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Archives of Business Review – Vol. 8, No.7
Publication Date: July 25, 2020
DOI: 10.14738/abr.87.7990.
Hlzane, M. P., Dlamini, D. V., & Ajetomobi, J. O. (2020). The Effect of Investment Climate on Productivity of Food and Beverages
Industries in Eswatini. Archives of Business Research, 8(7). 124-133.
The Effect of Investment Climate on Productivity of Food and
Beverages Industries in Eswatini
M. P. Hlanze
Agricultural Economics and Management,
University of Eswatini
D. V. Dlamini
Agricultural Economics and Management,
University of Eswatini
J. O. Ajetomobi
Agricultural Economics and Management,
University of Eswatini
ABSTRACT
Food security and job creation are among the top priorities of the
government of Eswatini. To address these priorities, the government
have made substantial amount of investment to support the
agricultural sector over the years. Regardless of these policy
intervention, Eswatini is still an importer of food and unemployment
remains substantial high. The study examined the influence of
investment climate on productivity of food and beverages industries in
Eswatini. Findings of this study will assist the government of Eswatini
to design focused policy interventions in order to attract more local and
foreign direct investment.A Cobb-Douglas production function for
Eswatini of manufacturing industries was estimated using 2006 cross
sectional data from the World Bank to produce a measure of TFP for
each industry. In addition, an extended Cobb-Douglas production
function was estimated with investment climate variables for selected
manufacturing industries.The results showed that manufacturing
industries in Eswatini are labour intensive and competition from
informal sectors is the main obstacle faced by manufacturing
industries in Eswatini. The findings of the study further reveals that an
increase in corruption cause a decrease in productivity. The study
recommends investment in institutional reforms especially to fight
against corruption and efficacy in the provision of public goods and
services. It further recommends a resilient support and regulation of
the informal sector.
Keywords: Investment Climate, Total Factor Productivity, Policy
Intervention, Food and Beverages Industries, Eswatini.
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INTRODUCTION
The economy of Eswatini is closely linked to the economy of South Africa, from which it receives
over 90% of its imports and exports about 70% of goods. Other key trading partners are the United
States and the European Union (EU), from which Eswatini received preferences for textile exports
under the African Growth and Opportunity Act (AGOA) to the USA and sugar exports to the EU.
Both textile and sugar exports did well under these agreements with rapid growth and a strong
inflow of foreign direct investment (FDI). Between 2000 and 2005 the textile exports grew by over
200% and the sugar exports by more than 50%. In 2014, the export sector was threatened by the
removal of the trade preferences for textiles and phasing out of the preferential prices for sugar to
the EU markets (Central Bank of Swaziland (CBS), 2016). Eswatini is now facing the challenge of
remaining competitive in the changing global environment. The investment climate is a crucial
factor in addressing this challenge. Firms in Eswatini are less productive than firms in most middle- income countries in other regions. Productivity of firms in Eswatini can be compared much easily
with firms from lower middle income countries but it is difficult to do so because firms in Eswatini
are hindered by inadequate government arrangements and infrastructure (World Bank, 2007).
In 2015, the World Bank approved a loan of $25 million to help the country to improve the
environment for private sector development and to catalyze new investments. The private sector
competitiveness project aims at supporting the economic diversification and job creation
particularly in the agribusiness and tourism sectors. Another aim of the project is to help in
implementation of the country’s Investor Road Map. The main objective of the Investor Road Map
is to create an enabling business climate. Private sector competitiveness project has to improve the
investment environment which focus on regulatory reform and export facilitation to increase
international and domestic investments and to increase exports. Through the private sector
competitiveness project, the financial sector has to develop in order to improve access to finance
for Micro, Small and Medium Enterprises (MSMEs). The development of the financial sector will
help in the reviewing of the Export Credit and Small Scale Enterprise Guarantee Schemes and assist
in the implementation of revised Schemes. These improvements has to facilitate private sector
activity across sectors, including agribusiness and tourism (Commonwealth Network of Nation
Report, 2017).
Objectives
The main objective of the study is to examine the influence of investment climate on the
productivity of food and beverages industries in Eswatini. The specific objectives are to
1. Investigate the most serious obstacles facing industries in Eswatini.
2. Estimate total factor productivity of food and beverages industries in Eswatini.
3. Examine the effects of investment climate on the productivity of food and beverages
industries.
