Evaluation of Training in African Enterprises*

Michael Rosholm**

Department of Economics, University of Aarhus, Denmark

Helena Skyt Nielsen

Department of Economics, University of Aarhus, Denmark

Andrew Dabalen

The World Bank, Washington DC

Abstract:

The importance of skill-shortage in Africa, combined with the extensive use of training,

necessitates appropriate evaluation of training programs. By using firm data from Sub-

Saharan Africa, we evaluate the effect of on-the-job training on log wages using

matching estimators. We find that training tends to improve wages, and that the effect is

larger and more well-determined when we focus on long training durations and large

firms.

JEL: J31.

Keywords: Evaluation, training, matching, endogenous variables, Sub-Saharan Africa.

* Acknowledgement: Financial support from The World Bank is gratefully acknowledged. The World Bank is not responsible for the results obtained in the paper, nor does it necessarily reflect the views of The World Bank. We are grateful to Richard K. Johanson, Hong W. Tan, Zafiris Tzannatos and two anonymous referees for useful comments on earlier versions of the paper. The usual disclaimer applies. ** Corresponding author. Mailing address: Michael Rosholm, Department of Economics, University of Aarhus, University Park, Bld. 322, DK-8000 Aarhus C. Email: mrosholm@econ.dk. Phone: +45 8942 1559

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1. Introduction For decades, training, entrepreneurship and vocational education have been seen as a

key to provision of skills for the large and - to a large extent - unskilled populations of

Africa.1 Though expensive, vocational education and training in general have been

viewed as a fast and efficient way to improve skills for the population in Africa.2 First

of all, the reason behind this great interest in vocational education and training is that

these remedies are directed towards development of skills that are expected to be useful

in the productive process of an enterprise operating in a competitive environment.

Training represents a flexible response to changes in market demand. Secondly, the

individuals are partly productive while being trained or educated. Whether in training

institutions or in employment based (on-the-job) training, the education is focused

around some sort of commercial production, which could reduce the invested amount

and thus increase returns. Thirdly, the educational system in many African countries has

been run down, which increases the relative efficacy of alternative ways of upgrading

the skills of workers outside the range of general and academic education.3

The obvious motivation behind this interest in training and vocational education

activities is the implicit assumption that these types of human capital investments

increase the productivity of workers. Middleton et al. (1993) and Grubb and Ryan

(1999) conclude that there is (almost) consensus that earnings increase after training.

According to the literature survey by Middleton et al. (1993), the magnitude of the

effect seems to be about 20% in many developing countries.4

1 Biggs et al. (1995b), World Bank (2001) and Middleton et al. (1993) confirm that this is high on the agenda of NGOs and international aid donors. The main focus, though, is on improving basic education. 2 However, market failure may result in too little training (Becker, 1975). Hence, donor assistance for capacity building and institution building is important (Chandra et al. 2001), and therefore, donors and NGOs have subsidised training institutions for years. 3 However, at the same time it may decrease the trainability of the individuals. 4 However, for the benefit to occur, employment should be available, of course. This is apparently not a great problem, since skill-shortage has been reported as a main obstacle to business growth; Chandra et

2

Another motivation for being interested in training is that returns to the acquired skills

are shared between employer and employee, so that training simultaneously benefits

both the workers and the producers of goods. In a previous study by Biggs et al.

(1995a), it is shown that training appears to benefit the employers by increasing output,

that is, training increases productivity. This is shown in a pooled regression, which

estimates the production function of firms. Specifically, they estimate log value added

as a function of inputs to the production process and other important factors. Including a

dummy for whether the firm provides training to (some of) its workers, they find a

significantly positive effect of training on productivity. They use OLS as well as two

stage least squares. Their estimates imply that firms that train their workers have 50-

127% higher outputs than firms that do not. They do, however, note that these estimates

may be biased upwards due to non-random selection.

The extensive use of and investment in training in Sub-Saharan Africa accentuates the

need for evaluations of its effects. Yet, to our knowledge, such evaluations are scarce

for African countries. Applying state-of-the art econometric methods, studies for

developed countries show that publicly financed on-the-job and classroom training

programs have had disappointingly low effects on the income of workers (and in some

cases even negative effects), see e.g. the survey by Heckman et al. (1999), or Lechner

(1999) for a recent example. Investigating firm-sponsored training, Lynch (1992) shows

that on-the-job (informal) training has low returns and is firm specific, whereas off-the-

job (classroom) training is more general in nature and has significantly positive returns.

In this paper, we apply matching techniques to estimate the effects of training conducted

in African enterprises, specifically, in formal sector enterprises in Kenya and Zambia.

al. (2001) report that 2/3 of firms view skill-shortage as an important obstacle to business growth in South Africa.

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The motivation for using matching techniques is that we have access to a rich set of

variables affecting the selection into training as well as the earnings of workers.

We find a positive return of roughly 20%, which is larger and more well-determined

when we focus on long durations training and large firms. This number is confirmed for

three waves of data for other Sub-Saharan African countries where more general

training indicators are available. One might expect different returns to training across

education levels because more educated individuals learn faster, but unfortunately the

number of trainees in our samples do not allow for such an analysis.

The paper is organized as follows: Section 2 describes the data sources. Section 3

discusses training institutions in Sub-Saharan Africa and describes the extent of training

plus the selection process into training in African enterprises. Section 4 contains a

discussion of theoretical considerations of the evaluation problem and presents the

matching technique used to estimate training effects. Section 5 contains results, and

section 6 discusses policy issues. Section 7 concludes.

