We are Being Anti-Keynesian !

In the recently presented budget, India’s fiscal deficit for FY 2017-18 got revised upwards from 3.2% to 3.5% of GDP and the target fiscal deficit for FY 2018-19 is set at 3.3%. Though Moody’s has said that a slight slippage in fiscal deficit has no material impact on overall economic strength, Indian rupee slide past 64 post budget and bond yields hardened substantially. This begs a question that Shouldn’t the government have some space to operate its fiscal policy?  I argue that the stringency we are placing on the path of fiscal consolidation makes us anti-Keynesian and I believe that that’s not optimal.

The first subtle and grossly unappreciated point is that expressing the fiscal target as a percentage of GDP makes our policy Pro-Cyclical or Anti-Keynesian. Fiscal deficit expressed as a percentage of nominal GDP can be breached even if expenditure in Rupee term is adhered to but the actual GDP falls short of forecast GDP based upon which government plans its budgetary expenditure. To achieve the fiscal deficit target as a percentage of GDP requires that government literally scales back the Rupee expenditure when economic growth slows down. Is this a desirable fiscal policy? No so much! That is what the evidence suggests so.

Greece is a leading example of what austerity measures could do to the country’s economic growth. Greece lost 30% of the GDP since 2010 after imposing the austerity measures. In fact, as IMF’s recent paper suggests that countries who employed severe austerity plans lost more in terms of GDP growth post-financial crises. (See Figure 1) When Canada employed massive fiscal consolidation in 1997-98, it was running a 4% plus GDP growth rate (which is very high for an advanced economy) and more importantly had high-interest rates. Bank of Canada could counter some of the negative impacts of austerity by reducing the interest rates. In India, where inflation is sitting right in the middle of inflation target band and where RBI has limited scope to tinker with the rates, fiscal austerity would have a much higher bite in India. Even generally, history suggests that countries have resorted to counter-cyclical policies. For example, take USA’s case. IMF has made available fiscal data from 1800 for the USA. The USA experienced a fiscal deficit of 30 basis points higher on average during worst 30 years in last two centuries compared with best 30 years.   

Let’s flip the side and ask if being-Keynesian helps. 2011 paper by Auerbach and Gorodnichenko in American Economic Review and a follow-up paper by Valerie Ramey and Sarah Zubairey both convincingly show that at least in the USA, fiscal multiplies in bad times are large; meaning that government expenditure helps during bad times. Richard Koo’s hypothesis also suggests that when everyone in the private sector is deleveraging their balance sheets, the government should not do so as it will reduce the aggregate demand. In short, evidence supports the being Keynesian in bad times. But Auerbach in his 2011 paper also shows that fiscal multiplies can be close to zero or even negative during good times. This can occur because the government can crowd out private borrowers from credit markets by putting upward pressure on the interest rates. This implies that it's important that government stick to counter-cyclical policies all the times.

The fear most economists have is exactly this that counter-cyclical policies hardly stay counter-cyclical by the time economy enters the boom phase. At that time governments find it tough to control the spending amidst high tax revenues and golden opportunity to move towards fiscal balance can be lost. But does India’s case look like that? Since 1990, India has been able to maintain fiscal in a broad range of 2%-4%, even after undergoing a wave of productive capital expenditure during NDA government at the turn of the century. More recently, we had a very high fiscal deficit in 2009 of roughly 9%. UPA pulled it back to 4.4% till 2014. But they also had good growth years to support fiscal consolidation. From 2014, the BJP-led government has been able to reduce the fiscal deficit further to 3.2%-3.5% range even after having sub-7% growth years. I feel that Indian governments have shown fiscal record which is credible enough to allow them some flexibility to counter the slow growth.

Observers got a bit worried that fiscal slippage was lead by the revenue deficit rising to 2.6% compared to the planned 1.9%. But many of the government expenditures on education, health, and rural programs are categorized as revenue and to call it wasteful is not really correct.

In summary, I propose that government should have a long-term plan for achieving fiscal balance. But the glide path should really be a function of the state of the economy that prevails in the future. It should allow for overshooting during tough times but requires undershooting during good times.  After all, being Keynesian is not being fiscally imprudent.