STYLIZED FACTS ABOUT INDUSTRIES IN ESWATINI
Following the ISIC (revision 3.1) classification, the following industries in Table 1 were covered by
the 2006 World Bank Investment Climate in Eswatini, namely: food and beverages, garments,
textiles, machinery and equipment, chemical, wood, wood product and furniture, metal and metal
products and other manufacturing.
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Hlzane, M. P., Dlamini, D. V., & Ajetomobi, J. O. (2020). The Effect of Investment Climate on Productivity of Food and Beverages Industries in
Eswatini. Archives of Business Research, 8(7). 124-133.
Table 1: Types of Industries
Industry Frequency Percentage Cumulative
Frequency
Food and beverages 14 20 20
Garments 15 21.4 41.4
Textiles 5 7.1 48.5
Machinery & equipment 3 4.3 52.8
Chemical 4 5.7 58.5
Wood, wood product & furniture 3 4.3 62.8
Metal & metal products 2 2.9 65.7
Other manufacturing 24 34.3 100
Total 70 100
Source: Own calculation, World Bank 2006
The importance of each industry on three factors: gross output, value added and employees is
presented in Table 2. Food and beverages sector as a whole is the second largest industry in terms
of gross output. In terms of number of employees the food and beverages industries employ a large
number of people as compared to other industries. In terms of value added, food and beverages
sector is the forth in the order of importance after garment, machinery and equipment and textiles
industries.
Table 2: Importance of Industries
Industry Gross Output Value added Employee
Chemical 2.56 4.13 2.10
Food and beverages 26.12 9.58 27.02
Garment 26.97 35.88 20.49
Machinery & Equipment 16.82 22.71 20.14
Metal 10.39 7.71 12.03
other manufacturing 5.54 3.43 6.88
Textile 10.81 15.56 10.61
Wood 0.77 0.99 0.72
Source: Own calculation, World Bank 2006
Empirical Model Specification and Estimation Technique
In this study Total Factor Productivity (TFP) was estimated for food and beverages, garment,
textile, chemical and other manufacturing industries. The data was collected in three cities
(Mbabane, Matsapha and Manzini) covered by the 2006 World Bank Enterprise survey for
Eswatini. The computational models used in the study are shown in the next section.
Firm – level Total Factor Productivity
A Cobb-Douglas production frontier is estimated for food and beverages, garment, textiles,
chemical and other manufacturing industries. The estimation of the firm-level total factor
production is derived from the production frontier. The model is adopted from Kinda, Plane and
Vganzons-Varondakis (2008). The production technology defines the relationship between the
Value added (Y) as the dependent variable and capital (K) and labour (L) as the independent
variables.
NOPmêE,Zp = ANOPm◊E,Zp + ÿNOPmNE,Zp + ŸE + aE,Z (1)
Where:
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êE,Z = Value added (Total sales less purchased total material)
◊E,Z = Capital (Net book value)
NE,Z = Labour (Total number of employees)
ŸE = city dummy variables
aE,Z = error term
⁄/ç = industries and cities
Assessment of the effects of Investment Climate on firm- level productivity
To assess the effects of investment climate variables on the productivity of industries, Cobb- Douglas production frontier with investment climate variables and firm characteristics were
estimated for food and beverages and garment industries. The World Bank Investment Climate (IC)
surveys made available information on a large number of investment climate (IC) variables as well
as general information on firms’ status, productivity, sales and supplies.