2. Data sources We have access to Regional Programme on Enterprise Development (RPED) data from

five Sub-Saharan African countries: Côte d’Ivoire, Ghana, Kenya, Zimbabwe and

Zambia. In each country, around 250 firms within the manufacturing sector are

interviewed two or three times. In addition to information about the firm and the owner,

the data sets contain information on roughly 1000 employees per wave. In this paper,

however, we will focus on two of these countries, Kenya and Zambia, since there is

most information about the receipt of training in the questionnaires conducted in these

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countries, see section 3. In the three other countries, there was merely a question on

whether the worker had received training in the past.5

The RPED data sets are samples of firms, and for each firm, a selection of workers has

been asked a number of questions concerning their employment relations. Because the

sample is essentially one of manufacturing firms, the sample of workers is not

representative, even of the labor force in the manufacturing sector. The sample of

workers may overlap over time, but there are no unique person identifiers in the data

sets. 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.6 This leads to overrepresentation of employees from

smaller firms and from smaller job categories. Unfortunately, there is not enough

information in the data to introduce weights. Thus, in the following we analyze

unweighted data. To some extent this problem is reduced by the presence of a rich set of

covariates one can condition upon in the econometric analyses.

In some cases, we had to merge data sets across different waves to be able to use

information on ownership status of the firm. The ownership information is often only

available in the first wave in a given country. The training questions we use in this study

are never asked in the first wave, however, hence the need to merge. Unfortunately this

implies a loss of data, as some firms are added to the data in each wave to compensate

for the process of attrition. For these firms, ownership information is not available and

we control for that in the analyses in order not to exclude these observations.

Table 1 presents descriptive statistics of the firms from both the countries analyzed in

this study, using the last wave of available data. Average firm size and the distribution

5 The RPED data sets are documented by e.g. Biggs et al. (1995a;1995b).

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of firm sizes is quite similar in the two countries. The same holds for the industry

composition of firms. The only difference is with respect to ownership, where Zambia

has 7 percent of firms owned by the state, while in Kenya state ownership does not

exist.

Table 1. Descriptive statistics, firm level

Kenya 1995

Zambia 1995

Number of observations (firms) 218 196 Mean firm size 94.6 75.6

Size 1-10 0.38 0.40 Size 11-50 0.29 0.34 Size 51-150 0.21 0.15 Size 150+ 0.12 0.11 Food industry 0.25 0.26 Textile industry 0.24 0.27 Wood industry 0.27 0.24 Metal industry 0.25 0.23 Private ownership 0.76 0.66 Joint state-private ownership 0.01 0.01 State ownership 0.00 0.07 Missing ownership 0.23 0.26 Foreign ownership 0.06 0.06 Joint domestic-foreign ownership 0.08 0.06 Domestic ownership 0.63 0.63 Missing ownership 0.23 0.26

3. Description of training in African enterprises To study the effect of training on the incomes of workers in African enterprises, we

focus on the logarithm of the monthly wage as the outcome variable and training as an

endogenous treatment as is standard in evaluation studies (see section 4).

Before describing the data and the training selection process, we will first briefly

discuss the general background as regards training in Sub-Saharan Africa. In this paper,

6 In some of the waves, it is made explicit that the distribution of selected workers across job categories should reflect the distribution in the firm.

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we only analyze formal sector firms, so training in the informal sector (mostly in terrms

of unstructured apprenticeships paid for by the trainees, see Ziderman, 2003) is not

discussed.

3.1 Training Institutions in Sub-Saharan Africa

Training in formal sector enterprises in sub-Saharan Africa during the nineties was

characterized as being mainly Enterprise Based Training (EBT), see e.g. Grierson

(2002b) and Johanson and Van Adams (2004), both of which are specifically concerned

with training in Sub-Saharan Africa in the nineties. Publicly provided on-the-job

training in Sub-Saharan Africa (particularly in Kenya and Zambia, two of the countries

studied by Grierson) has been decreasing in size for some time and is becoming

increasingly irrelevant. Indeed, Grierson (2002b) argues that

“…governments keep investing their scarce resources in pre-employment training for sectors that are not hiring…”7

This point of view is basically shared by Ziderman (2003) and Johanson and Van

Adams (2004), who both contend that public sector training is very inefficient.

Moreover, national trade and/or sector associations have not filled the gap left by the

stagnation in public sector training provision. Hence, the burden of provision of training

has fallen upon the firms themselves, with the advantages and drawbacks that entail:

EBT is flexible, efficient, demand driven, and it can easily be targeted towards firms’

specific needs. In addition, it may help to promote firm specific social skills and

traditions. The drawbacks are, naturally, that EBT is firm specific, that is, a large part of

the human capital invested in a worker is not transferable between firms. Moreover, the

quality of EBT may be low and vary a lot between firms. EBT also is not necessarily

conducive to long-term growth, as its objective is probably to boost short-term profits in

the firm.

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The financing of EBT falls largely upon the firms and to some extent also on the

trainees, mainly through lower wages during the training period. In some countries, e.g.

Kenya and Zimbabwe, publicly provided training (which is mainly pre-employment

training through formalized apprenticeship systems) is financed through training levies

on employers, either as payroll taxes or as a fixed amount per worker or per unit of

output produced.

As regards selection processes (who receives training?), not much is known in advance.

Grierson (2002b) as well as Johanson and Van Adams (2004) report that training

resources often are concentrated on already highly skilled ‘key’ personnel.

In the present study, we are not able to identify EBT as a specific training category, but

as will be evident from the next sub-section, it would appear that many of the

descriptive statistics are consistent with this broader picture, namely that EBT is the

predominant form of training and that mainly high skilled workers are trained.

3.2 Training

The survey contains various sources of information regarding the extent of training. We

have information about whether the worker has received training in the 12 months

leading up to the survey as well as information about training received during the first

year of employment at the current firm. We have chosen to focus on the impact of

training received in the year leading up to the survey in order to avoid clear endogeneity

biases of, say, tenure when focusing on past training in the econometric analysis.8

7 P. 1. 8 A potential problem of using past training arises if trained individuals leave the manufacturing sector at a rate different from that of non-trained workers. This problem is not addressed in the study, except by limiting the focus to training received during the past 12 months, due to lack of relevant information.

