Deprivation,social class and social mobility at Big Four and non-Big Four firms

Using the work of Bourdieu and Savage, this paper investigates social class and social mobility among chartered accountants who qualified with The Institute of Chartered Accountants of Scotland in 2009. We find that these accountants tend to come from privileged backgrounds and that those who qualified with Big Four firms possess more economic, social and cultural capital than those who qualify with other firms. Our study provides fresh insights into how elements of social class interact with social background. In contrast with the prevailing view that there is limited social mobility in the accountancy profession, we find some evidence of social mobility, suggesting that current debates are based on contestable assumptions. We also find that chartered accountants from more deprived backgrounds as indicated by childhood postcode often have a father who has a professional or managerial occupation, so are not deprived on all measures. Where those from more deprived backgrounds accessed chartered accountancy careers, this was at the expense of people whose parents held lower rather than higher professional or managerial jobs. This suggests that the most advantaged maintain access to chartered accountancy but those from more middling professional homes are displaced when those from more deprived backgrounds gain access.     

The effect of data availability in measuring fund managers’ after‐tax alphas

We examine potential sources of measurement error when evaluating the after‐tax performance of fund managers based on periodic snapshots of their holdings alone, compared with when daily transactions data are also available. To do this, we compare portfolio return estimates based on imputed trades from monthly, quarterly and semi‐annual snapshots with estimates that also incorporate daily trades for a sample of active institutional equity portfolios. This method allows us to directly measure the contribution of interim trading before tax, while more accurately estimating the tax effects associated with turnover through observing actual trade prices. Further, availability of both trade and holdings data permits the identification of how contributions and tax effects arise from income and capital gains sources, as well as how they vary across investment styles and market conditions.     

Effects of the Big Five personality traits on tipping attitudes,motives, and behaviors

Knowledge about the personality predictors of tipping attitudes, motives, and behaviors could shed light on the psychological processes underlying tipping and might allow service workers to better predict and manage their tip incomes. To those ends, analyses of online survey data revealed numerous direct and indirect (through tipping motives) Big Five personality trait effects on tipping attitudes and behavior. For example, Agreeableness, Conscientiousness and Openness affected tipping likelihood and tip sizes through its enhancement of intrinsic tipping motives. Also, the effects of Agreeableness, Conscientiousness, Extraversion, and Neuroticism on leaving sub-normative (<15%) or normative (15%–20%) restaurant tips were independent of the traits’ relationships with self-reported tipping motives. However, the sizes of these and other personality effects were small, so there is little to be gained from using customer’s personalities to predict their tipping behavior.     

A non-parametric index of corporate governance in the banking industry: An application to Indian data

This paper presents a methodological framework for constructing a non-parametric index of corporate governance for banks. The index is constructed by aggregating six distinct dimensional indices capturing different dimensions of corporate governance, namely board effectiveness, audit function, risk management, remuneration, shareholder rights and information, and disclosure and transparency. For aggregation, a tailored version of data envelopment analysis (DEA) approach which is popularly known as constrained ‘Benefit-of-the-Doubt (BoD)’ model is employed. This approach is unique and distinctive in the sense that it requires no a priori knowledge of weights, and assigns endogenous weights obtained from actual data to individual dimensions of bank governance in order to construct a composite index of corporate governance. This methodological framework has illustrated by applying it for a data set of 40 Indian banks operating in the year 2017. The data set has been compiled using 58 governance regulations as defined by relevant jurisdictions.     

Improving public services by sharing the right information

This article looks at the role that information sharing plays in supporting new models of public service delivery. It sets out the barriers to information sharing, attempts to overcome them and considers some of the factors involved in shaping a new direction for information sharing, such as changing public expectations and the rapidly changing regulatory environment.     

R&D performance assessment of industrial enterprises in China: A two-stage DEA approach

With the growing expenditure on the R&D activities of industrial enterprises above designated size (IEDSs) in China, it is important to evaluate the R&D efficiency of the Chinese IEDSs. However, few studies about R&D efficiency measurement of Chinese IEDSs have considered the internal structure of the R&D production process. To fill this gap, this paper investigates the R&D performance of IEDSs of 30 sample provinces on China's mainland from 2009 to 2014, based on a two-stage data envelopment analysis (DEA) model. The major findings from the empirical results are shown as follows: (i) serious imbalance exists in the R&D resources among Chinese IEDSs of 30 provinces; (ii) there is a decline in the average overall efficiencies after 2012; (iii) there are great differences regarding the performances of R&D activities among the Chinese IEDSs of 30 provinces; (iv) high attention to the R&D activities or strong scientific research atmosphere may promote the R&D efficiency of Chinese IEDSs; and (v) the IEDSs with the relatively high profitability or high government support in terms of R&D activities have relatively poor performance. Based on these findings, several policy suggestions are proposed for the R&D activities of Chinese IEDSs.     

