Melco Management Accounting Research
Discussion Paper Series
No.MDP2019-003
Short- and Medium-run Impacts of Management Training:
An experiment in Vietnam and Tanzania
February 2019

Yuki Higuchi*
Associate Professor, Nagoya City University
Graduate School of Economics
1, Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya-city, 467-8501, Japane-mail: higuchi@econ.nagoya-cu.ac.jp
Edwin Paul Mhede
Deputy Permanent Secretary
Ministry of Industry, Trade, and Investment, Dodoma, Tanzania
Vu Hoang Nam
Associate Professor
Foreign Trade University, Hanoi, Vietnam
Tetsushi Sonobe
Professor/ Vice PresidentNational Graduate Institute for Policy Studies, Tokyo, Japan*corresponding author

The Melco Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
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Short- and Medium-run Impacts of Management Training:
An experiment in Vietnam and Tanzania

Yuki Higuchi(Nagoya City University, Nagoya, Japan)

Edwin Paul Mhede(Ministry of Industry, Trade, and Investment, Dodoma, Tanzania)

Vu Hoang Nam(Foreign Trade University, Hanoi, Vietnam)

Tetsushi Sonobe(National Graduate Institute for Policy Studies, Tokyo, Japan)

We conducted a randomized controlled trial of management training inindustrial clusters in Vietnam and Tanzania, collecting baseline and followup data over the span of four years with negligible incidence of attrition.
The data reveal that the training intervention improved the management
practices of the treated firms, and that the impact remained significant twoto three years after the intervention. Moreover, the initially insignificant
impacts on value added became significant later in the study.

Keywords: Management training, Kaizen, Industrial Cluster, Vietnam, Tanzania

1 Introduction
Firms producing similar or related products tend to be located near one another, aphenomenon referred to as firm agglomeration. Areas with this firm agglomeration arecalled industrial clusters. While there are eye-catching clusters, such as the IT industries
in Silicon Valley and Mumbai, the majority of industrial clusters are local and obscure. Inreality, the vast majority of firms, both in the developed and developing worlds, arelocated in industrial clusters because they can take advantage of the various benefits ofagglomeration economies (e.g., Atkin et al., 2017; Fujita, Krugman, & Venables, 1999).
Based on a number of case studies in Asia and Africa, Sonobe and Otsuka (2011)
found that most firms in developing countries are indeed located in industrial clusters,
and there are both successful clusters and failed ones. They found that it is most likely themanagerial capacity of entrepreneurs that determines success. A capable entrepreneur
introduces advanced technology by borrowing it from nearby cities or neighboring
countries and instructs and motivates workers to produce upgraded products. A clusterwith no such entrepreneur will continue to maintain the status quo, their products will
eventually become old-fashioned, and the cluster will disappear.
In another strand of literature, Bloom and van Reenen (2007) developed amanagement score and found that managerial capacity is limited in developing countries
based on data collected from thousands of firms around the world. To improve managerial
capacity, several randomized controlled trials (RCTs) have been conducted to provide
management training in developing countries. According to a survey of managementtraining RCTs by McKenzie and Woodruff (2014), experimental training programs did
indeed improve the management score of treated firms, but the evidence on the impact ofsuch training on business performance is limited. Further, researchers have yet to arrive
at a consensus on why the training impacts on business performance are limited. Are they
due to inadequately designed training programs, too early assessment of training impacts,
or knowledge spillovers from training participants to non-participants?
Our study attempts to integrate these two strands of literature by conducting RCTs
on management training in industrial clusters. Our research question are whether
management training improves the managerial capacity of firms in industrial clusters,
whether it increases business performance, and as a result, whether it leads to dynamic
development in these industrial clusters. Since our ultimate goal is to prescribe aneffective policy toward income generation in developing countries, we are interested in
evaluating training impacts in labor-intensive industries, in which developing countries
have comparative advantages. Hence, we provided management training in labor-intensive garment industrial clusters in Vietnam and Tanzania (Higuchi, Nam, & Sonobe,2015; Higuchi, Mhede, & Sonobe, 2019).