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The specific question asked to the worker is

In the past 12 months, have you participated in any of the following

learning activities?

A. At a school, vocational or training institute

B. At an industry association training center

C. Formal training courses provided by your employer

D. Instructions from a supervisor or co worker

E. Watching others or learning on your own

The categories A to E represent a decreasing level of formality of training. In what

follows, an affirmative answer to any of A through C is considered to mean ‘formal

training’, whereas D is considered ‘informal training’. We have chosen to disregard

answer E.9 In addition to this, the worker is being asked to report the amount of time

spent in each of these learning activities during the 12 months period.

In Table 2, we look at training received during the previous year with the present

employer. As reported by Barron, Berger and Black (1997), the incidence of training

increases as we move down the table to less formal training modes.

The table shows that the fraction receiving formal training is similar in Zambia and

Kenya, and that informal training is used more frequently in Kenya. If we take a look at

the intensity of training, defined as the number of hours per year, some remarkable

differences reveal themselves.10 Category A training (i.e. training at a school) is

apparently much more intensive in Zambia compared to Kenya, and moreover, most of

9 Lumping of A, B, and C into a single indicator is needed in order to have enough trained individuals to perform the econometric analysis. Judging from Barron, Berger and Black (1997), there tends to be a lot of measurement error (i.e. disagreement about how to categorize training between employers and employees) if very detailed training categories are used. If more aggregate training categories are used, there tends to be agreement about the amount and incidence of training between the employer and employee. 10 In the table we report the mean and the median of the distribution of hours spent in training. There is not enough data points to generate plots of the distribution of hours within each category, but if we add – for each individual - the hours spent in training types A-D, the distribution is highly skewed with a probability density function which decreases with the number of hours in both countries.

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formal training is paid for by the firm (it is on company hours). Category B training (i.e.

training at industry associations) is used less intensively in both countries, and still, the

intensity of this type of training is about twice as large in Zambia compared to Kenya.

For category C training (i.e. courses), the intensity is about the same in the two

countries, but more hours are on company time in Kenya. For category D training,

which we denote informal training, the intensity is larger in Zambia, while its extent is

larger in Kenya. The general picture is that nearly all formal training is paid for by the

firm (on company hours).

Table 2. Worker training in the previous year of the present job (1995 waves) Fraction receiving training Previous year of present job Kenya Zambia A: At a school, vocational or training institute Of which public Mean and Median # hours on company time (if yes) Mean and Median # hours on own time (if yes)

2.4 -

57.1 15 76.0 1

2.8 0.9

230.1 36 188.6 0

B: At an industry association training centre Mean and Median # hours on company time (if yes) Mean and Median # hours on own time (if yes)

1.3 53.5 50

0.0 0

0.7 12.7 0 92.4 0

C: Formal training courses provided by employer Mean and Median # hours on company time (if yes) Mean and Median # hours on own time (if yes)

1.8 84.4 4 7.0 0

2.0 57.6 80 23.5 0

D: Instructions from supervisors or co-workers Mean and Median # hours on company time (if yes) Mean and Median # hours on own time (if yes)

6.3 91.1 30

0.4 0

4.7 266.4 140

1.0 0

E: Watching others or learning on your own Mean and Median # hours on company time (if yes) Mean and Median # hours on own time (if yes)

4.3 74.4 16 27.5 0

6.9 118.4 50

97.3 10

3.3 Dependent and explanatory variables

The dependent variable in this study is the natural logarithm of the monthly wage

measured in the local currency. In Table 3 we present the average log wages in the

samples studied, as well as the raw difference in the averages.

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Table 3. Average log wages. Kenya

1995 Zambia

1995 Avg. log wage 15.14

(0.74) 10.76 (0.72)

Avg. log wage, trainees, formal 16.09 (1.35)

11.44 (0.59)

Avg. log wage, trainees, informal 14.83 (0.65)

11.00 (0.76)

Avg. log wage, non-trainees 15.09 (0.65)

10.72 (0.71)

Raw difference in avg. log wages, formal and non-trainees

1.01 (0.18)

0.72 (0.10)

Raw difference in avg. log wages, informal and non-trainees

-0.26 (0.08)

0.16 (0.13)

Number of formal trainees Number of informal trainees Number of non-trainees Total number of workers

55 64 956

1075

41 36 643 720

Note: Formal training is the presence of one of the types of training ranging from A to C, and informal training is the presence of training of type D, during the previous year in the present firm.

It is seen from the table that trainees having received formal training have higher

incomes than non-trainees. However, we already know11 that those receiving training

are the better educated, work in larger firms, and have more tenure than non-trainees, so

it may just be these differences that explain the earnings differences in Table 3.

Looking at those receiving informal training there is no statistical difference in wages

compared to non-participants for the case of Zambia, whereas those participating in

informal training in Kenya receive, on average, 26 percent less per month than non-

participants.

The proportion of workers having received some kind of training during the past 12

months is 11 % for both Zambia and Kenya with an almost equal share between formal

and informal training.

To be able to balance the potential bias stemming from self-selection into training we

take advantage of the rich survey information at our disposal – firm level as well as

11 See Nielsen and Rosholm (2002).

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worker level information is available. On the firm level we use information that

potentially affects the likelihood and intensity with which training is used. This

information consist of the ownership type of the firm (privately or publicly owned or a

combination of the two, whether the company is domestically or internationally owned

or a combination thereof), industry (textile, wood, metal, or food sector), location of the

firm, the size of the firm with respect to number of employees in the years leading up to

the training period, the financial situation of the firm (whether profits went up or down

in the period), whether the firm was experiencing a shortage of skilled labor, the number

of people hired and fired, the degree of unionization within the firm, whether formal

training is provided within the firm, and the amount spent annually on training of

employees. The information used on the individual level is gender, ethnicity, age,

educational attainment, tenure in the firm, years of labor market experience prior to the

current employment spell, occupation, whether the individual is a relative to any of the

owners, and whether the individual is member of a labour union. The descriptive

statistics for the workers in the samples are shown in Appendix Table A1.