Dr. Apoorva Javadekar

05/02/2018

(Not So) Mysterious Link Between

Doing Business Index and Economy

Dr. Apoorva Javadekar

(Ph.D. Boston University)

India stands at 130^th place out of 190 nations in the 2017-Doing Business Rankings of the World Bank (WB), just a spot higher than the 2016 rankings. Is this a worrisome sign? Surprisingly, the answer is “Not so much” according to the recent research. (see the article published in mint newspaper and the paper by IIM Banglore faculty)  A curious case is being made in the recent times that the higher rankings do not really imply good economic outcomes like higher FDI inflows or higher GDP growth. I will argue that the case being made is curious because it is based on the dodgy economic foundations.

My argument is based on the simple logic that ease of business rank is a stock concept: It represents the till the date reforms that has taken place in a country on various issues like infrastructure, legal system etc. over the last couple of decades or even more. Hence this rank ought to be more intricately associated with corresponding macroeconomic stock variables like level of per capita GDP instead of flow variables like growth in GDP or additional FDI flows during a given year. With this concept in mind, figure 1 plots the average ease of business rank during 2010-2015 against the per capita GDP level. The message from the figure is clear: In the long-run, a country can’t become rich in per capita sense unless it has a high ease of doing business index. Only oil-rich countries like Kuwait, Qatar, Venezuela, and Angola managed to get sufficiently rich even with a very low ease of business score. On the other hand, if you have a sufficiently high ease of business score, then you are almost guaranteed to become rich. The outlier to a marginal degree to this rule is two emerging nations: Thailand and Malaysia. But both the countries are growing fast and in a sustainable way precisely because of solid infrastructure and institutions they have built and over the next half a decade these would move out of this marginal outlier list. This is a tremendous macro relevance attached to the ease index. It’s not the index that matters but the institutional and infrastructure set up that this index reflects that is important.

   Figure 1: Ease of Doing Business Score and Per Capital Income

Once we know that high ease of business index is almost equivalent to being a rich country, it is foolhardy to expect that high index would also imply higher GDP growth. Why? The answer lies in the convergence hypothesis proposed by the neoclassical growth theory which says that poorer countries tend to grow faster in per capita terms. Figure 2 illustrates the convergence result. A unit increase in log per capita GDP reduces the annual growth rate by almost 0.90%. We can then adjust the observed growth rates by this magnitude for each unit of per capita GDP while comparing the results for various countries. With this background let’s look at the growth data. 

   Figure 2: Convergence Result

Ease of Business Score	GDP per capita (in 2010 constant US Dollars)	Annual GDP Growth (2010-15)	Adjusted Annual GDP Growth (2010-15)
score<50	1587	2.35%	0.22%
50<score<70	6768	1.75%	1.62%
Score>70	38561	0.50%	1.88%

The adjusted GDP growth column adjusts the growth rate upwards or downwards considering how rich or poor the economy is compared to the mean economy. In statistical parlance, this is called controlling the effect of the GDP per capita. The result shows that after adjusting for the convergence effect, the countries with better ease of business score actually also register higher growth.

What about the other economic outcomes like FDI inflows? Again, one must compare the stock variable with a stock variable. OECD provides the data on the Inward FDI stock for various countries which represents the total FDI that a country has received so far over the long-run as a percentage of the country’s current real GDP. Again, ease of business index is associated with higher FDI stock. The only exception seems to be Korea Rep. and Japan on the downside. The problems with Japanese FDI are well known like Japanese customer’s liking for domestic known products and connected nature of Japanese firms. Hence it is possible that some country-specific cultural factors can play an important role in FDI outcomes, but on average, better ease of business score implies greater cumulative inward FDI for the country in the long-run.

   Figure 3: Ease of Business Score and Inward FDI Stock

In summary, the ease of business rankings might not be an end in itself, but the underlying developments the rankings capture like infrastructure, low corruption, efficient legal process, good bankruptcy laws etc. are vital for the long-run economic success. India’s quest for improving the rankings hence is not an unnecessary obsession!

     

     

Inequality, Poverty and Growth: A First Look !

