Conceptual And Theoretical Issues

Published Date: 02 Nov 2017

INTRODUCTION

Productivity and employment are issues that are central to the social and economic life of every country.

The extant literature refers to productivity and unemployment as constituting a vicious circle that explains the

endemic nature of poverty in developing countries. And it has been argued that continuous improvement in

productivity is the surest way to breaking this vicious circle. Growth in productivity provides a significant basis for

adequate supply of goods and services thereby improving the welfare of the people and enhancing social progress.

As pointed out by Dernburg (1985:63), "Without it there would be no growth in per capita income, and inflation

control would be all the more difficult". In fact, the observation has been made that continuous enhancement of

productivity has been very central to the brilliant performance of the Asian Tigers and Japan in recent years

(Simbeye, 1992; World Bank 1993). Recent developments in the world economy have also shown that countries

with high productivity are not only central to the determination of global balance of powers (e.g Japan and

Germany), but also serve as centres of stimulus, where world resources (including labour) are redirected to, as

opposed to countries with low or declining productivity. Recent studies, for example, Rensburg and Nande (1999)

and Roberts and Tybout (1997) have also shown that high productivity increases competitiveness in terms of

penetrating the world market. Thus, a country with high productivity is often characterized by a very high capacity

utilization (optimal use of resources), high standard of living, low rate of unemployment and social progress.

Unemployment, on the other hand, has been categorized as one of the serious impediments to social

progress. Apart from representing a colossal waste of a country's manpower resources, it generates welfare loss in

terms of lower output thereby leading to lower income and well-being (Akinboyo, 1987; and Raheem, 1993).

Unemployment is a very serious issue in Africa (Vandemoortele, 1991 and Rama, 1998) and particularly in Nigeria

(Oladeji, 1994 and Umo, 1996).

The need to avert the negative effects of unemployment has made the tackling of unemployment problems

to feature very prominently in the development objectives of many developing countries. Incidentally, most of these

countries' economies are also characterized by low productivity. Thus, it seems obvious to many policy makers that

there must be a straight forward connection between productivity and employment/unemployment. However, the

theoretical linkage between productivity and unemployment is yet to be settled in the literature. While some

researchers posit that higher productivity may increase unemployment (e.g. Diachavbre, 1991; Krugman, 1994),

some others argue that it could increase employment (e.g Yesufu, 1984; Akerele, 1994; CEC, 1993).

In view of the unfolding reality coupled with the protracted debates this paper attempts to examine the

linkage between productivity and unemployment. Specifically, it examines the dimensions of productivity and

unemployment in Nigeria as well as the direction of causality between them. To this end, the rest of the paper is

organized thus. Following this introduction is part II, which examines the conceptual and theoretical is sues. Part III

discusses the profile of productivity and unemployment in Nigeria while the empirical link between them is

examined in part IV. The final part contains the policy implications and conclusions.

II.

CONCEPTUAL AND THEORETICAL ISSUES

The literature is replete with varied categorizations of productivity and unemployment in terms of their

definitions, measurements and linkages. It is therefore important to make some clarifications on these issues.

2.1

Concept of Productivity

Productivity measures the relationship between the quantity and quality of goods and services produced

and the quantity of resources needed to produce them (i.e factor inputs such as labour, capital and technology)

(Simbeye, 1992; Okojie 1995; Roberts and Tybout, 1997). Mali (1978:6) defines it thus:

"The measure of how resources are being brought together in organizations and

utilized for accomplishing a set of results. It is reaching the highest level of

performance with the least expenditure of resources".

Productivity is viewed as the instrument for continuous progress, and of constant improvement of activities. It is

often seen as output per unit of input. Hence, higher productivity connotes achieving the same volume of output

with less factor inputs or more volume of output with the same amount of factor inputs. Thus, increased

productivity could result from the reduction in the use of resources, reduction in cost, use of better methods or

improvement in factor capabilities, particularly labour. Two variants of productivity measurements have been cited

in the literature: total factor productivity (TFP), otherwise known as multifactor productivity, and partial

productivity. Roberts and Tybout (1997) and Tybout (1992), assuming a neo-classical production function at the

2

sectoral or industry level, define total factor output to be a concave function of the vector of inputs and time (a proxy

for shift in technological innovation). To them, the elasticity of output with respect to time is the total factor

productivity. In a more general sense,

TFP

=

Total Output

..........(1)

Weighted Average of all inputs

Critical among these factor inputs are labour, capital, raw materials and purchase of spare parts, and other

miscellaneous goods and services that serve as inputs in the production process. In a more practical sense, these

factor inputs are reduced to the weighted average of labour and capital (Okojie, 1995; Roberts and Tybout, 1997).

The second variant, partial productivity (PP), is defined as:

PP

=

Total Output

............(2)

Partial Input

The partial input could either be labour or capital. This can be measured at the national level, sectoral level, industry

or factory level. Existing studies on productivity measurement show a predilection for productivity per labour input.

Several reasons have been put forward for the choice of labour as against other factors of production. First, Ilyin

and Motyler (1986) see labour as the "means and end of production". Labour is the only factor that creates value,

influences its prices and those of other factors and sets the general level of productivity. Second, it is the most easily

quantified factor of production (Okpechi, 1991). And finally, given the low technological base of developing

countries' economies, the quest for improved managerial capability and effectiveness should give the human factor

appropriate recognition and attention. While labour productivity seems to be the most convenient to use, it is

however important to note that this approach has an important limitation. It treats labour as being homogenous

instead of differentiating it according to age, sex, education, application of

skills, aptitude, among others. Nevertheless, this study applies productivity per worker as opposed to per capital or

total factor productivity.

2.2

Concept of Unemployment

There seems to be a consensus on the definition of unemployment. The International Labour Organization

(ILO) defines the unemployed as numbers of the economically active population who are without work but available

for and seeking work, including people who have lost their jobs and those who have voluntarily left work (World

Bank, 1998:63). Although there seems to be convergence on this concept, its applications have been bedeviled with

series of problems across countries. First, most published unemployment rates are recorded open unemployment.

People's attitude on this varies from country to country. While this may be high in developed countries and where

government is committed to resolving unemployment problems, it is likely to be very low in countries with the

opposite attributes.

