Final Draft - Long Paper 2015, DPNMAT001

The Impact of Institutional Challenges on Firm
Productivity: Evidence from a Zambian Enterprise
Survey
Abstract:
This paper seeks to analyse the impact that rigid factor market regulations, high regulatory costs
and infrastructural challenges have on firm level measures of performance as measured by labour
productivity and employment growth. This paper finds moderate firm-level productivity boosts
but decreased employment growth associated with rigid factor markets, particularly amongst
skilled workers. The effects were more pronounced amongst smaller firms, indicative of
differential effect with firm size. High costs of doing business impact negatively on firm
performance in terms of employment growth, however the benefits to the manufacturing industry
in terms of firm productivity are significantly positive due to inherent trade protections.
Infrastructural challenges were not altogether significant impacts on firm performance, although
individually the transport costs and electricity issues both had negative impacts on employment
growth. This paper then provides certain policy implications that coincide with the above results
that may aid firm performance and subsequently economic growth.

The Impact of Institutional challenges on Firm Productivity: Evidence from Zambia
1
Introduction
One of the most widely discussed topics in international economics surrounds the explanation of
the sources of economic growth. Several theories have been proposed, from Robert Solow’s (1956)
criticism of the Harrod-Domar model, and subsequent development of the Solow-Swan model, to
theories prioritizing human capital investment (Hanushek & Kimko, 2000: 1184-1208). One
agreement between these models, is that labour productivity – as measured by the ratio of value
output per labour input – has consistently been shown to be a crucial source of economic growth
(Roubini & Backus, 1961; Wang, 2015; Kendrick, 1961). Thus understanding the forces that
influence productivity, and therefore the most effective method of maximising firm productivity,
is key to developing economic policies aimed at stimulating economic growth. The question forms
part of a wider economic concern over institutional regulation and is one that has far reaching
implications for development across a number of emerging economies, in particular for policies
relating to business regulations.
A recent survey by Dollar, et al., (2002), similar in nature to that used in the Zambian Enterprise
Survey, interviewed 1000 manufacturing firms in India. They presented evidence that firm
performance could be depressed by up to 44 % in regions that were perceived to have a poor
business environment. More recently, Clarke (2005) has shown there to be a shift in emphasis
focused rather on interpreting the influence of the regulatory environment and “weak institutional
policy” observed in Africa. Similarly, Collier and Gunning (1999) propose that insufficient
infrastructure investment, inefficiency in public service administration and distorted credit and
labour markets are all significant inhibitors to investment by African firms. In particular this paper
will seek to establish a relationship between the impacts that certain institutional obstacles pose
and their subsequent impact on firm performance, as measured by employment growth and labour
productivity. Making use of the Zambian Enterprise Survey dataset analysing firm level data from
manufacturing firms in the period 2007 - 2013.
It is crucial to provide an insight into the overview of the business environment in Zambia. The
business environment can be summarized as aspects of the economy related to infrastructure,
factor markets, the legal system, social factors and the financial system. These aspects are
exogenously imposed on the individual firm, while having substantial implications for costs and
ease of business operations (Carlin & Seabright, 2007).

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Figure 1 below presents an initial look into the identified obstacles facing firms operating in the
Zambian economy. From the figure it can be perceived that labour regulations presents only one
obstacle of concern amongst many more impacts on the costs of doing business in Zambia. (The
World Bank, 2014).
It was decided that these intuition level obstacles could be effectively subdivided into three
categories. First would be the factor market institutions, which would contain labour regulation,
access to finance, and access to land or property regulations. Second would be the ease of doing
Figure 1: – Business Environment in Zambia, figure sourced from: (World Bank Group, 2015)

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business including, ability to obtain a business license, impact of corruption, crime, courts, trade
regulations, political instability, tax rates and tax administration. The third and last category would
contain infrastructural challenges including, access to electricity, telecommunications and
transport.
Background and Data Review
This paper makes use of an Enterprise Survey panel data set collected by the World Bank, with
surveys being conducted in 2007 and again in 2013. The survey aimed to provide an accurate
depiction of Zambia’s private sector through the collection of key business information. Thus
presenting a picture of the climate for investment, job creation and sustainable growth. This paper
focuses on, the series of questions surrounding ‘Obstacles to Business’ which will form part of the
analysis into the business environment faced in the Zambian economy. As mentioned in the
introduction, these obstacles will be categorized into factor market institutions, ease of doing
business and infrastructural challenges.
The various institutions were then scrutinized and analyzed as to their respective impact on a
number of set firm level performance indicators. When deciding on an appropriate method to
quantify firm performance it was critical to choose measures that would provide a logical and
precise indicator of how well the firm is doing. As such labour productivity, which looks at the
revenue each worker generates for their respective firm, was a logical choice
World Bank ‘Doing Business” Report: Zambia
Before any reasonable analysis can be made into the impact of these institutions, it is critical to
establish a background of the business environment in Zambia. The World Bank (2014) has
compiled a report called ‘Doing Business 2015: Going Beyond Efficiency’ which aims to shed
light on the degree of ease involved for a local entrepreneur to start and manage a small to medium-
size business. The report details the relevant regulations that a business owner is required to
comply with including any major changes concerning eleven key areas in a business’ life cycle.
These consist of: “starting a business, dealing with construction permits, getting electricity,
registering property, getting credit, protecting minority investors, paying taxes, trading across

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borders, enforcing contracts, resolving insolvency and labour market regulation” (The World
Bank, 2010).
Table 1 below represents the primary independent variables of interest. A complete list of variables
used in this paper can be found in the Appendix A.
Table 1: Primary independent variables of interest
Factor Market
Regulations
Cost of doing Business Infrastructure
Labour Regulations.
Access to finance.
Access to land.
Ease of obtaining a business license
Cost of tax administration and Rates.
Impact of corruption and Political
Instability.
Difficulties with the court system
Crime, theft and disorder.
Access to electricity.
Cost of transport.
Cost of
Telecommunications.
In terms of industry stratification, the Zambia Enterprise Survey dataset of interviewed firms was
stratified into four manufacturing industries (food, textiles and garments, chemicals and plastics,
other manufacturing) and two service sectors (retail and other services).
The Zambia Enterprise Survey had a well-defined size stratification throughout the survey process:
small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). In
terms of the actual data manipulation applied in this paper, a dummy variable was constructed
indication whether the firm was characterised as a SME (Small or Medium sized Enterprise) or a
large firm. The reason behind this was to isolate the unobserved firm specific characteristics that
exist in firms of a larger scale.
In terms of Regional Stratification, the Zambia Enterprise Survey categorised the survey into four
regions: Kitwe, Livingstone, Lusaka and Ndola. For the models used in this paper Kitwe was used
as a base category. It is important to note that Lusaka is the capital of Zambia, and facilitates the
bulk of economic activity in the country.

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Literature Review
The bulk of the analysis contained within this paper will focus on two major indicators of firm
performance; employment growth and labour productivity. As such, each indicator will be
provided with a background of previous literature that has demonstrated the reasoning behind their
use in quantifying the firm performance impacts of factor market regulations, costs of doing
business and infrastructural challenges.
Factor Market Regulations
Besley and Burgess (2002) provide perhaps the most compelling and comprehensive investigation,
similar in scope to that of this paper, concerning the manufacturing sector of India between 1958
and 1992. They hypothesized that labour regulations were a contributing factor to the poor growth
in India during this period, and as a result the poor poverty reduction in the country. The reason
they put forth for focusing on the manufacturing sector lies in the performance of successful Asian
countries post 1960 where growth was stimulated through structural development focused in the
manufacturing sector (Besley & Burgess, 2002).
In a similar manner to this paper, Besley and Burgess (2002) quantified firm performance as a
measure of output, employment and productivity. Besley and Burgess were interested in a sector
wide impact, categorized as an aggregation of firm performances within the sector. This provides
the backbone on which this paper seeks to build its analysis. The paper deals particularly with the
“Industrial Disputes Act” which guides the interaction between firms and employees in terms of
strikes, wage demands and other general disputes. It assess the impact this act had on the Indian
manufacturing sector stating that as a result of this act output, employment, investment and
productivity all fell. Besley and Burgess (2002) also cover a relevant welfare implication in their
paper. It confirms the relationship between the increased levels of urban poverty as a result of
falling employment rates.
There currently exists an extensive literature on the impact that hazard rates (collective term for
the costs involved in: employment protection and hiring and firing employees) have on the
representative firm’s ability to adjust employment levels to business cycle fluctuations (Kugler,

