GROWTH INTENSITY OF EMPLOYMENT IN AFRICA: A PANEL DATA APPROACH KAMGNIA, Dia B.1 Abstract:
The incidence of poverty has increased in the majority of African countries over the 1990, while a pro-employment growth is strongly believed to be an efficient means for fighting against poverty. Thus a quantitative analysis of the growth-employment nexus in Africa appeared necessary. More specifically, the growth intensity of employment is assessed based on some panel data models of employment, using annual data of 39 African countries, over the period 1995-2000. In the specific case of the dynamic panel model, a Fixed Effects estimation suggests that economic growth affects employment but with lags (up to 2 years). However GMM estimations indicate that the short run effects of GDP remain weak while credit to the private sector and FDI bear some substitution relationship with each other.
JEL classification: C33, J21, N17, O55.
Key words: Growth intensity; employment persistence; Panel model; Fixed Effects;
GMM estimations.
1. Introduction
Despite the believed virtue of a pro-employment growth in the fight against poverty, employment and economic growth do not seem to be getting along in Africa since the 1980s. Indeed, if some African countries are characterized by an evident synchronization of the evolutions of real GDP and growth in employment, either positive (Benin, Burkina-Faso, Cape Verde, Côte d'Ivoire, Senegal, Equatorial Guinea, Madagascar, Mozambique, South Africa, Namibia) or negative (Burundi, Kenya, Mauritius, Rwanda, and Zimbabwe), such synchronization is rather ambiguous in many more countries (Cameroon, Bostwana and Swaziland). Of course, the extent of lack of a co-movement in employment and growth in some countries has been lessened during the period of economic recovery (since 1995). But, there still are countries for which the linkage is not evident to seize: in some cases, employment and GDP evolve in opposite directions; in others, employment stabilizes when real GDP continues to grow. Two factors are thought to account for the observe ambiguity: the quality of the data on the one hand, and the interplay of economic factors on the other hand.
As pointed out by Saget (2000), the absence of a systematic relation between employment and growth in some countries can come out of the bad quality of the data on GDP. Indeed, if the informal sector grew out of proportion over the 1980s, its contribution to the economy is more often omitted from the national accounts in a number of African countries. Hence, employment and GDP could be presenting opposite dynamics without clear evidence popping out. The economic explanation is based on the thinking that the lack of synchronization between employment and growth in some countries (although weaker since 1995) could be an evidence on that there
1 University of Yaounde II P.O.Box 1365, Yaounde, Cameroon; E-Mail:
[email protected]. I am grateful to the African Economic Research Consortium, AERC, and the African Development Bank for the financial support of the background study. I also thank Ebeke Christian for research assistance
might be a good number of factors other than growth that determine employment, notably in Africa.
In the specific case of growth, two main questions could be thought of: i) have the authors always considered growth as a factor that is favourable to employment? ii) Are the points of views converging to the acceptance of employment as a determinant of growth? The answer to these questions gave rise to a rather abundant literature (Gordon, 1984; Lee, 2000; Harris and Silverstone, 2001; Virén, 2001; Brauninger and Pannenberg's, 2002), all elaborating around the Okun's (1962) law, which emphasizes the relationship between changes in unemployment and output. Harris and Silverstone (2001) specifically note that despite the theoretical and empirical usefulness of Okun’s law, most specifications assume a symmetric relationship (output expansions and contractions have the same absolute effect on unemployment); what might not be always appropriate. On that respect, Lee (2000) and Virén (2001) use contemporary econometric techniques to evaluate the asymmetry in Okun’s law. While Virén (2001) analyses the effect of the changes in output on the changes in unemployment, Lee considers the opposite relation.
Ericksson (1997) searched to know if production and employment could be simultaneously increased. More specifically he shows based on a theoretical model, that if one should impulse economic growth, then it should be through indirect measures such as modifications in the capital tax rate, or some unemployment - benefits mechanisms, or consumer preferences. So doing, growth and employment would be simultaneously stimulated. But, there would be a trade-off between employment and growth if one considers direct changes in the rate of growth; that is if one considers exogenous changes in the growth rate. Following Brauninger and Pannenberg (2002), a trade-off between economic growth and employment could hold given that the former is directly linked to factors such as R&D subsidies. In the specific case of Africa, Guisan, Aguayo and Exposito (2001 a,b), and Guisan and Neira (2006), among other authors, point to the role of education for sustaining the nexus of economic growth and employment.
