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©2017 International Bank for Reconstruction and Development / The World Bank

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Internet: www.worldbank.org

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Abstract

The 2016 Malawi Investment Climate Assessment (ICA) evaluates the performance of domestic private sector firms in

Malawi, based on the analysis of data from the 2014 Malawi Enterprise Survey. It identifies the barriers to increasing

private investment and analyses productivity of enterprises in Malawi.

This report also sets out prioritized policy recommendations aimed at addressing the constraints faced by enterprises.

Authors:

Efrem Chilima, William Mwanza, George Clarke and Richard Record.

2

http://www.worldbank.org/

mailto:[email protected]

GOVERNMENT FISCAL YEAR

July 1 to June 30

CURRENCY EQUIVALENTS

Currency Unit : Malawian Kwacha

WEIGHTS AND MEASURES

Metric System

ABBREVIATIONS AND ACRONYMS

ACB Anti-Corruption Bureau

ATM Auto Teller Machine

ATS Automated Transfer System

CSD Central Securities Depository

DFI Development Finance Institutions

EGENCO Electricity Generation Company

ESCOM Electricity Supply Corporation of Malawi

ESW Economic Sector Work

FDI Foreign Direct Investment

GCI Global Competitiveness Index

GDP Gross Domestic Product

GOM Government of Malawi

ICA Investment Climate Assessment

IFMIS Integrated Financial Management Information System

LP Labor Productivity

MCCCI Malawi Confederation of Chambers of Commerce and

Industry MFI Microfinance Institutions

MSMEs Micro Small and Medium Enterprises

MW Megawatts

POS Point of Sale

RBM Reserve Bank of Malawi

SACCO Savings and Credit Cooperative Organization

T&C Trade and Competitiveness

TFP Total Factor Productivity

3

4

Vice President : Makhtar Diop

Country Director : Bella Bird

Country Manager : Laura Kullenberg

Senior Director T&C : Anabel Gonzalez

Director T&C : Klaus Tilmes

Practice Manager T&C : Catherine Kadennyeka Masinde

Task Team Leader : Brian Mtonya/Efrem Chilima

Principal Authors : Efrem Chilima, William Mwanza, George Clarke,

Richard Record

ACKNOWLEDGEMENTS

The Malawi Investment Climate Assessment (ICA) 2016 was prepared by a World Bank team led by Efrem

Chilima (Senior Private Sector Specialist), under the guidance of Catherine Kadennyeka Masinde (Practice

Manager), Laura Kullenberg (Country Manager), Yutaka Yoshino (Program Leader), Steven Dimitriyev

(Lead Specialist, T&C), and Brian Mtonya (Senior Private Sector Specialist). The co-authors were William

Mwanza (Consultant), George Clarke (Consultant) and Richard Record (Senior Economist). The authors

benefited greatly from advice from peer reviewers, namely: Julian Latimer Clarke, Moses Kibirige, Madalo

Minofu, and Hardwick Tchale. The preparation of this document benefited from collaboration and support

from Esther Lozo, Zeria Banda, Kehinde Vivian Waterman, Tillmann Von Carnap-Bornheim, Ellie

Stylianou, Priscilla Kondoole and Mwayi Nampeya. Govati Nyirenda provided the photographs in the

publication.

The authors greatly benefited from interactions with stakeholders in Malawi including Government officials

from the Ministry of Industry, Trade and Tourism.

The team would like to thank the Enterprise Survey Unit for conducting the Enterprise Survey in Malawi.

5

Table of Contents Executive Summary .................................................................................................................................... 10

CHAPTER 1: Introduction ............................................................................................................................ 13

1.1 Background to the Economic Sector Work ................................................................................. 13

1.2 Investment Climate Assessment (ICA) ........................................................................................ 13

1.3 Enterprise Survey ........................................................................................................................ 14

1.4 Challenges With Using Enterprise Surveys ................................................................................. 14

CHAPTER 2: Context − Economy and Firm Competitiveness ....................................................................... 17

2.1 Low Income Agricultural Economy ............................................................................................. 17

2.2 Agriculture As The Main Sector .................................................................................................. 18

2.3 Financial Sector ........................................................................................................................... 18

2.4 Private Sector .............................................................................................................................. 21

2.5 Firm Competitiveness ................................................................................................................. 21

2.6 Infrastructure .............................................................................................................................. 23

CHAPTER 3: Enterprise Performance: How Does Firms Level Productivity in Malawi Compare with Other

Countries and Across Different Categories? ............................................................................................... 25

3.1 Partial Productivity Measures: Definitions and Cross Country Comparison ............................... 26

3.2 Partial Productivity Measures for Different Categories of Firms According to Size, Location,

Domestic or Export Oriented, Local or Foreign Owned and Whether or Not Credit Constrained .......... 27

3.3 Total Factor Productivity (TFP): Cross Country Analysis and Comparison with respect to

Location in Malawi .................................................................................................................................. 30

3.4 Labor Costs and Productivity: Country Comparison and with respect to location and firm size31

3.5 Employment Growth .................................................................................................................. 34

3.6 Total Factor Productivity and Investment Climate ..................................................................... 34

3.7 Policy Implications of Productivity Measures ............................................................................. 35

CHAPTER 4: Enterprise Constraints: What do Firms in Malawi View as The Major Challenges to Enterprise

Development and Growth? ........................................................................................................................ 38

4.1 Top three obstacles by Firm Size, Business Sector, Location and Gender Perspective ............... 38

4.2 Comparison with 2008 Survey .................................................................................................... 40

4.3 Cross Country Comparison on Top Most Obstacles .................................................................... 41

4.4 Comparison with the World Bank Doing Business Survey Results ............................................. 41

4.5 Comparison with the MCCCI Business Climate Survey ............................................................... 42

CHAPTER 5: Looking at Top Constraints in Detail ....................................................................................... 44

5.1 Access to finance is a major constraint ....................................................................................... 45

5.2 Electricity outages ....................................................................................................................... 51

6

5.3 Corruption ............................................................................................................................ …...55

CHAPTER 6: Other risk measures ................................................................................................................ 60

6.1 Tax Rates and Administration ................................................................................................ …...60

6.2 Practices of the informal sector ............................................................................................ …...61

6.3 Access to land and Political Instability .................................................................................. …...62

6.4 Business licensing and permits ................................................................................................... 63

6.5 Crime, theft and disorder ........................................................................................................... 65

6.6 Transport and Logistics ............................................................................................................... 68

6.7 Water Shortages ......................................................................................................................... 69

CHAPTER 7: Conclusions for Policy on Investment Climate in order to improve Productivity ................... 70

BIBLIOGRAPHY ............................................................................................................................................ 74

Annexes

Annex 1: Measuring Firm Performance Using Enterprise Survey Data .................................................. 77

Annex 2: Cross Country Comparison Using Enterprise Survey Data ...................................................... 80

Annex 3: Estimating Total Factor Productivity Using Enterprise Survey Data ....................................... 82

Annex 4: TFP and Investment Climate ................................................................................................. 90

Annex 5: What Is an Enterprise Survey................................................................................................. 93

Annex 6: Enterprise Survey in comparison to Doing Business methodologies ...................................... 94

TABLES

Table 1: Basic Statistics for 2014 Malawi Enterprise Survey ...................................................................................... 15

Table 2: Banking Sector Market Share as at December 2014 ...................................................................................... 19

Table 3: Breakdown of Partial Productivity Data by Firm Type ...................................................................................27

Table 4: TFP and Improvement in the Investment Climate ...........................................................................................35

Table 5: Three Biggest Obstacles to Establishments .....................................................................................................39

Table 6: Access to Finance, Electricity and Corruption ................................................................................................41

Table 7: Finance As a Major Obstacle ..........................................................................................................................45

Table 8: Firms with credit lines .....................................................................................................................................46

Table 9: Collateral Requirements ..................................................................................................................................47

Table 10: Working Capital Financed by Banks .............................................................................................................49

Table 11: Firms Using Banks to Finance Investment ....................................................................................................50

Table 12: Firms Identifying Electricity as Major Constraint .........................................................................................53

Table 13: Power Outages in a typical year in 2008 and 2014 ......................................................................................54

Table 14: Corruption and courts as Major Constraints ..................................................................................................57

Table 15: Firms Identifying Corruption as Major Constraint ........................................................................................58

7

Table 16: Corruption Levels – Country Comparison .................................................................................................... 59

Table 17: Tax Rates as a Major Constraint ................................................................................................................... 61

Table 18: Informal Sector Competition as Major Constraint ........................................................................................ 62

Table 19: Access to Land and Political Instability ........................................................................................................ 63

Table 20: Business Licensing and Permit ..................................................................................................................... 64

Table 21: Losses Due To Theft, Crime, Vandalism ...................................................................................................... 66

Table 22: Transport Challenges .................................................................................................................................... 68

Table 23: Water Insufficiencies .................................................................................................................................... 69

Table 24: Correlation Between Country Level Estimates of Technical Efficiency ...................................................... 84

Table 25: Breakdown of Partial Productivity Data by Firm Type................................................................................. 92

FIGURES

Figure 1: Illustration of details of the survey Sample ................................................................................................... 15

Figure 2: GDP Growth Rates, Exchange Rates and Foreign Exchange Positions ........................................................ 17

Figure 3: Holdings of Credit By Private Sector in Malawi ........................................................................................... 20

Figure 4: Labor Productivity ........................................................................................................................................ 25

Figure 5: Capital Intensity ............................................................................................................................................ 25

Figure 6: Capital Productivity ...................................................................................................................................... 25

Figure 7: ToTal Factor Productivity ............................................................................................................................. 25

Figure 8: Large Firms are More LABOR Productive and Capital Intensive ................................................................ 28

Figure 9: Blantyre Firms are more Productive ............................................................................................................ 29

Figure 10: Labor Costs ................................................................................................................................................. 33

Figure 11: Unit Costs .................................................................................................................................................... 33

Figure 12: Employment Growth ................................................................................................................................... 33

Figure 13: Major Obstacles identified in percentage shares ......................................................................................... 38

Figure 14: Top Ten Biggest Obstacles to Enterprises in Malawi, 2008 and 2014 ........................................................ 40

Figure 15: Top Three Obstacles - Country Comparison ............................................................................................... 41

Figure 16: Top Obstacles According to Survey By MCCCI ........................................................................................ 43

Figure 17: Access to Finance as Major Constraint - Country Comparison .................................................................. 45

Figure 18: Firms Owning Bank credit line /Loan - Country Comparison .................................................................... 46

Figure 19: Loans Requiring Collateral -Country Comparison ...................................................................................... 47

Figure 20: Comparison of Value of collateral and NPLs – Malawi and Comparator Countries in year of enterprise

survey .................................................................................................................................................................. 48

Figure 21: Percentage of Firms Using Banks to Finance Working Capital .................................................................. 49

Figure 22: Firms Using Banks to Finance Investment (%) ........................................................................................... 50

Figure 23: Investment Financing Options (proportion, %) ........................................................................................... 50

Figure 24: Electricity Outages And Associated Losses Across Countries .................................................................... 52

8

Figure 25: Firms Owning or sharing Generators (%) ....................................................................................................52

Figure 26: Number of Days to Obtain electricity connection ........................................................................................53

Figure 27: Firms Identifying Corruption as a Major Constraint (%) .............................................................................57

Figure 28: Bribery Rates ...............................................................................................................................................58

Figure 29: Top Ten Biggest Obstacles to Enterprises in Malawi, 2008 and 2014 .........................................................60

Figure 30: Licensing, Taxation and Tax Administration ...............................................................................................61

Figure 31: Competition from Informal Players .............................................................................................................62

Figure 32: Land and Political Instability as Obstacles - Country Comparison ..............................................................63

Figure 33: Firms Identifying Business Licensing and Permits as Major Constraints (%) .............................................64

Figure 34: Number of Days to get Licenses and Permits .............................................................................................65

Figure 35: Crime, Theft and Disorder - Country Comparison .....................................................................................66

Figure 36: Transport as a Major Constraint -Country Comparison ...............................................................................68

Figure 37: Water Insufficiencies in a Typical Month ....................................................................................................69

Figure 38: Malawi Labor Productivity - Higher than would be expected given the level of development ...................80

Figure 39: Malawi Capital intensity - higher than would be considering the country's level of development ..............80

Figure 40: Malawi's Labor productivity and capital intensity are high but capital productivity is low .........................80

Figure 41: TFP IS Higher in Malawi Than Countries at Similar Level of Development ..............................................80

Figure 42: Despite High Labor Costs, Unit Cost in Malawi Remains Reasonable .......................................................81

Figure 43: Over the Past Three Years, Employment Growth was Slow Among Manufacturing Firms ........................81

Figure 44: Few Firms In Malawi Provide Workers With Formal Training ...................................................................81

9

EXECUTIVE SUMMARY

The World Bank conducts investment climate assessments (ICAs) across the globe to analyze private sector

performance and to determine principal constraints affecting the private sector in respective countries. The

2016 Malawi ICA focuses on supporting dialogue on investment climate by analyzing the challenges

reported by private enterprises in the country. It is the second in a series, following the first one that was

published in 2006. The 2016 Malawi ICA is based on the analysis of results from the World Bank enterprise

survey conducted in 2014. Regression analysis on productivity levels of firms was conducted to present

some insights into the competitiveness of manufacturing firms.

