Model estimation and empirical results

B. Key control variables

V. Model estimation and empirical results

It is important to note that the choice of the methodology is conditioned on the availability of data. It would have been preferable to calculate and employ the structural transformation index, as in McMillan (2014); however, due to a lack of country-level sectoral data, this was not possible. To assess the effect that demand factors have on Africa’s structural transformation, the following three equations were estimated:

𝑉𝐴𝑚𝑎𝑛𝑢𝑓𝑎𝑐_𝑖𝑡 = 𝛿(𝐺𝐷𝑃𝑝𝑝𝑐𝑜𝑛_𝑖𝑡+ 𝐻𝐶_𝑖𝑡 + 𝐺𝐶_𝑖𝑡+ 𝐺𝐹𝐶𝐹_𝑖𝑡+ 𝐺𝑜𝑣𝑃𝑜𝑙𝑖𝑐𝑦_𝑖𝑡+ 𝑇𝑟𝑎𝑑𝑒) + 𝛽 (𝑈𝑟𝑏_𝑖𝑡+ 𝐶𝑎𝑝𝐿𝑎𝑏_{𝑅𝑎𝑡𝑖𝑜}_𝑖𝑡+ 𝐸𝑑𝑢𝑐_𝑖𝑡) + νi + 𝜇_𝑡 + εit...(2) 𝑉𝐴𝑆𝑒𝑟𝑣𝑖_𝑖𝑡 = 𝛿(𝐺𝐷𝑃𝑝𝑝𝑐𝑜𝑛_𝑖𝑡+ 𝐻𝐶_𝑖𝑡+ 𝐺𝐶_𝑖𝑡+ 𝐺𝐹𝐶𝐹_𝑖𝑡+ 𝐺𝑜𝑣𝑃𝑜𝑙𝑖𝑐𝑦_𝑖𝑡+ 𝑇𝑟𝑎𝑑𝑒) + 𝛽 (𝑈𝑟𝑏_𝑖𝑡+ 𝐶𝑎𝑝𝐿𝑎𝑏_{𝑅𝑎𝑡𝑖𝑜}_𝑖𝑡+ 𝐸𝑑𝑢𝑐_𝑖𝑡) + νi + 𝜇_𝑡+

εit...(3)

𝑉𝐴𝑎𝑔𝑟𝑖𝑐𝑢𝑙_𝑖𝑡 = 𝛿(𝐺𝐷𝑃𝑝𝑝𝑐𝑜𝑛_𝑖𝑡+ 𝐻𝐶_𝑖𝑡 + 𝐺𝐶_𝑖𝑡+ 𝐺𝐹𝐶𝐹_𝑖𝑡+ 𝐺𝑜𝑣𝑃𝑜𝑙𝑖𝑐𝑦_𝑖𝑡+ 𝑇𝑟𝑎𝑑𝑒) + 𝛽 (𝑈𝑟𝑏_𝑖𝑡+ 𝐶𝑎𝑝𝐿𝑎𝑏_{𝑅𝑎𝑡𝑖𝑜}_𝑖𝑡+ 𝐸𝑑𝑢𝑐_𝑖𝑡) + νi + 𝜇_𝑡 + εit...(4)

The abbreviations used for the equations signify the following: VAmanufactit is the value added of the manufacturing sector, (as a percentage of GDP) in country i for year t; VAagricultit

is the value added of the agriculture sector (as a percentage of GDP); VAserviit is the value added of the services sectors (as a percentage of GDP); 𝐺𝐷𝑃𝑝𝑝𝑐𝑜𝑛 is the GDP per capita (purchasing power parity (PPP), constant 2011); HC is the share of household consumption expenditure (as a percentage of GDP); GC is the share of general government final consumption expenditure (as a percentage of GDP); GFCG is the share of gross fixed capital formation (as a percentage of GDP); GovPolicy is a proxy for government policy; Trade openness is the ratio of the sum of exports and imports to GDP (percentage); Urb is the rate of urbanization (urban population as a percentage of total population); CapLabratio is the capital–

labour ratio, that is, capital stock (at current PPPs) divided by the number of employed persons;

Educ is the education enrolment ratio, secondary (net percentage); and 𝛿 and β are the coefficients of interest to be estimated. Information on the variables, definitions and sources of data are provided in annex I, table AI.

