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

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

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exogenously imposed on the individual firm, while having substantial implications for costs and

ease of business operations (Carlin & Seabright, 2007).

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).

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Figure 1: – Business Environment in Zambia, figure sourced from: (World Bank Group, 2015)


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

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

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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 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.

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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.

Literature ReviewThe 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.

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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,

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.

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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 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

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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.

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

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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 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

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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 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

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growth 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

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relates to flexibility of contracts in being able to match value added (labour productivity) to

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

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regressions, including an 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 (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

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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 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,

17


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

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-

18


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.

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

19


point lower trade costs, benefits to lower cost inputs, and increased competition will be required

for optimal performance for the manufacturing sector.

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.

20


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

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).

21


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 factormarkets+ε

The new model, with the inclusion of the interaction terms is as follows:

Labour Productivity=B0+B1×rigid factormarkets+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.

22


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.

23


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.

24


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

25


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 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.

26


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31


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 Variableregs How Much Of An Obstacle (1-5): Labor Regulationsregion Sampling Regiontotsales In Last Fiscal Year, What Were This Establishment’s Total Annual Salesyearest Year Establishment Began Operationscurremp Num. Permanent, Full-Time Employees At End Of Last Fiscal Yearorigemp Number Of Full-Time Employees Of The Establishment When It Started

Operationsforeign Percentage of firm owned by Private Foreign Individuals, Companies Or

Organizationslabour_cost Total Labor Cost (Incl. Wages, Salaries, Bonuses, Etc) In Last Fiscal Yearfirm_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 totsalessales_regs Interaction term of lnsales and labour regulationsforeign_regs Interaction term of foreign ownership and labour regulationssize_regs Interaction term of firm_size and labour regulationsage 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.

32


lnlabprod Natural logarithmic function of labprod.

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

33


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 0.001Notes: 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

Notes:

Standard errors in parentheses. t-statistics calculated using robust standard errors.*** significant at 1%, ** significant at 5%, * significant at 10%.

36

(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 0.010Observations 544 993


Appendix CThe 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*

37


(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)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 291R-squared 0.046 0.380 0.014 0.272 1,085 611

Notes: Standard errors in parentheses. t-statistics calculated using robust standard errors.*** significant at 1%, ** significant at 5%, * significant at 10%.

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 0.609 -1.753**

39


and recorded Media(0.584) (0.881)

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 1,008R-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)VARIABLES lnlabprod lnlabprod empgrowth empgrowthIndependent 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)Publishing, Printing 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)Machinery and 1.179*** 4.338**

41


Equipment(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)Observations 588 398 624 620R-squared 0.007 0.385 0.006 0.268


42


Appendix DThe 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)VARIABLES lnlabprod lnlabprod empgrowth empgrowthPrimary 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)

Publishing, Printing 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


Non-metallic Mineral 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**



44


Table 9: Results of Regression Analysis of Business Costs on Labour Productivity and Employment Growth: Interactions Included

(1) (2) (5) (6)VARIABLES lnlabprod lnlabprod empgrowth empgrowthPrimary 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)

Publishing, Printing 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)

45


Fabricated Metal Products 0.819*** 0.823*** -0.507 -0.509


(0.355) (0.356) (2.217) (2.219)Electronics 0.319 0.319 0.208 0.199


(0.0187) (0.0186) (0.0636) (0.0638)Age squared -8.54e-05 -8.20e-05 -0.00211* -0.00212*


(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**



46


Table 10: Results of Regression Analysis of Infrastructure Challenges on Labour Productivity and Employment Growth: Interactions Included

(1) (2) (5) (6)VARIABLES lnlabprod lnlabprod empgrowth empgrowthPrimary 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)Publishing, Printing 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)Non-metallic Mineral 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



(0.357) (0.358) (2.218) (2.218)Electronics 0.356 0.354 0.206 0.209


(0.0187) (0.0187) (0.0640) (0.0641)Age squared -0.000105 -9.65e-05 -0.00212* -0.00211*


(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**



48

final draft - long paper 2015, dpnmat001

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