Beware its not over yet!

Carmen Reinhart and Vincent Reinhart warn that the impact of financial crisis is not over yet. It will last for some years to come.

The basis for sustained recovery is in place, and canny Fed officials are now alive to the dangers of both deflation and inflation. Similarly Jean Claude Trichet, head of the European Central Bank, spoke about how the dust had begun to settle on the crisis. Policymakers and financial markets seem to be looking at what comes next. 

Such optimism, however, may be premature. We have analysed data on numerous severe economic dislocations over the past three-quarters of a century; a record of misfortune including 15 severe post-second world war crises, the Great Depression and the 1973-74 oil shock. The result is a bracing warning that the future is likely to bring only hard choices. 

Our research found real per capita gross domestic product growth tends to be much lower during the decade following crises. Unemployment rates are higher, with the most extreme increases in the most advanced economies that experienced a crisis. In 10 of the 15 episodes we studied, unemployment never fell back to its pre-crisis level, not in the following decade nor right up to the end of 2009. 

It gets worse. Where house price data are available, 90 per cent of the observations over the decade after a crisis are below their level the year before the crisis. Median prices are 15 to 20 per cent lower too, with cumulative declines as large as 55 per cent. Credit is also a problem. It expands rapidly before crises, but post-crash the ratio of credit to GDP declines by an amount comparable to the pre-crisis surge. However, this deleveraging is often delayed and protracted. 

Our review of the historical record, therefore, strongly supports the view that large destabilising economic events produce big changes in long-term indicators, well after the upheaval of the crisis. Up to now we have been traversing the tracks of prior crises. But if we continue as others have before, the need to deleverage will dampen employment and growth for some time to come. 

Part of these changed prospects after a crisis simply reflects the correction of expectations. During episodes of financial euphoria – from the diving bell, through the steam engine and thereafter – the old rules seem not to apply. Lenders provide easy credit, investors bid up asset prices, and businesses invest unwisely. Spending advances rapidly, and debt builds up. Yet recent discussions about the “new normal” leave the misleading impression that the pre-crisis environment was “normal”.

The role of state post-economic crisis

Ajay Chibber argues that the state is still an important player in economy. Its role, however, in the developed and developing countries will differ. He cautions that markets are wary of rising government debt in the developed countries that have launched massive fiscal stimulus to save the economy from going down the drain.

So what is the appropriate role of the state after the financial crisis? In the developed world, a permanent expansion is impossible, especially as ageing populations put further pressure on public finances. With almost half of GDP in state hands, it is not surprising that large stimulus packages helped stop the markets going over the edge. But with public debt in the developed world exceeding GDP, there is less scope for fiscal activism. If economies sink back into recession, further fiscal expansion could unnerve markets. In the long run, debt sustainability may require a fundamental review of the welfare state.

It is too early to predict the demise of the nation state. The state remains the ultimate protector of people’s interests as markets overreach, on both the upswings and the downswings of capitalism. Self-regulation – à la 16th-century Scottish bankers – or a light-touch regulatory system cannot be the solution for the modern financial world. A co-ordinated, activist and sceptical regulatory system is needed. The Group of 20 and more broadly the UN can play a bigger role.

Asian-style state-led capitalism has performed well during the crisis. With low public debt at around 40 per cent of GDP, Asia has shown the world that future capitalist development depends on an activist state, but not necessarily a large one. Unburdened by expensive welfare provision, developing countries in Asia and elsewhere must now build social protection systems but with “workfare” rather than European-style “welfare”.

With global warming – the mother of all market failures – looming, the role of the state becomes more critical. Investment in green technologies and public infrastructure must be the priority.

In terms of size, the state has reached its limits in the developed world. But there is a case for increasing its role in sectors such as banking and finance, as well as in addressing climate change. In the developing world, government needs to play a bigger role in social protection, basic services and rural infrastructure. Addressing corruption and ensuring delivery will be key to its legitimacy.

What will matter is what the state does, not how big it is. A smarter, more active state is the way forward.

What determines employment in NREGS?

India's Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is one of the largest and probably successful rural employment generation program in the world. Any adult from a household  who is willing to do manual labor at a given minimum wage rate is guaranteed a maximum of 100 days of employment. For more on introductory stuff about this program see this and this.At a cost of around 0.6 percent of GDP, this program provided employment to over 45 million households last year.

