Econometric Estimation of Distance Functions and Associated Measures of Productivity and Efficiency Change

The economically-relevant characteristics of multi-input multi-output production technologies can be represented using distance functions. The econometric approach to estimating these functions typically involves factoring out one of the outputs or inputs and estimating the resulting equation using maximum likelihood methods. A problem with this approach is that the outputs or inputs that are not factored out may be correlated with the composite error term. Fernandez, Koop and Steel (2000, p. 58) have developed a Bayesian solution to this so-called ‘endogeneity’ problem. O'Donnell (2007) has adapted the approach to the estimation of directional distance functions. This paper shows how the approach can be used to estimate Shephard (1953) distance functions and an associated index of total factor productivity (TFP) change. The TFP index is a new multiplicatively-complete index that satisfies most, if not all, economically-relevant tests and axioms from index number theory. The fact that it is multiplicatively-complete means it can be exhaustively decomposed into a measure of technical change and various measures of efficiency change. The decomposition can be implemented without the use of price data and without making any assumptions concerning either the optimising behaviour of firms or the degree of competition in product markets. The methodology is illustrated using state-level quantity data on U.S. agricultural inputs and outputs over the period 1960-2004. Results are summarised in terms of the characteristics (e.g., means) of estimated probability densities for measures of TFP change, technical change and output-oriented measures of efficiency change.

Estimating State-allocable Production Technologies When there are two States of Nature and State Allocations of Inputs are Unobserved.

Chambers and Quiggin (2000) have used state-contingent production theory to establish important results concerning economic behaviour in the presence of uncertainty, including problems of consumer choice, the theory of the firm, and principal-agent relationships. Empirical application of the state contingent approach has proved difficult, not least because most of the data needed for applying standard econometric methods are lost in unrealized states of the world. O’Donnell and Griffiths (2006) show how a restrictive type of state-contingent technology can be estimated in a finite mixtures framework. This paper shows how Bayesian methodology can be used to estimate more flexible types of state-contingent technologies.

An Econometric Approach To Estimating Support Prices And Measures Of Productivity Change In Public Hospitals

In industry sectors where market prices are unavailable it is common to represent multiple-input multiple-output production technologies using distance functions. Econometric estimation of such functions is complicated by the fact that more than one variable in the function may be endogenous. In such cases, maximum likelihood estimation can lead to biased and inconsistent estimates of the model parameters and associated measures of firm performance. We solve the problem by using linear programming to construct a quantity index. The distance function is then written in the form of a conventional stochastic frontier model where the explanatory variables are unambiguously exogenous. We use this approach to estimate productivity indexes and support (or shadow) prices for a sample of Australian public hospitals. We decompose the productivity index into several measures of environmental change and efficiency change. We find that the productivity effects of improvements in input-oriented technical efficiency have been largely offset by the effects of deteriorations in the production environment over time.

New Estimates and a Decomposition of Provincial Productivity Change in China

Productivity and efficiency change lies at the heart of some of the key challenges facing China’s economy. This paper contributes to an understanding of TFP change by computing and decomposing provincial-level Hicks-Moorsteen (H-M) total factor productivity (TFP) indexes for the period 1978 to 2008. The H-M index is appealing because, unlike the more commonly-used Malmquist TFP index, it can be decomposed into unambiguous measures of technical change and efficiency change. The efficiency change component can be further decomposed into measures of pure technical, scale and mix efficiency change. We also make use of new capital stock estimates that have been computed using province-specific investment price deflators and capital stock depreciation rates. On average across provinces, we find evidence of moderate TFP growth, as well as large changes in the components of TFP growth over the sample period. Considerable heterogeneity from province to province is also documented both with respect to the rate of TFP growth and its components.

Production Under Uncertainty: A Simulation Study

In this article we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without regard to the nature of producer risk preferences. In our model producers? risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technology we show that rational producers who encounter the same stochastic technology can make significantly different production choices. Further, we develop an econometric methodology to estimate the risk-neutral probabilities and the parameters of stochastic technology when there are two states of nature and only one of which is observed. Finally, we simulate data based on our state-general state-contingent specification of technology. Biased estimates of the technology parameters are obtained when we apply conventional ordinary least squares (OLS) estimator on the simulated data.

15-keto-prostaglandin E2 activates host peroxisome proliferator-activated receptor gamma (PPAR-γ) to promote Cryptococcus neoformans growth during infection

Cryptococcus neoformans is one of the leading causes of invasive fungal infection in humans worldwide. C. neoformans uses macrophages as a proliferative niche to increase infective burden and avoid immune surveillance. However, the specific mechanisms by which C. neoformans manipulates host immunity to promote its growth during infection remain ill-defined. Here we demonstrate that eicosanoid lipid mediators manipulated and/or produced by C. neoformans play a key role in regulating pathogenesis. C. neoformans is known to secrete several eicosanoids that are highly similar to those found in vertebrate hosts. Using eicosanoid deficient cryptococcal mutants Δplb1 and Δlac1, we demonstrate that prostaglandin E2 is required by C. neoformans for proliferation within macrophages and in vivo during infection. Genetic and pharmacological disruption of host PGE2 synthesis is not required for promotion of cryptococcal growth by eicosanoid production. We find that PGE2 must be dehydrogenated into 15-keto-PGE2 to promote fungal growth, a finding that implicated the host nuclear receptor PPAR-γ. C. neoformans infection of macrophages activates host PPAR-γ and its inhibition is sufficient to abrogate the effect of 15-keto-PGE2 in promoting fungal growth during infection. Thus, we describe the first mechanism of reliance on pathogen-derived eicosanoids in fungal pathogenesis and the specific role of 15-keto-PGE2 and host PPAR-γ in cryptococcosis