Efficiency changes in long-term care in OECD countries: A non-parametric Malmquist Index approach

Future demographic projections indicate that long-term care (LTC) demand will put significant pressures on the long-term sustainability of public finances. Efficiency gains could potentially compensate for increasing LTC expenditures. Nevertheless, among the factors that drive LTC expenditures, efficiency, or productivity discussions have received little attention. This paper provides the first analysis of the efficiency levels of LTC for a subset of OECD countries. In doing so, the study adopts a non-parametric approach and performs a Data Envelopment Analysis (DEA). The results of the DEA show that average efficiency scores have a slight tendency to increase. There was a 0.1% increase in productivity in the provision of LTC between 2009 and 2014, which mainly resulted from an increase in technological change. However, the slight increase in efficiency is insufficient to offset fiscal pressures led by the increasing number of LTC beneficiaries and amount spent on LTC. The second stage Tobit analysis results further indicate that the productivity of LTC provision does not differ across countries with different public LTC financing arrangements.     

Using accounting-based and loan-related information to estimate the cure probability of a defaulted company

The cure of a defaulted company has important implications for the estimation of the loss given default. In this study, we estimate the probability of a defaulted company being cured using data on a large international sample of defaulted companies. More specifically, we examine whether historic accounting information on a defaulted company and loan-related information are associated with that company's probability of being cured. The main finding of our analysis is that both accounting-based and loan-related independent variables increase the validity of cure prediction models.     

A graphically based machine learning approach to predict secondary schools performance in Tunisia

The main purpose of this paper is to identify the key factors that impact schools' academic performance and to explore their relationships through a two-stage analysis based on a sample of Tunisian secondary schools. In the first stage, we use the Directional Distance Function approach (DDF) to deal with undesirable outputs. The DDF is estimated using Data Envelopment Analysis method (DEA). In the second stage we apply machine-learning approaches (regression trees and random forests) to identify and visualize variables that are associated with a high school performance. The data is extracted from the Program for International Student Assessment (PISA) 2012 survey. The first stage analysis shows that almost 22% of Tunisian schools are efficient and that they could improve their students’ educational performance by 15.6% while using the same level of resources. Regression trees findings indicate that the most important factors associated with higher performance are school size, competition, class size, parental pressure and proportion of girls. Only, school location appears with no impact on school efficiency. Random forests algorithm outcomes display that proportion of girls at school and school size have the most powerful impact on the predictive accuracy of our model and hence could more influence school efficiency. The findings disclose also the high non-linearity of the relationships between these key factors and school performance and reveal the importance of modeling their interactions in influencing efficiency scores.     

Technological progress in the Brazilian agriculture

We analyze technical progress in the Brazilian agriculture in the period 1976–2016. Output is production value and inputs are expenses on land, labor, and other inputs. Data Envelopment Analysis (DEA) measures are used to capture technological progress via efficiency analysis. Filtering the data with a moving average and fractional regression we identify four periods with distinct behavior of technical efficiency under constant or decreasing returns to scale. From 1976 to 1988 and from 1996 to 2007 technical efficiency decreases at statistically significant rates of −1.74% and −0.26% respectively. From 1989 to 1995 and from 2008 to 2016 efficiency increases at statistically significant rates of 1.53% and 1.75% respectively. Efficiency of input use varies in these periods and is computed as a byproduct of the DEA analysis. We observe a more efficient usage of land and other inputs. A parametric fit using adjusted efficient inputs indicate the relative elasticities of 40.5%, 38.5% and 21.0% for other inputs, land and labor, respectively. Technological progress increases 3.73% in the period 1976–1988, decreases 1,58% between 1989 and 1995, increases 0.17% in the period 1996–2007 and increases 1.01% between 2008 and 2016. Technological inputs are statistically significantly influential on total factor productivity as indicated by an infinite distributed lag model. The long run elasticity effect is 39.5%. The effect of investments (public) in agricultural research has a significant elasticity of 0.835.