Growth Within a Country the Issue of Term Paper

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Growth Within a Country

The issue of development and growth within a country or across countries is such that attracts that attention across board. There are screaming headlines of different economic measures taken by economists and financial analysts of countries concerned. Tracking and recording growth in any country or comparing growth amongst country necessitates using diverse measures. Many measures in the name of models have been propounded of which some are accepted without contention. Many however are still undergoing different measures of experimentation. Augmented Solow Model that was developed by Mankiw et al. (1992) is extensively used in this project to measure growth across countries.

The variable of human capital has a general acceptance as a major contributor to economic development. The correlation between the variable, human capital and economic development will be calculated using time series data of 22 countries. Positive relationship that exists between the variable and time series data is further confirmed. A further sensitivity test is performed to verify the correctness and usability of the result obtained. Human capital has health and education as components and these two shall serve as proxies for human capital. Although the different models assume different states of technological advancement with regards to the sampled countries but in this write-up, the emphasis was placed on using different growth rates for the technological advancements as different countries have distinctive growth rates. This tends to give a better regression results in support of the Solowian concept. The final result obtained lay credence to the concept that all sampled countries move from being poor to a steady state which does not contradict the augmented Solowian theory. Effort was also made in using stock performance record to tract the various per capita incomes which also assisted in the final regression results.

INTRODUCTION

Comparative differences in the levels of development amongst nations have brought about some level of concern and this concern gave birth to so many explanations using different models for explanation. Solow model explained that increased development, measured in Gross Domestic Product (GDP) is as a result of increased use of capital and technological change. The former increased by 12.5% while the latter increased by 87.5 per man-hour in GDP, Solow (1956). It was however realized that the huge percentage of technological change which is 87.5% could also be as a result of human capital. In view of this realization, different models sprang up which include the one found in Lucas (2002). One of such models is Augmented Solow Model developed by Mankiw et al. In the year 1992. Some factors used by Mankiw et al. To explain this concept include trade, inequalities and some core values such as labour and capital which are considered variables in nature. These factors are highly debated by analysts. Others variable factors were added, with reasons while others contested the result obtained from these factors and variables. (Mankiw, Romer and Weil, 1992; Ram, 2007) made an inclusion of schooling as a variable while (Knowles and Owen, 1995) believes that health and longetivity must also be a factor.

The Mankiw, Romer and Weil (MRW) are used as a case study here. Two types of technological changes were observed. The types A and B. According to MRW, the countries studied were noticed to approach a steady state from backward position. This is argued by Cho and Graham (1996) that approaching this steady state is as a result of assuming that the various countries have similar rate of technological advancement. However, by applying different rates of technological advancement to the countries, the number of countries that approached steady states from backward reduced.

EMPERICAL EVIDENCE

The additional non-core variable of choice is technological changes. According to Mankiw, Romer and Weil, selected countries attained a steady state due to per capital income levels that are at variance with those countries that have already attained steady states. In view of this, the Mankiw et al. assumes that total factor product is negative for the selected countries. By applying 2% technological advancement to Solow model, a lower steady-state is obtained for a ratio of labour to capital rather than a zero value obtained by Cho and Graham (1996). What this translates to is that for countries to attain a steady state, the value for the ratio of capital to labor must be less. Mankiew et al. augmented the original Solow model by adding human capital variable. This gives the equation a look as presented below in three steps:

Yt = K (t H (t (A t L. t) 1-( - ( - (1)

k t = s k y t -- (n + g + () k t - (2)

h = s h y t (n + g + () h t - (3)

The parameters of the equation above are as represented below:

Y represents output

K represents physical capital

H represents human capital

L represents labour

A, technological level attained n and g represent the rates of growth of labour and technology. They are s h and s k represent amounts invested from the outputs of human and physical capital

( represents the depreciation rate for the amounts invested. However, accumulation function can be expressed in units of effective labour which can be expressed as below:

y = Y, k = K

AL AL

and h = H

AL

Mankiw et al. used the equation above to calculate the level of steady rate per capita income. This was based on time t, yt*= Yt

Lt

In (yt*) = In (A () + gt -- (( + () In (n + g + () + ( ( ) In (sk) + ( ( ) In (sh) - 4

1 - ( - ( 1 - ( - (

1 - ( - (

In order to calculate steady state making use of Mankiw et al. theory for the year in question, taken that ( = (n + g + ()

(1 - ( - ()

d In (yt) = g + ( (In (yt*) - In (yt))

dt

B: COLLATED DATA

The countries listed below are some of the countries used as sample in calculating relative percentage annual growth productivity.

