Opinion: Policy prescriptions for a falling rupee

The rupee seems to be in free fall, having breached the 60 Rs/$ barrier for the first time recently. Current account deficit for the last fiscal year 2012-2013 was a startling 4.8%, nearly double the RBI's 'safe' deficit level of 2.5%. I believe it's important to separate the jingoistic nationalism from the real policy prescriptions. We need to remind ourselves why a depreciated rupee is necessarily a bad thing.

Let me invoke the simple, static IS-LM framework for this elucidation. India is probably at a point like Y today - we are below our true economic potential (Y*) and have a Balance of Payments deficit*.

One of the 3 lines - LM, IS or BB (balanced BoP) - lines has to move to restore equilibrium. Based on this, one can come up with the following policy prescriptions to end the slide of the rupee.

(1) Monetary Contraction: One of the easiest ways to stop the rupee's slide would be for the RBI to raise interest rates. An undesirable result of this would be lower output levels. At a time when economic growth has already stumped to a very low level, there would not be many buyers for this policy prescription. However, hear me out. In its effort to save the rupee by selling dollars, the RBI is already reducing its monetary base (i.e. high-powered currency consisting of its foreign and domestic assets). In any case, this will lead to monetary tightening and higher interest rates - thus leading to lower output.

(2) Fiscal Expansion: An even more undesirable way of stopping the rupee's slide would be for the Government to start a fiscal expansion. Not only will it tend to appreciate the rupee (by raising domestic interest rates), but it will also increase output and take the economy out of the slump. However, we are unlikely to find many buyers for this option either. The fiscal deficit is already at a high level; any attempt to increase it further is likely to be resisted highly and also damage investor confidence (which shifts the BB curve to the left - hence increasing the deficit). A Government living well beyond its means has therefore closed this option.

(3) Currency Depreciation: I wonder why there is necessarily a problem to let the rupee depreciate. Over time, the J-curve effect takes place and the current account deficit will be cured - our imports will reduce, and exports will increase. The problem, however, is domestic inflation. Higher import prices, especially for products with inelastic demand (most notably petroleum), will spill into domestic inflation numbers. Not a feasible solution, therefore, for an economy still having high inflation.

(4) Investment Incentives: Another way of shifting the BB curve to the right is by increasing capital inflows without changing domestic interest rates. There are several innovative ways of doing this. One way is to offer higher interest rates to NRI depositors (but not to domestic depositors). This will likely lead to an inflow of funds, but will come along with a host of implementation problems. Another option is to attract FDI by freeing up certain sectors. This, again, will be politically difficult for a fragile Government.

As one can see, there is no solution that offers to stop the rupee without extracting a cost. A general principle applied in macroeconomics is that domestic problems should be solved using domestic tools (IS, LM) and foreign problems using foreign tools (BB). This removes monetary contraction and fiscal expansion from our menu. 

Hence, the choice before the Government is essentially to either take the politically volatile decision of courting foreign funds by freeing investments such as FDI **, or to risk inflation caused due to currency depreciation.

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* - Of course, a current account deficit need not necessarily result in a balance of payments deficit. However, because the RBI is having to sell dollars to stabilize the rupee, we can conclude that the present current account deficit is accompanied by a balance of payments deficit.

** - Note that this will merely increase the surplus in our capital account, but leave the current account unchanged. In fact, the current account might worsen further as imports increase due to greater economic activity.

Review: Inequality-adjusted Human Development Index for India's States - M H Suryanarayana, A Agrawal, K.S. Prabhu

Here is a simple empirical paper by M.H. Suryanarayana, Ankush Agrawal and K. Seeta Prabhu where they adjust HDI indicators of Indian states for inequality:

http://indiagovernance.gov.in/files/HDI_India.pdf

The adjustment is made using the Atkinson index of inequality, choosing the inequality aversion factor as 1, which implies that the index is more sensitive to changes at the lower end of the income distribution.

Some the the interesting points made by the paper are:

• Overall loss in HDI due to inequality is 32%; the highest loss is in Madhya Pradesh (36%).

• The loss in HDI due to inequality in income is 16%, in health it is 34% (global average: 21%) and in education it is 43% (global average: 28%).

• Loss in HDI due to inequality in income is highest in Maharashtra (19%) and Tamil Nadu (17%). Surprisingly, it is lowest in Bihar and Assam (9%). There is no observable correlation between income level of state and its inequality level. For example, Orissa has a high loss in income HDI (15%) whereas Punjab has a lower loss (13%).

• Loss in HDI due to inequality in education is highest for UP, Rajasthan and Jharkhand (46%) and lowest in Kerala (23%) and Assam (34%). After eyeballing the data in table 5, no clear correlation between income and educational inequality can be observed. Neither can we find an advantage for communist-ruled states, or a North-South divide.

