Economic Growth in Africa

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Seah Shuqi Gabriel   (FXXXXXX)

Edwin Brouwer         (XXXXXXX)

 

Macro-economics: economic growth

Drs. X. XXX

 

Utrecht School of Economics

Universiteit Utrecht

Contents

 

1      Introduction                                                                                                               3

2      The Solow Model                                                                                                       4

      2.1Solw model                                                                                                            4    

      2.2 Modification of the data set                                                                                  4

      2.3 Instruments used                                                                                                    4

      2.4 Instruments excluded                                                                                            6

      2.5 The Model                                                                                                              6

3      Procedure and results                                                                                                 7

      3.1 Procedure                                                                                                               7

      3.2 Discussion of factors                                                                                             9

      3.3 Average growth rates and residuals                                                                     11

5      Conclusion                                                                                                                12

References                                                                                                                        13

 

                                                                      


1        Introduction

 

 

Africa is the world's poorest continent. It has had an uncertain transition from colonialism, with the Cold War and increases in corruption being major contributing factors to its poor economic situation. While rapid growth in China and India, has lifted millions beyond subsistence living, Africa has stagnated, even going backwards in terms of foreign trade, investment, and per capita income. This poverty has widespread effects, including low life expectancy, violence, and instability. [1]

The central challenge facing African economies is to reduce poverty through higher levels of economic growth. Long term, broad-based economic growth is essential for Africa to increase incomes and reach its potential to become a significant trade and investment partner in the world economy.

Sub-Saharan Africa experienced strong economic growth rates in the latter half of the 1990s and in the past five years, reflecting the implementation of better economic policies and structural reforms. Sub-Saharan African countries therefore face major challenges, to raise growth and reduce poverty, and to integrate themselves into the world economy. Economic growth rates are still not high enough to make a real dent in the pervasive poverty and enable these countries to catch up with other developing nations. What is needed is a sustained and substantial increase in real per capita GDP growth rates in these countries, coupled with significant improvements in social conditions.[2]

 

2       The Solow model

 

 

2.1 Solow model

 

In this paper we will try to explain economic growth in Africa by using the Solow model of growth. Solow developed the idea that economic growth is an outcome of capital accumulation. Countries that experience per capita growth have increasing capital-labour ratios, which in turn result from high enough rates of savings to compensate for the cost of capital depreciation and population growth. The concept of capital was later broadened to include human capital. Based on the hypothesis of diminishing returns to capital, the Solow growth model predicted that growth in per capita income eventually ends in the long run. This was in contradiction with empirical data on a number of countries that displayed positive growth rates for more than a century (Keller & Poutvaara, 2003).[3]

 

Our regressions have the form:

 

gy = a + b0 · ln(y0) + b1 · I1 + b2 · I2 + . . .

 

Where gy is the growth of real per-capita GDP during some period, ln(y0) is the natural logarithm of the initial level of per-capita GDP, and Ix are the instruments which will be explained in a later section.

 

 

2.2       Modification of the data set

 

Due to a lack of data, we had to exclude some countries from our regressions. No data for 1960 GDP/capita was available for Eriteria, Liberia, Libya, Sao Tome and Principe, Somalia, Sudan and Swaziland, so they were eliminated from the data set we used. There was no value for Malaria Ecology for the Seychelles, so we also excluded those counties.

Tunisia and Sierra Leone lacked data on GDP/capita in 1960, but had data from 1965. Since there was a difference of only 5 years, we extrapolated 1960 GDP by assuming their economies grew (or shrunk) at the same rate from 1960-1965 as from 1965-2000. We used the same method to extrapolate Capital in 2000 for countries with only data for Capital in 1960 and 1998, since the gap was only 2 years in this case: the countries treated this way were Angola, Botswana, Central African Republic, Mauritania and Tunisia.

 

 

2.3       Instruments used

 

In our model we used different instrument to explain the growth of real per-capita GDP in Africa, but we also excluded some instruments. Here is a list of the instruments we used (and didn’t use) and why we expect these instruments to affect GDP per capita growth in Africa (or not).

 

Absolute latitude: We theorized that absolute latitude – distance from the equator – would help explain differences in countries’ economic growth, since a country’s latitude determines its climate. We also theorized that countries’ proximity to the Sahara desert would affect economic growth, since it is a barren wasteland – countries further from it should grow faster. As such, we also tried regressing GDP/capita growth on |Latitude-10|, since the Sahara desert is in the north of the continent.

 

Log(GDP/capita in 1960):According to the Solow model, ceteris paribus, countries which are further from their steady states will grow more quickly than those nearer to their steady states – beta convergence.

 

Log(Physical Capital per Capita in 1960):According to the Solow model, ceteris paribus, countries which have less physical capital per worker will accumulate capital at a faster rate than those with more physical capital. The former will thus grow more quickly than the later. If all differences between countries are explained by differences in capital per worker, we will have beta convergence as above.

 

Growth of Physical Capital per Capita: According to the basic formulation of the Solow model, differences in physical capital per capita explain countries’ different levels of GDP/capita. If workers have more capital to work with, their average output will naturally be higher.