Okigbo (1991) also points out the problem arising from the concept of labour force. In most countries,

particularly Nigeria, people below the age of 15 years and those above the age of 55, who are actively engaged in

economic activities are usually excluded from labour statistical surveys. All these factors have the tendency to

result in underestimation of unemployment thereby making international comparison very difficult. Factors such as

3

the preponderance of full housewives (but who are willing to be engaged in paid job) and unpaid family workers

also contribute significantly to the underestimation of unemployment1.

2.3

Theoretical Linkage between Productivity and Employment/Unemployment

The relationship between productivity and employment/unemployment is a complex issue. Increased

labour productivity connotes that the same volume of output can be produced with less labour. By implication, this

tends to contract employment (an increase in unemployment rate). The theoretical perspectives on this relationship

vary from one school of thought to another.

The classical economists hold the view that the relationship between employment and output is a one-way

relationship that goes from the input of labour to output2. The classical growth theory, as reflected in aggregate

production (mostly a variant of Cobb-Douglas function) derived essentially from the technical relations that make

the level of output a function of production inputs such as labour, capital, land, technology, etc. In the classical

model's steady state (conditions where the growth rate of capital stock and output are equal), the approach shows

that the rate of growth of labour force and technical progress ultimately determine the growth rate of output. And as

pointed out by McCombie and Thirlwall (1994) and Hussain and Nadol (1997), this model fails to explain the

ultimate determinant of labour force and technical progress. The premise of the classical model therefore is that the

growth rate of employment is exogenous to the growth rate of output.

This, however, does not preclude the classical economists' belief in the attaintment of a full employment

equilibrium. In this framework, the supply of labour is positively related to the level of real wage, while the demand

exhibits a negative relationship with real wage, but a positive relationship with productivity (Fashola, 1983; Todaro,

1990). As pointed out by these authors, if there is some `involuntary' unemployment at or below the current real

wage, the real wage would fail to induce employers to take more labour until all involuntary unemployment is

eliminated. However, if increases in labour productivity translate to increased wages and such increases induce the

substitution of capital for labour the effect on unemployment will be positive (Fajana, 1983; Krugman, 1994). The

policy implications of this have been viewed as misleading particularly, to developing countries (Todaro, 1990;

Hussain and Nadol, 1997). Evidence from the economic recession of the 1980s in Africa and Latin America clearly

show that real wages declined very sharply. This period of lower real wages coincided with high level of

1

2

We do not intend to do cross-country analysis, hence our unemployment data shall be restricted to the

officially published data. We believe the effect of underestimation will be relatively minor.

By referring to output instead of productivity, we invoke the Verdoorn's Law as espoused in Kaldor (1967).

The Law postulates that faster growth of output causes a faster growth of productivity. This

positive relationship is further confirmed by Dernburg (1985:55) thus: " .. a fall in output generally

brings with it a very sharp decline in productivity ...". In line with the above, both output and

productivity may be used interchangeable here.

4

unemployment than the available jobs (Todaro, 1990: 249). Also as argued by Hussain and Nadol (1997:3), the

policy implication of the neoclassical approach to primary commodities-producing countries is that, given the

existence of says Law, whatever that was produced is automatically sold irrespective of the characteristics of the

goods produced and the demand for them. Recent developments in the world market for primary commodities has

proved this to be wrong.

In contrast, Keynesian theory explains the determination of output or productivity and

employment/unemployment in terms of aggregate demand. This approach sees demand for labour as a derived

demand. Productivity growth (a la Verdoorn's Law), should increase the demand for labour thereby reducing

unemployment. The Keynesian framework, as examined by Thirlwall (1979), Grill and Zanalda (1995) and Hussain

and Nadol (1997), postulates that increases in employment, capital stock and technological change are largely

endogenous. Thus, the growth of employment is demand determined and that the fundamental determinants of long

term growth of output also influence the growth of employment.

Contrary to the strong belief of the neo-classicals that equilibrium wage rate, price, interest rate and real

cash balances guarantee the quality of national output and full-employment level, the Keynesians strongly believe in

the efficacy of aggregate demand. As shown in Figure 1, in the upper panel of the diagram, C+I+G yield a level of

national output (Y1) that is less than the potential full-employment output level (Yf). Consequently, the level of

unemployment will be given by the "gap" between Nf and N1 in the lower panel of the diagram. Rather than the

workings of the real wage, price, interest rate and real cash balances, what could guarantee the attaintment of full

employment is additional government spending from G to G1. The Keynesian prescription for reducing

unemployment is increase in aggregate total demand through direct increases in government spending or policies

that encourage more private investment. As argued by the Keynesians, as long as there is unemployment and excess

capacity in the economy, the supply of goods and services will respond automatically to this higher demand. A new

equilibrium will always be established with higher income and lower level of unemployment.

5

Figure 1

6

The extension of the Keynesian model dominated development theorizing in the 1950s and beyond. Such

extensions could be found in Okun's Law and the Harrod-Domar model. For instance, Arthur Okun developed the

relationship between the actual and potential output and between the actual and benchmark unemployment in an

equation called the "Okun's Law" thus (Dernburg, 1985):

Q* - Q

=

"(U - U*)

.............(3)

Q

where Q* is potential output, Q is actual output, U is the unemployment rate, U* is the benchmark unemployment

rate, and " is Okun's coefficient3. The implication of Okun's coefficient is that a 1 percentage rise in unemployment

causes the economy to lose " percent of its output. Okun's Law clearly gives a direct relationship between output

and unemployment and indirectly between productivity and unemployment (a la Verdoorn Law).

In a similar vein, the neo-keynesians, in their efforts to provide reasons as to why employment growth lags

behind growth of industrial output, came out with a typical variant of the Harrod-Domar unemployment equation

..........(4)

{ ) Y } over { Y } - { ) (Y/N) } over { Y/N } = { )N } over { N }

thus,

The import of this equation is that the rate of output growth (Y) minus the rate of growth in labour productivity

(Y/N) approximately equals the rate of growth of employment (N). The implication is that the gap between growth

rate of output and the growth of labour productivity accounts for the rate of labour absorption. As had been argued

hypothetically by Todaro (1990), if output is growing by 8 percent per year while employment is expanding by only

3 percent, the difference is due to the rise in labour productivity, and vice versa. By implication, rapid economic

growth could generate lagging employment creation. This tends to support Essenberg's (1996) argument that if the

reduction in labour demand resulting from productivity increases is more than compensated by overall increases in

output, then both productivity and employment can increase together. This is particularly so when higher

productivity leads to increased profit and higher rate of investment, which in turn results in higher rate of growth.