The country’s fixed effects (νi) control for all time-invariant differences between countries, and year fixed effects (μt) control for factors that may affect countries’ growth and structural transformation; and εit is the error term. It is important to note that as the effect of education on structural transformation may take time to materialize, the time lag will likely differ depending on the context, the country and the type of reform. Experimenting with various time lag lengths when estimating the effects of education quality, resulted in the five-year lag on the education variable being the most appropriate to use. Similarly, in the choosing of a one-year lag on the GDP per capita variable, given that the effects of any changes will take time to materialize. In the second part of the analysis, the sectoral value added for equations (2)–(4) was replaced by sectoral productivity growth as a measure of structural transformation.

15 In carrying out the initial analysis and tests, the Wooldridge (2002) test for autocorrelation showed the presence of first order autocorrelation among some variables involved in the analysis. In addition, the Breusch and Pagan (1979) and the White Tests (for the Pooled OLS estimation), and the Modified Wald Test for group-wise heteroscedasticity (for the fixed effect regression model), revealed the presence of heteroscedasticity (see annex II, tables AII.1–AII.4 for test results) (Wald, 1940; White, 1980a; White, 1980b). Accordingly, the IV-GMM regression method that allows for correcting unobserved fixed effects, heteroscedasticity, serial correlation, endogeneity and issues of reverse causality and simultaneity bias, was deemed appropriate for the analysis. More specifically, the two-step GMM that presents some efficiency gains over the traditional estimators derived from using the optimal weighting matrix, the over-identifying restrictions of the model, and the relaxation of the independent and identical distribution (iid) assumption (Baum, Schaffer and Stillman, 2007) is used.⁵ The data for the analysis applies to a period of 54 years (1960–2014) covering 54 countries.⁶ Equation (1)⁷ was therefore estimated using a IV-GMM estimation method, using the panel equivalent of the Newey-West variance covariance matrix.⁸ Standard econometric tests for instrument suitability were also carried out (see the table on regression results below). The Wald test indicated the inclusion of the time-specific fixed effects in the model.

A. Empirical results

All the models present a good quality of adjustment reflected in a higher adjusted coefficient of determination (𝑅̅2), and the F-statistic and the relevant p-values, indicate that all the models are well specified, that is to say, the explanatory variables relate statistically and significantly to the structural transformation dependent variables.

The results of the Hansen (J-test) for over-identification also show that the chosen instruments are valid. The Hansen J statistic, tests the hypothesis that the variables are jointly exogenous. The Chi-square p-values of the Hansen test (which are all greater than 10 per cent) fail to reject the hypothesis of joint exogeneity of the instrumental variables. In addition, Cragg-Donald Wald statistic (Cragg and Cragg-Donald, 1993) and Kleibergen and Paap (2006) Wald F-statistic at the bottom of the tables, indicate very strong instrumentation. As heteroscedasticity-robust standard errors are used in all regressions, the use of the Kleibergen-Paap statistic is more appropriate; it generalizes the Cragg-Donald statistic to the case of non-iid errors, allowing for heteroscedasticity, autocorrelation and/or cluster robust statistics Baum, Schaffer and Stillman (2007). In the special case of a single endogenous regressor considered in this section, though, the Kleibergen-Paap and Cragg-Donald Wald statistics are, respectively, simply the standard robust and non-robust first-stage F statistics.

5 Appropriate macroeconomic and country-specific indicators are derived from the literature and instrumented.

6 Some countries do not have the full set of data for the entire period (for example, the data for South Sudan since 2006). Using an IV-GMM allows to handle this unbalanced dataset.

