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Grace Alinaitwe Makerere University Business School 10th ORSEA-15-17October 2014 Motivation Literature review Methodology Conclusion Results Growth theories do not clearly specify the explanatory variables to include in the "true" regression. A few studies have looked at determinants of economic growth using a Bayesian averaging of classical estimates The debate of whether finance leads or follows economic growth Negative, positive and none relationships have been found between economic growth and financial intermediaries. To find the true determinants of economic growth in Africa Research Questions To determine whether financial intermediaries affect growth. What are the true determinants of economic growth in Africa To find out if Beta convergence exists in Africa Do financial intermediaries cause economic growth in Africa? Does Beta convergence exists in Africa? 1 • This study has contributed to the debate of whether finance causes or follows economic growth by finding that in Africa, financial development is not a significant determinant of economic growth 2 • It has improved upon other studies by using a new method called Bayesian Averaging of Classical estimates which takes into account all the possible models. 3 • Many financial intermediary indicators: Liquid liabilities/GDP (llgdp), Central bank assets/GDP (cdagdp) and Private credit by deposit money bank/GDP have been used. 4 • Used most of the African countries Relationship between economic growth and finance: Pagano, M (1993) Finance causes growth: King, R. G. and Levine, R. (1993), Spiegel, M. M. (2001), Fritzer, F. (2004), Odhiambo, N. M. (2009) McKINNON, R. I. (1989) Arestis, P. et al. (2001) and Ghani, E. (1992) Growth causes finance: Robinson (1952) The causal link between growth and finance is determined by the nature and operation of the financial institutions and policies pursued in each country: Demetriades, P. O. and Hussein, K. A. (1996) and Arestis, P. and Demetriades, P. (1997). Odhiambo, N. M. (2009) Data • Cross-section data of 37 countries over a period of 1986-2007 • 14 variables Maddison data set Penn world tables • • • • • GDP per capita, GDP per capita growth rate (gdpg), real GDP per capita in current prices (cgdp) Population (Pop) and population growth rate (popg) • Price level of investment (pi), • Investment share of real GDP (ki), • Openness in current prices (openc) • Liquid liabilities/GDP (llgdp), • Central bank assets/GDP (cdagdp) Financial structure • Private credit by deposit money bank/GDP (pcrdbgdp) dataset World bank data base • • • • • fertility rate(fert), inflation rate (inf), life expectancy (life), years of schooling (scho) and oil availability (oil) Bayesian Averaging of Classical Estimates y 0 1 x1 n xn p i y 1 2k j 1 1 y , m j p m j i y Posterior inclusion probability of a variable shows the importance of a certain variable in explaining the dependent variable Important variables must have a higher posterior inclusion probability than their prior one. p m j y pm j T 2k i 1 pm j T K I SSE JT 2 2 SSEiT 2 BIC weights penalize large models and helps address the problem of colinearity in large models. k P ( M j) k k j 2 kj k 1 k k k j Expected model size equals 5, the prior inclusion probability is 5/14 = 0.3571 The posterior model weights in the above equation are equal to the prior model weights times the Bayesian Information Criterion (BIC) developed by Schwarz (1978) divided by the sum of prior weights times the Bayesian Information Criterion of all possible models. Similar variables usually explain relatively less variation in the dependent variable and (BIC) implies less weight on such models. BACE combines the averaging of estimates across models with classical ordinary least-squares (OLS) estimation. Its advantages over model-averaging ◦ requires the specification of only one prior hyper-parameter the expected model size k ◦ estimates are calculated using only repeated OLS ◦ This method takes into account all the possible models Variable FDI posterior prob Posterior unconditional posterior conditional Mean Mean st. dev. st. dev 1 0.0021 0.0002 0.0021 0.0002 Llgdp 0.4108 0.0136 0.0197 0.0332 0.0173 Lcgdp 0.2995 -0.0071 0.0142 -0.0239 0.0166 Popg 0.2792 -0.2317 0.4688 -0.83 0.5391 Fert 0.2608 -0.0016 0.0038 -0.0063 0.0051 INFL 0.1929 0.0001 0.0002 0.0003 0.0002 pcrdbc 0.1873 0.006 0.0172 0.0321 0.0272 Oil 0.1498 -0.0014 0.0048 -0.0091 0.0092 Scho 0.1214 0 0.0001 0.0001 0.0002 Lpop 0.1026 0.0002 0.0016 0.0015 0.0047 cbagdp 0.1004 0.0002 0.0086 0.0021 0.0271 Lpi 0.0872 0.0002 0.0043 0.0025 0.0144 Open 0.0855 -0.0001 0.0029 -0.0007 0.0099 Life 0.0844 0 0.002 -0.0001 0.0069 variable Kbar=3 Kbar=5 Kbar=7 Kbar=9 Kbar=11 prior inclusion probalility 0.2143 0.3571 0.5 0.6429 0.7857 1 1 1 1 1 Llgdp 0.3369 0.4108 0.4733 1 1 Popg 0.2071 0.2792 0.3352 1 1 Fert 0.1704 0.2608 0.3618 1 1 Lcgdp 0.1492 0.2995 0.4936 1 1 Pcrdbc 0.1197 0.1873 0.2656 1 1 INFL 0.1003 0.1929 0.2955 1 1 Oil 0.0743 0.1498 0.215 1 1 Scho 0.0706 0.1214 0.1702 1 1 Lpop 0.0557 0.1026 0.1573 1 1 Cbagdp 0.0509 0.1004 0.1514 1 1 Life 0.0492 0.0844 0.151 1 1 Lpi 0.0454 0.0872 0.1489 1 1 Open 0.0443 0.0855 0.1452 1 1 FDI In Africa financial markets are positively correlated with economic growth but for most indicators, this relationship is very weak. Poor countries grow relatively faster than richer ones hence beta convergence. Strongest evidence in Africa is found for foreign direct investment. Positive but non-significant relationship between growth and financial intermediaries is probably due to: • Africa has not yet reached the required minimum development level of financial markets. • Africa has banks which lack transparency and good management • Poor policies could be in place. I thank you for your kind attention