Relative % annual growth, Total Growth Productivity 1970-1985, Young

Egypt

3.500

Turkey

0.900

Morocco

0.000

Pakistan

3.000

Netherlands

0.800

Nigeria

0.000

Botswana

2.980

Ethopia

0.700

Haiti

0.000

Congo

2.800

Austra

0.700

Benin

0.000

Syria

2.500

Australia

0.700

Madagascar

0.000

Hong Kong

2.500

Kenya

0.600

Sudan

0.000

Cameroun

2.400

Spain

0.600

Ivory Coast

0.000

Zimbabwe

2.400

France

0.500

Senegal

0.000

Tunisia

2.400

Liberia

0.400

Zambia

0.000

Uganda

2.100

Honduras

0.400

Mozambique

0.000

Bangladesh

1.900

Paraguay

0.400

Angola

0.000

Thailand

1.900

Portugal

0.400

Bolivia

0.000

Italy

1.800

Belgium

0.400

Philippines

0.000

Norway

1.700

U.S. A

0.400

Papua N.G

0.000

Finland

1.500

Algeria

0.300

Dominican R. 0.000

Burma

1.400

Canada

0.300

El. Salvador

0.000

Korea. R

1.400

Central A. R

0.200

Jordan

0.000

Ecuador

1.400

India

0.100

Guatemala

0.000

Mauritius

1.300

Sri Lanka

0.100

Jamaica

0.000

Denmark

1.300

Singapore

0.100

Nicaragua

0.000

Greece

1.200

Rwanda

0.000

Peru

0.000

Japan

1.200

Sierra Leon

0.000

Costa Rica

0.000

Israel

1.200

Burkina F.

0.000

Mexico

0.000

Tanzania

1.100

Niger

0.000

Ireland

0.000

Columbia

1.100

Zaire

0.000

S. Africa

0.000

Malawi

1.000

Burundi

0.000

Argentina

0.000

Brazil

1.000

Mauritania

0.000

Uruguay

0.000

Malaysia

1.000

Togo

0.000

Chile

0.000

Sweden

1.000

Nepal

0.000

T & Tobago

0.000

Panama

0.900

Indonesia

0.000

New Zealand

0.000

U. K

0.900

Somalia

0.000

Switzerland

0.000

Germany

0.900

Chad

0.000

Venezuela

0.000

0.800

Ghana

0.000

By using the equation and having in mind the various interpolations.

In (yo*) = (" - gt + (2 In (n + g + () + (3In (sk) + (4In (sh)

(1

(0

(1

(2

(3

(4

R-2

Non-Oil

(1) MRW-CG

3.022 -0.288

-0.506

0.524

0.231

0.463 49(98)

(3.651) (-4.863) (-1.752)

(6.029)

(3.887)

(2) Young

3.991 -0143

0.419

0.474

0.139

0.530 43(98)

(7.109) (-2.143) (4.093)

(5.749)

(2.408)

OECD

(3) MRW-CG

2.755 -0.398

-0.863

0.332

0.228

0.651 4(22)

(2.294)

(-5.668) (-2.557) (1.914)

(1.570)

(4) ) Young

5.064 -0. 363

0. 157

0.327

0.126

0.541 7(22)

(5.295) (-4.331) ().933)

(1.583)

(0.517)

By having a closer look at the analysis presented above, Cho and Graham reached a conclusion that rich and poor countries are not at par. There is a difference. It was estimated that comparatively poor countries attained their steady state positions from above as opposed to the rich countries that attained theirs from below.

For the countries analyzed which are oil producing countries and some others that have attained the steady state level, the various data collated for the is used in calculating per capita income.

In (yt) - In (yo) = (o + (1 In (yo) + (2In (n + g + () + (3In (sk) + (4In (sh)

By making use of the data of the oil producing countries of Makiw et al., Cho and Graham calculated that about half of all the country sampled assumed tha steady state from above. That is assuming that technological changes made…[continue]

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