6
1999). It has often been cited that a reduction in these hiring and firing costs allows a greater deal
of flexibility leading to increased dynamism in the labour market. Literature here offers mixed
conclusions. Grubb and Wells (1993) found that stricter regulations led to a decrease in
employment, whereas Bertola (1990) found an absence of any significant relationship between
labour regulation strictness and employment in either a long or medium run. Further papers by
Botero et al. (2004), Almeida and Carneiro (2009) and Micco and Pagés (2006) all found that
stricter regulation of the labor market are associated with decreased labor force participation rates
and extensively characterized by higher unemployment rates. The economic theory seemingly
demonstrated by Grubb and Wells (1993) would suggest that with a greater flexibility firms are
better able to react to business fluctuations, hiring and firing employees as the economic climate
dictates. There does however exist contrary economic theory that indicates the existence of an
ambiguous impact as higher hazard costs create a tax on firms that results in lower dismissal rates
but simultaneously lower hiring rates and this seems to correspond to the results found by Bertola
(1990) (Kugler, 1999: 389-410).
There is a certain degree of uncertainty when it comes to the second indicator of choice, labour
productivity. Economic theory suggests that the presence of substantial hiring and firing costs
distorts firm-level adjustment costs in operating decisions. This distortion leads to firms not hiring
workers even when their marginal revenue product surpasses their market wage, while continuing
to employ inefficient workers with a wage above their respective marginal revenue product
(Kugler, 1999: 389-410) (Blanchard & Portugal, 2001: 187-207). It is then clear that these
distortions would have a net effect of decreasing labour turnover as these inherent costs to the firm
discourage hiring and firing of workers, rather encouraging the substitution of capital to replace
labour. This effect is ambiguous in that a firm may have less of an incentive to fire unproductive
workers, but invests more in capital which would then have a positive effect on currently employed
labour in terms of productivity. This is of particular interest when considering, as this paper does,
the manufacturing sectors of a country’s economy.
Recent studies by Castany et al (2005) and Pagés (2010) have presented evidence pointing to
distinct productivity differences in small/medium sized firms and larger ones. These differences
match prediction by economic theory of productivity advantages due to factors such as: economies
of scale, better access to technology, higher levels of human capital in operating and managerial

7
skills development, access to finance/credit and better access to latest technological advances
(Pagés, 2010: 207-210). There does exist a body of literature that presents a different view. Geroski
(1998) and Tybout (2000) state that flexibility, non-hierarchical business structures and ease of
implementation offer productivity boosts to smaller firms, albeit in certain circumstances.
The conclusions reached by Kugler (1999) in her work on Columbian labour market analysis
provide a comparable case study to Zambia, she also offers evidence that seems to fit the economic
theory. Kugler found that a reduction in the dismissal costs imposed on firms resulted in an
increased hazard rates by just greater than one percent. This increase in the hazard rate implied
that firms are more likely to hire and fire workers thereby increasing labour market ‘dynamism’
and ultimately leading to an increased level of employment growth in the Columbian economy
(Kugler, 1999: 389-410).
Costs of Doing Business
Another theme focused on in this paper is the impact of the imposition of trade regulations on firm
productivity. Topalova and Khandelwal (2010) present evidence from an Indian case study that
offered a positive relationship between pro-competitive regulations and firm-level productivity.
The paper detailed the impact of lower tariffs on final goods and the effect this had on foreign
inputs, and concluded that both show evidence of increased firm performance. This is further
corroborated by Bernard, et al. (2006) who found that the implementation of trade deregulations
aimed at pro-competitive reforms may increase firm level productivity. This is as a result of a shift
in economic activity in favour of more efficient firms. In addition to this Topalova and Khandelwal
observed complementarities between trade deregulation and other industrial policy reforms.
On the other hand, proponents of trade regulations point out that by protecting domestic firms from
cheap imports local firms are allowed to increase productivity and grow their market share and
thus their overall operations. This is the view held by many developing economies seeking to
protect their infant sectors, and led many economists to accepting these regulations realizing the
imperfections inherent in the political nature (Vousden, 1990). A similar conclusion was made by
Vandenbussche and Konings (2008) where they observed “moderate productivity improvements”
by firms receiving protection, although also concluding that trade policy has varying impacts
depending on initial levels of productivity amongst firms prior to the trade regulations.

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It is not certain which effect will dominate in this Zambian case study, whether the industries in
the economy are efficient enough to survive the influx of cheap imports, or still require a certain
degree of protection. It is uncertain whether productivity boosts inherent in technological
externalities, and lower costs to inputs of production will outweigh the current regulatory
protection. As such, trade regulations as an obstacle to business operations in Zambia were
included in this investigation as to the determinants of firm performance in Zambia from 2007 to
2013.
There has been some issues raised as to the degree of certainty that can be drawn from analysis on
labour markets in developing countries due to the concern over lack of regulation enforcement and
presence of a sizeable informal sector. Naturally, such characteristics are difficult to accurately
measure and so their degree of impact is hard to gauge (Heckman & Pagés, 2004). A similar
concern was raised relating to the efficiency in the resolution of labour market disputes, leading to
distortions when considering the effects job security and contract labour laws have on productivity
and employment growth (Ahsan & Pages, 2007). Ahsan and Pages (2007) go on to conclude that
laws designed to increase job security lead to lower levels of sector employment.
For many years it has been the goal of governments across the globe to find the correct balance
between labour market ‘dynamism’ and employee protection. It has been a key issue for many
economies, one that many government policies aim to address (Porter, 2011: 115-120). The
conclusion suggests why and how more equitable linkages between the informal economy and the
formal economy should be promoted through an appropriate inclusive policy and regulatory
environment. While not sitting perfectly in line with our hypothesized view, it does offer a different
perspective in interpretation of market policies and their relation to institutional regulations.
Chen (2007) highlighted a similar relationship describing the tendency of developing countries to
employ strict regulations, often driving workers away from the regulated formal market. This
relation underpins the manner in which the economy changes over time, however it has been
suggested that there is a constant declining trend in the income generated by the informal sector
which could lead to a counter-balance effect (Tokman, 1978: 1065-1075). De Soto (2000) further
supports the benefits of market deregulation insisting that economic freedom of deregulation will
lead to far more entrepreneurial opportunities stimulating growth in the economy. Two papers
documenting their attempts at opening up businesses in Latin America, one by De Soto (2000) and

9
another by Tokman (1978), showed the high costs of operating in the formal sector underpinning
the reason many firms opt to operate in the informal sector rather than the regulated formal sector.
In one particular instance De Soto was confronted with ten situations where he and his team were
asked for bribes in order to fulfil the necessary paperwork to register their business (Loayza, 1996:
129-162).
There is a certain level of concern when drawing inferences on data relating informal-formal sector
relations. Thus implying that it is pertinent to establish the level of monitoring that takes place in
the informal sector. Almeida and Caneiro (2005) set out two factors to consider before any
conclusions are made. First requires data on the level of access given by firms to labour inspectors
and secondly the general level of law enforcement present in the firms vicinity.
Infrastructural Challenges
The final obstacle to be considered were the issues that arise from the lack of infrastructural
development often associated with developing economies. Infrastructure in this paper is broken
down into three sub sectors, transport infrastructure, telecommunication infrastructure and
infrastructure involved in the provision of electricity.
We first assess the impact that high transport costs due to poor infrastructure has on firm
operations. Venables and Limão (1999) investigated the impact of transport infrastructural
improvements on aggregate revenue growth, through increased trade volumes, in sub-Saharan
Africa. They concluded that simply halving the costs involved in the transportation of goods and
inputs trade volumes could increase fivefold, with the effect being translated to an increase in firm
profits. Elnadawi, et al., (2001) further corroborates these findings. They find that higher domestic
transport costs imply higher input costs which translate to lower levels of firm exports, and
subsequently lower performance.
One of the key facets of infrastructure is the provision of electricity to firms. The vast majority of
firms require electrical access in order for their business to operate efficiently, and as such issues
associated with its provision have substantial implications (Aschauer, 1989: 177-200). Further it
has been shown that high costs due to poor infrastructure and services, including electricity supply,
pose a “competitive burden on African firms” (Eifert, et al., 2008: 1531-1546). As such African

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firms face higher costs in sourcing inputs and so higher overall factor costs, which negatively
impacts on firm level growth (Eifert, et al., 2008: 1531-1546).
It turns out that these implications are actually subject to “severe simultaneity bias” and “spurious
correlations”, and their returns are greatly reduced once these are controlled for (Röller &
Waverman, 2001: 909-923).
The final aspect of infrastructure that we investigate is the provision of telecommunications. The
provision of this service creates economic growth in a number of ways. The first and most obvious
is the productivity boost it gives businesses in being able to communicate efficiently and
effectively (Röller & Waverman, 2001: 909-923). It allows them to develop and implement new
strategies and allows for better worker management interactions. Secondly, the implementation of
telecommunication is made up by a number of products each of which needs to be manufactured.
Thus there is a boost to the firms that manufacture these components (Röller & Waverman, 2001:
909-923). Thus it has been shown that the implementation of telecommunications creates positive
externalities and spill over effects (Röller & Waverman, 2001: 909-923).