3.4 Selection into Training

Examining the selection into formal training we first note that firms fully owned by the

state have no workers in training, and as a consequence these observations are excluded

in the selection analyses below. For the case of Kenya, the same is the case for firms

jointly owned by private firms and the state. Moreover, the group without any formal

education is also not participating in formal training and is excluded. The five percent

found to be relatives to either the owner of the firm or the plant manager do not

participate in training neither and are excluded from the analyses. We could potentially

leave this group in below, but we conjecture that they might differ systematically in

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their earnings potentials compared to their non-relative counterparts, and since the

number of non-participants in our sample is relatively large, we weigh the importance of

minimising the bias higher than the precision of our estimates. Finally, those working in

the occupational category ‘sales’ were found not to participate in formal training and we

exclude this population accordingly. This leaves us with 1075 individuals for Kenya

and 720 for Zambia, of whom 55 individuals in Kenya and 41 in Zambia receives

formal training. For informal training, the corresponding numbers are 64 and 36,

respectively.

We now proceed by conducting a more thorough investigation of participation in

training in the two samples. We do this by running a simple probit model. The

probability of receiving training plays a central role in our impact estimator below. In

this paper we employ the method of propensity score matching. We therefore need a

sufficiently rich set of attributes to balance the bias coming from self-selection into

training, see section 4 below.

Previous studies on the impact of training on earnings (see Heckman et al. (1999) and

Heckman & Smith (1999)) have shown the importance of labour market transitions as

key predictors of training participation. The questionnaires at our disposal do not lend

themselves to constructions of such a set of conditioning variables besides tenure and

the amount of experience the individual had prior to joining the current firm. However,

what we lack in information on the individual worker level, we have on the firm level,

where a variety of information is available, as discussed in section 3.3. Table 4 reports

the results from the participation probit. Here we report only the overall probit model

for each country, that is, where participation into formal or informal training is

estimated.

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Table 4. Coefficient estimates and asymptotic errors from a probit for participation in training. Kenya 1995 Zambia 1995

Variable Coefficient Std.err. Coefficent Std.err.

Constant -0.690 0.560 -0.335 0.753

Ownership missing -0.391 0.167 0.087 0.348

Domestic/foreign ownedb -0.065 0.191

Domestic ownedb -0.104 0.365

Foreign ownedb 0.231 0.222 0.182 0.302

Private/stately ownedb 0.511 0.495

Relative to the owner 0.052 0.393

Textileb 0.022 0.213 -0.046 0.297

Wood b -0.227 0.188 0.235 0.266

Metalb 0.592 0.177 -0.406 0.298

# of workers, 1994 /1000b -0.566 0.582 -0.181 0.407

Profit upb 0.003 0.179 -0.321 0.250

Profit downb 0.272 0.198 -0.950 0.249

Shortage of skilled laborb 0.140 0.136 0.058 0.210

# of workers hired /100b 1.746 0.383 -0.203 0.537

# of workers fired /100b -1.115 0.577 -0.245 0.278

Fraction of workforce unionisedb 0.056 0.202 1.698 0.296

Formal training provided inhouseb -0.475 0.830 -0.497 0.325

Annual cost of training / 10,000,000b 7.834 1.540 822.183 175.713

Male -0.053 0.168 -0.106 0.220

Native 0.219 0.351 0.261 0.449

Primary education -0.116 0.150 -0.168 0.252

Further education 0.164 0.202 0.081 0.186

Tenure /100 0.422 1.265 1.269 1.782

Member of labor union -0.601 0.186 -0.994 0.253

Missing information on occupation 0.279 0.160

Production worker -0.380 0.290 -0.087 0.180

Age/100 -1.986 0.966 -4.232 1.491

Regional dummies Yes Yes

Notes: Bold letters indicate significance at a 5%-level and italics indicate significance at a 10%-level. a The omitted owner type is ‘private’, the omitted owner location is ‘domestic’, omitted industry is ‘food’, omitted profit rate is ‘the same’, omitted worker type is ‘Service’. b Standard errors are not corrected for sampling procedure /clustering.

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Ownership and industry dummies have some explanatory power for Kenya. If the firms

profit went down from past years, workers are less likely to have been trained in

Zambia, but more likely to have received training in Kenya. In Kenya, the number of

hires the previous year increases the probability of participation, whereas the number of

fires decreases the probability. The larger the fraction of the workforce in the firm,

which is unionized, the larger is the probability of training in Zambia. However, if the

worker him or herself is unionized participation decreases. Training probabilities

increase strongly with the annual cost of training in the firm, naturally. Less educated

workers receive less training, while workers with a further education receive more

training than the reference category with secondary schooling. None of these effects are

significant, though. Recall that workers without formal education are excluded since

they never received training. Finally, tenure does not appear to have any explanatory

power for the receipt of training, but higher age does indeed decrease the participation.12

In Figures 1 and 2, we plot the estimated densities of the probability of training receipt

for those who received training - the treated – and those who did not- the non-treated. It

can be observed that the distribution of the propensity scores differ widely among

treated and non-treated, but still, there appears to be an area of common support below a

probability of 0.7.

12 Notice that: age=7 + education + experience in previous jobs + time in unemployment + tenure with firm. Although time in unemployment was not included as an explanatory variable, we had to exclude experience in previous jobs as well to estimate the propensity scores. Although we try to identify as detailed effects as possible, only age turn out to be significant.

