Purpose: The purpose of this paper is to examine the relationship between quality ambidexterity (QAMB), competitive strategies (cost leadership, differentiation, and focus), and firm performance in Palestinian industry, and to analyze the combination of quality exploitation (QEI) and quality exploration (QER) (QAMB) associated with the different levels of each competitive strategy.Design/methodology/approach: Using data collected through a survey of 205 Palestinian industrial firms, the study conducted structural equation modeling to test the proposed relationships. Additional statistical analyses were applied to the combinations of QEI and QER for each competitive strategy.Findings: The results show a positive and significant relationship between QAMB and three competitive strategies, and between competitive strategies and financial performance, focus strategy excepted. Balanced combination with similar levels of QEI and QER is found to be more suitable for higher levels of competitive strategies implementation, whereas an excess of QER over QEI is associated with lower levels of strategies implementation.Research limitations/implications: Although Palestine has two regions, the West Bank and the Gaza Strip, all survey respondents were from the West Bank. The data used in this study come from the industrial sector only.Originality/value: This study is the first empirical test to examine the impact of QAMB on financial performance through competitive strategies. The study results may help managers to implement QEI and QER practices in order to allocate resources effectively and ultimately improve financial performance.

The problem of missing data is a common feature in any study, and a single imputation method is often applied to deal with this problem. The first contribution of this paper is to analyse the empirical performance of some traditional single imputation methods when they are applied to the estimation of the Gini index, a popular measure of inequality used in many studies. Various methods for constructing confidence intervals for the Gini index are also empirically evaluated. We consider several empirical measures to analyse the performance of estimators and confidence intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely large biases under certain non-response mechanisms, and this problem gets noticeably worse as the proportion of missing data increases. For a large correlation coefficient between the target and auxiliary variables, the regression imputation method may notably mitigate this bias problem, yielding appropriate mean square errors. We also find that confidence intervals have poor coverage rates when the probability of data being missing is not uniform, and that the regression imputation method substantially improves the handling of this problem as the correlation coefficient increases.

The ratio method is commonly used to the estimation of means and totals. This method was extended to the problem of estimating the distribution function. An alternative ratio estimator of the distribution function is defined. A result that compares the variances of the aforementioned ratio estimators is used to define optimum design-based ratio estimators of the distribution function. Different empirical results indicate that the optimum ratio estimators can be more efficient than alternative ratio estimators. In addition, we show by simulations that alternative ratio estimators can have large biases, whereas biases of the optimum ratio estimators are negligible in this situation.

Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty measures. The proposed estimation methodology is based on this idea. The variance estimation and the construction of confidence intervals are also topics addressed in this article. Real survey data, extracted from the European Union Survey on Income and Living Conditions and based on various countries, are used to investigate some empirical properties of our estimators via Monte Carlo simulation studies. Empirical results indicate that the suggested methods can be more accurate than the customary estimator. Desirable results are also obtained for the proposed variances and confidence intervals. Various populations generated from the Gamma distribution also support our findings.