But, an equally important issue to highlight is how quickly employment responds to changes in economic growth, given its persistence throughout the years. The general objective of the current paper therefore is to investigate a dynamic panel model in analyzing the growth intensity of employment in Africa over the 1990s. More specifically, we first define an autoregressive panel model as a framework for the analysis of the growth elasticity of employment in Africa, and then discuss the results on the different scenarios of estimation of the intensity of employment with respect to growth.
2. An autoregressive panel model of employment in Africa.
The primary concern in our modeling process is the sustainability of job creation, as expressed by the degree of persistence of employment over the years. Hence, assuming that all the dynamics could be captured by the first lag, then a AR(1) would be specified as:
1
1 ,
,
it i it
t
i Lemp
Lemp (1)
163 with:
itLempi,t1,...,Lempi,0, i
0E (2)
Country specificities (political regimes, location, colonial ties, to name but a few of these) are captured in the term αi. Lempitis the log of workforce for country i in year t.
At this point, a suitable analysis is that of a fixed effects model; given the implicit correlation between the lagged dependent variable and the country heterogeneity.
Unfortunately, the fixed effects estimator happens to be biased and inconsistent forn ,but fixed T. Indeed for
n i
T
t it i
n i
T
t it i it i
n i
T
t it i
n i
T
t it i it i
FE
p m Le nT Lemp
p m Le Lemp
p m Le Lemp
p m Le Lemp
p m Le Lemp
1 1
2 1 , 1
,
1 1 , 1 , 1
1 1
2 1 , 1
,
1 1 , 1 , 1
1 ˆ
(3)
Nickell (1981)1, as well as Hsiao (1986)1 show that
1 0
1
lim 1 2 2
2
1 1 , 1 ,1
n T
i T
t it i it i
n
T T p T
m Le nT Lemp
p (4)
Relation (4) converges to 0, thus establishes the consistency of the fixed effect estimator, only under the conditions thatT ,along with n .
Basically, the problem stems from the fact that the within transformed lagged dependent variable is correlated with the within transformed error. A solution then is to start by differencing away the individual effect (Verbeek, 2000). That is:
, 1 , 2
, 1
, 2,...,T1
,
Lemp Lemp Lemp t
Lempit it it it it it (5)
Unfortunately,Lempi,t1 and i,t1are, by definition, correlated even ifT ; what necessitates the use of an instrumental variables estimation approach. The question then lies in the selection of the instruments.
Following Anderson and Hsiao (1981), an appropriate instrument isLempi, t 2, given that it is correlated withLempi,t1-Lempi,t2 but not with i,t1. Furthermore, admitting the assumption of absence of autocorrelation ofit, the Instrumental Variable estimator is expressed as:
n
i T
t it it it
n i
T
t it it it
IV Lemp Lemp Lemp
Lemp Lemp
Lemp
1 2 , 2 , 1 , 1
1 2 , 2 , 1
ˆ (6)
ButLempi,t2 -Lempi,t3 is as well correlated withLempi,t1-Lempi,t2 but not with i,t1; thus equally qualifies as a viable instrument. UsingLempi,t2 -Lempi,t3 , these authors’ alternative estimator is expressed as:
n
i T
t it it it it
n i
T
t it it it it
IV Lemp Lemp Lemp Lemp
Lemp Lemp
Lemp Lemp
1 2 , 2 , 3 , 1 , 1
1 2 , 2 , 3 , 1
) 2
ˆ(
(7)
While the consistency of both estimators is guaranteed by the assumption that the errors do not exhibit some autocorrelation, the second estimator, (7), requires an additional lag to construct the instrument. Following Verbeek (2000), a method of moments approach would not only unify the two estimators but also eliminate the disadvantages of reduced sample sizes while dealing with the problem of autocorrelation of the errors. Such development constitutes the thrust of Arellano and Bond (1991)’s work on the efficiency of the instrumental estimators under a number of moment conditions. Those moment conditions are all exploited in a Generalized Moment Method (GMM) framework, in the presence of strictly exogenous variables.
Hence, the model needs to be expanded to include such variables.