In the questionnaire, the IC variables are classified into 6 broad categories: (a) Infrastructures and
Services, (b) Finance, (c) Business-Government Relations, (d) Conflict Resolution/Legal
Environment, (e) Crime, and (f) Capacity, Innovation, Learning. The surveys contain multiple
indicators for different categories. Within the same category, the correlation between indicators is
high. One solution applied in some studies has been to restrict the analysis to a limited number of
indicators and accept the omitted variable bias. This was also adopted in this study. Based on
availability of data, the IC indicators used in this analysis were limited to the following: Average
number of days to claim goods from customs (X1), Total losses for the year as the % of annual sales
(X2), informal payments/gifts given to public officials as a % of total sales (X3), number of days to
obtain telephone lines (X4), obtain a loan from a financial institution dummy (Yes =1, No = 0) (X5),
number of unskilled production workers (X6), influences of the pressure from domestic
competitors on production (X7). The empirical model is shown below:
NOPmêE,Zp = €NOPm◊E,Zp + ÿNOPmNE,Zp + Ÿ(‹%) + õ(‹Ä) + :(‹›) + fi(‹fl) + h(‹·) + ⁄(‹‚) +
ç(‹„) + ◊(‰⁄Â:) + =(:DoOÊt) + Á(Os>) + ŸE + aE,Z (2)
Where:
êE,Z = Value added (Total sales less purchased total material)
◊E,Z = Capital (Net book value)
NE,Z = Labour (Total number of employees)
‹% = Average number of days to claim goods from custom
‹Ä = Total losses for the year as the % of annual sales
‹› = Informal payment/gifts given to public officials as a % of total sales
‹fl = Number of days to obtain telephone lines
‹· = obtain a loan from a financial institution dummy (Yes =1, No = 0)
‹‚ = number of unskilled production workers
‹„ = influences of the pressure from domestic competitors on production
è⁄Â: = size of firms ranking: Small= 1, Medium = 2 and Large = 3
QDoOÊt = percentage of establishment’s sales scheduled for direct exports
Ës>:ʉh⁄o = percentage of firm owned by largest shareholder(s)
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Hlzane, M. P., Dlamini, D. V., & Ajetomobi, J. O. (2020). The Effect of Investment Climate on Productivity of Food and Beverages Industries in
Eswatini. Archives of Business Research, 8(7). 124-133.
ŸE = city dummy variables
aE,Z = error term
⁄/ç = industries and cities
Other individual variables that have been included in the model are: size of firm (Size), percentage
of establishment’s sales scheduled for direct exports (Export) and percentage of firm owned by
largest shareholder(s) (Ownership).
Following Kinda, Plane and Varondakis (2011) one step procedure was used to estimate all the
coefficients. That is the production frontiers and the factors contributing to the firms’ productivity
were estimated at the same time. This is to solve the problem of possible correlation between the
production function inputs and the firm level productivity.
RESULTS AND DISCUSSIONS
Obstacles faced by industries in Eswatini
The section discusses the obstacles faced by industries in Eswatini. The results are presented in
figure 1. Competition from informal sectors is the main problem faced by industries. One may say
that competition is good for the industries because it encourages industries to provide quality
goods and services to their customers. On the other hand, competition from informal sectors can
reduce profits of industries. Most informal sectors do not pay rents where they are operating so
they sell their goods and services at a low price. The low prices of informal sectors attract more
customers. Also informal sectors do not have trading licenses, thus they do not pay tax. This can
affect the country as a whole because tax cannot be collected from the informal sectors. Also
informal sectors are not regulated.
Fig.1. Obstacles faced by industries
Source: Author, 2018
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The second obstacles faced by industries are access to finance and electricity. This means that
industries in Eswatini cannot access capital from money institutions to start their businesses or to
expand their businesses. The World Bank in 2007 also found that banks in Eswatini provide less
finance to businesses. Power outages in Eswatini affects production of industries thus affecting
their profits.
The third problem facing industries in Eswatini is crime, theft and disorder. Inadequately educated
workforce, access to land and corruption are the forth obstacles faced by industries in the country.
Other serious problems facing industries in Eswatini are tax rates, labour regulations, business
licensing and permits, telecommunication, customs and trade regulation, functioning of courts and
tax administration.
Firm-Level Total Factor Productivity
The estimates of the firm level total factor productivity using the stochastic production frontier for
five industries is discussed in this section. Table 3 includes the coefficients and significance levels
of labour and capital. R2 and number of observations are also presented in Table 3 together with
F-statistic. The R2 give some information about the goodness of fit of a model. R2 ranges between 0
and 1, 0 indicates that the model explains none of the variability of the response data around its
mean. While 1 indicates that the model explains all the variability of the response data around its
mean. R2 in all the industries is above 0.5 that indicate a good fit of the model.
