scholarly journals Factors Affecting Human Capital in Southern Punjab: An Empirical Analysis

2021 ◽  
Vol 4 (1) ◽  
pp. 179-191
Author(s):  
Salyha Zulfiqar Ali Shah ◽  
Imran Sharif Chaudhry ◽  
Fatima Farooq
Keyword(s):  
Human Capital ◽  
Rural Areas ◽  
Household Head ◽  
Household Survey ◽  
Ordinary Least Squares ◽  
Primary Data ◽  
Factors Affecting ◽  
Household Level ◽  
Annual Budget ◽  
Per Capita

Analyzing the factors affecting human capital at household level increases the productivity of people living in developing countries. A primary data was collected through a household survey to study the factors affecting human capital in Southern Punjab using Ordinary Least Squares (OLS) regression technique. The findings show that the location of the household in rural areas, occupation of the household head in the primary sector, household size, household poverty and female/male ratio shows significant and negatively affecting the human capital of the households in Southern Punjab. Moreover, per capita income, number of earners in the household, remittances are significant and positively affecting the human capital of the households in Southern Punjab. The annual budget allocation for education in Pakistan is very low, so Government should allocate a significant amount of funds to the education sector.

scholarly journals Factors Affecting Human Capital in Southern Punjab: An Empirical Analysis

2021 ◽  
Vol 4 (1) ◽  
pp. 189-200
Author(s):  
Salyha Zulfiqar Ali Shah ◽  
Imran Sharif Chaudhry ◽  
Fatima Farooq
Keyword(s):  
Human Capital ◽  
Rural Areas ◽  
Household Head ◽  
Household Survey ◽  
Ordinary Least Squares ◽  
Primary Data ◽  
Factors Affecting ◽  
Household Level ◽  
Annual Budget ◽  
Per Capita

Analyzing the factors affecting human capital at household level increases the productivity of people living in developing countries. A primary data was collected through a household survey to study the factors affecting human capital in Southern Punjab using Ordinary Least Squares (OLS) regression technique. The findings show that the location of the household in rural areas, occupation of the household head in the primary sector, household size, household poverty and female/male ratio shows significant and negatively affecting the human capital of the households in Southern Punjab. Moreover, per capita income, number of earners in the household, remittances are significant and positively affecting the human capital of the households in Southern Punjab. The annual budget allocation for education in Pakistan is very low, so Government should allocate a significant amount of funds to the education sector.

scholarly journals Poverty Human Capital and Economic Development Nexus: A Case Study of Multan Division

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Salyha Zulfiqar Ali Shah ◽  
Imran Sharif Chaudhry ◽  
Fatima Farooq
Keyword(s):  
Human Capital ◽  
Economic Development ◽  
Poverty Alleviation ◽  
Household Head ◽  
Household Survey ◽  
Primary Data ◽  
Household Poverty ◽  
The World ◽  
Per Capita

Countries across the world have acknowledged that poverty alleviation has to be of critical importance among the objectives of economic development. This paper sheds light on the Multan division, as one the important division of Southern Punjab, Pakistan. The primary data was collected through a household survey during the year 2019.The study concluded that occupation of the household head in the primary sector and household size are significant and positively associated with household poverty. However, human capital of the household is found to be significant and negatively related to household poverty in the Multan division. Economic development or per capita income of the households are found to be significant and positively related with human capital of the households.

scholarly journals FOREST RESOURCE INCOME VARIATION IN MID-HILLS OF NEPAL: A CASE STUDY FROM TWO CFUGS OF PARBAT DISTRICT, NEPAL

2015 ◽  
Vol 4 (3) ◽  
pp. 1-10 ◽  
Author(s):  
Ganesh Paudel
Keyword(s):  
Survey Methods ◽  
Group Discussion ◽  
Household Head ◽  
Sustainable Use ◽  
Household Survey ◽  
Benefit Sharing ◽  
Primary Data ◽  
Forest Resource ◽  
Household Level ◽  
Forest Income

