scholarly journals Implementation of the Two Step Cluster Method on the National Labor Force Survey Data (Sakernas)

2018 ◽  
Vol 2 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Maya Deanti ◽  
Farit Mochamad Afendi ◽  
Aam Alamudi
Keyword(s):  
Cluster Analysis ◽  
Labor Force ◽  
Survey Data ◽  
Occupational Safety ◽  
Mixed Data ◽  
Cluster Method ◽  
Foreign Workers ◽  
Data Types ◽  
Labor Force Survey ◽  
Force Data

MAYA DEANTI. Implementation of Two Step Cluster Method on National Labor Force Survey Data (Sakernas) 2017 Bogor Regency. Supervised by FARIT MOCHAMAD AFENDI and AAM ALAMUDI.        Five labor issues in Indonesia that have not been resolved by 2017 are termination of employment due to digitalization or automation, labor informalization, BPJS, high accident and occupational safety (K3), and outsourcing. In addition, the increasing number of Foreign Workers (TKA) in Indonesia can affect the decrease in local employment opportunities. Therefore, in this study will be carried out clustering to the labor force data to determine the condition of employment in Indonesia, especially Bogor regency. However, this labor force data has considerable observation with mixed data types, namely numerical and categorical. Regular cluster analysis can not be applied directly to the condition of the data, so that to be used in this research is a Two Step Cluster analysis which is a modification of existing cluster analysis. This Two Step Cluster analysis produces 3 clusters, with the characteristics of each cluster that is cluster 1 consisting of resident households or unemployed, cluster 2 consists of self-employed residents, and cluster 3 with the majority of the population working as laborers or employees. This clustering is based on work aspect only because the demography and education aspect of Bogor Regency is quite uniform.   Keywords: cluster analysis, cluster, Two Step Cluster, uniform

scholarly journals Wages and Informality in Developing Countries

2015 ◽  
Vol 105 (4) ◽  
pp. 1509-1546 ◽  
Author(s):  
Costas Meghir ◽  
Renata Narita ◽  
Jean-Marc Robin
Keyword(s):  
Developing Countries ◽  
Labor Market ◽  
Labor Force ◽  
Survey Data ◽  
Informal Sector ◽  
Heterogeneous Firms ◽  
Total Output ◽  
Compensating Differentials ◽  
Labor Force Survey

We develop an equilibrium wage-posting model with heterogeneous firms that decide to locate in the formal or the informal sector and workers who search randomly on and off the job. We estimate the model on Brazilian labor force survey data. In equilibrium, firms of equal productivity locate in different sectors, a fact observed in the data. Wages are characterized by compensating differentials. We show that tightening enforcement does not increase unemployment and increases wages, total output, and welfare by enabling better allocation of workers to higher productivity jobs and improving competition in the formal labor market. (JEL E26, J24, J31, J46, O15, O17)

scholarly journals Detecting Fraudulent Interviewers by Improved Clustering Methods – The Case of Falsifications of Answers to Parts of a Questionnaire

2016 ◽  
Vol 32 (3) ◽  
pp. 643-660 ◽  
Author(s):  
Samuel De Haas ◽  
Peter Winker
Keyword(s):  
Cluster Analysis ◽  
Data Quality ◽  
Survey Data ◽  
Empirical Research ◽  
A Priori ◽  
Cluster Method ◽  
Clustering Methods ◽  
New Approach ◽  
Bootstrap Approach ◽  
Synthetic Datasets

Abstract Falsified interviews represent a serious threat to empirical research based on survey data. The identification of such cases is important to ensure data quality. Applying cluster analysis to a set of indicators helps to identify suspicious interviewers when a substantial share of all of their interviews are complete falsifications, as shown by previous research. This analysis is extended to the case when only a share of questions within all interviews provided by an interviewer is fabricated. The assessment is based on synthetic datasets with a priori set properties. These are constructed from a unique experimental dataset containing both real and fabricated data for each respondent. Such a bootstrap approach makes it possible to evaluate the robustness of the method when the share of fabricated answers per interview decreases. The results indicate a substantial loss of discriminatory power in the standard cluster analysis if the share of fabricated answers within an interview becomes small. Using a novel cluster method which allows imposing constraints on cluster sizes, performance can be improved, in particular when only few falsifiers are present. This new approach will help to increase the robustness of survey data by detecting potential falsifiers more reliably.

scholarly journals Wages Over the Course of Structural Transformation: Evidence from India

2019 ◽  
Vol 36 (2) ◽  
pp. 131-158
Author(s):  
Rana Hasan ◽  
Rhea Molato
Keyword(s):  
Labor Force ◽  
Survey Data ◽  
Structural Transformation ◽  
Urban Areas ◽  
Formal Sector ◽  
Labor Force Survey ◽  
Wage Regressions ◽  
Wage Employment ◽  
Big Cities

This paper uses labor force survey data from India for 2000 and 2012 to examine how wages behave over the course of structural transformation. We find that wage employment between 2000 and 2012 displays the patterns one would expect for an economy undergoing structural transformation, with employment shares shifting from agriculture to industry and services, and from rural to urban areas and larger cities within urban areas. These shifts, as well as a shift to nonroutine occupations and routine manual occupations outside of agriculture, are associated with an improvement in average wages. Finally, simple Mincerian wage regressions confirm that jobs in larger firms and big cities are associated with significantly higher wages—even more so for women. Overall, our results are consistent with the notion that policies that encourage the expansion of the formal sector and employment in larger firms are crucial for development.

scholarly journals Characteristics Of The Unemployed In Canada: Leavers, Losers And Layoffs

2010 ◽  
Vol 9 (12) ◽  
Author(s):  
Nita N. Chhinzer ◽  
Khaldoun I. Ababneh
Keyword(s):  
Human Capital ◽  
Labor Force ◽  
Survey Data ◽  
Work Related ◽  
Logit Regression ◽  
Labor Force Survey ◽  
The Unemployed

This research identifies the need to disaggregate unemployed persons into job leaver, job loser and job layoff categorizations. Multinominal logit regression on Labor Force Survey data (n=38,546) confirms that demographic, human capital and work-related variables account for almost a third of the variance in likelihood to fall into the disaggregated unemployment categories in Canada.

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

Sign in / Sign up

Export Citation Format

Share Document