Labor Markets and Enterprise Training in African Manufacturing

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Draft: 24 January 2003

Labor Markets and Enterprise Training in African Manufacturing∗

Andrew Dabalen, Helena Skyt Nielsen Michael Rosholm 1818 H St. NW, Department of Economics Department of Economics MSN J7-702, Aarhus School of Business University of Aarhus, The World Bank, Prismet, Silkeborgvej 2, 8000 University Park, Bld 326 Washington DC 20433 DK-Aarhus C DK-8000 Aarhus C Phone: 202-473-9159 Phone: 45-8948-6412 Phone: 45-8942-1559 Email: [email protected] Email: [email protected] Email: [email protected] Abstract: This paper explores the current on- the-job training by enterprises in the manufacturing sectors of five African countries. Unlike the predic tions of the competitive models of training, we find that enterprises pay for and provide general and specific training. In the context of imperfectly competitive markets, concerns about enterprises failing to invest in general skills, which are essential for preparing workers not only with the skills of the firm, but a wider range of skills that can benefit the economy and the worker by creating a more adaptable workforce, may be over-emphasized. We also find that firms that are foreign or large provide more training than domestic and small firms. Among workers, those with more education receive more training. These findings point to the selectivity of access to training. Those in the favored tradeables sector will more likely have access to skills development for future income growth, as will those with higher levels of education. Exporting industries use higher levels of technology and, therefore, more educated workers. The importance of this is that there would be a large number of other workers outside these sectors and with lower levels of education, who would have lower access to skills development through the enterprise. This offers some guidance to targeting of training access on equity grounds to non-tradeables and to workers with lower educat ion levels. Finally, we show that despite non-competitive nature of labor markets, trained workers receive significant wage premiums using propensity score matching methods. The policy importance of this finding should not be under-estimated. It tells us that the incentives for private investment in skills development are there in enterprises. It is a mutually beneficial situation for enterprises and workers with access to this enterprise training. Further incentives for enterprise training may be unnec essary for some workers. Again, this re-enforces the argument for targeting of public incentives for skills development. It does raise a policy issue for future research involving the potential widening of income distribution between those with access to training and those who have not. This is a concern for equity in many countries where growth and development and their benefits are unevenly distributed, and should be monitored over time. Our findings suggest that targeted subsidies, especially at low skilled workers and employees in small enterprises, supplemented by regulation to certify skills and monitor training quality hold the potential to improve overall training in Africa. Targeting access to training can make sense under the conditions found in this study. The case for certification of skills and quality is more subtle. It may be important in an environment where there is asymmetric information (a) on the skills of the worker that the employer cannot judge and appropriately value in terms of wages, and (b) on the quality of training provided by employers contracted by a government agency to provide training, both of which are common under imperfectly competitive markets. JEL: J31, J41 Keywords: Non-competitive labor markets, returns to training, matching models, Sub-Sahara Africa.

∗ The background studies for this paper were funded by the Africa Human Development Network, World Bank, as part of its Vocational Skills Development in Sub-Sahara report. The views and interpretations expressed in this study are solely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors or its affiliated organizations. We would like to thank Arvil Van Adams, Richard Johanson, Hong Tan, and P. Zafiris Tzannatos for invaluable comments.

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1. Introduction. For many years, governments of Sub-Sahara Africa invested significant resources in

improving the skills of their labor forces through vocational training, in addition to the

general-purpose education, for numerous reasons. First, vocational training was

regarded to be closer to the spirit of on-the-job training. The latter is believed to be

more responsive to the market as it allows for continuous learning and adaptation to new

technologies , which general-purpose education has failed to do in Africa. Second,

vocational training, which can be delivered at short duration, was viewed as an

alternative route to expand access to training. Such training was also believed to be a

vehicle for achieving social goals and equalizing opportunities, especially when targeted

at low education workers. Finally, and more persuasively, public provision of direct

training was justified on the assumption that firms would not have an incentive to

provide genera l training in competitive markets where worker mobility was common

(Acemoglu and Pischke, 1999).

Therefore, although the reasons for and the performance of public intervention in on-

the-job training are well known, less is known about the reasons for and the extent to

which enterprises in Sub-Sahara Africa provide such training. This paper explores the

current practice in on-the-job training by enterprises in African manufacturing. It

provides evidence for three inter -related questions. First, under what environment

would enterprises provide on the job training? Second, how much training takes place,

and for what skills and kinds of employees, do employers provide on the job training?

And finally, what is the size of benefits to training in African ma nufacturing? Our study

finds four main results. First, we find that both general and firm-specific training are

widespread. Moreover, we find that manufacturing enterprises in Africa bear a

substantial cost of employee training. Second, workers with more education and those

working in occupations requiring higher skills also receive more training. Third, larger

and foreign-owned firms provide more training than smaller and domestic -owned firms.

Finally, we find that the returns to enterprise-based training in Sub-Sahara Africa are

substantial. Our estimates indicate that the median wage premiums received by those

who train range between 15% and 21%.

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The rest of the paper is organized as follows. Section 2 provides a brief description of

the rationale for why firms will pay for, and provide training to, their employees.

Section 3 provides the evidence on the extent of enterprise training in the manufacturing

sectors of five African countries. Section 4 assesses the benefits of training, using recent

evaluation methods in social studies. Section 5 explores some policy implications for

training in Africa in light of the evidence, while section 6 concludes.

2. Why Do Enterprises Provide and Pay for Training?

The standard theory stipulates that under competitive markets, firms will not provide

any training because workers, whose wages reflect their marginal product, will receive

all the benefits of training. Therefore, since enterprises receive no benefits from the

training of their workers, they will under-invest, or worse not invest at all, and this

would lead to market failure. Becker’s (1964, 1975) work was the first to challenge this

view. By separating training into general and firm-specific, he argued that there need

not be any market failure in training at all. General skills are defined as those that are

also useful to other employers, and specific skills as those, which can be used only at the

worker’s current job. He then argued that, even under competitive markets, only the

benefits of general training would be received entirely by the worker. In contrast,

workers will not benefit from increased productivity arising from training in specific

skills if they changed jobs, as their skill will be useless outside of their current job. This

means that, under competitive markets firms will not pay for general training. Instead,

the workers, who are the sole beneficiaries of the higher productivity from training, will.

However, firms will be willing to share in the cost of specific training, as they benefit

from productivity increase from such training. Furthermore, since workers will be

willing to pay for general training either directly out of their pockets, through borrowing

or by accepting lower wages, there is no reason for market failu re, as long as the credit

markets are working well. In this case, public action should focus on facilitating the

proper functioning of credit markets.

Studies of training since Becker, have found numerous instances in which the evidence

contradicts his conclusions (Bishop, 1991: Barron, Berger and Black, 1997). These are

instances where firms have been found to finance training in general, in addition to

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specific, skills. The German apprenticeship system and the temporary agencies in the

United States are two examples (Acemoglu and Pischke, 1999). Becker’s theory is

inadequate to understand such practices. Since his prediction rests on the assumption

that labor markets are competitive, which ensures that workers are paid their marginal

product, it is not surprising that theories that provide better explanation to empirically

observed training patterns will start with a look at non-competitive behavior in labor

markets, which is increasingly recognized as being widespread (Bhaskar, Manning and

To, 2002; Acemoglu and Pischke, 1999; Idson and Oi, 1999).

To see why firms will pay for general training, suppose that all training is general1. A

worker with training level τ has a productivity level )(τf and receives a wage level

)(τw . Under competitive markets, )()( ττ wf =′ , and all the productivity increase from

training will be captured by the trainee. Therefore, the firm will have no incentive to

train. However, under imperfect markets (e.g. oligopsonistic or monopsonistic

competition), marginal product need not be equal to wage. For a number of reasons, the

firm can set the wages below the marginal product. This way, the firm can recover the

costs of training, which are often paid up-front. The ability of the firm to extract rents

from a worker provides one incentive for the firm to provide general training. One way

for the firm to do this is to compress the wage structure so that wages rise more slowly

than productivity. This means that the gap between productivity and wage is higher at

higher levels of training. Consequently, the firm’s profits are higher at higher levels of

skills, and this provides a strong incentive for it to provide and pay for the general

training of its workers. Another source of training is the complementarity between firm-

specific and general training. To the extent that firm-specific training makes general

skills more productive or vice -versa, the firm will have an incentive to train its workers.