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Figure 1. Estimated densities for Kenya.

Figure 2. Estimated densities for Zambia.

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4. The Evaluation Problem Suppose we have a random variable Y on which we want to measure the impact of

receiving training. Let D be an indicator variable denoting receipt of training; if D=1,

the individual received training, and D=0 otherwise. Let 1Y be the outcome in the

presence of training and 0Y the outcome in the absence of training. Think of 1Y and 0Y

as two latent variables, of which only one is observed.

Hence, for a given person, the effect of training is the effect of shifting from ‘D=0’ to

‘D=1’, or 1 0Y Y− . The mean effect of training in the population is [ ]1 0E Y Y− while the

average effect of training on the trained (ATET) 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 outcome in the presence of training among those

receiving training – is directly observed in the data. The second term – the outcome in

the absence of training for a trained person – is not observed, and herein lies the

evaluation problem. To estimate the impact of training one thus needs to estimate the

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

of the appropriate measure of the impact of training is highly relevant, we will skip it

here and refer the interested reader to the discussion in Heckman et al. (1999). We only

note that the parameter that we estimate in this paper is the average effect of training on

the trained (ATET), that is, the parameter ∆ above.

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4.1 Matching estimators

The matching estimator tries to mimic the laboratory experiment by equalizing trained

and untrained workers along measurable dimensions. The assumption required for

consistent estimators is that

(3) 0 |Y D X⊥

where X is a set of observed variables. The conventional term for this condition in the

econometrics literature has become the ‘conditional independence assumption’ (CIA).

The problem with this assumption is evident if, for instance, X is multidimensional or

contains continuous variables, because in those cases matching on X becomes very

tedious. Rosenbaum and Rubin (1983) prove that (3) actually implies that

(4) 0 | ( )Y D p X⊥

where [ ]( ) Pr 1|p X D X= = is the propensity score, that is, the probability of

participating in training. This means that, instead of conditioning on X, all that is

necessary is to condition on the propensity score, which is one-dimensional.13

Given the access to rich information as regards the selection into training, we argue that

many of the variables affecting selection into training are observed, and we will

therefore proceed by employing matching estimators.14

13 Of course, if the propensity has to be estimated non-parametrically, the ‘curse of dimensionality’ re-emerges, see Smith & Todd (2003) for discussion.

18

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

matching, kernel and local linear matching. In this paper we employ the last of the four.

Local linear matching uses multiple comparison sample members to construct kernel

weighted averages to use 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.

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

estimator because of some desirable statistical properties (Heckman et al., 1997). As

kernel function, we use the biweight kernel

(10) 2 215 (1 ) for | | 1

( ) 160 otherwise

s sK s

− <=

The rule of thumb by Silverman (1986) is used for the choice of bandwidth, and a 3%

trimming level is applied to determine the area of common support.

5. Results Trainees generally receive higher log wages than non-trainees. When estimating the

average effect of training on the trained, the exercise is to find out whether this

relationship reflects a mere productivity enhancing effect of training, or whether

trainees just have favorable characteristics, which would have given them higher wages

even in the absence of training. In this section we present estimates of the average effect

of training (during the last 12 months before the survey) on the monthly log wages of

trained workers. First, we report the raw log wage differences and estimated training

effects using a propensity score matching estimator. Then, to the extent that data allows,

14 In previous versions of the paper we tried to estimate models which take into account selection on

19

we check robustness of the results by looking at variation in training effects by duration

of training and by firm characteristics.15 Finally, we check robustness by comparing

with the training effects as estimated from three other Sub-Saharan African countries

where more general training indicators are available.

5.1 Average training effects

Table 5 below presents the estimates of training effects on log wages of workers. Using

a local linear matching estimator, we find that the training effect is significantly positive

for Kenya, but not well-determined for Zambia. We also distinguish between formal

and informal training. We denote training which takes place at a formal training course

as ‘formal training’, whereas training which consists of instruction by a supervisor or a

co-worker is denoted ‘informal training’.16

For Kenya, the average effect of training on the wages of trained workers is 24.1%, and

it is significantly positive at a 10% level. The effect stems mainly from formal training.

For Zambia, the average effect of training on wages is small and insignificant. Looking

separately at formal and informal training, we find that informal training has a

significantly positive effect in Zambia, while formal training does not.

unobservables, but we were unable to come up with convincing instruments/exclusion restrictions. 15 We also tried with individual characteristics like the educational level, but were unable to obtain meaningful results, due to there being too few individuals irrespective of the grouping chosen. 16 Section 3 gives more details on the training indicator. ‘Formal training’ refers to category A-C, whereas ‘informal training’ refers to category D.

20

Table 5. Average training effect on the trained.

Kenya 1995 Zambia 1995

All Formal Informal All Formal Informal

Raw difference in log wages 0.366 1.008 -0.258 0.427 0.664 0.181

ATET (Local linear matching)

0.241

(0.124)

0.402

(0.318)

Too few

Observ.

0.112

(0.119)

0.083

(0.338)

0.286

(0.147)

Sample size 1075 720

Sample size after elimination of zero densities 986 929 . 672 545 587

# treatments (after trimming) 81 26 . 53 20 23

# controls (after trimming) 847 849 . 580 494 532

Note: Std. errors are based on 999 bootstraps with 100% resampling. Trimming level 0.03. Bold letters indicate significance at a 5%-significance level, whereas italics indicate significance at a 10%-significance level.

In the appendix, we report balancing tests to investigate how well the propensity score

matching succeeds in matching on a long range of variables. In general, the means are

similar and biases moderate. We experimented with interactions and polynomials for

each estimation. Still, there are a few variables where the match is not very good on

average.

