4 research outputs found
The Effect of Educational-job Mismatch on Company's Productivity: A Panel Data Approach (Case study from Afghanistan)
No full textThe mismatch of education, especially over-education, leads to inefficient allocation of scarce resources and becomes a public policy problem. The purpose of this study is to analyze the impact of educational-job mismatch on firm productivity. For this, we used a panel dataset for the period 2010-2016 obtained from the Central Statistical Organization (CSO) of Afghanistan, the World Bank database, and the Ministry of Labor and social welfare. Methodologically, we aggregated at the company level using the ORU (Overeducation, Required Education. and Undereducation) criteria. We also applied the OLS model, a fixed effects model, and a GMM estimator for dynamic systems. We investigate the significantly positive impact of university-level demands on company productivity while attempting to control for simultaneity concerns, unobserved work values that do not change over time, peer group consequences, and the interplay of the production process of adjustment; additional or longer periods of over-education or over-skilling (for younger as well as older workers) are detrimental to company productivity; additional years of under-education and under-skilling (for young employees) are not good (bad) for enterprises productivity. Governments should use greater levels of education and over-education of young and elderly employees as a policy instrument to boost productivity. The Generalized method of moments (GMM) approach is used in this work, which adds to the Afghan literature
The Effect of Educational-job Mismatch on Company's Productivity: A Panel Data Approach (Case study from Afghanistan)
No full textThe mismatch of education, especially over-education, leads to inefficient allocation of scarce resources and becomes a public policy problem. The purpose of this study is to analyze the impact of educational-job mismatch on firm productivity. For this, we used a panel dataset for the period 2010-2016 obtained from the Central Statistical Organization (CSO) of Afghanistan, the World Bank database, and the Ministry of Labor and social welfare. Methodologically, we aggregated at the company level using the ORU (Overeducation, Required Education. and Undereducation) criteria. We also applied the OLS model, a fixed effects model, and a GMM estimator for dynamic systems. We investigate the significantly positive impact of university-level demands on company productivity while attempting to control for simultaneity concerns, unobserved work values that do not change over time, peer group consequences, and the interplay of the production process of adjustment; additional or longer periods of over-education or over-skilling (for younger as well as older workers) are detrimental to company productivity; additional years of under-education and under-skilling (for young employees) are not good (bad) for enterprises productivity. Governments should use greater levels of education and over-education of young and elderly employees as a policy instrument to boost productivity. The Generalized Motion Estimation (GMM) approach is used in this work, which adds to the Afghan literature
The effect of roses crops on households income in Afghanistan: Case study from Dari Noor district, Nangarhar Province
Full text linkAbstract. Afghanistan is an agricultural country, employing 85% of the population. Nangarhar province is the main source of food in Afghanistan. Most of the crops are grown and consumed in the different districts of the province. This study aims to examine the impact of rose crops on household income in Dari Noor district of Nangarhar province. For this study we are uded time series data from the period of 2015 to 2018. This is the first attempt to study the impact of rose crops on household income at the country level. Both quantitative and qualitative research designs were used in this study. A sample of 300 farmers was used for the study. Primary data were collected using well-structured and planned questionnaires. Secondary data were obtained from various official sources, including Afghanistan's Ministry of Agriculture, Irrigation and Livestock, the World Bank, German Agro Action (GAA), and the International Center for Agricultural Research in Dry Areas (ICARDA). The data was analyzed using inferential statistics such as the Heteroskedasticity test, Granger causality test, Multicollinearity test, multiple regressions, and descriptive statistics. The findings of the study revealed that rose cultivation starting time, the farmer's age, the farmer's education, the farmer's training, work experience, the number of employed males, rose yields, agricultural yields, and the government policies all had a significant effect on households income. Furthermore, the number of employed females has a positive but insignificant impact on household income. These findings suggest that the Afghan government should consider using the farmer's education, working age, farmer's training,and work experience as policy tools to increase household income from rose cultivation. By using the OLS estimation method, this study contributes to the literature in Afghanistan.Keywords. Household income; Rose crops; Time series data; OLS; Afghanistan.JEL. Q11; Q47
Analyzing female labor force participation in Afghanistan: Panel data approach
Full text linkAbstract. In comparison to other countries, female labor force participation in Afghanistan is the lowest. Afghanistan currently has the lowest labor force participation rate in the world, at 16%. According to the 2015 UN Gender Inequality Index, women own only 5% of Afghan businesses. The aim of this paper is to examine female labor force participation in Afghanistan. This is the first study of women's labor force participation in Afghanistan. Data were obtained from a variety of official sources, including the Central and Statistical Organization of Afghanistan, the World Bank, the Ministry of Labor, and the Ministry of Women's Affairs. The dataset covers 20 provinces in the different time periods from 2016 to 2020. In a panel data approach, we used a fixed effects model and a generalized method of moments (GMM) to analyze the effect of minimum wage, female education, female age, mother age, household size, father's education level, and female labor skills (work experience) on female labor force participation. Our findings show that the minimum wage, female education, female age, father's education level, and female work skills (work experience) all have significant and positive effects on female labor force participation. However, the mother's age has no effect on women's labor-force participation. There is a strong, statistically significant, and negative relationship between household size and female labor force participation. These findings imply that the Afghan government should consider using minimum wages, education, working age, and work experience as policy tools to increase female labor force participation. Using a panel data approach, this study contributes to the literature in Afghanistan.Keywords. Female labor force; Household size; Education; Minimum wage; Labor market; Panel data, Afghanistan.JEL. J20; J21; P21