7 𝒚𝒊𝒕= 𝜹𝑿𝒊𝒕+ 𝜷𝒁𝒊𝒕+ 𝝁𝒊𝒕 …………..(1) (also provided in the first paragraph of the present section).

8 The panel data version of the estimation command “ivreg2” with the “gmm2 cluster (iid)” options from the statistical software Stata 14.

16 According to Stock and Yogo (2005), the Cragg-Donald F statistic must exceed five if statisticians are to be confident at the 5 per cent level that the bias to the coefficient estimate on the structural transformation variable is less than 30 per cent of the GMM bias. Critical values have not been tabulated for the Kleibergen-Paap rk statistic as the specific thresholds depend on the type of violation of the iid assumption, which invariably differ widely across applications. Nevertheless, standard F statistics well below unity are unlikely to exceed even the most generously low thresholds

The table on regression results below shows the estimates for equations (2)-(4). The coefficients of the GDP per capita are positive and statistically significant in the three models.

These results are also economically (quantitatively) significant given the elasticity estimates of 1.01, 1.14 and 0.98 for manufacturing, services and agriculture sectors, respectively, derived from the IV-GMM estimation results as expected theoretically. For structural transformation to take place, one should first and foremost observe economic growth. The interesting part, regarding the present study, is how this growth has had an impact on the reallocation of resources in the three sectors.

Among the three sectors, income has the highest impact on services (explained by the growing importance of the service sector in Africa, previously mentioned, which contributes to approximately 50 per cent of GDP). The results show that an increase in GDP per capita of 1 per cent will boost the value added of the manufacturing sector by 1 per cent. This means that the relationship between economic growth and structural transformation in Africa is very strong. The result also indicates that growth has enhanced the service sector (1.1 per cent) more, in relative terms, than the manufacturing (1.0 per cent) and agricultural sectors (0.9 per cent), indicating that relatively more resources have been shifted towards the service sector.

17 Table

Regression results for the structural transformation equation

Manufacturing Services Agriculture

Household consumption 1.342*

(0.2963)

0.859*

(0.1592)

0.469*

(0.1429)

Government consumption 0.699*

(0.2231) (Kleibergen-Paap rk LM statistic): 17.227

Chi-sq (5) P-val = 0.0041 (Kleibergen-Paap rk LM statistic): 17.227

Chi-sq (5) P-val = 0.0041 Hansen J statistic (overidentification

test of all instruments): 6.236 Chi-sq (4) P-val = 0.1822

Instrumented: Per capita gross domestic product

Included instruments: Household consumption expenditure, general government final consumption expenditure, gross fixed capital formation, capital-labour ratio, trade openness, urbanization, education and government policy.

Excluded instruments: Life expectancy, oil production, aid flow democracy, FDI.

Note: In the service value-added equation, the null hypothesis of the J Hansen test of over-identification rejects its null, casting doubt on the choice of instruments used to explain the service sector. These instruments explain the two other services.

Note: Standard errors in parentheses. Hansen J statistic over-identification 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.

Household consumption and government consumption have the highest impact on the manufacturing sector, compared with the other variables. While household consumption

18 expenditure has a positive and significant impact on all three categories, it has the highest impact on the manufacturing sector, followed by the service sector. An increase of household consumption of 1 per cent increases the manufacturing sector value added by 1.34 per cent, while it increases the services and agriculture sectors by 0.86 per cent and 0.47 per cent, respectively. As income rises, a shift in the composition of consumption expenditure is observed. Households spend rising proportions of their rising incomes on manufactured goods more than services. This demand shift towards manufactured goods and services leads to changes in the price of output and productivity in the manufacturing sector relative to the agriculture sector, which would trigger the reallocation of labour between the sectors. The shift in the relative output prices of the sectors of origin and destination brought about by a shift in the composition of consumption spending or change in their relative cost of production, translates into intersectoral differences in marginal productivity; hence, the reward to labour, which in turn would drive the reallocation of labour between the sectors, generating structural change. In principle, either one of these two phenomena could be sufficient to trigger resource allocation, even in the absence of the other (Echevarria, 1997; Ngai and Pissaradi, 2007);

however, it is more likely that, in practice, the two forces may reinforce each other (Beura and Kabuski, 2009; Duarte and Restuccia, 2010).