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Summary of Labour Regulations and Reforms in Zambia in the period
2007-2013
In 1990, after the legalization of opposition parties, a new government came into power. The
primary objective for this governing body was to stir economic growth through the use of
“sweeping economic reforms” which focused on promoting the ease of doing business in the
economy (The World Bank, 2014). The economic principles they followed were those that
economists have been advising governments on for years. Regulations on hiring and firing,
including generous unemployment welfare packages, were the fundamental reason for labour
productivity being low and unemployment being so high in developing nations compared to more
developed countries. Understanding this many developing economies introduced reform programs
that aimed to increase market dynamism, referred to by the IMF and the World Bank as “second-
generation reforms” (The World Bank, 2014).
Zambia has not been stagnant in implementing market reforms, but there exists room for
improvement in terms of labour deregulation. Zambia has introduced 15 reforms however only
one that relates to labour market reform, in 2008, increasing mandatory paid annual leave. While
countries such as Mauritius, Rwanda, and South Africa have introduced 23, 31 and 12 reforms
respectively. Accordingly Mauritius, Rwanda and South Africa are ranked 28th, 46th and 43rd
respectively in the world for ease of doing business (The World Bank, 2014). Mauritius is the
highest of any African country and can be seen as an example of how deregulating an economy’s
business environment can lead to increased economic growth while South Africa, as the most
developed economy in Sub-Saharan Africa, falls 15 places short due to a lack of economic reform
and enduring strict labour regulations (The World Bank, 2014).
A paper by Lall and Wignaraja (1998) which attempted to identify the primary obstacles
encountered by firms operating in the Mauritian economy provides an insight into doing business
in the economy. They identified a number of factors influencing firm performance including: high
interest rates; substantial bureaucratic requirements foreign investment approvals; difficulty
accessing credit, delays in import duties; difficulty obtaining work permits especially for foreign
employees and issues in accessing finance for smaller firms. Mauritius has since implemented
economic reforms to adjust for these obstacles, and the result has been favorable economic growth

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and increased foreign investment. The paper by Lall and Wignaraja (1998) has provided a context
for this paper’s scope of analysis into the impact of market regulations in a Zambian context.
To date Zambia has implemented 15 economic reforms surrounding the registration of property,
access to credit, paying taxes, starting a business, resolving insolvency, trade regulations,
insolvency, permits and labour market regulations of which only those concerning access to credit,
labour market regulations, registration of property and starting a business are of concern to this
paper.
Thus the following reforms are of interest to the analysis of this paper. In 2012 Zambia increased
the costs involved in registering property as they increased the property tax rate, making it more
difficult for firms to transfer land. In 2011 Zambia eliminated minimum capital requirements for
business start-ups, allowing for more entrepreneurial opportunities for smaller firms. Zambia
implemented an economic reform that introduced a “one-stop border post” with Zimbabwe
allowing online customs declaration and installing scanning machines at all major border posts.
The aim behind this reform was to encourage trade between the nations and limit the corruption
costs often associated with trade across this border. Also in 2011 Zambia again improved their
credit information system by requiring credit reports and additional information from “banks and
nonbank financial institutions registered with the central bank”. In 2009 Zambia reduced
registration requirements involved in registering a property and streamlined their registry process
through the implementation of customer service center. In 2008 Zambia implemented a labour
market reform that increased annual mandatory paid leave for all workers, a regulation that sought
to directly benefit workers, but also meant addition costs for businesses (World Bank Group,
2015).

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The Model of Firm Performance
As a result of the obstacles highlighted in the World Bank Report, a regression analysis was run
to determine the relation that existed, if any, between the reported obstacles and the measures of
firm performance as set out in this paper. The results of this regression are contained in table 2 in
the appendix of this paper and are split up to indicate the impact each of the obstacles had on labour
productivity and employment growth respectively. The results indicated the potential of significant
impact on labour productivity due to the impact of the informal sector, difficulty accessing finance,
difficulty accessing land, high costs in obtaining business licenses, high costs in trade regulations
or customs and to a lesser degree the impact of political instability. A note on political instability
and corruption, these obstacles often manifest themselves in the form of increased costs to other
regulations. For example, bribes involved in registering a new company, registering property or in
streamlining credit approval procedures. As such they were not modelled individually, but
discussed in their influence on the other independent variables.
Methodology
The independent variables of interest inherent in this paper were that of labour productivity and
employment growth. These indicators of firm performance were chosen as they represent a more
comprehensive view of firm growth, rather than simply comparing total sales of the firm (Besley
& Burgess, 2002).
The primary independent variables of interest are the reported ‘obstacles’ identified in the Zambia
Enterprise Survey and are as follows: Factor market regulations (labour regulations, access to
finance and or credit, access to land), Costs of doing business (ease of obtaining a business license,
impact of corruption, difficulties with the court system, impact of crime, theft and disorder,
difficulties in trade regulations) and infrastructural challenges (access to electricity, political
instability, costs of tax administration and rates, cost of telecommunications and lack of transport).
Factor market regulations have ties to the hazard rates experienced by firms, as measured by
rigidity of employment. Rigidity of employment is comprised of three facets, difficulty in hiring,
rigidity of work hours and difficulty in dismissal. In particular difficulty in hiring and dismissal
relates to flexibility of contracts in being able to match value added (labour productivity) to

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minimum wage. In addition, a term used by the World Bank in their reports is ‘redundancy costs’
an added cost imposed on the firm in order to issue notice, pay severance packages and penalties
in dismissal (World Bank Group, 2015). This is then translated into the Zambia ES through a
questionnaire of the degree to which labour regulations are an obstacle for the firm.
Labour productivity was calculated as the average revenue product, or the additional revenue each
worker generates for their firm. As there exists significant differences between skilled and
unskilled workers, we felt it prudent to compare the impact on each group separately, thus labour
productivity measure was set up whereby marginal revenue per unskilled worker and labour
productivity per skilled worker was regressed against the measures of obstacles (Knowles &
Robertson, 1951: 109-127). A critical aspect of labour productivity is the relation it has with
capital. Output that a worker is able to generate is dependent on the machinery and equipment at
their disposal
Additionally when analysing each of the categories, we felt it prudent to control for the size and
age of the firm (including age squared to control for differing impacts at different stages of the
business life cycle) , as positive impacts of economies of scale and operating efficiencies may exist
that could skew our conclusions (Stigler, 1951). Similarly we controlled for potential regional
differences in regulatory implementation, varying formal-informal market compositions and other
region specific characteristics that have not been recorded in the Enterprise Survey.
As a firm is impacted more by factor market regulations, the costs involved in hiring and firing
rise. This is an additional expense incurred by the firm when employing workers, the cost of hiring
an ineffective worker rises and so only the highly productive workers are hired (Kugler, 1999:
389-410). There also exists evidence that suggests that due to high costs of dismissing workers,
workers whose productivity falls below their corresponding wage may not be fired, which has a
negative impact on labour productivity (Blanchard & Portugal, 2001: 187-207).
When using variables such as book value of capital, total number of employees and sales we took
the natural log of these variables to ensure they followed approximately a normal distribution
giving us a better measure of firm size across the industry (Audia & Greve, 2006: 83-94).
Similarly, to test the interaction relationships with firm size, we re-ran the respective regressions,
including an interaction terms constructed between each institution and the natural log of firm size.

15
We decided to standardize firm size between zero and one as it simplifies the interpretation of the
reported coefficients (Audia & Greve, 2006: 83-94).
Contained within factor market institutions is labour regulations, which includes laws governing
the rigidity of hours, imposing certain restrictions on weekend, night and overtime work as well
as total hours worked and requiring a certain number of paid mandated leave days. This has a
particularly significant effect on the manufacturing industry, where continuous operation is
necessary for efficient operations. As such we decided to focus our analysis for labour regulations
on the manufacturing industry as we felt this would provide more useful insights (Besley &
Burgess, 2002).
To test this relationship the above regressions were re-run including interaction terms constructed
between each institution and the natural log of firm size. We decided to standardize firm size
between zero and one as it simplifies the interpretation of the reported coefficients. Thus the
primary effect is the previously reported effect on the smallest firm (0) and the addition of this
coefficient and the interaction coefficient is the impact on the largest firm (1), while all other fall
in-between (Audia & Greve, 2006: 83-94).
We have chosen to use a Wald test (χ2, one degree of freedom) to test whether the sum of the rigid
market coefficient and the interaction term are significantly different from zero. We use a similar
approach in each of our regressions (Audia & Greve, 2006: 83-94).