• Loss in HDI due to inequality in health is highest for Madhya Pradesh (43%) and lowest for Kerala (11%). In the case of health, however, one can observe that the poorer states - MP, UP, Bihar, Orissa, Assam, Rajasthan - tend to have much higher inequality in healthcare.

• After adjusting for inequality, Kerala is an outlier in both education and healthcare. Its HDI is 24% higher than the second best (Punjab) for health and 59% higher than the second best (Himachal) for education. This is truly remarkable in that not only are social indicators much better in Kerala, but are also more equitably distributed.

Review: India's Patter of Development: What Happened, What Follows - K Kochar, U Kumar, R Rajan, A Subramanian, I Tokatlidis

Here is an extremely interesting paper by Kalpana Kochhar, Utsav Kumar, Raghuram Rajan, Arvind Subramanian and Ioannis Tokatlidis on India's development pattern:

www.imf.org/external/pubs/wp/2006/wp0622.pdf

Section II of the paper introduces the key features of the Indian economy before 1980. Most of the statistics quoted here would be quite surprising to the average reader such as me:

• Value added shares by sector: Indian manufacturing, contrary to belief, was contributing a far greater percentage (~5% more) than the average for countries at India's income level. Even after correcting for India's geographic size, the gap reduces to only ~2% (though the latter is not statistically significant). Services was a negative outlier; India's services sector contributed ~4% less than it should given India's income level.

• Employment share: Employment share in manufacturing was in keeping with India's income level. However, services employed lesser people  (by ~ 8%) than a country of India's income should.

• Labour and Skill Intensity: It was noted, surprisingly, that both value added ratio and employment  share in labour intensive industries in India is negative (though not statistically significant). Productivity in labour intensive industries, however, was found to be higher given size and income. Similarly, both value added ratio and employment share in skill-intensive industries is high; and so is productivity - all of these corrected for income and size.

• Scale: It was found that while within India, the larger industries contributed a greater share of value added and employment share than similar nations; Indian industries on an average are smaller than their counterparts elsewhere.

• Diversification: In terms of both employment share and value added ratio, India was found to be more diversified than other similar nations.

Section III of the paper presents the post-1980s period. It first briefly enlists the changes that were introduced in the 1980s and 1990s. However, the interest part of this section is when they talk about certain key indicators of the economy.

• Sectoral Composition: Manufacturing was seen to perform badly after reforms, both in the level of value add ratio, and the change in value add ration (though both coefficients are not statistically significant). It is services which grew immensely in the period. India outperformed the average in similarly placed economies by ~ 4% in the level of services value add ratio, and ~10% in the change in services value add ratio. However, employment in services continued to be lower (by ~17%) than similarly placed economies.

• Labour and Skill Intensity: Ratio of value added in labour-intensive industries continued to fall in India. At the same time, skill-intensive industries continued to increase their ratio of value added. Moreover, both the level and change in diversification in India has been higher than other countries.

Section IV of the paper deals with the performance of the states post-reforms. In sub-section A, the authors talk about sectoral composition of the states' economies - they note that change in share of manufacturing in GDP and change in ratio of value added in labour-intensive units are uncorrelated with the growth in per capita GDP over the year 1980-2000. They also note that either of the two - decline in share of manufacturing or decline in share of labour intensive units - has happened in every fast growing state. As is common belief, the share of services in GDP increased in all states (except Nagaland). Moreover, this share was positively correlated with growth of per capita GDP. However, an interesting point is that growth in per capita GDP was negatively correlated with growth in share of public services - thus implying that in the laggard states (J&K, Bihar, Orissa, Assam, MP, UP), the role of Government in the provision of services grew. The authors then point out that diversification is still correlated, albeit midly, with growth in per capita GDP. Some states, they note, have already started becoming more concentrated, thus behaving like advanced nations in the Imbs and Wacziarg (2003) relationship.

Subsequently in Section V, the authors attempt to explain what caused growth to accelerate by looking at what determined the success of the individual states. 

• Pre-existing capability: The authors take the Herfindahl index as a proxy for this. The relation between annual growth of per capita GDP and the Herfindahl index is strongly negative, implying that states that were more diversified grew more after the 1980s. To test whether such a relationship was observed in previous decades, a similar exercise was done for previous decades and it was observed that the negative relationship was significant (despite correcting for quality of institutions and literacy level) only in the 1990s, and not in the 1980s or before. Finally, the correlation of value added by an industry in 1980 and 1997 was found to be least for the fast-growing economies, thus implying that the faster growing states did not simply do more of what they were doing better anyway.

• Decentralisation: If decentralisation after the 1980s caused growth to accelerate, then one would expect state-level institutions to determine growth. When per capita income growth was regressed on transmission and distribution (T&D) losses in electricity transmissions (a proxy for the quality of institutions), it was seen that the coefficient was negative. This implies that states that had managed to cut down T&D losses, or had better institutions as is implied, saw better growth.

