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Case of Nepal: The percentage of people who are MPI poor (headcount) is 84.7 percent. The average intensity of MPI (average number of depravations each household faces) is 54 percent. Nepal is ranked 82 with a score of 0.350. The percentage of population at risk (deprived in at least one indicator) is 76.73 percent. There are 18.3 million MPI poor people in Nepal. Compare this with the World Bank’s estimate of poverty: 55.10 percent (15.59 million) of the population living below income poverty line of $1.25 a day. The national poverty line estimates 30.90 percent of population living below the poverty line. The incidence of poverty is 64.7 percent and average intensity across the poor is 54 percent.

In South Asia, Sri Lanka has the best MPI index with a value of 0.021 and ranks 32 in the world (first in South Asia). It followed by Pakistan, Bangladesh, India and Nepal with world ranking of 70, 73, 74, and 82 respectively. 

 
 

Hans Rosling on global population growth

He argues that living standard of the poorest has to be improved in order to check population growth. The population of the world is projected to reach 9 billion over the next 50 years.