Asset Pricing and the closely related research agenda on equity premium puzzle is a fascinating research agenda and I am always looking for some pointers to what is happening in developing countries like in India in this context. Here is a very well written entry on the Equity Premium in India by Rajneesh Mehra the author of the original article on equity premium puzzle with Ed Prescott .
India is quite notorious when it comes to availability of timely and quality data and when it comes to unemployment more so. Till recently, the NSSO conducted the employment-unemployment survey only every five years. The reasons might be many- lack of funding, tailoring the survey dates to five year plans, using it as a report card on government in office, etc. But given that unemployment is an important macroeconomic indicator, you would want it to be available at a much higher frequency. Thankfully, the government seems to have woken up to this reality. Thanks to the Labor Bureau from Ministry of Labor and Employment, we now have an annual survey on employment and unemployment starting from the year 2009-10! You can access the reports here.
Given this late start, we have two rounds of this survey till now. How does the picture look like? In the following table, I have collected data from two rounds for unemployment rate based on the usual status. Note that Unemployment Rate (UR) is defined as the number of persons unemployed per 1000 persons in the labour force (employed & unemployed). The usual principal status approach means asking the question about employment situation in the past 365 days. Accordingly, the major time spent by a person (183 days or more) is used to determine whether the person is in the labour force or out of labour force. A person found unemployed under this approach reflects the chronic unemployment (Labor Bureau 2010).
Overall in past two years, unemployment declined from 9.4% to 3.8% and most of this decline seems to be coming from decline in rural unemployment; rural unemployment declined by 66% as against 33% decline in urban unemployment with the total decline in unemployment being 60%. This decline in total unemployment because of a relatively higher decline in rural unemployment may be behind the recent decline in poverty. In a recent article, Kotwal and Sen argue that this poverty decline might be because of the success of Mahatma Gandhi National Rural Employment Guarantee Scheme (MNREGA) and Pradhan Mantri Gram Sadak Yojana (PMGSY). This is not only because of improved employment opportunities from the scheme but also that the offered wage under the schemes pushed the rural wage up ensuring improved terms of trade for agriculture. So the decline in poverty story seems to be going hand in hand with the phenomenal decline in rural unemployment.
It might be tempting to put the blame for higher inflation on increased government spending under the above mentioned schemes, but the causality does not seem to be that clear. The increase in the rural wage and the beneficial effects of better rural roads may have opposite effects on inflation. We will have to wait for more research on these links as data becomes available.
Labour Bureau (2010), Report on Employment- Unemployment Surveys 2009-2010, Ministry of Labour and Employment, Government of India.
Labour Bureau (2012), Report on Employment- Unemployment Surveys 2011-2012, Ministry of Labour and Employment, Government of India.
While I was studying in New Delhi, I used to pine for food from my homestate (may be even hometown or just homefood!). That is what migrants usually do and you find great markets in some pockets of bigger cities which cater to migrants preferences for certain kinds of food. This explains why there is a China Town, a Little India or a Little Italy in almost all bigger cities in North America. Hell, there is even little Madras in an area called Rasta Peth in Pune! New Delhi’s Delhi Hat has food stalls from all over India, but I am pretty sure most of the visitors flock to their state’s food stall. Does such kind of food preference have any economic implications at large? The answer is yes according to David Atkin from Yale.
In an innovative paper, he shows that habit persistence in food preferences among people from different regions in India imply much lower gains from trade than otherwise, should we decide to allow a freer movement of commodities between states. It also might illustrate how our somewhat fixed preferences for certain kinds of food may hold us back from getting the necessary nutrition for a healthy life!
Have not blogged for a while as the semester got busy and the teaching honeymoon (2 courses) was over. Nonetheless here are a few of the papers that have been piling up on my desk to read and blog!
Why do many households remain exposed to large exogenous sources of nonsystematic income risk? We use a series of randomized field experiments in rural India to test the importance of price and nonprice factors in the adoption of an innovative rainfall insurance product. Demand is significantly price sensitive, but widespread take-up would not be achieved even if the product offered a payout ratio comparable to US insurance contracts. We present evidence suggesting that lack of trust, liquidity constraints, and limited salience are significant nonprice frictions that constrain demand. We suggest possible contract design improvements to mitigate these frictions. (JEL D14, D81, O12, O13, O16, O18, Q12)
We study the impact of loan regulation in rural India on child labor with an overlapping-generations model of formal and informal lending, human capital accumulation, adverse selection, and differentiated risk types. Specifically, we build a model economy that replicates the current outcome with a loan rate cap and no lender discrimination by risk using a survey of rural lenders. Households borrow primarily from informal moneylenders and use child labor. Removing the rate cap and allowing lender discrimination markedly increases capital use, eliminates child labor, and improves welfare of all household types.
Resource misallocation can lower aggregate total factor productivity (TFP). We use microdata on manufacturing establishments to quantify the potential extent of misallocation in China and India versus the United States. We measure sizable gaps in marginal products of labor and capital across plants within narrowly defined industries in China and India compared with the United States. When capital and labor are hypothetically reallocated to equalize marginal products to the extent observed in the United States, we calculate manufacturing TFP gains of 30%-50% in China and 40%-60% in India.
An interesting article on how neglecting relative price changes might lead to misleading conclusions on welfare gain comparisons. It suggests that welfare gains in the post reform era in India might be overstated if we consider the effect of inequality and relative price changes.
Dynamic Stochastic General Equilibrium modeling is now the dominant paradigm in macroeconomics. However, there is still research done in older ways and such ways continue to be favorites amongst many economists especially in India. A recent paper by Sudipto Mundle, N R Bhanumurthy and Surajit Das certainly reflects this preference. Although, many arguments they make for not using DSGE are somewhat valid, one of them especially stands out. As a response to the Lucas critque, they say,
“First, not all policy choices are choices between alternative policy rules, and some choices may merely represent alternative values of policy variables within a given policy rule, and these need not affect behaviour. For this class of policy choices, Tinbergen models are no more subject to the Lucas critique than the models based on the ‘deep’ micro-foundation variables that he recommended.”
The reason why I say it stands out is because it betrays a half correct interpretation of the Lucas critique. What the authors are essentially saying is that not all changes in policy variables constitutes a change in policy regime and hence an eventual change in the way people respond to such change. So now the question is what constitutes a policy regime change and hence warrants the application of Lucas critique? Lets say that current policy rule is something like M2-M1=600 + 0.3 (Y2-Y1). Let us say that people also know this rule and hence any change in money supply for a given change in output is going to be judged by this rule. If the change in money supply falls short or is more than what the rule predicts what would people think? They would think that something fundamentally has changed and hence they will revise their expectations implying a changed rule. What Lucas critique says is that now you cannot use the first rule to predict the effects of change in money supply which is out of sync with earlier policy rule. Any such change is bound to have effect on peoples’ expectations and hence the way they respond to changes in policy variables.
The above example demonstrates that if the money keeps on changing according to the rule that every one has come to expect then we could very well engage in policy analysis based in Tinbergen kind of models. But if the changes are not in sync with the rule then Lucas critique applies and we cannot use such policy analysis. So using Tinbegrgen models for analysis without a test of regime change implies that the authors beleive that all the data for the given period has been generated by one policy regime. Is this assumption valid? The authors do not address this issue.
On another note, I did find a paper which engages in a DSGE analysis of the Indian economy and the model includes an informal sector too! You can find that paper here.
There are many valid arguments for not believing the DSGE modeling exercise completely. One of them is the considerable evidence that real decision making does not follow the text book model of rational decision making. Financial crisis also brought forth the network effects in people’s decision making. For a critique based on these lines see my earlier post.