 

Malaria Ecology: Malaria is a disease caused by a mosquito parasite that affects many inhabitants of developing countries. It causes “high fevers, shaking chills, and flu-like illness”[4]. As might be expected from the symptoms, people afflicted with Malaria would find it hard to work and study, if they were able to do so at all (either being too sick to do so or losing their lives to the disease), and this would severely impact economic growth.

In a 2004 paper[5], Sachs et al. came up with a Malaria Ecology Index based on a country’s geography and mosquito biology and behavior to measure how susceptible a country naturally is to Malaria. Ceteris paribus, a country with a higher Malaria Ecology Index would suffer from a slower rate of GDP/capita growth due to its people suffering from the effects of the disease.

 

Population growth: In the theory of the Solow growth model, population growth acts in the same way as capital dilution – each worker has less capital to work with and the country moves proportionately further from its steady state.

In reality, the profile of the people a country gains through population growth is never identical to the prior composition of its population. In developed countries, rapid population growth resulting from more babies being born would dampen economic growth in the years before the children join the workforce as they are educated and their human capital is built up. In Africa, many countries suffered civil wars during the period studied[6], wreaking havoc with their educational systems.

Also, population growth resulting from refugee flows would reduce economic growth more than would be expected by capital dilution, since the sort of refugees who would migrate to other African countries would be destitute; the well off would likely migrate to safer, richer countries outside Africa.

 

 

2.4 Instruments excluded

 

For various reasons, we found some instruments to be unhelpful in our data analysis.

 

Malaria exposure (1994) had too many countries with values of 0 and 1, making us suspicious of the data. Data from one year was also, we felt, insufficient to capture the impact of malaria over 40 years of economic development. Malaria ecology, a constant value, was far more suitable as a regressor, since it would not change significantly in the time period studied.

 

Carbon Dioxide (CO2) emissions was another factor we decided not to use since it would be a proxy for development rather than a factor affecting it – more developed countries emit more carbon dioxide due to bigger manufacturing sectors, higher electricity consumption, higher motor vehicle use and other reasons.

 

It might have been informative to regress against labor's share of national income (1-α) in the Solow Growth Model, but we were missing data for almost all countries.

 

National saving and average investment rates were also unused, since they would determine physical capital accumulation, which was what we actually regressed against.

 

 

2.5 The model

 

Explained the used instruments our regression will have the following form:

 

Growth of real per-capita GDP (1960-2000)  = a + b0 · Log(GDP/capita in 1960) + b1· Log (Physical Capital per Capita in 1960) + b2 · Growth of Physical Capital per Capita + b3 · Malaria Ecology + b4 · Population growth + b5 · Absolute latitude + u

 

Where ‘a’ is the meaningless constant term and ‘u’ are the unobservables.

 

In the next chapter we will evaluate the procedure and the results of our regression.

3       Procedure and Results

 

 

3.1 Procedure

 

Setting the test size at 10%, we carried out our regressions. First, we used the dataset including data on physical capital accumulation. Regressing growth of GDP/capita from 1960-2000 on the logarithm of GDP/capita in 1960, absolute latitude, the growth of physical capital/capita, malaria ecology, population growth between 1960-2000 and the logarithm of Physical Capital/capita in 1960, we obtained:

 

Table 1: Solow model Africa

Dependent Variable: GDP_GROWTH_60_00_BASIC

 

Method: Least Squares

 

 

Date: 03/30/06   Time: 14:34

 

 

Sample (adjusted): 1 32

 

 

Included observations: 32 after adjustments

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

 

 

 

 

 

 

 

 

 

 

LOG(GDP_CAP__1960_BASIC)

-1.480231

0.620415

-2.385873

0.0249

ABS_LAT

0.077207

0.037906

2.036813

0.0524

GROWTH_OF_PHYS__CAP__POP

0.027823

0.040513

0.686769

0.4985

MALARIA_ECOLOGY_GEOGRAPH

-0.031767

0.029053

-1.093423

0.2846

POP_GROWTH_YR

-0.245797

0.671848

-0.365853

0.7176

LOG(PHYS__CAP__POP__1960_FAC)

0.475230

0.248371

1.913388

0.0672

C

10.81991

5.115542

2.115105

0.0446

 

 

 

 

 

 

 

 

 

 

R-squared

0.377936

Adjusted R-squared

0.228641

 

Population growth was extremely insignificant (0,7176 > our test size of 10%), so excluding this you get:

 

Table 2: adapted Solow model Africa, without population growth

Dependent Variable: GDP_GROWTH_60_00_BASIC

 

Method: Least Squares

 

 

Date: 03/30/06   Time: 14:39

 

 

Sample (adjusted): 1 32

 

 

Included observations: 32 after adjustments

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

 

 

 

 

 

 

 

 

 

 

LOG(GDP_CAP__1960_BASIC)

-1.427070

0.593027

-2.406418

0.0235

ABS_LAT

0.080684

0.036078

2.236394

0.0341

GROWTH_OF_PHYS__CAP__POP

0.025987

0.039526

0.657463

0.5167

MALARIA_ECOLOGY_GEOGRAPH

-0.032153

0.028546

-1.126382

0.2703

LOG(PHYS__CAP__POP__1960_FAC)

0.458742

0.240145

1.910270

0.0672

C

9.782413

4.186045

2.336911

0.0274