 Inequality persists in Nepali society and various source of income including forest resource contributes to it. This study was conducted in two CFUGs of the Parbat district, Nepal and examines the variability in the forest resource income at the household level. Focus group discussion, Key informants survey and Household survey methods were employed to collect the primary data and random sample of 120 respondents were surveyed. Along with simple mathematical calculations statistical test ANOVA was performed for data analysis. Rich categories of users derive significantly more income from the CF than the middle and poor categories of users although being the larger share of CF income to the total forest income for poor users. The CF income also varies with the sex of the household head and caste of the users in absolute terms but not significant. The Gini coefficient of the income distribution is found 0.48, 0.34 and 0.31 for CF income, Private trees income and total forest income respectively. It is recommended to the CFUGs that the benefit sharing should be in the equitable basis for the sustainable use of the resources. International Journal of EnvironmentVolume-4, Issue-3, June-August 2015Page: 1-10

scholarly journals Human capital and job opportunities according to educational level in Perú

2021 ◽  
Vol 25 (110) ◽  
pp. 48-57
Author(s):  
Freddy Carrasco Choque ◽  
Rudy Francheska Castillo Araujo
Keyword(s):  
Human Capital ◽  
Rural Areas ◽  
Work Experience ◽  
Returns To Education ◽  
Household Survey ◽  
Healthcare Spending ◽  
Middle Income ◽  
Heckman Model ◽  
Schooling Experience ◽  
Mincer Equation

Education promotes progress and economic and social growth, improves the quality of life of the population. The first objective of the study was to identify people's income according to the years of schooling, the second was to estimate the income gap according to gender, residence and working conditions, the third was to identify the return of education, work experience towards the income of the Peruvian inhabitants. Parametric tests and the two-stage Heckman model were used to obtain the results. The data come from the National Household Survey. Income differs according to schooling. There are gaps in earned income. For one more year of education, the monetary return amounts to 12,46%, if it is a woman, it is 13,23%, if it is a man, it is 11,51%, if it resides in an urban area it amounts to 10,62%, if it is a resident in rural areas it amounts to 9,83%. Keywords: Labor income, returns to education, Mincer equation, Heckman methodology. References [1]J. Mincer, “Schooling, Experience, and Earnings,” Natl. Bur. Econ. Res., 1974, [Online]. Available: https://www.nber.org/books-and-chapters/schooling-experience-and-earnings. [2]T. W. Schultz, “Investment in Human capital,” Am. Econ. Rev., vol. Vil. (1)2, 1961. [3]J. Freire and M. Teijeiro, “Las ecuaciones de Mincer y las tasas de rendimiento de la educación en Galicia,” Investig. Econ. la Educ. 5 - Univ. A Coruña, 2010. [4]K. Ogundari and A. Abdulai, “Determinants of Household’s Education and Healthcare Spending in Nigeria: Evidence from Survey Data,” African Dev. Rev., vol. Vol. 26, N, pp. 1–14, 2014. [5]C. Montenegro and H. Patrinos, “Comparable estimates of returns to schooling around the world,” Policy Res. Work. Pap. Ser. 7020, World Bank., 2014. [6]G. Fink and E. Peet, “Returns to Education in Low and Middle-Income Countries: Evidence from the Living Standards and Measurement Surveys,” Progr. Glob. Demogr. Aging Harvard Univ., vol. PGDA Worki, 2014, [Online]. Available: https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1288/2015/06/PGDA_WP_120_Fink.pdf. [7]L. Godínez, E. Figueroa, and F. Pérez, “Rentabilidad privada de la educación en el Estado de México,” Papeles Poblac. - Univ. Auton. Mex., vol. Vol. 22 N°, 2016. [8]M. Diaz, “Brecha Salarial por Género en Colombia.,” Econ. y Finanz. Int. - Univ. la Sabana - Colomb., 2014. [9]M. Urroz and M. Salgado, “La relación entre educación e ingresos: estimación de las diferencias salariales por nivel educativo alcanzado,” Fund. Zamora Terán, 2014. [10]E. Tarupi, “El capital humano y los retornos a la educación en Ecuador,” Gest. - Rev. Int. Adm., 2015, [Online]. Available: https://revistas.uasb.edu.ec/index.php/eg/article/view/571. [11]R. Arpi and L. Arpi, “Retornos Heterogeneos a La Educación En el Mercado Laboral Peruano, 2015,” Rev. Investig. Altoandina, vol. Vol. 18, 2016. [12]R. Paz and J. C. Quilla, “Retornos a la Educación de los Jefes de Hogar en la Región de Puno, 2011 – 2015,” Rev. Investig. Altoandina, vol. V. 18, 2016. [13]INEI, “Instituto Nacional de Estadistica e Informatica - Evolucion de la Pobreza Monetaria 2008 - 2019,” 2020. [Online]. Available: https://www.inei.gob.pe/media/cifras_de_pobreza/informe_pobreza2019.pdf. [14]A. Smith, An Inquiry into the Nature and Causes of the Wealth of Nations. Londres: Londres - Reino Unido, 1776. [15]G. Becker, “A Theory of the Allocation of Time,” Econ. J., vol. Vol. 75 N°, p. pp.493-517, 1964. [16]R. Hernández, C. Fernández, and M. del P. Baptista, Metodologia de la Investigación, vol. 6ta Ed. 2014. [17]W. Mendoza, Cómo Investigan los Economistas, 1ra Ed. Lima - Perú, 2014. [18]D. Alfaro and E. Guerrero, “Brechas de genero en el ingreso: Una mirada mas alla de la media en el sector agropecuario,” Consorc. Investig. Econ. y Soc. - CIES, 2013, [Online]. Available: http://cies.org.pe/sites/default/files/investigaciones/1_informe_final_pb19_-_alfaro_y_guerrero_final.pdf. [19]J. Wooldridge, Introduccion a la Econometria. Un enfoque moderno, 4ta Ed. Mexico, D.F., 2009. [20]D. Gujarati and D. Porter, Econometría. 2010.