Moreover, labor market institutions that compress wages such as minimum or unionized

wages, could encourage the firm to provide and pay for wages. This happens because

with compressed wages, productivity increases from training will be captured by the

firm.

The potential existence of non-competitive markets where firms exercise pricing power,

create a condition where wages are set below the marginal product and skills from

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general training are treated as if they are specific (Acemoglu and Pischke, 1999). In

such an environment, firms will have an incentive to train their workers in specific and

general skills. How do the predictions of the non-competitive theory of labor markets

accord with observed training practices in Africa? In the next section we provide some

supporting evidence.

3. The Extent of Training in African Manufacturing Enterprises.

This section provides the evidence on the extent of training in African manufacturing

enterprises. After a brief description of the data, we highlight key observations in the

prevailing practices in training within African manufacturing enterprises.

3.1 Data Sources This study uses data from the Regional Program on Enterprise Development (RPED)

project for Côte d’Ivoire, Ghana, Kenya, Zambia, and Zimbabwe. The inf ormation is

drawn from a sample of 250 firms within the manufacturing sector in each of the

countries, who were surveyed repeatedly, two or three times, between 1993 and 1996.

The surveys contain various sources of information regarding the extent of training. For

example, in certain rounds of the multi-year surveys, questions on training were asked

both at the firm level, specifically owners and managers, and to selected workers at each

firm.

In one set of questions the firms’ managers or owners were asked about the extent of

training they offered to their workers. Training is provided in two forms: formal training

and informal training. Formal training is defined as training provided by training

specialists employed in the firm, in-house training provided by external trainers, and/or

training outside the company. Informal training is defined as the use of

foremen/supervisors to provide informal instructions and training to workers.

The worker-reported levels and types of training rely on information from roughly 1000

employees in each country. The individual workers were asked whether they received

1 The exposition of the theoretical section follows Acemoglu and Pischke, 1999.

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any training currently or in the past. In response to these questions, they reported if they

had 1) no training, 2) training in the firm, 3) training outside the firm, and in some cases

(4) training in and outside the firm. For two countries, Kenya and Zambia, there is a

more detailed set of question regarding the type, timing, and extent of training received

in the 1995 survey. The information obtained from this source is much more detailed

and provides a much richer picture concerning the extent of training among workers.

With regard to training, the questions asked whether it was obtained, i) at a school,

vocational or training institute, ii) at an industry association training center, iii) through

formal training courses provided by employer, iv) through instructions from supervisors

or co-workers, or v) watching others, or learning-by-doing. This survey also collected

whether above training was received in a previous job, during the first year in the

present job, or during the last twelve months. For purposes of simplification, training

obtained in the forms i) to iii) will be referred to as “general training” while the rest will

be classified as “job-specific-training.”

Training information from managers and owners and a sample of workers was obtained

for one round, the 1995 survey, of Kenya, Zambia and Zimbabwe (see Table 1). Ghana

and Cote d’Ivoire have only worker-reported information on training. In our discussion,

we exploit training information from worker and firm-reported levels and costs of

training, where applicable, to provide general statements on training practices in African

manufacturing sector.

It is important to keep in mind that because the sample is essentially one of

manufacturing firms, the sample of workers is not representative of the labor force.

Moreover, the worker sampling frame within firms is not described in the underlying

documentation of the data sets, except that at least one worker was selected from each

job category present in the firm. This leads to overrepresentation of employees from

smaller firms and from smaller job categories. This should be kept in mind when reading

the parts of the paper that portray the distribution of training among workers.

Table 1 gives descriptive statistics on the characteristics of the firms in which both

owner- and firm-reported training was available.

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Table 1. Characteristics of firms in the samples Kenya 1995 Zimbabwe

1995 Zambia 1995

Number of observations (firms) 218 187 196 Firm size, (number of employees)

Mean firm size 94.6 263.9 75.6 Proportion of firms with 1-10 employees 0.38 0.24 0.40 11-50 employees 0.29 0.17 0.34 51-150 employees 0.21 0.22 0.15 150+ employees 0.12 0.37 0.11 Distribution by Sector Food industry 0.25 0.24 0.26 Textile industry 0.24 0.45 0.27 Wood industry 0.27 0.13 0.24 Metal industry 0.25 0.18 0.23 Private-State ownership Private ownership 0.76 0.88 0.66 Joint state-private ownership

0.01 0.04 0.01

State ownership 0 0.01 0.07 Missing ownership 0.23 0.07 0.26 Foreign-Domestic ownership Foreign ownership 0.06 0.07 0.06 Joint domestic-foreign ownership

0.08 0.13 0.06

Domestic ownership 0.63 0.72 0.63 Missing ownership 0.23 0.07 0.26 Source: Authors’ calculations from da ta.

Table 1 shows that firms in Zimbabwe are much larger and more concentrated in the

textiles sector than firms in Kenya and Zambia. The average firm size in Zimbabwe is

more than three times the average in Zambia and more than twice the average in Kenya .

And whereas in Zimbabwe, 37% of firms have more than 150 employees, only 12% of

Kenyan firms and 11% of Zambian firms are of that size category. About 45% of firms

in Zimbabwe are in the textiles sector, while in Kenya and Zambia, the distribution

across sectors is more even. Finally, there is very little difference, in terms of ownership

structure, across the three countries: most firms are domestic and privately owned.

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3.2 The extent and cost of firm-provided training in African Manufacturing.

Firms provide substantial levels of training: Table 2 shows that firm provided training is

widespread. Informal training is more common than formal training. More than 63% of

firms in all three countries provide informal training. In Zimbabwe, this rises to 90% of

firms. By contrast, only 20% of firms in Kenya and Zambia and less than 50% of firms

in Zimbabwe provide formal training. The more common form of training, informal

training, is also more commonly targeted at new employees. In Zambia, a new

employee receives about 4 times more days of informal training than the average days

per worker, while in Kenya a new employee receives 10 times more days of informal

training for every day of informal training received by the average worker.

Table 2. The Size and Costs of Formal and Informal Training in Firms Kenya 1995 Zimbabwe

1995 Zambia 1995

% of firms providing formal training

22.9 47.6 19.4

Formal company training 0.52 12.3 4.1 Avg. number of workers trained, if positive

201 212.8 45

Avg. cost per worker trained, if positive

0 KSh 883 ZWD 52 K

In-house training by external trainers 11.5 24.6 6.6 Avg. number of workers trained, if positive

20.6 66.5 10.7


8209 KSh 2773 ZWD 228 K

Training outside the firm 16.1 36.9 15.3 Avg. number of workers trained, if positive

23.3 8.5 4.2


17035 KSh 6867 ZWD 575 K

% of firms providing informal training

62.4 91.4 62.8

Number of days training in one year, new employee

86.4 10.8 26.3

Number of days training in one year, all employees

8.5 2.0 7.4

2 Just one firm provides this form of training

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Note: Informal training refers to on-the-job training, shop -floor training undertaken in the presence of

supervisors and foremen. Formal training refers to structured training provided by (a) hired specialists in

the premises of the firm (or internal training), or (b) outside specialists in training centers, such as

employer or trade association training centers, outside the premises of the firm and training institutes.

Source: Authors’ calculation from survey data

Workers receive both firm-specific and general training: Training outside the firm is the

most common form of formal training (Table 2). If training outside the firm is more

likely to be general than specific, then Table 2 implies that over 16% of firms provide

some form of general training. Further evidence can be seen in Table 3. The proportion

of workers who reported currently receiving training outs ide the firm do not differ much

from the proportion who receive their current training inside the firm. Assuming that

current training in the firm is more likely to be firm-specific, while training outside the

firm is more general, the implication is that a significant proportion of workers receive

general training. The last two columns of Table 3 provide further support to this

conclusion. About the same proportion of workers claim to have obtained their past

training outside the firm as did inside the firm. The last column also suggests that

“poaching” of trained workers is common in the market, which is consistent with the

view that such practices are more common in markets where general training is

prevalent. Table A1 shows that more than 40% of the workers who received training in

a previous job in Kenya and Zambia, received general training.