The productivity analysis reveals that firms in Malawi have higher levels of labor productivity and capital

intensity, but lower levels of capital productivity than their counterparts in neighboring countries. Unit labor

costs are lower than in most comparator countries. The results are predominantly with respect to large firms,

with medium firms in Malawi facing lower levels of labor productivity and capital intensity, but higher

levels of labor costs than small and large firms. Apart from the foregoing measures of partial productivity,

total factor productivity is lower than in most comparator countries.

Turning to the analysis of the results of the enterprise survey themselves, the most frequently cited obstacles

to growth and efficient performance of enterprises in Malawi are grouped in three main categories: finance,

infrastructure, and business environment. The most significant constraint is access to finance, followed by

unreliable electricity supply and frequent outages. The next most cited obstacles are the costs of dealing

with corruption, particularly with regard to Government-to-business services and the burden of an

unfavorable tax administration and rates. Other obstacles include unfair competition due to the unregulated

practices of the informal sector, difficulty in accessing land, perceptions of political instability, excessive

bureaucratic practices experienced when obtaining business licenses and permits, growing concerns over

crime, theft and disorder, and high costs and poor transport infrastructure.

The effects of limited access to finance are quite far-reaching, and felt more severely by Malawian owned

local firms especially those that are rural-based, female headed, domestic market oriented, small scale and

those engaging in the service industries. The high collateral requirements from commercial banks deter

investment, especially by the small and the medium enterprises (representing the majority of enterprises in

Malawi), resulting in heavy reliance on internal resources for financing investments, other than from the

formal banking system. The interest rates are very high, sometimes running at around 40 per cent.

Commercial banks offer only a narrow range of financial instruments. The country does not have long term

financing schemes. As part of efforts to address this challenge, the Reserve Bank of Malawi is developing

a leasing framework together with an examination of the applicability of a long term finance guarantee

scheme.

The deterioration in the reliability of electricity supply across Malawi forces many enterprises to sometimes

convert to costlier diesel-powered generators. This increases their costs of production and operations, and

worsens their overall performance. Per the survey, the manufacturing firms were the most affected by

electricity outages.

Another growing concern since 2008 is corruption, which was cited as the 3rd biggest obstacle in 2014,

compared to 8th in 2008. Incidences of informal payments to public officials including tax officials, seem to

have increased considerably. Perceived occurrences of bribery in form of “gifts” was noted when securing

government contracts, obtaining business operating and import licenses, getting construction permits,

meeting with tax officials, as well as getting connected to the electricity grid and water supply.

10

There are considerable challenges in dealing with the bureaucracy in government administration especially

in obtaining business licenses and permits. Within categories of firms in the survey, medium-scale

enterprises complained mostly about the exorbitant tax rates. The small-scale enterprises and the firms

headed by female managers, mostly complained about the difficulty and costs of obtaining business licenses

and permits. Growth of medium and small-scale enterprises was affected by practices in the unregulated

informal sector. All the industry categories complained about the increasing difficulty to obtain rights to land1

for their business operations, and about increasing political instability. The former seems to have more

severe impacts in Malawi than in some of the countries in the region.

In contrast, business managers’ perceptions on transport challenges improved in 2014 compared to 2008.

However, this is still worse than the average for the Sub-Saharan region. Moreover, the impact of transport

and logistic challenges for Malawi is more pronounced because the country is landlocked. Similarly, the

perception of crime, theft and disorder as a major constraint has lessened, but remains a concern to the

private sector.

Insufficient water supply is another infrastructure-related constraint to the growth of the manufacturing

sector. Water disruptions occurred frequently in 2014 at 5.3 days a month on average (compared to 0.5 days

in 2008). This was the worst performance amongst comparator countries in the region.

The principal conclusion from the 2016 Malawi ICA is that policymakers face one distinct pathway if private

sector will have to play its role as engine of growth; namely a radical reinforcement in the approach to

private sector development, in which case, given all the challenges the country faces, the country’s pace of

reform needs to be focused, be much more rapid, deep and consistent. This is necessary if Malawi is to

enhance and maintain its competitiveness in a fast moving world. To do this, four priority steps are

recommended:

(1) Take advantage of Malawi’s higher levels of labor productivity and lower unit cost levels than

neighboring countries to attract investment into labor-intensive industries. While doing this,

labor productivity of medium firms needs to be improved, as does their use of capital. An increase

in labor-intensive industries can create jobs that through increasing incomes, can positively

reinforce capital utilization for domestic markets. Market access to regional and global markets can

also enhance capital utilization as more products are produced for those markets. Further to this,

total factor productivity needs to be improved inter alia through development of an innovation

ecosystem and frameworks that facilitate high levels of technology diffusion; and research and

development, education and skills development that promote agricultural production and feed into

the labor needs of emerging manufacturing sub-sectors.

(2) Ensure macroeconomic stability and provide financial infrastructure and instruments to

reduce risks to lenders and the costs to borrowers with a view to effectively enhance access to

finance by businesses. Based on the identified challenges, measures need to be taken to reduce

interest rates and to prioritize the improvement of financial infrastructure to achieve broader access

to finance. This should be complemented by measures to improve financial literacy and to develop

financial products specially tailored towards the needs of small and medium-sized businesses, with

attention to the disadvantaged identified across the different categories. This should also be

accompanied by effective implementation of the warehouse receipt system, collateral registry,

credit reference and long term financing infrastructure.

1 The World Bank supported the Ministry of Lands review 10 land laws which were enacted in 2016. The Bank has also

supported the development of an integrated land information system and registry.

11

(3) Focus finite public sector resources in investment in sectors such as energy, water and

transport that would provide the greatest contributions to growth of industries. Significant

infrastructure improvements, especially for the availability of electricity (and water), should be

undertaken if the productivity of firms is to be enhanced. This will require deeper efforts to improve

the governance of utility suppliers and to encourage private investment to boost installed generation

and transmission capacity. The generation capacity, including through alternative sources of

energy, should be increased to ensure adequate supply of electricity to both rural and urban areas.

Efforts to implement interconnectors with Malawi’s neighbors such as Mozambique and Zambia

must also be reinforced to ensure that Malawi accesses energy from the Southern African Power

Pool (SAPP). A dilapidated national grid network should be rehabilitated and expanded to uptake

and efficiently transmit the increased generation of electricity that would result.

(4) Address policy and regulatory challenges to increase predictability and transparency, and

implement strong measures to curb corruption, especially in government to business services.

Strong institutional measures are required to ensure that corruption and bribery are curbed.

Regulatory processes including tax and licensing requirements should be made simpler, be online

and automated and made more accessible to ensure these areas are not breeding grounds for

corruption. This would ensure transparency and accountability, and help save the private sector

from losing time and other resources. Improvement of IFMIS (the public financial management

system) should be prioritized. The Anti-Corruption Bureau (ACB) and other governance

institutions should be fully supported and financed. Implementation of reforms in land governance

(new set of land laws) that have commenced should be effectively implemented to ensure

predictability and ease in acquiring land by investors. Automation of land registration should be

prioritized to ensure improvement on transparency. Government institutions mandated to provide

land to investors, such as the Malawi Investment and Trade Centre, should be empowered to

undertake their function effectively. Government should also ensure a more predictable and

transparent investment climate for commercial agriculture and agribusiness, and specifically

provide a legal framework that allows for clearly defined and transparent procedures for the

regulation of commodity imports and exports. The review process of the Control of Goods Act

should be expedited and Special Crops Act should be reviewed or repealed to remove

unpredictability in this area.

12

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND TO THE ECONOMIC SECTOR WORK

The Malawi Growth and Competitiveness Economic Sector Work (ESW) is an analytical piece of

work by the Trade and Competitiveness (T&C) Global Practice of the World Bank Group, designed

in 2015 to complement the preparatory work for a lending operation - the Shire Valley Transformation

Project (SVTP) - and to promote dialogue around the SVTP and Government’s overall strategy on

agriculture commercialization. The SVTP is a World Bank project aimed at supporting the Government to

develop a gravity-fed irrigation scheme covering around 42,000 hectares to the west of the Shire River in

Chikwawa and Nsanje districts. The Bank is also financing another operation, the Malawi Agricultural

Commercialization (AGCOM) project, which is supporting farm level productivity, with significant efforts

to build productive alliances as an agglomeration strategy aimed at improving farmer access to inputs,

technology, and markets. The Bank is also financing the Malawi Resilience Development Policy Financing

(DPO) to address policy and regulatory reforms impeding the effective functioning of agricultural markets

in order to achieve structural transformation. An analytical work was designed: (a) to identify private sector

investment opportunities and spill overs from the SVTP in the Shire Valley area; and, (b) to identify key

investment climate and trade policy/logistics constraints as well as necessary policy action reforms that need

to be taken for the positive spill overs of the SVTP to result in increased investment.

The 2016 Malawi ICA aims to support dialogue on the investment climate in the country by analyzing

challenges reported by the private sector. This analyzes firm level constraints based on a World Bank

Enterprise Survey that was conducted from April 2014 to February 2015. The survey was conducted in the

form of interviews with business owners and top managers from 523 formal private firms, categorized into

small, medium and large firms in the manufacturing, service and retail sectors. The survey covered six

districts: Blantyre, Kasungu, Lilongwe, Mangochi, Mzimba, and Zomba.2

1.2 INVESTMENT CLIMATE ASSESSMENTS (ICAS)

ICAs provide a standard for the measurement and comparison of investment climate conditions in a

country, creating the platform for the identification of the features of the investment climate that

matter most. ICAs track changes in the investment climate and make international comparison between

countries regarding firm level characteristics and constraints. This kind of analysis provides insights to

policy makers and development specialists to effectively formulate policies and propose steps that can be

implemented to improve the business climate and increase investment.

The Malawi ICA 2016 is the second report in series. The first was done in 2006 based on the 2003 World

Bank Enterprise Survey. This report is based on analysis of the enterprise data which was collected and

processed in 2014 and part of 2015. The Malawi Confederation of Chambers of Commerce and Industry

conducted its own surveys in 2015 and 2016, and the results mirror the findings of the Malawi ICA 2016,

indicating that the findings are still prevalent currently. The assessments drew comparisons between

selected countries in the region3 and compared its findings with similar assessment conducted in 2008. It

also analyzed some of the risks across geographical regions in Malawi, different sizes of enterprises, and

analyzed productivity measures.

3 The following comparative countries in the Region were used. Note that the latest enterprise surveys in these countries were

conducted at different times as indicated in brackets: Zambia (2013), Mozambique (2007), South Africa (2007), Zimbabwe

(2016), Tanzania (2013), and Kenya (2013).

13

1.3 ENTERPRISE SURVEY

A World Bank Enterprise Survey (WBES) broadly explores challenges faced by firms with respect

to the most significant obstacles to the daily operations of their businesses, as well as the degree to

which each element poses as an obstacle. First, the survey questionnaire requests respondents to indicate

which element, from a list, represents the biggest obstacle faced by its establishment. The elements are

ranked according to the percentage of firms that selected each constraint. The second question is on the

degree to which each element of the business environment is an obstacle to the current operations of the

establishment, whereby firms provide a rating in a range of 0 for ‘no obstacle’ to 4 for ‘very severe obstacle’.

More details are provided in Annex 5.

The Malawi 2014 enterprise survey is the third to be undertaken, building from previous assessments

in 2008 and 20034. In 2014, the survey covered 523 business establishments registered as privately-owned

with sampling stratified by sector, size, and gender of owner. The survey involved business owners and top

managers who were interviewed between April 2014 and February 2015. The survey sample included panel

interviews from those conducted in the previous round, so that the performances and constraints are

compared over time. Table 1 and Figure 1 below provide some descriptive statistics of the 2014 Malawi

Enterprise Survey.

1.4 CHALLENGES WITH USING ENTERPRISE SURVEYS

ICAs and analyses from Enterprise Surveys have proven to be an important method of assessing the

investment climate of countries. Despite concerns that subjective views and ranking constraints based on

perception-based measures pose analytical challenges, this approach has been seen to provide useful

insights. A strong assumption is that managers and owners know more about the constraints and obstacles

facing their businesses hence their views are significantly representative of the actual situation (Clarke,

2010). For the ICA, regression analysis was conducted to determine labor, capital and total factor

productivity levels in Malawi. This analysis was also conducted on the data from the Enterprise Survey, to

determine the extent to which the identified constraints affect total factor productivity, and hence long term

growth in Malawi.

4 The 2003 survey covered 306 firms, and the 2008 survey covered 160 firms (World Bank, 2006) (World Bank, 2006, and

World Bank, 2017c)).

14

Table 1: Basic Statistics for 2014 Malawi Enterprise Survey5 ______________________________________________________________________________________ Total Number of Survey Respondents 523

Small Enterprises (5-19 employees) 291

Medium Enterprises (20-99) 148

Large scale Enterprises (100+) 84

Location of Firms

Blantyre 256

Lilongwe 183

Zomba 36

Mzimba 30

Mangochi 14

Kasungu 4

Sectors

Manufacturing 197

Retail 118

Other 208

Source: World Bank Enterprise Survey 2014

FIGURE 1: ILLUSTRATION OF DETAILS OF THE SURVEY SAMPLE

SURVEY SAMPLE BY LOCATION

Kasungu Mangochi Mzimba

Blantyre Kasungu Zomba

Mangochi

Mzimba

Zomba Lilongwe

Lilongwe

Blantyre

FIRM SIZE

Largescal e

Medium

Smallscale

BUSINESS SECTOR

Manufacturing

Other

Retail

GENDER

percentage of firms

with a female top

manager

percentage of firms

with a male top

manager

Source: Authors’ calculation based on data from WBES

5 It will be noted that the districts included in the survey are based on the number of firms operating in them. There is quite some

activity in other districts such as Chikwawa, Thyolo and Mulanje where there is agro-processing of sugar and tea, respectively.

