Macroeconomic effects of public investment in infrastructure in India

This paper attempts to build an aggregative, structural, macroeconometric model for India. Investment and output in the model are disaggregated into four sectors, viz., (a) agriculture including forestry & fishing, (b) manufacturing, (c) infrastructure, which includes power, transport, communication and construction and (d) services sector, covering all other activities. The model emphasizes the interrelationships between internal and external balances and also the relation between money, output, prices and balance of payments. A unique feature of the model is that it incorporates the savings-investment identity. The model also tries to link economic growth with poverty reduction. Annual time series data for the period 1978-79 to 2002-03 are used for this purpose. Three-stage least squares method is used to estimate the model. The model is validated for its in-sample forecasting ability. A few counter factual policy simulations relating to public investment in infrastructure are undertaken to illustrate the usefulness of the model for analyzing the policy options in a simultaneous equations framework. A preliminary trend analysis has shown slowing down of the economy during `90s and thereafter. There are also significant structural shifts in production from agriculture to infrastructure and services in the Indian economy. The estimated model indicated significant crowding-in effect between private and public sector investment in all the sectors. Counter factual policy simulations of sustained increase in public sector investment in infrastructure, financed through borrowing from commercial banks, shows substantial increase in private investment and thereby output in this sector. Further, due to increase in absorption, real output in the manufacturing and services sectors also seem to increase, which sets-in motion all other macro economic changes. Due to rise in sectoral (and aggregate) output, price level and money supply seem to decline in the short-run. Due to sustained nature of the policy change, the impacts get strengthened over time and benefit the economy. A 10 sustained increase in public sector investment in infrastructure, which is less than 0.4 of GDP, can accelerate the macro economic growth by nearly 2.5 without causing any inflation. Further, this increase in income will lead to nearly 1 reduction in poverty in India. This re-assures the potential for achieving the much debated 10 aggregate real GDP growth in the Indian economy.

Effects of Public Investment in Infrastructure on Growth and Poverty in India

Counter factual policy simulations of sustained increase in public investment in infrastructure, financed through borrowing from commercial banks, shows substantial increase in private investment and thereby output in this sector. Further, due to increase in absorption, real private investment and thereby output in all the other three sectors also seems to increase, which sets-in motion several other macro economic changes. A 20% sustained increase in public investment in infrastructure, which is 0.5% of GDP and 2.7% of total govt. revenue in 2000-03, can accelerate the real macro economic growth by 1.8% in the medium to long-run (6-10 years after the policy change). This will be accompanied by a 1.4% fall in wholesale price index and 0.2% decline in the rate of inflation. Sectoral prices, except that of agriculture, also decline to varying extent, the steepest decline being for infrastructure price. Further, this increase in income will lead to 0.7% reduction in poverty in rural India. This shows the potential for achieving the much-debated 10% aggregate real GDP growth in the Indian economy.Public Inversment, Infrastructure, Growth, poverty, India

Effects of public investment in infrastructure on growth and poverty in India

Counter factual policy simulations of sustained increase in public investment in infrastructure, financed through borrowing from commercial banks, shows substantial increase in private investment and thereby output in this sector. Further, due to increase in absorption, real private investment and thereby output in all the other three sectors also seems to increase, which sets-in motion several other macro economic changes. A 20 sustained increase in public investment in infrastructure, which is 0.5 of GDP and 2.7 of total govt. revenue in 2000-03, can accelerate the real macro economic growth by 1.8 in the medium to long-run (6-10 years after the policy change). This will be accompanied by a 1.4 fall in wholesale price index and 0.2 decline in the rate of inflation. Sectoral prices, except that of agriculture, also decline to varying extent, the steepest decline being for infrastructure price. Further, this increase in income will lead to 0.7 reduction in poverty in rural India. This shows the potential for achieving the much-debated 10 aggregate real GDP growth in the Indian economy.

Coherent network analysis for continuous gravitational wave signals in a pulsar timing array: Pulsar phases as extrinsic parameters

Supermassive black hole binaries are one of the primary targets for gravitational wave searches using pulsar timing arrays. Gravitational wave signals from such systems are well represented by parametrized models, allowing the standard Generalized Likelihood Ratio Test (GLRT) to be used for their detection and estimation. However, there is a dichotomy in how the GLRT can be implemented for pulsar timing arrays: there are two possible ways in which one can split the set of signal parameters for semi-analytical and numerical extremization. The straightforward extension of the method used for continuous signals in ground-based gravitational wave searches, where the so-called pulsar phase parameters are maximized numerically, was addressed in an earlier paper (Wang et al. 2014). In this paper, we report the first study of the performance of the second approach where the pulsar phases are maximized semi-analytically. This approach is scalable since the number of parameters left over for numerical optimization does not depend on the size of the pulsar timing array. Our results show that, for the same array size (9 pulsars), the new method performs somewhat worse in parameter estimation, but not in detection, than the previous method where the pulsar phases were maximized numerically. The origin of the performance discrepancy is likely to be in the ill-posedness that is intrinsic to any network analysis method. However, scalability of the new method allows the ill-posedness to be mitigated by simply adding more pulsars to the array. This is shown explicitly by taking a larger array of pulsars.Comment: 30 pages, 11 figures, revised version, published in Ap