An interesting question is: what determines employment in NREGS? Jha, Gaiha and Pandey, in a recent ASARC Working Paper 2010/17, argue that the ratio of NREGS wage to agriculture wage, marital status, age, gender, and education determines employment in the rural employment guarantee program. Their conclusion is based on household level survey data from three states: Rajasthan, Andhra Pradesh, and Maharashtra.

While it is broadly true that the selection of workers for NREGS favours illiterate workers and those from deprived backgrounds, female workers appear to have a lower chance of being selected. In two of the three states, the ratio of NREGS wage to agricultural wage has significant effects. Marital status and age also affect the chances of getting employment in NREGS. Within each state, workers in some districts have higher chances of being employed in NREGS. 

Once employed in NREGS, the duration of such employment is affected by social background or educational status. Factors relevant for selection for NREGS are not necessarily so for the duration of employment.

Is highly skilled migration good?

Gibson and McKenzie argue that there is no conclusive evidence that countries either suffer or lose from highly skilled migration. One thing that is certain is that the biggest impact is on the migrants themselves.

Our findings question both the pessimistic view that high-skilled migration hurts development, and the optimistic view that most countries can benefit to the extent Taiwan, China and India have from trade and investment flows. For most countries, the first-order effects are mostly an individual phenomenon – individuals stand to gain a lot from migration, and the second-order effects on others are small in comparison and seem to at least balance one another out if not also be a net positive. In the absence of compelling evidence for massive externalities from their presence, we argue governments should not be so concerned about high rates of skilled emigration, but focus instead on the basics of providing the policy environment needed to foster growth and innovation at home.

The two sides of India very few know of…

An excellent piece about the Maoist insurgency in India in Foreign Policy magazine. It is a story of neglect of market forces to honor indigenous people’s rights and livelihoods, the state’s incapacity to realize this, and the capitalization on anger (due to poverty, lack of justice, and political disenfranchisement) of frustrated people by the Maoists in their bloody insurgency against the central government. Nepal endured this situation for ten bloody years between 1996 and 2006.

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[…]It wasn't supposed to be this way -- not in 21st-century India, a country 20 years into an experiment in rapid, technology-driven development, one of globalization's most celebrated success stories. In 1991, with India on the brink of bankruptcy, Singh -- then the country's finance minister -- pursued an ambitious slate of economic reforms, opening up the country to foreign investment, ending public monopolies, and encouraging India's bloated state-run firms to behave like real commercial ventures. Today, India's GDP is more than five times what it was in 1991. Its major cities are now home to an affluent professional class that commutes in new cars on freshly paved four-lane highways to jobs that didn't exist not so long ago.

But plenty of Indians have missed out. Economic liberalization has not even nudged the lives of the country's bottom 200 million people. India is now one of the most economically stratified societies on the planet; its judicial system remains byzantine, its political institutions corrupt, its public education and health-care infrastructure anemic. The percentage of people going hungry in India hasn't budged in 20 years, according to this year's U.N. Millennium Development Goals report. New Delhi, Mumbai, and Bangalore now boast gleaming glass-and-steel IT centers and huge engineering projects. But India's vast hinterland remains dirt poor -- nowhere more so than the mining region of India's eastern interior, the part of the country that produces the iron for the buildings and cars, the coal that keeps the lights on in faraway metropolises, and the exotic minerals that go into everything from wind turbines to electric cars to iPads.

If you were to lay a map of today's Maoist insurgency over a map of the mining activity powering India's boom, the two would line up almost perfectly. Ground zero for the rebellion lies in Chhattisgarh and Jharkhand, a pair of neighboring, mostly rural states some 750 miles southeast of New Delhi that are home to 46 million people spread out over an area a little smaller than Kansas. Urban elites in India envision them as something akin to Appalachia, with a landscape of rolling forested hills, coal mines, and crushing poverty; their undereducated residents are the frequent butt of jokes told in more fortunate corners of the country.

Revenues from mineral extraction in Chhattisgarh and Jharkhand topped $20 billion in 2008, and more than $1 trillion in proven reserves still sit in the ground. But this geological inheritance has been managed so disastrously that many locals -- uprooted, unemployed, and living in a toxic and dangerous environment, due to the mining operations -- have thrown in their lot with the Maoists. "It is better to die here fighting on our own land than merely survive on someone else's," Phul Kumari Devi told us when we visited her dusty mining village of Agarbi Basti in June. "If the Maoists come here, then we would ask their help to resist."