2 Study Sites
The garment cluster in Tanzania is located in the country’s largest city, Dar es
Salaam, and has more than 700 garment producers, including the self-employed. We
randomly selected 113 firms out of the 250 members of three major associations ofgarment firms. They mostly supply their products to the domestic market and occasionally
export to neighboring countries by participating in trade fairs (Higuchi, Mhede, & Sonobe,2019). In Vietnam, the study site is located on the outskirts of the capital city, Hanoi. It
contains numerous subcontractors, but they are not included in the sample. Instead, thesample covers all 161 final product producers in the cluster. They supply their products
to the domestic market, and a few of them export to Eastern Europe as well (Higuchi,Nam, & Sonobe, 2015).
Conducting an RCT of management training in an industrial cluster has bothadvantages and disadvantages. A major advantage is that the sample firms face the same
prices for products, factors, and intermediate inputs, and have the same access toinfrastructure because they produce the same products in geographical proximity to one
another. This reduces heterogeneity among the sample firms, thereby facilitating
statistical inference.
A major disadvantage is that imitation is rampant in industrial clusters.
Management practices and business performance might improve even for those firms that
did not receive training, which would lead to an underestimation of training impacts
unless a special method of impact evaluation, such as the one proposed by Baird, Bohren,
McIntosh, and Özler (2014), is applied. Having said that knowledge spillovers make
impact evaluation difficult, we note that such spillovers have a positive effect, making thesocial benefit of training greater than its private benefit; this warrants further research on
management training in industrial clusters. Although there is suggestive evidence for theexistence of spillovers, we have not applied any special method in this paper, and hence,
our results are likely to understate the impacts.

3 Experiments
Table 1 shows the sample size, the average number of employees, and other dataon our sample firms. Typical firms in the Tanzania study site employ about 5 workers,while the firms in Vietnam employ about 20 workers. When a firm has no employees,
business owners must know about self-management, financial management, andmarketing. When a firm has many employees, owners need to know how to coordinate
the division of labor as well. Thus, our experimental training programs covered not only
basic accounting, marketing, and business strategy, as often adopted in the existing studies,but also elementary training in Kaizen management. Kaizen is an approach to production
management and quality control, aimed at improving coordination among workers.
In both study sites, the training programs consisted of two components: one offeredclassroom lectures for about 45 hours, and the other sent trainers to participants on several
occasions to provide coaching tailored to the respective firms. In each site, the sample
was randomly divided in half, and one-half was invited to participate in the classroom
training component. Then, independently of this, the sample was randomly divided in halfagain, and one-half was invited to participate in the on-site training component. We referto those firms that were invited to either one component or both as the treatment groupand those that were not invited to either component as the control group. The two groups
in each study site differ in the baseline average firm size in terms of value added due tosome outliers and the small sample sizes, but the difference is not statistically significant(see the p-values reported in columns 3 and 6).
After the training programs were implemented in 2010, follow-up surveys wereconducted twice in Vietnam and thrice in Tanzania from early 2011 through early 2014.
As shown in Table 1, the incidence of sample attrition was low, probably because thesample firms were sufficiently large to survive and because even the control group
continued to cooperate with our repeated surveys, expecting to participate in an advanced
training program that we plan to provide in the future.

4 Major Results
An outcome variable of interest is the management score, or the number of goodpractices adopted by a firm. It was constructed based on enumerators’ visual inspections
and personal interviews with the owners/managers of the sample firms.1 In both sites, as
shown in Table 1, the treatment and control groups share about the same baseline scores,
but their scores diverged from the first follow-up survey onwards (see the p-values forthe difference in the changes from the baseline level reported in columns 3 and 6). The

The score ranges from 0 to 27 in Tanzania and 0 to 30 in Vietnam, reflecting
differences in training content.
1control group’s average score increased from the baseline through the second follow-up
survey, suggesting knowledge spillovers from the treatment group to the control group.2
In Tanzania, the management scores at the third follow-up survey were lower than at thesecond, indicating that the firms stopped using some practices that they had adopted
earlier. Still, the p-value shows that the difference in changes from the baseline issignificant.
Another interesting variable is the annual value added, which is defined as sales
revenue minus the costs of materials, electricity, other intermediate inputs, andsubcontracting. In both sites, the product markets worsened throughout the post-training
period, mostly because of competition with imported goods, and the average value added
declined substantially for the control group. In Tanzania, it continued to decline even for
the treatment group, although the magnitude of decline is smaller among the treated firms.
Table 1 shows that the difference in changes from the baseline level was initially small
and insignificant, but it became significant at the 5 percent level in 2012 in Vietnam andin 2013 in Tanzania.3
The bottom two rows of Table 1 show the fraction of firms that were willing to pay
the local currency equivalent of 150 USD for training participation. The fraction was very
low in Vietnam, indicating that few business owners in this cluster knew the value oflearning about management. Consistently, there were many firms that were invited to thetraining program free of charge but did not participate in it. In Tanzania, the fraction ofwilling firms was much higher and reached the upper bound after the training intervention.
Indeed, the take-up rate was nearly 100 percent. The firms in this cluster were more
willing to learn, probably because some successful entrepreneurs had started theirbusinesses after participating in business training programs provided by internationalorganizations and NGOs. In addition, the increased willingness to pay in the control groupsuggests that the training was favorably received by non-participants due to knowledge
spillovers.