Review: State Level Performance under Economic Reforms in India - M.S. Ahluwalia

Here is a short paper written by Montek Singh Ahluwalia (2000), which has come to be very important in the field of inter-state inequality in India:

http://planningcommission.nic.in/hindi/aboutus/speech/spemsa/msa007.pdf

The purpose of the paper is to analyse whether growth became more unequal in the post-reform period, and if yes, what the causes of this inequality are. The first part of the paper reviews the growth performance of states in both the pre-reform and post-reform period through a number of summary statistics such as coefficient of variance. I have mentioned a few of these in the last two posts. There is a brief section of what this means for poverty.

The second part of this paper, which deals with the determinants of growth in the states, is the section I find most insightful. The author picks up possible culprits behind the divergence in growth between states and then tests their validity at a very basic level. It would be worthwhile to mention a few of his results here:

• Investment Ratios: After recognising the inadequacy of investment data at the state level, the author runs regressions of the growth rate on (1) public investment (2) private investment (3) sum of both. He finds, not so surprisingly, that the only statistically significant coefficient in the post-reform period was found for the model containing only private investment. This could support the hypothesis that after reforms, public investments had to be reined in due to aggressive fiscal targets, and this increased the importance of private investment. Private investment, however, is not as equally distributed as public investment and hence would tend to go to states that are more efficient at using their resources, and hence are richer. This would be a worthy topic of future research.

• Plan Expenditure: Using only the available data of state-level plans, the author runs regressions of growth rate on the planned expenditure as a percentage of state GDP. Expectedly, he finds that the coefficient was significant in neither the pre-reform or the post-reform period. This would support the assertion made earlier that planned or public expenditure has begun to matter less.

• Human Resources: Taking the literacy rate as a proxy for the quality of human resources, the author runs regressions of the growth rate on the literacy rate. In the pre-reform period, the sign of the literacy rate comes out to be negative, which is counter-intuitive. In the post-reform period, it comes out positive as would be expected; however, in neither period is the coefficient statistically significant. When combined with the investment ratio to create an interactive dummy, however, the coefficients turn out to be statistically significant. Hence, it could be concluded that investment yields high growth only in the presence of good human resources.

• Infrastructure: The independent variable used here is the CIME composite index of the relative infrastructure capacity of different states. When a regression is run, both the coefficients and the R-square are not statistically significant. The author notes that three components of this index - percentage of villages electrified, per capita energy consumption and teledensity - have a statistically significant relation with growth rate; he however cautions that no major implication should be drawn from this.

Review: Regional Growth and Disparity in India: a comparison of pre and post-reform periods - B.B. Bhattacharya, S. Sakthivel

Here is a paper by Bhattacharya and Sakthivel on regional divergence between the pre-reform and post-reform period.

http://iegindia.org/workpap/wp244.pdf

The paper, albeit slightly dated, attempts to look at the divergence from several perspectives, among them population, inflation and sectoral composition. For most students of economics, this paper will seem very simplistic in its approach. For example, covergence cannot be measured solely on the basis of variance of state-level growth without accounting for differences in savings rates across states. With that caveat in mind, I have summarised the most important results that this paper presents.

• Convergence: As evidence of divergence, the authors first use the variance of SDP (State Domestic Product) growth rates, which increased from 0.14 in the 1980s to 0.29 in the 1990s; at the same time, variance of per-capita SDP growth rate increased from 0.22 in the 1980s to 0.43 in the 1990s. They also quote Ahluwalia (2000), who pointed out that the Gini coefficient, which remained stable till the mid-1980s, then increased from 0.16 in 1986-87 to 0.23 in 1997-98. Finally, the correlation between average SDP growth rates in the 1980s and 1990s comes out to be 0.5, which is statistically significant at the 5% level of significance.

• Growth and Population: Controlling for the abnormal circumstances in Assam and Orissa, the correlation coefficient between SDP growth and population growth in the 1990s was found to be -0.69, which is significant at the 1% level of significance (and note that SDP growth is not in per-capita terms). However, the result can be interpreted in two ways depending on the direction of causality. If economic growth results in lower population growth, then it is a good thing; because then, all we need to do to control population is to ensure higher economic growth. However, if it is the other way round, i.e. population growth reducing economic growth, then we have a problem at hand, because economic growth cannot take off until population growth is controlled. The authors do not attempt to find out the direction of causality.

• Growth and Inflation: The growth-inflation trade-off is fairly well-known, yet contentious. The authors take a preliminary view on this by finding out the correlation coefficient between SDP growth and inflation rate. They find that such a trade-off did not exist in the 1980s (correlation coefficient of -0.69, significant), however this trade-off began to appear in the 1990s (correlation coefficient of 0.25, not significant). The authors use this data to infer that it was supply-side economics that dominated in the 1980s, but that going ahead, it is likely to be demand that is going to be the major constraint.