[…] But in our visits to the region and dozens of interviews there -- with miners and politicians, refugees and paramilitary leaders, cops and go-betweens for the guerrillas -- we found a far more complex reality. Mining companies have managed to double their production in the two states in the past decade, even as the conflict has escalated; the most unscrupulous among them have used the fog of war as a pretext for land grabs, leveling villages whose residents have fled the fighting. At the same time, the Maoists, for all their communist rhetoric, have become as much a business as anything else, one that will remain profitable as long as the country's mines continue to churn out the riches on which the Indian economy depends.

[…] In a sense, however, India has already lost this war. It has lost it gradually, over the last 20 years, by mistaking industrialization for development -- by thinking that it could launch its economy into the 21st century without modernizing its political structures and justice system along with it, or preventing the corruption that worsens the inequality that development aid from New Delhi is supposed to rectify. The government is sending in Army advisors and equipment -- for now, the war is being fought by the Indian equivalent of a national guard, not the Army proper -- and spending billions of dollars on infrastructure projects in the districts where the Maoists are strongest. But it hasn't addressed the concerns that drove the residents of Chhattisgarh and Jharkhand into the guerrillas' arms in the first place -- concerns that are often shockingly basic.

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Remittance’s ugly face in Nepal: Change in consumption pattern & investment behavior

My latest piece is about the impact of remittances in the Nepalese economy. No doubt that remittances have been vital in holding the economy from falling apart. However, it is also changing the way people make consumption and investment decisions. This is concerning because at a time when GDP is pretty much below 4 percent mark for years, consumption is increasing. Worse, consumption of imported goods is increasing and investment is increasing in few unproductive sectors. This is leading to widening balance of trade deficit and also putting deficit pressure on balance of payments, which usually is positive due to high inflow of remittances. For previous pieces on remittances click here, remittances in Nepal during the global economic crisis, brain drain could be good, and a good review of remittances market in Nepal.

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Remittance's ugly face

While at transit at Doha International Airport (DIA), I saw migrant workers, mostly from South Asia, working in pretty much all the stores and cafes in and around the airport. There are numerous Nepali workers, who constantly remit money back home, working in places ranging from airports to restaurants. These migrant workers are one of the backbones of our economy. This fact was acknowledged lately by Nepali policymakers. In doing so, they eulogized remittances inflow to such an extent that they either neglected or forgot to track the amount of money entering the country from multiple sources and, importantly, how and where it is spent. The result: Change in consumption habit and investment behavior, leading to an ever-surging imports and balance of trade deficit.

In DIA, Aakash Gaihre, a young Nepali man working at Coffee Beanery, a coffee shop in the airport, curiously asked me if I was Nepali. I replied affirmatively. After a brief informational conversation, he was busy serving other customers pretty much nonstop. Each hour he spends working there, he gets paid and saves whatever is left after factoring essential expenditures to maintain basic living standard in Doha. It is typical of numerous Nepali migrants like Gaihre working at various labor destinations overseas. They work long hours to save and send money back home for the convenience of their family members. They have done their part in uplifting income (and social) status of their families. Importantly, they have constantly played their part in rescuing the Nepali economy from plunging headlong into an abysmal economic mess.

At Tribhuvan International Airport (TIA), scenes of youths like Gaihres forming serpentine lines to board airplanes headed toward major labor destinations, mostly an unchartered territory for most of them, is not uncommon. Their aim is to reach the intended destination, not get duped by manpower agencies, and land on a decently-paying job. The expectation of their families is likewise.

Enter Kathmandu and head toward the Ministry of Foreign Affairs (MoFA) and you will see a much more chaotic scene: Anxious, curious, and confused aspirant migrants waiting to get their passport issued. Thanks to litigious, indecisive, and non-transparent multi-million dollar deal to print machine readable passports, hundreds of thousands of citizens are deprived of an internationally accepted (and required) quality of passport. Hence, the haphazard queue of people at the MoFA premises. No wonder it is a fertile market for commission-sucking brokers.

Go farther away from the city center and you will see a completely different terrain. New buildings are popping up everywhere and there is an influx of migrants in and around city centers. Some of the villages lack the backbone of local economy i.e. youths. Elderly and kids are the main inhabitants of villages as youths have/are headed either to overseas labor destinations or to major city centers. Daily wages for manual labor have more than doubled. Interestingly, each alternate house either has a ‘cold store’ or a retail store—one wonders from where demand comes from. Perhaps, this is the best way to kill time. The opportunity cost of labor appears zero to them. There is no better way to waste labor than be self-employed—unproductive sales person waiting for customers in a place where pretty much every household owns a retail store!