The analysis may have been helped by insights from firms in these districts. However, these were not in the scope of the

Enterprise Survey.

15

16

Tea plantation in Thyolo

CHAPTER 2: CONTEXT - ECONOMY AND FIRM COMPETITIVENESS

The 2014 Malawi Enterprise Survey was conducted between April 2014 and February 2015. This was

also a period when the country faced significant business challenges and an economic slowdown. The

manufacturing sector was especially affected by insufficient electricity supply and a significantly

depreciated currency. The manufacturing base was, and remains, small, dominated by informal Small and

Medium Enterprises (SMEs). A few large firms operate in the country and only a few of them produce for

export. The enterprises face stiff competition on the international markets because of high costs of imported

raw materials, worsened by the landlocked6 position of the country.

2.1 LOW INCOME AGRICULTURAL ECONOMY

Malawi is categorized as a low-income economy. The country’s Gross Domestic Product (GDP) per

capita (current US$) was estimated at US$372.47 in 2015. About half of the population lives below the

national poverty line and a quarter is estimated to live in extreme poverty. The poverty levels declined, but

only slightly, from 52.4 percent to 50.7 percent between 2005 and 2011, a period when GDP grew strongly

(on average by 6.5 per cent). This points to the worsening of the income distribution, not in favor of the

majority of the population in Malawi living in the rural areas. Those living in rural areas, have their

economic output revolving around a narrow resource base of primary agricultural commodities. Due to a

combination of weather related shocks, macroeconomic instability and other economic challenges, the

country’s GDP growth has not been steady over recent years (see illustration in Figure 2).

FIGURE 2: GDP GROWTH RATES, EXCHANGE RATES AND FOREIGN EXCHANGE POSITIONS

Source: World Bank (2017a)

Malawi experiences consistent macroeconomic shocks. Overall, the country’s foreign exchange market

has been under pressure following the persistent depreciation of the Kwacha (from trading at MK405.2 to

the US dollar in 2013 down to MK730.08 in November, 2016 – and at MK734.7 as at April, 2017). GDP

growth has slowed in recent years from 5.7 percent in 2014 to 2.8 percent and 2.5 percent in 2015 and 2016,

respectively. The manufacturing sector declined by 1.1 percent in 2012. In 2013/14, the country experienced

lapses in public financial management with the systematic embezzlement of public funds which was dubbed

“cashgate” leading to donors withholding budgetary support. The country also experienced high inflation

rates, fuel shortages and frequent electricity load shedding.

6 Malawi shares borders with Zambia to the West, Tanzania to the North, and Mozambique to the South and East 7 Or US$493.7 in constant 2010 US$ prices (World Bank, WDI) 8 http://www.rbm.mw/Statistics/MajorRates

17

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Malawi’s rate of inflation has been very high but decreased rapidly in 2017 due to good harvests, a

stable exchange rate and efforts in implementing fiscal discipline. Inflation, which was at 34.6 percent

in December, 2012, peaked at 37.9 percent in February 2013, before dropping to 25.9 percent in January

2014. The rate had consistently been above 20 percent since 2012, but started showing signs of declining

in 2016/17. As a result of the consistently high inflation rates, interest rates were above 40 per cent at the

time that the 2014 Enterprise survey was conducted. As at May 2017, the inflation rate had dropped to 12.3

percent.

Malawi’s lending rates and interest spreads are very high. Since 2012/13, the nominal lending rate and

interest rate spreads (gap between lending rates and deposit rates) have been widening. A number of key

variables have contributed to the spread and the persistently high lending rates, including: borrower credit

risk, high operating costs for banks and cost of funding as banks bid their large deposit base on high yielding

treasury bills. Malawian banks have high exposure to the public sector which not only crowds out the private

sector through heavy borrowing, but also exerts upward pressure on the interest rate (through high treasury

bill yields).

The country had neither a well-functioning credit reference nor collateral system for some time. In

2016, a collateral registry was established, and the credit reference system was strengthened, though these

had not yet been widely used to have an impact on interest rates. The pre-2016 collateral system placed

foreign owned firms at an advantage as they had larger immovable asset bases and were perceived less

risky.

2.2 AGRICULTURE AS THE MAIN SECTOR

Malawi’s economy is predominantly dependent on agriculture. The sector is dualistic in nature. It

comprises the smallholder subsector which is dominated by maize production, and the large-scale sector

focusing on the commercial agricultural cash crops such as tobacco, tea, and sugarcane. The agriculture

sector accounts for 30 percent of GDP, generates over 80 percent of export earnings, and accounts for over

64 percent of the country’s workforce9. Several challenges make it difficult for the manufacturing sector to

benefit fully from agriculture. These include: unpredictable agriculture output levels and poor quality and

standards, predominance of maize staple in the sector (lack of diversification), and impediments to the

manufacturing sector (details of these challenges will be discussed in subsequent chapters).

Exports are predominantly primary agricultural, including tobacco, tea and sugar. The overall trend

of external trade balance points to rapid growth of imports as compared to exports. The country’s imports

are dominated by petroleum products, fertilizers, and medicine. Tobacco dominates Malawi’s export basket

accounting for 60 percent in 2016.10 The country’s main export markets are in Europe, Asia, South Africa,

and more recently Zimbabwe and Mozambique.

2.3 FINANCIAL SECTOR

Malawi’s financial sector is small, shallow and commercial bank-dominated. When the enterprise

survey was conducted in 2014, the banking sector comprised 11 banks, one discount house and one leasing

company. Two banks, namely Malawi Savings Bank and Inde Bank, have since been taken over by FDH

Bank and National Bank respectively, and a new bank has been established. The rest of the banking sector

comprises two discount houses, one leasing company, four development finance institutions (DFIs), 22

microfinance institutions (MFIs), 41 savings and credit cooperative organizations (SACCOs), and a nascent

capital market. There is also a small life and general insurance subsector.

9 Government of Malawi, 2013, Malawi Labor Force Survey, National Statistics Office 10 ITC Trade Map. Available online at: http://www.trademap.org

18

http://www.trademap.org/

There are a number of challenges affecting the ability to borrow from financial institutions. The judicial

system does not seem able to provide full protection to the banks as it appears to be too easy to obtain court

injunctions stopping the financial institutions from enforcing loan recovery procedures in cases of default,

which makes financial institutions tighten their lending policies, thereby making it difficult for average

firms to qualify for credit.11 Loans in Malawi are sometimes construed to be free by borrowers especially in

cases where Government or development partners are involved or are deemed to be providing some form of

subsidy. Heavy default rates are also a result of unfairly high and non-transparent borrowing costs, with

some institutions charging 120 per cent interest rates per annum. There is currently no robust system of

disclosure of financial products by financial institutions.

Banking Sector

The country's major banks, National Bank and Standard Bank, account for over 50 percent of assets, deposits and capitalization, and command the majority of loan portfolio. See table 2 for details.

TABLE 2: BANKING SECTOR MARKET SHARE AS AT DECEMBER 2014

Assets Loans Deposits Capital

Name of Bank MK’ billion MK’ billion MK billion MK billion billion Share billion Share billion Share billion Share

National Bank of Malawi (NBM) 206.3 26.3% 80.7 25.7% 136.3 25.1% 41.7 29.4%

Standard Bank Malawi (STD Bank) 191.8 24.4% 57.6 18.3% 140.1 25.8% 37.0 26.1%

First Merchant Bank (FMB) 81.4 10.4% 31.7 10.1% 46.2 8.5% 22.7 16.0%

NBS Bank Limited (NBS) 75.3 9.6% 41.2 13.1% 49.1 9.0% 11.7 8.3%

Ecobank Malawi Limited (Ecobank) 50.3 6.4% 22.8 7.3% 34.0 6.3% 5.7 4.0%

Malawi Savings Bank (MSB) 49.8 6.3% 26.5 8.4% 40.9 7.5% 4.8 3.4%

First Discount House (FDH) 49.5 6.3% 21.2 6.7% 39.1 7.2% 7.4 5.2%

Investment and Development Bank (INDE Bank)

23.4 3.0% 13.3 4.2% 17.3 3.2% 1.7 1.2%

Opportunity Investment Bank of Malawi (OIBM)

21.5 2.7% 6.5 2.1% 12.1 2.2% 3.5 2.5%

Continental Discount House (CDH Bank)

19.1 2.4% 8.0 2.5% 14.6 2.7% 3.0 2.1%

NED Bank Malawi Limited (NED Bank)

16.5 2.1% 4.8 1.5% 12.8 2.4% 2.4 1.7%

Total 784.9 100% 314.3 100% 542.5 100% 141.6 100%

Source: Reserve Bank of Malawi (2014a)

Bank lending rates and credit risks to the financial system continue to be high in Malawi. Monetary

conditions remained tight with the bank rate at 25 percent for most of 2013. The rate increased to 27 percent

in November 2015. Commercial banks’ lending rates were as high as 42 per cent by December, 201612.

Credit risks to the financial system increased as non-performing assets in the banking system (as a ratio of

gross loans and leases) rose to 15.7 percent in March 2014 from 13.6 percent in September 2013. The

outturn was mostly due to the high inflation rates that prevailed in the economy. Inflation has been on a

downward trend from the second half of 2016, and this has led to the Reserve Bank of Malawi cutting the

bank rate from 27 per cent to 24 percent in November, 2016 and 22 percent in March, 2017.

11 This is a challenge that possibly requires stakeholder consultations between the Judiciary, the Executive and all relevant

stakeholders in the public and private sector. 12 http://www.rbm.mw/Statistics/BankRates

19

http://www.rbm.mw/Statistics/BankRates

Credit to the private sector continued to increase in 2013/14 in favor of the corporate sector. The

corporate sector (commercial and industrial) was the largest holder followed by credit to individuals as at

March 2014. Credit to the agriculture sector was the lowest as at March 2014.

FIGURE 3: HOLDINGS OF CREDIT BY PRIVATE SECTOR IN MALAWI

Source: Authors’ calculations based on Reserve Bank of Malawi June 2014 Financial Stability Report

Insurance and Pension

Insurance and pension sectors are growing but are not fully developed. Total insurance assets – both

general and life insurance - reached 9.7 percent of GDP in 2014, from 6.9 percent in 2010. Meanwhile,

following the adoption of the 2011 Pension Act, the total assets of the pension sector grew to 9.6 percent of

GDP in 2014 from 5.7 percent in 2010. Assets stood at MK365 billion in September 2016.13

E-Payment Innovations

There were some improvements in financial sector innovation. In February 2015, the Malawi National

Switch system was launched to facilitate inter-operability of ATMs and point of sale (POS) terminals in the

country. This was the third major national payments infrastructure improvement to be implemented in the

country following the Automated Transfer System (ATS) and Central Securities Depository (CSD) which

went live in December, 2014. Since the introduction of non-bank led mobile payments in Malawi, the

number of active subscribers to mobile payments has been growing. In terms of service distribution

channels, there was notable growth of mobile money agents, contributing considerably to financial

inclusion. Internet banking maintained an increasing trend and subscriber base for bank-led mobile payment

schemes continue to register increases reaching 520,959 by November, 2016. The volume of transactions

through this channel has significantly expanded.

Financial Inclusion

Financial inclusion improved in the period between 2008 and 2014. According to the 2008 and 2014

Malawi Finscope Survey report, the banked population in Malawi increased from 18.9 percent in 2008 to

33 percent in 2014 (by about 2,390,605). A number of initiatives were instituted to develop institutions that

13 https://www.rbm.mw/FinancialStability/FinancialStabilityReports/

20

http://www.rbm.mw/FinancialStability/FinancialStabilityReports/

would pursue the access to finance agenda. An example is the Export Development Fund, a development

financial institution established in 2012 whose mandate is to increase the productive potential of the country

through provision of finance, equity participation, performance bonds or guarantees, and advisory service

for the set-up, expansion and modernization of viable enterprises in the medium and large scale enterprises

sector. Beyond such kind of new initiatives, there has been a growth in the “village banking”, “mobile

banking” services and other new similar innovations with outreach to even the small scale enterprises and

the people living in rural remote areas of the country.

2.4 PRIVATE SECTOR

The Private Sector is characterized by a few large private firms and a large number of informal small

and micro enterprises. Large firms are often engaged in agriculture or agro-processing. The micro and

small enterprises are primarily informal and operate in the services sector, mainly in retail. The 2012 Malawi

FinScope MSME Survey estimated that there are almost a million MSMEs in the country, employing over

a million people in total, with their total income equivalent to a sum greater than 30 percent of the annual

GDP in 2011. Private Sector in Malawi is characterized by a “missing middle”. This absence of a larger

group of middle-sized firms points to the difficulties in the business environment, which constrains the

growth of small firms. Along with the difficult business environment, the risks that are brought about by

persistent instability in the macro-economy and regulatory deficiencies are more navigable by larger firms

due to their broad networks and larger financial resources.