In conclusion, the neo-classical approach posits that the rate of growth of employment (unemployment) is

exogenous to the rate of growth of output (productivity). In contrast, the Keynesian argument is premised on the fact

that it is the strength of demand that determines the amount of resources utilized. As such, employment is demand

determined and the rate of output growth is itself an important determinant of the rate of growth of employment.

Thus, output, productivity and employment are determined endogenously. This approach therefore suggests the

possibility of a bi-causal relationship.

III.

3.1

PROFILE OF PRODUCTIVITY AND UNEMPLOYMENT IN NIGERIA

Trends in Productivity

The centrality of continuous productivity improvement in advancing societal development has been well

acknowledged in the literature. In spite of the general consensus on the importance of productivity, many countries

3

Okun's coefficient (") was estimated for the American economy between (1970-82) to be 3.2 percent.

7

have not paid serious attention to improving the level of productivity in their economies. Evidence from Nigeria has

shown that both the national and sectoral productivity measures have generally reflected a declining trend over the

past three decades.

Given the data limitation on total factor productivity in Nigeria, our analysis is restricted to labour

productivity. As shown in Table 1, gross productivity (i.e. real GDP per worker) consistently rose between 1973

and 1977 as a result of the appreciable improvements in the level of economic activities immediately after the oil

boom of 1973/74. The motivation associated with the Udoji salary award and the consequent spread to the private

sector also contributed to productivity improvement during the period.

The sectoral analysis clearly shows that productivity in the industrial and service sectors are higher than in

the agricultural sector (Table 2). The productivity in the former is more than three times higher than in the latter

during this period. This finding conforms with the outcome of Dike and Ezenwe (1986) who also found that

agricultural productivity was the least among the three sectors examined above. Phillips (1983) and Udokporo

(1983) provided the reasons for low productivity in this sector. Critical among the factors are: subsistence

production, prevalence of redundant labour, low income and lack of proper training on issues relating to agricultural

activities.

Total labour productivity declined consecutively from 5.53 in 1977 to 3.36 in 1983 with the highest rate of

decline experienced in 1982 (-29.53 percent) (Table 1). Meanwhile, the performance varied across the sectors.

Though agricultural productivity was at its lowest ebb during the period, it, however, increased marginally from

2.02 to 2.11 in 1983, perhaps as a result of the implementation of the Green Revolution Programme during the

period. Productivity in both the industrial and services sectors consistently declined during the period. For instance,

they declined at an annual average of 8.02 and 2.40 percent for industry and services, respectively.

The institutionalization of the War Against Indiscipline (WAI) by the Buhari/Idiagbon administration in

1984/85 yielded some positive impacts on national productivity as it recorded the highest growth rate of 20.73

percent in 1985. The ouster of this regime weakened the implementation of WAI and hence ushered in a period of

relatively low productivity. Thus, productivity dropped from 3.74 in 1985 to 3.22 in 1987 ( the lowest ever). The

introduction of the Structural Adjustment Programme (SAP) led to marginal improvement in national productivity

during the period. Though the three sectors recorded some improvements, during this period, those of the industrial

and services were more pronounced than the agricultural sector. While agricultural productivity fluctuated between

2.32 and 2.49 during 1987-1992, the industrial and services productivity fluctuated between 3.84-7.39 percent and

4.49-5.67 percent, respectively.

In spite of the improvement in real GDP between 1993 and 1996, the political upheavals experienced

during the period seriously affected the overall productivity. Thus, the rate of productivity decline fluctuated

between 0.24 and 2.03 during 1993-95 period. And as shown in Table 2, the rates of decline were much more

pronounced in the industrial and services sectors than the agricultural sector. Evidence from the Central Bank of

Nigeria's survey of industrial enterprises attributed the sector's dismal performance largely to low capacity

8

utilization and high cost of productioN4. For instance, capacity utilization fluctuated between 29.6 and 30.4 percent

during the period. This was further compounded by the increasing cost of operation which rose by 75.6 percent in

1995. This arose largely from the continuous depreciation of the domestic currency during the period.

Consequently, the cost of

Table 1: Labour Productivity in Nigeria (Gross )

Note:

The growth rate was computed on the basis of the immediate past year rather than the interval of two years

given in the table.

Sources:

Computed by the authors from CBN: Statistical Bulletin (various issues), Nigeria: Economic,

Financial and Banking Indicators (various issues); National Planning Commission: National

Development Plans (various issues); FOS: Annual Abstract of Statistics (various issues); ILO

(1996) and World Bank: African Development Indicators (various issues) and World Tables

(various issues).

4

Year

Gross Productivity ('000)

Annual Growth Rate

1973

1975

1977

1979

1981

1983

1985

1987

1989

1990

1991

1992

1993

1994

1995

1996

1974-80

1981-90

1991-96

1974-96

4.59

4.69

5.53

4.88

3.54

3.36

3.74

3.22

3.61

3.79

3.86

3.87

3.86

3.86

3.78

3.80

5.09

3.46

3.84

4.01

-

-5.77

-1.72

-1.39

-29.53

-3.93

20.73

-3.12

4.59

5.26

1.78

0.08

-0.24

-0.05

-2.03

0.58

1.71

-1.91

0.03

-0.17See the details in CBN (1995): Annual Report and Statement of Accounts, December.

9

raw materials (mostly imported) accounted for 72.3 percent of the total cost of operation while salaries and wages

accounted for only 6.6 percent (CBN, 1995). Besides the low value added that could result from these

developments, the relatively low share of salaries and wages in the total cost of production is a reflection of low

motivation in the sector. Low motivation, an important determinant of low productivity is also prevalent in the

services sector, especially the public service. For instance the index of real wages for public officers on Grade Level

08 declined from 242 in 1980 to 107, 40 and 32 in 1986, 1990 and 1992, respectively (Odusola, 1997). The same

rate of decline applied to other categories of workers in the public service.