The influx of money sent by migrants sweating and saving pennies overseas is changing the way we consume and invest. While consumption accounts for over 90 percent of GDP, gross domestic savings is equivalent to a mere 9.7 percent. Banks are becoming big fat kids from slim ones as remittances are constantly pouring in, facilitating instant easy lending to a handful of sectors. Due to political instability, squeezing returns on investment and pressure to maintain comfortable profit margin, banks are eschewing lending to traditional employment-generating sectors. Instead, money is channeled into construction, real estate, and import-consumption sectors. These sectors are referred to as “unproductive” i.e. they do not absorb much labor for employment given the scale of domestic investment.

In the last five years, construction and real estate sectors grew at an average of 4.5 percent and 7 percent annually, respectively. In real estate, credit flow doubled from Rs 7.71 billion to Rs 14.92 billion in the past two fiscal years. Unfortunately, GDP growth rate was around 3 percent and industrial sector growth just over 1 percent. Due to neglect and flawed priority, the contribution of remittances in stimulating the real sectors is minimal.

With bitter political bickering for control of state powers, managing uncontrolled flow of remittances hasn’t been a priority. Fierce competition is ensuing among banks to attract deposits and to issue loans with flamboyant, unsustainable schemes. New financial institutions are popping out of nowhere and clogging the already saturated market, especially in the urban centers. The central bank watched this unwanted drama unfold until it was unbearable to do so, chiefly due to the pressure to reduce BoP deficit, tame high inflation rate, and maintain monetary stability.

With these developments in the banking sector, the construction and real estate sectors are transforming from thin and healthy kids to very obese ones. Easy and cheap loans, thanks to remittances inflow, are helping to increase breadth and height of houses in and around major cities. A large proportion of the construction materials are imported. It would have been fine if they were sourced from domestic economy. It isn’t. The demand for these items is so high that the existing supply cannot satisfy it, leading to price-quantity equilibrium at a high and potentially unsustainable point.

The high price is not due to changes in demand and supply at the factor market, but due to a mismatch of desire, demand and supply at the product market. As a result, general prices are rising without an increase in per capita income that is backed by stimulation of domestic economy. The cost of living has gone up both in real and nominal terms. As these undesirable developments unfolded, the central bank and policymakers remained mere spectators, basking on the boon of remittances. Now, they are realizing how the same blessing can be an anathema.

While remittances have taken out hundreds of thousands of households from poverty by increasing household income to bridge poverty gap, it has also changed the way we consume and invest. Things are not boding well for the economy. The policymakers and central bank must look for actionable options to manage the inflow of remittances, and encourage consumption and investment patterns that are consistent with our long-term growth objectives. We should administer and appropriately utilize the pennies saved by thousands of migrants like Gaihres. Their sweat should not go for waste.

[Published in Republica, August 18, 2010, pp.6]

Exports sophistication of Nepal

Finally, I have a stable internet connection at my place, at least for now (for how long? don’t ask!) I will now be updating this blog more frequently than during the past two weeks.

This blog post is an extension to my previous piece about the future of Nepal’s exports. I remain optimistic that exports will rebound if non-economic constraints are addressed. It is based on this paper by Jesus Felipe et al. The position of Nepal in ranking of each of the indicators discussed below is shown with a red pointed arrow in the figures.

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 the average annual growth rate of 5.49-6.69 percent over the period 2010-2030.

In the “Index of opportunities”, Nepal ranks third in South Asia with a score of 0.4729, following India and Malaysia with scores of 0.8590 and 0.6822 respectively. 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’s rank is 33 while China’s and India’s second and third respectively.

Among the 96 non-high income countries, Nepal has a score of 0.4112 in the sophistication level of the export basket, called EXPY. It is calculated as the weighted average of the sophistication of the products exported by a country. Generally, countries with high EXPY tend to have high per capita income. A 10 percent increase in EXPY at the beginning of the period raises growth by about half a percentage point, according to ADB economist Jesus Felipe.

 

For its given level of income Nepal already has a pretty impressive level of sophistication of exports basket, although it has to be improved to if we want to enjoy the same level of export sophistication and future India and China have. With the existing state of capabilities and export potential, the average annual growth rate is projected to be between 5.49 and 6.69 percent between 2010 and 2030.

