Lesotho’s unemployment, poverty and income inequality and other social ills remain pervasive in the face of non-inclusive growth. Using structural vector autoregressive framework and annual time series data spanning from 1980 to 2014, this paper investigates the sources of the high unemployment in Lesotho, a small landlocked developing country whose currency is pegged to that of South Africa. The impulse response functions, forecast error variance decomposition and historical decomposition jointly revealed that unemployment dynamics in Lesotho largely emanate from shocks to employment, unemployment itself, productivity, real wages and inflation. The importance of positive shocks to employment and unemployment itself in explaining unemployment variations decline over time while the importance of positive shocks to productivity, real wages and inflation grow with time. In light of this, Lesotho’s government should promote private sector development, diversify the economy’s markets and invest in human and physical capital development with a view to increase employment .
In recent years, business cycle research has devoted much attention to understanding the patterns of unemployment dynamics. The net changes of unemployment dynamics shape the adjustment of unemployment and provide a useful indicator of the economic situation [
The objective of this paper is to investigate the patterns of unemployment in Lesotho not only with a view of contributing to the general body of knowledge on causes of unemployment in developing countries that are landlocked and of which the national currency is pegged to that of a highly dominant trading partner but also to offer a more nuanced understanding of the factors that drive unemployment in Lesotho as this will better inform policy decision-making in the country. To this end, the paper employs a structural vector autoregressive (SVAR) model and identifies variable shocks that are widely considered important in the explanation of labour market fluctuations. These shocks include productivity shocks, real wage shocks, inflation shocks and labour market shocks.
The subsequent parts of the paper are structured as follows: Section 2 provides a brief account of Lesotho’s economy and unemployment dynamics. Section 3 gives the review of relevant theoretical and empirical literature. Section 4 presents the econometric methodology. Section 5 provides detailed analysis of the empirical results. The last section offers some concluding remarks together with policy recommendations.
Lesotho is a small and mostly mountainous country that is largely rural with a population of approximately 2 million people. It is completely enveloped by South Africa and through its membership in the Common Monetary Area (CMA)1 its currency; the Loti (plural Maloti), is pegged at par to the South African Rand. Apart from CMA membership, the country is also a member of Southern African Customs Union (SACU)2 and Southern African Development Community (SADC). The country is not only one of the poorest in Southern Africa; but it is also one of the most unequal in the world. It has an open economy that is traditionally focused on trade. Textiles, water and diamonds are its main exports and its main trading partners are South Africa and the United States (US); with the former being its main trading partner. The national head count poverty rate was 57.1 percent in the fiscal year 2010/11 according to the 2010/11 household survey. This figure was virtually unchanged from the 2002/03 household survey. The level of inequality as measured by the Gini coefficient increased from 0.51 to 0.53 between 2002/03 and 2010/11. The main driver of growth in the country is government spending. Public spending in the 2014/15 financial year rose to about 63.1 percent of Gross Domestic Product (GDP) from 44.4 percent of GDP registered in the 2004/05 financial year [
The terms and conditions of employment in Lesotho are governed by the Lesotho's Labour Code Order of 1992 and its subsequent amendments. The Labour Code regulates terms of employment and conditions for worker health, safety, and welfare. It law permits union organization. The Labour Court and the Labour Court of Appeal are the key judiciary entities dealing with labour disputes. In addition, the Labour Code Amendment Act of 2000 established the Directorate of Industrial Dispute Prevention and Resolution (DDPR), which is a semi-autonomous labour tribunal independent of the government, political parties, trade unions, employers and employers’ organizations. Lesotho National Development Corporation (LNDC) is another key institution that deals with labour disputes especially those arising from the manufacturing sector. The function of LNDC in this realm is to bring parties together before any formal process is set in motion. For example, LNDC intervenes in strikes and tries to reconcile workers and employers. When this informal process fails, the more formal process of the DDPR can be engaged which can consist of conciliation and arbitration.