In effect, many more exogenous factors do condition the evolution of employment in Africa. For instance, the external demand could be taken as a substitute for domestic demand. Indeed, it is often stated that the low level of domestic demand occurs as a result of declining job opportunities in a given economy (Saget, 2000). As well, a strong international demand could be an important source of job creation. More specifically, openness could offer opportunities for expanding domestic employment, while Foreign Direct Investments (FDIs) constitute potential sources of job creation.
That is especially the case when FDIs comprise a good deal of productive investments.
Another important source of employment creation is the financial system, as clearly pointed out in relevant economic literature. On that ground, specific channels are credits made available to the private sector. More specifically, if the efficiency of financial resources could be guaranteed, then one can be sure that the financial system in any economy would contribute significantly to the development of productive capacities in general and to employment creation in particular. Defining the set of those exogenous factors by a vector z, an alternative specification of our model is:
it i
it i it
t t i
i Lemp z
Lemp
it
1 ,
x
(8)
The estimation approach remains that of first differencing away the individual effect and then resorting to an instrumental estimation of the differenced equation.
Viable instruments in that case are zit,zi,t1,zi,t2,Lempi,t2,Lempi,t3(Wooldridge, 2002).
But, we would not omit real GDP growth, the explanatory factor of our primary interest. Of course, the search for the determinants of employment could take us into questioning the complexity of growth in its conceptualization. But economic growth, as considered in the current analysis, is nothing else than the rate of change in real GDP. GDP can be perceived either from a supply side, or on a demand stand. On a supply side, an expansion in GDP would be interpreted as an intensification of the productive capacities, an increase in value added from all the sectors of the economy.
165
From a demand perspective, the expansion in GDP would be considered as an increase in the domestic demand for local goods. In Africa, the imbalance between capital accumulation and technological progress had stood for an important constraint for a sustainable intensification of productive capacities; with well documented effects on job creation. On the demand side, it seems that the absence of a clear and systematic relation between employment and economic growth in Africa is the consequence of a hysteretic effect of the recession of the 1980s which tended to crowd out the positive impact of the economic recovery which is underway since the mid 1990s.
Despite the economic recovery underway on the African continent since 1995, the observed growth does not seem to be correcting for the still widespread low level of domestic demand. Indeed intuitively in the early stages of a recovery, one would expect firms to be hesitant to hire more workers until they are convinced that the recovery will be sustained. Thus, though economic growth will have an immediate impact on employment, some of its effects may not be felt for a period of time. Hence, a distributed lag model might be suitable.
In sum, we propose to evaluate an autoregressive distributed lag panel model of employment in Africa, expressed as:
it i q it q
q l
it l
l
k it k
k j
it j
j p
it p
i it
open fodi
cred Lgdp
Lemp lemp
(9)
where: Lemp is the logarithm of total employment;Lgdp is the logarithm of Real GDP;
open is the degree of openness; fodi is the ratio of Foreign Direct Investments to GDP; cred is the ratio of credit to the private sector over GDP.
Moreover, we test the effects of the selected factors on the evolution in employment in Africa since the mid 1990s, using a sample of countries for which data on employment as well as growth are available.
The estimation of the specified models is done using annual data for 39 African countries2, over the period 1995-2000. Employment data are those of the International Institute for Applied Systems Analysis (IIASA3). Data on GDP, exports and imports (in 2000 US$), rate of credit to the private sector, contribution of the sectors to GDP, all are obtained from the World Bank Africa Database of 2005.
2 The 39 countries which are considered in the sample are: South Africa, Angola, Benin, Botswana, Burkina-Faso, Burundi, Cameroon, Cape Verde, the Comoro Islands, Congo, Côte d'Ivoire, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Equatorial Guinea, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Swaziland, Tanzania, Chad, Togo, Uganda, Zambia and Zimbabwe.
3 the data base of IIASA can be checked on its website http://www.iiasa.ac.at/Research/ECS/data_am
3. Discussion of the results
Two scenarios of estimation for the growth elasticity of employment are looked at: i) the growth intensity of employment in the face of financial conditions and resource inflows, and ii) the growth intensity with lag effects. Les results are discussed along with the characteristics of the key variables of the model.
Growth intensity of employment in the face of financial conditions and external resource inflows: The first set of estimations is a FE of the Saget (2000) type of model. The key results are as shown in Table 1.