The household consumption expenditures have, therefore, the most important role in stimulating structural transformation and long-term sustainable growth. With the African urban and middle class projected to experience a sizeable growth, as mentioned previously, the demand for consumer goods and services would increase and could be fully harnessed to further enhance the continent’s economic growth. The middle-class population will not only demand access to more goods and services, they will also require higher quality services beyond what is currently available to them. Based on 2008 spending by the middle class (estimated at $680 billion and representing nearly a quarter of Africa’s GDP), the African Development Bank projected that, if consumption grows at the same rate, expenditure would reach $2.2 trillion by 2030 and Africa would contribute approximately 3 per cent of global consumption (AfDB, 2011). Accordingly, this presents an investment opportunity for African countries to meet the growing consumer demand, thereby creating jobs for the rising middle class leading to income growth and further increased consumption.

Public expenditure has a positive and significant impact on the manufacturing, services and agriculture sectors. An increase of 1 per cent in public expenditure leads to an increase of 0.7 per cent and 0.28 per cent in manufacturing and service sectors, respectively. The effect it has on the agriculture sector is negligible, as an increase of 1 per cent in public expenditures leads to an increase of only 0.003 per cent of the value added of the agriculture sector. The effect that it has on the manufacturing sector is marginal, possibly because the government expenditure, strategies and priorities of most African countries may not have had a direct impact or have targeted the development of the manufacturing sector, suggesting the existence of government policies that do not directly talk to each other (ECA, 2017).

If, in theory, educational attainment can stimulate structural transformation and government expenses in education are increasing, the results of this study show that investment in education in Africa is still too low to make a big difference on the structural change. Two main reasons explain why the effect that structural transformation has on education is not significant. First, a large part of investment in education in Africa goes to primary education;

while the much needed highly skilled workers require the development of secondary and tertiary education. Higher education enrolment, however, is still low in Africa. For example, secondary schools can accommodate only 36 per cent of qualified secondary school students (UNESCO, World Bank, 2015). In addition, the increasing investment in education is not a

19 sufficient condition to achieve structural transformation, but it should be accompanied by an improvement in the skills that are associated with high levels of value-added activities (Marouani and Mouelhi, 2016). Accordingly, improving education quality at all levels is imperative for Africa’s structural transformation and growth.

The capital–labour ratio is used to capture technological developments because of innovation and capital deepening that leads to increases in efficiency and productivity. Policies and technological change, which influence the capital–labour ratio during the take-off stages of economic growth, therefore, have important implications for structural transformation. The result confirms this trend. Indeed, an increase of 1 per cent in the capital–labour ratio leads to increases in value added of the manufacturing and services sectors by 0.26 per cent and 0.019 per cent, respectively, but decreases value added of the agriculture sector. This could be due to the lack of modern farming methods in Africa, such as the use of mechanized ways of farming, with the largest proportion of the population employed in the sector.

While investment should theoretically yield a positive impact on structural transformation, its effect, as identified in this study, is insignificant in Africa, which seems to imply that the current level of investment in Africa is below the threshold level required to induce a process of structural change. It could also imply that the investments made in the various sectors are not going directly to the countries’ productive sectors. In turn, that could have a significant impact on productivity and structural change in these economies.

Urbanization has a negative impact on the manufacturing sector and a positive impact on services. An increase of 1 per cent in the rate of urbanization leads to a decrease of 0.53 per cent in the value added of manufacturing sectors and to an increase of 0.22 per cent in services.

