scholarly journals Labour Force Participation Rate in Agricultural and Non-Agricultural Activities in Sikkim with Special Emphasis on Women’s Work Participation: Variation Across Districts

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Kesang Wangmo Bhutia
Keyword(s):  
Labour Force ◽  
Participation Rate ◽  
Labour Force Participation ◽  
Women's Work ◽  
Work Participation ◽  
Agricultural Activities ◽  
Labour Force Participation Rate ◽  
Women’S Work

scholarly journals Long-term Growth–Employment Relationship in India

2021 ◽  
pp. 2633190X2097649
Author(s):  
Arup Mitra
Keyword(s):  
Rural Women ◽  
Labour Force ◽  
Women's Work ◽  
Work Participation ◽  
Employment Relationship ◽  
Strong Impact ◽  
Labour Force Participation Rate ◽  
Women’S Work ◽  
Physical Infrastructure

This article focuses on the growth–employment relationship and the determinants of labour force participation rate. In the time-series framework, employment is seen to have a greater impact on GDP rather than vice versa. This is quite consistent with the literature that employment contracts can be long term in nature, and they are usually not flexible in the short run. Hence, fluctuations in the commodity market do not affect employment immediately. The effect of employment on growth through the demand linkage is usually overlooked, which is brought out by this study, suggesting that demand deceleration caused by sluggish expansion in jobs can make economic growth unsustainable in the long run. From supply side of labour, poverty-induced participation in the job market is evident, and women are seen to be largely engaged in the agricultural sector. The effect of physical infrastructure on women’s work participation is positive. Large household size and child to women ratio affect women’s work participation adversely. On the whole, the positive effect of health and education and a strong impact of physical infrastructure on labour market participation of rural women are evident.

scholarly journals Education and Unemployment in Israel, 1976-1994: Reducing the Anomaly

2005 ◽  
Vol 54 (4) ◽  
pp. 673-693 ◽  
Author(s):  
Jacob (Yaacov) Weisberg ◽  
Noah M. Meltz
Keyword(s):  
Unemployment Rate ◽  
Labour Force ◽  
Participation Rate ◽  
Labour Force Participation ◽  
Multivariate Regression Analysis ◽  
Government Support ◽  
Labour Force Participation Rate ◽  
Education Group ◽  
Education Levels ◽  
Low Educated

In industrialized economics, unemployment rates are inversely related to education levels. Data from 1963 to 1994 show that Israël is an anomaly exhibiting an inverted U-shaped relationship. Workers with 9-12 years of schooling consistently experienced a higher level of unemployment than the schooling groups with less and more education. Multivariate regression analysis of data for Israël during the 1976-1994 period indicates that this inverted U-shaped relationship is moderating. The national unemployment rate and a time trend variable had positive and significant effects tending to strengthen the inverted U-shaped relationship. However, an increase in the unemployment rate within the 0-8 education group relative to the 9-12 group and a decline in the labour force participation rate of the 0-8 group overrode these factors, resulting in a flattening of the inverse relationship. The major factor responsible for the anomaly in the education-unemployment relationship in Israël appears to be government policies intended to protect low-educated immigrants with large familles. A reduction in government support over recent years seems to have increased the exposure of the least educated to labour market forces.

scholarly journals Fertility and socio-economic determinants: evidence from co-integration and multivariate Granger casualty tests

2016 ◽  
Vol 4 (1) ◽  
pp. 134
Author(s):  
Prakash Kengnal ◽  
Asha Bullappa
Keyword(s):  
Labour Force ◽  
National Income ◽  
Participation Rate ◽  
Labour Force Participation ◽  
Fertility Rate ◽  
Gross National Income ◽  
Female Labour Force Participation ◽  
Labour Force Participation Rate ◽  
Female Labour ◽  
Per Capita

Background: The empirical work on fertility determinants widely discusses the role of socio-economic factors like female labour force participation rate, urban population and per capita gross national income in determining fertility rates. The India’s high fertility rate began to decline gradually after late 1950s and continued to fall since then. India achieved almost 31 per cent decline in fertility rate from 1990 to 2012. The objective was to examine the relationship between fertility rate, urbanization, female labour force participation rate and per capita gross national income for India.Methods: This study covers the sample period from 1990-2012. Moreover, the direction of causality between fertility rate, urbanization, female labour force participation rate and per capita gross national income in India using Granger Causality test within the Vector Error-Correction Model (VECM) are examined.Results: As a summary of the empirical results, we found that fertility rate, urbanization, female labour force participation rate and per capita gross national income in India are co-integrated and there is unidirectional Granger Causality between the four variables in long and short-run.Conclusions: The growth in urban population, female labour force participation rate and per capita gross national income are responsible for the decrease in fertility rate in India.

The Australian labour market in 2019

2020 ◽  
Vol 62 (3) ◽  
pp. 341-364 ◽  
Author(s):  
Elisa Birch ◽  
Alison Preston
Keyword(s):  
Labour Market ◽  
Employment Growth ◽  
Labour Force ◽  
Participation Rate ◽  
Labour Force Participation ◽  
Policy Formulation ◽  
Total Employment ◽  
Labour Force Participation Rate ◽  
Minimum Number ◽  
Part Time

1 This article provides an overview of the key features of the labour market in 2019, with historical data providing insight into recent trends. In 2019, the female labour force participation rate reached an all-time high of 61.3%, 10 percentage points lower than the male rate. Disaggregated analysis shows this growth stems from rising participation amongst older women. This, in turn, is underpinned by a growth in feminised sectors of the labour market, notably the Health Care and Social Assistance sector. Since 2000 this sector has contributed 22.6% to total employment growth and at 2019 accounted for 13.5% of the Australian workforce. There has also been a growth in part-time and casual employment over recent years, with the latter now accounting for 25% of all employees. These are concerning developments, with estimates showing that 58.6% of casuals are not guaranteed a minimum number of hours of work in their job. The article notes that wages growth remains below that required to stimulate employment growth, and that a continued focus on conventional labour market indicators has the potential to lead to misguided policy formulation.

Structure and growth of employment: evidence from India KLEMS data

2019 ◽  
Vol 12 (2) ◽  
pp. 202-228
Author(s):  
Suresh Chand Aggarwal ◽  
Bishwanath Goldar
Keyword(s):  
Growth Rate ◽  
Employment Growth ◽  
Labour Force ◽  
Participation Rate ◽  
Labour Force Participation ◽  
Recent Period ◽  
Data Set ◽  
Labour Force Participation Rate ◽  
Content Type ◽  
Employment Elasticity

Purpose This study aims to analyze the structure and trend in employment in the Indian economy between 1980-8081 and 2015-2016. Design/methodology/approach Use of India KLEMS data set. Estimate growth rate of employment and discuss employment prospects using “Point” employment elasticity. Findings Whilst India’s GDP growth rate has been quite impressive since the reforms of 1991, the rate of employment growth, especially in the recent period of 2003-2015, has been quite slow (1 per cent) with low employment elasticity (0.1). The pattern of employment growth has also been imbalanced with slow rate of employment growth in manufacturing and rapid growth rate in the construction sector. India now also has low labour force participation rate and a large share of informal employment in the economy. Research limitations/implications The limitation is the lack of reliable data on employment for the recent period. Practical implications With overall low employment elasticity, India would have to explore sectors where more employment opportunities could be created. Social implications India has to create not only more jobs but also “good” jobs. Originality/value The India KLEMS data provide a time series for employment, which has been used in this paper to find “Point” elasticity instead of arc elasticity of employment and is an improvement over existing employment elasticity estimates.

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