Background:

Inequality, be it of income, access to credit, health or education has become one of the most important issues in political and policy circles. In simple terms, inequality means the gap between the means of wealthy and poor. A simple way to measure inequality is to take the difference between the share of the national income earned by richest 20% of the population and the share earned by the poorest 20% of the population. For example, consider India’s case. In the last decade on an average, richest 20% of the population earned 44% of the national income as against mere 8% by poorest 20%. The difference of 36% gives a pretty good measure of the income inequality. Using this measure, the inequality in India and the USA was around 31% and 37% in 1980’s which grew to 36% and 41% respectively by 2010.  The fact that income inequality has risen in many of the advanced and developing nations over last two decades is considered by many as a major socio-economic problem.  Of course, many economists do not see inequality as a problem. Barro (2000) for example argues that inequality, especially in the developing world where the average income is low implies that there are at least certain individuals who have a bear minimum capital to innovate or be the entrepreneurs and kick-start the development process. One can find plenty of arguments on both the sides. But I feel that most of these arguments miss the fundamental point that inequality is not the same as poverty (in data as well as in principle) and that we need to start seriously thinking about the two separately and at the same time find any links between these two concepts. The purpose of this note is twofold: first, I argue that poverty and not the inequality should be our concern. Second I try to unearth some links between inequality and poverty.

Some Philosophy:

From a social perspective, do we care about the Inequality or the economic state of the poorest section? Inequality is a relative concept where we compare the rich against the poor.  But inequality tells us little about the financial condition of the poor. If the most miserable person in the country has a beautiful house, car, and a good job, do we care about how much more the richest guy in the country has? I doubt it. From a welfare perspective, we are more concerned about ensuring a reasonable lifestyle to the poorest section and not about the gap between the rich and the poor. Until the gap is an outcome of the market forces, what we care is just the absolute condition of the poorest section. If certain people as a combination of talent, efforts, and opportunities made a good fortune for themselves and at the same time also created opportunities for the underprivileged class, we should not be much concerned about the gap or the inequality.  So the first point I would like to stress is that in principle, we are more concerned about poverty reduction as against inequality reduction.

Some Empirics:

Given this view, let’s try to look at the data on inequality and poverty.

Figure 1:

Figure 1 shows inequality-poverty combination for each of the country averaged over the period 2008-2015.  Red lines indicate the global means of respective variables which divide the plot into four regions. The first striking observation is that no region is significantly relatively more populated or empty. There is at the best a weak positive relationship between the inequality and poverty. A substantial number of countries has above-average inequality but below-average poverty level, for example, Brazil.  The mean level of poverty for countries with above-average inequality is 47%, and that for the countries with below-average inequality is 33%. It means that variation in inequality explains only 14% of the poverty level difference, while the poverty ranges between 10% and 55% even leaving the bottom and top 10% of the countries. This analysis makes it clear that poverty and inequality are two different dimensions for a good deal of the countries.  

So now we know that an unequal society does not necessarily need to be a poor country. So why should we care about inequality? We must if higher inequality hampers the future economic development or the speed of poverty eradication. I will discuss the theory later, but first again let’s take a look at the data.

Figure 2:

In figure 2, I plot the initial inequality in 1980’s for each country on X-axis and the percentage of poverty change from 1980 to 2015 on Y-axis. (Note that the negative change means poverty reduction). Looking at the graph, one can see that most countries have reduced the poverty level over last three decades. But the magnitude of the reduction is not linked with the initial inequality as can be seen from the almost flat line of best fit and also from the simple observation that enough countries lie above and below the average poverty reduction line (red horizontal line) for any level of inequality.

Figure 3:

I do a similar analysis in figure 3, with the percentage change in GDP per capita on Y-axis. Because countries which started as poor in 1980’s had more scope to improve the income level, I plot the graph separately for countries which were rich in 1980’s and the countries which were poor in 1980’s. But scatter is flat for both the type of the countries, suggesting that initial inequality countries have not done poorly in terms of per capita GDP growth or in terms of eradication of poverty.

Figure 4:

But the level of growth is not all that one cares. The volatility of growth matters too as it feeds into the decision making of the consumers and producers. So I look at the link between the starting inequality level and the volatility of growth over next three decades for each country. Figure 4 plots the relationship. What we see is a flat average link between the two. If at all, the extreme long-term growth volatility countries all had low inequality to start with. Again, inequality does not seem to be associated with high volatility.

Figure 5:

Before turning to theory and economics behind the results, let’s look at the last graph. The idea is to understand if there is a notion of Inequality Trap. I plot initial inequality on X-axis and change in inequality over 1980-2015 on Y-axis in figure 5.

