Business reform outcomes: Why so different?

Post-crisis policy making increasingly focuses on doing business reforms. We argue that the effects of those reforms will be different across countries. To understand the reasons for the reform outcome divergence, we advance a novel firm-size distribution (FSDs) argument. At the center of the argument is the fact that FSDs are different across countries and stable over time. Then, if a given doing business reform induces firms of different size to grow differently, this will produce a variety of reform outcomes across countries. To advance the argument, we set up a tractable general equilibrium (GE) model and study how firms of different size grow after a doing business reform. The model predicts that larger firms will grow faster than smaller firms after the reform. The model predictions are tested on the Enterprise Surveys (ES) data, merged with the Doing Business indicators. We confirm that firms of different size grow differently after a Doing Business reform. Thus, based on the notable differences of firm size distributions across countries, identical reforms to start, operate and close a business will produce a variety of reform outcomes across countries.     

Missing value imputation method for disaster decision-making using K nearest neighbor

Due to destructiveness of natural disasters, restriction of disaster scenarios and some human causes, missing data usually occur in disaster decision-making problems. In order to estimate missing values of alternatives, this paper focuses on imputing heterogeneous attribute values of disaster based on an improved K nearest neighbor imputation (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers (TFNs) are introduced and three types of attributes (i.e. linguistic term sets, intervals and real numbers) are converted to TFNs. Then the correlated degree model is utilized to extract related attributes to form instances that will be used in K nearest neighbor algorithm, and a novel KNNI method merging with correlated degree model is presented. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and effectiveness.     

Estimation and goodness-of-fit procedures for Farlie–Gumbel–Morgenstern bivariate copula of order statistics

In this study, we provide the Farlie–Gumbel–Morgenstern bivariate copula of rth and sth order statistics. The main emphasis in this study is on the inference procedure which is based on the maximum pseudo-likelihood estimate for the copula parameter. As for the methodology, goodness-of-fit test statistic for copulas which is based on a Cramér–von Mises functional of the empirical copula process is applied for selecting an appropriate model by bootstrapping. An application of the methodology to simulated data set is also presented.     

Percentile Related Bounds and Estimator for the Mean of the Negative Binomial Distribution

Upper and lower bounds are obtained for the mean of the negative binomial distribution. These bounds are simple functions of a percentile determined by the shape parameter. The result is then used to obtain a robust estimate of the mean when the shape parameter is known.     

Towards expenditure rules and fiscal sanity in the euro area

The study demonstrates the key role of expenditure policies in explaining fiscal developments during EMU in the euro area, its three largest members and four “macro-imbalances” countries. It compares actual primary expenditure trends with those that would have prevailed if countries had followed neutral policies based on expenditure rules since the start of EMU. Moreover, the implications for debt trends are calculated. Results show that all sample countries except Germany applied expansionary expenditure policies already before the crisis. Consequently, expenditure and debt paths were much higher compared to a counterfactual neutral expenditure stance. Rule-based expenditure policies could have led to much safer fiscal positions much more in line with the EU's Stability and Growth Pact. An empirical analysis of the determinants of countries’ expenditure stance confirms the need for stronger fiscal rules and institutions in the euro area.     

THE INEFFICIENCY OF WORKER TIME USE

Much work is carried out in short, interrupted segments. This phenomenon, which we label task juggling, has been overlooked by economists. We study the work schedules of some judges in Italy documenting that they do juggle tasks and that juggling causally lowers their productivity substantially. To measure the size of this effect, we show that although all these judges receive the same workload, those who juggle more trials at once instead of working sequentially on few of them at each unit of time, take longer to complete their portfolios of cases. Task juggling seems to have no adverse effect on the quality of the judges' decisions, as measured by the percent of decisions appealed. To identify these causal effects we estimate models with judge fixed effects and we exploit the lottery assigning cases to judges. We discuss whether task juggling can be viewed as inefficient, and provide a back‐of‐the‐envelope calculation of the social cost of longer trials due to task juggling.     

An omnibus two-sample test for ranked-set sampling data

We develop an omnibus two-sample test for ranked-set sampling (RSS) data. The test statistic is the conditional probability of seeing the observed sequence of ranks in the combined sample, given the observed sequences within the separate samples. We compare the test to existing tests under perfect rankings, finding that it can outperform existing tests in terms of power, particularly when the set size is large. The test does not maintain its level under imperfect rankings. However, one can create a permutation version of the test that is comparable in power to the basic test under perfect rankings and also maintains its level under imperfect rankings. Both tests extend naturally to judgment post-stratification, unbalanced RSS, and even RSS with multiple set sizes. Interestingly, the tests have no simple random sampling analog.