TOTAL FACTOR PRODUCTIVITY AND THE WIDE SPREAD CROSS-COUNTRY GROWTH DISPARITIES IN AFRICA

Paper Presented during the 10th ORSEA annual International Conference held at the

University of Nairobi School Of Business, Lower Kabete Road, Nairobi, October 16-18, 2014

AuthorsDickson Turyareeba

Makerere University Business School,Email: dturyareba@mubs.ac.ug, turyareeba@yahoo.co.uk

Joyce Abaliwano,

Makerere University Business School,Email: jabaliwano@mubs.ac.ug

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Outline

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Introduction

• Cross country differences in income growth ‐are widely known to be enormous (Kanczuk, et al., 2009,….)

• Increasing and stable economic growth is a fundamental policy objective in nearly all modern societies (Malmaeus, 2010; ….)

• High income growth improves income distribution- Kuznets curve (Kuznets, 1955)

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Introduction(cont)

• Economic growth is positively associated with reductions in poverty (Roemer & Gugerty, 1997; …)

• Citizens’ welfare increases if national income grows bigger (Stevenson & Wolfers, 2010)

• ….• To sum it up: Most other goals in society will

be more effectively achieved if the economy gets bigger (Malmaeus,2010)

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Introduction(cont)• Persistence of pervasive cross-country growth

disparities perpetuates inequalities between countries, perpetuates dependency and puts the low income economies at risk of insurmountable impoverishment and miserable welfare standards

• On African continent, growth disparities are indeed pronounced

• The so called ‘lion economies’ of Africa have outpaced the rest of countries in the rate at which they generate their economic wealth.

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Table1:Average Annual growth rates of selectedAfrican countries (2001-2012)

Source: Based on World Bank data bank (last updated December 2014)

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'fast growing economies' 'slow growing economies'

Country Average annual growth

in GDP Country Average annual growth in

GDP

Angola 11.3 C. African Rep. 1.0

Chad 9.8 Guinea-Bissau 1.3

Sierra Leone 9.7 Comoros 2.1

Liberia 8.6 Gabon 2.3

Ethiopia 8.5 Seychelles 2.3

Rwanda 8.0 Swaziland 2.3

Mozambique 7.8 Guinea 2.6

Uganda 7.5 Cameron 3.3

Tanzania 7.0 Burundi 3.3

Nigeria 6.5 Djibouti 3.6

Country group average 8.5 Country group average 2.4

MotivationUntil recently, there has been a debate on the drivers of the impressive growth recorded by the ‘fast growing economies’ on the African continent (see Radelet, 2007; McKinsey & Company, 2010; AfDB, OECD, UNDP & UNECA, 2011: 2012; Aryeetey, Devarajan, Kanbur & Kasekende, 2012).

Although a number of growth drivers have been identified as sources of rapid growth in a handful of countries in Africa (natural resources, Agricultural commodity booms, diversification, better governance and stability, structural reforms, FDI,…), the empirical growth literature largely misses the recognition of the role of variations in TFP in explaining the sources of growth differences existing in Africa.

Yet TFP has gained credible importance in explaining cross country growth differences (see Harrigan, 1995; Comin and Hobijn 2004; Hafiz, Mohammad & Mohammad, 2010 ). The current study intends to fill this knowledge gap

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Methods and procedures

Measurement of TFP•Common methods: Growth accounting and frontier analysis.But our study adopts:•Growth accounting, based on the estimation of aggregate production function to produce a measure that approximates technological progress; Solow residual

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Methods and procedures (ctd)• The objective of this method is to determine

how much economic growth can be attributed to advances in technological and organizational competences.

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Methods and procedures• Data and the sample . Panel data- Secondary . A total of 20 African countries . 10 ‘fast growing’ economies and 10 ‘slow growing countries’ . Study period: 2001-2012• Sample selection . Purposive: 10 ‘lion economies’ ( The Economist, 2012); bottom10 slowest growing economies (according to growth averages:2001-

2012)

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Methods and procedures• Data sources . WB Data Bank ( last updated, 2014)• Research design: Quantitative• Empirical model(s):Two econometric models, each specified for a

particular country group:(i) a pooled cross-section model (ii) fixed effects

panel model

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Methods and procedures (ctd)

• Pre-estimation diagnostic tests (To a panel model)(i) Panel unit root tests to avoid possibility of spurious

regression estimates . Two panel unit root tests adopted: IPS (2003) and the Fisher-type (Maddala and Wu,1999).(Why?)(ii) The cointegration test . Study adopts the Pedroni (2004) cointegration test(ii) Causality test- Only for the panel model Study adopts the Granger (1969) causality test- on ‘stacked data’ that disregards space but not time.

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Methods and procedures (ctd)• Estimation techniques(i) Panel FE estimator on panel growth specifications, not

RE because the later is appropriate for large panels(T>20, n=?) and the former largely exploits the advantages of panel data and controls for unobserved heterogeneity.