5.2 Training effects for different training durations

If training has a productivity enhancing effect, one should see wage effects increasing

with the amount of training. Therefore, we investigate whether training effects vary

with the duration of training. Due to the low number of trainees in the samples, this is

done by gradually removing workers receiving only small amounts of training. Table 6

shows the results. The columns ‘10+’ include only trainees with more than 10 hours of

training, while the columns ‘80+’ include trainees receiving more than 80 hours of

training. For Kenya, the effect of training tends to increase and become more well-

determined when we exclude individuals with a short training duration, whereas for

Zambia the results are inconclusive.

21

Table 6. Variation in estimated training effect by hours of training.

Kenya Zambia

Hours of training All 10+ 80+ All 10+ 80+

Raw difference in log wages 0.366 0.526 0.564 0.427 0.417 0.435

ATET (Local linear matching)

0.241

(0.124)

0.358

(0.150)

0.472

(0.172)

0.112

(0.118)

0.086

(0.130)

0.142

(0.175)

Sample size 1075 720

Sample size after elimination of zero densities 986 962 919 672 660 639

# treatments (after trimming) 81 60 25 53 44 29

# controls (after trimming) 847 847 841 580 578 574

Note: Std. errors are based on 99 bootstraps with 100% resampling. Trimming level 0.03. Bold letters indicate significance at a 5%-significance level, whereas italics indicate significance at a 10%-significance level.

5.3 Variation in training effect across firms

In Table 7 below we present the estimated training effects when we exclude the smallest

firms with less than 10 employees. We find that the effect is larger and more well

determined when we exclude the smallest firms. For both countries, the effect is a 26%

wage increase after a training episode.

Table 7. Training effect and firm size. Kenya Zambia

Size of firm All 10+ All 10+

Raw difference in log wages 0.366 0.428 0.427 0.410

ATET (Local Linear Matching)

0.241

(0.124)

0.265

(0.124)

0.112

(0.118)

0.259

(0.124)

Sample size 1075 720

Sample size after elimination of zero densities 986 854 672 553

# treatments (after trimming) 81 70 53 49

# controls (after trimming) 847 735 580 472

Note: Std.errors are based on 99 bootstraps with 100% resampling. Trimming level 0.03. Bold letters indicate significance at a 5%-significance level, whereas italics indicate significance at a 10%- significance level.

22

There are several potential explanations why the return to training increases with firm

size. One is that large firms have internal labor markets that reward increased

productivity by promotion (Lazear, 1995). Another explanation is that there may be

economies of scale in training provision and economies of scale in access to

government sponsored training programs. The existence of internal labour markets

allows large firms to invest more in training and get a higher pay-off to training for at

least two reasons (Middleton et al., 1993). The promotion possibility makes it more

attractive for workers to stay in the firm, and therefore the potential market failure in the

training market is reduced. Moreover, large firms may be more inclined to hire low-

educated workers and train them themselves, if the skills from the school system are

inappropriate or insufficient. On the other hand, they may use education as a screening

device since education is expected to be positively correlated with favorable unobserved

traits (Stiglitz, 1975).

The effect of training may also vary across individuals. Middleton et al. (1993) report

several explanations why the training effect might vary with gender or education.

However, we are not able to identify gender specific training effects, since females

constitute a small proportion of the samples, and hence the overall effect stems from

training of males. Neither is it possible to detect variation in the training effect across

education. The only educational group where we succeed to estimate the training effect

separately consists of individuals with secondary or further education in Zambia in

which case the effect was slightly more well-determined (ATET 0.15, std.err. 0.11) than

for the overall sample.

23

5.4 Training effects for alternative data sources

As a further check of the robustness of the results, we use other available waves of

African firm data. We use three waves of RPED data collected in Ghana, Zimbabwe and

Kenya in 1994. For these three waves of data we know whether an employee has

received training in the past or not. However, the training indicator is more general

(“Did you receive training in the past?” Possible answers: Within, outside or no) and the

information about the selection process is more scarce than for Kenya and Zambia.

As a robustness check, we report the estimated average training effect on the trained for

these three waves in Table 8. The training effects for these waves are found to be 15-

20%, which is similar to the findings from the data sets, where we have more details on

the type of training and the selection process.

Table 8. Estimates of average training effects for alternative data waves. Ghana

1994

Zimbabwe

1994

Kenya

1994

Raw difference in log Wages 0.18 0.39 0.17

ATET (Kernel Matching) 0.22

(0.10)

0.22

(0.07)

0.15

(0.05)

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 999 bootstraps with 100% re-sampling

6. Policy issues The first crucial policy question is whether training increases wage and productivity at

all. Like most other studies17 evaluating the average effect of employer-sponsored

training, we tend to find a positive wage effect. We find an average effect of training on

the trained, which is roughly a 20% wage increase. If wages reflect productivity, both

employers and employees seem to benefit from training.

24

The second crucial policy question is whether the net benefit after considering costs

associated with training is also positive. Our data for Kenya and Zambia show that

training (in terms of time spent) is employer financed rather than employee financed.

The amount of individual hours spent on training is lower than the number of company

hours spent, cf. Table 2.18 Hence, the employers seem to carry most of the costs, which

may lead to under-investment in training, at least if the skills are transferable across

firms (Becker, 1975).

If skills are fully transferable from firm to firm, and if labor markets are competitive

such that wages reflect productivity only, the simple evaluation studies performed in the

previous section of this paper are valid. For the UK, Blundell et al. (1996) find that

benefits of employer-sponsored in-service training has long-lasting effects and that the

acquired skills are almost completely transferable. To our knowledge no such analysis

exists for Sub-Saharan Africa.