A coherent method for the detection and estimation of continuous gravitational wave signals using a pulsar timing array

The use of a high precision pulsar timing array is a promising approach to detecting gravitational waves in the very low frequency regime ($10^{-6} -10^{-9}$ Hz) that is complementary to the ground-based efforts (e.g., LIGO, Virgo) at high frequencies ($\sim 10 -10^3$ Hz) and space-based ones (e.g., LISA) at low frequencies ($10^{-4} -10^{-1}$ Hz). One of the target sources for pulsar timing arrays are individual supermassive black hole binaries that are expected to form in galactic mergers. In this paper, a likelihood based method for detection and estimation is presented for a monochromatic continuous gravitational wave signal emitted by such a source. The so-called pulsar terms in the signal that arise due to the breakdown of the long-wavelength approximation are explicitly taken into account in this method. In addition, the method accounts for equality and inequality constraints involved in the semi-analytical maximization of the likelihood over a subset of the parameters. The remaining parameters are maximized over numerically using Particle Swarm Optimization. Thus, the method presented here solves the monochromatic continuous wave detection and estimation problem without invoking some of the approximations that have been used in earlier studies.Comment: 33 pages, 10 figures, submitted to Ap

Statistics of Conductances and Subleading Corrections to Scaling near the Integer Quantum Hall Plateau Transition

We study the critical behavior near the integer quantum Hall plateau transition by focusing on the multifractal (MF) exponents $X_q$ describing the scaling of the disorder-average moments of the point contact conductance $T$ between two points of the sample, within the Chalker-Coddington network model. Past analytical work has related the exponents $X_q$ to the MF exponents $\Delta_q$ of the local density of states (LDOS). To verify this relation, we numerically determine the exponents $X_q$ with high accuracy. We thereby provide, at the same time, independent numerical results for the MF exponents $\Delta_q$ for the LDOS. The presence of subleading corrections to scaling makes such determination directly from scaling of the moments of $T$ virtually impossible. We overcome this difficulty by using two recent advances. First, we construct pure scaling operators for the moments of $T$ which have precisely the same leading scaling behavior, but no subleading contributions. Secondly, we take into account corrections to scaling from irrelevant (in the renormalization group sense) scaling fields by employing a numerical technique ("stability map") recently developed by us. We thereby numerically confirm the relation between the two sets of exponents, $X_q$ (point contact conductances) and $\Delta_q$ (LDOS), and also determine the leading irrelevant (corrections to scaling) exponent $y$ as well as other subleading exponents. Our results suggest a way to access multifractality in an experimental setting.Comment: 7 pages and 4 figures, plus Supplemental materia

Imitation in Large Games

In games with a large number of players where players may have overlapping objectives, the analysis of stable outcomes typically depends on player types. A special case is when a large part of the player population consists of imitation types: that of players who imitate choice of other (optimizing) types. Game theorists typically study the evolution of such games in dynamical systems with imitation rules. In the setting of games of infinite duration on finite graphs with preference orderings on outcomes for player types, we explore the possibility of imitation as a viable strategy. In our setup, the optimising players play bounded memory strategies and the imitators play according to specifications given by automata. We present algorithmic results on the eventual survival of types

Evaluation of yogurt with enhanced cysteine content

Amino acids are the building blocks of protein and assist with metabolism in the body. In the human body, the amino acid cysteine can be synthesized from methionine by the enzyme Î¥-cystathionase. Because certain human subpopulations such as those prone to cataracts have decreased Î¥-cystathionase activity, dietary cysteine may be beneficial. Nutritionally, yogurt mix is one of the best dairy food sources of methionine and cysteine, but the heat treatment used in manufacturing yogurt decreases the dietary availability of cysteine. Last year, it was shown that supplementing yogurt mixes with whey protein isolate (WPI) (\u3e90% protein) and processing yogurt mixes at a lower temperature produced yogurts with increased cysteine. Because the quality or cysteine content of the yogurt during the expected storage life is unknown, this study was conducted to determine if a combination of WPI addition and non-optimal process conditions could produce a yogurt with higher cysteine content and an acceptable shelf life. In this study, control yogurt mixes were made with nonfat dry milk (NDM) and processed at 90oC for 7 minutes, whereas the experimental yogurt mixes were made with NDM and WPI and processed at 70oC for 20 minutes. Both mixes were cooled, inoculated, fermented into yogurt, stored at 4°C, and evaluated periodically over a 60-day period. The experimental yogurts had ~2X more cysteine than the control yogurt; this trend was present throughout storage. After 60 days of storage, the water-holding capacity (WHC) and firmness was greater and the syneresis was less for the experimental yogurt than the control yogurt. These results show that yogurt supplemented with WPI and processed at less optimal conditions may be a good source of the conditional amino acid cysteine during storage.; Dairy Day, 2012, Kansas State University, Manhattan, KS, 2012; Dairy Research, 2012 is known as Dairy Day, 201

Particle Swarm Optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

The detection and estimation of gravitational wave (GW) signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Due to noise in the data, the function to be maximized is often highly multi-modal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the Particle Swarm Optimization (PSO) method in this context. The method is applied to a testbed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that PSO works well in the presence of high multi-modality, making it a viable candidate method for further applications in GW data analysis.Comment: 13 pages, 5 figure