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 in EXPY-core. Note that even though the exports basket of Pakistan, Sri Lanka and Bangladesh are less sophisticated than ours, they nevertheless have high EXPY-core score, meaning that their exports sector has high-valued products that constitutes a major portion of the overall exports basket. No wonder they have higher exports market and high income per capita. Given Nepal’s income level, the EXPY-core score is not that bad but it can be improved further.

 

In terms of diversification of exports, Nepal has the second highest score (0.4032) in South Asia, following India (0.8611). Diversification is measured by the absolute 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 the 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. Generally, except for heavily natural resource dependent nations, countries with high income per capita tend to have higher degree of diversification.

In order to gauge the level differentiation of products exported with comparative advantage, the authors constructs share-core score, which shows the number of commodities with revealed comparative advantage in the core as a ratio of the total number of commodities in which that country has comparative advantage. Nepal scores 0.3569, the third best in South Asia,  in share-core category. China and India scored 0.6497 and 0.6148, respectively. To increase per capita income, Nepal has to acquire more capabilities, “both by increasing the absolute number of core commodities with comparative advantage and by shifting the composition of products with comparative advantage towards core commodities.”

 

The uniqueness of products exported by a country also matters in ensuring sustainability of exports sector and in undergoing the resulting 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. The standardness level of Nepal is better than that of countries with the same income level. Nepal has a good degree of exports uniqueness and diversification. This is an encouraging news for the listless, demoralized exports sector.

Policymakers and investors want to know the probability of producing new products and exporting them with comparative advantage. This can be figured out from “open forest” analysis in the product space. It provides a measure of the (expected) value of products that could be potentially exported with comparative advantage. We can figure out how far products currently not exported with comparative advantage are from the ones that are exported with comparative advantage and are within nearby range of existing production capacities of an economy. It is easier to produce “nearby” products than a product that is “far away” because the capabilities to produce similar but slightly differentiated products already exists in the economy.

Nepal has a score of 0.2041 in “open forest” dimension of the Index of Opportunities. It is among the lowest among the South Asian countries. The low score 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 resources requirements. Poland, India, Turkey, China, and South Africa have one of the highest open forests among developing countries.

My point: The exports sector is not done yet and there is no need to resort to widespread import curbing measures, especially in daily consumption goods. We can increase exports if we could judiciously and decisively address non-economic constraints ailing the industrial sector and implement highly targeted and appropriate industrial and trade policies. The product level analysis of sophistication of our exports basket reaffirms this conviction.

Links of Interest (08/09/2010)

Prem Khanal argues that deposit is interest elastic in Nepal and proposes an interest floor at 7 percent.

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“However, many economic commentators had refused to buy the theory and had argued the growing mismatch between inflows and outflows of resources from banking system, which fuelled a record liquidity shortage, was due to prolonged negative interest rate. The slowly unfolding scenarios reflected in terms of encouraging increment in banking deposits have proved that the orthodox theories were wrong. Granted, the lingering political uncertainty was weakening depositors’ confidence on banks that caused deposits to disappear from banking system. But my questions is: Has there been any significant development to justify that the country was moving toward political stability within the last one month when banking system experienced a record Rs 25 billion rise in deposits? No. The level of political stability in fact has worsened lately compared to last year.

[…] Everyone should be aware, especially dogmatic bankers, that it is not the right time to rejoice the rise in deposits. As the economy has already learnt a bitter lesson of sustained negative interest rates, the most serious challenge for monetary authorities is to keep it positive so as to prevent reemergence of the same ills that shook the very foundation of the economy. As the banks amass more deposits, it is time for the monetary authorities to take some measures to check the possibilities of another downward trajectory of interest rates. I propose that the central bank should direct commercial banks to keep minimum interest rate on one-year deposit rate equal to bank rate, which is currently fixed at 7 percent.”

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Phase out spending but phase in social protection, writes Guillermo Calvo

Nepalese government mulls export incentives

Uncertainty and information asymmetries are barriers to entry for exporters

The evolution of agricultural trade flows

Dilli Raj Khanal explains why import substitution makes sense and no-sense at the same time. A good article but I think he misses the point by a small margin. Here, here, and here are my take on that one.

[My internet is still so slow. I cannot post a lot of blog post. Importantly, I am missing out on so many new papers and blogs. I feel I am handicapped. It is painful.]

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.”