The labour market of Lesotho is characterized by a low employment-to- working age population ratio. The government is the main employer in the formal sector of the domestic economy while the largest informal employer is the manufacturing sector. However, high levels of unemployment has led to increased number of the economically active being employed in South Africa; the main destination for Lesotho’s migrant labour. In 2008, Lesotho had a total of 1,461,763 persons that were eligible for entry into the labour market. In the same year, 42.3 percent of this economically active population was employed while the remaining was either unemployed or inactive (not actively seeking work and/or not available for work). The highest number of employed persons was in the 25 - 29 age group while the highest number of unemployed was in the 20 - 24 age group with the proportion of unemployed males exceeding that of unemployed females. This shows that the level of unemployment is highest amongst the country’s youthful males. The nature of the labour market remained broadly unchanged in 2012. The majority of the economically active population was employed with a salary in all the age groups except the age group 60 - 64 where most of the population was engaged in subsistence farming. Of those employed with a salary, 56.1 percent were females while 44.6 percent were males. Furthermore, the rates of unemployment were most predominant in the lowlands and foothills while lower in the country’s mountainous districts [
The unemployment rate in the second quarter of 2014/15 was an estimated 32.8 percent which translated into an economic dependency ratio of 1.41 during that time. 38.4 percent of the working population was engaged in elementary occupations which were highly labour intensive and used mostly hand-held tools.
The main factors inhibiting returns to labour and jobs in Lesotho are lack of skills, high burden of disease especially HIV/AIDS and tuberculosis; poor investment climate and lack of key infrastructure [
Since the launch of the NSDP, some strides have been made by the Government of Lesotho towards the improvement of the country’s investment climate as well as the development of key infrastructure. For instance, in 2014, Parliament approved the Industrial Licensing Act of 2014 whose purpose is to facilitate and promote industrial development and micro, small, and medium enterprises (MSMEs) through a new regulatory regime that is simple, short, and cost
Labour Force | Frequency | Percentage |
---|---|---|
Employed | 729,418 | 74 |
Unemployed | 253,238 | 26 |
Total | 982,656 | 100 |
Source: [
effective. This initiative complements the amendment of the Trading Enterprises Regulations (1999) in December 2011 that has made it easier to obtain a trade license through the establishment of a One-Stop Business Facilitation Center (OBFC) and replacement of pre-inspection with post inspection for businesses with low health and environmental risks. Furthermore, there has been considerable improvement in the financial sector as it has become much easier for MSMEs to access credit due to reforms in the lending environment that reduced risk for banks. The land administration reform has greatly increased the availability of leasehold titles and streamlined the process of registering mortgage securities, leading to rapid growth in mortgage lending. In addition, The Financial Sector Development Strategy (FSDS) adopted by the government in 2013 has seen encouraging increases in the banking system’s domestic loan-to-deposit ratio and a corresponding decrease in the portion of bank assets invested abroad, primarily in South Africa [
Last but not least, the Government of Lesotho in partnership the United Nations agencies launched the National Volunteer Corps Project (NVCP) and the Youth Employment Project (YEP) as part of its strategy to respond to high youth unemployment and poverty reduction. Furthermore, the Government of Lesotho in concert with commercial banks established of a M50 Million Partial Credit Guarantee Fund (PCGF) in May 2012 while another Partial Credit Guarantee Fund (PCGF) was launched by the Lesotho National Development Corporation (LNDC) in the same year. The main aim of these facilities was not only to encourage investors to start or expand MSMEs but also to support large businesses in the country. This was against the background that efforts to start or expand businesses particularly the MSMEs were fruitless due to lack of collateral. Therefore, these facilities encouraged the commercial banks to introduce easily accessible loan instruments for the business sector, especially the MSMEs, as they are able to recover a portion of their money in the event of a default.
1) Classical Theory of Unemployment
Under the Classical theory of unemployment, the labour market is understood to be a single, static market that is characterised by perfect competition, spot transactions and institutions for double auction bidding. In this scenario, workers supply labour while employers demand it. The quantity of labour supplied can be interpreted by, for example, the number of workers working full days over a given period. On the other hand, the price of labour is the real wage per day. The theory assumes that every unit of labour services is the same and every worker in the labour market gets exactly the same wage. Moreover, under the classical theory, unemployment is a temporary phenomenon and the workings of the free market forces would ensure that the economy is restored to full employment given the flexible wages and perfect information assumptions [
2) Keynesian Theory of Unemployment
The Keynesian school of thought explains unemployment as a cyclical and involuntary phenomenon where aggregate demand deficiencies over certain periods in the business cycle lead to a situation where there are not enough job opportunities to meet the number of people looking for work. Reference [
Considering
There are many studies that analyse the sources of unemployment in various countries around the world using Structural Vector autoregressive (SVAR)
framework. In the case of developed and emerging markets economies, [
In a different study [
In the context of Austria [
Using the SVAR model with long-term restrictions proposed by [
In the case of developing countries especially in Africa, studies of this nature have also been done though the coverage remains low compared to developed and emerging markets economies. This is despite the importance of the issue of unemployment and/or employment for policy formulation in developed and developing countries alike. Even where such studies have been done they have not used the SVAR approach. For instance [
In a different study [
In light of this review of the literature, it is obvious that the studies that examine the sources of unemployment using SVAR methodology in developing countries, especially in landlocked countries that are economically characterised by poverty and close relationship with a more economically developed country, whose currencies are pegged to that of a dominant trading partner such as Lesotho6, remain scant. Therefore, the current study analyses the sources of unemployment in Lesotho that was ranked a middle income country in 2016, despite the existence of extreme poverty, high income inequality and prevalence of HIV/AIDs as well as persistently high unemployment rate. This combination of traits arguably has the potential to lead to some interesting revelation with respect to the sources of unemployment and may have interesting economic policy implications regarding what could be done to reduce high level of unemployment in Lesotho.