Surface-wave imaging of the weakly-extended Malawi Rift from ambient-noise and teleseismic Rayleigh waves from onshore and lake-bottom seismometers

Located at the southernmost sector of the Western Branch of the East African Rift System, the Malawi Rift exemplifies an active, magma-poor, weakly extended continental rift. To investigate the controls on rifting, we image crustal and uppermost mantle structure beneath the region using ambient-noise and teleseismic Rayleigh-wave phase velocities between 9 and 100 s period. Our study includes six lake-bottom seismometers located in Lake Malawi (Nyasa), the first time seismometers have been deployed in any of the African rift lakes. Noise-levels in the lake are lower than that of shallow oceanic environments and allow successful application of compliance corrections and instrument orientation determination. Resulting phase-velocity maps reveal slow velocities primarily confined to Lake Malawi at short periods (T 25 s) a prominent low-velocity anomaly exists beneath the Rungwe Volcanic Province at the northern terminus of the rift basin. Estimates of phase-velocity sensitivity indicates these low velocities occur within the lithospheric mantle and potentially uppermost asthenosphere, suggesting that mantle processes may control the association of volcanic centers and the localization of magmatism. Beneath the main portion of the Malawi Rift, a modest reduction in velocity is also observed at periods sensitive to the crust and upper mantle, but these velocities are much higher than those observed beneath Rungwe

Government Assistance and Total Factor Productivity: Firm-level Evidence from China

Industrial policy, particularly through the provision of large-scale assistance to industry in the form of ‘tax holidays’ and subsidies to firms, is very important in China. A major contribution of this paper is to introduce firm-level measures of assistance directly into industry-level production functions determining firm output using Chinese firm-level panel data for 1998-2007 and analysing the impact of government assistance on TFP at the firm-level. Our results indicate inverted U-shaped gains from assistance: across the 26 industries considered, firms receiving assistance rates of 1-10%, 10-19%, 20-49% and 50+% experienced on average 4.5%, 9.4%, 9.2% and -3% gains in TFP level, respectively. We then decompose the growth of TFP and relate it to assistance and formal political connections between firms and the government. We find in general firms receiving assistance contributed relatively more to TFP growth than non-assisted firms. However, this was largely through new firms being ‘encouraged’ to start-up rather than through firms open throughout 1998 to 2007 improving. There is also evidence that closure rates were truncated as a result of assistance. Moreover, the better results for assisted firms was very much ‘driven’ by a sub-group that received assistance but had no formal political connections and were not State-owned

Genome-wide transcriptome study using deep RNA sequencing for myocardial infarction and coronary artery calcification

Background: Coronary artery calcification (CAC) is a noninvasive measure of coronary atherosclerosis, the proximal pathophysiology underlying most cases of myocardial infarction (MI). We sought to identify expression signatures of early MI and subclinical atherosclerosis in the Framingham Heart Study (FHS). In this study, we conducted paired-end RNA sequencing on whole blood collected from 198 FHS participants (55 with a history of early MI, 72 with high CAC without prior MI, and 71 controls free of elevated CAC levels or history of MI). We applied DESeq2 to identify coding-genes and long intergenic noncoding RNAs (lincRNAs) differentially expressed in MI and high CAC, respectively, compared with the control. Results: On average, 150 million paired-end reads were obtained for each sample. At the false discovery rate (FDR) < 0.1, we found 68 coding genes and 2 lincRNAs that were differentially expressed in early MI versus controls. Among them, 60 coding genes were detectable and thus tested in an independent RNA-Seq data of 807 individuals from the Rotterdam Study, and 8 genes were supported by p value and direction of the effect. Immune response, lipid metabolic process, and interferon regulatory factor were enriched in these 68 genes. By contrast, only 3 coding genes and 1 lincRNA were differentially expressed in high CAC versus controls. APOD, encoding a component of high-density lipoprotein, was significantly downregulated in both early MI (FDR = 0.007) and high CAC (FDR = 0.01) compared with controls. Conclusions: We identified transcriptomic signatures of early MI that include differentially expressed protein-coding genes and lincRNAs, suggesting important roles for protein-coding genes and lincRNAs in the pathogenesis of MI

Low energy analysis techniques for CUORE

CUORE is a tonne-scale cryogenic detector operating at the Laboratori Nazionali del Gran Sasso (LNGS) that uses tellurium dioxide bolometers to search for neutrinoless double-beta decay of 130Te. CUORE is also suitable to search for low energy rare events such as solar axions or WIMP scattering, thanks to its ultra-low background and large target mass. However, to conduct such sensitive searches requires improving the energy threshold to 10 keV. In this paper, we describe the analysis techniques developed for the low energy analysis of CUORE-like detectors, using the data acquired from November 2013 to March 2015 by CUORE-0, a single-tower prototype designed to validate the assembly procedure and new cleaning techniques of CUORE. We explain the energy threshold optimization, continuous monitoring of the trigger efficiency, data and event selection, and energy calibration at low energies in detail. We also present the low energy background spectrum of CUORE-0 below 60keV. Finally, we report the sensitivity of CUORE to WIMP annual modulation using the CUORE-0 energy threshold and background, as well as an estimate of the uncertainty on the nuclear quenching factor from nuclear recoils inCUORE-0