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Empirical Analysis, Results and Discussion
General Comments
Table 2 contains results of a regression analysis aimed at investigating any possible relationship
between the three institutional categories - factor markets, business costs and infrastructure – and
our two main dependent variables of interest, labour productivity and employment growth. We see
evidence of a relationship between factor markets and productivity at the 10 percent level, as well
as a negative relationship between costs of doing business and employment growth at a 5 percent
level. This provided grounds for further analysis into the individual components that underpin
these relationships. Table 3 shows just this breakdown, from which we observe significant
relationships for finance, land, business licenses, and political instability and trade regulations.
Factor Market Institutions
This paper begins its analysis investigating the section of the report related to factor market
institutions. As mentioned in our methodology, these institutions relate to the direct costs involved
in the everyday operations of a firm and are a crucial component of any business environment.
The first of these factors markets is the labour market, which is governed by a set of labour
regulations, a crucial system of laws and institutions that were put in place to protect workers and
ensure that all employees are guaranteed a certain standard of living (The World Bank, 2014).
These regulations play a critical role in allowing efficient operation of contract agreements
between employers and employees by protecting the employee from “discriminatory or unfair”
conduct by their respective employers (The World Bank, 2010). The labour market is often
discussed using a measure of ‘dynamism’ or the flexibility of regulations relating to hiring,
dismissal and working hours according to the standards set out by the International Labour
Organisation (The World Bank, 2014).
Table 4 reports the results from the regression of labour productivity (skilled and unskilled) and
employment growth against rigid factor market conditions. The results are shown first without
control variables and then once they have been included, this is so that the reader is afforded a
better understanding of the inherent relationship and how adding controls influences that
relationship. From Table 4 we observe that there exists strong positive relationship between factor

17
market regulations and labour productivity of skilled workers. This result is in line with predictions
presented by Besley and Burgess (2002), Brown and Medoff (1978) and Kugler (1999). This then
presents evidence to suggest that the increased productivity is a result of increased efforts by firms
to hire only the more productive workers owing to the comparatively higher costs involved in
dismissal should the firm hire a sub-standard worker. These effects would be more pronounced in
skilled workers as there are higher screening costs involved in determining the implicit
productivity of a skilled worker versus an unskilled worker, which explains the absence of a
significant relationship between labour regulations and unskilled worker productivity (Stigler,
1962: 94-105).
When controlling for a number of firm level variables we see a far less conclusive relationship.
This suggests that the conclusion reached above may not describe the entire relationship, the test
statistic relating to skilled labour productivity is only 1.23 with an associated p-value of 0.22,
which is just not enough to conclusively state that a rigid labour market has a significant influence
on overall productivity. In fact, when we consider productivity of unskilled workers, once
controlling for certain factors, the relationship adopts an inverse nature. One plausible explanation
is that workers may become complacent when they know there is greater protection (Autor &
Kugler, 2007: 189-217) which does make economic sense given the high levels of unionization
amongst manufacturing workers (which we pointed out earlier makes up a huge portion of the
Zambian economy), and the increasing the costs as trade unions offer resources for workers to
oppose their dismissal (Brown & Medoff, 1978: 355-378). We also note that this relationship does
not appear to be a particularly significant one, due in part to the limited nature of the given data
set where only 291 observations were obtained for this given regression analysis. Thus it does not
make sense to draw any definitive conclusions on the basis of this regression alone.
As has already been discussed, current literature points to employment regulation generally
increasing the length of employment these regulations have however also been shown to be
characterized by less desirable effects. Such effects include lower levels of employment growth,
longer periods of structural unemployment and as a result, lower productivity (The World Bank,
2014). Table 4 contains the result of a regression analysis constructed to test the above hypothesis,
by analysing the relationship between a rigid factor market and employment growth levels.
Interestingly there appears to be a positive relationship between a rigid factor market and

18
employment growth. This seem to contradict economic theory, although there has been some
evidence to suggest that, at a theoretical level at least, a properly designed labour contract can
negate the implied negative effects of labour regulations (Lazear, 1990: 699-726). The more likely
case is that there exists some unobserved firm specific characteristic that accounts for this
relationship, which can only be tested through a fixed effects regression which we discuss later.
For many years it has been the goal of governments across the globe to find the correct balance
between labour market ‘dynamism’ and employee protection. In searching for this balance it has
been shown that developing countries tend to one extreme or the other, often resulting in many
workers and employers turning to the informal sector to escape rigid regulations discussed so far
in this paper (Chen, 2007: 6-13). We note that to provide a more comprehensive view of the
relationship between factor market institutions and the informal sector is scope for further research,
it is sufficient in terms of this paper to understand that this relationship does exist as set out by
Chen (2007) and may vary depending on the sector in question. In particular the role of formal
regulation on the aforementioned relationship, with a greater degree of regulation relating to a
more active and growing informal sector and the subsequent impact this has on firm performance
in the economy as a whole.
The literature surrounding access to finance or credit indicates that government deregulation would
result in higher levels of economic freedom, for both employers and workers, leading to increased
entrepreneurial opportunities (de Soto, 2000). Such opportunities would be prominent in countries
that have not yet experienced large amounts of entrepreneurial development, and thus have large
potential for further growth, such as is seen in developing economies (de Soto, 1989). There is
some evidence to support this hypothesis in the existence of a strong negative relationship between
strict factor market regulations and employment growth, at a 5 percent level. The implication then
as suggested by Tokman (1978) and Sethuraman (1976) is that an economy seeking to promote
growth should worry far less about labour regulations and focus rather on the government
provision of credit and business development services (Chen, 2007: 6-13). However when
controlling for certain factors the relationship cannot be considered significant, with a test statistic
of .0.90, and so does not allow us to draw any meaningful inferences.

19
Costs of Doing Business
The second institution category we investigated related to the costs of doing business in Zambia
outside of business operational costs. What this means is costs due to regulations, licensing, court
administration, tax impacts and the volatile environment caused by political instability, crime and
corruption. All these factors impose a cost on firms wishing to do business in Zambia, thus we
sought out to investigate the implications these costs have on firm performance.
First, we consider trade regulations, a contentious area that is constantly being debated. One such
contention surrounds the common practice of developing nations, such as Zambia, to attempt to
protect their ‘infant’ industries by imposing trade regulations that prohibit the dumping of cheap
imports by foreign firms. There are some that believe that the benefit is still apparent in
manufacturing firms operating in developing economies (Rosenstein-Rodan, 1957) (Myrdal, 1960:
146-147). Indeed Rosenstein-Rodan’s analogy of a plane needing to gain speed before it can
attempt to take-off seems intuitively appealing. Current literature however has indicated that the
effects of this protection is ambiguous, suggesting that each economy will react differently to the
imposed regulations. The benefit of technological externalities and lower input costs must be
compared against increased market share, and subsequent boost to efficiency, of trade protection
(Topalova & Khandelwal, 2010) (Konings & Vandenbussche, 2008: 371-383).
If we consider Table 5 we see that there exists a significantly positive relationship between trade
regulations and firm level labour productivity, reporting significance at the 1 percent level for both
specified models. We conclude from this result that Rosenstein-Rodan and Myrdal still have valid
points when it comes to manufacturing firms in developing countries and that the hypothesis set
out by Koning and Vandenbussche (2008) is supported by our results from Zambia, that labour
regulations do provide a moderate boost to labour productivity. The implication then is that the
Zambian government should continue to provide some level of protection to the manufacturing
sector, however this will not be the case in perpetuity. There will come a point where the benefit
of protection will hinder performance in the industry, at which point lower trade costs, benefits to
lower cost inputs, and increased competition will be required for optimal performance for the
manufacturing sector.