Private Sector development is undermined by a number of significant challenges. Enterprises operate

under a challenging business environment such as macroeconomic instability, corruption (including on land

acquisition), inconsistent policies, and access to good markets. Other challenges are infrastructural such as

poor road networks, inadequate provision of electricity and water which further undermine private sector

development. Interest rates in the informal sector can go as high as 60 percent while prime lending rates are

still above 30 percent per annum making it prohibitive for the private sector to borrow, thus restricting

investment.

2.5 FIRM COMPETITIVENESS

While there have been some improvements recently according to the Doing Business (DB) reports,

the country’s ranking remains low. The DB ranking worsened consistently for five years reaching its

trough of 164 in 2014. According to DB17 Report, Malawi ranked low, mostly ranked outside the top 100

economies against all 10 indicators, including those affecting competitiveness: Starting a Business

(150/190), Trading across Borders (118/190), Paying Taxes (102/190), and Getting Electricity (169/190).

Overall, the country’s DB ranking has recently shown some improvement from 144 th in 2015 to 141st in

2016, and 133rd in 201714.

Malawi fares poorly on the competitiveness indicators and rankings. The Global Competitiveness Index

(GCI) placed Malawi at 135 out of 140 countries in 2015/16. The GCI attempts to quantify the impact of a

number of key factors which contribute to creating conditions for competitiveness, with particular emphasis

on the macroeconomic environment, the quality of the country’s institutions, and the state of the country’s

technology and supporting infrastructure. Unbundling the index reveals low scores by Malawi on basic

requirements for business competitiveness, including the macroeconomic environment and other efficiency

enhancers such as education standards, market size and efficiency, financial markets, innovation and

technological readiness.

14 http://www.doingbusiness.org/data/exploreeconomies/malawi/

21

http://www.doingbusiness.org/data/exploreeconomies/malawi/

Power lines in Kanengo, Lilongwe

2.6 INFRASTRUCTURE

Electricity

In 2014, Malawi enterprises experienced one of the worst electricity crisis in years, characterized by a

widespread national load-shedding program. At that point, the country had a total installed hydropower

capacity of 351MW, generated from the Shire River. The peak demand was estimated to exceed 400 MW,

and the deficit caused chronic shortage of power and increased frequency of unscheduled outages. The

challenge of prolonged load-shedding continued through 2015 and 2016 due to low water levels in the Shire

River because of severe droughts experienced in those years – making evident the continued vulnerability

of Malawi’s energy security to weather shocks. To date, only nine percent of the population has access to

electricity. For the 80 percent of the population who live in rural areas, the access rate is limited to only

about four percent. Until recently, the Electricity Supply Corporation of Malawi (ESCOM), was the only

Government-owned electricity utility that generated, transmitted and distributed power. By 2015, the

customer base was about 312,857 (from about 200,000 in 2014), while the installed generation capacity

remained at 351MW, against a demand potential around 440 MW.

The energy sector has faced numerous other challenges, apart from limited investment in generation and

reliance on one river as a source15, including inadequate transmission and distribution networks and

relatively low tariffs that affect the economic viability of other sources of energy. These tariffs together

with uncertainties associated with the regulation and governance of the energy and electricity sector –

including limited autonomy of utility operations and the energy regulator from Government - have been

limiting factors for reinvestment in the network and undertaking new investments in the sector.

A number of reforms have been implemented in the energy sector. The power system in Malawi until

recently has been vertically integrated with ESCOM being the only utility in charge of generation,

transmission, distribution and sale of electricity. The Electricity (Amendment) Act was passed in 2016 to

allow for the involvement of the private sector in generation. The new law also created a single buyer and

a system and market operations segment in the power sector. This resulted in the unbundling of ESCOM

into two segments i.e. Electricity Generation Company (EGENCO) to handle generation and ESCOM to

handle transmission and distribution.

Water

Malawi enterprises face challenges in the provision of water. Per capita water availability has been

declining at a rapid rate due to population growth which comes with increased demand. Further, water

resources in Malawi are highly variable between wet and dry seasons and from year to year, and the

country’s stock of water storage infrastructure is one of the lowest in the region. There is inequitable water

supply coverage especially in rural areas, as well as non- functionality of water points. In the cities and

towns, water supply is characterized by intermittent and unreliable supply.

Some of the key issues and challenges in the sector include: (i) increasing climatic and hydrological

variability and limited resilience to floods and droughts; (ii) limited stock of water storage and irrigation

infrastructure; (iii) degradation of watersheds leading to increased soil erosion and sedimentation; (iv)

inefficient water utilities that lack autonomy and accountability; and (v) limited maintenance of distribution

infrastructure resulting in significantly high leakage rates.

Transport

15 Malawi is still not connected to the Southern Africa Power Pool (SAPP), therefore not yet able to trade electricity through the

SAPP.

23

Being landlocked, the transport sector plays a key role in the country- and firm-level competitiveness.

Producers in Malawi face high trade and transport costs in sourcing inputs and in delivering outputs

to domestic, regional, and international markets. Road transport, which is comparatively more expensive

than other modes of transport such as rail and water, accounts for 70 percent of all freight, and 99 percent

of all passengers. The overall quality of the road network, particularly at the secondary and tertiary level,

and connectivity to some rural areas is poor, further increasing challenges of accessibility to markets.

There have been efforts to develop other modes of transport in recent years. For example, the rail route

from Tete province in Mozambique, through Malawi, to Nacala port has been rehabilitated. The full

potential benefits for Malawi in terms of reduced import and export costs by utilization of this railway line

is yet to be realized in part due to the absence of adequate port handling infrastructure in key production

areas connected to the railway such as Lilongwe and Blantyre. The southern-most leg of the rail network as

well as the connection to the central region of the country and to Zambia is not functional.

24

CHAPTER 3: ENTERPRISE PERFORMANCE: HOW DOES FIRM LEVEL PRODUCTIVITY IN MALAWI

COMPARE WITH OTHER COUNTRIES AND ACROSS DIFFERENT CATEGORIES?

This chapter looks at measures of firm-level productivity in Malawi, starting with comparisons on partial

productivity, labor productivity, capital intensity, capital productivity and total factor productivity. The

analysis focuses only on manufacturing firms interviewed in the Enterprise Survey (i.e. 197 out of 523 firms

surveyed). This is the case in order to draw more accurate findings on the factors of production and to also

draw more accurate comparisons of firms across the country and in comparator countries (details on this and

the calculation of the productivity measures are provided in Annexes 1,2 and 3). Regression analysis was

used to determine productivity levels in Malawi with respect to labor, capital and total factor productivity,

as well as labor costs and growth in employment; and the extent to which the investment climate constraints

identified in the enterprise survey affect total factor productivity.

Source: Authors’ calculation based on data from World Bank Enterprise Surveys (WBES)

Note: The country estimates are from the LAD regressions.

Notes:

1. Partial productivity measures are measured in 2010 US$. See annexes, 1,2,3 for details. All data points are for the median firm on each

measure of performance.

2. For Figure 9.4, Country estimates are from the LAD regression

3. Comparisons made were for median firms in terms of the different measures of performance. Medians are preferred to means because means

can be affected greatly by outliers—possibly due to misreported or miscoded information.

25

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3.1 PARTIAL PRODUCTIVITY MEASURES: DEFINITIONS AND CROSS COUNTRY COMPARISON

Labor Productivity

Labor productivity is defined as the rate of output or value added per worker (or a group of workers)

per unit of time. It is calculated by subtracting raw materials and intermediate input from sales and dividing

by number of workers, and tends to be higher where firms substitute capital for labor. Although in theory

this is only a partial measure, and does not take into account capital contribution, it does provide useful

information and is easy to calculate. Labor productivity is considered higher for firms that produce more

output with less raw materials and fewer workers (or where firms substitute more capital for labor). This

reflects better worker skills, management ability, and use of technology. Figure 4 compares labor

productivity for Malawi and other countries. Labor productivity tends to be higher in countries with higher

per capita income.

Analysis shows that Malawi has higher labor productivity in comparison to some of the countries in

the region. The country’s labor productivity is higher than neighboring countries - Mozambique, Tanzania and Zambia, but it lags behind the levels of other countries such as South Africa, Zimbabwe and Kenya. At

$5,100, Malawi’s labor productivity is higher than would be expected given the country’s low level of per

capita income. One of the weaknesses of labor productivity as a measure of firm productivity is that it does

not take into account the use of capital, i.e. equipment and machinery.

Capital Intensity

Capital intensity is a measure of the relative use of capital, compared to other factors such as labor,

in a production process. It is defined as the amount of fixed or real capital present in relation to other

factors of production. Sometimes capital intensity is also defined as a measure of a firm's efficiency in

deployment of its assets, computed as a ratio of the total value of assets to sales revenue. This analysis

considers capital intensity as capital to labor ratio, measured as the value of the firms’ machinery and

equipment divided by number of workers.

Median firms in Malawi appear relatively more capital intensive than some of the other countries in

the region. According to this analysis, firms in Malawi are relatively more capital intensive, in comparison

to similar firms in neighboring countries (Tanzania, Mozambique and Zambia) (See Figure 5 and detailed

explanations in Annex 2). For Malawi’s case, deeper analysis shows that only large firms appear to be

particularly capital intensive, over four times the capital intensity of SMEs.

Just like the case of labor productivity, capital intensity is higher in Malawi than other countries at similar levels of development. Comparing Malawi with a broader group of countries at similar levels of

development, the median firm in Malawi appears to be more capital intensive than the median firms in the

other countries (at similar levels of development).

Capital Productivity

From the analysis above, both capital intensity and labor productivity are relatively high in Malawi,

but it would be necessary to look at the value added of machinery and equipment. Capital productivity

measures the ratio of value added to the value of machinery and equipment. It is the degree to which physical

capital is used to provide goods and services. Capital productivity is high when firms produce more output

with only a small amount of machinery and equipment.

Analysis shows that capital productivity is low in Malawi. For the median firm in Malawi, capital

productivity is about 115 percent, which is lower than in any of the comparator countries (see Figure 7).

26

Although both capital intensity and labor productivity are relatively high, capital productivity is not,

implying that Malawi’s firms produce less output for the available machinery and equipment, that firms use

capital inefficiently, or that firms are highly labor intensive. Enterprises in Malawi seem to fall under these

explanations. This points to the need to improve usage of more efficient technology, as well as more

technical skilled labor.

3.2 PARTIAL PRODUCTIVITY MEASURES FOR DIFFERENT CATEGORIES OF FIRMS ACCORDING TO SIZE,

LOCATION, DOMESTIC OR EXPORT ORIENTED, LOCAL OR FOREIGN OWNED AND WHETHER OR NOT CREDIT

CONSTRAINED

This section presents a further breakdown of partial productivity measures for various groups of firms with

sufficient data including firm size, location, whether the firm is an exporter or domestically or foreign

owned and whether the firm is credit concerned. It was noted earlier on that the sample of manufacturing

firms that actually provided performance data in Malawi is small (197 out of 523 firms surveyed). As a

result, only a limited number of breakdowns will be provided.16

The labor productivity and capital intensity results for these categories are presented in table 3 below. This

disaggregation makes clearer the main sources of the high productivity noted in section 3.1 above.17 The

table also includes a disaggregation across the categories for unit labor costs and labor costs per worker,

which are discussed in section 3.4.

TABLE 3: BREAKDOWN OF PARTIAL PRODUCTIVITY DATA BY FIRM TYPE

Number of Workers

Labor Productivity

Unit Labor Costs

Labor Costs per Worker

Capital Intensity

Small (5-19 workers) 10 $4,774 18% $1,071 $2,082

Medium (20-99 workers) 41 $2,905 25% $1,257 $1,487

Large (100 workers and up)

200 $8,855 16% $1,115 $9,700

Blantyre 20 $8,855 15% $1,190 $2,082

Lilongwe 19 $4,417 17% $1,071 $2,844

Non-exporter 18 $4,417 16% $1,071 $2,082

Exporter 161 $10,632 18% $1,741 $9,700

Domestic 15 $3,844 19% $1,062 $2,082

Foreign 150 $10,297 16% $1,611 $7,808

Credit constrained 18 $4,601 17% $1,171 $2,082

Not credit constrained 100 $7,928 12% $1,071 $4,183

Note: All measures are described in Annex 1. Groups with less than 10 observations are excluded from the table.


16 Further breakdowns with respect to manufacturing sub-categories are not possible at this point due to the small number of

respondent firms in the respective sub-categories covered by the Enterprise Survey. 17 It will be noted that it is not possible to disaggregate the data further given the small number of firms in the different sub-

sectors in manufacturing.

27

Partial Productivity with respect to Firm Sizes

Larger firms in Malawi are on the whole more productive than small firms. Overall, labor productivity

and capital intensity are significantly high among large firms, followed by small firms and medium sized

firms (see Table 3 and Figure 8). The fact that large firms use much more capital intensive relative to other

factors of production than smaller and medium firms is perhaps unsurprising. Interesting however, is the

indication that medium sized firms are less capital intensive than small firms. This may mean they use more

labor productive relative to capital productive, but it is noted that their labor costs per worker are also higher.

The trend is also noted for labor productivity, which is lower for medium sized firms than small firms, and

unit labor costs, which are higher for medium sized firms than they are for small firms and large firms,

respectively. Higher labor costs are normally indicative of workers with higher skill levels being hired.

Taken together, therefore, the above scenario of low labor productivity among medium sized firms may

point at relevant skills not being available to them, at the same time that they use less capital relative to

labor. These challenges in factors of production certainly reflect the “missing middle” that was highlighted

in section 2.4, and require further interrogation to ensure the growth in medium sized firms.

