scholarly journals Rates of Return on Physical and Human Capital in Africa's Manufacturing Sector

2000 ◽  
Vol 48 (4) ◽  
pp. 801-827 ◽  
Author(s):  
Arne Bigsten ◽  
Anders Isaksson ◽  
Måns Söderbom ◽  
Paul Collier ◽  
Albert Zeufack ◽  
...  
2018 ◽  
Vol 17 (1) ◽  
pp. 52 ◽  
Author(s):  
Mukhamad Azhar ◽  
S. Suwatno ◽  
Amir Mahmud

Badan Pusat Statistik. (2016). Penduduk Berumur 15 Tahun ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan. Jakarta: Badan Pusat Statistik.Badan Pusat Statistik.(2016). Keadan Angkatan Kerja Provinsi Banten Agustus 2016. BPS Banten.Becker, Gary S. (1975). Human Capital, A Theoretical and Empirical Analysis with Special Reference to Education, 2nd Edition. Diakses dari http://www.nber.org/Deolalikar, Anil. (1993). Gender Differences in the Returns to Schooling and in School Enrollment Rates in Indonesia. Journal of Human Resources. 28 (4), 899-932[Friedman, Howard S., Schustack, Miriam W. (2008). Kepzribadian Teori Klasik dan Riset Modern. Jakarta: Penerbit Erlangga.Heckman, James J., Lochner, Lance J., dan Todd, Petra E. (2003) Fifty Years of Mincer Earnings aKrueger, Alan B., and Lindahl, Mikael. (2000). Education for Growth: Why and For Whom?. Working Paper No. 7591.Megasari,  Diah Nurulia, (2014). Analisis Tingkat Pengembalian Investasi Pendidikan Antara Laki-Laki Dan Perempuan Di Provinsi Jawa Barat Tahun 2014. Universitas Negeri YogyakartaOECD Stat. Extract. Dzaiakses dari: http://stats.oecd.org, pada 1 April 2015.OECD. (2000). Estimating Economic and Social Returns to Learning: Session 3 Issues for Discussion.Perkins, D.H, Radelet, S, Snograss, R.R, Gillis, M, and Roemer, M. 2001. Economics of Development.WW. Norton & Company, Inc. United States of America.Psacharopoulos, G. 1985. “Returns to education: A further international update andimplication”. The Journal of Human Resources, 20 (4), 583-597.Psacharopoulos, George 1994 “Returns to Investment in Education: A Global Update”.World development vol. 22 no. 9 pp 1325-43.Psacharopoulos, George. (1993). Return to Investment in Education: A Global    Update.               Diaksesdari:             http://www- wds.worldbank.org/servlet, pada 10 Agustus 2015.Psacharopoulos, George. (2006). The Value of Investment in Education: Theory, Evidence, and Policy. Journal of Education Finance. 32(2), 113-136.Purnastuti, L., dkk. (2011). Economic Return to Schooling in a Less Developed Country: Evidence for Indonesia. Diakses dari: http://kastoria.teikoz.gr/icoae2/, pada 20 Desember 2014.Purnastuti, L., dkk. (2015). Analisis Tingkat Pengembalian Investasi Pendidikan di Daerah Istimewa Yogyakarta. Prosiding Seminar Nasional 9 Mei 2015. Hlm. 797-806Purnastuti, L., Miller, P., dan Salim, R. (2013). Decilining Rates of Return to evidence for Indonesia. Bulletin of Indonesia Economic Studies.49(2), 213-236.Purnastuti, Losina., Miller, Paul., and Salim, Ruhul (2012). Economic Returns to Schooling in A Less Developed Country: Evidence for Indonesia. Journal of European Economy. Vol. 11. Sepecial Issue.Purnastuti, Losina., Miller, Paul., and Salim, Ruhul (2013). Declining rates of return to education: evidence for Indonesia, Bulletin of Indonesian Economic Studies.Schultz, Theodore, W (1961). Investment in Human Capital. Diakses dari: www.ssc.wisc.edu, pada 23 Februari 2015.


2011 ◽  
Vol 17 (6) ◽  
pp. 1325-1345 ◽  
Author(s):  
Alejandro García Pozo ◽  
Andrés J. Marchante Mera ◽  
José Luis Sánchez Ollero

This study analyses the returns on human capital in the Spanish hospitality and travel agency industries across seven occupational categories by gender. It is motivated by evidence that there is great variation between jobs in this sector and that estimations of the rate of returns for each component of human capital for the entire sector may not be accurate. The main results indicate that the rates of return on human capital are lower in these industries for most occupations than in the private services sector. Given that differences in the returns on human capital components across occupational categories are statistically significant and large, then the relative weight of each segment within the aggregate may explain the results obtained in previous studies.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nagwan Abdulwahab AlQershi ◽  
Sany Sanuri Mohd Mokhtar ◽  
Zakaria Bin Abas