Table 3. Proportion of workers who receive training currently and in past, within

and outside the firm (%).

Proportion receiving training currently

Received training in the past

In the firm

Outside the firm

Both in & out-side the firm

While in this firm

While in another firm

Côte d’Ivoire 1995 1996

3.3 3.1

1.1 4.0

0.4 -

Ghana 1992 1993 1994

7.2

3.9

-

27.3

36.1

Kenya 1993

2.7

3.8

0.7

10

1994 1995

9.7 5.2 18.5 30.3

Zambia 1993 1994 1995

4.5 5.1

8.2 7.3

1.0 -

Zimbabwe 1993 1994 1995

5.4 12.1

8.0 12.9

3.2 -

24.7

23.9

Source: Authors’ calculation from survey data. Firms bear a significant financial cost to provide general training to workers: The most

commonly provided form of formal training, is also the most expensive. In Kenya, the

average cost per work trained outside the firm was 17,000 Kenya shillings (about 283

US dollars using 1997 exchange rate, 1 USD=60Ksh in 1997) in 1995. Training by

external trainers on the firms’ premises cost another 8000 Kenya shillings per worker

(about 133 USD using 1997 exchange rate). The average training bill for training

outside the enterprise translates to 33% of the average annual wage in manufacturing in

1995. It reaches 48% of annual average salary if training costs for in -house training are

included. Similarly, firms in Zambia and Zimbabwe appear to have spent a substantial

amount of money in training workers on general training. Table A2 shows another way

in which the cost of general training is picked up by the firm—through company time.

The costs continue to be substantial even when measured by hours and not the local

currency.

Large firms train more than small firms: Table 4 shows that a higher proportion of

larger firms rely on formal training. About 80% of firms in Kenya and Zimbabwe with

over 150 employees use formal training compared to 7% and 5% respectively for firms

with less than 10 employees. There is less varia tion in the use of informal training

across firm size, perhaps because this form of training is more firm-specific, and less

costly. In Zimbabwe, for example, roughly the same proportion of firms across size

categories used informal training.

Table 4. The percent of firms providing formal and informal training, by size,

industry, and ownership

Kenya 1995 Zimbabwe Zambia 1995

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1995 Formal training, all firms 22.9 47.6 19.4 Firm size 1-10 employees 7.2 4.6 3.8 11-50 employees 11.1 34.4 12.1 51-150 employees 34.8 47.6 44.8 151+ employees 80.8 81.2 63.6 Industrial sector Food industry 31.5 56.8 30.0 Textile industry 13.5 44.1 13.2 Wood industry 17.2 40.0 8.3 Metal industry 29.6 50.0 26.7 Public – Private ownership Missing info 30.0 57.1 12.0 Private ownership 19.9 43.9 15.4 Joint state and private ownership 100.02 100.0 50.0 State ownership - 100.03 78.6 Domestic and Foreign ownership Missing info 30.0 57.1 12.0 Domestic owners 16.1 38.5 18.6 Joint domestic and foreign owners 38.9 72.0 45.5 Foreign owners 46.2 84.6 36.4 Informal training 62.4 91.4 62.8 Firm size 1-10 employees 47.0 95.5 36.7 11-50 employees 55.6 81.3 72. 7 51-150 employees 82.6 88.1 89.7 151+ employees 92.3 95.7 90.9 151+ employees 92.3 95.7 90.9 Industrial sector Food industry 57.4 95.5 72.0 Textile industry 46.2 89.3 45.3 Wood industry 62.1 88.0 62.5 Metal industry 83.3 94.1 73.3 Public – Private ownership Missing info 60.0 100.0 48.0 Private ownership 62.5 90.2 66.2 Joint state and private ownership 100.02 100.0 100.0 State ownership - 100.02 78.6

3 Just one state owned firm.

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Domestic and Foreign ownership Missing info 60.0 100.0 48.0 Domestic owners 59.9 91.1 62.9 Joint domestic and foreign owners 77.8 92.0 90.0 Foreign owners 76.9 84.6 100.0 Note: Informal training refers to on-the-job training, shop -floor training undertaken in the presence of

supervisors and foremen. Formal training refers to structured training provided by (a) hired specialists in

the premises of the firm (or internal training), or (b) outside specialists in training centers, such as

employer or trade association training centers, outside the premises of the firm and training institutes.

Source: Authors’ calculation from survey data.

Table 5 shows that the proportion of workers receiving current training in firms

employing more than 150 employees is higher, or the same but certainly never less, than

the proportion receiving current training in each of the other size categories.

Furthermore, it is higher than the overall proportion of workers receiving current

training. A similar observation obtains with past training and when we look at the more

detailed questionnaires from Kenya and Zambia (see Table A3).

Table 5. Proportion of workers who receive training currently and in the past (%),

by firm size

Employer size, (number of employees in the firm)

Country and (Year of survey)

1-10 11-50 51-150 150+

Overall


9.7 15.0

2.3 3.0

12.1 15.8

4.5 7.1

4.8 7.2

Ghana Current Training

1992 1993 1994

Past Training 1994

13.2 52.7

9.7 67.3

11.0 62.9

13.2 62.9

11.1 63.4

Kenya Current Training

1993 1994 1995

Past Training 1994

3.6 19.6 58.2

5.3 10.4 41.2

6.7 12.4 48.9

13.5 24.2 55.1

7.2 14.9 48.8

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Zambia Current Training

1993 1994 1995

9.4 9.6

9.7 13.3

13.9 12.7

21.2 12.7

13.7 12.4

Zimbabwe Current Training

1993 1994 1995

Past Training 1994

17.0 60.4

13.6 41.8

21.3 39.8

34.2 56.2

25.0 48.5


Foreign -owned firms train more than domestic firms. Firms that are partially or wholly

foreign-owned provide significantly more formal training. Results in Table 4 show that

the proportion of fully foreign-owned firms providing formal training ranged from a low

of 36% in Zambia to a high of 86% in Zimbabwe. By comparison, only 38% of fully

domestic-owned firms in Zimbabwe provided such training. A possible explanation for

this observation may be that perhaps foreign-owned firms use more complex and risky

technologies. Moreover, they have better information and experience with regard to the

benefits of training. Therefore, training can be more productive.

There is no age or gender discrimination in training : Table 6 shows that firms train

men and women in roughly the same proportions. This is true both in the type and

timing of training. Although the fraction of females who obtain training is slightly

higher, especially in the last twelve months preceding the survey (see Table A4), this

may be because of the small sample of female workers overall. Table 6 also shows that

young, defined as less than 25 years old, and older workers, defined as those above 36

years old, receive training at roughly the same proportion. The link between age and

training follows the familiar inverted U-shape, commonly observed in developed

countries, where workers between ages 26 and 35 receive proportionately more training

(see Table A4). One hypothesis for observed inverted U-shape is reluctance of firms to

train young workers who are believed to have a higher rate of job mobility, since they

search more to find the best job-match.

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Table 6. Proportion of workers who receive training currently and in the past (%), by age and gender. Age categories (years) Gender categories Current training less than

25 26 to 35

more than 35

Men Women


7.1 6.7

7.3 18.0

10.3 11.0

4.9 7.0

3.8 8.8

Ghana 1992 1993 1994

12.4

26.1

16.4

10.1

15.4

Kenya 1993 1994 1995

9.4 27.0

20.2 26.2

8.7 21.0

6.1 14.6

17.4 16.9

Zambia 1993 1994 1995

16.9 17.1

31.0 27.6

23.6 19.7

13.1 11.4

17.7 16.3

Zimbabwe 1993 1994 1995

20.1 23.8

38.4 63.4

28.0 42.2

16.8 25.3

15.3 23.1

Proportion with past training (%) Past training Less

than 25 26 to 30

more than 40

Men Women

Ghana, 1994 69.3 62.7 59.9 64.7 58.1 Kenya, 1994 49.4 50.5 45.0 48.6 50.0 Zimbabwe, 1994 43.6 47.8 51.1 50.7 37.5 Source: Authors’ calculation from survey data

Workers with more education also receive more training : There is substantial evidence

that cognitive ability increases with the general educational level (Middleton, Ziderman

and van Adams (1993)). This means that workers with more education learn more

efficiently. Secondly, more advanced tasks generally require more education but may

also require more training, either on-the-job or classroom train ing. Finally, if training

makes more educated workers more productive, thus allowing the firm to appropriate

more rents from such workers, the firm would have an incentive to train them more.