Table 1: Employment intensity with openness, financial conditions and external resource inflows
Regressors Coefficient t-Statistic α-Probabilities
lnGDP 0.36 16.14 0.0000
Foreign Direct Investment 0.27 6.95 0.0000
Openness -0.08 -2.95 0.0036
Credit to the Private Sector 0.08 3.12 0.0021
Total panel 234
Source: Constructed by the author
The coefficients are all significant and have the expected signs in the majority of the cases. But, the elasticity of employment with respect to growth is only 0.36 percent.
To that respect, although Demeke et al. (2003) admit that the growth elasticity of employment is expected to decrease gradually over the years as a country grows and its labour endowment decreases, these authors think that the fall in the level of the growth elasticity of employment in a number of African countries should not be interpreted as a sign of maturity of these economies. Rather, the observed reduced level should be attributed to the low reliability in the data collected on employment: those data barely distinguish between transient and permanent unemployment.
Concerning the contribution of FDIs, the UNCTAD’s report (2006) indicates that foreign capital continued to flow in LDCs, reaching record levels since 2000. Of those increases, FDIs accounted for 16 per cent, while being focused on resource-rich oil and mineral economies. The effect of credits to the private sector was rather marginal, although significantly positive. Indeed, domestic capital formation and use remained weak throughout the continent, and the trends are not any better since 2000. If gross domestic saving increased in countries such as Senegal, Chad, Central African Republic and Zambia, it stagnates in Ethiopia, while decreasing in Angola, Burkina Faso, Benin, to become more negative in Eritrea, Cape Verde, and Sierra Leone. A sustainable impact of the financial sector would require a reactivation of its activities.
As regards the degree of openness, it significantly, but negatively explains the variations of the workforce in Africa since the mid 1990s; what might be contradicting the growing importance of trade on the continent since the second half of the 1990s.
Indeed, improvement in growth appeared to be associated with a significant increase in the share of the continent’s exports in the world exports (Figure 1), and following the UNCTAD (2006), only a few of the African LDCs did not participate in the increase in the merchandise exports of Africa.
167
Figure 1: Growth and trade in Africa, 1990-2004
-35 -30 -25 -20 -15 -10 -5 0 5 10 15
1990-95 1995-00 2000-04
p e r i od s
Real GDP Growt h Perc ent age change in t he shar e of export s
Source: Kamgnia Dia (2006)
Between 2002 and 2003, the value of exports decreased in nominal terms in Central African Republic, the Gambia, Guinea, Mauritania and Somalia; and between 2003 and 2004, Cape Verde, Eritrea, Liberia, and Malawi experienced a decline in their exports. However, Countries such as Angola, Equatorial Guinea, Senegal, and Yemen were among the 10 best-performing LDCs during the two periods in terms of the nominal value of exports. Senegal was among the countries whose good performance in merchandise trade was driven by exports of manufactured goods; for a number of the other countries, it is driven by oil exports. We would react to such a counterintuitive result by noting that the majority of African countries, notably the non-oil-exporting countries still rely heavily on imports, such that their trade balances are negative most of the time. Moreover these countries, being net food importers for the majority of them, tend to be vulnerable to swings in the prices of food items.
Hence, one would expect the combination of rising food and fuel prices to have a marked negative impact on their trade balance, thus to translate into a negative effect of openness.
Employment intensity and lags in growth: To the question to know how quickly employment responds to changes in economic growth, we could reply that the data at our disposal allow us to indicate that a two years adjustment could be necessary (Table 2). Globally, it could be said that economic growth has a positive effect on labor force in Africa, although such an effect still remains weak. More importantly, the magnitude of the effect first increases significantly over the one-year lag, then decreases to become negative and non significant after a two-year lags, notably when financial conditions and external resource inflows are accounted for (Table 2.b). Even the autoregressive distributed lag format of the model seems to be conveying interesting information, by allowing determine a long-run multiplier effect of 0.25. That is, a 1 percent increase in output would result in 0.25 percent increase in labor force; as compared to the 0.01 percent instantaneous effect.