Of concern is the illustration in Figure 8, that labor productivity of medium-sized firms is less than that of

both large and small firms. This is consistent with the “missing middle’ of firms highlighted in section 2.4.

It may point to demand-side constraints on labor and needs to be interrogated further as it has implications

on job creation.

With regard to labor costs (see section 3.4), evidence from World Bank investment climate assessments

generally shows that in most countries there is a gap in productivity between large and small firms, and that

large firms also pay their workers more than small firms. For example, in Zambia, labor costs per worker

are close to three times greater for large firms than they are for small firms. This is not the case in Malawi.

Both large and small firms report labor costs close to US$1,000 per worker. This suggests that large firms

are not passing the benefits of higher productivity to workers or are not hiring more skilled or better

educated workers than small firms are.

FIGURE 8: LARGE FIRMS ARE MORE LABOR PRODUCTIVE AND CAPITAL INTENSIVE


Note: Partial productivity measures are measured in 2010 US$. See Annex for details. All data points are for the median firm on each


28

$10,000

$8,000

$6,000

$4,000

$2,000

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With respect to Location (Lilongwe and Blantyre)

There are some notable differences in productivity across Malawi, with labor productivity much

higher in Blantyre compared to Lilongwe (see Figure 9). The median firm in Blantyre produces about

US$8,855 of output per worker. In comparison, the median firm in Lilongwe produces only US$5,112 per

worker. On the other hand, firms in Lilongwe are more capital intensive than those in Blantyre. Whereas

the median firm in Blantyre has US$ 2,082 of capital per worker, the median firm in Lilongwe has US$

2,844 per worker. Unit labor costs are relatively similar at 15% and 17%, respectively (see table 3 above).

FIGURE 9: BLANTYRE FIRMS ARE MORE PRODUCTIVE


Note: Partial productivity measures are measured in 2010 US$. See Annex for details. All data points are for the median firm on each


With respect to whether the firms engage in exports or produce for domestic market

Firms that are export oriented are by and large more productive than those that are domestic

oriented. A possible reason for higher productivity among firms that are export oriented is that these firms concentrate in capital intensive high skill sectors or that they have larger markets to work with and hence

larger revenues for the same amount of labor. The median firm that is export oriented produces about

$10,632 per worker, compared to US$4,417 per worker for a median firm that is not export oriented. While

labor unit costs for the two categories are not too far apart - at US$1,000 and US$1,700 - the main difference

between them is capital intensity. The export oriented firms use much more capital relative to other factors

of production such as labor as compared to domestically oriented firms. (see Table 3).

With respect to Credit Constraints

Firms that have less credit constraints are more productive than those that are credit constrained. The reason seems to be that firms in this category have resources that make them access efficient machinery

and are able to attain higher levels of operational efficiency, including getting more from the labor they

employ. The median firm is more capital intensive, producing double output per available capital compared

29

Labor productivity Capital intensity Labor Costs

Lilongwe All firms Blantyre

$2,000

$1,000

$0

$2,082 $3,000

$2,844 $2,844 $4,000

$4,417 $5,000

$5,112

$9,000

$8,000

$7,000

$6,000

$8,855

to those that are credit constrained. Their labor productivity is much higher at US$7,928 per worker,

compared to US$4,601 per worker for those firms that are credit constrained. (see Table 3).

With respect to foreign or domestic ownership

Foreign owned firms in Malawi tend to be more productive as they use labor and available capital

more efficiently. In this analysis, the median foreign owned firm produced US$10,297 per worker

compared to US$3,844 for the locally owned median firm. Similarly, a median foreign owned firm uses

relatively more at US$ 7,808 per worker compared to US$2,082 for a locally owned median firm. These

foreign owned firms are predominantly large scale, export oriented, in large cities such as Blantyre, and

have better access to financing for their operations.

3.3 TOTAL FACTOR PRODUCTIVITY (TFP): CROSS COUNTRY ANALYSIS AND COMPARISON WITH

RESPECT TO LOCATION IN MALAWI

Partial productivity analysis can be misleading if considered in isolation. A firm could have high labor

productivity because it is efficient, but it could also have high labor productivity simply because it uses a

capital intensive production process. When multiple inputs are considered, such as labor and capital, the

unaccounted for level of output compared to the level of inputs is commonly referred to as the Total Factor

Productivity (TFP) or Multi Factor Productivity (MFP). TFP is calculated using regression analysis (For

details about calculation and estimation of TFP, see Annex 3). In this analysis, results (in comparison to

other countries) are presented as a percentage in relation to Malawi. When the median firm’s TFP is higher

in a country than in Malawi, TFP will be greater than 100 percent. When it is lower, TFP will be less than

100 percent. In theory, firms with higher TFP are more efficient because they produce higher output with

fewer inputs. TFP measures how productive firms are based on technological progress, market structure

and institutional effects, or other residual factors, after taking into account the firms’ use of both capital and

labor.18 Typically, TFP would be higher when workers are better educated, more highly skilled, when

managers are more efficient and when firms use advanced technology.

Cross Country Analysis

Analysis suggests that TFP is lower in Malawi than in most of the comparator countries. Although

TFP is slightly higher for the median firm in Malawi than in Mozambique or Tanzania (57 and 77 percent

of TFP in Malawi – see Figure 8), it is lower than in the other regional comparator countries. Although

labor productivity in Malawi is higher than in Zambia, TFP is lower. This suggests more capital intensity

in Malawi for example, than in firms in Zambia and Tanzania. Malawi’s reliance on tobacco processing

might play a role, as that dominating sector is highly capital intensive. This might also be a reflection of

Malawi’s low per capita income19. As TFP tends to be higher in countries where per capita income is greater,

it is not surprising that TFP is relatively low in Malawi. Malawi might only compare favorably with other

countries at similar levels of development.

18 See van Ark, B. (2014). Total Factor Productivity – Lessons from the Past and Directions for the Future. [Online. Available at:

https://www.nbb.be/doc/ts/publications/wp/wp271en.pdf 19 Malawi’s per capita GNI was only $815 in purchasing power parity (PPP) US$ in 2014 (World Bank, 2015). This is lower

than in all but three of the 188 countries where the World Bank has computed per capita GNI.

30

http://www.nbb.be/doc/ts/publications/wp/wp271en.pdf

TFP with respect to Location

A comparison is now drawn on TFP in different locations in Malawi using the same basic approach as was

done to compare Malawi with other countries. Because the sample of manufacturing firms is relatively

modest in Malawi—and because many manufacturing firms did not answer enough questions to calculate

TFP — we calculate median levels of TFP not according to firm size but only with respect to three regions:

Blantyre, Lilongwe and the rest of the country.20

Source: Authors’ calculation based on data from World Bank Enterprise Surveys (WBES)

Note: The country estimates are from the LAD regressions.

According to the TFP results above, firms in Blantyre are more productive than firms in other regions

of the country. The median firm in Blantyre is about 41 percent more productive than the median firm in

Lilongwe and about 31 percent more productive than the median firm in other regions. As the commercial

hub of the country, Blantyre is home to a significant number of manufacturing firms. The higher output per

worker could be reflective of competitive factor markets in which Blantyre participates, as well as its

proximity to Malawi’s main trade routes through Mozambique and South Africa and hence greater access

to raw materials and output markets in Malawi and outside.

3.4 LABOR COSTS AND PRODUCTIVITY: COUNTRY COMPARISON AND WITH RESPECT TO

LOCATION AND FIRM SIZE

Although TFP provides some useful incites about the competitiveness of firms relative to firms in

other developing economies, it can be misleading when considered in isolation. One problem with TFP

analysis is that it does not distinguish between highly skilled and unskilled workers. Productivity and wages

will mostly be higher when firms employ highly skilled workers. However, firms can also remain

competitive when productivity is low if wages are comparatively lower. For this reason, it would be useful

to look at the cost and growth of labor as well as TFP when assessing competitiveness. The next sections

therefore also consider labor costs, unit labor costs, and employment growth to assess competitiveness of

enterprises in Malawi.

20 There were about 176 firms that were given the manufacturing survey. Many of these firms, however, were outside of

manufacturing according to the ISIC codes that they gave during the interview. This can often happen when service or retail

firms have some manufacturing (e.g. bakers). In total, of the 176 firms that were given the manufacturing survey, over half

reported being in areas outside of manufacturing. In comparison, less than 2 percent of firms given the manufacturing survey in

Zambia reported being in sectors outside of manufacturing. As a result, only 62 manufacturing firms answered enough questions

to calculate TFP and reported doing business in areas that are considered manufacturing.

31

MED

IAN

TE

REL

ATI

VE

TO

BLA

NTY

RE

59

%

69

%

10

0%

Steelworks production in a local firm

Labor costs in Malawi are relatively high. Per worker labor costs are measured by dividing total

expenditures on wages, salaries, and other benefits by the number of workers. The measure reflects

payments to all workers including professionals and managers—not just production workers. For Malawi,

they are estimated at US$1,071, which like labor productivity is higher than in neighboring Mozambique,

Tanzania and Zambia, but less than Kenya, Zimbabwe and South Africa. As noted in table 3 and in the

foregoing discussion, labor costs are highest among medium firms, followed by large and small firms. This

suggests that large firms are not passing the benefits of higher productivity to workers and probably are not

hiring more skilled or better educated workers than small firms are. This situation is not encouraging for

the growth of small enterprises that are in majority in Malawi.

Source:

Authors’ calculation based on data from World Bank Enterprise Surveys (WBES)

Labor Costs and Productivity

Unit labor costs allow for comparison of labor productivity and labor costs and how they might affect

competitiveness. Unit worker labor costs are measured by dividing labor costs by unit of value added.

Firms with high labor costs can still be competitive if they have relatively higher rates of labor productivity.

On the other hand, unit labor costs are higher when high labor costs are not fully reflected in high

productivity. When this is the case, firms will find it difficult to compete on international markets, all else

equal.

33

3% 3% 5%

13%

16% 20%

15%

10%

5%

0%

Notes:

1. Partial productivity measures are measured in

2010 US$. See Annex for details. All data points

are for the median firm on each measure of

performance

2. Unit labor costs are the ratio of labor costs to

labor productivity. See Annex for details

3. Means not medians. See Annex for details on calculations.

12% 17% 21% 27% 30%

45% 49% 60%

40%

20%

0% $871 $750 $913 $1,071$1,561

$3,900

$9,673 $12,000 $10,000

$8,000 $6,000 $4,000 $2,000

Ke

nya

Mala

wi

Zam

bia

Zimb

ab

we

Ta

nza

nia

Sou

th A

frica

Mo

zam

biq

ue

Sou

th A

frica

Zimb

ab

we

Zimb

ab

we

K

en

ya

Ke

nya

M

alaw

i

Mala

wi

Zam

bia

Zam

bia

Mo

zam

biq

ue

Ta

nza

nia

Sou

th A

frica

Mo

zam

biq

ue

Ta

nza

nia

Labor Costs Across the country and cities

Unit costs in Malawi are relatively low. They are lower than most of the comparable countries, except

Kenya (see figure 10). In countries where labor costs are lower than in Malawi, such as Tanzania and

Mozambique, labor productivity is also lower. This suggests that Malawian firms might be able to remain

competitive even though labor and total factor productivity is lower than in the best performing comparator

economies. Although low salaries are not good for workers, they do allow firms to remain competitive when

productivity is low, and can offset higher costs in other areas that are hard to reduce (such as the naturally

higher transport costs associated with manufacturing in a landlocked economy).

3.5 EMPLOYMENT GROWTH

Employment growth has been relatively modest in recent years in Malawi. Employment growth in the

country averaged about 3 percent in the three years prior to the survey. This was lower than in any of the

comparator countries except Zimbabwe, where employment growth was highly negative in the three years

before that country’s 2011 survey. The status of job creation in and employment in respective sectors,

challenges and suggested pathways are discussed in more detail in the 2017 Country Economic

Memorandum for Malawi. In terms of demand, growth in manufacturing can create substantial scope for

increased job creation. As different sectors grow and particularly as Malawi moves into production of higher

value added goods and services, skills development that respond to current and emerging sectors is critical.

3.6 TOTAL FACTOR PRODUCTIVITY AND INVESTMENT CLIMATE

An interesting question is how much TFP would improve if Malawi’s investment climate was more

favorable. In this analysis, as indicated earlier, standard regression analysis is utilized to gain insights into

this question. See Annex 3 for details on the methodology.

Malawi’s investment climate is somewhat favorable, particularly after taking into account Malawi’s

low per capita income - many investment climate variables tend to be consistently lower in poorer

countries. The regression results (see table 4) illustrates that in the median country, firms spend less on

bribes, are more likely to have bank credit and to have training programs, are more likely to license foreign

technologies and are more likely to be foreign-owned than domestic firms in the median country.

Comparisons are drawn with firms in Malawi at those in the 20th percentile (better than the median). Malawi

does not compare favorably on a number of the measures, such as time spent dealing with regulation, losses

due to crime and power outages, firms with bank credit and training programs and those likely to be

exporters. This reaffirms some of the challenges that will be identified in chapters 4 to 6, which were based

on the perceptions of firms. The gains that would accrue if Malawi improved towards levels of the median

country and those in the 20th percentile, indicate the extent to which they affect TFP and would result in its

increase if addressed.

