Tag: Education

AN EMPIRICAL STUDY ON FAMILY BUSINESS FINANCING

Brikena LEKA

Associate Professor, University of Tirana

brikenaleka@feut.edu.al

Etleva BAJRAMI

Associate Professor, University of Tirana

etlevabajrami@feut.edu.al

Gentiana SHARKU

Professor, University of Tirana

gentianasharku@feut.edu.al

Abstract

Family business in Albania, as well as in the other countries, is the oldest dominant form of business. Usually for the small businesses which are part of this study these businesses are managed by family members, and some of family members are engaged in this business. These businesses started to recover in Albania, after the 1990 with the overthrow of communism regime. Although during this system the word “private property” almost completely disappeared from the vocabulary, again its traces remained in the focus of the family business (FB). This study is based on primary data, collected through questionnaires for family businesses. A total of 327 questionnaires are considered, covering micro and small family businesses. The questionnaires are completed by directly interviewing the individual who runs their business. The data are elaborated in SPSS, since the data were mainly of a qualitative nature. The study consists of two statistical analyses. First, Chi- square tests are performed to analyze the significance of the relationship and Second, a regression equation was performed to analyze the main factors that determine the way these businesses are financed. This study finds that the owners with high level of education are more prone to use external source of financing, the “older” businesses will finance the greater part of their activity by their own funds, and as the turnover of the previous year increases, a major part of the profit will be reinvested in the business for short-term and long-term investment.

Keywords: family business, financing, lifetime, education, turnover

JEL classification: D14, G51, M13

 pp. 81-91

read more

HUMAN CAPITAL FORMATION AND ECONOMIC GROWTH RELATIONSHIPS: PANEL DATA INSIGHTS FOR THE INDIAN STATES

Imran HUSSAIN

Department of Economics, Vidyasagar University, Midnapore, India

 imranhussaingrp@gmail.com

Ramesh CHANDRA DAS

Department of Economics, Vidyasagar University, Midnapore, India

ramesh051073@gmail.com

(corresponding)

Abstract

The various endogenous growth theories as well as empirical studies have proved that human capital works as an important factor for economy’s growth. The role of income on human capital formation cannot be overlooked so far as the essences of the endogenous growth theories are concerned. Considering this interconnection among the human capital and income of the economy, the present study provides quantitative evidence to show the associations amongst human capital formation as quantified by the governments’ health and education expenditures and income of the economy measured by states’ gross domestic products for the panel of states and union territories of India during the period from 1998-99 to 2018-19. The technique of panel cointegration is used to show the long run relationships among human capital investment and income of the economy, and then the Wald test is used to examine the direction of short-run causality. The empirical results demonstrate that human capital and state incomes have a long-term relationship. The Wald test reveals a short-run linkage between human capital and income of the state economies, with the causality running from human capital investment to output of the economy. i.e., human capital has an immediate influence on the progress of the economy. It is consequently suggested that the governments of the states and union territories make additional investments in sectors such as education and health in order to secure long-term economic prosperity.

Keywords: Human capital, education, health, growth, panel cointegration, Indian states

JEL classification: I1, I2, O3, C32, C33

pp. 57-71

read more

A MODEL FOR THE JOB DEMAND FORECASTING IN THE ARCTIC ZONE OF THE RUSSIAN FEDERATION BASED ON TIME SERIES

Zhanna PETUKHOVA

Professor, Department of Economics, Management and Organization of Production, Norilsk State Industrial Institute

zh-petukhova@ust-hk.com.cn

Mikhail PETUKHOV

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

mpetukhov@nanyang-uni.com

Igor BELYAEV

Senior Lecturer, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 belyaev@lund-univer.eu

Lyudmila BODRYAKOVA

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 ln-bodryakova@lund-univer.eu

Abstract

The Russian Federation is the largest country in the world, whose territory includes the Arctic regions. The area of the land territories of the Arctic Zone of the Russian Federation (AZRF) is approximately 3.700,000 km2. The population of the Arctic Zone of Russia is approximately 7 million people, which is equal to 5% of the population of the entire Russian Federation. The purpose of this study is to investigate and analyse regression models for predicting the time series of the number of jobs in the labour market of the Russian Federation, to select an adequate model characterised by a minimum average relative error and a maximum lead time, or to select several adequate models for different forecasting periods: short-term, medium-term and long-term. The study examines the possibilities of predicting the situation in the labour market of the Arctic Zone of the Russian Federation, the demand for specialists in various industries using regression models for forecasting a time series. The simulation was performed using the Statistica software. As a result of the conducted studies, adequate forecasting models were obtained in the time period from 01.01.2020 to 01.01.2021, taking into account the epidemiological situation in the country. Thus, the best model with the smallest error was determined.

Keywords: labour market, regression models, education, autocorrelation function, autoregression.

JEL classification: I15, J11, J01

 pp. 291-298

read more