Market deregulation,trade liberalization and productive efficiency in Bangladesh agriculture: an empirical analysis

The impact of trade liberalization and of market deregulation in general, on the performance of agriculture remains contentious and empirical issue in the literature. Following the random coefficient frontier modelling framework, this paper attempts to contribute to this debate by computing the farm-specific productive efficiency indices in Bangladesh agriculture before and after reform. It also examines the impact of some farm-specific and policy variables on productive efficiency. The empirical results show that there are wide variations in productive efficiency across farms and regions and the average efficiency of all regions increased modestly by 8 percentage points from the pre-reform to post-reform period. The efficiency differentials are largely explained by farm size, infrastructure, households' off-farm income and the reduction of government anti-agricultural bias in relation to trade and domestic policies. The implication of these results suggests the need for further policy reform to augment productive efficiency.     

Six Decades of Total Factor Productivity Change and Sources of Growth in Bangladesh Agriculture (1948–2008)

This study applies the Färe–Primont index to calculate total factor productivity (TFP) indices for agriculture in 17 regions of Bangladesh covering a 61‐year period (1948–2008). It decomposes the TFP index into six finer components (technical change, technical‐, scale‐ and mix‐efficiency changes, residual scale‐ and residual mix‐efficiency changes). Results reveal that TFP grew at an average rate of 0.57% p.a. led by the Chittagong, Rajshahi, Rangpur, Dinajpur and Noakhali regions. TFP growth is largely powered by technological progress estimated at 0.74% p.a. Technical efficiency improvement is negligible (0.01% p.a.) due to stagnant efficiency in most of the regions. Decline in scale efficiency is also negligible (0.01% p.a.), but the decline in mix efficiency is high at 0.19% p.a. Decomposition of the components of TFP changes into finer measures of efficiency corrects the existing literature’s blame of a decline in technical efficiency as the main cause of poor TFP growth in Bangladesh. Among the sources, farm size, R&D investment, extension expenditure and crop specialisation positively influenced TFP growth, whereas the literacy rate had a negative influence on growth. Policy implications include encouraging investment in R&D and extension, land reform measures to increase average farm size, promotion of Green Revolution technology and crop diversification.     

Exploring the impact of R&D and climate change on agricultural productivity growth: the case of Western Australia*

This article empirically examines the impact of R&D and climate change on the Western Australian Agricultural sector using standard time series econometrics. Based on historical data for the period of 1977–2005, the empirical results show that both R&D and climate change matter for long‐run productivity growth. The long‐run elasticity of total factor productivity (TFP) with respect to R&D expenditure is 0.497, while that of climate change is 0.506. There is a unidirectional causality running from R&D expenditure to TFP growth in both the short run and long run. Further, the variance decomposition and impulse response function confirm that a significant portion of output and productivity growth beyond the sample period is explained by R&D expenditure. These results justify the increase in R&D investment in the deteriorating climatic condition in the agricultural sector to improve the long‐run prospects of productivity growth.     

Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design

Bankruptcy prediction has received a growing interest in corporate finance and risk management recently. Although numerous studies in the literature have dealt with various statistical and artificial intelligence classifiers, their performance in credit risk forecasting needs to be further scrutinized compared to other methods. In the spirit of Chen, Härdle and Moro (2011, Quantitative Finance), we design an empirical study to assess the effectiveness of various machine learning topologies trained with big data approaches and qualitative, rather than quantitative, information as input variables. The experimental results from a ten-fold cross-validation methodology demonstrate that a generalized regression neural topology yields an accuracy measurement of 99.96%, a sensitivity measure of 99.91% and specificity of 100%. Indeed, this specific model outperformed multi-layer back-propagation networks, probabilistic neural networks, radial basis functions and regression trees, as well as other advanced classifiers. The utilization of advanced nonlinear classifiers based on big data methodologies and machine learning training generates outperforming results compared to traditional methods for bankruptcy forecasting and risk measurement.     

The effects of venture capital syndicate diversity on earnings management and performance of IPOs in the US and UK: An institutional perspective

This study examines the extent to which principal–principal agency conflicts within venture capital (VC) syndicates lead to additional principal–agent conflicts in IPO firms in two institutional contexts. Using a matched sample of 274 VC-backed IPOs in the US and the UK, it shows that the diversity of a VC syndicate increases pre-IPO discretionary current accruals, used as a proxy for earnings management, but the impact of such diversity is higher in the US. There is also evidence of higher underpricing and lower aftermarket performance in firms with higher earnings management and VC diversity, and these negative performance effects are also higher in the US. Our findings indicate that local and informal institutions have a significant effect on multiple agency conflicts in IPO firms and performance outcomes.     

A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry

This paper proposes a semiparametric smooth-varying coefficient input distance frontier model with multiple outputs and multiple inputs, panel data, and determinants of technical inefficiency for the Indonesian banking industry during the period 2000 to 2015. The technology parameters are unknown functions of a set of environmental factors that shift the input distance frontier non-neutrally. The computationally simple constraint weighted bootstrapping method is employed to impose the regularity constraints on the distance function. As a by-product, total factor productivity (TFP) growth is estimated and decomposed into technical change, scale component, and efficiency change. The distance elasticities, marginal effects of the environmental factors on the distance elasticities, temporal behavior of technical efficiency, and also TFP growth and its components are investigated.