20
When we look at employment growth however we observe a negative ‘sign’ on the coefficient for
trade regulations, this suggests that the increased protection the Zambian government in providing
its domestic firms is hurting employment growth. These results are in line with the conclusions
drawn by Bernard, et al. (2006) and Topalova & Khandelwal (2010) who hypothesized and
demonstrated that falls in trade costs may lead to increased efficiency in some firms, resulting in
firm level growth. However we must bear in mind that this boost to employment growth is felt by
only a certain number of firms, the more productive ones, and may lead to the foreclosure of
smaller, less efficient firms. This is a trade-off that must be considered when determining the
policy implications on the implementation of trade regulations.
As was mentioned above, business costs involved in firm operations is used here as a catch all
term to describe the difficulty in obtaining a business license, costs involved due to corruption,
crime and political instability. Our model uses difficulty in obtaining a business license as one of
the variables for estimating these costs to the firm, and as a result their impacts on labour
productivity and employment growth. From Table 5 we see a negative relationship significant at
the 10 percent level, implying that high costs in obtaining business licenses is hurting productivity.
When we consider this with the equally significant negative relationship with corruption, there
seems to be strong evidence to support the views posed by De Soto (2000) and Tokman (1978).
Both De Soto and Tokman experienced high levels of costs to registering businesses due largely
in part to high levels of political instability, crime and subsequently high levels of bribery and
corruption. Interestingly but maybe not surprisingly, both de Soto and Tokman encountered these
similar experiences in a Latin American and Ghanaian contexts respectively, indicating that
corruption and its implications have huge consequences for doing business in developing
economies.
Infrastructural Challenges
Infrastructural challenges in this paper have been restricted to impacts caused by three divisions
of infrastructure: transport, telecommunications and electricity. The results of our regression
analysis of the impact of these three divisions on firm performance are contained in Table 6.
Literature suggests that we should expect to see significant negative relationships caused by high
transport costs, as seen by Limão and Venables (1999). The second hypothesis suggested by

21
literature is that telecommunications have significant spill overs and generate positive
externalities, although are somewhat reduced by spurious correlation between other infrastructural
systems.
Looking at the results we are not able to make any definitive significant conclusions, only
suggestion inferences on relationships. The negative relationship on the electricity variable follows
from the literature presented in that without consistent electricity access productivity of workers
falls as they are limited in their use of capital. In the second model, after controlling for capital in
each firm, as well as costs involved in running capital, we find a significant and positive
relationship with electricity and labour productivity. This suggests that in the absence of consistent
access to electricity and holding capital constant, labourers tend to be around 12 percent more
productive on average. This coincides with the literature posed by Röller & Waverman (2001) that
suggests that individual returns by each of these components may be reduced due to the impacts
inherent in the synnergies between the components.
Interaction Effects
Of particular interest to this paper is the interpretation of the interaction effects that exist between
our institutional variables and firm size. We hypothesized that regulations that impose costs on
businesses may have larger effects on firms that are smaller in size. The logic behind this
hypothesis is that larger firms, with larger revenue streams are better able to absorb the impact
these costs impose. This hypothesis stems from the intuition behind the literature presented by De
Soto (2000), Sethuraman (1976) and Chen (2007) who determined that labour market regulations
had a larger impact on smaller firms.
To test this relationship the above regressions were re-run including interaction terms constructed
between each institution and the natural log of firm size. We decided to standardize firm size
between zero and one as it simplifies the interpretation of the reported coefficients. Thus the
primary effect is the previously reported effect on the smallest firm (0) and the addition of this
coefficient and the interaction coefficient is the impact on the largest firm (1), while all other fall
inbetween (Audia & Greve, 2006: 83-94).

22
We have chosen to use a Wald test (χ2, one degree of freedom) to test whether the sum of the rigid
market coefficient and the interaction term are significantly different from zero. We use a similar
approach in each of our regressions (Audia & Greve, 2006). The results of these regressions are
reported in Table 7-9. The Basic model is as follows, with the control variables not shown for
logistic reasons:
𝐿𝑎𝑏𝑜𝑢𝑟 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝐵0 + 𝐵1 × 𝑟𝑖𝑔𝑖𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 + 𝜀
The new model, with the inclusion of the interaction terms is as follows:
𝐿𝑎𝑏𝑜𝑢𝑟 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝐵0 + 𝐵1 × 𝑟𝑖𝑔𝑖𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 𝑚𝑎𝑟𝑘𝑒𝑡𝑠 + 𝐵2 × 𝑓𝑎𝑐𝑡𝑜𝑟 ∗ 𝑓𝑖𝑟𝑚𝑠𝑖𝑧𝑒 + 𝜀
Now if we take a look at Table 7, with particular interest paid to the interpretation of the interaction
term ‘factor * firmsize’ coefficient. When considering labour productivity, the sum of the
coefficients is positive and statistically significant at the 1 percent level (Wald test generated a
statistic of 8.82, Prob > F = 0.0002). The implication therefore is that larger firms incur heavier
productivity declines due to rigid factor market conditions. In terms of employment growth, the
sum of the coefficients is negative but not statistically significant at the 10 percent level (Wald test
generated a test statistic of 0.46, Prob > F = 0.6290). This suggests, although tentatively, that larger
firms are better equipped to cope with rigid factor market impacts on employment growth, while
smaller firms struggle to generate employment growth when these regulations are increased
(Almeida & Caneiro, 2009: 28-46).
Now if we take a look at Table 8, we focus on the interpretation of the interaction term ‘business
* firmsize’ coefficient. When considering labour productivity, the sum of the coefficients is
negative and is not significant at the 10 percent level (Wald test generated a statistic of 0.53, Prob
> F = 0.5904). The implication therefore is that larger firms incur less productivity declines due to
business operating costs than smaller firms do. This is aligned with the hypothesis generated in the
literature, suggesting that larger firms have the revenue streams to deal with these costs. In terms
of employment growth, the sum of the coefficients is positive but not statistically significant at the
10 percent level (Wald test generated a test statistic of 0.09, Prob > F = 0.9133). This implies that
to make any inference would be misleading, as the interaction effect is simply not significant.

23
Finally, we take a look at Table 9, and again focus on the interpretation of the interaction term
‘infrastructure * firmsize’ coefficient. When considering labour productivity, the sum of the
coefficients is negative and is not significant at the 10 percent level (Wald test generated a statistic
of 0.90, Prob > F = 0.4068). The suggesting is therefore that larger firms incur less productivity
declines due to infrastructural challenges than smaller firms do. This may be due in part to larger
firms possessing the capital to circumvent these challenges (purchasing generators or hiring
transportation fleets rather than rely on the given infrastructure). In terms of employment growth,
the sum of the coefficients is again negative but not statistically significant at the 10 percent level
(Wald test generated a test statistic of 0.75, Prob > F = 0.4732). From this we are able to suggest
that large firms experience lower impacts to employment growth compared to smaller firms and
that large firms may be able to adapt to these challenges better than small firms can.

24
Scope and Limitations
This paper will attempt to provide an insight into which effect dominates, by providing an analysis
of the impact rigid factor markets, costs of doing business and infrastructural challenges have on
employee productivity and firm-level employment growth across manufacturing firms in the
Zambian Economy from the period 2007-2013.
The questionnaire-nature of the Zambian Enterprise Survey limits the inferences that can be made
on a number of relationships. In particular, the small number of panel observations contained in
this data set severally limit the interpretation of any fixed effects regressions. As a result there may
be certain firm-specific characteristics that we have not controlled for that may be influencing our
results.
There is a growing body of literature investigating the costs to business operations imposed by
environmental protection regulations (Balchin & Edwards, 2008). These regulations are concerned
with reducing carbon emissions, minimizing water contamination and consumption and
maximizing efficiency and sustainability in resource use. These regulations impose additional
costs to business operations, particular in the mining and manufacturing sectors, which have not
been taken into account in this paper and should be kept in mind when making policy decisions.