The long-term productivity growth rate for Nigeria (1974-1996) is disappointing. It recorded an average

growth rate of -0.17 percent during the period (Table 1). This is quite disheartening when compared with the 5.0

percent in Japan for the period 1960-1990. Other countries with remarkable performances include Italy (3.8%),

France (3.5%) and Germany (2.8%) (Krugman, 1994:34).

Why is Nigeria's productivity performance so low relative to other countries? The issues raised above are

quite germane for this performance. Besides the factors raised above, inadequate training has been a major

productivity factor in Nigeria. As pointed out by the National Manpower Board (NMB) (1991), only 5.34 percent of

the total employees were sent for training in 1991 in both the private and public sectors in Nigeria. This comprises:

Federal Government Civil Service (2.60%), Federal Parastatals (5.32%), State Government Civil Service (3.94%),

State Government Parastatals (3.65%), Local Government (3.20%), Joint Ownership by Federal and State (24.87%),

Joint Ownership by Government and Private (4.26%), Purely Private Enterprises (5.14%) and Voluntary Agency

(7.79%). Given the recent endogenous growth model, which sees continuous training (human capital investment) as

a crucial factor in national productivity, then this proportion of trained staff to the total number of employees is too

small for continuous productivity growth in Nigeria.

Table 2: Sectoral Labour Productivity (Agriculture, Industrial and Services) ('000)

Year

Agriculture

Industry

Services

Productivity

Annual

Productivity

Annual

Productivity

Annual

Growth

Growth

Growth

10

1973

1975

1977

1979

1981

1983

1985

1987

1989

1990

1991

1992

1993

1994

1995

1996

1974-80

1981-90

1991-96

1974-96

2.49

2.44

2.20

2.02

2.05

2.11

2.61

2.32

2.49

2.44

2.47

2.47

2.45

2.46

2.45

2.54

2.21

2.29

2.47

2.31

-

14.86

-9.67

-7.52

-2.15

1.22

33.81

-5.75

0.57

-1.95

1.12

0.17

-1.16

0.71

-0.57

3.78

-2.04

2.01

0.68

0.41

8.56

6.23

6.31

5.87

5.82

4.78

5.00

3.84

6.72

7.11

7.39

7.04

6.69

6.62

6.34

6.43

6.48

5.16

6.75

5.73

-

-24.68

-0.91

-5.97

-4.67

-13.41

8.11

-5.51

63.35

5.85

3.88

-4.70

-4.91

-1.15

-4.17

1.43

-4.32

3.37

-1.60

-0.23

7.55

7.09

7.56

7.02

5.74

5.61

5.54

5.02

4.49

5.30

5.32

5.67

5.56

5.59

5.36

5.43

7.24

5.27

5.51

5.68

-

-7.37

-7.75

-5.50

-0.43

-1.28

11.22

2.09

-16.51

18.04

0.23

6.76

-1.88

0.49

-4.14

1.32

-3.31

-0.32

0.46

-1.02

Note:

The growth rate was computed on the basis of the immediate past year rather than the interval of two years

given in the table.

Sources:

Computed by the authors from CBN: Statistical Bulletin (various issues), Nigeria: Economic,

Financial and Banking Indicators (various issues); National Planning Commission: National

Development Plans (various issues); FOS: Annual Abstract of Statistics (various issues); ILO

(1996) and World Bank: African Development Indicators (various issues) and World Tables

(various issues).

Evidence from NCEMA and ASCON (2000) also identified low labour compensation (remuneration and

motivation), inadequate training, political interference, and inadequate provision of opportunity to use talents and

initiatives effectively as the bane behind low productivity in the Nigerian public sector. In addition to some of these

factors, Balogun (1983) and Oloko (1983) also identified lack of technical support staff and equipment, ineffective

supervision and gross indiscipline as important constraints to civil service productivity. This clearly shows that

factors militating against productivity growth in Nigeria are multi-dimensional.

3.2

Trends in Unemployment

The problem of unemployment has posed a great challenge to many countries (both developed and

developing). In recent times, the incidence of unemployment in Nigeria has been deep and widespread, cutting

11

across all facets of age groups, educational strata and geographical entities. One peculiar feature of the

unemployment problem in Nigeria is that it was more endemic in the early 1980s than any other period (a la official

statistics). This is clearly evident in Table 3. For instance, the unemployment rate rose from 4.3 percent in 1976 to

6.4 percent in 1980. Though it recorded some marginal decline between 1981 and 1986, the rates were relatively

higher than what obtained in the 1960s and 1970s. The unemployment rate oscillated

between 5.3 and 6.4 percent during 1980 - 85 period. This development was as a result of the lull in the economy

during the period. The economic down-turn did not only discourage new investment but also forced government to

implement stabilization measures including restrictions on importation. Given the high import-dependency of most

manufacturing enterprises, the import restriction forced many companies to operate below installed capacity,

causing most of them to close down or retrench a significant proportion of their workforce. For instance, the survey

of manufacturing companies undertaken by the Manufacturers Association of Nigeria (MAN) showed that 61.0

percent of the companies surveyed were shut down for different periods of not less than three months while between

62.0 and 63.9 percent of them disengaged over 100 workers (CBN; 1993). This development made job placement

for fresh school leavers to be exceedingly difficult. In addition, the government also placed embargo on

employment from September 1981, though relaxed in some periods (e.g. November 1982). This was implemented

pari-passu with the public sector retrenchment. Accordingly, the total disengagement from

Table 3: Nigeria: Unemployment Rates by Urban, Rural and National Classification (1976 - 1997)

Year

Urban

Rural

National

12

1976

1980

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

-

-

7.9

9.8

9.1

9.8

7.8

8.1

5.9

4.9

4.6

3.8

3.2

3.9

3.9

8.5

-

-

4.4

5.2

4.6

6.1

4.8

3.7

3.0

2.7

3.2

2.5

1.7

1.6

2.8

3.7

4.3

6.4

6.2

6.1

5.3

7.0

5.3

4.5

3.5

3.1

3.4

2.7

2.0

1.8

3.4

4.5

Sources:

Data for 1976 and 1980 were obtained from FOS (1997:99) while the rest were compiled from:

CBN - Nigeria: Major Economic, Financial and Banking Indicators , April 1998.

the federal civil service rose from 2,724 in 1980 to 6,294 in 19845. The Structural Adjustment Programme (SAP),

adopted in 1986, had serious implications for the short run unemployment problem. Contrary to the expectations of

SAP, which was geared towards encouraging greater employment opportunities in the private sector (especially

among the small-medium enterprises), the unemployment rate rose from 5.3 percent in 1986 to 7.0 percent in 1987.