scholarly journals Correlates of human capital expenditure among rural households in Nigeria

2018 ◽  
Vol 40 (335) ◽  
pp. 41-53 ◽  
Author(s):  
Oluwakemi Adeola Obayelu ◽  
Ayodeji Ojo ◽  
Olamide Oladoyin
Keyword(s):  
Human Capital ◽  
Capital Expenditure ◽  
Rural Households ◽  
Household Head ◽  
Household Survey ◽  
Educational Interventions ◽  
National Bureau ◽  
Access To Education ◽  
Heckman Selection Model ◽  
Rural Nigeria

Abstract Human capital development is increasingly gaining policy relevance especially with the implementation of the Sustainable Development Goals (SDGs). This study examined the correlates of human capital expenditure in rural Nigeria. General Household Survey dataset collected by the National Bureau of Statistics was used for this study. Descriptive statistical tools, principal components analysis and the Heckman selection model were used to analyse relevant data. The study found majority of the households were male-headed, with an average size of 7 people. In terms of access to education, 62.1% of the surveyed households had access to education and spent an average of NGN 12,570.56 on education. The age of household head, access to loans, marital status and household size were the correlates of human capital expenditure in rural Nigeria. Also, school fees and registration accounted for 41.2% of households’ expenditure on education. The study found paucity of funds, low priority placed on education and low interest were the main constraints to human capital expenditure. The study recommended the design and implementation of pro-poor educational interventions especially for children from rural households. Also, there is the need for government, multilateral organisations and financial institutions to position rural households for financial inclusion.

scholarly journals The relationship between household income and educational level. (south Darfur rural areas-Sudan) statistical study

