Managing Risk in Africa through Institutional Reform

Phillip LeBel, Ph.D. Professor of Economics

School of Business Montclair State University

Upper Montclair, New Jersey 07043Lebelp@mail.montclair.edu

Understanding Africa’s Weak Economic Performance

• By almost any measure, Africa’s economic growth has been weak over the past twenty-five years

• While mean PPP GDP per capita has increased by modest amounts, median PPP GDP per capita has hardly increased at all.

The Mixed Record of African Development Indicators:

Adult Illiteracy Rate

Drought Frequency in Africa

Cereal Crop Yields

Africa’s Share in World Trade

Africa’s Share in Global Merchandise Trade

Reframing African Development Strategies Must Begin With An Emphasis on Risk Management

• Aggregate country risk, which includes political, economic, financial, and environmental factors, is inversely related to the level of per capita GDP

Corruption is a Determinant of Risk

• Corruption is directly related to the level of risk.

In turn, corruption is a function of several factors:

• Judicial independence is inversely related to the level of corruption

• Economic freedom, an important contributor to development, has improved little in Africa over the past twenty-five years.

• Economic Freedom Affects the Level of Per Capita GDP

• International aid, which has often served political over economic interests, has largely failed to raise per capita income in Africa, even when increases have been made.

International Aid has had a negative impact on per capita GDP

Rising International Aid In Africa

• As a result, even rising levels of FDI have but a limited impact on economic development in Africa. In part this is because the level of FDI is so small relative to GDP, which in turn reflects the overall level of aggregate country risk.

As a result, even rising levels of FDI have had a lower impact than

elsewhere

• Despite political instability, military spending in Africa has been lower than in some developed countries. At the same time, it has been higher than in most parts of Asia, China and North Korea being the principal exceptions.

Despite political instability, military spending in Africa has remained

relatively low

• Property rights in Africa have experienced some modest improvements, with some relapses in more recent years. The effect has been negative on the level of economic freedom, with higher risk resulting in lower levels of per capita GDP.

The Quality of Institutional Governance is a Key to Managing Risk

• Democracy, which in addition to transparent and fair elections, consists in the level of civil liberties and political rights. Despite some gains in Africa, the effects on per capita GDP have been weak.

Democracy Offers Some Modest Gains in Per Capita GDP

Some Stylized Patterns of African Development

Determinants of Risk in Africa

Panel Nested Regression Equations

Basic Model Estimating Equations:

Dependent Variable: DEMOC3 .

ECFREE3 .

JUDIND4 .

CORRUPA4.

AIDGNI6 .

RCCRISK4 .

GNSGDP7 .

TRDEP3 .

MKTCAPRATE9 .

FDIGDP8 .

PPPRPCGDP4.

CONSTANT -8.173 1.245 2.769 7.918 11.482 21.701 577.806 133.203 11.262 171.300 -1662.856CIVLIBS 2.730

(42.534)POLRTS 3.737

(67.292)PROPRT 0.106

(7.142)DEMOC* 0.025

(10.675)ECFREE* 0.979 0.448

(15.225) (3.480)JUDIND* -0.138

(3.168)MILBURD -1.124

(6.134)DEBTSRAT 0.114

(6.137)AIDGNI* 0.314

(8.041)CORRUPA* 2.903

(8.465)RCCRISK* -12.234 -1.670 -3.675

(3.225) (2.818) (5.474)GNSGDP* 141.747

(15.120)TRDEP* 16.021

(14.388)MKTCAPRATE* 52.745

(53.057)FDIGDP* 3.209

(6.842)

Number of Observations 750 750 750 750 750 750 750 750 750 750 750

Adj. R-Sq. 0.9830 0.9701 0.9336 0.9789 0.7229 0.8402 0.4769 0.8020 0.8932 0.8130 0.8998

F 1395.21 810.92 5267.19 34796.17 64.04 196.24 98.89 357.25 2243.20

Notes:

1. T-statistics are reported in parentheses

2. Starred variables are based on predicted values from a nested regression.

3. Estimate based on fixed effects using cross-section weights

4. Estimate based on no effects with cross-section weights.

5. Estimate based on two-stage least squares with cross-section weights and fixed effects.

6. Estimate based on fixed cross-section specification with period GLS weights.

7. Estimate based on two-stage least squares with rccrisk instrument, fixed cross-section specification with cross-section GLS weights.

8. Estimate based on two-stage least squares, with fixed effects and cross-section weights, d(RCCRISK) as instrument.

9. Estimate based on two-stage least squares with ecfree(-1) as instrument, fixed cross-section and cross-section GLS weights.

• Estimates are based on parametric changes in selected independent variables

Basic Model Institutional Valuations:

Regional Variation Valuations:

Africa Sample Profile:

Model Descriptive Statistics:

Model Definitions and Sources:

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