25
Conclusion and Welfare Consequences
The conclusions set out in this paper have far reaching implications for proposed policies in
Zambia. If the Zambian government is to reach its goal of creating 200 000 jobs each year, they
will need to understand both the benefits and drawbacks to regulation, or in some cases
deregulation, while bearing in mind the knock on effects this will have on other areas of their
economy. One of the most fundamental methods for job creation is through improved labour
productivity. Simply put more productive workers lead to greater revenue generation and greater
growth of firms. This in turn will lead to firm expansions, given that firms are able to efficiently
and effectively gain access to financing or credit options, offering the opportunity for job creation
at existing firms as well as through additional entrepreneurial opportunities.
Through the first model set out, this paper has shown that strong positive relationship between
factor market regulations and labour productivity of skilled worker exists and further that a
moderate degree of labour regulation is needed to ensure efficient operation of the labour market,
and that worker protection can lead to moderate productivity boosts. Policy makers should bear in
mind the employment decrease these regulations could have, but given the evidence presented here
labour contracts do offset a large portion of this decrease.
Thus, using the results found in this paper, it is evident that when designing factor market
regulation laws specific to the manufacturing sector, the moderate boosts to productivity associated
with more effective hiring procedures give credibility to the notion that a certain degree of
regulation is necessary for business operations. The surprising positive relationship between
employment growth and labour regulations gives further credence to moderate and introspective
regulations for the identified sectors. We have shown that the effect of properly defined labour
contracts is able to counteract the negative influence these regulations would have on employment
growth, which when coupled with the benefits to job security and resolutions of contract and labour
disputes these regulations allow for, present a meaningful case for labour regulation.
The second model set forth in this paper has shown costs to doing business can be substantial,
often having detrimental effects on both productivity and employment growth. Policy makers
should aim to increase monitoring to ensure fair and legal regulation in all aspects of the economy.
Corruption, crime and bribery are all issues that are difficult to solve, but by streamlining and

26
improving administrational procedures their effects can be minimized. The key implication from
this second model is the strong positive relationship that trade regulations had on firm performance
both in terms of productivity and employment growth. This shows us that the Zambian
manufacturing industry benefits from ‘infant industry’ protection in accordance with the
predictions by Topolova and Khandelwal (2010) and Vousden (1990).
The third model set out in this paper has shown that the inherent costs imposed by infrastructural
inferiorities were not statistically significant when considered in their entirety. There did exist
some evidence to suggest that investments in infrastructure will have positive returns as a
collective, initially in the demand boost for manufacturing firms and secondly through positive
externalities, improved productivity and positive spill overs as hypothesized by (Röller &
Waverman, 2001: 909-923).
In terms of the welfare implications implied by the interpretation on the interaction terms, there
exists a scope for policy makers to aim to lessen the burden of business costs on smaller firms as
they feel a pronounced effect in comparison to larger firms. In terms of factor market regulations
small firms are impacted more heavily in terms of productivity, but larger firms experience
substantial employment growth knocks due to these regulations. Thus there is scope for a policy
that aims to streamline contracts to further negate the negative impacts, or for smaller firms to be
given a different set of regulatory guides to dampen these effects. Lastly in terms of infrastructure
impacts, large firms are better suited to dealing with the high transport and electricity costs. If the
Zambian policy makers are looking to boost entrepreneurial opportunities and small to medium
enterprise growth, infrastructure development will have a substantial effect at aiding this
endeavour.

27
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32
Appendix A
Table 1 below contains the unabridged list of variables used in this paper and also includes a
description of each variable.
Table 2: List of variables used in this paper with corresponding descriptions.
Variable Name Description of Variable
regs How Much Of An Obstacle (1-5): Labor Regulations
region Sampling Region
totsales In Last Fiscal Year, What Were This Establishment’s Total Annual Sales
yearest Year Establishment Began Operations
curremp Num. Permanent, Full-Time Employees At End Of Last Fiscal Year
origemp Number Of Full-Time Employees Of The Establishment When It Started
Operations
foreign Percentage of firm owned by Private Foreign Individuals, Companies Or
Organizations
labour_cost Total Labor Cost (Incl. Wages, Salaries, Bonuses, Etc) In Last Fiscal Year
firm_size Dummy variable indicating a 0 if the firm is considered a small (5-19) or medium
(20-99) sized enterprise and 1 if the firm is a large enterprise (greater than 99).
lnsales Natural logarithmic function of totsales
sales_regs Interaction term of lnsales and labour regulations
foreign_regs Interaction term of foreign ownership and labour regulations
size_regs Interaction term of firm_size and labour regulations
age Years since the firm began operations.
age_sqr The age variable multiplied by itself.
kitwe Dummy variable indicating a 1 if the sampling region is Kitwe and 0 if elsewhere.
livingstone Dummy variable indicating a 1 if the sampling region is Livingstone and 0 if
elsewhere.
lusaka Dummy variable indicating a 1 if the sampling region is Lusaka and 0 if
elsewhere.
ndola Dummy variable indicating a 1 if the sampling region is Ndola and 0 if elsewhere.
empgrowth Percentage growth in employment over the enterprises period of operation.
empgrowth_year Annualized percentage growth in employment over the enterprises period of
operation.
labprod Labour productivity, defined as marginal revenue product per worker.
lnlabprod Natural logarithmic function of labprod.

33
labprod_skill Labour productivity of skilled workers.
labprod_unskill Labour productivity of unskilled workers.
sector Industry Screener Sector
manufacturing Dummy variable indicating a 1 if the firm falls into the manufacturing sector (as
defined the World Bank Group) and 0 if elsewhere.
retail Dummy variable indicating a 1 if the firm falls into the retail sector (as defined
the World Bank Group) and 0 if elsewhere.
other Dummy variable indicating a 1 if the firm falls into neither the manufacturing
sector nor the retail sector (as defined the World Bank Group) and 0 if elsewhere.
informal How Much Of An Obstacle (1-5): Practices Of Competitors In Informal Sector
finance How Much Of An Obstacle (1-5): Access To Finance
land How Much Of An Obstacle (1-5): Access To Land
business_license How Much Of An Obstacle (1-5): Business Licensing And Permits
corruption How Much Of An Obstacle (1-5): Corruption.
courts How Much Of An Obstacle (1-5): Courts.
crime_theft_disorder How Much Of An Obstacle (1-5): Crime, Theft And Disorder.
trade_regs How Much Of An Obstacle (1-5): Customs And Trade Regulations.
electricity How Much Of An Obstacle (1-5): Electricity To Operations Of This
Establishment.
education How Much Of An Obstacle (1-5): Inadequately Educated Workforce
political_instability How Much Of An Obstacle (1-5): Political Instability
tax_admin How Much Of An Obstacle (1-5): Tax Administrations
tax_rates How Much Of An Obstacle (1-5): Tax Rates
telecommunications How Much Of An Obstacle (1-5): Telecommunications To Operations Of This
Establishment
transport How Much Of An Obstacle (1-5): Transport.
idPANEL2007 Panel ID

34
Appendix B
Table 2 below represents the results of the regression of each of the institutional categories on
Labour Productivity and Employment Growth.
Table 3: Results of Regression Analysis of Institutional Categories on Labour Productivity and
Employment Growth
(1) (2)
VARIABLES Labour Productivity Employment Growth
Institutional Categories:
Rigid Factor Markets 0.792* 0.856
(0.459) (0.881)
Infrastructure -1.023 -0.378
(0.638) (0.649)
Costs of Doing Business -0.274 -0.836**
(0.320) (0.335)
Constant 18.46*** 2.038***
(0.0651) (0.244)
Observations 593 1,116
R-squared 0.008 0.001
Notes: Standard errors in parentheses.t-statistics calculated using robust standard errors.
*** significant at 1%, ** significant at 5%, * significant at 10%.

35
Table 4: Obstacles to Business Operations: 2007-2013
(1) (2)
VARIABLES Labour Productivity Employment Growth
Finance -0.106** -0.129
(0.0520) (0.207)
Land -0.213*** -0.145
(0.0507) (0.203)
Business License -0.162** -0.217
(0.0764) (0.295)
Corruption -0.0551 -0.0810
(0.0580) (0.240)
Courts 0.134 0.0839
(0.0823) (0.342)
Crime Theft Disorder 0.0823 -0.0165
(0.0651) (0.258)
Trade Regulations 0.184*** 0.105
(0.0649) (0.264)
Electricity 0.0662 0.310
(0.0577) (0.227)
Political Instability -0.144* -0.0516
(0.0799) (0.323)
Tax Administration 0.00996 -0.242
(0.0824) (0.331)
Tax Rates 0.0818 0.229
(0.0644) (0.263)
Telecommunications 0.0847 0.386
(0.0743) (0.307)
Transport 0.0642 -0.209
(0.0631) (0.257)
Constant 18.46*** 2.158***
(0.147) (0.569)
R-squared 0.119 0.010
Observations 544 993