Table 2: Effects of lags in growth 2a. Pure distributed lags of GDP
Regressors Coefficient t-Statistic α-Probabilities
lnGDP 0.04 0.759 0.449
lnGDP(-1) 0.18 3.161 0.002
lnGDP(-2) 0.08 1.716 0.088
lnGDP(-3) -0.01 -0.387 0.699
2b. A distributed lags of GDP comforted by financial conditions and external resource inflows.
Regressors Coefficient t-Statistic α-Probabilities
lnGDP 0.18 3.114 0.002
lnGDP(-1) 0.12 2.185 0.030
lnGDP(-2) 0.10 2.274 0.024
lnGDP(-3) -0.04 -1.185 0.237
Credit to the Private Sector
0.06 2.368 0.019
Openness -0.05 -1.780 0.077
Foreign Direct Investment 0.26 6.691 0.000 2c. Appreciation of long term effects
Regressors Coefficient t-Statistic α-
Probabilities
lnGDP 0.01 0.407 0.684
lnGDP(-1) 0.02 1.823 0.070
lnGDP(-2) -0.02 -2.845 0.005
lnEmployment (-1) 0.96 58.857 0.000
25 . 96 0
. 0 1
02 . 0 02 . 0 01 . multiplier 0 run
Long
; 01 . 0 multiplier Impact
Total panel (balanced) 234
Source: Constructed by the author
Unfortunately over the decade 1990-2000, the average rate of annual growth of the real GDP exceeded its value of 1980-1990 only in two regions on five, namely in West Africa and in Southern Africa (Figure 2). Central Africa registered the weakest performance with a rate of 0.42 percent over this period 1990-2000; what could be explained by the political instability that plagues a great part of the region (for instance, Democratic Republic of the Congo, the Central African Republic, the Republic of Congo).
169
Figure 2 : Real GDP growth per region in Africa 1970-2000.
3,97
6, 62
3,80
6,02 7, 34
4,05
2, 86 2,75
1, 93 5, 10
1,44 2,35
1,50
0,42
3, 39
0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00
West Af ric a Cent r al Af ric a East Af r ica Sout hern Af ric a Nor t h Af r ic a
1970-1980 1980- 1990 1990- 2000
Notes: Constructed was based on data from the World Bank Development Indicators (WDI2005). Source: Kamgnia Dia (2006)
Nevertheless, the global outlook of a number of African economies reveals a good number of growth upsurges since the second half of the 1990s. Indeed, from an annual rate of 4.5 percent in 2003, the growth rate of real GDP increased to 6.5 percent in 2004, ranking African LDCs right behind the group of other developing countries, but consistently before Asian LDCs and Island LDCs on the ranking scale, as shown in Table 3. In total, African countries constituted the majority (12 out 15) of LDCs which had a real GDP growth rate of 6 percent and above in 2004; only Benin, Guinea, Mali, Liberia, Comoros, Eritrea, Niger and Central African Republic appeared among the LDCs that UNCTAD (2006) classified as countries that had a real GDP rate below 3 percent.
Table 3: Real GDP growth rates of LDCs and other developing countries since 1990 Period
LDCs
1990–
2000
2000–
2002
2002–
2004
2003 2004 LDCs
of which:
3.9 4.9 5.2 4.6 5.9
Bangladesh 4.8 4.8 5.4 5.3 5.5
Other LDCs 3.5 4.9 5.2 4.4 6.0
African LDCs 2.7 5.2 5.5 4.5 6.5
Asian LDCs 5.7 4.6 4.9 4.8 5.0
Island LDCs .. 2.2 4.2 3.4 5.0
Other developing countries 4.9 3.0 5.9 5.1 6.7
Source: UNCTAD, 2006
Two groups of authors proceeded into the search of growth accelerations analysis in an attempt of explanation of the recent growth upsurges in Africa. On the one hand, Hausmann, Pritchett and Rodrik (hereafter HPR, 2004) compared seven-year forward
and backward-looking growth rates of per capita GDP, departing from a given year.
Acceleration is identified when the forward-looking rate exceeds the backward-looking rate by at least 2% and the level of the jump is at least 3.5%, with an additional condition that the level of the post-acceleration GDP exceeds that of the pre- acceleration GDP (excluding crisis recovery periods). On the other hand, Patillo, Gupta, and Carey (hereafter PGC, 2005) reconsidered HPR’s analysis by defining an acceleration window of five years, in order to allow the identification of acceleration episodes which begin as late as 1999. Hence, instead of a 3.5% cut-off for the post- jump growth rate, they considered 2% as an admissible jump in per capita growth in a five-year window period. But PGC maintained the requirement that the level of GDP per capita must exceed the pre-acceleration level.