Table 7 confirms that current training is given proportionately more to more educated

workers. This is particularly evident in Cote d’Ivoire, Ghana and Zimbabwe. However,

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past training appears to have been equally obtained across education levels. This is

because less educated workers probably start working earlier than more educated

workers, so that their accumulated training would not necessarily be lower than those of

more educated workers. The more detailed questionnaires asked of Kenyan and Zambia

workers, show that most low educated workers do not receive the more general training

that is offered at company premises, schools, training institutes, and industry association

centers. Instead, much of training comes through instructions supervisors and learning-

by-doing (Table A5).

Table 7. Proportion of workers who receive training currently and in the past (%), by education. Proportion in current training Current training

Missing No education

Primary education

Secondary education

Higher education


0.04 0.05

1.1 6.1

2.6 5.9

5.0 7.6

21.3 10.5

Ghana 1992 1993 1994

0.0

6.0

3.3

11.0

12.9

Kenya 1993 1994 1995

2.4 19.5

0.0 6.7

2.6 14.2

11.3 16.4

12.1 13.9

Zambia 1993 1994 1995

0.0 -

15.4 8.0

7.0 12.8

13.2 12.3

20.8 12.7

Zimbabwe 1993 1994 1995

0.0 -

0.0 9.1

10.2 20.3

21.3 28.4

27.1 35.5

Past training Proportion with past training Ghana, 1994 60.0 61.5 70.0 69.3 57.1 Kenya, 1994 42.7 44.2 42.6 52.5 50.1 Zimbabwe, 1994

- 26.9 46.9 47.2 83.9

4 Just one observation with missing info on education 5 Just four observations

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Training is provided more equitably to workers with varying levels of experience, but

more training is provided to employees in occupations with higher skill content: The

relationship between training and tenure shows that firms provide training more broadly,

to workers grouped into a range of experience levels (Table 8). The same Table shows

that workers with technical and managerial skills receive more training, perhaps because

the returns to training may be higher from such workers, or lack of it much more costly.

The technical and managerial workers receive dispr oportionately more formal training,

while informal training is obtained more broadly across occupations (Table A6 and A7).

Table 8. Proportion of workers who receive training currently and in the past (%), by tenure and occupation. Tenure categories

(years) Occupation categories

Current training

Less than 1

1 to 6 Greater than 6

Manager

Technician

Office worker

Prod. worker


8.1 9.3

7.1 13.5

4.5 6.8

12.1 7.3

10.6 10.5

2.1 8.1

2.9 6.2

Ghana 1992 1993 1994

15.8

20. 1

10.3

12.9

14.9

10.6

10.1

Kenya 1993 1994 1995

7.8 24.5

19.4 34.1

4.8 9.7

15.2 7.6

16.9 15.4

7.2 22.1

5.0 14.7

Zambia 1993 1994 1995

15.8 13.3

27.2 28.7

12.3 9.4

19.5 14.4

29.5 11.9

19.7 16.9

8.0 12.1

Zimbabwe 1993 1994 1995

13.3 27.8

38.2 49.8

15.6 24.5

22.4 27.6

35.9 0.0

32.3 50.0

12.3 25.8

Proportion with past training

Proportion with past training

Past training Less than 1

1 to 6 Greater than 6

Manager

Technician

Office worker

Prod. worker

Ghana, 1994 70.0 63.8 59.6 74.2 55.2 54.3 65.5 Kenya, 1994 55.5 46.2 48.3 43.4 40.3 54.4 50.8 Zimbabwe,1994

36.1 48.8 50.3 62.1 37.5 75.0 47.6


17

In concluding this section, we point to four key findings.

First, African enterprises provide significant on the job training. A large proportion of

this training is general. The proportion of firms providing formal training within and

outside firm premises using external trainers—what we have referred to as more general

training— range from 20% in Kenya and Zambia to 48% in Zimbabwe. Furthermore,

the proportion of workers reporting currently receiving training in and outside the firm is

about the same.

Second, firms bear a substantial cost of general training . Financial and time costs, at

the expense of the firms, are significant in all the countries for which this was reported.

As an example, Kenyan firms paid more than 400 USD per worker in training costs in

1995. Moreover, formal training consumed 221 hours of company time on average, and

only 117 hours of a worker’s own time

Third, employees with more education and those in occupations requiring higher skill

content are also more likely to receive training. In all countries, current and formal

training offered at company premises, institutes and industry association sites, is

targeted more at workers with more education. Furthermore, employees in managerial

and technical occupations receive more training.

Fourth , larger and foreign-owned firms provide more training than smaller and

domestic firms. Significantly more firms with more than 150 employees reported

providing formal training to their workers. Additionally, proportionately more firms

who are partially or wholly-foreign-owned provide formal training.

4. Wage structure and returns to training

The review of training practices in section 3 shows that firms do provide training, and

contrary to the predictions of the competitive models of training, this training includes

general skills. Furthermore, firm size and workers’ education levels matter. A crucial

assumption for why firms will undertake training is that they have the ability to

compress wages and therefore share in the benefits of productivity increase from

training. This begs two questions. How much do firms benefit from the training of their

workers? And what is the return to training for a worker? The first question, had been

18

analyzed by Biggs, Shah, and Srivastava (1995a, 1995b) using these very data sets.

They found that among the learning technologies that had unambiguously positive

impact on productivity, on-the -job training of workers had the largest relative impact on

value-added. They show that if the percent of trained workers increased by one percent

from its sample average of 9, value added would increase by 60 percent. In this section

we look at the benefits of training to workers.

The ability of firms to extract rents from their workers through wage compression

implies that we should be able to see a connection between the level of training and

wage structure in different settings. One question is whether countries in which

enterprises train more, also have more compressed wages. We would expect that, if

non-competitive labor markets prevail, the returns to training would be lower in

countries with more training. Furthermore, we would expect the returns to decline with

education. The next section examines, the link between returns to skill and training

across countries with different wage structures.

4.1 Wage structure and Training :

The connection between wage structure and training is shown in Table 9. The ratio of

log wages suggest that wages in 1994 were more compressed in Ghana and Zambia,

compared to Kenya and Zimbabwe. The wage gap between the highest paid workers

(90th percentile) and the lowest paid (10th percentile) was 20% in Ghana and Zambia, but

23% and 32% in Kenya and Zimbabwe, respectively. Since more compressed wages

provides incentives for firms to train more, we would expect training levels in Ghana

and Zambia to be higher than in Kenya and Zimbabwe. Among employees in current

training, we find that more training is reported in Kenya and Zimbabwe. However, if we

consider all training, including past training in other firms (last column), more

employees in Ghana reported receiving training. Therefore, this simple evidence we

provide for the connection between wage compression and training gives a mixed result:

with current training, countries with less wage compression provide more training, but

with past training, the opposite occurs, the latter being consistent with non-competitive

view of labor markets.

19

Table 9: The ratio of log wages at the 90 th , 50th and 10 th percentiles.

Percentile log wage ratio Proportion of trained

workers in their current firm

Country and Year 90/10 90/50 50/10

In current training

Trained in the past (in and outside

the firm) Cote d’Ivoire 1995

4.8

1996

7.1

Ghana 1994 1.20 1.09 1.10 11.1 63.4

Kenya 1994 1.23 1.15 1.07 14.9 48.8 1995 1.17 1.10 1.06

Zambia 1995 1.19 1.11 1.07 12.4*

Zimbabwe

1994 1.32 1.22 1.08 16.6 47.6 Source: Table 3 and authors’ calculation from survey data. For Zambia, the “current training” rates refer to the 1994 data.

4.2 Evaluation of the Returns to Training:

This section discusses the evaluation of the benefits of training to a trainee. Evaluation

of the returns to training in the labor market faces the twin problem of selection and

missing data. The selection problem arises when workers who have high ability and

would benefit most from training are more likely to train. Because training is not

randomly given to workers, a simple difference between pre- and post-training wages

will be a biased estimate of impact of training on wages. Missing data poses another

challenge. To see the difficulty, consider this simple illustration.