This research paper makes use of annual time series data from 1980 to 20147.
In order to obtain the dynamic interactions among LPRD, LEMP, LRWG, INF and LUEM in Lesotho, the study used a structural VAR (SVAR) model whose data generation process (DGP) is underpinned by a reduced form VAR. The VAR has superiority over other econometric methods such as simultaneous equations due to its capability to quantify the average contribution of a given structural shock to the variability of the data over time through what are known as forecast error variance decompositions (FEVDs) (See, [
The reduced form VAR is presented as:
Z t = G 0 + G 1 Z t − 1 + G 2 Z t − 2 + … + G s Z t − s + ε t (1)
where Zt is a (5 × 1) vector of endogenous variables (LPRDt, LEMPt, LRWGt, INFt, and LUEMt) observed at time t. G0 is a (5 × 1) vector of constants, G1,2,…,s is a (5 × 5) matrix of coefficient estimates, ε is a (5 × 1) vector of serially uncorrelated system innovations and s is the optimal lag length of each variable. When unpacked, the VAR model in Equation 1 is a system of five equations. Therefore, equation 1 can be estimated using the ordinary least squares (OLS) method after the choice of optimal lag length (VAR order) has been selected. Reference [
Prior to undertaking any empirical analysis of the data, the paper determines the order of integration of the variables. For this purpose, the study utilised the Augmented Dickey and Fuller (ADF) by [
It is essential to establish the integrity of Equation (1) in the DGP before the SVAR can be estimated. To this end, the study focuses on tests for residual autocorrelation8, heteroskedasticity and structural stability. Normality tests were not conducted since normality is not a necessary condition for the validity of statistical procedures related to VAR models [
Once all the necessary model checking has been done and it has been confirmed that equation 1 passes the relevant residual diagnostics and structural stability tests, the next step is to specify and estimate the SVAR model. From the SVAR model, structural shocks can be isolated. This allows for the generation of IRFs, FEVDs and the historical decomposition [
A X t = β 0 + β 1 X t − 1 + β 2 X t − 2 + … + β s X t − s + υ t (2)
where; A is a (5 × 5) matrix of instantaneous relations among the endogenous variables where the diagonal elements are normalized to equal one but the off diagonal elements may be arbitrary. Xt is a (5 × 1) vector of endogenous variables (LPRDt, LEMPt, LRWGt, INFt, and LUEMt) observed at time t. β0 is a vector of constants, β1,2,…,s is a (5 × 5) matrix of coefficient estimates, υ is a (5 × 1) vector of serially uncorrelated structural errors and s is the optimal lag length of each variable
The SVAR model cannot be estimated with OLS because of the contemporaneous relations between the endogenous variables in matrix A that are correlated with the structural errors. Therefore, to estimate the SVAR model and develop IRFs and FEVDs, Equation (2) needs to be identified. This is done by imposing restrictions on elements of matrix A in Equation (2). Reference [
Z t = A − 1 A X t (3)
The relationship between the forecast errors and structural shocks is represented by Equation (4)
ε t = A − 1 υ t (4)
In order to obtain the structural innovations in equation (4), the study employed a strictly recursive Cholesky decomposition technique where
( n 2 − n ) 2
zero (exclusion) restrictions9 are imposed. The Cholesky decomposition used in this study has the following ordering: LPRDt, LEMPt, LRWGt, INFt, and LUEMt. This ordering was used by [
1) Productivity (LPRD) is not contemporaneously affected by any of the shocks. It is only affected by own shocks while all other shocks will affect it with a lag.