Based the modified framework for the identification of the acceleration episodes, Patillo, Gupta, and Carey (2005) determined 34 growth acceleration episodes in SSA, with more such episodes in the 1990s than in the 1980s, including several episodes currently under way. Moreover, the modified approach identified six countries which experienced two accelerations over the full period; findings which led these authors to conclude that accelerations are a surprisingly widespread phenomenon in Africa.
Under normal circumstances, a high economic growth should be associated with an expansion of employment in a given economy or region. Guisan, Aguayo and Exposito(2001a) point out that in the case of Africa, Northern and Southern areas have performed generally much better than Western, Central and Eastern Areas, both due to higher rates of growth in real GDP and to lower rates of increase in Population. But over the 1990s, while the rate of growth of real GDP remained significantly below the rate of growth of population in Central Africa, it increase slightly over the rate of growth in the population in Western and Eastern Africa; it decreased in Southern Africa, as shown in figure 3.
Figure 3: Real GDP and demographic growth rates in Africa over the period 1990–2000
2,86
0,42
2,75
1,93
3,39
2,77 2,72
2,43 2,27
2,00
0, 00 0, 50 1, 00 1, 50 2, 00 2, 50 3, 00 3, 50 4, 00
West A f r i ca Cent r al A f r i ca E as t A f r i ca Sout her n A f r i c a Nor t h A f r i c a
gdp gr ow t h 1990-2000 popul at i on gr owt h 1990-2000
Note: The data are from the WDI of 2005.Source : Kamgnia (2006)
Likely, all depends on the evolution of the productive capacities; which we take to be the productive resources, entrepreneurial capabilities and productions linkages underlying the capacity of a country to produce goods and services and enable it to
171
grow and develop4. Productive resources, comprising natural and human resources, financial and physical capital resources could not suffice on their own. They need to be combined to entrepreneurial capabilities on the one part and to production linkages flows of goods, flows of information and knowledge, flows of productive resources on the other part, so as to sustain production and impulse employment.
Following Islam (2004), when high rates of economic growth lead to sustained increase in productive capacity, employment opportunities with rising productivity are generated. This in turn allows for a progressive absorption and integration of the unemployed and the underemployed into expanding economic activities with higher levels of productivity. To that respect, Guisan, Aguayo and Exposito (2001 a,b), and Guisan and Neira (2006), among other authors, indicate that the increase of the educational level of people is the best way to diminish excessively high rates of population growth and to favour an increase of capital formation per inhabitant, rates of employment, real wages, productivity and income per inhabitant.
But overall in Africa, employment intensity of growth has remained low, which coupled with still low and stagnating economic growth, is an indication of low labor productivity. Tahari, Ghura, Akitoby and Brou (2004) agreed with Bosworth and Collins (2003) that stagnant total factors productivity (TFP) contributed significantly to the weak growth of SSA over the period 1960–2002. However, they pointed out that the economic recovery which is observed in a number of African countries since 1996 could be attributed to the recent surge in the growth of TFP.
Indeed, the contribution of TFP to growth in Africa turned positive (0.8%) in 1997/2002, from its negative value (-0.8%) in 1990–1996, as shown in Table 4.
Table 4 : Sources of growth in sub-Saharan Africa
Contribution of (to per capita real GDP growth) Period
Real GDP growth rate
% Physical
capital
Employment Total factor Productivity
1990–1996 2.1 1.3 1.6 -0.8
1997–2002 3.6 1.3 1.4 0.8
Note: The original table was drawn from Tahari, Ghura, Akitoby and Brou, 2004 Source : Kamgnia (2006)
It should be pointed out that one would accept the current results only if the inference done on the model is robust. Indeed, following the discussion on the inferential framework of the model, an appropriate fit is GMM estimations. On that respect, the results of the Arellano-Bond dynamic panel-data estimation are as shown in Table 5. In such a dynamic specification, only the first difference of the log of GDP significantly affects employment in Africa. The short run effect, though significant following the GMM estimation, remains low at 0.02 percent. Although the significantly positive effect of Openness agrees with current trends in trade statistics in
