Measuring Trade Profile with Granular Product‐Level Data

2019 ◽  
Vol 64 (1) ◽  
pp. 102-117
Author(s):  
In Song Kim ◽  
Steven Liao ◽  
Kosuke Imai
Keyword(s):  
Product Level ◽  
Level Data

scholarly journals The employment and wage effects of export VAT rebates: evidence from China

2020 ◽  
Author(s):  
Bo Gao ◽  
Jing Ma ◽  
Zheng Wang
Keyword(s):  
Employment Effect ◽  
High Productivity ◽  
Product Level ◽  
Level Data ◽  
Wage Effects ◽  
Reallocation Of Resources

AbstractThis paper studies the employment and wage effects of VAT rebates to exporters with comprehensive firm-product-level data of China. It is found that the adjustments in VAT rebates significantly and positively affect firm’s employment but have no statistically significant effect on firm’s wage. Moreover, this paper finds that the employment effect of VAT rebates is heterogeneous across firms. In particular, low-productivity firms are more sensitive to the adjustments of VAT rebates than high-productivity firms, suggesting that an increase of VAT rebates may cause mis-reallocation of resources.

Product-level profitability

2019 ◽  
Vol 33 (1) ◽  
pp. 214-237
Author(s):  
Hannu Hannila ◽  
Joni Koskinen ◽  
Janne Harkonen ◽  
Harri Haapasalo
Keyword(s):  
Business Processes ◽  
Data Driven ◽  
Product Portfolio ◽  
Data Governance ◽  
Content Type ◽  
Technical Product ◽  
Product Level ◽  
Level Data ◽  
Corporate Level

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

Six comparisons of firm-level and product-level data

2013 ◽  
Vol 20 (4) ◽  
pp. 382-385 ◽  
Author(s):  
A. Cassey ◽  
K. Schmeiser
Keyword(s):  
Firm Level ◽  
Product Level ◽  
Level Data

What makes a successful export? Evidence from firm‐product‐ level data

2012 ◽  
Vol 45 (4) ◽  
pp. 1332-1368 ◽  
Author(s):  
Holger Görg ◽  
Richard Kneller ◽  
Balázs Muraközy
Keyword(s):  
Product Level ◽  
Level Data

scholarly journals Does Trade Liberalization Boost Quality Upgrading? Evidence From Indonesian Plant-Product-Level Data

2017 ◽  
Vol 55 (3) ◽  
pp. 171-188 ◽  
Author(s):  
Kazunobu Hayakawa ◽  
Toshiyuki Matsuura ◽  
Sadayuki Takii
Keyword(s):  
Trade Liberalization ◽  
Product Level ◽  
Plant Product ◽  
Level Data ◽  
Quality Upgrading
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