B. Key control variables
VI. Conclusion and policy implications
Notwithstanding the extensive work on structural transformation, empirically identifying the key economic forces that shape structural transformation remains an open question. One issue is the role played by aggregate demand. Specifically, while it is known theoretically that households’ consumption, public expenditures, human capital, capital–labour ratio and trade, among others, have an impact on resource allocation, their impact on structural transformation has not been empirically established, especially in Africa. Analysis on this issue has important implications as to how policies and technological change influences structural transformation.
In contrast to the structural transformation process emphasized in the conventional narrative, the present study exams the effect that aggregate demand components have on structural transformation in African countries.
The IV-GMM methodology is applied on an unbalanced data set covering 54 countries for a period of 54 years. The results reveal that household consumption, public expenditures and the capital–labour ratio have a positive and significant impact on sectoral outputs, while urbanization has a negative and significant impact on it, and human capital accumulation, trade openness and investment have no significant impact on sectoral outputs that could constitute sectoral resource shift leading to structural transformation.
Overall, household and government consumption are found to have the largest impacts on the manufacturing sector, compared with other demand-related variables. Given the fact that the manufacturing sector plays a significant role in a country’s structural transformation process, the conclusion is that the household consumption expenditures play a significant role in stimulating resource allocation in Africa. Private and public expenditures can therefore play an important role in stimulating structural transformation in Africa. Policies that enable capital deepening and technology transfer that allows labour to be more productive should be enhanced among African countries, as they have important implications for structural transformation.
As a priority, policies that stimulate private consumption and investment are needed, such as policies to increase credit facilities to the private sector in particular, by reducing the cost of having access to these services would play a great role in this regard. Maintaining stability, financial inclusion and the fight against money laundering along with illicit financial flows could strengthen the financial sector and increase its ability to gain access to credit for the private sector. Measures to encourage private sector investment, such as those aimed at improving the business climate should be encouraged.
10 The composition of the industrial sector could be obscuring many issues in relation to the effect of demand on productivity in the sector.
23 FDI inflows can contribute to structural change through the modernization of production capacity; and by ensuring that funds are channelled to the countries’ productive sectors and FDI inflows into resource-rich sectors, such as those in extractives. Policies that will encourage FDI away from extractives activities and lower regulatory supply-side barriers for investors are required to increase investment spending towards achieving structural transformation.
Empirical and theoretical literature suggests that there has been a positive impact on education, urbanization and trade on structural transformation; however, the results highlighted in the present study do not show this positive and significant relationship for Africa. This could suggest a disconnection between Africa’s industrial and urban development processes. The structural transformation that is taking place in many African cities is falling short of its productive potential. The African industrial sectors fail to create broad-based jobs, develop functional value chains and support rural-urban linkages, which manifests in cities dominated by poverty, informality and inequality (ECA, 2017). Understanding the complexity and interplay of the various urban sectors, and managing the forces driving growth is fundamental in tackling all issues (social, environmental or economic) and is vital to reconnect urbanization and structural transformation through strategic policies. Among others, the quality and form of urban development must be considered at an early stage of the process to avoid severe economic, environmental and social problems in the long term.
The results from this study, however, reveal that even with continued demand pressures (due to increased urbanization and a rising middle-class population), which create continued stimulus for investment and consumption as well as the reallocation of resources throughout the sectors in accordance with the empirical results outlined in figure II, it fails to enhance productivity growth in Africa. Estimation results reveal an increase in the level of income and trade openness that have a positive impact on agriculture productivity growth, and urbanization that has a positive impact on productivity growth in the services sector. Nevertheless, there is a need to focus on policies that lead to an increase in manufacturing productivity. African countries could achieve this through value addition on the various raw commodities and products by focusing and directing investment into productive sectors.
In addition, structural transformation requires the absorption of labour into job-rich industrial sectors. To stimulate structural transformation, advantage should be taken of the demographic dividend, and reduce poverty. Policies should focus on supporting high growth sectors that could generate jobs and raise people’s income. Diversification of exports out of traditional exports, in particular natural resource-based products, is needed to take advantage of the effects of trade openness. Industrial development strategies based on the development of the relative advantages of the various countries will help to foster structural transformation.
Structural transformation, inclusive growth, coherent macroeconomic policies, innovative ways of financing and long-term development planning for structural transformation that are both sustainable and inclusive, should all be promoted in Africa.
24 Annex I
Variables, definitions and sources of data Table A1.