This was partly accounted for by the organizational down-sizing, re-engineering and rationalization policies which

accompanied the introduction of SAP, especially in the private sector. This was further compounded by the

continuation of staff retrenchment and placement of embargo on employment in the public sector. Besides, the new

policy orientation brought about some structural changes within the Nigerian labour market. Sectors such as the oil,

banking and the external sectors became the "blue chips" as against the public and industrial sectors which used to

be the "prime" of the labour market prior to the adoption of SAP in 1986. This development consequently created

some structural and frictional unemployment problems in the country. When this structural and frictional

unemployment is considered along with the lack of job placement for fresh graduates, the situation becomes more

precarious. As pointed out by Umo (1996), an annual average of about 2.8 million fresh graduates enter the

5

For details see the Annual Abstract of Statistics of the Federal Office of Statistics (various issues), Lagos

13

Nigerian labour market, with only about 10 percent of them getting employment. This, no doubt, portrays

unemployment as a very serious problem in the country.

Evidence from Table 3 shows that unemployment fell very significantly after 1987. It fell consistently

from 7.0 percent in 1987 to 3.1 percent in 1991. Although it rose marginally to 3.4 percent in 1992, the

unemployment rate, however, consistently declined appreciably to 1.8 percent in 1995 before rising to 3.4 and 4.5

percent in 1996 and 1997, respectively. However, the estimated unemployment gap for Nigeria, indicates that the

unemployment rate varied between 7.27 and 8.0 between 1990 and 19986. Why is the gap between the estimated

and the actual unemployment rate as high like this? Raheem (1993) and Ohiorhenuan (1986) exp lained that only

recorded open unemployment is published by the official statistics. Many people who felt disenchmented with

searching for jobs refused to register thereby leading to gross under-estimation of the unemployed. Okigbo (1986,

1991) also pointed out that the concept of labour force adopted in the Nigerian Labour Force Statistical Survey,

which

excluded people that were less than 15 and above 55 years but actively working, is an important factor for gross

underestimation of unemployment in the country. This is further compounded by gross inconsistency in government

documents. For instance, all surveys prior 1983 used 55 years as the cut-off point for working age but in 1983, it

was raised to 59 years which was later raised to 64 in 1997. Yet, some categories of people above the age of 64 still

remain government employees e.g. Judges. This again gives room to underestimation. As argued by Okigbo

(1991), it also excludes people who have been categorized as incapable of working but are willing to work (e.g. the

handicapped). Also excluded from the labour force are the full housewives who are willing to be engaged in a paid

job. The preponderance of unpaid family workers as a proportion of active workers, as presented by the World

Bank (1999) is also a potential source of underestimation of unemployment or underemployment in the country.

Thus, taking cognisance of the above, Okigbo (1991: 13), estimated the unemployment rate for 1986 to be 28

percent.

6

For details see Federal Republic of Nigeria: Fourth National Development Plan (1981 - 1985) , Federal

Ministry of National Planning, Lagos, and Federal Republic of Nigeria: National Rolling Plan ,

Abuja (Various Issues).

14

In spite of the differences, the official unemployment rate appears to be on a declining trend. The observed

downward trend may be attributed partly to the intensification of the implementation of the Agricultural

Development Programmes (ADPs) and the Accelerated Development Area Programmes (ADAPs). The latter was

later transformed into the Directorate of Food, Roads and Rural Infrastructure (DFRRI). The activities of the

National Directorate of Employment (launched in 1986), the Peoples Bank, Better Life for Rural Women

Programme, among others, may have also accounted for the decline. The intensification and expansion of the

informal sector activities could also be an important factor during this period. Besides the consistent view of the

CBN's annual reports on this issue, the evidence from DPC (2000) also shows that the informal private sector

expanded in scope of activities and in pattern of employment, with more graduates participating in the sector.

Available data also suggest that unemployment rates vary by rural-urban residence, education, age,

professional classification and states. Evidence from Table 3 shows that the average annual rate of unemployment

was higher in the urban areas than in the rural areas for each year between 1984 and 1997. The influx of rural

dwellers into the urban centres in search of better employment opportunities could have accounted for the observed

pattern.

The dynamics of the linkage between educational status and the unemployment rate in Nigeria is of crucial

importance. In the 1970s, the people most seriously affected by unemployment were those with no schooling or

those with primary education. As shown in Table 4, "no schooling" category accounted for 22.6 and 65.4 percent of

the unemployed in 1974 and 1976, respectively, while the primary school leavers correspondingly accounted for

64.3 and 26.5 percent. The incidence of unemployment on these categories of people declined very significantly in

the 1980s and 1990s. The severity of this problem varies according to

Table 4: Composite, Urban and Rural Distribution of Unemployed by Educational Level December 1993 to

December 1997 (Per Cent).

Educational

No. Schooling

Primary

Secondary

Post Secondary

All Levels

Level

Composite

1974

1976

1983

1985

Dec. 1990

Dec. 1992

22.6

65.4

7.1

22.6

12.2

19.1

64.3

26.5

43.5

23.9

22.9

10.4

11.8

0.3

48.7

51.1

60.9

65.6

0.3

0.0

0.2

3.3

4.0

4.9

100.0

100.0

100.0

100.0

100.0

100.0

15

Note:

Sources:

The Data for Primary for the period 1974 - 1985 contained below primary and primary education

levels.

The figures for 1974 - 1985 were compiled from Ige, C. S. (1986:20) "Unemployment in Nigeria:

Spatial and Sectoral Patterns and Trends," Annual Conference of the Nigerian Economic Society

1986, Kaduna, May 13 - 16, pp. 20, while those for 1990 and 1992 were obtained from FOS

(1997:101). The data for 1993 - 1997 were compiled from Federal Republic of Nigeria: The

Economic and Statistical Review , The National Planning Commission, Abuja (1996 - 1998 issues)

residential classification. For instance, while the problem was more severe for the "no schooling" rural dwellers, the

primary school leavers residing in urban centres had a greater burden than their rural counterparts. In contrast, the

incidence of unemployment on secondary school and post secondary school leavers increased very substantially

during the period.