2) Employment (LEMP) is contemporaneously affected by productivity shocks and own shocks while all other shocks will affect it with a lag.
3) Real wage (LRWG) responds contemporaneously to productivity shocks, employment shocks and own shocks, while all other shocks will affect it with a lag.
4) Inflation (INF) responds contemporaneously to own shocks and all other shocks except the unemployment shock
5) Unemployment (LUEM) contemporaneously responds to own shocks and shocks from all the variables in the model.
There are five endogenous variables. Therefore, Equation (2) is just-identified when ten zero exclusion restrictions are imposed on matrix A in Equation (2).
As a normal practice in time series analysis, the paper performs the standard Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests to check the order of integration of the variables prior to estimating the reduced form VAR model in Equation (1).
Given the results of the unit root tests above, the issue would now be on the choice of the appropriate econometric strategy (analytical technique), which allows for the empirical analysis of the sources of unemployment in Lesotho in the presence of a mixture of [I(0)] or [I(1)] variables. There are various econometric tools which could be employed under this circumstance. For instance, one would estimate a cointegration relationship among these variables through use of analytical tools such as autoregressive distributed lag (ARDL) bounds testing procedure or estimate a VAR model in which the INF variable is treated as an exogenous variable, etc.
Variables | ADF Test | Phillips Perron(PP) Test | |||
---|---|---|---|---|---|
Level | First Differences | Level | First Differences | ||
LEMP | −1.5157 (0.5137) | −6.0057* (0.0000) | −1.2776 (0.6285) | −7.5751* (0.0000) | |
LPRD | −1.0573 (0.7210) | −6.3937* (0.0000) | −0.9628 (0.7552) | −12.2156* (0.0000) | |
LRWG | −0.8078 (0.8041) | −6.1214* (0.0000) | −0.9167 (0.7706) | −10.0063* (0.0000) | |
LUEM | −2.1048 (0.2441) | −5.5953* (0.0000) | −0.1718 (0.2197) | −5.7171* (0.0000) | |
INF | −5.9362* (0.0000) | −5.9474* (0.0000) | |||
However, the current study follows existing studies in the literature (see [
According to Khan and Ahmed [
As mentioned earlier, the main objective of this paper is to track out responses of unemployment to shocks in productivity, employment, and real wage and inflation by means of impulse response analysis. For this purpose, we have estimated SVAR model in levels and derived the impulse response functions (IRFs).
Roots of Characteristic Polynomial | |
---|---|
Endogenous variables: LPRD LEMP LRWG INF LUEM | |
Root | Modulus |
0.998654 | 0.9986539041076444 |
0.704411 − 0.224126i | 0.7392071701120861 |
0.704411 + 0.224126i | 0.7392071701120861 |
−0.100606 − 0.219044i | 0.2410433043411363 |
−0.100606 + 0.219044i | 0.2410433043411363 |
No root lies outside the unit circle. | |
VAR satisfies the stability condition |
The impulse response of unemployment to the positive productivity shock is positive and immediate registering approximately 0.04 percent and peaks in the 2nd year at 0.063 percent before decreasing and becoming statistically insignificant at the beginning of the 4th year. Thereafter, unemployment’s response is negative and stays negative for the remaining period until the tenth year. This finding implies that productivity initially increases unemployment up to 4 years in Lesotho. Thereafter, the impact becomes negative. This empirical finding is inconsistent with that of [
It appears that employment immediately and significantly lowers unemployment by 0.12 percent with the impact remaining negative up to the 3rd year. This empirical finding is consistent with the standard economic theory. However, after the 3rd year, the response of unemployment to a one percent positive shock in employment becomes positive and fairly stable until the 10th year. This empirical finding is consistent with that of [
short term nature.
