This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Constructing data series from various sources, I do comprehensive growth accounting for the Indian Economy. Without accounting for human capital, total factor productivity differences over time accounts for 48% to 69% of output variation. TFP growth accounts for 35% to 70% of the total GDP growth between 1960 and 2004 depending on measure of human capital. Even after using the Mincer wage regression coefficients, TFP growth still remains significant in explaining the output growth.Starting from a modest rate in 60s Productivity growth dipped and became negative in 70s. This productivity growth rate started accelerating in 80s (much before the reform-period of early 90s) and is estimated between 3% and 4.5% in 2000s.Variance decomposition of growth rates show negative relation because input and output growth accelerated in different periods. Capital-Output ratio seems to transition from one-steady state to another. Capital-per-Worker has reached a constant rate of growth.Accounting estimates, decompositions and period-wise trends point toward Indian growth being triggered by overall efficiency improvement (TFP) rather than input accumulation growth.

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Studies on Indian manufacturing have been unable to provide consistent estimates of productivity and its growth rates. This paper performs detailed and exhaustive set of accounting exercises for the period 1970-2003 using production function, index number and envelopment analysis methods. TFP growth rate average is 1.1% for both gross output based and net value added based measures. In gross output production, share of materials is 0.6, much larger than the capital and labor shares. Share of capital is constantly increasing. For the period just after the reforms (1991)(1992)(1993)(1994)(1995)(1996)(1997), input growth jumps but TFP growth is negative. But after 1998, the trend reverses and output grows slowly despite negative input growth due to large TFP growth. Aggregated TFP growth rates (Domar-weighted and Fisher index) also follow the same pattern; showing upward trends after mid1990s. There are no significant differences in TFP growth rates among different-sized firms. After the reforms, TFP growth increases substantially in the public corporations. Productivity transition seems to be random across different (3-digit NIC code) industries. Industries with focus towards services experienced higher productivity growth than others. These results show that the lack of productivity growth was the reason for unimpressive performance of Indian manufacturing earlier.

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This paper compares and contrasts the growth experience of India with that of China. Chinese economy has grown at much faster rate than Indian, but India seems to be catching up. The average estimated productivity growth rate of China (5.9%) is more than double that of India (2.4%). The difference between same-deflator average growth rates of India and China reduces significantly (by as much as 70%) for manufacturing sector.While increased growth of spending are accompanied by increase the growth rate of productivity in China, in India the correlation is negative. For India, service sector growth trend is more strongly correlated with government spending and infrastructure.2

This paper discusses the effect of technological innovation on consumption side. Apart from Quality effect (improvement in the quality of service or reduction in constant-quality price), there is another "Consolidation effect".This takes form of more features (which can be enjoyed together) being included in the same service. This effect is driven by the Time-constraint in form of technological limit on consumption per unit of time. The effect is stronger if features being bundled together are complementary to each other.Another aspect is shown by offering an alternative explanation of Engel's Law. If complementarity of a sector is affected by technological externalities, the income share spent on that sector changes. The direction of movement depends whether the tech progress has developed "enhancing" (positive) or "impeding" (negative) complements. Service sector has more of these enhancing complements and hence income share spent on service sector has gone up.