In all the industries except food and beverages industries, the sum of the coefficients relative to
labour and capital is less than one. Which means that all the industries except food and beverages
industries are probably mostly exposed to competition in the developing country like Eswatini.
The coefficients of capital are insignificant in all the industries except in other manufacturing
industries where it is significant at 5%. It means that 1 unit increase in capital will increase total
output by 0.34 units.
Table 3: Estimates of the Stochastic Production Frontier
Food Garment Textile Chemical Other
manufacturing Total
Log (Capital) 0.221
(0.176)
0.332
(0.294)
0.218
(0.149)
0.142
(0.089)
0.335**
(0.052)
0.228***
(0.052)
Log (labour) 0.935**
(0.350)
0.318
(0.346)
0.738***
(0.205)
0.806***
(0.174)
0.488**
(0.098)
0.749***
(0.077)
Constant 8.793***
(1.421)
8.786
(3.868)
9.152***
(1.875)
9.913***
(1.172)
8.337***
(0.607)
9.036***
(0.673)
Observations 14 15 5 4 24 70
R2 0.799 0.730 0.654 0.592 0.985 0.727
F Statistic 21.816***(d
f = 2; 11)
1.349 (df =
2; 1)
11.338***
(df = 2; 12)
15.215*** (df
= 2; 21) 67.450** (df = 2; 2) 89.185***
(df = 2; 67)
Note:*p,0.1**p<0.05***p<0.01
Source: Own calculation, World Bank 2006
While the coefficients of labour are significant in all the industries except in garment industry
where it is insignificant. Coefficients of labour in food and beverages and other manufacturing
industries are significant at 5%. While in textile and chemical industries, the coefficients of labour
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Hlzane, M. P., Dlamini, D. V., & Ajetomobi, J. O. (2020). The Effect of Investment Climate on Productivity of Food and Beverages Industries in
Eswatini. Archives of Business Research, 8(7). 124-133.
are significant at 1%. A unit increase in labour (number of employees), total output will increase
by 0.94 units, 0.76 units, 0.81 units and 0.49 units in food and beverages, textile, chemical and other
manufacturing industries respectively.
Stochastic Frontier Model with Investment Climate Variables
Table 4 shows the results of the stochastic production frontier with IC variables, the model is
estimated at the sector level and the sample size varies from 14 observations in food and beverages
industries, 15 in garment industry and 70 in total of the industries. The Cobb-Douglas production
function was estimated with the production frontier. According to Ajetomobi, Ajagbe and Dlamini
(2017) the merit of the model is that the coefficient of labour and capital expressed in logarithmic
form can be treated as the variable’s direct elasticity.
The results reveal that the elasticities of labour and capital are different in food and beverages,
garment and total industries. The coefficients of labour and capital are strongly significant in the
total industries. In food and beverages industries, the coefficients of labour and capital are
significant at 5% and 10% respectively. The coefficient of labour is higher than that of capital in
food and beverages, garment and total industries. This shows that all industries in Eswatini are
labour intensive. The results are in line with what Ajetomobi et al. (2017) found in Nigerian
industries. Thus there is a need to improve capital technologies in all sectors in Eswatini just like
in Nigeria.
The coefficient of labour for the food and beverages industries is higher than the garment industry
and the total industries in Eswatini. This means that food and beverages industries in Eswatini are
more labour intensive than other industries.
Regarding the effects of investment climates, the results clearly reveal that differences in firm level
efficiencies across industries in Swaziland can be pointed to discrepancies in investment climate.
The results show that most of the investment climate variables are significant in food and
beverages industries. An interesting aspect of the results is that corruption and unskilled
workforce negatively and significant influences productivity of food and beverages industries. It is
in line with what Ajagbe and Ajetomobi (2017) found in Nigeria in terms of corruption. Nguyen
and Taisen (2017) also found that corruption in Vietnamese destroys markets and incentives for
productive investments. Access to finances is insignificant in food and beverages, garment and the
total industries. This might be a good reason for relatively low performance of industries in
Eswatini because they are not able to obtain capital to expand their businesses. Poor finance,
asymmetric information, inadequate modern technologies, power interruption, government
bureaucratic bottleneck and corruption need to be given enough attention in order to address the
challenges of poor returns and high production costs in food industries (Clement and Reiner, 2009
in Ajetomobi et al., 2017).