36
Appendix C
The following tables (5-7) below represents the results of the each regression performed against
each of the institutional categories on Labour Productivity and Employment Growth.
Table 5: Regression Output of Factor Market components on Labour Productivity and
Employment Growth
(1) (2) (3) (4) (5) (6)
Primary Independent
Variable:
Lnlabprod
skill
Lnlabprod
skill
Lnlabprod unskill
Lnlabprod
unskill
empgrowth empgrowth
Labour Regulations 0.125** 0.0790 0.0758 -0.0348 0.357* 0.221
(0.0597) (0.0643) (0.0804) (0.0871) (0.208) (0.219)
land -0.204*** 0.00675 -0.109** 0.0229 -0.235* 0.0531
(0.0487) (0.0534) (0.0549) (0.0608) (0.130) (0.130)
finance -0.106** 0.0429 -0.0417 0.0251 -0.240** -0.235
Year:
2013 -0.384* 0.131 0.882
(0.203) (0.233) (0.637)
Sector:
Food 0.941*** 0.576*** -0.407
(0.188) (0.215) (0.718)
Textiles 0.327 0.0741 -3.288
(0.436) (0.539) (2.050)
Garments -0.419** 0.133 0.376
(0.207) (0.321) (0.761)
Leather -0.703 0.408
(1.187) (1.056)
Wood 0.121 0.221 -0.991
(0.545) (0.626) (0.847)
Paper 1.111*** 0.106 -1.108
(0.324) (0.410) (2.508)
Publishing, Printing
and recorded Media
0.992 1.020* -1.075
(0.602) (0.581) (1.055)
Chemicals 1.098*** 0.556* -0.776
(0.299) (0.334) (0.939)
Plastics and Rubber 0.661** 0.122 -1.590*
(0.316) (0.280) (0.880)
Non-metallic
Mineral Products
0.573 0.231 -1.537
(0.461) (0.424) (0.960)
Basic Metals 1.109* 0.188 0.369
(0.590) (0.486) (1.479)

37
Fabricated Metal
Products
0.854*** 0.741** 0.0109
(0.271) (0.289) (0.836)
Machinery and
Equipment
1.146*** 1.547*** 3.876*
(0.365) (0.393) (2.139)
Electronics 0.345 1.867*** 0.269
(0.521) (0.614) (1.197)
Control Variables:
Age 0.0188 -0.00583 0.169**
(0.0186) (0.0255) (0.0788)
Age squared -0.000151 0.000181 -0.00280*
(0.000261) (0.000393) (0.00153)
Region:
Livingstone 0.110 -0.141 -0.0820
(0.227) (0.215) (0.616)
Lusaka 0.350** 0.203 -0.396
(0.176) (0.191) (0.661)
Ndola -0.154 -0.111 -0.367
(0.195) (0.260) (0.458)
lncapital 0.233*** 0.226***
(0.0400) (0.0379)
firm_size -0.000251 0.000139 0.0269*
(0.000877) (0.000757) (0.0143)
Constant 18.77*** 13.24*** 18.94*** 13.91*** 2.370*** -0.834
(0.115) (0.722) (0.113) (0.808) (0.456) (1.222)
Observations 584 398 425 291
R-squared 0.046 0.380 0.014 0.272 1,085 611

38
Table 6: Results of Regression Analysis of Ease of Doing Business on Labour Productivity and
Employment Growth
(1) (2) (5) (6)
VARIABLES lnlabprod lnlabprod empgrowth empgrowth
Primary Independent
Variable:
Business License -0.131* -0.0485 -0.178 -0.0348
(0.0727) (0.0738) (0.172) (0.160)
Political Instability -0.0974 -0.0696 0.0245 0.00608
(0.0771) (0.0890) (0.177) (0.199)
Corruption -0.0912* -0.101* -0.119 -0.0575
(0.0545) (0.0611) (0.152) (0.137)
Courts 0.118 0.0891 0.119 -0.0762
(0.0791) (0.0931) (0.243) (0.245)
Trade Regulations 0.203*** 0.212*** 0.155 0.0494
(0.0616) (0.0715) (0.194) (0.184)
Tax Rates -0.0451 -0.0226 -0.261 -0.0955
(0.0755) (0.0738) (0.232) (0.213)
Tax Admin 0.0891 0.00279 0.212 0.0937
(0.0626) (0.0625) (0.195) (0.182)
Crime Theft and
Disorder
0.0792 0.113* -0.0782 0.192
(0.0700) (0.0683) (0.191) (0.142)
Year:
2013 -0.269 1.376**
(0.203) (0.549)
Sector:
Food 0.873*** -0.238
(0.196) (0.784)
Textiles 0.114 -2.741*
(0.414) (1.623)
Garments -0.348 0.0982
(0.217) (0.692)
Leather -0.694 0.350
(1.463) (1.015)
Wood -0.185 -1.517*
(0.625) (0.844)
Paper 1.441*** -0.797
(0.401) (2.565)
Publishing, Printing and
recorded Media
0.609 -1.753**
(0.584) (0.881)

39
Chemicals 1.137*** -1.273
(0.307) (0.851)
Plastics and Rubber 0.548* -1.589**
(0.311) (0.797)
Non-metallic Mineral
Products
0.475 -1.752*
(0.437) (0.930)
Basic Metals 1.430** 0.468
(0.703) (1.609)
Fabricated Metal
Products
0.832*** -0.0375
(0.269) (0.825)
Machinery and
Equipment
1.163*** 3.576
(0.368) (2.318)
Electronics 0.201 -0.000144
(0.544) (1.168)
Control Variables:
Age 0.00313 0.193***
(0.0197) (0.0519)
Age squared 6.17e-05 -0.00253***
(0.000274) (0.000782)
Region:
Livingstone 0.204 -0.369
(0.234) (0.540)
Lusaka 0.347* -0.0759
(0.182) (0.489)
Ndola -0.162 -0.0536
(0.193) (0.449)
lncapital 0.241***
(0.0403)
firm_size -0.000512 0.0177*
(0.000785) (0.0100)
Constant 18.77*** 13.29*** 2.370*** -1.601
(0.115) (0.726) (0.456) (0.973)
Observations 584 385 1,016 1,008
R-squared 0.046 0.407 0.002 0.194

40
Table 7: Results of Regression Analysis of Infrastructure on Labour Productivity and Employment
Growth
(1) (2) (5) (6)
Independent
Variable:
Electricity -0.00201 0.0917 0.446 0.0607
(0.0591) (0.0614) (0.483) (0.128)
Telecommunications 0.0612 -0.00721 0.248 0.199
(0.0746) (0.0673) (0.239) (0.188)
Transport 0.0781 0.0566 -0.454* -0.214
(0.0602) (0.0634) (0.263) (0.172)
Year:
2013 -0.309 0.927
(0.188) (0.589)
Sector:
Food 0.882*** -0.286
(0.193) (0.707)
Textiles 0.259 -3.274
(0.459) (2.094)
Garments -0.427** 0.302
(0.214) (0.763)
Leather -0.796 0.460
(1.111) (1.150)
Wood -0.0123 -0.941
(0.558) (0.861)
Paper 1.346*** -0.641
(0.317) (2.472)
and recorded Media
0.886 -1.048
(0.585) (1.018)
Chemicals 1.102*** -0.632
(0.313) (0.928)
Plastics and Rubber 0.682** -1.545*
(0.314) (0.852)
Non-metallic
Mineral Products
0.504 -1.425
(0.460) (0.956)
Basic Metals 0.975* 0.377
(0.514) (1.459)
Fabricated Metal
Products
0.844*** -0.0653
(0.278) (0.817)

41
Machinery and
Equipment
1.179*** 4.338**
(0.369) (2.131)
Electronics 0.364 0.180
(0.509) (1.215)
Control Variables:
Age 0.0134 0.173**
(0.0193) (0.0807)
Age squared -5.45e-05 -0.00295*
(0.00027) (0.0016)
Region:
Livingstone 0.0946 0.0300
(0.224) (0.592)
Lusaka 0.373** -0.200
(0.180) (0.656)
Ndola -0.102 -0.252
(0.201) (0.445)
lncapital 0.236***
(0.0415)
firm_size -.00023 0.0277**
(.00084) (0.0139)
Constant 18.32*** 13.18*** 1.881*** -1.200
(0.116) (0.733) (0.525) (1.108)
R-squared 0.007 0.385 0.006 0.268

42
Appendix D
The following tables (8-10) below represents the results of the each regression performed against
each of the institutional categories on Labour Productivity and Employment Growth. They
include the interaction terms between the relevant institution and firm size.
Table 8: Results of Regression Analysis of Rigid Factor Markets on Labour Productivity and
Employment Growth: Interactions Included
(1) (2) (5) (6)
Primary Independent
Variable:
Rigid Factor Market 1.100*** 2.483** 1.337 -3.009
(0.305) (1.033) (1.468) (4.769)
Interaction Term:
Factor_Market -
Firm Size Interaction
-0.327 1.371
(0.212) (1.780)
Year:
2013 -0.289 -0.288 0.930 0.901
(0.187) (0.187) (0.589) (0.593)
Sector:
Food 0.880*** 0.882*** -0.202 -0.212
(0.188) (0.189) (0.834) (0.837)
Textiles 0.227 0.222 -2.298** -2.231**
(0.457) (0.457) (1.115) (1.118)
Garments -0.378* -0.375* 0.888 0.882
(0.202) (0.203) (0.778) (0.779)
Leather -0.691 -0.701 0.462 0.530
(1.048) (1.049) (0.949) (0.943)
Wood 0.178 0.175 -0.759 -0.723
(0.533) (0.534) (0.924) (0.920)
Paper 1.202*** 1.195*** -1.273 -1.193
(0.256) (0.257) (2.621) (2.624)
and recorded Media
1.071* 1.074* -1.011 -0.900
(0.577) (0.579) (0.979) (0.980)
Chemicals 1.040*** 1.031*** -1.374 -1.340
(0.311) (0.312) (0.914) (0.909)
Plastics and Rubber 0.625** 0.608** -1.835** -1.776*
(0.303) (0.304) (0.903) (0.910)

