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This study analyzes provincial productivity growth in China for the period of 1979-2001. The Malmquist Index approach allows us to decompose productivity growth into two components, technical progress and efficiency change. Considerable productivity growth was found for most of the data period, but it was accomplished mainly through technical progress rather than efficiency improvement. Although China's capital stock has been accumulated at the speed of a historical record in recent years, our findings show that TFP growth slowed significantly during 1995-2001. The study thus raises serious questions on whether China's recent growth pattern is consistent with its comparative advantages, and whether its reliance on capital accumulation can be sustained in the long run.Journal of Economic Literature Classification Numbers: O47, O53, D24

Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional correlations in rs-fMRI timeseries are often interpreted as brain connectivity, yet the links between brain networks and neurobiology have until now been largely speculative. Here, we show that the topology of rs-fMRI brain networks is caused by the spatial and temporal autocorrelation of the timeseries used to construct them. Spatial and temporal autocorrelation show high test-retest reliability, and are correlated with popular measures of network topology. A generative model of spatially and temporally autocorrelated timeseries exhibits similar network topology to brain networks, and when fit to individual subjects, it captures near the reliability limit of subject and regional variation. We demonstrate why spatial and temporal autocorrelation induce network structure, and highlight their ability to link graph properties to neurobiology during healthy aging. These results offer a reductionistic account of brain network complexity, explaining characteristic patterns in brain networks using timeseries statistics.

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