-FGLS(ii) OLS on pooled model

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Methods and procedures (ctd)

Model variables•The study uses the growth rate in GDP per worker (ggdpw) as the dependent variable. The study includes growth in Total Factor Productivity per worker (gtfpw) and lags of growth rate in GDP per worker as control variables.

Model1:

Model2:

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Results

• Unit roots ( Model 2): ggdpw and gtfw were stationary in Levels i.e. all variables were I(0).

• Cointegration: No evidence of cointegration in all specifications.

• Causality (Model2): gtfpw was not an endogenous regressor

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Results(ctd)

• Model1 Regression estimates

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‘Fast growing economies’ (n=110a); Method: OLS ‘Slow growing economies’(n=110a); Method: OLS

Dep. Var.= ggdpw Dep. Var.= ggdpw

I ndep.vars. coef. St. Error p-value I ndep.vars. coef. St. Error p-value

gtfgw 0.209 0.0723 0.005* gtfpw 0.133 0.0714 0.066** Cons 14.417 1.5718 0.000* Cons 7.621 1.1804 0.000* R2 = 0.072

2R = 0.063 Prob(F) = 0.0046* Prob(Breusch-Pagan Chi2)= 0.74

R2 = 0.031 2R = 0.022

Prob(F) =0.0663 Prob(Breusch-Pagan Chi2)= 0.52

Results (ctd)• Heteroscedasticity test after Model 1 Regression did

not show evidence of heteroscedastic residulas from the regression: Prob(B.Pagan stastistic) > the level of significance (0.05)

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Results (ctd)

Model2 Regression results: The Fixed effectsThe Fixed effects results were unreliable because there were plunged with heteroscedasticity(Table of results not shown here).

To fix the problem, we adopted the FGLS Estimator

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Results(ctd)

Model2 Regression estimates: FGLS

•The heteroscedasticity test after the FGLS did not detect heteroscedastic residuals

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‘Fast growing economies’' (n=10,T=11) , Method: FGLS ‘Slow growing economies’(n=10,T=11), Method: FGLS

Dep. var.= ggdpw Dep. var.= ggdpw

I ndep.vars. coef. St. Error p-value I ndep.vars. coef. St. Error p-value

ggdpw(-1) 0.280 0.0921 0.002* ggdpw(-1) -0.081 0.0989 0.413 gtfpw 0.156 0.0748 0.037** gtfpw 0.149 0.0751 0.048** Cons 10.761 2.0628 0.000* Cons 8.902 1.513 0.000*

Prob(Chi2)=0.0003* Prob(Chi2)=0.1072

Discussion of the results• Models 1&2 regression results agree: growth in TFP

per worker has a positive causal effect on growth in GDP per worker in both country groups.

• Implication: GTFP is an important predictor of growth in Africa, regardless of country groupings.

However,• Higher and stronger gains from growth in TFP

separates the ‘fast growing economies’ from the ‘slow growing economies’ in terms of the pace at which the two country groups generate their economic wealth.

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Discussion of the results (ctd)But also: The first lag of the dep. Variable is statistically significant for the ‘fast growing economies’ BUT NOT for the ‘slow growing economies’.Implication: Current good growth performance in the ‘fast growing economies’ is influenced by the past good growth performance. This is why fast growing economies continue to do better: Important results but it does not address the study problem holistically.

Caveat!Our study results only provide a precursor to the prominence of TFP in growth accounting in Africa but do not provide a sufficient explanation for the existing widespread cross-country growth differences on the continent. Already highlighted by IMF (2013).

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Conclusion

• TFP matters for growth (both country groups)• Larger and ‘stronger’ gains from growth in TFP

has put the ‘ fast growing economies’ a head; only a partial explanation for the existing growth disparities.

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Policy Implications• To accelerate rates of income growth of countries in Africa, it is important

for the governments, especially those of low income and slow income growth, to put in place policies that raise TFP; and provide incentives that nurture the determinants of TFP.

Policy interventions that may raise TFP include: infrastructural development (Inmaculada, Osvaldo & Laura, 2011;

Mushtaq, Ali, Ashfaq, Abedullah & Dawson, 2012); industrial development (Conway & Meehan, 2013); enhancement of human capital development (Jorgenson and Fraumeni,

1993); Economic diversification (Zilibotti,1997) increased participation in international trade [Enrique & Ouattara, 2011);

promoting institutional efficiency especially in the public sector (Acemoglu, Johnson & Robinson, 2004);

promotion of female labor participation [for instance McGuckin and Van Ark (2005);

enactment of priorities to develop the financial sector (Isaksson, 2007) ; adequate investments in Information and Communications Technology

(Kretschmer & Strobel, 2013), among others.

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Thank You All for

Lending me your ears

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