PurposeThis paper examines the interaction of human capital and CRM on the performance of SMEs in Yemen.Design/methodology/approachThe study used a quantitative approach in investigating the interacting effect of human capital on the relationship between CRM and SMEs' performance in Yemen. The PLS-SEM analysis was performed to test the hypotheses.FindingsIt was observed that key customer focus, technology-based CRM and CRM knowledge management were effective drivers of SME performance, but not CRM organization tools. It was also ascertained that human capital has no moderating effect on the key customer focus and knowledge management relationships with performance, although it does moderate the relationships between performance and CRM organization and technology-based CRM respectively.Research limitations/implicationsBecause this study is limited to manufacturing SMEs in Yemen, the results cannot be generalized to other types of industry such as services, whose structure and vision differ from those of manufacturing SMEs. While the current results may be appropriate for SMEs in other developing countries, the researcher believes they are unsuitable for SMEs in advanced economies with different financial structures and employee and management cultures.Practical implicationsThe empirical insights of this study are valuable for the owners, managers and professionals in the SMEs manufacturing sector in developing countries, to enrich their organizational performance through CRM adoption, while considering the moderating effect of human capital.Originality/valueThis is the first empirical work to confirm way the main drivers of human capital, including in the analysis the impact of CRM dimensions and SME performance, in the context of the manufacturing sector. In support of an original conceptual model, the insights contribute to the literature on CRM, SMEs in the manufacturing sector, human capital and emerging economies.


2020 ◽  
Vol 12 (6) ◽  
pp. 2300 ◽  
Author(s):  
Livia Anastasiu ◽  
Ovidiu Gavriş ◽  
Dorin Maier

This article argues for adapting Porter’s Five Forces Model to strategic human resources management. The world business environment is facing real challenges: Shortage of talents, ageing of the world population, and disappearance of repetitive jobs. For a sustainable approach, the quality and stability of human capital should be analyzed strategically, based on the influence of five forces which act in the market: Competition in the industrial sector between specialists with core competencies (rivalry), demands of the hiring companies in terms of the number of employees and updated skills (organizations as buyers), recruitment companies and schools (suppliers), effects of globalization on people’s migration (new entrants), and modern technologies and innovation (substitutes). The stronger the forces are, the harder it will be for the organization to select or retain valuable employees who will add value to products/services. Actual and future employees should analyze the intensity of these forces when they plan to prepare for jobs or change their career. This analysis was focused mainly on the manufacturing sector, where jobs based on repetitive or dangerous tasks may disappear in time.


2019 ◽  
Vol 8 (1) ◽  
pp. 103
Author(s):  
Jude Ohi Ikhatua ◽  
Peter Okoeguale Ibadin

Today, countries, especially the developing ones rebase their Gross Domestic Product (GDP) to determine their economic strength. Nigeria as an acclaimed giant in Africa cannot but continuously examine variables which may impact the economy. It is in this light that this study was intended to investigate the Determinants of Tax Revenue Effort in Nigeria. To achieve this, secondary data, as time series data, covering a period of 1980 to 2015, were used and sourced from the Central Bank of Nigeria Statistical Bulletin, Annual Abstract from the Office of the National Bureau of Statistics and the Federal Inland Revenue Service, both in Nigeria. The dependent variable of Tax Revenue Effort (TTAXeff) was regressed on macro independent variables of Agricultural Sector Productivity(AGRICSP), Manufacturing Sector Productivity (MANSP), Tourism Sector Productivity(TOURSP), Telecommunication Sector Productivity(TELCOMSP), Capital Flight(CAPFR), Trade Openness (TOPEN) and Human Capital Development(HCD). The study adopted a longitudinal research design and used the Autoregressive Distributed Lag (ARDL) technique to evaluate the models. The findings revealed that Agricultural Sector Productivity, Tourism Sector Productivity, Trade Openness and Human Capital Development had significant and positive effects on Tax Revenue Effort in Nigeria. The Manufacturing Sector Productivity, Telecommunication Sector Productivity and Capital Flight had significant but negative effects on Tax Revenue Effort in Nigeria. There is however the need to consistently ensure better performance of tax efforts in the country through strict and meticulous enforcement of tax rules and tax administrations procedures in the country.


1989 ◽  
Vol 49 (4) ◽  
pp. 939-957 ◽  
Author(s):  
John R. Hanson

I test the hypothesis advanced by Richard Easterlin and others that the importation of modern technology and prospects for economic development in the Third World are principally a function of the local population's formal schooling. According to orthodoxy, manufacturing more than any other sector should repay investment in human capital. Yet the correlation of schooling with the manufacturing sector is much lower than with the mineral sector, an enclave in colonial economies and a symbol of underdevelopment.


2014 ◽  
Vol 104 (9) ◽  
pp. 2736-2762 ◽  
Author(s):  
Rodolfo E. Manuelli ◽  
Ananth Seshadri

We reevaluate the role of human capital in determining the wealth of nations. We use standard human capital theory to estimate stocks of human capital and allow the quality of human capital to vary across countries. Our model can explain differences in schooling and earnings profiles and, consequently, estimates of Mincerian rates of return across countries. We find that effective human capital per worker varies substantially across countries. Cross-country differences in Total Factor Productivity (TFP) are significantly smaller than found in previous studies. Our model implies that output per worker is highly responsive to changes in TFP and demographic variables. (JEL E23, I25, J24, J31, O47)


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