Explaining agricultural productivity growth: an international perspective

This article presents multi-output, multi-input total factor productivity (TFP) growth rates in agriculture for 88 countries over the 1970–2001 period, estimated with both stochastic frontier analysis (SFA) and the more commonly employed data envelopment analysis (DEA). We find results with SFA to be more plausible than with DEA, and use them to analyze trends across countries and the determinants of TFP growth in developing countries. The central finding is that policy and institutional variables, including public agricultural expenditure and proagricultural price policy reforms, are significant correlates of TFP growth. The most significant geographic correlate of TFP growth is distance to the nearest OECD country.     

Trends and projections for Bangladeshi food production : An alternative viewpoint

This article analyses growth in Bangladeshi food production and yields, by reviewing past evidence and critically examining the contribution of James K. Boyce. General statistical evidence is considered on whether the past growth rate is being sustained; the evidence suggests that past growth is unlikely to continue. Data from a recent field survey in two different areas of Bangladesh add weight to this view. On both ecological and economic grounds, it seems likely that Bangladesh's ability to feed its population will continue to decline.     

Hedging performance of multiscale hedge ratios

In this study, the wavelet multiscale model is applied to selected assets to hedge time-dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in-sample and out-of-sample portfolio variances, the wavelet-based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet-GARCH model has the lowest minimum capital risk requirements. Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models.     

Law‐abiding organizational climates in developing countries: The role of institutional factors and socially responsible organizational practices

The institutional environment of developing countries may lead firms to engage in unlawful firm conduct, which is a pervasive problem in this context. Our paper examines the effectiveness of organizational practices for ensuring that firms adhere to the law in the light of pressures from the institutional environment to be unlawful. Using the lens of anomie theory, we investigate: (a) the negative effect of aspects of the institutional context—regulatory burden and lack of industry munificence—on a law‐abiding climate, a type of organizational climate related to unlawful conduct, and (b) the role of socially responsible organizational practices in combating these negative effects. Survey data were collected from 118 firms and analysed using OLS moderated regression. Our results indicate that a manager's perceptions of regulatory burden and lack of industry munificence are negatively related to the extent to which the firm has a law‐abiding climate. Furthermore, our findings shed light on the ability of socially responsible practices to countervail this effect. While the negative effect of perceived regulatory burden on law‐abiding climate weakens when codes of ethics are used more extensively by a firm, it strengthens when firms hold a CSR certification. The latter finding may be due to the lack of enforcement associated with the specific certification considered in our study.     

A hybrid clustering and classification approach for predicting crash injury severity on rural roads

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.     

Conceptualization of the real economy and Islamic finance: Transformation beyond the asset‐link rhetoric

A key factor behind the chronic crises in modern economies is the growing dominance of financialization and the resulting disconnect between the real economy and the financial sector, to which conventional financial industry is a key contributor. In contrast, Islamic finance claims to be real‐economy‐oriented, as all transactions must be asset‐linked (asset‐backed or asset‐based). However, is Islamic Finance based on proper conceptualization of the real economy? What is the real economy and its dynamics? How Islamic finance is interfaced with the real economy? In this article, the claimed real‐economy‐orientation is examined by presenting a conceptualization of the real economy and then evaluating the related discourse accordingly. The analysis presented, especially for noneconomists, exposes a serious gap between the claims about Islamic finance and the real economy it is supposed to advance. Appropriate conceptualization of the real economy can help the industry to be relevant and effective for broader socioeconomic transformation.     

A Flexible Neuro‐Fuzzy Approach for Improvement of Seasonal Housing Price Estimation in Uncertain and Non‐Linear Environments

Changes in housing price affect both individuals and government since they have substantial influence on the socio‐economic conditions. Valuations of housing are necessary in order to assess the benefits and liabilities in housing sector. This study presents a flexible meta‐modelling approach for improvement of housing price estimation in ambiguous and complex environments. It is composed of artificial neural network (ANN) and fuzzy linear regression (FLR). Seven FLR models are considered to cover latest approaches and viewpoints. Also, ANN is applied to data sets. The preferred FLR model is selected via mean absolute percentage of error (MAPE) for further considerations, and then the preferred FLR model and the best structure of ANN are applied to the data set. Finally, the preferred model is selected based on MAPE. The intelligent approach of this study is applied for estimation and forecasting housing price in Iran. The housing price in Iran mainly is based on eight economic indices including currency, oil income, general index, house service pricing index, rate of informal market, gross domestic production in basic price, added value of oil group and construction materials price. FLR is identified as the preferred model with lowest MAPE for housing price forecasting in Iran. This shows that the housing market of Iran is associated with severe environmental fuzziness and ambiguity. This is the first study that introduces a flexible neuro‐fuzzy approach for improved estimation and forecasting of housing price in noisy, complex and uncertain environments.     

Effects of communication and financial concerns on banking attitude-behaviour relations

Despite extensive discussion of the concept of Islamic banking, which is based on the fundamental doctrines of Islamic law (Shariah) and Islamic economics (prohibition of interest and profit-loss sharing), few studies have explored the factors that potentially affect behaviour intentions among customers. This study investigates direct and moderating effects of communication and financial factors on customer attitudes and behavioural intentions towards Islamic banking. Data were collected from the customers of Islamic banks and the Islamic banking divisions of conventional banks in Malaysia using online surveys. The hypotheses were tested using hierarchical regression analysis. The results suggest that relationship marketing, informative advertising, perceived benefits, and profit-loss sharing are important determinants of behavioural intentions among both Muslim and non-Muslim customer groups. This study also found moderating effects for most of the conceptualized moderating variables, particularly attitude interactions with informative advertising, ease of online banking, and the principle of profit-loss sharing. There are both notable similarities and differences in the results for the two customer groups. The findings imply that different strategies should be used both to retain existing customers and attract new ones.     

Entrepreneurial,market, learning and networking orientations as determinants of business capability and international performance: the contingent role of government support

International Entrepreneurship and Management Journal - This study presents a business capability model that connects strategic orientations, business capability, government support, and...     

Auditors' and governmental financial officers' views on expanding the Sarbanes–Oxley Act provisions to state and local governments

This paper examines whether certain provisions of the Sarbanes–Oxley Act (SOX, 2002) should be expanded to include state and local governmental entities. Surveying governmental financial officials (GFOs) and their external auditors to gauge support for SOX-like legislation for governmental entities, we find the strongest support for auditor independence rules similar to SOX, management assessment of, and reporting on, internal controls, and severe penalties for destruction of records, fraud, and failure to report fraud.