After a period of training, pay rises may occur due to a productivity increase or due to

promotion. We are not able to distinguish the two, and hence we just attribute pay rises

to training, irrespective of the source of the increase in pay, productivity increase or

promotion, since both are caused by training. We estimate that the average effect of

training on the trained is a 20% wage increase, and if wages do indeed mirror

productivity, both employers and employees benefit from training. For these numbers to

be useful, the associated costs need to be considered also. For Kenya and Zambia, the

indirect costs (output lost) of training are indicated by Table 2, which shows that the

17 See Grubb and Ryan (1999). 18 In 1991, the World Bank recommended to move the focus from supply driven to demand driven training, and to switch from public towards employer and private training, see Bennell (1996) and Middleton et al. (1993). Bennell (1996) indicates that the policy has not been implemented in Sub-Saharan Africa, and that it is not likely to be successfully introduced either. It is not evident from our data whether this switch has actually taken place, but in-depth interviews (Grierson, 2002a) indicate that it has taken place in Zambia.

25

invested company hours range from 50 to 400 hours per trainee, which is roughly 3-

25% of annual working time. There is no direct evidence on the direct costs of training,

partly due to the fact that training is heterogeneous (see Table 4). However, some of the

training is informal, involving foremen and/or co-workers. If we make the ad hoc

assumption that informal training involves a one-to-one relation, then the total costs are

approximately twice the indirect costs. Hence, total costs add up to 6-50% of average

annual wages. However, informal training shows less variation in terms of the number

of hours used, which is around 250, corresponding to 15% of annual working time.

Assuming that these numbers are generally valid for the countries in this study, and

adding a little costs for formal training, we can perform an approximate cost-benefit

analysis for training. Moreover, assume that labor’s share of output is two-thirds -

which is roughly in accordance with Biggs et al. (1995a) – and that a productivity

increase is shared in the same proportion between the capital owner(s) and the workers.

We use discount rates of 0, 0.05, and 0.10. The formula used for the analysis is the

following:

16

0 2rtPDV c e dt−∆

⋅= − + ∫

PDV is present discounted value of training, expressed in ‘annual output per worker’

units, c denotes the initial cost of training (as a fraction of annual output per worker), ∆

is the return to training, and r is the discount rate. The upper limit of the integral is set to

16 years; in the descriptive statistics in Table A1, reported tenure levels are around 8

years. Assuming an exponential distribution, the expected duration of a stock sampled

observation is twice the elapsed duration, that is, 16 years. The division by 2 is simply

because the firm owner gets 1/3 of the output generated, and ∆ corresponds to 2/3. We

also calculate the amount of tenure required for the investment to be worthwhile for the

26

firm, that is, the time until the PDV is positive; we just replace the upper integral limit

with an x and solve for the value of x that sets PDV=0. Table 9 shows this back-of-the-

envelope calculation of PDV and break-even of employer-financed training.

Table 9. PDV and break-even of employer-financed training. Rate of return, ∆ 20% PDV Break-even 0% discount rate Costs 6% Costs 15% Costs 80%

- annual output -

1.54 1.45 0.80

- years in the firm until break-even - 0.60 1.50 8.00

5% discount rate Costs 6% Costs 15% Costs 80%

1.04 0.95 0.30

0.61 1.56 10.22

10% discount rate Costs 6% Costs 15% Costs 80%

0.74 0.65 -0.00

0.62 1.63

-

Note that the PDV is positive in all except one case. For example, with a 5% discount

rate and 15% costs, the firm experiences an accumulated gain corresponding to the

annual output of 0.95 workers, implying that the investment pays off as long as the

worker stays in the firm at least 1.56 years after being trained.

Based on these calculations, we would argue that the case for training is very good.

Firms have gained from investing in training, and since the gain is increasing in tenure,

firms should construct training schemes in such a way that they induce the worker to

stay in the firm. To the extent that training produces firm specific productivity

increases, that is not a problem, but if some of the productivity increase is transferable

to other firms, it may be worthwhile to design pay profiles in such a way that the worker

has an incentive to stay in the firm at least until the firm reaches its break-even point.

27

These relatively optimistic conclusions hold for the trained on average, and they do not

necessarily carry over to the marginal trainee. Therefore, it is not possible to infer

anything about prospects for future policy. Furthermore, the conclusions do not

necessarily carry over to public sponsored training programs.

7. Conclusion Econometric evaluation of training programs in Sub-Saharan Africa is scarce. Biggs et

al. (1995a) show that African manufacturing firms gain from training its workers, and in

this paper we show that workers also experience wage gains from participation in

training. We find that the returns to training are roughly 20% in general. Returns are

larger for formal than informal training in Kenya, while the reverse appears to be the

case in Zambia. We find, moreover, indications that the returns to training increases

with the amount of training received and that the returns to training increase with firm

size. Cost-benefit analyses show that the case for training is very good, since the

calculated present value is positive under most assumptions. Under training schemes

with high costs, the break-even point may be sufficiently far out in the future that it may

be worthwhile to design pay profiles such that the worker has an incentive to stay in the

firm at least until the firm reaches its break-even point.

References

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Becker, G. (1975), Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education. New York: NBER.

Bennell, P. (1996), “Privatization Choice and Competition: The World Bank’s Reform Agenda for vocational Education and Training in Sub-Saharan Africa.” Journal of International Development 8 (3): 467-487.

Biggs, T., M. Shah and P. Srivastava (1995a),”Training and Productivity in African Manufacturing Enterprises”, RPED Discussion Papers. The World Bank: Washington D.C.

Biggs, T., M Shah and P. Srivastava (1995b), “Technological Capabilities and Learning in African Enterprises”. Technical Paper No. 288. Africa Technical Department, World Bank, Washington, D.C.

28

Blundell, R., L. Dearden, and C. Meghir (1996), The Determinants and Effects of Work Related Training in Britain. IFS, London.

