ISSN: 2067 – 9211 Performance and Risks in the European Economy

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The Analyze of the Link between

Minimizing Production Costs and Maximizing Profits

Cătălin Angelo Ioan1

Abstract: The article examines the link between minimizing production costs and maximizing profits. We will

analyze the phenomenon of demographics and poverty for developing countries and regions of the World for

developing countries and regions of the World for each of the developing countries or regions of the World.

The source of the statistical data present in the analysis is the World Bank, all the indicators and regression

models being the contribution of the authors.

Keywords: cost; profit; minimizing; maximizing

Let us consider a firm F whose activity is formalized with a production function Q that depends on a

number of production factors x1,..., xn, n≥2. In order to ensure its competitiveness on the market, its

main purpose is to reduce its total cost, which will implicitly lead to the output of its products at the

lowest possible cost. On the other hand, the company wants to maximize its pr ofit. For example, we

consider the production function as Cobb-Douglas type, which is equivalent to a constant production

elasticity in relation to the production factors, which is not restrictive, at least in the short term.

1. Main Notions

In the following we will analyze the phenomenon of demographics and poverty for developing countries

and regions of the World for developing countries and regions of the World for each of the developing

countries or regions of the World

The source of the statistical data present in the analysis is the World Bank, all the indicators and

regression models being the contribution of the authors.

Before starting the analysis, we will briefly outline the significance of some (less usual) indicators.

The annual population growth rate for a given year is calculated as the exponential growth rate of the

population from the previous year to the current one, expressed as a percentage.

Birth rate, crude is calculated by reporting the number of live births that occurred in one year per 1,000

inhabitants.

Life expectancy at birth indicates the number of years of life of a newborn if the data that influence

the mortality are kept constant.

The mortality rate, adult is the probability of dying between the ages of 15 and 60.

1 Associate Professor, PhD, Danubius University of Galati, Romania, Address: Blvd. Galati no. 3, Galati, Romania,

Corresponding author: catalin_angelo_ioan@univ-danubius.ro.

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The maternal mortality ratio (according to WHO, UNICEF, UNFPA, World Bank Group, and the

United Nations Population Division) is “the number of women dying from pregnancy during pregnancy

or within 42 days of termination of pregnancy for 100,000 live births”.

People using at least basic sanitation services refer to people using basic sanitation services.

The rural poverty gap (according to World Bank, Global Poverty Working Group) is “the low poverty

line as a percentage of the poverty line”.

The Gini index measures the extent to which income distribution between individuals or households in

an economy deviates from a perfectly equal distribution (0 - perfect equality, 100 - perfect inequality).

International migrant stock is the number of people born in a country other than the one they live in.

Refugees are persons who are recognized as individuals who have been granted refugee status and

persons enjoying temporary protection.

2. The Analysis

2.1. Aruba

The analysis of indicator: Population, total during - highlights an average of 74712.37. Also for

Population, total the region ranks on the first 91% in the World.

The analysis of indicator: Population, female (% of total) during 1960-2014 highlights an average of

51.44 bigger than the World average: 49.74. Also for Population, female (% of total) the region ranks

on the first 5% in the World.

The analysis of indicator: Population growth (annual %) during 1960-2014 highlights an average of 1.21

smaller than the World average: 1.62. Also for Population growth (annual %) the region ranks on the

first 80% in the World.

The analysis of indicator: Urban population (% of total) during 1960-2014 highlights an average of

48.27 bigger than the World average: 42.81. Also for Urban population (% of total) the region ranks on

the first 73% in the World.

Rural population (% of total population) during 1960-2014 highlights an average of 51.73 smaller than

the World average: 57.19. Also for Rural population (% of total population) the region ranks on the first

28% in the World.