Suppose we want to measure the impact of training, D, on wages, Y. Let D, receipt of

training, take two values: D=1 , if the individual received training, and D=0 otherwise.

Let 1Y be the wage an individual receives after training, and 0Y the wage in the absence

20

of training. For a worker, the effect of training is the effect of shifting from D=0 to

D=1, or 1 0Y Y− . If training was randomly given, the average effect of training in the

population is [ ]1 0E Y Y− , which is the difference in the average post-and pre-training

wages, while the mean effect of training on the trained is

(1) [ ]

[ ] [ ]1 0

1 0

| 1

| 1 | 1

E Y Y D

E Y D E Y D

∆ = − =

= = − =

The first term in the second line – the wage after training among those who received

training – is directly observed in the data. The second term – the pre-training wage for a

trained person – is not observed, and herein lies the evaluation problem. The problem of

missing data arises because in most survey data, we do not have pre-and post-training

wages of the trained. To estimate the impact of training one thus needs to estimate the

unobserved counterfactual ( [ ]0 | 1E Y D = ) in the equation above.

In this paper, we estimate the effect of training on the trained, that is, the parameter ∆

above 6. To measure the impact of training on the trained, let

(2) 0 0 0

1 1 1

,andY x

Y x

β ε

β ε

= +

= +

In this case,

( ) [ ]1 0 1 0 | 1x E Dβ β ε ε∆ = − + − =

which is just a difference in the intercepts in the special case we study here, where we

assume that 1 0ε ε= and 1 0β β= , except the constant term. To measure∆ , we try two

types of estimators: matching and endogenous variable estimators, both of which are

summarize below.

Matching estimators: The matching estimators attempt to obtain the counterfactual pre-

training wage of the trained by matching observationally equivalent individuals that

differ only in their receipt of training. The idea of matching is to approximate random

6 There is a rich debate on the appropriate measure of the impact of training in the training and program evaluation literature in general. We refer the interested reader to the discussion in Heckman et al. (1999).

21

experimental situation. The goal is to match each trained person to a non-trained person

that has the same observable characteristics ( X variables), such as education, gender,

age, tenure, and so on. If two people have the same characteristics, but one is trained

and the other is not, then using the untrained person’s wage as the pre-trained wage of

the trained introduces no bias. Effectively, wages become independent of training

assignment conditional on observable characteristics: that is, 1 0, |Y Y D X⊥ (or “strict

ignorability” assumption of Rubin (1977)).

Notice that if the observable characteristics, X, upon which one wants to match are

multidimensional or contain continuous variables, then matching on X becomes very

difficult, if not impossible. Rather than match on X, one could match on a single index

variable that summarizes all the information contained in X (Rosenbaum and Rubin

(1983)). Such an index function has come to be known as the propensity score, or the

probability of participating in training, defined as [ ]( ) Pr 1|p X D X= = . It can be shown

that conditioning on propensity score achieves the same randomizing effect as

conditioning on the X variables: that is, 1 0, | ( )Y Y D p X⊥ ,

Several matching algorithms are available, including nearest neighbor matching, caliper

matching, kernel and local linear matching. In most cases it has become common to

impose some kind of restriction on the matching process (see Heckman et al. (1999) for

more details on matching). The most common form of matching is the nearest neighbor

matching, which has been found to provide estimates which are not sensitive to changes

in the matching algorithm, once a common support restriction is imposed (see e.g. Smith

and Todd (2001)).

Nearest Neighbor Matching: The algorithm for the nearest neighbor matching is as

follows. For each person i in the group of trained individuals who are inside the area of

common support, find individual j in the control group such that

(3) ( ) argmin ( ) ( )i jjj i p X p X

∈Ω= −

where Ω denotes the common support area. Equation (3) is a rule that pairs a trained and

an un-trained individual with the same propensity score. Nearest neighbor matching can

22

be extended to include three, five, ten, or any other number of nearest neighbors.

Increasing the number of nearest neighbors has been shown to improve the efficiency of

the estimator at the cost of increasing the bias (Smith and Todd (2001)). Nearest

neighbor matching can be performed with or without replacement. Here we mostly use

matching with replacement, due to the limited sample sizes.7 Estimation of ∆ then

proceeds in the following way. First, estimate the probability of participation in

training, say ˆ ( )p X , as a function of some explanatory variables, X. This may be done

using logit, probit or any other specification for the estimation of binary outcome

variables. In this study, we use the probit model. Second, match each individual in the

sample of trainees to the nearest neighbor in the comparison sample of non-trainees

using the nearest neighbor criterion in (3). Finally, estimate ∆;

(4) ( )

1, 0, ( ):

1ˆi

i j ii I p

Y YN ∈ ∈Ω

∆ = −∑

where I denotes the set of trainees, and N is the number of individuals in the sample of

trainees fulfilling the condition in brackets.

Another way to proceed is to use regression adjusted matching (Rubin (1979);

Heckman, Ichimura and Todd (1997, 1998)). In this case, the idea is to replace Y in

equation (4) above with the residuals Y X β− from an OLS regression, using all

explanatory variables except the training indicator, D . Differences in earnings stemming

from training will then be embedded in the residuals. The propensity score is then

estimated using an extended set of explanatory variables (using also the exclusion

restrictions, discussed below), and a common support condition is once again imposed.

Caliper Matching : Small comparison groups introduce the risk of making bad matches.

To avoid this, we extend the traditional nearest neighbor matching algorithm to caliper

matching (Cochran and Rubin, 1973). With caliper matching, the idea is to accept only

7 To match without replacement, the control sample has to be much larger than the sample of trained individuals.

23

matches within a maximum distance. Hence, a match is found for a trained individual

only if ( ) ( )i jp X p X ε− < , where ε is a pre-specified tolerance. Treated people for

whom no matches can be found are, therefore excluded from the analysis.8

Kernel and local linear regression matching: All the above-mentioned matching

estimators use at most one person from the comparison group in the construction of a

match for a given trained individual. In contrast, kernel matching and local linear

regression matching use multiple comparison sample members to construct kernel

weighted averages as matches. Relative to simpler techniques, kernel techniques allow a

reduction of the variance of the matching estimate by using more individuals for each

match. The cost is a small increase in bias due to the increased average distance between

trained and matched non-trained individuals. For the kernel matching estimator, the

weights used are the following,

(5)

( ) ( )

( , )( ) ( )

i j

ijn

iki kk J

k J n

P X P XK

KhW i j

KP X P XKh ∈

∈

− = =

−

∑∑

While for the local linear regression matching estimator, the weights are

(6)

2

22

( ( ) ( )) ( ( ) ( )) ( ( ) ( ))( , )

( ( ) ( )) ( ( ) ( ))

ik ik i k j i ij j ik J k J

ij ik i k ik i kj J k J k J

K K P X P X K P X P X K P X P XW i j

K K P X P X K P X P X

∈ ∈

∈ ∈ ∈

− − − −=

− − −

∑ ∑

∑ ∑ ∑

The matching estimator now is

(7) 1 0

1ˆ ( , )i ji I j J

Y W i j YN ∈ ∈

∆ = −

∑ ∑

8 Deheija and Wahba (1999) apply the caliper matching algorithm slightly differently. They use all individuals within the calliper tolerance, assigning the same weight to each of them. Should the calliper be empty, they simply use the nearest neighbor outside the calliper, instead of dropping the observation, as we do.

24

The local linear regression matching estimator has a slight advantage over the kernel

matching estimator because of some desirable statistical properties (Heckman et al.,

1997). For the kernel function, we use the biweight kernel,

(8) 2 215

(1 ) for | | 1( ) 16

0 otherwise

s sK s

− <=

We also tried to stratify the common support into a set of intervals, and computed the

weighted mean difference between trained and non-trained individuals within each

stratum. We then computed an overall estimate of the effect of training by weighting the

strata-specific estimates according to the fraction of the trained population in each

stratum. However, due to the modest sample size, this estimator had much larger

standard errors than the other estimators applied, so we have not reported these

estimates.