2016 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Sofian A. A. Saadv ◽  
Amin Adam
Keyword(s):  
Rural Areas ◽  
Household Survey ◽  
Level Of Education ◽  
Household Level ◽  
Income Sources ◽  
Dependent Variables ◽  
Independent Variable ◽  
High Level ◽  
Education Groups ◽  
The Relationship

<p>The main aim of this paper is to find out the relationship between the household level of income and the level of education for the household being lived in southern Darfur state (Sudan) since they were seriously affected by the war conflict and lost most of their income sources. One-way Analyses of Variance (ANOVA) have been used to asses this relation where the independent variable and dependent variables are categorical and continuous respectively. Data gathered from Household survey (HHS) is analyzed. The results indicate the existence of relationship between certain education groups and the level of income, mainly high level of education comparing with the low level.</p>

scholarly journals Seasonality and household diets in Ethiopia

2015 ◽  
Vol 19 (10) ◽  
pp. 1723-1730 ◽  
Author(s):  
Kalle Hirvonen ◽  
Alemayehu Seyoum Taffesse ◽  
Ibrahim Worku Hassen
Keyword(s):  
Food Security ◽  
Energy Intake ◽  
Rural Areas ◽  
Urban Areas ◽  
Dietary Diversity ◽  
Rural Households ◽  
Household Level ◽  
Rural And Urban ◽  
Per Capita ◽  
Nationally Representative

AbstractObjectiveTo revisit seasonality by assessing how household diets vary across agricultural seasons in rural and urban Ethiopia. The role of seasonality on the sources and intake of energy (per capita) and household dietary diversity score (HDDS) was analysed.DesignThe use of nationally representative household-level data collected each month over one year to study the seasonal changes in the sources and intake of energy and HDDS.SettingEleven regions of Ethiopia, including rural and urban settings.SubjectsTotal of 27 835 households were interviewed between July 2010 and July 2011 in all eleven regions of the country. On average each month saw 2300 household interviews, yielding nationally representative data for each calendar month.ResultsFor rural households, the mean daily per capita energy intake was 10 288 kJ (2459 kcal) in February (post-harvest period) and lower in the lean season: 9703 kJ (2319 kcal) in June (P<0·05) and 9552 kJ (2283 kcal) in July (P<0·001). HDDS for rural households was highest in February (6·73) and lowest in June (5·98; P<0·001) but high again in July (6·57). Urban energy intake was also lower in the lean season but HDDS varied less by season. Considerable seasonal variation was also found in energy sources in rural areas, less so in urban areas.ConclusionsHousehold diets in Ethiopia remain subjected to significant seasonal stress. HDDS and food security measured using energy intake do not always agree. Preferably, HDDS and energy intake data should be used together to assess food security.

scholarly journals Estimation of the Effect of Cassava Commercialization on Different Household Income Measurements in Kilifi County, Kenya

2020 ◽  
Vol 13 (1) ◽  
pp. 44
Author(s):  
Florence Opondo ◽  
George Owuor ◽  
Patience Mshenga ◽  
Andre Louw ◽  
Daniel Jordan
Keyword(s):  
Household Income ◽  
Household Head ◽  
Value Added ◽  
Household Survey ◽  
Climatic Conditions ◽  
Farm Income ◽  
Switching Regression ◽  
Development Discourse ◽  
Per Capita ◽  
Cassava Crop

The transformation of agricultural production from subsistence to commercially oriented outcomes is a topical matter in the rural and socio-economic development discourse. Cassava crop is being promoted for commercialization because of its tolerance to harsh climatic conditions experienced in arid and semi-arid areas. Furthermore, there is high potential for the tuber crop to improve household income. In Kenya, a number of interventions have been directed towards commercializing cassava. The effect of commercialization on household income has not been established. Distinct from other studies, this study estimated the effect of cassava commercialization on three different income measures namely per capita, annual and per acre revenue. A household survey was conducted in Kilifi County in Kenya where 200 respondents were randomly selected. Data was collected using a structured questionnaire. A two-stage endogenous switching regression model was fitted to determine the effect of commercialization on the different income measures. The proportion of households that commercialized was 69% while the remaining 31% did not. The study found that majority of the households marketed low value-added cassava products. The results reveal that farmers who engaged in cassava commercialization enjoyed relatively more income than their counterparts. Off-farm income, age of the household head and distance to market had a negative significant influence in all the income estimates. Group membership was only significant for the per acre income while household size was negative and statistically significant in both per acre and per capita incomes. Findings point out the importance of promoting policies that will enhance cassava commercialization.

