A STATA Code for Computing a Distance-Based Firm-Level Cluster Index

2018 ◽  
Author(s):  
Astrid Krenz
Keyword(s):  
Firm Level ◽  
Cluster Index ◽  
Level Cluster

Detecting Spatial Clustering Using a Firm-Level Cluster Index

2014 ◽  
Vol 50 (6) ◽  
pp. 1054-1068 ◽  
Author(s):  
Tobias Scholl ◽  
Thomas Brenner
Keyword(s):  
Spatial Clustering ◽  
Firm Level ◽  
Cluster Index ◽  
Level Cluster

Marshallian Cross: An Alternative Justification and Related Issues

2013 ◽  
pp. 108-120 ◽  
Author(s):  
L. Grebnev
Keyword(s):  
Alternative Treatment ◽  
Supply And Demand ◽  
Firm Level ◽  
Economics Courses ◽  
Introductory Economics

The paper provides a justification of the laws of supply and demand using the concept of a marginal firm (technology) for the case of perfect competition.The ideological factor of excessive attention to the analysis of marginal parameters at the firm level in the introductory economics courses is discussed. The author connects these issues to the ideas of J. B. Clark and gives an alternative treatment of exploitation.

International Productivity Comparisons Built from the Firm Level

CFA Digest ◽  
2002 ◽  
Vol 32 (1) ◽  
pp. 38-40
Author(s):  
Keith H. Black
Keyword(s):  
Firm Level

The End of Multi-Fibre Arrangement and Firm Performance in the Textile Industry: New Evidence

2013 ◽  
Vol 52 (2) ◽  
pp. 97-126 ◽  
Author(s):  
Zara Liaqat
Keyword(s):  
Textile Industry ◽  
Manufacturing Industry ◽  
Firm Heterogeneity ◽  
Production Functions ◽  
Firm Level ◽  
Quality Ladders ◽  
Sectoral Differences ◽  
The Mean ◽  
New Evidence ◽  
Empirical Methodology

Using a sample of 321 textile and clothing companies for the years 1992 to 2010, this paper analyses the effect of quota phase-outs on firm-level efficiency in Pakistan following the end of the Multi-Fibre Arrangement (MFA). It highlights sectoral heterogeneity within the manufacturing industry as a result of MFA expiration. The empirical methodology uses the structural techniques proposed by Olley and Pakes (1996), and Levinsohn and Petrin (2003) in order to take care of endogeneity in the estimation of production functions. The results differ for the two industries: MFA expiration lead to an increase in the average productivity of textile producing firms but a significant reduction in the mean productivity of clothing producers. We offer a number of explanations for this outcome, such as a change in the input and product mix, entry by non-exporters in the clothing sector, and sectoral differences in quality ladders. A number of crucial policy lessons can be drawn from the findings of this study. JEL Classification:F13; F14; D24; C14; O19 Keywords: Multi-Fibre Arrangement, Trade Liberalisation, Productivity, Firm Heterogeneity, Simultaneity and Production Functions, Endogeneity of Protection

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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