Variable information and definitions
Variables Description of variables
Description Sources Expected
sign Manufacturing value
added
Value added of the manufacturing sector as percentage of GDP
Penn World Table (PWT) version 9.0
Services value added Value added of the services sector as percentage of GDP
Penn World Table (PWT) version 9.0
Agriculture value added Value added of the agriculture sector as percentage of GDP
Penn World Table (PWT) version 9.0
Per capita GDP GDP per capita based on PPP. Data are in
constant 2011 international dollar World Bank (2017) (+)
Household consumption expenditure
The share of household final consumption expenditure as a percentage of GDP
Penn World Table (PWT) version
9.0 (+)
Government consumption expenditure
The share of general government final consumption expenditure as a percentage of GDP
Penn World Table (PWT) version
9.0 (+)
Capital–labour ratio Capital stock (at current PPPs)/employment (number of persons engaged)
Penn World Table (PWT) version
9.0 (+)
Investment Share of gross capital formation as a percentage of GDP
Penn World Table (PWT) version
9.0 (+)
Urbanization
Urban population (per cent of total population) refers to people living in urban areas as defined by national statistical offices
World Bank (2017) (+)
Education
Total enrolment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age
World Bank (2017) (+)
Level of democracy Average of Freedom House and Polity IV scores (0–10)
Marshall and Jaggers, 2002;
Teorell and others, 2011 (+) Natural resources
endowment Oil production in metric tons
Michael L Ross
https://dataverse.harvard.edu/datas et.xhtml?persistentId=doi:10.7910/
DVN/ZTPW0Y
(-)
Trade openness Ratio of the sum of exports and imports to GDP
(per cent) World Bank (2017) (+)
Life expectancy Life expectancy at birth, total (years), proxy for
health World Bank (2017) (+)
Aid flow Sum of commitments received from donors (including international organizations)
investment Net inflows (per cent of GDP) World Bank (2017) (+)
25
Note: The Prob>F is < 0.05, the null that the coefficients for all years are jointly equal to zero is rejected, therefore, time fixed effects are needed, the fixed effects regression should include time effects.
Table AII.2
Testing for heteroscedasticity
. xttest3: Modified Wald test for groupwise heteroscedasticity in fixed effect regression model
p< 0.5, heteroscedasticity; The null hypotheses of the test are rejected. The errors exhibit groupwise heteroscedasticity whether fit by fixed effects or by feasible GLS estimators.
Table AII.3
Testing for serial correlation
Wooldridge test for autocorrelation in panel data Table AII. 4 Note: The null hypothesis of no serial correlation is strongly rejected except for agriculture sector. The data for manufacturing and services have first-order autocorrelation.
Hausman test, Random of fixed effects?
The null is that the two estimation methods are both OK and that therefore they should yield coefficients that are “similar”. The alternative hypothesis is that the fixed effects estimation is OK, and the random effects estimation is not; if this is the case, then the expectation would be to see differences between the two sets of coefficients. The findings reveal that both estimations are found to be appropriate, however, further tests reveal that the results from the random effects estimation method are more robust, which are very close to the IV-GMM estimation results.
Manufacturing Services Agriculture
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
chi2(31) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 160.91
Prob>chi2 = 0.000
(V_b-V_B is not positive definite)
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
chi2(31) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 157.83
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
chi2(31) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 160.91
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
Annex III Robustness test
The robustness test uses random-effects feasible generalized least squares (FGLS) and ordinary least squares (OLS) methodologies.
Table AIII Robustness test
Dependent variable = VA manufacturing Dependent variable = VA services Dependent variable = VA agriculture Random effect-FGLS OLS Random
effect-FGLS
27 Annex IV
Regression results
Table AIV
Regression results for the productivity equation
Manufacturing Services Agriculture (Cragg-Donald Wald F statistic): 23.370
Underidentification test (Cragg-Donald Wald F statistic): 23.370
Underidentification test
Included instruments: Household consumption expenditure, General government final consumption expenditure, General government final consumption expenditure, Gross fixed capital formation, Capital-labour ratio, Trade openness, Urbanization, Education, Government policy
Excluded instruments: Life expectancy, Oil production, Aid flow, Democracy, Foreign direct investment N.B: Dropped for collinear: GovPol1 GovPol2 GovPol3
Note: Standard errors in parentheses. Hansen J statistic overidentification test is asymptotically chi-sq.
distributed under the null of exogeneity, with p-values reported in brackets. * = Significant at the below 1 per cent; ** = Significant at below 5 per cent; *** = Significant at below 10 per cent level.
28
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