Firms’ TFP would increase only modestly if Malawi’s investment climate was as favorable as in the

median country21 in all areas where Malawi’s performance does not already exceed the median country’s

performance. The estimates suggest that if Malawi improved along all dimensions, average TFP would be

about 10.4 percent higher than it is currently. The biggest gain would come from reducing crime—if losses

due to crime were the same as in the median country, where TFP would be about 5.1 percent higher. Most

of the other increases would be relatively modest.

21 Given that Malawi’s investment climate compares favorably with the median country’s investment climate, it is not surprising

that the median manufacturing

34

TABLE 4: TFP AND IMPROVEMENT IN THE INVESTMENT CLIMATE

Malawi Country

at 20th

percentile

Median

Country

TFP Improvement

if Malawi

improved to

20th

percentile

TFP Improvement if

Malawi

improved to

median

Time dealing with regulation (%) 11.31 5.09 9.41 1.3% 0.4%

Bribes (% of sales) 0.01 0.00 0.01 0.0% ---

Losses to crime (% of sales) 1.85 0.13 0.35 5.8% 5.1%

Losses due to power outages (% of sales) 0.78 0.01 0.11 1.7% 1.5%

% of firms with bank credit 49% 78% 60% 3.3% 1.2%

% of firms with own website 48% 62% 34% 4.5% ---

% of firms that license foreign technology

26% 21% 14% --- ---

% of firms with training programs 23% 49% 32% 3.3% 1.1%

Firm is an exporter (dummy) 18% 45% 24% 5.2% 1.0%

Firm is foreign-owned (dummy) 29% 17% 8% --- ---

Total 25.1% 10.4% Source: Authors’ calculations based on World Bank Enterprise Survey data.

Note: For variables that are entitled “% of firms” or “dummy variable”, the averages are weighted averages for manufacturing firms in the country.

For the other (continuous) variables, the numbers are the weighted average response for manufacturing firms in the country. The median country

(country at 20th percentile) is an unweighted median (country at 20th percentile) of country averages for that variable. Outliers more than 3 standard

deviations are dropped for the averages for continuous variables with no upper bound (bribes, losses due to crime and losses due to power outages).

See text for additional notes.

If Malawi’s investment climate was equal to the best performing countries ranked in the 20 th

percentile on each dimension, TFP would increase significantly by about 25.1 percent. In addition to

the large gains from reducing crime, there would be additional gains from firms engaging more in exports

(5.2 percent), increasing website use (4.5 percent), increasing access to bank credit (3.3 percent), increasing

training programs (3.3 percent), decreasing power outages (1.7 percent) and time spent dealing with

regulation (1.3 percent).

3.7 POLICY IMPLICATIONS OF PRODUCTIVITY MEASURES

The foregoing discussion on productivity measures in Malawi highlights a number of implications for

policy design and implementation, particularly with regard to developing the manufacturing sector:

(a) Malawi’s higher labor productivity figures and lower unit labor costs as compared to its

neighboring countries makes it competitive in attracting and channeling foreign and domestic investment

into labor-intensive manufacturing industries. This is important for positioning Malawi to increase

investment inflows vis-à-vis its neighboring countries, as well as address its low employment growth as

highlighted in 8.3 above. While doing this, labor productivity needs to be further enhanced, particularly for

medium-sized firms, as does their use of required capital.

(b) The low capital productivity rates for Malawi as compared to all other countries in the region need

to be addressed. The challenges in production due to low supply of electricity could be one way of ensuring

greater utilization of capital. Further, increased earnings of income by individuals can create domestic

demand for greater utilization of capital. Flowing from (1) above, an increase in labor-intensive industries

can create jobs that can result in such increased incomes, thereby positively reinforcing the capital

35

utilization for domestic markets. Market access to regional and global markets can also enhance capital

utilization as more goods are produced for those markets, of course along with the addressing of other

supply-side constraints to production in Malawi.

(c) After considering labor and capital, the TFP measure is one that requires attention if long term

growth is to be sustained. As noted, TFP is only slightly higher than in Mozambique and Tanzania, but

mostly lower than other regional comparator countries. As elucidated in Annex 4, there are a number of

factors pertaining to the environment for investment that can be addressed to enhance TFP. These include:

▪ Intangible investments such as education and skills, research and development (R&D), patents,

licenses, organizational change and product marketing;

▪ Skills, motivation and competencies – in part affected by intangible investments above;

▪ Innovation and technological change – in part also affected by the intangible investments above; and

▪ Markets, institutions and regulations.

It is important that Malawi’s innovation ecosystem be effectively developed, coupled with frameworks that

will encourage high levels of diffusion of technology. In this regard, R&D, education and skills

development need to be emphasized, particularly as they have constrained productivity, growth and

competitiveness thus far and are critical to enhancing it going forward. At the operational level, challenges

have been noted in sectors such as agri-business, where progress has been constrained by lack of

entrepreneurial skills, business organization, financial literacy, and other cognitive skills resulting from

high illiteracy levels. Outside agriculture, skills development also has to respond to emerging sectors of

high value added goods as alluded to in section 3.3 above. Progress in these areas needs to take into account

the differences in productivity identified with respect to location as well as difficulties encountered

according to gender of managers of firms highlighted in chapters 4 to 6.

Specifically, drawing from the regression results presented in Table 4 above, firms are more productive

when they spend less time dealing with regulation, have smaller losses due to power outages and crime, pay

less bribes, have better access to credit and training programs and engage more in exports. These issues need

to be addressed to increase productivity levels.

On the whole, it will be noted that the favorable levels of productivity mostly reflected the position of large

firms, which as mentioned earlier, are more able to navigate risks brought about by a difficult business

environment. Efforts to raise productivity levels should, therefore, focus on facilitating the growth of small

and medium sized firms.

The next chapters explore the challenges to firms as reported in the enterprise surveys in more detail,

highlight some efforts currently underway in different areas, and make policy recommendations for further

improvement.

36

A bank pafupi customer

CHAPTER 4: ENTERPRISE CONSTRAINTS: WHAT DO FIRMS IN MALAWI VIEW AS

THE MAJOR CHALLENGES TO ENTERPRISE DEVELOPMENT AND GROWTH?

The most frequently cited obstacle to business enterprises in Malawi is financing. This was identified

as a major obstacle in the 2014 enterprise survey followed by electricity, corruption and tax rates. It was

also cited as biggest obstacle in 2008 enterprise survey.

FIGURE 13: MAJOR OBSTACLES IDENTIFIED IN PERCENTAGE SHARES


4.1 TOP THREE OBSTACLES BY FIRM SIZE, BUSINESS SECTOR, LOCATION AND GENDER

PERSPECTIVE

Enterprises in Malawi experience serious challenges to access financing. 29.9 percent of surveyed

enterprises identified access to finance as the top most obstacle in the 2014 enterprise survey (was 45.7 per

cent in 2008). A large group of respondents from the manufacturing sector (35.5 percent), identified this as

the top most obstacle compared to 25.9 percent and 27.1 percent from retail and “other” services sectors,

respectively. In terms of size of firms, a bigger proportion (37.3 per cent) of small scale enterprises

perceived this as top most obstacle compared to medium scale (22.1 per cent) and large scale (17.1 per cent)

enterprises. A bigger proportion (38.6 percent) were from firms that had female top management, compared

to 28.7 percent from those with male top management. The majority of the firms were those that were

producing goods primarily for the domestic markets.

38

TABLE 5: THREE BIGGEST OBSTACLES TO ESTABLISHMENTS


Access to Finance was a major concern in all three regions of the country. Between 17.6 and 37.8

percent of firms in the districts across the country indicated that this is a big obstacle for their firms. Most

of those who held this view however were from cities of Zomba and Blantyre, and with a slightly lower

percentage from Lilongwe.

Electricity was the second biggest obstacle, and a major challenge especially to large enterprises.

About 14.2 percent of enterprises in the 2014 survey indicated that electricity is the biggest obstacle that

they faced. Large-scale firms (about 31.6 percent) reported this as their biggest obstacle, compared to 13.8

percent by medium scale enterprises and 9.2 percent by small scale enterprises. This points to some

correlation between the size of firms and the electricity dependence in the firms’ operations, and hence the

larger firms felt the impact more. Most of the respondents who cited electricity as the biggest obstacle are

based in the cities of Lilongwe, Blantyre and Zomba, other than smaller urban towns such as Mangochi and

Mzimba. Although firms in all regions were concerned about electricity, quite a significant proportion (20.3

percent) were from Lilongwe.

Corruption was cited as the third biggest obstacle, and a major challenge mostly faced by the retail

firms. Overall, 10.7 percent of enterprises indicated that this is the biggest obstacle they faced. About 12.0

percent and 12.1 percent of both the small and large-scale firms respectively, reported this as the biggest

obstacle to their businesses, while only 7.2 percent of medium-sized firms cited corruption as the biggest

obstacle. Corruption was reported as the biggest obstacle particularly by retail firms (18.9 percent), followed

by services firms (13.1 percent), and manufacturing firms (6.1 percent). Lilongwe firms cited corruption as

the biggest obstacle in comparison with Blantyre and other places. The problem seems to be more prevalent

in cities other than the rural urban areas.

39

Access to

Finance

Electricity Corruption

All 29.9 14.2 10.7

Business Sector

Manufacturing 35.5 17.0 6.1

Services 27.1 12.7 13.1

Retail 25.9 9.3 18.9

Other Services 27.7 14.7 9.9

Firm Size

Small 37.3 9.2 12.0

Medium 22.1 13.8 7.2

Large 17.1 31.6 12.1

Location

Blantyre 35.6 10.3 9.2

Lilongwe 21.7 20.3 14.4

Mangochi 13.9 18.4 0

Mzimba 17.6 7.5 4.0

Zomba 38.0 17.7 0

Status

Exporters 19.8 19.7 18.1

Non exporters 28.4 14.3 11.5

Gender of Top Manager

Top Manager, Female 38.6 11.4 0.2

Top Manager, Male 28.7 14.8 12.6

Ownership

Domestic 37.5 8.3 11.4

More foreign 11.5 37.5 10.5

4.2 COMPARISON WITH 2008 SURVEY

Access to finance was the most commonly cited concern in both the 2008 and the 2014 enterprise

surveys. About 45.7 percent of firms reported that access to finance is the biggest constraint in the 2008

survey, but this reduced to 29.9 percent in the 2014. In both surveys, there is a large gap between access to finance and the next obstacle. This gap, however, narrowed, in the 2014 survey findings, reflecting a

decrease in perception of access to finance as a major obstacle and an increased concern for power outages.

FIGURE 14: TOP TEN BIGGEST OBSTACLES TO ENTERPRISES IN MALAWI, 2008 AND 2014


In the 2014 enterprise survey, the second on the list as top most obstacle was electricity outages, while

in 2008, transportation was second. 14.2 percent of firms view power outages as the biggest challenge in

2014. This represents an increase from the 8.6 percent in 2008. The proportion of firms that cited

transportation as their biggest obstacle reduced from 11.4 percent in 2008 to 2.7 percent in 2014. This is the

most notable difference in the two survey results and may be due to progressive improvements in the quality

of both national and regional transport infrastructure over the years.

Corruption has been on the rise in the country and was significantly reported to be rampant at the time of the Enterprise Survey. This became third on the list of obstacles to enterprises in 2014, replacing

practices of the informal sector, which was third on the list of obstacles in 2008. 10.7 percent of respondents chose corruption as the top most obstacle, up from a much less prominent 2.7 percent in the 2008 survey.

40

Access to finance

Electricity

Corruption

Tax rates

Practices of the informal sector

Access to land

Political instability

Business licensing and permits

Crime, theft and disorder

Transportation

29.9 45.7 14.2

10.7

10.1

7.6

7.1

4.2

3.6

3.2

2.7

0.0 10.0 20.0 30.0 40.0 50.0

Malawi 2014 Malawi 2008

4.3 CROSS COUNTRY COMPARISON ON TOP MOST OBSTACLES

TABLE 6: ACCESS TO FINANCE, ELECTRICITY AND CORRUPTION


FIGURE 15: TOP THREE OBSTACLES - COUNTRY COMPARISON

Evidence from enterprise surveys suggests that access to finance is a problem in most Sub-Saharan

African countries. The percentages of firms that reported access to finance as biggest obstacle in Malawi

is higher than the regional average. Access to finance is also a top obstacle in Tanzania, Zambia,

Mozambique and Zimbabwe. This is however not the case in Kenya where corruption is the biggest obstacle

and South Africa where electricity is the biggest obstacle. Access to finance and electricity are the first and

second biggest obstacles for firms in Sub-Saharan Africa.

4.4 COMPARISON WITH THE WORLD BANK DOING BUSINESS SURVEY RESULTS

Drawing comparisons between the DB 2015 report and the ES 2014, which were conducted around the

same time, shows similarities in the key challenges facing businesses in Malawi for example in access to

finance, electricity and getting services from Government institutions. See the table below for details. A

summary of the ES and DB methodologies is in Annex 6.