Endogenous variables estimators: Matching estimators attempt to mimic the

experimental approach of the natural and medical sciences by equalizing the treated and

untreated along observable and measurable dimensions. To the extent that training is

assigned randomly or that selection into training is done solely on observable

characteristics, they “solve” both the missing data and selection problems alluded to

above. However, to the extent that important variables determining both selection into

training and wages are unobserved – that is, if there is selection on unobservables – the

matching estimator does not consistently estimate ∆ , since participation in training is

now an endogenous variable. In this case, an alternative is to model the selection

process along with the equation of interest (that is, the wage premium due to training)

using traditional endogenous variables model estimators (see Heckman (1978) and

Björklund and Moffitt (1987)). Let

25

(9)

*

* 0

i i i i

i i i

i i

y x d

d z

d d

β ε

γ η

= + ∆ +

= +

= Ι >

where *id is a latent variable measuring the expected returns to training for the

individual, and the individual participates in training, if id is positive. ∆ measures the

effect of training on the wage of the individual, iy . Assume that the unobservable

determinants of wages and training are jointly distributed normal—that is, bivariate

normal, ( ), ~ 0,0,i i BVNε η Σ , with 2

1ε

ε

σρσ

Σ =

. Then maximum likelihood

techniques can be used to estimate the parameters of the model above. To obtain

unbiased estimates, it is necessary to impose restrictions, exclusion restriction, which

can be accomplished by excluding one or more variables in z , from the variables

included in x. As an alternative to maximum likelihood, one could use a two-step

estimator in the spirit of Heckman (1979), but that estimator is not efficient, unlike the

MLE. The likelihood function to be maximized is thus

(10) ( ) ( )

( )

2|

1

1|

( , , , , ) 1 |

|

i

i

dN

i i i i i i ii

di i i i

L f y x d F z y x d

F z y x d

ε ε η ε

η ε

β δ γ ρ σ β γ β

γ β

=

−

= − − ∆ ⋅ − − − − ∆ ⋅

− − − ∆

∏

where fε is a normal density with mean zero and variance 2εσ , and |Fη ε is a normal

distribution function with mean / ερε σ and variance 21 ρ− .

This estimator differs from the matching estimators in several ways: first, the matching

estimators are semi-parametric, while the endogenous variables estimator above is fully

parametric. Second, the matching estimators assume conditional independence of wages

and the training indicator, while the endogenous training estimator does not. Third,

26

endogenous estimator does not require exclusion restrictions to identify the parameter of

interest and the correlation in the bivariate normal error distribution.

Exclusion restrictions: In some of the econometric analyses of the impact of training,

we need exclusion restrictions. The effect of training is only identified in these models if

we can identify variables that affect the probability to participate in training, but do not

affect the earnings of the workers. We have several candidates, since our sample is

essentially a matched employer-employee sample. Under the maintained assumption that

workers are randomly allocated between firms, firm specific information can be used as

exclusion restrictions. Specifically, we have used (combinations of) the following

exclusion restrictions: an indicator for whether the owner has been trained in the past

(excluding military training); an indicator for whether the firm has received external

training support (also referred to as business support) in terms of funding from external

sources (government, aid organizations etc.); an indicator for whether the firm provides

formal and informal training, which come from the firm questionnaire. These are likely

to be strong predictors of training probabilities, but still independent of (latent) earnings

given the maintained assumption of random allocation of workers to firms. We also use,

as an instrument, the training costs per worker in the firm.

In order to test for the validity of exclusion restrictions, we need at least two variables in

z, which are not in x. We then sequentially include one of them in the wage equation, in

order to see if it has explanatory power. Since there are two identifying variables, the

parameters of the equation system are still identified. If an identifying variable does not

have explanatory power in the wage equation, then it is a valid exclusion restriction. If,

in addition is has explanatory power in the selection equation, then it is a good exclusion

restriction. Unfortunately, in some of the estimation, this strategy of choosing exclusion

restrictions left us unable to use some or all of our preferred identifying variables, since

they all had explanatory power in the wage equation. In these situations we had to resort

to a more pragmatic approach, namely to look for variables which did not have any

effect in the wage equation and still had some explanatory power in the selection

equation. This led us to use mostly gender and ownership information as exclusion

restrictions in these cases.

27

4.3 Estimated Benefits of Training

The range of estimated wage premiums due to training, obtained using a number of

estimators, including raw log wage difference between the trained and un-trained, OLS,

four matching, and the endogenous variable estimators, are reported in Table 10.

Detailed results from each of the estimators and for each country, are reported in the

more comprehensive study by Nielsen and Rosholm (2002c). For two of the matching

estimators, we report the premiums when the matching is done with and without

replacement. For nearest neighbor matching, we report additional estimates with and

without exclusion rest rictions.

Table 10. Estimates of average return to training. Ghana

1994 Zimbabwe 1994

Kenya 1994

Kenya 1995

Zambia 1995

Raw difference in log Wages 0.18 0.39 0.17 -0.03 0.32 OLS 0.29

(0.08) 0.20 (0.06)

0.16 (0.04)

-0.01 (0.04)

0.17 (0.05)

Nearest Neighbor Matching

No exclusion restrictions, with replacement

-0.12 (0.16)

0.31 (0.10)

0.21 (0.07)

-0.03 (0.07)

0.19 (0.10)

No exclusion restrictions, without replacement

. . . -0.03 (0.05)

0.20 (0.08)

Exclusion restrictions , with replacement

0.08 (0.12)

0.22 (0.10)

0.11 (0.06)

-0.06 (0.07)

0.08 (0.12)

OLS-residuals, with exclusion restrictions, with replacement

0.19 (0.11)

0.21 (0.09)

0.11 (0.06)

-0.06 (0.06)

0.04 (0.11)

Nearest Neighbor Caliper Matching

With replacement 0.15 (0.15)

0.15 (0.10)

0.12 (0.07)

-0.09 (0.07)

0.06 (0.12)

Without replacement 0.19 (0.09)

0.20 (0.09)

0.16 (0.05)

-0.05 (0.06)

0.15 (0.10)

Kernel Matching

0.22 (0.10)

0.22 (0.07)

0.15 (0.05)

-0.03 (0.05)

0.18 (0.07)

Regr. Adj. Local Linear Matching

0.37 (0.07)

0.21 (0.06)

0.16 (0.04)

-0.02 (0.04)

0.16 (0.07)

28

Endogenous Variables Estimator

0.17 (0.41)

0.70 (0.20)

-0.13 (0.40)

0.74 (0.10)

0.81 (0.13)

Past training

Past training

Past training

Training first year

Training first year

Note: Bold letters indicate significance at a 5%-significance level. For the kernel and the regression adjusted local linear matching estimators, standard errors have been obtained by 1000 bootstraps with 100% re-sampling.

The main conclusion from the various estimators is that training is beneficial to trainees.

The median estimates are 19% (Ghana), 15% (Kenya), 16% (Zambia), and 21%

(Zimbabwe). Therefore, these median estimates suggest that the return to training in

African manufacturing is within the range of 15% to 21%. When we restrict ourselves to

estimates that are statistically significant (at 5% level), the range widens significantly;

19%-37% in Ghana; 15%-21% in Kenya; 16% -81% in Zambia; and 20%-70% in

Zimbabwe.

However, two other observations are worth noting about the estimates we obtain. First,

depending on the type of estimator used, within country estimates can be volatile: from

–12% to +37% in Ghana; –13% to +21% in Kenya; from 4% to 81% in Zambia; and,

from 15% to 70% in Zimbabwe. Only in Zimbabwe is the conclusion about the effect of

training robust across the range of estimators. Second, in general, the endogenous

variable matching estimator produces the largest deviations from the OLS estimates.

However, this is also the only estimator that allows for selection on unobserved

characteristics, and for that reason, if selection on such variables is important, we would

expect this estimator to produce different estimates from the others.

Training effect by education and firm size: In the competitive model, where workers are

paid their marginal product, returns to training are independent of firm size and

education levels. That means that the coefficients of training or education interacted by

firm size should be zero. Table 11 shows the results when training is interacted with

firm size and education. The estimated model is the endogenous variables model which

corrects for unobserved characteristics.

