scholarly journals Skill complementarity in production technology: New empirical evidence and implications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Andrey Stoyanov ◽  
Nick Zubanov
Keyword(s):  
Empirical Evidence ◽  
Production Technology ◽  
Data Driven ◽  
Firm Productivity ◽  
Wage Dispersion ◽  
Manufacturing Firm ◽  
Technological Differences ◽  
Worker Skill ◽  
Firm Data

Abstract Danish manufacturing firm data reveal that 1) industries differ in within-firm worker skill (= wage) dispersion, and 2) within-firm skill dispersion positively correlates with firm productivity in industries with higher average skill dispersion. We argue that these patterns reflect technological differences between industries: firms in the “skill complementarity” industries profit from hiring similarly able workers, while the “skill substitutability” firms thrive on skill differences. Our study produces a robust, data-driven and theoretically validated classification of industries into the complementarity and substitutability groups, unveils hitherto unnoticed technological heterogeneity between industries within the same economy, and illustrates its importance through simulations.

scholarly journals Adopsi IFRS dan Pengaruhnya Terhadap Manajemen Laba Akrual dan Manajemen Laba Riil Pada Perusahaan Manufaktur Di Bursa Efek Indonesia

InFestasi ◽  
2018 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
Ismawati ◽  
Rita Yuliana ◽  
Yuni Rimawati
Keyword(s):  
Earnings Management ◽  
Empirical Evidence ◽  
Production Cost ◽  
Secondary Data ◽  
Real Earnings Management ◽  
Financial Statement ◽  
Manufacturing Firm ◽  
Ifrs Adoption ◽  
Study Find ◽  
Firm Data

<p>This study aims to provide empirical evidence on adoption of IFRS and the effect to<br />accrual earnings management and real earnings management. This study used<br />manufacturing firm data which is listing on Indonesia Stock<br />Exchange in 2010-2013 and there are 63 firm used as sample. This study used<br />secondary data. 2010 and 2011’s financial statement used to test descriptive<br />statistic at the beginning stage adoption of IFRS. 2012 and 2013’s financial<br />statement used to test descriptive statistic at the advance stage adoption of IFRS.<br />The technique of data analysis in this study using multiple linear regression<br />analysis. <br />The result of this study find empirical evidence that IFRS adoption has negative<br />influence to accrual earnings management. This finding confirmation the result of <br />descriptive statistic that accruals earnings management stage descend at the <br />advance stage adoption of IFRS compared to the beginning stage adoption of IFRS.<br />IFRS adoption doesn’t have influence to real earnings management through proxy<br />as operation cash flow, production cost, and descretioner cost after firm adopted<br />IFRS at the beginning stage and also at the advance stage adoption of IFRS.</p>

Rule based classification of neurodegenerative diseases using data driven gait features

2018 ◽  
Vol 9 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Kartikay Gupta ◽  
Aayushi Khajuria ◽  
Niladri Chatterjee ◽  
Pradeep Joshi ◽  
Deepak Joshi
Keyword(s):  
Neurodegenerative Diseases ◽  
Data Driven ◽  
Rule Based ◽  
Gait Features ◽  
Using Data

scholarly journals A Recommender for Choosing Data Systems based on Application Profiling and Benchmarking

2021 ◽  
Author(s):  
Elton Figueiredo de Souza Soares ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Marcelo de Oliveira Costa Machado ◽  
Leonardo Guerreiro Azevedo
Keyword(s):  
Empirical Evidence ◽  
Informed Decision ◽  
Data System ◽  
Data Driven ◽  
Data Systems ◽  
Ongoing Work ◽  
Wide Range

In our data-driven society, there are hundreds of possible data systems in the market with a wide range of configuration parameters, making it very hard for enterprises and users to choose the most suitable data systems. There is a lack of representative empirical evidence to help users make an informed decision. Using benchmark results is a widely adopted practice, but like there are several data systems, there are various benchmarks. This ongoing work presents an architecture and methods of a system that supports the recommendation of the most suitable data system for an application. We also illustrates how the recommendation would work in a fictitious scenario.

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