41

Tanzania

Malawi

Zambia

Mozambique

Zimbabwe

Kenya

South Africa

Sub−Saharan Africa

0 10 20 30 40

Access to

Finance

Electricity Corruption

(percentages)

Malawi 29.9 14.2 10.7

Sub-Sahara 21.9 12.5 8.0

Zambia 27.5 13.1 2.5

Zimbabwe 20.6 3.3 8.3

Kenya 9.6 9.6 12.3

Tanzania 37.9 24.9 2.5

South Africa 7.5 14.7 7.1

Mozambique 23.2 9.1 4.1

Comparison of Findings in DB and ES22

Indicator Doing Business Enterprise Survey

Access to

Finance

Electricity

Paying

Taxes

Trading

Across

Borders

Dealing with

Construction

permits

Ranking of the ‘Getting Credit’ indicator worsened in the 2015 DB

report to 151st from 147th in 2014.

Access to Finance was the biggest

Obstacle to enterprises in Malawi

The ‘Getting Electricity’ indicator

ranked 181st in 2015, which was the

worst ranking across all indicators.

The 2015 DB report indicated that

it took 222 days to get connected to

the electricity grid, compared to

109 days in Tanzania, 34 days in

Rwanda, and 107 days in

Mozambique.

Electricity was perceived by the

largescale enterprises as the biggest

obstacle to business development in

the Enterprise survey. There is a

difference in the findings of the

specific number of days to get

connected to the electricity grid, but

both findings indicate that the

severity of the electricity challenge

in Malawi.

Malawi’s ranking deteriorated from

92nd in the 2014 DB report to 103rd

in 2015.

Tax rates were ranked 4th biggest

obstacle by enterprises in 2014, and

10 percent indicated that this is a

major problem (compared to 7.0

percent in 2008. Similarly, larger

percentage of firms mentioned tax

administration as a major obstacle,

compared to 9.0 per cent in 2008.)

The DB indicated that the time that

it took to export and import was

significantly higher than

comparator countries. Similarly,

the amount of documentation was

more than in comparator countries.

In the ES, firms reported that they

were expected to give gifts in order

to get necessary documentation

such as import licenses (see table

14)

The DB indicated that it took 60

days to get a construction permit

In the ES, businesses reported that

they were expected to give gifts to

get a construction permit (see table 14)

Source: Source Enterprise Survey (World Bank, 2017c)

4.5 COMPARISON WITH THE MCCCI BUSINESS CLIMATE SURVEY

In 2015, the Business Climate Survey by the Malawi Confederation of Chambers of Commerce and

Industry (MCCCI), asked business owners/leaders the extent to which they perceived several factors

as challenges to doing their businesses in Malawi. Participants were asked to rate challenges on a scale

of 1 to 10 (1 to 5 being minor, 6 to 7 being moderate and those above 7 considered as major obstacles). The

findings are similar to those from the 2014 enterprise survey, and also consistent with the DB findings. Cost

of finance was cited as one of the top three obstacles. This comes from high interest rates in the country as

a result of the persistently high inflation and weak productive structure. The other big obstacles were

telecommunication, electricity, economic uncertainty and policy inconsistency (see figure 16).

22 Note: Most of the included indicators have improved in the 2016 and 2017 DB Reports on account of some reforms

implemented in the respective areas. For example, the ‘Getting credit’ indicator has improved to 101 in 2017, following

implementation of the Collateral Registry in 2016.

42

FIGURE 16: TOP OBSTACLES ACCORDING TO SURVEY BY MCCCI

Cost of Finance remains biggest obstacle, followed by electricity

Source: Malawi Confederation of Chambers of Commerce and Industry (2016)

43

Cost of Finance 9.6

Telecommunication 9.1

Electricity 8.7

Economic uncertainity & Regulatory Policies 8

Water 7.6

Crime 7.6

Parliament effectveness 7.4

Customs regulations, procedures,… 7.3

Taxes 7.1

8 10 12

CHAPTER 5: LOOKING AT TOP CONSTRAINTS IN DETAIL

In Chapter 2, it was noted that the 2014 enterprise survey took place against a backdrop of

pronounced economic and financial sector challenges. The banking sector was dominated by a few large

commercial banks commanding a majority loan portfolio. There were few lending instruments, mostly on

commercial terms without long term development finance instruments. Macroeconomic conditions were

significantly adverse. Inflation was very high, at 25.9 percent in January 2014. Monetary conditions were

tight with policy rate above the 25 per cent. This translated into significantly high commercial bank interest

rates of above 40 per cent, which were largely unaffordable to the common enterprise firms. This, together

with a largely undeveloped credit system, made financial institutions reluctant to lend out funds, especially

to small scale enterprises. The financial sector was not well developed (without interoperability and limited

transactional innovations such as e-money or e-banking), making it difficult for those in rural areas to access

financial services.

Analysis of the 2014 enterprise survey results confirms these challenges and provides insights on the

profile across sectors and demographics - showing for example that access to finance is a bigger

challenge for service industries, small scale enterprises, rural based, female headed, non-exporting,

and domestic oriented firms. Few firms have access to loans, but this challenge is probably less

problematic for those firms that are retailers and engaging in exports. This is also the case for firms with

foreign ownership. Malawi is amongst the countries that have the highest percentage of loans requiring

collateral and also highest loan-to-collateral-value ratios. Only a small proportion of working capital in

Malawi is financed by banks, and this is mostly to the largescale, the manufacturers, the export oriented and

the foreign owned firms. As a result, the majority of the firms in the country depend heavily on funding their

investments through own funds. Only a third use banks to finance investment. This chapter explores

questions related to access to finance in more detail.

Chapter 4 focused on the question requesting respondents to indicate an element from a list, that represents

the biggest obstacle to their enterprises. The analysis in the next section of this chapter focuses on the

degree23 to which an element of business environment is an obstacle, starting with the key challenges

identified by the Enterprise Survey.

23 Whether to a “minor” or “major” degree

44

Zimbabwe 55.9

Mozambique 50.1

Sub−Saharan Africa 37.8

0.0 10.0 20.0 30.0 40.0 50.0 60.0

5.1 ACCESS TO FINANCE IS A MAJOR CONSTRAINT

TABLE 7: FINANCE AS A MAJOR OBSTACLE


FIGURE 17: ACCESS TO FINANCE AS MAJOR CONSTRAINT - COUNTRY COMPARISON

Tanzania 43.9

Malawi 34.9

Zambia 27.4

Kenya 17.2

South Africa 15.5

Access to finance was cited as the biggest obstacle that firms face in their daily operations in Malawi

and as a major constraint. In response to a question of what managers consider, out of a list of 15, as the

biggest obstacle to their establishments, 29.9 percent indicated that access to finance is the biggest obstacle

(from 45.7 per cent in 2008). Further, when asked to what degree access to finance is an obstacle on a scale

of 1 to 424, 34.9 percent responded that this was a major constraint. Malawi is however, not the worst

amongst comparable economies. This finding is better if compared with the average for Sub-Saharan

countries, and it represents an improvement from 2008 when 51.0 percent identified access to finance as a

major constraint. Larger proportions of firms in Zimbabwe (55.7 percent), Mozambique (50.1 percent) and

Tanzania (43.9 percent) also identified this as a problem.

Access to finance seems to be a bigger challenge for service industries. 35.6 percent of service

enterprises identified access to finance as a major constraint compared to 33.7 percent for firms engaged in

manufacturing. Similarly, 40.0 percent of small scale enterprises identified access to finance as a major

obstacle compared to 33.4 percent and 19.4 percent for medium scale and large scale enterprises,

respectively. A proportion of manufacturing and service firms that indicated access to finance as a challenge

reduced to a third in 2014 compared to around half in 2008.

24 The question was asked on a basis of scale 1 to 4 (1 = no obstacle, 2 = minor obstacle, 3 = moderate obstacle, 4 = very severe

obstacle)

45

(percentage)

2008 2014

Access to Finance as biggest obstacle 45.7 29.9

Percentage of firms identifying Access to Finance as a major constraint

51.0 34.9

Sector

Manufacturing 48 33.7

Services 52.1 35.6

Retail 33.4

Other Services 36.7

Size

Small 54.2 40.0

Medium 52.9 33.4

Large 30.5 19.4

Location

Blantyre 32.1

Lilongwe 38.1

Mangochi 52.9

Mzimba 28.7

Zomba 56.4

Exporter (portion of sales are exports) 47.9 26.7

Domestic only firm (no exports) 51.5 37.0

Top Manager, Female 64.7 41.9

Top Manager, Male 48.5 33.6

Firm has no foreign ownership 57.6 43.1

Firm has at least 10% foreign ownership 30.3 16.3

The rural based, the female headed, the non-exporting, and the domestic oriented firms seem to face

more challenges with access to finance than their respective counterparts. A higher proportion of firms

in Mangochi (52.9 percent) identified this as a major constraint than from the more urban cities of Lilongwe

(38.1 per cent) and Blantyre (32.1 percent). A significant number of firms in Zomba (56.4 percent) also

cited this challenge. Of the total that cited access to finance as a major constraint, 41.9 percent were firms

with a female top manager against 33.6 percent from firms with a male top manager. 43.1 percent were

firms with no foreign ownership while 16.3 percent were those which had at least 10 percent foreign

ownership. 37 percent were firms selling to the domestic markets only, while 26.7 percent were those that

exported some portion of their sales.

Access to Loans

Access to credit/loans remains low in Malawi. Only 26.7 percent of firms interviewed in the 2014

enterprise survey indicated that they have a loan or line of credit with a bank. This is low compared to 2008

(40.1 percent), but higher than the average for Sub-Sahara (22.6 percent) and neighboring countries:

Tanzania (16.6 percent), Mozambique (14.2 percent percent) and Zambia (8.8 percent).25

TABLE 8: FIRMS WITH CREDIT LINES


FIGURE 18: FIRMS OWNING BANK CREDIT LINE /LOAN - COUNTRY

COMPARISON

The large, the retail, the export oriented, and the firms with foreign ownership are more likely to have a bank loan or line of credit. Access to loan facilities is skewed towards the large-scale enterprises

(40.2 percent), compared to the medium (30.0 percent) and the small scale enterprises (21.3 percent). There are also more retail firms (28.0 percent) that reported to have such a facility than the manufacturing firms

25 Given the tough macro-economic environment some/most of the firms surveyed in 2008 may have closed (fallen out) with less new

fims formed, contributing to the lower percentage(what is termed ‘Survivorship Bias‘).If more new firms were formed, then the new

are less likely to access credit from financial institutions

46

Zimbabwe 7.8

Zambia 8.8

Mozambique 14.2

Tanzania 16.6

Malawi 26.7

South Africa 30.1

Kenya 35.6


10 20 30 40

(percentage)

2008 2014

Proportion with bank credit line 40.1 26.7

Small scale 27.7 21.3

Medium scale 59.9 30.0

Large scale 51.9 40.2

Business Sector

Manufacturing 42.5 25.8

Retail n.a 28.0

Services 39.3 27.2

Exporter (portion of sales are

exports)

51.5 53.3

Domestic only firm (no exports 40.0 23.9

Ownership



No foreign ownership 40.3 24.9

At least 10% foreign ownership

39.5 25.3

Location

Lilongwe 29.0

Blantyre 25.8

Mangochi 9.2

Mzimba 22.4

Zomba 28.3

(25.8 percent). Similarly, it is skewed towards the firms engaged in exports (53.3 percent) compared to

those with domestic orientation (23.9 percent). The firms that are headed by female top managers still

reported having more access to bank credit than their counterparts, as did those firms with foreign

ownership. Those firms located in urban areas such as Lilongwe and Blantyre reported as having more

access to a line of credit than in the rural urban centers such as Mangochi.

Collateral Requirements

Malawi is amongst the countries that have the highest percentage of loans requiring collateral and

highest collateral to loan value ratio. In the 2014 enterprise survey, 92.6 percent of loans in Malawi

required collateral (see Table 9). This compares unfavorably with results from the 2008 enterprise survey

which indicated that 88.1 percent required collateral. The value of collateral needed for a loan is equally

large at 293.6 percent of the value of a loan. This translates to over three hundred percent above 2008 level

and is the highest in the region.

TABLE 9: COLLATERAL REQUIREMENTS

Proportion

of loans

requiring

collateral

(92.6 %)

Value of

collateral

needed

for a

loan

(293.6 %)

Firm Size




Business Sector


Retail 89.7 346.3

Services 89.2 340.8

Exporter (portion of sales

are exports)

97.4 160.7

Domestic only firm (no

exports

96.2 308.2

Gender of Top Manager

Top Manager, Female 100 360.7


Ownership

No foreign ownership 96.3 311.5

Firm has at least 10% foreign ownership

97.1 247.0

Location

Lilongwe 92.6 324.1

Blantyre 92.3 286.4

Mzimba 100 240.1


FIGURE 19: LOANS REQUIRING COLLATERAL -COUNTRY COMPARISON

The Value of collateral in Malawi and comparator countries is very high despite low bank non- performing loans to total gross loans (NPLs). For example, the NPLs for Mozambique for the year that

the Enterprise Survey was conducted was 2.6 and the value of collateral needed for a loan was 92 per cent

of the loan amount (Figure 20). For Tanzania, the NPLs was only 5.0 but the value of collateral was as exorbitant as 240.2 per cent. For Malawi, the value of collateral was 293 % of loan value, but the NPL to

47

350.0

300.0

250.0 213.2

200.0

293.6

223.4 236.6 240.2

188.4

150.0

100.0

50.0

0.0

92.0 103.6 92.6

Value of collateral needed for a loan (% of the loan amount)

Proportion of loans requiring collateral (%)

Linear (Proportion of loans requiring collateral (%))

ratio was 15.6%. This evidence suggests very little, if at all any, strong relationship between value loan

requirements and NPLs26.