scholarly journals Socio-Economic and Demographic Factors of Poverty Alleviation in Pakistan: A Case Study of Southern Punjab

2020 ◽  
Vol 6 (2) ◽  
pp. 525-538
Author(s):  
Rashid Ahmad ◽  
Muhammad Zahir Faridi
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Capita Income ◽  
Household Head ◽  
Rural Poverty ◽  
Least Square ◽  
Per Capita Income ◽  
High Dependency ◽  
Per Capita ◽  
Dependency Rate

This study aims to explore the socio-economic and demographic determinants of poverty in Southern Punjab by using the cross sectional data consisting of 785 household heads. Binary logistic regression  and ordinary least square method are used for estimation. The findings exhibit that the variables like family system, household size, presence of disease and status of employment of household head are positively and significantly related to  poverty whereas household head age, rural-to-urban migration,  years of schooling,  number of earners, women status of work, remittances, the physical assets value and ownership of house significantly and negatively influence the likelihood of poverty and positively influence the per capita income of the households in Southern Punjab. The study also provides the comparison of regional and division level. It is concluded that DG Khan division is the poorest among all the divisions of the southern Punjab. In DG Khan Division, the households have less education, high dependency ratio. In rural areas of southern Punjab, there is more poverty as compare to urban areas. The rural poverty is due to many factors like high dependency rate, lower level of education, adoption of profession, lower per capita income, dissaving. It is suggested that education should be promoted, employment opportunity should be provided so that dependency rate may be reduced, rural areas should be restructured by provision of basic necessities of life.

scholarly journals Influence of Private Forest Land to Farmers Welfare in Yogyakarta Special Province

2004 ◽  
Vol 13 (2) ◽  
Author(s):  
Su Ritohardoyo
Keyword(s):  
Land Use ◽  
Rural Areas ◽  
Secondary Data ◽  
Structured Interview ◽  
Primary Data ◽  
Cluster Sampling ◽  
Social Forestry ◽  
Forest Land ◽  
Policy Makers ◽  
Factors Affecting

Forest people land use pratices with economic and ecological orientation have been developed in the vicinity of forest areas. Nevertheless, policy makers view this development as detrimental to the forest it self. Forestry programs directed to accommodate public participation are uniformly designed and implemented, ignoring the physical and social variantion of rural areas. Thus there is a need for research on spatial variation of the social forestry program especially related to its success and impact on welfare. The research is carried out in Yogyakarta speial province employed a survei method and spatial approach. Stratified cluster sampling is used on the basis of variations in physiographic characteristics. Secondary data are also collected in forms of literatures and previous research in the field. Primary data are obtained through a structured interview using a set of questionnaires. Data are analyzed using simple tabulation method as well as statistical techniques.The research shows that in general people forest land use has been sucessfully implemented in the province. It is important to note that a more successfull implementation of people forest land use is found in the private rather than government initiated forest. In the less accessible areas people forest land use activities are much more successfull than in the accessible ones. Social forestry land use in the volcanic slope physiography is much better than that in either undulating or karst areas. Among important factors affecting the success of people forest land use are land ownership, education level, technology, land accessibility and percentage of plant growth. It is also confirmed that accessibility is the most important factor affecting the success of an people forest land use. The research also reveals that people forest land use has been very important in improving the farmers welfare. A composite indicator shows that the welfare of farmers is much improved after the adoption of people forest land use, with the highest achievement is obtained by farmers in the volcanic slopes.

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