The analysis of: Birth rate, crude (per 1,000 people) during 1960-2014 highlights an average of 19.80

smaller than the World average: 26.17. Also for Birth rate, crude (per 1,000 people) the region ranks on

the first 81% in the World. Time regression analysis reveals a correlation coefficient value: -0.97 and a

value of R Square: 0.94. The equation of linear regression is therefore: -0.394*Year+802.303. From this

equation we can note that, every year, the indicator decreases with 0.394. The analysis of indicator: Life

expectancy at birth, total (years) during 1960-2014 highlights an average of 72.07 bigger than the World

average: 63.96. Also for Life expectancy at birth, total (years) the region ranks on the first 33% in the

World. The analysis of indicator: Life expectancy at birth, female (years) during 1960-2014 highlights

an average of 74.48 bigger than the World average: 66.16. Also for Life expectancy at birth, female

(years) the region ranks on the first 38% in the World. The analysis of indicator: Life expectancy at

birth, male (years) during 1960-2014 highlights an average of 69.77 bigger than the World average:

61.92. Also for Life expectancy at birth, male (years) the region ranks on the first 34% in the World.

Time regression analysis reveals a correlation coefficient value: 0.95 and a value of R Square: 0.90. The

ISSN: 2067 – 9211 Performance and Risks in the European Economy

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equation of linear regression is therefore: 0.148*Year-223.447. From this equation we can note that,

every year, the indicator grow with 0.148.

The indicator: Mortality rate, adult, male (per 1,000 male adults) during 1960-2014 highlights an

average of 157.64 smaller than the World average: 244.07. Also for Mortality rate, adult, male (per

1,000 male adults) the region ranks on the first 15% in the World. Time regression analysis reveals a

correlation coefficient value: -0.95 and a value of R Square: 0.91. The equation of linear regression is

therefore: -1.814*Year+3763.848. From this equation we can note that, every year, the indicator

decreases with 1.814.

The analysis of indicator: People using at least basic sanitation services (% of population) during 2000-

2015 highlights an average of 97.86 bigger than the World average: 63.09. Also for People using at least

basic sanitation services (% of population) the region ranks on the first 26% in the World. Time

regression analysis reveals a correlation coefficient value: -0.99 and a value of R Square: 0.97. The

equation of linear regression is therefore: -0.057*Year+212.130. From this equation we can note that,

every year, the indicator decreases with 0.057.

International migrant stock (% of population) during 1990 -2015 highlights an average of 7.12 bigger

than the World average: 0.69. Also for International migrant stock (% of population) the region ranks

on the first 9% in the World.

2.2. Afghanistan

The analysis of indicator: Population, total during - highlights an average of 17040452.79. Also for

Population, total the region ranks on the first 32% in the World.

The analysis of indicator: Population, female (% of total) during 1960-2014 highlights an average of

48.70 smaller than the World average: 49.74. Also for Population, female (% of total) the region ranks

on the first 94% in the World.

The analysis of indicator: Population growth (annual %) during 1960-2014 highlights an average of 2.40

bigger than the World average: 1.62. Also for Population growth (annual %) the region ranks on the first

14% in the World.

The analysis of indicator: Urban population (% of total) during 1960-2014 highlights an average of

17.61 smaller than the World average: 42.81. Also for Urban population (% of total) the region ranks

on the first 92% in the World. Time regression analysis reveals a correlation coefficient value: 1.00 and

a value of R Square: 1.00. The equation of linear regression is therefore: 0.327*Year-632.601. From this

equation we can note that, every year, the indicator grow with 0.327.

Rural population (% of total population) during 1960-2014 highlights an average of 82.39 bigger than

the World average: 57.19. Also for Rural population (% of total population) the region ranks on the first

9% in the World. Time regression analysis reveals a correlation coefficient value: -1.00 and a value of

R Square: 1.00. The equation of linear regression is therefore: -0.327*Year+732.601. From this equation

we can note that, every year, the indicator decreases with 0.327.

The analysis of: Birth rate, crude (per 1,000 people) during 1960-2014 highlights an average of 47.97

bigger than the World average: 26.17. Also for Birth rate, crude (per 1,000 people) the region ranks on

the first 15% in the World. The analysis of indicator: Life expectancy at birth, total (years) during 1960-

2014 highlights an average of 47.62 smaller than the World average: 63.96. Also for Life expectancy at

birth, total (years) the region ranks on the first 83% in the World. Time regression analysis reveals a

correlation coefficient value: 1.00 and a value of R Square: 0.99. The equation of linear regression is