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

 

 

 

 

 

 

 

 

 

 

R-squared

0.374606

Adjusted R-squared

0.254338

 

 

 

 

 

 

From this data, we can see that the rate of accumulation of physical capital/capita is only significant at a 52% test size. Thus, physical capital accumulation is not a significant factor in explaining economic growth in Africa during this period. However, the logarithm of physical capital per capita in 1960 was significant at a 7% test size (0,0672).

 

Excluding the rate of accumulation of physical capital/capita, we obtained:

 

Table 3: adapted Solow model Africa, without physical capital/capita

Dependent Variable: GDP_GROWTH_60_00_BASIC

 

Method: Least Squares

 

 

Date: 03/30/06   Time: 14:43

 

 

Sample (adjusted): 1 32

 

 

Included observations: 32 after adjustments

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

 

 

 

 

 

 

 

 

 

 

LOG(GDP_CAP__1960_BASIC)

-1.494514

0.577913

-2.586052

0.0154

ABS_LAT

0.081200

0.035688

2.275267

0.0310

MALARIA_ECOLOGY_GEOGRAPH

-0.029620

0.027985

-1.058406

0.2992

LOG(PHYS__CAP__POP__1960_FAC)

0.440382

0.235995

1.866066

0.0729

C

10.68258

3.913992

2.729332

0.0110

 

 

 

 

 

 

 

 

 

 

R-squared

0.364208

Adjusted R-squared

0.270017

 

Since Malaria Ecology is only significant at 30%, we dropped it and ran the regression again:

 

Table 4: adapted Solow model Africa, without malaria ecology

Dependent Variable: GDP_GROWTH_60_00_BASIC

 

Method: Least Squares

 

 

Date: 03/30/06   Time: 15:46

 

 

Sample (adjusted): 1 32

 

 

Included observations: 32 after adjustments

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

 

 

 

 

 

 

 

 

 

 

LOG(GDP_CAP__1960_BASIC)

-1.600410

0.570407

-2.805733

0.0090

ABS_LAT

0.093512

0.033812

2.765678

0.0099

LOG(PHYS__CAP__POP__1960_FAC)

0.464814

0.235367

1.974849

0.0582

C

10.94374

3.914584

2.795633

0.0093

 

 

 

 

 

 

 

 

 

 

R-squared

0.337830

Adjusted R-squared

0.266883


The logarithm of physical capital per capita in 1960 is significant at a test size of 6%, but the logarithm of per capita GDP in 1960 and absolute latitude were significant at 1%.

 

The value for R-squared shows that the variables used explain 33.8% of the variation in the growth of per capita GDP between the countries, the average value for which is 0.7694% per year. There is also strong evidence for beta convergence (which will be explained in a later in this chapter).

 

We suspected that using a value for |Latitude-10| might improve the accuracy of the regression, so we ran a new regression and got:

 

Table 5: adapted Solow model Africa, with Latitude – 10

Dependent Variable: GDP_GROWTH_60_00_BASIC

 

Method: Least Squares

 

 

Date: 03/30/06   Time: 15:50

 

 

Sample (adjusted): 1 32

 

 

Included observations: 32 after adjustments

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

 

 

 

 

 

 

 

 

 

 

LOG(GDP_CAP__1960_BASIC)

-1.101626

0.546954

-2.014113

0.0537

LOG(PHYS__CAP__POP__1960_FAC)

0.423489

0.253906

1.667900

0.1065

_LATITUDE_10_

0.056906

0.026735

2.128489

0.0422

C

7.880784

3.892344

2.024688

0.0525

 

 

 

 

 

 

 

 

 

 

R-squared

0.274351

Adjusted R-squared

0.196603

 

Apparently, absolute latitude explains economic growth in Africa in this period better than |Latitude-10|, since the P values have all increased and R-squared and especially adjusted R-squared have dropped greatly.

 

 

3.2 Discussion of factors

 

Absolute latitude: We found that absolute latitude was the only instrument that explained the steady state of a country in Africa[7]. If an African country was 1 degree further from the equator, it had 0.09% added to its yearly growth rate between 1960 and 2000.

Contrary to our predictions, regressing on |Latitude-10| yielded a worse result than regressing on absolute latitude. African countries further away from the Equator do better, as opposed to those further away from the Sahara desert. This is probably because near the Equator are located countries which have suffered devastating civil wars, like Sudan, Ethiopia, Somalia and the Democratic Republic of Congo. In contrast, countries in the far north like Morocco and Egypt, and those in the South like South Africa, Botswana and Zimbabwe (at least until 1999, when land redistribution began and just before our data ends) enjoy greater political stability and are better governed (by African standards), skewing the results for the Northern and Southern parts of the continent.

 

Thus, it is not absolute latitude per se that explains economic growth in countries further from the equator, but the fact that these countries are more politically stable and enjoy better governance. Thought of in this way, the fact that absolute latitude affects a country’s steady state makes sense.

 

Log(GDP/capita in 1960): We found strong evidence for conditional beta convergence, since countries with a lower initial level of GDP/capita grew more quickly than those with higher initial levels, even when we only corrected for absolute latitude and malaria ecology; many other factors which we did not correct for affect countries’ steady states.

With a p-value of 0.0090 which is way below our test size of 10%, there is very strong evidence for beta convergence among the countries; for every increase of 1 in the value of log(GDP/Capita in 1960), a country had slower growth to the tune of -1.6% per year from 1960-2000.