This result could be interpreted as supporting the notion that urbanization in Africa has been associated with the growth of low-productivity service sectors as labour migrates from rural to urban areas. Studies have shown that Africa’s urbanization is without industrialization, as countries are urbanizing rapidly without large shifts of economic activity towards manufacturing and modern services (Bouare, 2006; Ravaillon, 2002; World Bank, 2000;

Gollin, Jedwab and Vollrath, 2016).

Studies have also shown that, as Africa’s growth rebounded in the 1990s, instead of expanding industrial activities, labour relocated to the service sector. It has been argued that developing countries, especially those in Africa, are moving into the service sector too early in their development trajectory, and this “premature de-industrialization” may damage future growth prospects by depriving economies of the benefits of industrialization for sustained growth and economic convergence. In addition, if people are moving out of farming into service jobs, the sectors they move into are likely to see productivity growth slowed by this influx of cheap labour as stipulated by the productivity growth results outlined in figure II. A large portion of the urban labour force in Africa remains thus trapped in low-productivity informal service activities (AfDB, 2016).

The effects of trade openness are only significant for the agriculture sector, which is affected negatively. An increase of 1 per cent of trade openness leads to a decrease of 0.67 per cent of value added in the agriculture sector. This could be attributed to the fact that most of the industries before trade liberalization were agro-based. Most of these firms and industries closed down or reduced their production capacities due to competitive pressures from cheap product imports into Africa, negatively affecting the manufacturing sector in most countries.

Again, though Africa has vast agricultural potential, population growth, low and stagnating agricultural productivity, weak institutions and policy distortions, have led to Africa’s food–

20 trade deficit since the mid-1970s, and the continent has become a net importer of food and agricultural products (FAO, 2012).

The impact of natural resource endowment and FDI was also looked at (in order to avoid autocorrelation problems), and though the results have not been reported in the table on regression results provided above, they were, however, consistent with the results presented in Annex III, table AIII. The result showed that the impact of natural resources endowment on manufacturing value added is negative and significant, and the value of its coefficient is almost negligible. This supports the notion that natural resource dependence without value addition to its products negatively affects employment prospects and revenue. The same trend is observed for aid flow, which has a positive but negligible impact, which might suggest that a greater percentage of aid is spent on non-productive activities or sectors, that do not have a significant contribution to Africa’s structural transformation.

Regarding FDI flows, its effect on structural transformation is negative, as an increase of 1 per cent in FDI leads to a decrease of 0.13 per cent in manufacturing value added. This result could be supporting the narrative that most FDI funds are channelled into countries’ extractive sectors, with minimal attention given to the manufacturing sector and therefore hindering the diversification and employment generation efforts made in these countries. Studies have also shown that FDI could have “crowded out” domestic investment and suppressed local entrepreneurship in some of these countries (Bruton, 1998). This negative effect could also be due to deteriorations in the countries’ balance of payments because of increased imports, profit repatriation and reduced tax revenue caused by transfer-pricing practices, tax allowances and other financial incentives granted to foreign firms.

B. Robustness analysis

To test the robustness of the results of the study, with regard to the composition of the sample, the relationship between variables capturing the structural transformation process and the components of demand, the model is estimated with two other estimation techniques, a random-effects generalized least squares (GLS) regression model and an ordinary least square (OLS) method.

The Hausman test on the full model yielded results that conclusively accepts a random-effects GLS regression as a valid estimation model (see annex II, table AII. 4). In addition to that, a simple OLS was performed. Table AIII shows the yielded results of the main variables of interest found to be consistent with the results from the GMM model estimation.

Similarly, expansionary private and public expenditures have a significant and positive effect on the manufacturing, services and agriculture sectors, with a relatively higher effect on the manufacturing sector than the two other sectors. Notwithstanding its low level of structural transformation, the results signify the importance of policies aimed at promoting demand factors, such as private and public investment through enhancing the shift of resources towards the manufacturing sector. Technological developments also have a positive impact on structural transformation. As is the case with results from the IV-GMM estimates, the results also show a negative correlation between structural transformation and FDI, urbanization and natural resources endowments. This highlights the need for industrialization,

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