5 Conclusion
This study presents new evidence indicating that, while management training can increase
not just management scores but also value added or firm income, it takes a few years to
As additional direct evidence of spillovers, we have data on conversations between
sample firm owners about the training content.
3The results of a more rigorous evaluation are reported in Higuchi, Nam, and Sonobe(2015) and Higuchi, Mhede, and Sonobe (2019).
2

experience a significant impact on incomes. The majority of firms are located in industrial
clusters, where knowledge tends to spill over, and they may not know the value of learning
about management. Such ignorance and spillovers, together with the favorable training
effects, suggest a need for policy intervention and, hence, warrant a considerable
compilation of future research.

References
Atkin, D., Chaudhry, A., Chaudry, S., Khandelwal, A. K., Raza, T., & Verhoogen, E.
(2017). On the origins and development of Pakistan’s soccer-ball cluster. WorldBank Economic Review, 30, s34-s41.
Baird, S., Bohren, A., McIntosh, C., & Özler, B. (2014). Designing experiments tomeasure spillover effects. World Bank Policy Research Working Paper, 6824.
Bloom, N., & van Reenen, J. (2007). Measuring and explaining management practices
across firms and countries. Quarterly Journal of Economics, 122(4), 1341-1408.
Fujita, M., Krugman, P. R., & Venables, A. J. (1999). The Spatial Economy, Cities, Region
and International Trade. Cambridge, Massachusetts: MIT Press.
Higuchi, Y., Mhede, E. P., & Sonobe, T. (2019). Short- and medium-run impacts ofmanagement training: An experiment in Tanzania. World Development, 114, 220236.
Higuchi, Y., Nam, V. H., & Sonobe, T. (2015). Sustained impacts of Kaizen training.
Journal of Economic Behavior and Organization, 120, 189-206.
McKenzie, D., & Woodruff, C. (2014). What are we learning from business training andentrepreneurship evaluations around the developing world? World Bank Research
Observer, 29(1), 48-82.
Sonobe, T., & Otsuka, K. (2011). Cluster-Based Industrial Development: A Comparative
Study of Asia and Africa. New York, New York: Palgrave Macmillan.

Table 1. Sample Size, Management Score, and Value Added by Treatment Status
Vietnam

Tanzania

(1)

(2)

(3)

(4)

(5)

(6)

TreatedControl

p-value

TreatedControl

p-value

Baseline (early 2010)

107

54

-

81

32

-

1st follow-up (early 2011)

107

54

-

81

32

-

2nd follow-up (late 2012/early 2013)

102

46-

78

32

-

3rd follow-up (early 2014)

---

7530

-

17.2

22.4

(0.37)

5.4

4.8(0.37)

Baseline

13.4

13.3

(0.89)

10.9

10.3(0.36)

1st follow-up

17.1

13.9

[0.00]

16.5

12.8[0.00]

2nd follow-up

19.2

15.0

[0.00]

20.1

17.5[0.05]

3rd follow-up

-

-

-

16.5

13.1

[0.00]

Baseline (mean of 2008 and 2009)

171.8

292.6

(0.15)

16.7

27.6(0.13)

2010 (2010 was the training year)

181.1

196.2

[0.28]

26.7

32.4[0.52]

2011

-

-

-

22.2

25.4

[0.18]

2012

186.0

96.9

[0.03]

17.2

13.3[0.06]

2013---18.413.1[0.04]

0.23

0.11(0.06)

0.68

0.71(0.82)

Number of observations

Baseline number of workers(mean of 2008 and 2009)Management Score

Value added

Willingness to pay (yes = 1)
Baseline

1st follow-up
0.53
0.13
[0.00]
1
1[0.82]
Notes: The p-values in parentheses are for the t-tests of the null hypothesis that two groups share thesame mean. The p-values in brackets are for the t-tests of the null hypothesis that the difference inchanges from the baseline between two groups is zero. Management score ranges from 0 to 27 inTanzania and 0 to 30 in Vietnam. Value added is expressed in terms of PPP-adjusted 1,000 USD. We
assume that closed firms have zero value added.