 

Log(Physical Capital per Capita in 1960): It is interesting that the logarithm of the 1960 level of physical capital per capita was a significant regressor – the more physical capital per capita a country had in 1960, the faster it grew in the next 40 years. This result is hard to explain, because Solow’s theory tells us the opposite. According to his  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 is the case with Log(GDP/capita in 1960). In this case there is divergence between the countries which we can not explain.

 

Growth of Physical Capital per Capita: Was a relatively insignificant variable. This is probably because economic activity in Africa is not capital intensive, and Africans make poor use of the physical capital they already have. Also, much modern capital requires a certain level of education (human capital) and/or tacit knowledge to use, and this might not be present in some African workforces.

 

Malaria Ecology: Malaria Ecology was only significant at the 30% test size – more significant than the other factors we dropped, but less so than those we kept. This is probably because the effect of malaria is ameliorated by international aid, and by the fact that countries with a naturally more serious malaria problem would do more to combat it.

 

Population growth (theory vs practice): The results showed that population growth is not a significant factor affecting the growth of per capita GDP. This shows that the composition of refugee inflows and newly born generations is not radically different from that for the population as a whole.

 


3.3 Average growth rates and residuals

 

The average growth rate of GDP per capita between 1960 and 2000 is 0,7694 percent.

The countries that perform better than expected are the countries that show a positive residual value (country 1 – country 13 in table 6). While the countries that are performing less than expected are those countries witch show a negative residual value (country 14 – country 32 in Table 6). So there are more countries that are performing below our expectations.

 

Table 6: Deviation of the different countries in Africa

No

Country

Deviation

No

Country

Deviation

1

Botswana

3,83606

17

Algeria

-0,21882

2

Mauritius

3,09565

18

Togo

-0,22415

3

Congo, Republic of

1,7373

19

Burundi

-0,26167

4

Uganda

1,69891

20

Malawi

-0,38075

5

Egypt

1,22518

21

Mauritania

-0,47207

6

Cameroon

1,19002

22

Zimbabwe

-0,56832

7

Tunisia

0,69237

23

Senegal

-0,59631

8

Rwanda

0,68841

24

Nigeria

-0,86276

9

Kenya

0,66988

25

Angola

-1,02551

10

Cote d'lvoire

0,63771

26

Mozambique

-1,08612

11

South Africa

0,21942

27

Tanzania

-1,15139

12

Benin

0,19085

28

Mali

-1,23524

13

Ghana

0,02851

29

Central African Republic

-1,24394

14

Burkina Faso

-0,14013

30

Madagascar

-1,86078

15

Morocco

-0,15078

31

Niger

-2,1021

16

Ethiopia

-0,19836

32

Zambia

-2,13109

 


4 Conclusion

 

 

In this Paper we tried to explain economic growth in African countries by using Solow’s optimal growth model. We used his model because the model can tell us if there is any convergence between the countries, and which factors can explain the steady state of a country.

In our adapted Solow model we used different instrument to explain the growth of real per-capita GDP in Africa. We included the natural logarithm of the initial level of per-capita GDP in the model to find out if there where any beta convergence. 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. With our data we found there is very strong evidence for beta convergence among the countries.

But on the other hand including the natural logarithm of Physical Capital per Capita in 1960 leaded to divergence of African countries. The more physical capital per capita a country had in 1960, the faster it grew in the next 40 years.

Future more we included other variables as growth of physical capital per capita, malaria ecology, population growth and absolute latitude to see which instruments can change the steady state in African countries. We found that absolute latitude was the only significant instrument that explained the steady state of a country in Africa. And we think this is caused by the fact that countries further from the equator are more politically stable and enjoy better governance. If countries want to influence the level of there steady state they therefore have to stabilize their political situation (less civil wars) and improve their governance.


References

 

 

 

Fafchamps, Teal & Toye (2001), Towards a Growth Strategy for Africa.

 

Keller & Poutvaara (2003), Do the Augmented Solow Models Rule? A Contribution to the

Empirics of Human Capital, R&D, and Economic Growt.

 

Kiszewski, Mellinger, Spielman, Malaney and Sachs (2004), A Global Index of the

Stability of Malaria Transmission. American Journal of Tropical Medicine and

Hygiene, 70(5), May, pp. 486-498.

 

Nunn (2005), Slavery, Institutional Development, and Long-Run Growth in Africa, 1400–

2000, pp. 3-7

 

 

WebPages:

 

http://www.cdc.gov/malaria/faq.htm

 

http://www.ppu.org.uk/war/countries/africa/africa_index.html



[1] Nunn. 2005, Slavery, Institutional Development, and Long-Run Growth in Africa, 1400–2000, pp. 3-7

[2] Fafchamps, Teal & Toye. 2001, Towards a Growth Strategy for Africa

[3] http://www.cebr.dk/upload/cebr_dp_2003_13_final.pdf

[4] http://www.cdc.gov/malaria/faq.htm

[5] "A Global Index of the Stability of Malaria Transmission," with Anthony Kiszewski, Andrew Mellinger, Andrew Spielman, Pia Malaney, and Sonia Ehrlich Sachs.   American Journal of Tropical Medicine and Hygiene, 70(5), May 2004, pp. 486-498.

[6] http://www.ppu.org.uk/war/countries/africa/africa_index.html

[7] Depending on how strict one is with test sizes, Malaria could also influence a country’s steady state, but we chose to drop it.