With respect to real wage, the IRFs indicate that a positive shock to real wages immediately and significantly lowers unemployment with the highest dip of 0.03 percent in the 2nd year. Thereafter the impact remains negative until the 7th year after which it becomes positive until the 10th year. The impact of a wage shock on unemployment from the 1st year to the 7th year contradicts economic theory, where an increase in real wages lowers unemployment. A possible explanation could be that firms’ initial gain in competitiveness after an increase in labour productivity enables them to set higher prices that temporarily offset any increase in wages. This finding is similar to that of [
Regarding inflation, it appears that a positive shock to inflation immediately and significantly increases unemployment for the first 3 years, peaking in year 2 at 0.03 percent, after which the impact becomes negative. This empirical evidence is in line with the economic theory and may be on account of increased cost of labour and production in the first 3 years. Therefore, price inflation shocks are a critical factor for high unemployment in Lesotho within a short time horizon. These findings are in line with [
In summary, the preceding analysis shows that shocks to the level of employment, productivity, real wages, inflation and unemployment itself are critical factors explaining unemployment dynamics in Lesotho.
According to [
Period | S.E. | LPRD | LEMP | LRWG | INF | LUEM |
---|---|---|---|---|---|---|
1 | 0.288348 | 6.131027 | 78.07416 | 0.729601 | 0.097615 | 14.96760 |
2 | 0.444564 | 17.14009 | 51.39727 | 4.308218 | 3.340607 | 23.81381 |
3 | 0.452775 | 15.92130 | 44.28794 | 5.898842 | 3.278075 | 30.61384 |
4 | 0.460350 | 15.10720 | 41.57523 | 6.287392 | 3.315325 | 33.71485 |
5 | 0.468976 | 15.55946 | 40.36243 | 6.138150 | 3.806087 | 34.13387 |
6 | 0.477925 | 16.49755 | 39.84452 | 5.868429 | 4.353753 | 33.43576 |
7 | 0.487382 | 17.32106 | 39.62462 | 5.701674 | 4.746214 | 32.60644 |
8 | 0.497214 | 17.81720 | 39.52686 | 5.692193 | 4.943168 | 32.02057 |
9 | 0.507265 | 18.02337 | 39.45781 | 5.811277 | 5.002862 | 31.70469 |
10 | 0.517400 | 18.05142 | 39.37663 | 6.007297 | 4.997163 | 31.56748 |
Cholesky Ordering: LPRD LEMP LRWG INF LUEM.
shocks to productivity, shocks to real wages and last but not least the shocks to price inflation. For instance, in the 1st year, approximately 78 percent of total variation in unemployment is explained by shocks to the level of employment while own shocks account for approximately 15 percent of the variation in unemployment. Shocks to productivity, real wages and inflation collectively explain only the small fraction of the variation in unemployment. Specifically, the shocks to these variables explain approximately 6.13 percent of the variation in unemployment rate in Lesotho in the 1st year. In the 5th year approximately 40 percent of variation in unemployment is explained by shocks to the level of employment; while approximately 30 percent is explained by own shocks and about 16 percent explained by shocks to the level of productivity in the economy. Real wages and inflation collectively account for only about 9.94 percent of the total variation in unemployment.
In the 10th year, 39 percent of variation in unemployment is explained by shocks to the level of employment whereas approximately 31 percent is explained by own shocks. In addition, shocks to productivity explain about 18 percent of the variation in unemployment while shocks to real wages and inflation collectively explain only about 11 percent of the variation in unemployment during the same time horizon. In this regard, the importance of the shocks emanating from productivity, real wages and inflation in explaining variation in unemployment grows over time while that of shocks to employment and own shocks decline over time.
In summary, the results reported in
Apart from employing IRFs and FEVD, the paper further uses historical decompositions to estimate the individual contributions of each structural shock to the movements in unemployment in Lesotho over the sample period. According to [
Y t = A t Y 0 + ∑ k = 1 t A t − k e k (5)
Equation (5) states that the model variables at each point in time (Yt) are represented as the function of initial values (Y0) plus all the structural shocks of the model.
from 1980 to 1988. From 1990 to 1994 unemployment was in decline. This was mostly affected by productivity, real wage and own shocks that applied downward pressure on the level of unemployment while employment had mixed impacts during this time.
During the period 2000 to 2010, the shocks to real wages, inflation, productivity and employment generally contribute positively to explain the dynamics of unemployment in Lesotho. On the other hand, the own shocks make a negative contribution to level of unemployment during that time horizon. From 2010 to 2014, the shocks to real wages, inflation, and employment as well as own shocks play a dampening role in unemployment dynamics. Productivity shocks positively contribute to unemployment between 2010 and 2012 in Lesotho, and thereafter put downward pressure on unemployment dynamics until the end of the sample period.
To sum up, employment shocks, own shocks and productivity shocks are the most important drivers behind the dynamics of unemployment in Lesotho while real wage and inflation only moderately contribute in explaining such dynamics. This empirical evidence is exactly congruent to the finding derived from the IRFs and FEVD.