Figure 5:

The graph clearly indicates a negative link. That is those countries which started with high initial inequality have reduced the inequality on an average. It shows that there is no apparent evidence of inequality trap. The reduction in inequality could be due to aimed policies by the governments or the market forces. The graph tells us nothing about that, but it shows one thing that countries with high inequality to begin with have been able to reduce it more than the countries with lower levels of inequality.

    

None of these graphs presented above have got a causal interpretation in the sense that X-axis variable is not causing Y-axis variable necessarily. The graph should be seen only from correlation perspective. For example, figure 5 cannot be interpreted to mean that high inequality to begin with increases the speed of inequality reduction. A more rigorous regression analysis can shed light onto robustness of the correlation as well as the causation.

So looking at the evidence, we know four things: First poverty and inequality are different dimensions. Second, higher inequality cannot be associated with lower long-term growth rate or lower rate of poverty reduction. Third, that inequality does not increase the growth volatility and fourth that high inequality can be reduced. Recent IMF paper documents that high inequality reduces the future growth. But the horizon they consider is a five-year window as against thirty years in my analysis which leads to different results.  In summary, it seems difficult to understand the inherent negativism towards inequality. Inequality neither implies poverty nor the hindrance to the poverty reduction process, at least in the data. 

Reputation and Risk-Taking By Mutual Fund Managers

In the last post (paste link here) I discussed how the reputation of a mutual fund plays a crucial role in determining how investors react to fund's recent performance. In this post, I shift my attention from investors to mutual fund managers. Here I discuss some results from my latest work on the behavior of fund managers. By behavior I mean the way manager forms his portfolio (portfolio is nothing but a bundle of asset and it is usual in finance it sometimes also means the investment strategy) and manage the risk in his investments. In my paper I study how the historic performance of a fund alters its risk taking behavior. Before proceeding, let's understand what matters for fund manager. Managerial incentives are two dimensional: on one hand he has to attract capital from investors as ultimately his compensation depends upon the size of the fund he manages. On the other hand he is answerable to fund management and his employment contract depends upon his performance. We have already seen in the last post that investor reaction depends upon historic performance together with current performance. Data suggest that reputation or history of performance also matters for employment continuation. In my sample, 30\% of the managers who are fired from their job ranked within bottom 20\% in terms of their historic performance. Clearly, having a poor history raises the probability of firing. (Else the number should have been 20\% instead of 30\%. Think!) Clearly we know now that a poor history fund manager is facing a serious unemployment risk as compared to a good history fund manager. So there is a strong case to believe that reputation must matter for the way managers handle their investments during current period.

In 1996, Brown, Harlow and Starks (BHS hereafter) came up with a study which found that a midyear losing manager is more likely to bump-up the risk of his portfolio in the second half of the year. But later studies did not find strong support for this evidence. Kemph, Ruenzi and Theile published a research in 2007 doing a very simple trick. They argued that compensation incentives are more dominant during the period when stock market performs well but employment incentives are strong during bad years. So managers might manage risk differently over good and bad years. I combine Kemph's approach and include the reputation as a new driver of managerial risk taking ability. So we are interested in analyzing how managers change their investment risk profile in the later half of the year as compared to first half of the year, given their mid-year performance position. It is important to know a good measure of risk in this context: it is precisely the extent to which manager deviate away from what their peers are doing. So risk is measured using how much manager's returns deviate away from benchmark. Then change in risk is just risk in the second half of the year minus risk in the first of the year. I prefer to take the ratio of second half risk to the first half risk, so that the ratio of 1 is status quo.

What I find is pretty interesting. First I find that midyear position has no impact whatsoever on risk-shifting during good years. Only during bad years, when unemployment risk is higher do the managers try to adjust the risk. Following figure shows the results for bad years: On X-axis we have mid-year rank (0 being lowest and 1 being highest) and on Y-axis we have risk-shifting ratio: ratio of risk during second half to first half. What we have on our hands is really a fascinating result: As the mid-year performance gets better, top reputed funds reduce this ratio and low reputed funds increase this ratio. (Note the ratio is more than 1 for any reputation and mid-year position. So it is a general trend that all funds bump-up their risk during second half anyway. We want to understand how the extent of that change with mid-year performance).  This is a dramatically opposite behavior.