The evidence from the educational classification is further reinforced by the evidence from the registered

unemployed. As shown in CBN (1997: 170 and 171), more than 90.0 percent of the registered unemployed belong

to the lower level workers. The number of this category of people registered with the Ministry of Employment,

Labour and Productivity rose from 11,732 in 1970 to 23,239 in 1975 and 256,623 in 1980. The figure however

declined thereafter. In contrast, the number of registered unemployed professionals which dropped from 518 in

1970, to a mere 135 in 1978, rose very remarkably from 1984. It rose from 2,514 in 1984 to 16,293, 22,206 and

32,942 in 1988, 1992 and 1995, respectively. This represents 1.8, 12.3, 19.7 and 28.7 percent of the total registered

Dec. 1993

Dec. 1994

June 1995

Dec. 1996

Dec. 1997

17.2

13.3

16.2

48.0

21.1

17.9

13.2

13.4

10.8

11.8

60.9

68.7

59.5

52.8

46.2

4.0

4.8

5.8

18.4

20.9

100.0

100.0

100.0

100.0

100.0

Urban

Dec. 1993

Dec. 1994

June 1995

Dec. 1996

Dec. 1997

15.3

16.3

17.7

6.8

13.4

17.7

17.2

18.8

11.9

16.8

60.0

71.8

58.3

62.7

48.3

7.6

4.7

5.2

18.6

21.5

100.0

100.0

100.0

100.0

100.0

Rural

Dec. 1993

Dec. 1994

June 1995

Dec. 1996

Dec. 1997

17.6

14.8

9.4

20.4

22.8

17.9

12.3

16.8

10.6

10.7

61.1

68.0

65.4

50.7

45.7

3.4

14.9

8.4

18.3

20.8

100.0

100.0

100.0

100.0

100.0unemployed people, as opposed to an annual average of 1.7 percent between 1970 and 1978.

16

The demographics of unemployment is shown in Table 5. Unemployment has been unevenly distributed

across the age groups with young people bearing the burden of unemployment. As shown in the table, the

unemployed persons are mostly youths aged 15 - 24 years. The proportion of this category of unemployed

fluctuated between 41.6 and 70.4 percent during 1993 - 1997 period. It recorded an annual average of 56.3 percent

during the period. This observation is a reconfirmation of the dominance of secondary school leavers among the

unemployed, since most of them fall into this age group. Another prominent age group is 25 -44. It is worrisome to

observe that while the percentages of other groups unemployed have been declining consistently over time, those of

this group have been on the upward trend. This perhaps portends the widening gap between the output produced by

the tertiary institutions and the skill requirements of the labour market. The rising trend of graduate unemployment,

as observed by many analysts, may have contributed very significantly to the rising wave and sophistication of

crime in the country (e.g. Albert, 2000). As also shown in Table 5, an inverted U-shaped trend is observed for the

age group 45 - 59, with 1995 recording the peak of 13.8 percent. The current wave of self-employed activities may

have partly accounted for this observation. The inclusion of age group 60 - 64 in the current labour force statistical

survey

Table 5:

Unemployment by Age Groups (1993 - 97)

(Per Cent)

Source: Compiled and Calculated from FOS: Annual Abstract of Statistics 1998.

is an advancement on the previous exercises. The inclusion of this set of people will reduce, to some extent, the

wide gap between the published unemployment rate and the actual one. The exclusion of this group in the past led

to serious underestimation of unemployment.

In recent times, attempts have been made to characterize unemployment by its duration (long and short

term unemployment). The increase in duration of unemployment represents the most serious labour market

15-24

25-44

45-49

60-64

1993

69.0

25.2

5.8

N.A

1994

70.4

21.0

8.6

N.A

1995

57.5

28.7

13.8

N.A

1996

42.9

46.0

11.1

N.A

1997

41.6

49.7

6.0

2.7

Annual Average

1993-97

56.3

34.1

9.1

-development. Long term unemployment has become a chronic problem in Nigeria (Okigbo, 1986; Oladeji, 1994).

17

As pointed out by Oladeji (1994), 75.5 and 13.61 percent of those sampled in the Graduate Employment Tracer

Study of the Manpower Board in 1986 has been unemployed for 13 - 34 and 25 - 30 months, respectively. Only

10.8 percent were unemployed for the duration of 1 - 12 months. This type of unemployment has been linked to job

transition patterns. This approach emphasizes hiring people from the public sector by the private sector, or between

firms, than from the unemployed people. It thereby makes the pool of the unemployed to be increasingly

homogenous. The risk attached to long- term unemployment has been well acknowledged in the literature (e.g.

Okigbo, 1986; Alhson and Ringold, 1996). The longer an individual is unemployed, the more difficult it is to find

work. It is therefore important to put up active labour market programmes for this category of people.

The national unemployment rates mask the peculiarities of the states. For instance, states such as

the Old Bendel, Imo, Rivers and Cross Rivers generally experienced very high unemployment rates as

opposed to the low rates experienced in Niger, Katsina, Kwara and Kano. Rural unemployment was

common in Borno and Kwara States while Anambra, Lagos, Plateau, Sokoto, Ogun and Oyo mostly

experienced high urban unemployment rates. (See FOS (1985:112-123) and FOS (1990:269-270) for

details). An important feature of this approach is the gender structure of unemployment. As shown in

Table 6, about 19 states (including Abuja) of the Federation clearly indicate higher female unemployment

rates, with twelve of them from the northern part of the country. This perhaps indicates that more females

are now interested in paid employment. An important feature of female unemployment is that, this period

coincided with the time of high female criminality. As pointed out by Oloruntimehin (2000), since 1980s,

female criminality has not only increased in number but has also become more serious and significant

over the years. The existence of this linkage therefore calls for an urgent attention to female

unemployment in the country.

The incidence of underemployment or disguised unemployment has been acknowledged in the

literature as a serious constraint to economic progress. In fact, its effects could be worse than those of

open unemployment (Raheem, 1993). FOS (1997) considers underemployment as a reflection of the

extent to which some human resources are rendered potentially idle.