Having estimated the IRFs, FEVDs and historical decompositions for the sources of unemployment based on SVAR in Lesotho, it is crucial to confirm that the robustness of the obtained results to ensure their reliability for policy analysis. As an initial robustness check, the errors in the reduced form VAR, Equation (1), were tested for the presence serial correlation and heteroskedasticity at the optimal lag order of 1 (chosen by the information criteria, see
Last but not least, the robustness test similar to that used by Bank (2011), which involves the analysis of different specifications of the reduced form VAR by changing the ordering of the endogenous variables using Cholesky decomposition was also conducted. Reference [
In the past decade, Lesotho’s growth rate averaged about 3.8 percent with growth primarily driven by substantially expanding public activities especially government spending. Despite this, unemployment, poverty and income inequality and other social ills remained stubbornly pervasive in the face of non-inclusive growth in the economy. Labour market deficiencies, undiversified markets and small private sector, among others, are to blame for this high unemployment in the economy. With few job opportunities at home and higher wages in the neighbouring South Africa, a sizeable proportion of Lesotho’s labour force used to seek employment in South Africa. However, South Africa’s policy shift towards the use of local labour significantly reduced the demand for labour from Lesotho. This situation not only worsened the already obstinately high unemployment but also increased income inequality and poverty and other social ills in the country.
This paper employed SVAR framework to investigate the sources of the high unemployment in Lesotho during the period 1980 to 2014 with a view of contributing to the general body of knowledge on causes of unemployment in developing countries that are landlocked and of which the national currency is pegged to that of a highly dominant trading partner. The empirical analysis derived from the IRFs reveal that positive productivity shocks increase unemployment for up to 4 years but thereafter decreases it. This could perhaps be attributed to, inter alia, “technological bias” which explains unemployment as a temporary phenomenon. In addition, a positive employmentshock has a short-lived negative impact on unemployment and this result could be due to the short-term nature of employment in the country. Furthermore, a positive real wages shock was found to decrease unemployment for a period of 7 years before increasing it. Lastly, the empirical analysis also shows that a positive shock to inflation instantaneously and significantly increases unemployment for the first 3 years after which the impact becomes negative with unemployment probably decreasing as firms internalise the inflation costs. On the same token, a positive shock to unemployment itself immediately and positively increases unemployment for a period of 8 years and decreases it thereafter. These empirical findings were also corroborated by results of the FEVD and historical decomposition, both of which reveal that the variation in unemployment largely emanates from shocks to employment, unemployment itself, productivity, real wages and inflation. However, the importance of positive shocks to employment and unemployment itself in explaining the variation in unemployment decline over time while of the impact of positive shocks to productivity, real wages and inflation grow over time.
In light of these empirical findings, the study recommends that the government should invest in activities that are geared towards the promotion of the private sector and economic diversification in order to increase the level of output while at the same time creating various employment opportunities. This will also help to relieve pressure on the public sector and enhance labour market competitiveness and flexibility, reduce labour costs and encourage the creation of more employment opportunities. In addition, the government’s fiscal policy should ensure that expenditure is channelled towards investment in human and physical capital development. This will not only aid in closing the existing skills gap but will also result in positive spillovers that will increase economic growth, translate in to job creation and a smoothing of the business cycle.
