Decomposing total factor productivity while treating for misspecification

Decomposing firm performance has been challenging for some time. Yet the importance of accurately measuring performance is unequivocal. We propose a flexible functional of total factor productivity (TFP) that measures firm performance and treats misspecification. We argue that measuring bank productivity at global level, which is provided by our model based on bank micro-foundations, is better suited than other measures. Our results suggest that there is not much convergence in TFP across the world, though the technology has positively contributed to bank TFP growth. However, nonperforming loans have had the opposite effect. Furthermore, we show that bank risk-taking and raising capital by equity are negatively related to TFP growth; instead, liquidity has a positive impact

Why do households repay their debt in UK during the COVID-19 crisis?

This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. We use data information related to COVID-19, financial markets, and household finances. In this paper, we investigate whether COVID-19 impacts household finances, like household debt repayments in the UK. Our results show that household debt repayments’ response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when we employ specific COVID-19 related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. We also report low persistence in household debt repayments. Generalized impulse response functions confirm the main results. As draconian measures, the lockdowns are eased it appears that the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. To the best of our knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. Our findings show that policy response in the future should prioritise innovation of new vaccines and testing

Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions

Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved productivity term into the DDF. For an unbalanced panel of U.S. electric utilities, a model assuming profit-maximization outperforms one assuming cost-minimization, while lagged productivity and energy price have the greatest effect on productivity

Measuring persistent and transient energy efficiency in the US

The promotion of US energy efficiency policy is seen as a very important activity. Generally, the level of energy efficiency of a country or state is approximated by energy intensity, commonly calculated as the ratio of energy use to GDP. However, energy intensity is not an accurate proxy for energy efficiency given that changes in energy intensity are a function of changes in several factors including the structure of the economy, climate, efficiency in the use of resources, behaviour and technical change. The aim of this paper is to measure persistent and transient energy efficiency for the whole economy of 49 states in the US using a stochastic frontier energy demand approach. A total US energy demand frontier function is estimated using panel data for 49 states over the period 1995 to 2009 using two panel data models: the Mundlak version of the random effects model (which estimates the persistent part of the energy efficiency) and the true random effects model (which estimates the transient part of the energy efficiency). The analysis confirms that energy intensity is not a good indicator of energy efficiency, whereas, by controlling for a range of economic and other factors, the measures of energy efficiency obtained via the approach adopted here are. Moreover, the estimates show that although for some states energy intensity might give a reasonable indication of a state’s relative energy efficiency, this is not the case for all states.ISSN:1570-646XISSN:1570-647

Measuring productivity and efficiency: a Kalman filter approach

In the Kalman filter setting, one can model the inefficiency term of the standard stochastic frontier composed error as an unobserved state. In this study a panel data version of the local level model is used for estimating time-varying efficiencies of firms. We apply the Kalman filter to estimate average efficiencies of U.S. airlines and find that the technical efficiency of these carriers did not improve during the period 1999-2009. During this period the industry incurred substantial losses, and the efficiency gains from reorganized networks, code-sharing arrangements, and other best business practices apparently had already been realized

Foreign presence, technical efficiency and firm survival in Greece: a simultaneous equation model with latent variables approach

The aim of the paper is to explain the role that technical efficiency and foreign spillover effects have on firm survival. Panel data from Greek manufacturing industry (3142 firms) in 1997-2003 are used. Technical efficiency is estimated through a CES translog production function. A hazard function is then used (corresponding to the Exponential and Weibull distributions as well as the Cox model) to estimate survival probabilities. While foreign spillovers exercise a positive impact on hazard, foreign firms do not have any distinctive survival advantage compared to their domestic rivals. On the contrary, technical efficiency affects hazard in a negative way, improving survival expectations

Quantile regression for overdispersed count data: a hierarchical method

Abstract Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count data. This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors. The technique is first illustrated with simulated overdispersed counts subject to contamination, such that estimates from conditional mean regression are adversely affected. A real application involves ambulatory care sensitive emergency admissions across 7518 English patient general practitioner (GP) practices. Predictors are GP practice deprivation, patient satisfaction with care and opening hours, and region. Impacts of deprivation are particularly important in policy terms as indicating effectiveness of efforts to reduce inequalities in care sensitive admissions. Hierarchical quantile count regression is used to develop profiles of central and extreme quantiles according to specified predictor combinations