This problem has contributed significantly to the widening gap between the reported and actual

unemployment in Nigeria. Underemployment has been particularly high in the country. In 1984, 7.1 and

21.1 percent was recorded for the urban and rural areas, respectively. This later rose to 11.2 (urban) and

28.7 (rural) percent in 1992. As shown in Table 7, underemployment rates were higher in the rural areas

than the urban centres. In almost all the cases, the rural underemployment rate is twice the rate of urban

18

underemployment. Besides, irrespective of the place of residence, female underemployment has been

higher than that of their male counterparts. The predominance of full housewives in the labour force may

partly account

19

Table 6: Unemployment Rates By States in Nigeria (1991 and 1993)

Source: National Population Commission (1998): 1991 Population Census of the Federal Republic of Nigeria; Analytical

Report at the National Level, Abuja. The figures for 1993 were obtained from FOS (1997): Socio-

Economic Profile of Nigeria 1996, Lagos , p. 102.

Table 7: Under-employment Rates in Nigeria (1984 - 1996)

Year

Urban

Rural

Male

Female

Total

Male

Female

Total

December 1984

December 1992

September 1993

June 1996

1997

7.1

9.5

17.3

8.9

NA

8.1

14.3

18.0

14.1

NA

7.1

11.2

16.4

11.2

9.8

21.1

27.8

20.0

20.0

NA

25.3

30.4

24.9

20.6

NA

21.1

28.7

21.8

20.6

10.7

Source: Compiled from FOS (1997: 103). The data for 1997 were sourced from CBN: Annual Report and Statement of

Accounts , 1997.

States

1991

1993

Both Male and Female

Male

Female

Male and Female

Unemployed

Population

Unemployed

Rate

Unemployed

Population

Unemployed

Rate

Unemployment

Population

Unemployment

Rate

Unemployment

Rate

Abia

Akwa-Ibom

Adamawa

Anambra

Bauchi

Benue

Borno

Cross Rivers

Delta

Edo

Enugu

Imo

Jigawa

Kaduna

Kano

Katsina

Kebbi

Kogi

Kwara

Lagos

Niger

Ogun

Ondo

Osun

Oyo

Plateau

Rivers

Sokoto

Taraba

Yobe

Abuja

Nigeria

79,335

76,021

31,589

49,322

32,425

30,129

23,526

50,534

64,824

56,030

77,707

92,792

18,772

46,331

39,580

21,734

8,160

47,655

11,135

92,825

16,622

15,053

42,086

13,728

20,208

33,500

176,214

11,401

13,861

9,544

8,900

1,311,603

9.0

9.2

5.1

4.8

3.2

3.6

3.1

7.8

7.2

7.6

7.0

11.8

3.1

5.0

3.0

2.8

1.7

6.6

1.8

3.7

2.5

1.4

2.9

1.6

1.3

3.9

12.6

1.1

3.2

2.7

6.8

4.7

37,856

40,999

21,522

21,778

21,413

21,506

15,197

29,680

38,992

35,592

34,828

42,663

14,023

30,400

28,799

16,074

5,841

27,323

5,718

53,171

11,522

8,067

23,246

7,255

11,122

22,236

102,529

7,611

10,249

6,693

5,910

753,909

8.3

9.9

5.0

3.8

2.5

4.3

2.7

8.4

8.5

8.8

6.1

10.3

2.6

4.1

2.5

2.3

1.5

7.3

1.8

3.6

2.2

1.5

3.3

1.8

1.5

3.6

13.1

0.9

3.2

2.3

5.6

3.4

41,479

35,022

10,067

27,544

11,012

8,623

8,329

20,854

25,832

20,434

42,879

50,129

4,749

15,931

10,981

5,660

2,319

20,332

5,417

39,654

5,100

6,986

18,840

6,473

9,086

11,324

73,685

3,790

3,612

2,851

2,990

548,794

9.7

8.5

5.3

6.0

7.5

2.5

4.5

7.0

5.9

6.2

7.7

13.4

6.9

9.4

5.8

7.0

3.3

5.8

1.8

3.8

3.4

1.3

2.5

1.3

1.2

4.9

12.0

2.8

3.1

5.0

11.3

5.3

4.2

5.4

1.5

2.8

1.0

1.2

0.5

3.4

5.9

5.1

3.5

9.1

0.2

3.6

1.3

0.5

0.6

2.8

0.7

2.8

0.5

1.7

1.1

1.6

1.3

1.4

7.4

0.1

0.9

0.2

4.2

-

20

for the higher rate of female underemployment. A large proportion of unpaid family workers as a share of active workers

which was estimated by the World Bank (1999: 285) at 23.5 percent could also be a factor contributing to the bourgeoning

rate of underemployment in Nigeria. To further reinforce the reason for higher female under-employment, we decompose the

unpaid family workers-active workforce ratio into gender classification. The females constituted 14.9 percent as opposed to

8.6 percent for male.

The rates of underemployment also vary across the states. For instance, in 1993 high rates of

underemployment featured in Enugu (5.74%), Ondo (3.50%), Sokoto (5.12%), Adamawa (4.80%) and Taraba

(4.61%). States with less than 1 percent underemployment rate were Delta, Abia, Cross Rivers, Oyo, Kaduna, Kogi

and Niger. Female underemployment was also serious in the following states. Jigawa (10.4%), Sokoto (10.13%),

Taraba (7.5%), Adamawa (7.13%) Enugu (5.4%) and Bauchi (5.15%) (FOS, 1997).

The seriousness of the unemployment problem has attracted government attention over the years.

Employment generation featured prominently in the past medium-term National Development Plans (1962 - 1985).

This led to the establishment of several government parastatals (whose primary objective was to create employment

opportunities) in addition to the creation of institutions such as the Industrial Training Fund (ITF), to drastically

reduce the problem of underemployment. The adoption of Structural Adjustment Programme also ushered in the

National Directorate of Employment (NDE) whose primary responsibility was to generate employment

opportunities with emphasis on the development of entrepreneurship and self employment. Besides NDE, other

programmes, with employment implications, established by the government include: the Directorate of Food, Roads

and Rural Infrastructure; the Better Life for Rural Women/Family Support Programme; the Development of Small-

Medium Scale Enterprises; the Raw Materials Research and Development Council; the Peoples' Bank of Nigeria and

the Community Banks. The current poverty alleviation programme also focuses on the unemployed. In spite of

these efforts, unemployment remains a grave problem in Nigeria.