Damane, M. and Sekantsi, L.P. (2018) The Sources of Unemployment in Lesotho. Modern Economy, 9, 937-965. https://doi.org/10.4236/me.2018.95060
Age group | Armed forces occupations | Managers | Professionals | Technicians and associate professionals | Clerical support workers | Service and sales workers | Skilled agriculture, forestry and fishery worker | Craft and related trades workers | Plant and machine operators, and assemblers | Elementary occupations | Total | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
15 - 19 | 0.0 | 0.0 | 0.0 | 1.1 | 0.0 | 1.8 | 3.4 | 0.9 | 1.4 | 10.1 | 5.1 | |
20 - 24 | 0.0 | 0.0 | 5.9 | 4.6 | 13.0 | 14.0 | 7.4 | 7.9 | 5.2 | 16.5 | 11.5 | |
25 - 29 | 7.2 | 8.1 | 14.8 | 9.7 | 32.0 | 18.5 | 8.1 | 18.7 | 15.0 | 13.9 | 14.1 | |
30 - 34 | 25.8 | 11.8 | 21.5 | 23.2 | 14.8 | 18.7 | 10. | 19.1 | 22.4 | 13.7 | 15.5 | |
35 - 39 | 36.2 | 12.8 | 16.0 | 14.9 | 12.8 | 15.4 | 8.4 | 18.9 | 17.4 | 11.9 | 13.1 | |
40 - 44 | 0.0 | 11.5 | 7.8 | 10.0 | 11.2 | 11.1 | 7.6 | 11.6 | 17.8 | 9.3 | 9.8 | |
45 - 49 | 2.8 | 16.6 | 10.2 | 12.4 | 5.1 | 6.9 | 6.2 | 9.0 | 8.3 | 6.9 | 7.6 | |
50 - 54 | 23.6 | 8.8 | 8.8 | 15.8 | 5.6 | 4.0 | 9.1 | 5.5 | 5.6 | 6.7 | 7.1 | |
55 - 59 | 0.0 | 7.2 | 6.8 | 1.2 | 2.3 | 4.1 | 8.1 | 4.6 | 4.9 | 4.9 | 5.4 | |
60 - 64 | 0.0 | 4.5 | 3.5 | 3.4 | 2.5 | 2.5 | 11.3 | 1.3 | 1.8 | 2.5 | 4.1 | |
65+ | 4.5 | 18.7 | 4.8 | 3.7 | 0.8 | 3.2 | 19.8 | 2.5 | 0.4 | 3.5 | 6.5 | |
Total (%) | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | |
Total (N) | 2706 | 15,650 | 60,073 | 19,868 | 16,894 | 82,789 | 132,481 | 70,902 | 34,076 | 293,980 | 729,418 |
Source: [
Variable Name | Acronym | Variable Explanation | Data Source |
---|---|---|---|
Total Labour Force | LFC | Due to lack of data on the labour force for the period 1980 to 1989, the main method used to generate the values for the labour force for the period 1980 to 1989 is the backward extrapolation technique similar to that used by Smith and Sincich (1988), Chow and Lin (1971), Smith (1987), Chang et al. (2007), Tsonis and Austin (1981) and Sunde and Akanbi, (2016). | World Bank-World Development Indicators and author calculations |
Unemployment | UEM | Due to lack of data on the labour force for the period 1980 to 1989, the main method used to generate the values for the labour force for the period 1980 to 1989 is the backward extrapolation technique similar to that used by Smith and Sincich (1988), Chow and Lin (1971), Smith (1987), Chang et al. (2007), Tsonis and Austin (1981) and Sunde and Akanbi, (2016). | World Bank-World Development Indicators and author calculations |
Employment | EMP | Total employment is equivalent to total labour force minus total unemployment. | World Bank-World Development Indicators and author calculations |
Gross Fixed Capital Formation | GFCF | Gross fixed capital formation as a percent of GDP | SADC |
Real Gross Domestic Product | GDP | GDP in constant local currency | World Bank-World Development Indicators |
Lending Rates | LER | Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability. | World Bank-World Development Indicators |
Real Wage | RWG | Note that capital stock and labour are the major inputs in the production process. To derive wages, the following identity is used: G F C F / G D P + E M P / G D P = G D P / G D P = 1 Thus, ( G F C F ∗ L E P ) / G D P + ( E M P ∗ R W G ) / G D P = G D P / G D P = 1 GFCF × LER represents the total value of capital in the economy and EMP × RWG represents the total wage bill in the economy. This implies that: R W G = [ 1 − ( ( G F C F ∗ L E R ) / G D P ) ] ( G D P / E M P ) = [ ( G D P − G F C F ∗ L E R ) / E M P ] This calculation was used by Akanabi and Du Toit (2011) as well as Sunde and Akanbi, (2016). | Calculated using, GFCF, GDP, EMP and LER using indicated formula |
---|---|---|---|
Productivity | PRD | This is calculated as the ratio of real GDP over total employment (GDP/EMP) | Calculated using GDP and EMP |
Consumer Price Index | INF | Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. Data are period averages. | World Bank-World Development Indicators |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | −28.56344 | NA | 7.57e−06 | 2.397389 | 2.635282 | 2.470115 |
1 | 110.6372 | 218.7439* | 2.24e−09* | −5.759803* | −4.332441* | −5.323444* |
2 | 127.1718 | 20.07765 | 4.96e−09 | −5.155127 | −2.538297 | −4.355136 |
Where, FPE and AIC represent Final prediction error (FPE) and Akaike information criteria, respectively while SC and HQ represent Schwarz information criterion and Hannan-Quinn information criterion, respectivey.* indicates lag order selected by the criterion.
