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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
Avg. cost per worker trained, if positive
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
Avg. cost per worker trained, if positive
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
Côte d’Ivoire 1995 1996
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
Source: Authors’ calculation from survey data
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
Côte d’Ivoire 1995 1996
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
Côte d’Ivoire 1995 1996
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
Côte d’Ivoire 1995 1996
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
Source: Authors’ calculation from survey data
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
Mean # hours on company time (if positive) Mean # hours on own time (if positive)
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
Mean # hours on company time (if positive) Mean # hours on own time (if positive)
20.9 124.4 19.9
12. 4 235.6 52.4
4.3 74.4 27.5
6.9 118.4 97.3
Source: Authors’ calculation from survey data.
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
B: At an industry association training center
Firm size 1-10 Firm size 11-50 Firm size 51-150 Firm size 151+
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
C: Formal training courses provided by employer
Firm size 1-10 Firm size 11-50 Firm size 51-150 Firm size 151+
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
D: Instructions from supervisors or co-workers
Firm size 1-10 Firm size 11-50 Firm size 51-150 Firm size 151+
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
E: Watching others or learning on your own
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
Source: Authors’ calculation from survey data.
.
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
B: At an industry association training center
Less than 26 26 to 35 more than 35
0.8 2.0 3.6
2.6 2.8 5.4
0.4 2.0 3.9
1.3 0.6 1.5
C: Formal training courses provided by employer
Less than 26 26 to 35 more than 35
0.8 2.7 4.2
1.9 7.6 5.5
1.2 3.1 4.7
1.9 2.7 5.5
D: Instructions from supervisors or co-workers
Less than 26 26 to 35 more than 35
40.3 71.8 54.3
22.4 38.8 32.9
14.2 10.3 5.7
10.3 8.5 6.5
E: Watching others or learning on your own
25. 7
10.9
11.9
5.8
40
Less than 26 26 to 35 more than 35
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
B: At an industry association training center
Men Women
1.1 1.7
2.4 1.9
1.3 1.1
0.7 0.6
C: Formal training courses provided by employer
Men Women
1.7 1.1
2.5 4.9
1.5 3.4
1.7 3.1
D: Instructions from supervisors or co-workers
Men Women
33.4 36.3
18.9 15.4
5.7 10.1
5.8 4.3
E: Watching others or learning on your own
Men Women
21.2 19.6
12.3 13.0
4.1 5.6
6.9 6.8
Source: Authors’ calculation from survey data.
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
Kenya Zambia Kenya Zambia A: At a school, vocational or training institute
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
41
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
C: Formal training courses provided by employer
Missing None Primary School Secondary School Further education
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
D: Instructions from supervisors or co-workers
Missing None Primary School Secondary School Further education
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
E: Watching others or learning on your own
Missing None Primary School Secondary School Further education
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
Source: Authors’ calculation from survey data
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
Source: Authors’ calculation from survey data
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
Kenya Zambia Kenya Zambia A: At a school, vocational or training institute
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
B: At an industry association training center
Manager Office worker Technician Production worker
- 0.0 - 1.2
6.2 2.6 5.2 1.3
- 0.0 - 1.3
3.1 1.3 0.0 0.2
C: Formal training courses provided by employer
Manager Office worker Technician Production worker
- 0.0 - 1.7
4.6 2.6 7.8 2.0
- 0.0 - 1.8
7.7 1.3 5.2 0.9
D: Instructions from supervisors or co-workers
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
E: Watching others or learning on your own
Manager Office worker Technician Production worker
- 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
Q:\My Documents\ENDATA\Thematic Fields\Labormarkets\RPEDTraining\Synthesis of Enterprise Training in Africa January
2003.doc
January 23, 2003 8:53 AM