3.3

Trend Analysis of Productivity and Unemployment

A review of the existing descriptive analysis of the linkage between productivity and unemployment shows

some degree of variations. Maddison (1982) showed that the growth of total employment since 1970 paralleled that

of real GDP in industrial countries. They both accelerated and decelerated in the same direction. By implication,

productivity and unemployment are inversely related. Schaik and Groot (1997) also presented the European

countries' experience of high growth of industrial productivity with unprecedented low rates of unemployment in the

21

1950s and 1960s. Grilli and Zanalda (1995) also observed that growth of total employment maintained a positive

relationship with real GDP in developing countries between 1960s and 1980s. In contrast, Krugman (1994) found

no visible pattern among some developed countries between productivity and unemployment. Some countries with

the best unemployment performances turned out to be the worst productivity performances. What is the pattern of

relationship between productivity and unemployment in Nigeria? A brief highlight of the stylized facts is provided

below.

A cursory look at Figure 2, shows that for most part of the period of analysis, unemployment and

productivity moved in opposite direction. For instance, between 1981 and 1990, periods of high rate of

unemployment were associated with period of declining/low productivity. Labour productivity was relatively higher

between 1990 and 1996 than what obtained in the 1980s, and the unemployment rate declined up to 1995. The wide

gap between unemployment and productivity between 1991 and 1996 tends to suggest that productivity and

employment were correlated during the period.

The trend analysis seems to suggest an inverse relationship between unemployment and productivity, thus

supporting a positive linkage between employment growth and higher productivity. However, it is difficult to use

this type of analysis to determine the direction of causality between the variables, hence one cannot clearly show

which of the theoretical postulates holds in the Nigerian situation. This, therefore, informs the use of causality tests

as is done inthe next section.

IV.

EMPIRICAL LINK BETWEEN PRODUCTIVITY AND UNEMPLOYMENT IN

NIGERIA

4.1

Methodology

The existence of correlations in descriptive analysis may not necessarily imply causality as two variables

may show some correlations even when they are not directly related. It might be possible that they share the same

trend from a third variable i.e. an external factor may influence the two variables in the same way. The use of

causality tests, therefore provides the opportunity to carry out a more scientific analysis of the issues in question. As

argued in the literature, the use of causal hypotheses makes scientific analysis more determinate and the resulting

conclusions more specific.

22

23

Figure 2

24

The commonly used causality tests in econometric modelling are Granger and Sims tests. While the former

uses the lagged values of a particular variable to explain the behaviour of another variable, the latter uses lead

values. The loss of degrees of freedom often associated with the use of the Sims approach makes its application

restricted in econometric analysis. Hence this study employs the Granger causality test.

The standard Granger causality test examines whether past changes in one variable, X (say, productivity)

help to explain the current changes in another variable Y (e.g. employment/unemployment), over and above the

explanation provided by past changes in Y. If, otherwise, then one concludes that X (productivity) does not Granger

cause Y (employment/unemployment). To determine whether causality runs in the other direction, from Y to X (or

employment/unemployment to productivity), one simply repeats the experiment, but with X and Y interchanged.

.....(5)

y_t = SUM from { i=1 } to { k } "_i { Y } _ { t-i } + SUM from { i=1 } to { k } beta_t~X SUB {t-i} +~epsilon _t

.....(6)

X_t = SUM from { t=1 } to { k } { 1_i } X _ { t-i } + SUM from {t=1 } to {k } { (_t } Y_ { t-i } + V_t

The above scenario may be given in a Granger causality sense thus:

where y and x could stand for either of the variables under consideration (productivity,

employment/unemployment). If ß i = ß2 = ..... = ß k = 0 then, x does not Granger cause y, hence, we accept the null

hypothesis. The same applies to equation 6.

The use of Granger causality test is an important scientific way of determining the direction of causation.

However, determining the nature of the relationship is outside its scope. This, therefore, informs the fitting of

simple regression equations, with a view to making the conclusions and policy deductions more determinate and

focussed. Depending on the outcome of the Granger causality tests, a bivariate model is fitted with any of the

variables (productivity or unemployment) serving as the dependent variable and the other serving as the explanatory

variable, with an adjustment mechanism of one lag and a disequilibrium term. The simplicity of this model does not

warrant an explicit specification here.

The data for this analysis were obtained from many sources: FOS, Annual Abstract of Statistics (various

issues) and Social Statistics in Nigeria (various issues); CBN, Statistical Bulletin (various issues) and Nigeria: Major

Economic, Financial and Banking Indicators , April 1997; ILO, Employment Policy Strategy Formulation Mission to

25

Nigeria , 1996, and International Labour Statistics and World Bank: African Development Indicators , World

Development Indicators (various issues) and World Tables (various issues).

4.2

Empirical Results

The Granger Causality tests carried out examine the direction of relationships between productivity and

employment, and productivity and unemployment. In order to get a clearer picture of the structure of production

and employment, the economy is divided into three sectors: agriculture, industry and services. However, the non-

availability of public data on the services sector unemployment could not allow us to consider the services sector in

the analysis. The results of the Granger Tests are in Table 8.

Evidence from productivity and employment linkage shows bi-causal relationships in all the cases except in

the agricultural sector. This evidence tends to reject the neoclassical framework for productivity and employment

linkage, which proposes a unidirectional relationship running from employment to output. As shown in Table 8, bi-

causal relationships exist between industrial employment and industrial productivity. However, this could not be

established in the agricultural sector. The rejection of the existence of a feedback relationship running from the

sector's employment to productivity could be due to the prevalence of redundant workers in the sector. The

historical antecedent of the sector tends to support the result. For instance, the sector constituted the largest sectoral

employment in the country. As pointed out by ILO (1996), the sector employed 71.7, 60.0, 60.7 and 59.8 percent of

the total workforce in 1960, 1980, 1990 and 1996, respectively. Thus, given the subsistent nature of the sector's

production, the tendency of diminishing marginal productivity seems operative. Thus, increased productivity in the

sector may not require additional employment but rather an optimal utilization of the existing underutilized

resources such as labour