Review: India’s Growth in the 2000s: Four Facts – Utsav Kumar, Arvind Subramanian

Here is a paper by Utsav Kumar and Arvind Subramanian (2011) that presents four facts about state-level growth in the 2000s:

http://www.iie.com/publications/wp/wp11-17.pdf

The paper is very simple, seeking only to present and prove the facts, rather than trying to explain the causation. The four stylised facts presented by the author are:

• Growth in the main states, except three, increased in 2001–09 compared to 1993–2001: The three laggard states are identified as  West Bengal, Himachal Pradesh and Rajasthan. This is, however, not as much an indictment of these states as it is reflective of their solid performance in the 1990s. I was surprised to learn that Bengal was among the top performers in the 1990s.

• Despite the strong performance of the hitherto laggard states, we find that divergence in the growth performance across states continues: The authors prove this using a number of models. In essence, they run a regression of the growth rate of the states in different time periods (2001-09, 1970-2009, 1994-2009 etc) versus their initial income level and find that the coefficient of initial income level is positive, i.e. richer states grew faster. What they also find is that divergence has been a constant phenomenon since the 1970s, and that the pace of divergence has only increased in recent years.

• States with the highest growth in the pre-crisis years, 2001–07, suffered the largest deceleration during the crisis years (2008 and 2009): The authors identify Karnataka, Maharashtra and Gujarat as those states whose growth rate decelerated most sharply in the crisis years. These were incidentally also the states that grew the most in the pre-crisis years. The laggard states, notably Assam, Madhya Pradesh and Bihar, were the ones that continued to maintain high growth rates even in the crisis years. The authors then hypothesize that this deceleration was a function of the openness of the state economy. Since no metric for a state's global economic integration exists, the authors use the share of the manufacturing and services sector in the SGDP as a proxy for openness. They find a negative relationship between the change in growth rate and the share of the manufacturing/services sector, thus implying that states that were most open to the global economy also suffered the most.

• For the period 2001–09 we do not find any positive effect of the so-called demographic dividend: To me, this was the most surprising observation. However, a deeper reading made it evident why it was so. 49% of our demographic dividend is supposed to come from the BIMARU states. However, these states were also the poorest performers in the time period that the paper covers. Hence, the clear demographic dividend that was observed in previous years was reversed in the 2000s. This is also a dire warning for India's continued growth - unless the youth in the BIMARU states are either allowed to migrate to other states or employment opportunities are generated for them within their states, India's date with the demographic dividend might never come.

Review: Competitiveness Indicators - AC Rodriguez, G Perez-Quiros, RS Cayuela

Here is a paper by Crespo Rodiguez, Gabriel Perez-Quiros and Ruben Seguera Cayuela that talks about the challenges faced in coming up with an indicator for competitiveness in international trade:

Competitiveness Indicators - AC Rodriguez, G Perez-Quiros, RS Cayuela

Whenever I have studied international trade, relative prices has been taken as the best indicator for competitiveness. The argument is simple - if you can produce goods at a cheaper price (after accounting for exchange rates) than your competitors, then you are more competitive in the international market.

However, the authors take a European focus, and challenge such simplistic arguments on two fronts:

• real exchange rate explains well below 10% of the variance in exports
• the 'Spanish Paradox', where Spain has lost a lesser share of its exports than would be indicated by the relative price indicators

In Table 1, the authors show that in most cases, over 80% of the variance of exports is explained by a country's world trade volume. As of now, this argument seems a bit circular to me. The correlation between variance of exports and world trade volume is obvious.

The paper then explores the well-documented 'Spanish Paradox', under which Spain has out-performed its relative-price performance. If one was to construct a regression line between the decrease in relative price competitiveness (on the horizontal axis), and decrease in export share (on the vertical axis), then Spain would lie to the right of this line, implying that its export share has decreased less than what its relative price would suggest (Netherlands would be the only other major European country to be on the right; Greece would be on the line and all other countries to the left).

The authors then attempt to explain the Spanish Paradox. Table 2 shows that larger companies (ones with >249 employees) participate more in trade in Spain as compared to Germany or other large European countries. It is also these firms that have experienced the best Unit Labour Costs (ULCs) over the past few years.

The authors divide the change in ULC into three components - constant shares (assuming constant share of the different industry sizes), reallocation (considering the change in distribution) and an interaction term. One can then observe that while Spain has experienced a significant decline in ULC, then reallocation component is much smaller than other nations. What this means is that Spain could have benefited more from declining ULC if resources were allowed to move freely to those industries that had low ULC.

The Spanish Paradox is thus settled - loss of competitiveness (as reflected by ULC) was lowest among the largest firms, with the greatest presence in international trade. Competitiveness could have been boosted by initiating reforms to allow better allocation of resources.