43
Products
0.528 0.526 -0.926 -0.826
(0.435) (0.436) (0.888) (0.880)
Basic Metals 1.209** 1.207** -0.115 -0.0836
(0.585) (0.586) (1.381) (1.387)
Fabricated Metal
Products
0.804*** 0.799*** -0.531 -0.478
(0.274) (0.275) (0.839) (0.842)
Machinery and
Equipment
1.155*** 1.156*** 3.764* 3.761*
(0.356) (0.356) (2.210) (2.210)
Electronics 0.368 0.368 0.247 0.295
(0.459) (0.460) (1.219) (1.220)
Control Variables:
Age 0.0120 0.0112 0.136** 0.140**
(0.0185) (0.0187) (0.0643) (0.0625)
Age squared -0.000104 -9.79e-05 -0.00210* -0.00216*
(0.000258) (0.000259) (0.00113) (0.00110)
Region:
Livingstone 0.128 0.132 -0.337 -0.315
(0.223) (0.223) (0.713) (0.721)
Lusaka 0.346* 0.349* -0.196 -0.241
(0.178) (0.178) (0.622) (0.616)
Ndola -0.132 -0.136 -0.445 -0.483
(0.192) (0.192) (0.477) (0.474)
Capital 0.202*** 0.203***
(0.0459) (0.0459)
Firm Size 0.130 0.134 1.532*** 1.491***
(0.0890) (0.0901) (0.514) (0.526)
Constant 13.62*** 13.61*** -4.299** -4.216**
(0.683) (0.684) (1.931) (1.964)
R-squared 0.390 0.390 0.143 0.145

44
Table 9: Results of Regression Analysis of Business Costs on Labour Productivityand Employment
Growth: Interactions Included
(1) (2) (5) (6)
Primary
Independent
Variable:
Costs of Doing
Business
-0.404 -1.478 1.026 3.274
(0.332) (2.375) (1.374) (3.133)
Interaction Term:
Business_Cost -
Firm_Size
Interaction
0.359 -1.082
(0.784) (1.566)
Year:
2013 -0.240 -0.239 0.980* 0.996*
(0.184) (0.184) (0.583) (0.587)
Sector:
Food 0.883*** 0.883*** -0.201 -0.199
(0.189) (0.189) (0.834) (0.835)
Textiles 0.199 0.200 -2.338** -2.349**
(0.453) (0.453) (1.116) (1.119)
Garments -0.384* -0.386* 0.877 0.876
(0.203) (0.204) (0.781) (0.781)
Leather -0.749 -0.752 0.409 0.402
(1.045) (1.048) (0.956) (0.957)
Wood 0.126 0.124 -0.804 -0.816
(0.534) (0.536) (0.930) (0.931)
Paper 1.148*** 1.147*** -1.347 -1.359
(0.254) (0.254) (2.626) (2.631)
and recorded Media
1.012* 1.011* -1.017 -1.027
(0.574) (0.574) (0.983) (0.984)
Chemicals 1.062*** 1.061*** -1.307 -1.314
(0.314) (0.315) (0.913) (0.915)
Plastics and Rubber 0.643** 0.643** -1.878** -1.888**
(0.311) (0.311) (0.902) (0.905)
Non-metallic
Mineral Products
0.482 0.481 -0.958 -0.976
(0.433) (0.434) (0.881) (0.883)
Basic Metals 1.182** 1.184** -0.152 -0.167
(0.586) (0.589) (1.379) (1.381)
Fabricated Metal
Products
0.819*** 0.823*** -0.507 -0.509

45
(0.277) (0.278) (0.835) (0.835)
Machinery and
Equipment
1.117*** 1.117*** 3.776* 3.761*
(0.355) (0.356) (2.217) (2.219)
Electronics 0.319 0.319 0.208 0.199
(0.453) (0.454) (1.218) (1.219)
Control Variables:
Age 0.0118 0.0116 0.137** 0.138**
(0.0187) (0.0186) (0.0636) (0.0638)
Age squared -8.54e-05 -8.20e-05 -0.00211* -0.00212*
(0.000260) (0.000260) (0.00112) (0.00112)
Region:
Livingstone 0.123 0.123 -0.337 -0.316
(0.224) (0.224) (0.722) (0.718)
Lusaka 0.364** 0.366** -0.176 -0.182
(0.178) (0.180) (0.624) (0.625)
Ndola -0.115 -0.115 -0.438 -0.439
(0.192) (0.192) (0.475) (0.476)
Capital 0.201*** 0.200***
(0.0467) (0.0480)
Firm Size 0.133 0.134 1.541*** 1.543***
(0.0899) (0.0903) (0.517) (0.518)
Constant 13.63*** 13.65*** -4.353** -4.366**
(0.698) (0.725) (1.931) (1.937)
R-squared 0.386 0.386 0.142 0.142

46
Table 10: Results of Regression Analysis of Infrastructure Challenges on Labour Productivityand
Employment Growth: Interactions Included
(1) (2) (5) (6)
Primary Independent
Variable:
Infrastructure 0.430 -0.430 0.0707 -1.136
(0.421) (1.264) (0.592) (2.755)
Interaction Term:
Infrastructure -
Firm_Size Interaction
0.246 0.423
(0.412) (1.086)
Year:
2013 -0.272 -0.276 0.991* 0.977
(0.187) (0.188) (0.591) (0.594)
Sector:
Food 0.888*** 0.886*** -0.199 -0.195
(0.188) (0.189) (0.834) (0.833)
Textiles 0.224 0.234 -2.339** -2.309**
(0.457) (0.457) (1.120) (1.125)
Garments -0.374* -0.373* 0.870 0.872
(0.203) (0.203) (0.780) (0.779)
Leather -0.673 -0.665 0.404 0.412
(1.046) (1.056) (0.967) (0.965)
Wood 0.131 0.156 -0.833 -0.797
(0.525) (0.532) (0.924) (0.903)
Paper 1.204*** 1.210*** -1.350 -1.323
(0.261) (0.263) (2.633) (2.634)
and recorded Media
0.968* 0.961* -1.034 -1.063
(0.552) (0.537) (0.983) (0.998)
Chemicals 1.100*** 1.104*** -1.315 -1.322
(0.317) (0.317) (0.912) (0.912)
Plastics and Rubber 0.629** 0.622** -1.881** -1.888**
(0.306) (0.306) (0.907) (0.909)
Products
0.496 0.505 -0.929 -0.884
(0.428) (0.431) (0.887) (0.871)
Basic Metals 1.053 1.145* -0.174 -0.128
(0.653) (0.613) (1.382) (1.388)
Fabricated Metal
Products
0.794*** 0.804*** -0.503 -0.502

47
(0.275) (0.275) (0.833) (0.834)
Machinery and
Equipment
1.142*** 1.146*** 3.765* 3.767*
(0.357) (0.358) (2.218) (2.218)
Electronics 0.356 0.354 0.206 0.209
(0.458) (0.459) (1.223) (1.222)
Control Variables:
Age 0.0135 0.0132 0.138** 0.138**
(0.0187) (0.0187) (0.0640) (0.0641)
Age squared -0.000105 -9.65e-05 -0.00212* -0.00211*
(0.000261) (0.000260) (0.00113) (0.00114)
Region:
Livingstone 0.123 0.121 -0.305 -0.306
(0.224) (0.224) (0.721) (0.722)
Lusaka 0.324* 0.324* -0.181 -0.182
(0.180) (0.180) (0.632) (0.632)
Ndola -0.130 -0.124 -0.450 -0.443
(0.192) (0.193) (0.479) (0.481)
Capital 0.209*** 0.209***
(0.0457) (0.0454)
Firm Size 0.118 0.110 1.534*** 1.506***
(0.0884) (0.0868) (0.514) (0.542)
Constant 13.49*** 13.52*** -4.340** -4.259**
(0.691) (0.693) (1.928) (2.016)
R-squared 0.388 0.389 0.142 0.142

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