How can we explain it? There are two dimensions we need to explain. First is how the manager behavior change as mid-year position change and second comparing the levels of the risk-shifting for top and low reputation managers. I answer these one by one. First, top reputed manager has lower risk of getting fired with one-off bad performance, so these managers bump-up the risk more during second half of the year as compared to low reputed managers when in a mid-year losing position. That explains why the curve for top guys starts at a higher level. Second, as mid-year performance improve, top guys want to play it safe. This is because even a reasonable performance during the year assures them employment next period and also we know from my last post that a bad performance by top guys is penalized more by investors. So given a reasonable mid-year position there is no point in bumping up the risk. That explains why risk-shifting curve slopes downwards for top guys. Now coming to low reputed managers: For low reputation guys, if they are in a reasonable mid-year position, it gives them some head-start to capitalize and they have incentive to bump-up the risk. This is gambling for resurrection. But as their mid-year performance gets bad, their risk appetite goes down very much as unemployment risk bites them. So their objective is not to be a topper anymore but just be a median guy, so they keep if very safe, having portfolios very similar to their peers. This explains why curve slopes upwards for low reputed guys.

Of course all these are conjectures and it is important to write down a proper theory to see if this logic holds in a rational model. But what the results show is very interesting and important from the compensation designing perspective. Fund management teams can optimally construct the salary schemes so as to keep managerial risk-shifting in control and what my results shows is that managers need to have element in their salary to hedge against unemployment risk. Otherwise, managers are probe to risk-shifting!

Mutual Fund Reputation and Investor's Reaction !! (My PhD Job Market Paper)

Does mutual fund's historic performance matter while investors determine their portfolios after observing a recent period's fund performance? In my PhD thesis, I solve this interesting question using a large dataset of US equity mutual funds. Before starting with the paper, I had this intuitive notion that a good history mutual fund can escape a one-off bad performance since investors are more likely to attribute this to a bad luck rather than lack of skill. Such behavior is also supported by behavioral traits such as confirmation bias where investors tend to disregard the new information if it does not match with their prior information. So a bad performance by a good history fund is exactly a type of information they are ready to ignore. But what I found in the data was exactly opposite to this intuition: A bad performance by a good-history fund experiences more fraction of capital outflow as compared to a bad-history fund. This left me with a small puzzle on my hands. Just to state findings complete, I also find that a good performance by a good-history fund attracts a lot more percentage inflows as compared to a bad-history fund. In summary, good-history funds experience very sensitive capital flows to their recent performance but bad-history funds neither lose lot of money on bad performance nor gain any significant capital with a reasonable performance. Just to give sense of magnitude, I report the numbers from my regression analysis. Consider a worst fund and a best fund. Worst fund has a bad history and also performs badly this period. On the other hand a best fund has an excellent history and it also performs nicely this period. Then best fund receives 50\% (as a percentage of asset size) as compared to worst fund. Out of which one-fifth can be attributed to the fact that best fund has a better recent period performance, one-tenth to the fact that it starts the period with higher reputation due to better historic performance, but whole of the remaining two-third to the fact that it performed well this period and it also had a good reputation to start with. This last effect is the joint effect and shows that the importance of the recent period's performance grows with good historic performance.

So there are two immediate questions: First is why this result is interesting? Second how one can explain this counter-intuitive evidence? To answer first question, it's important to know what was known till the day about capital flows in and out of a mutual fund. The notion of 'return chasing' was pervasive: meaning that investors chase recent winner funds and drop out of recent losing funds. But what my data shows is that the extent of this tendency is virtually determined by the historic performance. The good-history funds experiences this return chasing type of investor's behavior, but flows in and out of a bad-history fund are insensitive. So my results are important to qualify the applicability of this return chasing notion. Second important reason is that the investor's reaction determines the risk that a fund manager is ready to undertake. To this extent, good-history and bad-history funds can showcase very different risk taking appetite. I present the evidence on risk taking in a second post.

Coming to the second question, I have a simple story to explain these results. Imagine a world with two types of investors: Some investors are more attentive to the information than others. In this world, attentive investors always update their beliefs about the fund manager after each performance and shift out if fund keeps on performing badly. Necessarily they shift to good performing funds. Only inattentive investors stick with badly performing funds. So good-history funds are owned by attentive investors and bad-history funds are owned by inattentive investors in large part. This implies that a bad-performance by a good-history fund will be penalized more.

In summary, these results give a very different picture than what was thought to be true earlier and importantly can help funds managers understand the type of investor behavior they are likely to face given their historic performance.