Chandra, V, L. Moorty, B. Rajaratnam, and K. Schaefer (2001), “Constraints to Growth and Employment in South Africa.” Informal Discussion Paper on Aspects of the Economy of South Africa #14. The World Bank, Washington D.C.

Grierson, J., editor, (2002a), “Enterprise-based Training in Africa: Case Studies from Kenya and Zambia.” Occasional Paper, International Training Centre of ILO, Turin.

Grierson, J. (2002b), “Practices and Trends in formal Sector Enterprise-Based Training in Africa.” Occasional Paper, Occasional Paper, International Training Centre of ILO, Turin.

Grubb, W. N. and P. Ryan (1999), “The Roles of Evaluation for Vocational Education and Training: Plain Talk on the Fields of Dreams.” Kogan Page, UK.

Heckman, J. J., R. J. LaLonde, and J. A. Smith (1999), “The Economics and Econometrics of Active Labour Market Programs.” In Ashenfelter and Card (eds.) Handbook of Labor Economics. North Holland: Amsterdam.

Heckman, J. & Smith, J. (1999), “The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies.” The Economic Journal 109: 313–348.

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Lechner, M. (1999), “An Evaluation of Public-Sector Sponsored Continuous Vocational Training Programs in East Germany.” Journal of Human Resources 35 (2): 347-375.

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Middleton, J., A. Ziderman, A. Van Adams (1993), Skills for Productivity: Vocational Education and Training in Developing Countries. The World Bank, Washington D.C.

Nielsen, H. S. and M. Rosholm (2002), “The Extent of Training Among African Enterprises and African Workers.” Manuscript.

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29

APPENDIX Table A1. Descriptive statistics for the workers in the two samples Kenya

1995 Zambia

1995 Firm Level: Ownership type:

1. Private 2. Public 3. Jointly private and public 4. Ownership missing

0.761 0.000 0.014 0.225

0.704 0.158 0.029 0.109

Ownership nationality 1. Domestic 2. Foreign 3. Jointly domestic/ foreign 4. Ownership missing

0.588 0.074 0.113 0.225

0.731 0.082 0.079 0.109

Industry 1. Textile 2. Wood 3. Metal 4. Food

0.203 0.285 0.244 0.288

0.276 0.176 0.249 0.299

Firm location 1. Nairobi / Lusaka 2. Mombasa / Livingstone 3. Nakuru / Kitwe 4. Eldoret / Ndola

0.642 0.179 0.105 0.074

0.399 0.093 0.139 0.369

Total number of employees 1. 1993 2. 1994

123.6 127.9

178.1 162.7

Profit rate compare to previous year 1. Higher 2. Lower

0.375 0.388

0.276 0.621

Shortages of skilled labor an obstacle (1=yes)? 0.442 0.151 Number of permanent employees

1. Hired previous year 2. Fired previous year

7.2 6.6

8.4

16.7 Fraction of employees member of a labor union 0.396 0.467 Provision of training within firm (1=yes)? 0.013 0.097 Total amount spend on training previous year 124,608 1503 Worker level: Gender (1=male) 0.854 0.812 Ethnicity (1=African) 0.969 0.975 Educational Attainment, highest:

1. None 2. Primary 3. Secondary 4. Further

0.028 0.434 0.438 0.101

0.011 0.228 0.481 0.281

Tenure 7.70 7.40 Experience minus tenure 3.16 4.99 Whether relative to firm owner or manager (1=yes) 0.044 0.053 Member of labour union 0.339 0.310 Job previous year:

1. Sales 2. Production 3. Service

0.028 0.963 0.01

0.056 0.545 0.038

Age 33.56 35.40

30

Table A2. Balancing tests. Kenya – all Zambia – all

Treated

means

Matched

means Bias-%

Treated

means

Matched

means Bias-%

Ownership missing 0.163 0.117 13.145 0.075 0.059 6.532

Freign owned 0.113 0.101 3.586 0.113 0.098 4.878

Textile 0.088 0.104 -5.667 0.302 0.286 3.506

Wood 0.188 0.187 0.197 0.208 0.226 -4.379

Metal 0.438 0.442 -0.940 0.245 0.217 6.608

Nairobi/Lusaka 0.563 0.561 0.350 0.453 0.445 1.544

Nakuru/Kitwe 0.138 0.146 -2.400 0.208 0.196 2.742

Eldoret/Ndola 0.100 0.101 -0.341 0.264 0.244 4.617

# of workers, 1994 /1000 0.097 0.100 -1.894 0.198 0.221 -7.327

Profit up 0.500 0.502 -0.437 0.415 0.336 16.244

Profit down 0.300 0.293 1.584 0.509 0.538 -5.650

Shortage of skilled labor 0.488 0.505 -3.408 0.226 0.220 1.609

# of workers hired /100 0.046 0.051 -7.613 0.109 0.109 -0.165

# of workers fired /100 0.008 0.029 -18.284 0.185 0.170 3.964

Fraction of workforce unionised 0.315 0.338 -5.775 0.596 0.616 -5.540

Whether formal training provided inhouse 0.000 0.000 . 0.132 0.131 0.317

Annual cost of training / 10,000,000 0.011 0.010 3.792 0.000 0.000 0.158

Male 0.813 0.848 -9.399 0.755 0.793 -9.236

Native 0.963 0.978 -9.256 0.943 0.971 -13.557

Primary education 0.325 0.351 -5.425 0.113 0.119 -1.709

Further education 0.175 0.156 5.134 0.377 0.347 6.258

Tenure /100 0.059 0.056 5.084 0.067 0.071 -5.680

Member of labor union 0.150 0.155 -1.471 0.189 0.185 1.031

Production worker 0.925 0.904 7.625 0.434 0.519 -16.940

Age 0.301 0.306 -6.070 0.325 0.334 -10.389