4 We borrowed such a definition from UNCTAD (2006).
Africa, it needs to be reinforced by a sectoral analysis. Another interesting result is the substitution relation that Credit and Foreign Direct Investment appear to be bearing with each other: while Credit significantly and positively affects employment, the effect of FDIs is rather significantly negative. Such an effect of FDIs could be due to the fact that only a few African countries benefited from the FDIs upsurge of the late 1990s: Equatorial Guinea, Sudan, the Democratic Republic of Congo, and Angola (UNCTAD, 2006). The concerns then should be how to channel those financial opportunities into productive investments, so as to sustain the development requirement for productive capacities.
Table 5: Effects of lags in growth
Variable Coefficient t-Statistic α-Probabilities
lnGDP(Difference 1) 0.018 8.387 0.000
lnGDP(-1, Difference 1) 0.003 1.260 0.210
lnEmployment(-1) 0.459 17.895 0.000
Openness(Difference 1) 0.008 5.704 0.000 Credit to the Private Sector 0.002 2.074 0.040 Foreign Direct Investment -0.006 -3.910 0.000
Constant 0.013 14.388 0.000
Source: Constructed by the author
In effect, if all the countries in our sample showed a sustained capital accumulation over the years, such an accumulation did not always go along with the technological effort. One can therefore doubt about the real efficiency of the observed capital accumulation. Without getting into abusive exaggeration, we can assert that evidence on the continent certainly favored an accumulation of capital over the considered decade. But the technological effort deemed to go along did not always follow; what harms capacity building, and in turn, employment creation.
4. Conclusion
African countries are experiencing a fast growing population, which as it inflates the labor force, further constrains the other productive capacities, as evidenced by authors such as Guisan, Aguayo and Exposito (2001 a,b) and Guisan and Neira (2006).
In the few countries where economic growth tends to be accompanied by effective labor absorption, the productivity of labor remains weak. Globally, the dynamics of the growth – employment nexus in Africa revealed a great deal of heterogeneity.
In the attempt to explain such heterogeneity, we developed an employment model, considering different scenarios for combining the potential determinants of employment. A Fixed Effects estimation suggests that economic growth does affect employment but with lags (up to 2 years). Moreover, the degree of openness significantly, but negatively explains the variations of the workforce in Africa, while FDI significantly and positively affect workforce in Africa. However following GMM
173
estimation, the short run effect of GDP remains weak while credit made to the private sector and FDI evolve under a substitution relationship.
Overall, it appears quite clearly that the ongoing economic recovery constitutes a sound framework for expanding employment in Africa. That is especially the case if, as external resources flow in it could reinforce accumulated domestic capital; all translating into productive investments. More specifically, recommendations would be made to increase output on the one hand, and to diversify and improve the flow of FDI;
of course without creating a FDI dependency.
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Appendix
xtabond lemp, lags(1) diffvars(cred fodi) pre(lgdp ouv , endogenous) pre(lgdpl) artests(2)twostep small
Arellano-Bond dynamic panel-data estimation Number of obs = 156 Group variable (i): pays Number of groups = 39 F(6, 149) = 126.88 Time variable (t): temps Obs per group: min = 4, avg = 4, max = 4 Two-step results
--- lemp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---+--- lemp |
LD | .4593542 .0256698 17.89 0.000 .4086304 .510078 lgdp |
D1 | .0177741 .0021191 8.39 0.000 .0135866 .0219615 ouv |
D1 | .0083561 .0014649 5.70 0.000 .0054614 .0112508 lgdpl |
D1 | .0026642 .0021152 1.26 0.210 -.0015155 .0068439 cred | .0016403 .0007908 2.07 0.040 .0000777 .0032028 fodi | -.0061408 .0015705 -3.91 0.000 -.0092441 -.0030376 _cons | .0127001 .0008827 14.39 0.000 .0109558 .0144443 ---
Sargan test of over-identifying restrictions: chi2(36) = 22.94 Prob > chi2 = 0.9552
Arellano-Bond test that average autocovariance in residuals of order 1 is 0: H0: no autocorrelation z = -1.00 Pr > z = 0.3196. Arellano-Bond test that average autocovariance in residuals of order 2 is 0: H0: no autocorrelation z = 3.14 Pr > z = 0.0017
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