Table 11. Variation in estimated training effect by firm size (ML-selection). Variable Ghana Kenya Kenya Zambia Zimbab

29

1994 1994 1995 1995 we 1994

Training and firm size Training 0.20

(0.42) -0.09 (0.40)

0.74 (0.10)

0.86 (0.13)

0.70 (0.24)

Firm size/1000 1.83 (0.59)

0.59 (0.17)

0.15 (0.15)

0.43 (0.17)

0.49 (0.14)

Training x firm size/1000

-0.44 (0.92)

-0.35 (0.19)

-0.13 (0.14)

-0.19 (0.21)

-0.36 (0.14)

Training and Education Training 0.06

(0.44) -0.13 (0.40)

0.70 (0.10)

0.84 (0.13)

0.86 (0.21)

Training x No education 0.08 (0.45)

-0.07 (0.23)

-0.09 (0. 32)

-0.28 (4.2)

Training x Primary -0.07 (0.30)

-0.04 (0.14)

0.06 (0.08)

0.02* (0.14)

-0.30 (0.14)

Training x Higher education

0.43 (0.32)

0.10 (0.10)

-0.005 (0.12)

-0.11 (0.11)

-0.43 (0.70)

Note: Bold letters indicate significance at a 5%-significance level. For education, the omitted variable is secondary interacted with education. The Zambia coefficient on primary x training, also includes those with no education.

In all cases, the effect of training declines with firm size, but this is statistically

significant only for Zimbabwe and Kenya 1994 data. As the magnitude of the

parameters indicates, only in the very large firms, with more than 2000 employees, does

the training effect decline. A look at the returns to training for workers with varying

levels of education shows no significant variation, with the one exception of Zimbabwe

1994, where individuals with primary education have a 30% lower premium from

participating in training than those with secondary education. If more educated wor kers

are more productive, the firms have the incentive to train them more. However, the

desire to share in the rents from increased productivity of trained educated workers

would also encourage firms to compress wages for these types of workers. So while the

educated workers get compensated for their investment in training, the firms do not have

any extra incentive to reward them for their past investment in obtaining better

education. This explains the observed lack of additional premium to more educatio n

among the trained.

30

In conclusion, the analysis of the returns to training shows that trained workers receive

significant wage premiums. In addition, we find that among the trained, returns to

training are lower for those in larger firms and for more educated workers. Our

evidence linking wage structure and training is mixed: countries with more compressed

wages report lower rates of workers in training at the time of the surveys, but more

workers with past training. These findings, coming from five different countries with

diverse forms of industrial organization, are nonetheless consistent with the predictions

of the non-competitive labor markets.

5. Policy implications

This review of enterprise training in Africa has shown that firms provide specific and

general training to their workers, to the benefit of both parties. It also finds that large

rather than small, and foreign rather than domestic firms train more of their workers.

Furthermore, the more educated workers receive more training than the less educated.

These findings contradict the predictions of competitive market models of training in

which firms do not provide general training and size or ownership does not matter.

Instead, they are consistent with results from non-competitive labor markets, where

market power, size, and capital structure all matter.

So does this mean that there is no market failure in training and therefore no role for

public action? Do enterprises provide sufficient training? Not quite. There is still

market failure in training and total investment in training can still be sub-optimal. The

sources of market failure come in two forms. First, under imperfect markets, the desire

of the firms to share in the benefits from productivity increase from training will lead to

a compression of wages. The question is who will undertake and pay for training? The

level of training achieved would be either that desired by the firm or that desired by the

worker. If the desired level of training by the worker exceeds that desired by the firm,

then the worker would meet the full cost of training. However, if the firm’s desired

level of training exceeds the worker’s, then the firm would meet the full cost of training,

even when the training is general and the worker has the resources to pay for it. This is

because if the firm chooses and pays for a level of training that is higher than the

worker’s desired level, the latter would have no incentive to invest in more training,

much less pay for it (Acemoglu and Pischke, 1999, F125). Because these decisions are

31

made individually, that is either the firm or the worker makes them, the level of training

will be less than would occur if the decisions were co-ordinated. Second, the firms also

know that, the presence of mobility is likely to lead to higher profits for a future

employer who employs a worker with a higher level of training. Similarly, the worker

knows that the compressed wage structure means that his/her training will benefit a

future employer. Both these facts would lead to sub-optimal levels of training. The

firms will invest less than they would in the absence of mobility, and the worker would

invest less than he/she would in the absence of compressed wage structure. It is

important to note that sub-optimal levels of training under non-competitive markets will

result even when workers can borrow all they need to finance their training. The source

of market failure is the “positive externality” that training will have on future employers,

and not the failure of credit markets to finance training.

What does this mean for training policy in Africa? First, even without government

support, firms will have private incentives to provide and pay for training, including

general training, as indeed we find in this review because they capture part of the

benefits from increased worker productivity. This reduces the need for governments to

provide general subsidies for enterprise -based training. More of the training resources

can then be used to upgrade the basic education of the workforce. Second, the

persistence of sub-optimal levels of training investment because of the failure of the

trainees or training firms to capture all the future benefits (some will go to future

employers) of training, means that public action would still be needed. In particular,

while subsidies can still be an effective instrument to increase training investments, they

must be carefully targeted in order to (a) avoid windfalls, that is compensating firms for

training they would have undertaken without the subsidy, and (b) assist those who are

more likely to receive less training. Targeted subsidies could be especially important for

equity reasons for low educated workers who may not receive training from the self-

interested decisions of firms. The effective public intervention would then be a mix of

subsidies and a regulatory framework where the latter is targeted at ensuring that the

contracted level of training is delivered and quality (perhaps through certification) is

attained (Acemoglu and Pischke, 1999).

32

This policy prescription calls for strengthening of the private -public partnerships. The

tragedy is that in recent years, public-private training linkages appear to be weakening

and contracting (Grierson, 2002).

6. Conclusion

The discussion in this paper has shown that the stylized facts of training imply that the

labor markets in Africa are characterized by non-competitive behavior. We find that,

unlike the predictions of competitive labor markets, firms provide and pay for general

training. One implication of these findings is that more training than would be predicted

under competitive markets would be achieved even in the absence of government

intervention. In the context of imperfectly competitive markets, concerns about

enterprises failing to invest in general skills may be over-emphasized. The paper’s

finding further endorses the importance of enterprise training as a tool for preparing

workers not only with the skills of the firm, but a wider range of skills that can benefit

the economy and the worker by creating a more adaptable, more trainable work force for

the future, capable of responding to technological changes. This reduces the argument

that pre-employment training investments are necessary to prepare such workers for life-

cycle skills.

We also find that training is provided unequally to workers with different levels of

education. More educated workers and those in large or foreign-owned firms are

favored in training. These findings point to the selectivity of access to training. Those in

the favored tradeables sector will more likely have access to skills development for

future income growth, as will those with higher levels of education, especially if higher

ability (signaled by more education) and training are complements, and lead to higher

productivity. Having more education probably intersects with the working in the

tradeable sector. Exporting industries use higher levels of technology and, therefore,

more educated workers. The importance of this is that there would be a large number of

other workers outside these sectors and with levels of education, who would have lower

access to skills development through the enterprise. The policy issue here is how to

provide these workers with access to skills development either in or outside enterprises?

33

This offers some guidance to targeting of training access on equity grounds to non-

tradeables and to workers with lower education levels.

Finally, we show that, despite the non-competitive nature of markets, trained workers

receive significant wage premiums. These observations are consistent with non-

competitive markets, where firms have ability to compress wages and share the benefits

of increased productivity from trained workers. The policy importance of this finding

should not be under-estimated. It tells us that the incentives for private investment in

skills development are there in enterprises. It is a mutually beneficial situation for

enterprises and workers with access to this enterprise training. It suggests that further

incentives for some enterprise training may be unnecessary. However, it must be noted

that even when workers can pay for their training, non-competitive nature of markets

will lead to sup-optimal training levels as neither the workers nor the firms receive the

full benefits of their investment in training. Again, this re-enforces the argument for

targeting of public incentives for skills development. It does raise a policy issue for

future research involving the potential widening of income distribution between those

with access to training and those who have not. This is an equity issue that should be

monitored over time. These are concerns in many countries where growth and

development and their benefits are unevenly distributed. This may be such a case in the

making that for social harmony should be followed closely. Our findings suggest that

targeted subsidies, especially at low skilled workers and employees in small enterprises,

supplemented by regulation to certify skills and monitor training quality hold the

potential to improve overall training in Africa. Targeting access to training can make

sense under the conditions found in this study. The case for certification of skills and

quality is more subtle. It may be important in an environment where there is asymmetric

information on (a) the skills of the worker that the employer cannot judge and

appropriately value in terms of wages, and (b) the quality of training provided by

employers to the contracting government agency, both of which are common under

imperfectly competitive markets.