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Table 4: Stochastic Production Frontier with Individual IC Variables
Variable Food & beverages Garment Industries
log(capital)
0.546*
(0.062)
0.289
(0.140)
0.221***
(0.058)
log(labour)
2.506**
(0.155)
1.877
(0.762)
0.738***
(0.245)
X1
-0.405*
(0.042)
0.171
(0.159)
0.082
(0.088)
X2
0.046
(0.024)
0.066
(0.111)
0.050
(0.047)
X3
-0.959*
(0.095)
0.297
(0.112)
0.087*
(0.049)
X4
0.269*
(0.025)
-0.176
(0.062)
-0.013
(0.009)
X5
0.767
(0.186)
0.431
(0.634)
-0.168
(0.222)
X6
-0.022*
(0.002)
-0.001
(0.0005)
-0.0002
(0.001)
X7
1.008**
(0.061)
0.058
(0.281)
0.232**
(0.113)
Size -2.991**
(0.188)
-2.132
(1.182)
0.089
(0.392)
Export
0.010
(0.002)
-0.017
(0.015)
0.001
(0.004)
Ownership
0.004
(0.002)
0.012
(0.017)
-0.005
(0.004)
Constant
1.322
(0.690)
6.827*
(1.680)
8.835***
(1.032)
Adjusted R2 0.996 0.921 0.732
F statistic 291.451**(df=12; 1) 14.635*(df= 12; 2) 16.668***(df=12;57)
Observations 14 15 70
Note: *p<0.1; **p<0.05; ***p<0.01
Number of days to claim goods from customs is significant and negatively affect productivity of
food and beverages industries. The results correspond with what Subramanian, Anderson and Lee
(2005) found in their study in China and Brazil.
The importance of export and firm ownership is not significant in Eswatini in all the industries.
This shows that industries in Eswatini are not willing to improve their productivity by learning
from customers and by facing international competition. In addition industries in Eswatini are not
willing to improve their productivity by allowing foreign investors to bring new technologies and
management techniques.
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Hlzane, M. P., Dlamini, D. V., & Ajetomobi, J. O. (2020). The Effect of Investment Climate on Productivity of Food and Beverages Industries in
Eswatini. Archives of Business Research, 8(7). 124-133.
Size of firms in food and beverages industries are significant but negative. According to kinda et al.
(2011) the expected sign of size of firms is negative due to the fact that the one step procedure
explains firm level inefficiency. The size of firms in food and beverages industries are significant
and negative which means that small firms are more efficient than large firms. Small firms are able
to manage their inputs in order to produce outputs. Large firms require large inputs which they
cannot manage.
CONCLUSIONS AND POLICY RECOMMENDATION
This study examines the effects of investment climate on the total factor productivity (TFP) of food
and beverages industries in Eswatini. The study is conducted in two phases namely: an estimation
of firm-level productivity and the differences in TFP across firms is statistically related to
indicators of investment climate, taking into consideration firms characteristics. The analyses used
2006 World Bank Enterprise survey data on Eswatini.
Competition from informal sectors is a main problem in all industries in the country not sparing
food and beverages food and beverages industries. The results of the study show that Industries in
Eswatini are labour intensive so there is a need to improve capital technologies in all industries
including food and beverages industries. Investment climate indicators which are important for
food and beverages industries are: competition from domestic pressure, average number of days
to claim goods from custom, corruption, uneducated workforce and number of days to obtain
telephone lines.
The following policy is recommended to enhance competitiveness of Eswatini manufacturing
industries: Appropriate measures should be put in place to decrease the rate of unofficial payments
in the country. The country can invest much more effort in institutional reforms especially to fight
against corruption and efficacy in the provision of public goods and services.
This study would not have been possible without the guidance and direction of my supervisors, Dr
D.V. Dlamini and Professor J.O. Ajetomobi for guidance and direction. I am also grateful to the Head
of Department and the entire staff of the Department of Agricultural Economics and Management
, Faculty of Agriculture , University of Eswateni knowledge and skills impacted in to complete this
study.
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