Null hypothesis: No serial correlation at lag h | ||||||
---|---|---|---|---|---|---|
Lag | LRE* stat | df | Prob. | Rao F-stat | df | Prob. |
1 | 26.81364 | 25 | 0.3653 | 1.092345 | (25, 57.2) | 0.3803 |
2 | 26.04318 | 25 | 0.4053 | 1.054649 | (25, 57.2) | 0.4204 |
Null hypothesis: No serial correlation at lags 1 to h | ||||||
---|---|---|---|---|---|---|
Lag | LRE* stat | df | Prob. | Rao F-stat | df | Prob. |
1 | 26.81364 | 25 | 0.3653 | 1.092345 | (25, 57.2) | 0.3803 |
2 | 66.12913 | 50 | 0.0628 | 1.444640 | (50, 49.0) | 0.0999 |
*Edgeworth expansion corrected likelihood ratio statistic.
Joint test: | |||||
---|---|---|---|---|---|
Chi-sq | df | Prob. |
151.3009 | 150 | 0.4549 | |||
---|---|---|---|---|---|
Individual components: | |||||
Dependent | R-squared | F(10,19) | Prob. | Chi-sq(10) | Prob. |
res1*res1 | 0.305484 | 0.835717 | 0.6019 | 9.164515 | 0.5166 |
res2*res2 | 0.497635 | 1.882107 | 0.1131 | 14.92904 | 0.1347 |
res3*res3 | 0.355562 | 1.048307 | 0.4436 | 10.66687 | 0.3841 |
res4*res4 | 0.283983 | 0.753569 | 0.6691 | 8.519492 | 0.5782 |
res5*res5 | 0.554184 | 2.361848 | 0.0513 | 16.62552 | 0.0831 |
res2*res1 | 0.368155 | 1.107068 | 0.4054 | 11.04466 | 0.3540 |
res3*res1 | 0.321211 | 0.899103 | 0.5518 | 9.636337 | 0.4730 |
res3*res2 | 0.434161 | 1.457844 | 0.2299 | 13.02482 | 0.2223 |
res4*res1 | 0.413043 | 1.337036 | 0.2806 | 12.39130 | 0.2597 |
res4*res2 | 0.200030 | 0.475088 | 0.8859 | 6.000889 | 0.8152 |
res4*res3 | 0.346609 | 1.007906 | 0.4714 | 10.39826 | 0.4063 |
res5*res1 | 0.413937 | 1.341972 | 0.2784 | 12.41811 | 0.2580 |
res5*res2 | 0.508912 | 1.968957 | 0.0978 | 15.26735 | 0.1226 |
res5*res3 | 0.449314 | 1.550240 | 0.1971 | 13.47941 | 0.1981 |
res5*res4 | 0.244372 | 0.614464 | 0.7833 | 7.331154 | 0.6939 |
Period | S.E. | LPRD | LUEMP | LEMP | LRWG | INFL |
---|---|---|---|---|---|---|
1 | 0.270769 | 7.207876 | 92.79212 | 0.000000 | 0.000000 | 0.000000 |
2 | 0.421283 | 16.91439 | 72.17888 | 7.870931 | 0.617690 | 2.418112 |
3 | 0.427235 | 16.56833 | 64.19303 | 15.92834 | 0.891283 | 2.419025 |
4 | 0.433974 | 15.15398 | 59.71432 | 21.90117 | 0.979470 | 2.251053 |
5 | 0.442092 | 14.33803 | 56.49603 | 25.82921 | 0.980523 | 2.356204 |
6 | 0.451773 | 14.05057 | 54.33130 | 28.08691 | 0.950994 | 2.580218 |
7 | 0.463003 | 14.04683 | 52.99393 | 29.23345 | 0.928543 | 2.797238 |
8 | 0.475565 | 14.14646 | 52.23965 | 29.72698 | 0.932432 | 2.954476 |
9 | 0.489171 | 14.25433 | 51.85371 | 29.87519 | 0.969558 | 3.047207 |
10 | 0.503517 | 14.33191 | 51.67373 | 29.86461 | 1.039453 | 3.090300 |
Cholesky Ordering: LPRD LUEM LEMP LRWG INF |