34

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37

A. Appendix

Table A1. Proportion of workers who received training in their previous jobs .

Kenya Zambia A: At a school, vocational or training institute Of which public

14.4 7.1

15.5 10.9

B: At an industry association training center 1.5 4.9 C: Formal training courses provided by employer 3.6 6.6 D: Instructions from supervisors or co-workers 19.4 12.3 E: Watching others or learning on your own 9.7 10.1 Source: Authors’ calculation from survey data.

Table A2. Propo rtion of workers trained, and the hours in training, in the first year and the last 12 months at current job. Proportion trained (%)

and the hours in training in the first year at current job.

Proportion trained (%) and the hours in training in the last 12 months at current job.

Kenya Zambia Kenya Zambia A: At a school, vocational or training institute:

Of which public Mean # hours on company time (if positive) Mean # hours on own time (if positive)

2.9 - 83.7 106.2

5.1 3.1 735.5 209.7

2.4 - 57.1 76.0

2.8 0.9 230.1 188.6

B: At an industry association training center

Mean # hours on company time (if positive) Mean # hours on own time (if positive)

1.2 71.8 9.5

2.3 331.9 211.1

1.3 53.5 0.0

0.7 12.7 92.4

C: Formal training courses provided by employer


1.6 64.9 0.9

2.9 383.6 11.1

1.8 84.4 7.0

2.0 57.6 23.5

D: Instructions from supervisors or co-workers

Mean # hours on company time (if positive)

33.8 257.1 3.2

18.3 303.0 1.9

6.3 91.1 0.4

4.7 266.4 1.0

38

Mean # hours on own time (if positive)

E: Watching others or learning on your own


20.9 124.4 19.9

12. 4 235.6 52.4

4.3 74.4 27.5

6.9 118.4 97.3


Table A3. Proportion of workers who received training in the first year of current job and in the past year (%), by firm size. Type of Training Proportion tra ined in

the first year at current job (%)

Proportion trained in the past year (%) of current job

Kenya Zambia Kenya Zambia A: At a school, vocational or training institute

Firm size 1-10 Firm size 11-50 Firm size 51-150 Firm size 151+

0.5 1.5 4.3 4.8

3.0 1.2 8.9 8.7

0.0 1.5 3.5 4.4

0.0 1.2 4.4 5.4



0.0 0.8 1.9 1.8

0.8 0.6 3.0 5.0

0.0 0.3 1.3 3.9

0.8 0.3 0.5 1.2



0.0 0.8 2.9 2.2

0.0 0.9 0.5 9.5

0.0 0.3 3.7 2.6

0.0 0.0 2.5 5.4



32.8 29.8 31.9 44.5

19.4 17.9 15.8 20.3

9.2 6.4 3.5 8.3

7.5 4.7 3.5 4.2


Firm size 1-10

24.1

12.7

5.1

8.2

39

Firm size 11-50 Firm size 51-150 Firm size 151+

19.0 21.5 20.5

8.2 20.2 11.6

3.3 4.3 5.2

4.1 12.8 5.0


.

Table A4. Proportion of workers who received training in the first year of current job and in the past year (%), by age and gender. Proportion

receiving training in the first year of present job

Proportion receiving training in the past year of present job

Age categories Kenya

Zambia

Kenya

Zambia

A: At a school, vocational or training institute

Less than 26 26 to 35 more than 35

2.4 5.1 8.1

5.1 13.1 7.2

2.8 4.9 4.9

3.2 8.5 2.3



0.8 2.0 3.6

2.6 2.8 5.4

0.4 2.0 3.9

1.3 0.6 1.5



0.8 2.7 4.2

1.9 7.6 5.5

1.2 3.1 4.7

1.9 2.7 5.5



40.3 71.8 54.3

22.4 38.8 32.9

14.2 10.3 5.7

10.3 8.5 6.5


25. 7

10.9

11.9

5.8

40


45.5 31.6

29.7 21.1

3.9 5.6

14.5 13.6

Proportion receiving training in the first year of present job

Proportion receiving training in the past year of present job

Training by Gender Kenya

Zambia

Kenya

Zambia

A: At a school, vocational or training institute

Men Women

2.6 4.5

5.2 4.9

2.2 3.9

2.1 6.2


Men Women

1.1 1.7

2.4 1.9

1.3 1.1

0.7 0.6


Men Women

1.7 1.1

2.5 4.9

1.5 3.4

1.7 3.1


Men Women

33.4 36.3

18.9 15.4

5.7 10.1

5.8 4.3


Men Women

21.2 19.6

12.3 13.0

4.1 5.6

6.9 6.8


Table A5. Proportion of workers who received training in the first year of current job and in the past year (%), by education. Proportion receiving

training in their first year of current job

Proportion receiving training in the past year of current job


Missing None Primary School Secondary School Further education

0.0 0.0 1.0 4.3 5.8

0.0 0.0 1.5 3.8 10.7

5.9 0.0 0.8 2.6 8.3

0.0 0.0 0.5 1.6 7.2

B: At an industry association

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training center Missing None Primary School Secondary School Further education

0.0 0.0 0.4 0.4 8.3

0.0 0.0 0.0 3.4 2.4

0.0 0.0 0.2 1.4 5.8

0.0 0.0 0.0 0.7 1.2



0.0 0.0 1.0 0.8 8.3

0.0 0.0 0.0 3.4 4.8

2.9 0.0 0.2 2.2 6.6

0.0 0.0 0.0 1.6 4.4



23.5 25.0 32.2 37.0 32.2

0.0 10.0 15.5 24.6 10.3

5.9 0.0 6.1 7.3 5.0

0.0 0.0 2.4 7.2 2.4



0.0 21.9 22.3 22.2 15.7

0.0 20.0 8.7 14.2 12.3

0.0 0.0 3.4 4.5 9.1

0.0 20.0 2.9 6.5 10.4


Table A6. Proportion of workers who received training in the past year of current

job (%), by tenure.

Kenya Zambia A: At a school, vocational or training institute

Less than 1 year 1 to 6 years

2.0 8.0

1.0 7.7

42

More than 6 years 1.3 2.8 B: At an industry association training center

Less than 1 year 1 to 6 years More than 6 years

0.5 2.6 1.5

1.0 0.6 0.8

C: Formal training courses provided by employer Less than 1 year 1 to 6 years More than 6 years

1.5 3.0 2.0

2.1 0.6 3.6

D: Instructions from supervisors or co-workers Less than 1 year 1 to 6 years More than 6 years

15.7 13.0 2.8

9.3 6.0 3.9

E: Watching others or learning on your own Less than 1 year 1 to 6 years More than 6 years

13.2 8.1 1.3

5.7 14.8 7.0


Table A7. Proportion of workers who received training in the first year of current job and in the past year (%), by occupation. Proportion receiving

training in the first year of current job

Proportion receiving training in the past one year of current job


Manager Office worker Technician Production worker

- 0.0 - 3.0

10.8 3.5 13.0 4.1

- 0.0 - 2.5

3.1 4.7 10.4 0.9



- 0.0 - 1.2

6.2 2.6 5.2 1.3

- 0.0 - 1.3

3.1 1.3 0.0 0.2



- 0.0 - 1.7

4.6 2.6 7.8 2.0

- 0.0 - 1.8

7.7 1.3 5.2 0.9


Manager

-

12.3

-

4.6

43

Office worker Technician Production worker

21.4 - 34.3

18.1 19.5 19.1

9.5 - 6.2

5.2 1.3 5.0



- 23.8 - 20.8

20.0 13.8 16.9 10.4

- 2.4 - 4.4

15.4 6.9 7.8 5.8

Source: Authors’ calculation using survey data.

Andrew Da balen

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Labor Markets and Enterprise Training in African Manufacturing

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