FIGURE 20: COMPARISON OF VALUE OF COLLATERAL AND NPLS – MALAWI AND COMPARATOR COUNTRIES IN YEAR OF

ENTERPRISE SURVEY

293.0%

300.0%

250.0% 236.6% 240.2%

200.0% 188.4%

150.0% 92.0%

103.0%

100.0% 0.5

50.0% 2.6% 1.4% 6.9% 5.1% 15.6%

0.0%

NPL

Value of Loan Requirements

Source: Authors’ calculation based on data from WBES and the WDI (World Bank, 2017b)

Complaints about the high proportion of loans requiring collateral and high value of collateral

requirements seem to cut across all firms irrespective of size, sector, location, and whether domestic

or foreign oriented. One interesting observation is a gender issue, with more reported disadvantages for

firms and enterprises whose managers are women. For example, the value of collateral needed for a loan

from such firms were at 360.7 percent of loan value, against 274.6 percent of loan value for firms headed

by males. The Personal Property Security Registry system implemented by the Government in 2016 with

support from the World Bank provides a good opportunity for countering this situation, as business owners

can now use their movable assets to secure loans27.

Working Capital

A sizable number of firms, especially large scale in size, exporting, with foreign ownership and those

located in cities, utilize banks more as a source of financing for working capital. Almost a third (29.2

percent) of firms surveyed reported that they used banks to finance working capital. 42.9 percent of large

scale enterprises utilize banks this way compared to 22.0 percent of small-scale enterprises. 30.3 percent of

firms in the manufacturing sector also use banks as a source of financing for working capital compared to

22.6 percent for retail firms. Firms with foreign ownership utilizing this facility for working capital more

than those with only domestic ownership. An interesting aspect is that a higher percentage of firms with

26 Available data shows that NPL in Malawi jumped from around 2.0 per cent in early 2012 to 13.6 per cent in late 2013. And

the value of collateral jumped from 161.1 per cent of vale of loan to 293.6 per cent in 2014, according to RBM Stability Report

and Enterprise survey) 27 http://www.worldbank.org/en/news/feature/2016/02/04/new-online-collateral-registry-system-facilitates-increased-access-to-

business-loans-in-malawi

48

http://www.worldbank.org/en/news/feature/2016/02/04/new-online-collateral-registry-system-facilitates-increased-access-to-

female top managers indicated utilizing this feature for working capital compared to those firms headed by

male top managers.

TABLE 10: WORKING CAPITAL FINANCED BY BANKS

Working

capital

financed by

banks

(10%)

Percentage of firms

using banks to

finance working

capital (29.2%)





Retail 7.0 22.6

Services 9.3 28.6

Exporter (portion of

sales are exports)

16.3 48.8

Domestic only firm

(no exports

9.9 27.9

Top Manager, Female

13.0 36.9


Firm has no foreign

ownership

9.3 24.7

Firm has at least

10% foreign

ownership

13.5 48.4

Lilongwe 13.3 36.4

Blantyre 8.3 26.1

Mangochi 2.3 16.2

Mzimba 19.7 45.2


FIGURE 21: PERCENTAGE OF FIRMS USING BANKS TO

FINANCE WORKING CAPITAL

Financing of working capital by banks is skewed towards the largescale, the manufacturers, the

export oriented, the female headed, and the foreign owned firms. 14.4 percent of working capital was

financed by banks for the large scale enterprises compared to 7.6 percent for the small scale enterprises. For

the manufacturers, 11.1 percent of firms were financed by banks compared to 7.0 percent for the retailers.

16.3 percent of working capital was financed for the export oriented firms, compared to 9.9 percent for the

domestic oriented firms. 13.0 percent of working capital for the firms with female top managers was

financed by banks, against 9.5 percent for their male counterparts. 13.5 percent of working capital was

financed for the firms with some foreign ownership component against 9.3 percent for those without.

Investments

The firms in Malawi depend heavily on funding their investments through own funds, with occasional

recourse to other sources. 65.8 percent of total investment is financed internally, and the rest by banks,

with little amounts coming from supplier credit, equity or stock sales (Figure 23. While in small proportions,

there is some growth in investment financing through supplier credit, and equity or stock sales, compared

to 2008. Between 2008 and 2014 the proportion of investments financed by supplier credit increased from

2.5 percent to 7.4 percent and proportion for investment financed by equity increased from 2.9 percent to 7.7 percent. The proportion of investments financed internally reduced from 75.4 percent to 65.8 percent.

49

Kenya 41.1

Malawi 29.2

South Africa 21.1

Tanzania 14.7

Zimbabwe 12.0

Zambia 9.9

Mozambique 8.5


0 10 20 30 40 50

A third of the firms in the country use banks to finance investment, and this proportion compares

well in the region. The 2014 enterprise survey results indicate that 30.3 percent (compared to 20.6 percent

in 2008) of firms in Malawi use bank financing for investment (Table 9). Whilst this is higher than the

average for Sub-Sahara, it is better than the neighboring and comparable economies such as Zambia (12.2 percent), Tanzania (18.5 percent), Mozambique (10.5 percent), and Zimbabwe (16.7 percent). It is, however, lower than Kenya (43.2 percent), and South Africa (34.8 percent).

TABLE 11: FIRMS USING BANKS TO FINANCE INVESTMENT

FIGURE 22: FIRMS USING BANKS TO FINANCE INVESTMENT (%)

FIGURE 23: INVESTMENT FINANCING OPTIONS (PROPORTION, %)


The largescale, the export oriented and the foreign owned firms seem to have more access and use

banks to finance investments than their local small scale domestic oriented counterpart. 42.0 percent

of the large scale enterprises use banks to finance investment against 34.5 percent and 23.5 percent of the

medium scale and the small scale enterprises, respectively. 52.0 percent of the firms that export portions of

their sales use banks to finance investment, against 26.1 percent for the domestic oriented enterprises. 39.2

percent of the firms with at least 10 percent of foreign ownership use bank financing for investment against

26.7 percent for those with no foreign ownership.

50

Kenya

South Africa

Malawi

Tanzania

Zimbabwe

Zambia

Mozambique


43.2

34.8

30.3

18.5

16.7

12.2

10.5

21.3

10 20 30 40 50

80 65.8

60

40 13.8

20 7.7 7.4

Internally By Banks By By Supplier

Percent of

firms using

banks to

finance

investments

(30.3%)

Size

Small scale 23.5

Medium scale 34.5

Large scale 42.0

Sector

Manufacturing 29.9

Retail 28.8

Services 30.6

Exporter (portion of sales are exports)

52.0

Domestic only firm (no exports

26.1

Management and

Ownership

Top Manager, Female 31.2

Top Manager, Male 30.5

Firm has no foreign ownership

26.7

Firm has at least 10% foreign ownership

39.2

Location

Lilongwe 27.8

Blantyre 30.2

Mangochi 7.5

Mzimba 50.3

5.2 ELECTRICITY OUTAGES

It was noted in the contextual background that only about 9 percent of the population has access to electricity in Malawi. Demand for power has been growing, and as indicated the number of customers in

2015 was 312,857 while the generation capacity remained at 350MW (as in 2014), against the demand of

440 MW.

There are a number of other challenges in the sector apart from the low installed capacity, against rising demand and the reliance on Shire River for generation28. These include inadequate transmission and distribution networks, low tariff rates affecting economic viability of new projects, as well as

uncertainties in the regulation and governance of the sector.

Analysis from the 2014 enterprise survey shows the extent to which electricity outages have affected

businesses in Malawi, with it emerging as their second biggest obstacle. In Chapter 3, it was noted that

about 14.229 percent of top managers interviewed in the 2014 enterprise survey considered electricity

outages as the biggest obstacle to their enterprises. In a typical month, firms reported experiencing 6.7

outages, each lasting an average of 3.5 hours. On a scale of 1 to 4 concerning severity of the problem, 24.8

percent identified this as a major obstacle. This chapter analyzes this challenge and compares it with

previous survey results, and situations in neighboring countries.

Electricity outages are a big challenge in Malawi, although this is better than in some of the

comparator countries. According to the enterprises survey, Tanzania experiences the largest number of

outages in a typical month (8.9 outages a day), followed by Malawi (6.7), Kenya (6.3) and Zambia (5.2).

Outages in Tanzania, Zambia and Kenya were reported to have lasted longer than in Malawi. Kenya

reported experiencing biggest losses as a percentage of annual sales due to the electricity outages (Figure

24).

As a coping mechanism, firms turn to generators to make up for the lost energy. About 40.9 percent

of firms in Malawi own or share a generator. This is less than the average for Sub-Sahara (50.9 percent),

and also less than Zimbabwe (62.3 percent), Kenya (57.4 percent) and Tanzania (43.0 percent). But it is

worse than Zambia, South Africa and Mozambique. With wide usage of power back-up systems, the average

cost of power in Malawi and other countries in the region is high. While Malawi’s grid electricity is

generally recognized to be reasonably low-cost, this is offset at the enterprise level by the cost of investing in

and running back-up generating capacity.

28 Malawi is still not connected to the Southern Africa Power Pool (SAPP), therefore not yet able to trade in electricity through

the SAPP. 29 Slightly higher than the Sub-Saharan Africa average (13.7 percent)

51

FIGURE 24: ELECTRICITY OUTAGES AND ASSOCIATED LOSSES ACROSS COUNTRIES

FIGURE 25: FIRMS OWNING OR SHARING

GENERATORS (%)


Comparison with 2008 Survey

There were more electricity outages in Malawi in 2014 compared to 2008. 24.8 percent of the firms in

the 2014 enterprise survey identified electricity as a major constraint, compared to 37.6 percent in the 2008

survey, but firms reported 6.7 outages in a typical month in 2014, compared to only 0.8 outages in 2008 on

average. A typical outage lasted longer (3.5 hours) in 2014 compared to 2.4 hours in 2008. This is a drastic

deterioration that does not bode well for the operations of enterprises and their continued growth.

The electricity outages translate into significant losses to industry revenue in Malawi. About 5.1

percent of annual sales were reported to have been lost due to electricity outages in 2014, down from 8.0

percent in 2008. This decline in losses can probably be explained by an increase in the usage of generators,

as the number of firms owning generators almost doubled (from 25.3 percent in 2008 to 40.9 percent in

2014) (Table 10).

52

Zimbabwe

Kenya

Tanzania

Malawi

Zambia

South Africa

Mozambique


62.3

57.4

43.0

40.9

27.3

18.4

12.6

0.0 20.0 40.0 60.0 80.0

8.9

8.3

6.7 6.3 5.2

4.5

1.6 0.9

Tan

zan

ia

Mal

awi

Ken

ya

Zam

bia

Zim

bab

we

Mo

zam

biq

ue

Sou

th A

fric

a

Sub

−Sah

aran

Afr

ica

TABLE 12: FIRMS IDENTIFYING ELECTRICITY AS MAJOR CONSTRAINT

FIGURE 26: NUMBER OF DAYS TO OBTAIN ELECTRICITY

CONNECTION


There were more electricity outages in Malawi in 2014 compared to 2008. 24.8 percent of the firms in

the 2014 enterprise survey identified electricity as a major constraint, compared to 37.6 percent in the 2008

survey, but firms reported 6.7 outages in a typical month in 2014, compared to only 0.8 outages in 2008 on

average. A typical outage lasted longer (3.5 hours) in 2014 compared to 2.4 hours in 2008. This is a drastic

deterioration that does not bode well for the operations of enterprises and their continued growth.

The electricity outages translate into significant losses to industry revenue in Malawi. About 5.1

percent of annual sales were reported to have been lost due to electricity outages in 2014, down from 8.0

percent in 2008. This decline in losses can probably be explained by an increase in the usage of generators,

as the number of firms owning generators almost doubled (from 25.3 percent in 2008 to 40.9 percent in

2014) (Table 10).

Malawi firms face challenges to get connected to the electricity grid. Firms reported that it takes 50.4

days to obtain an electrical connection (upon application) in Malawi. While this is a slight improvement

compared to 59.2 days in 2008, it is still very high and is worse than some of the countries in the region

such as Kenya (43 days), Zimbabwe (22.2 days), Zambia (18.9 days), and Mozambique (12.7 days). It is

only better than Tanzania at 52.6 days (see Figure 26).

Perceptions with Respect to Size of Firms

Large scale and medium scale enterprises were more concerned about electricity outages in 2014,

compared to 2008. In the 2014 Enterprise Survey, large scale enterprises reported 9.4 outages in a typical

month as compared to one outage in a typical month in 2008. Medium scale enterprises reported 7.5 outages in 2014 compared to 0.9 in 2008.

53

Tanzania 52.6

Malawi 50.4

43.0

Zimbabwe 22.2

Zambia 18.9

South Africa 15.8

Mozambique


20 40 60

2008 2014

% of firms identifying electricity as a major constraint

37.6 24.8

Number of outages in a typical

month

0.8 6.7

Large 1.0 9.4

Medium 0.9 7.5

Small 0.7 Na

Duration of typical outages

(hours)

2.4 3.5

Large 4.7 94

Medium 2.7 7.5

Small 1.8 5.7

Losses due to outages (% of

annual sales)

8.0 5.1

Large 5.6 3.2

Medium 15.3 7.3

Small 4.8 4.6

Firms owning generator (%) 25.3 40.9

Large 46.0 74.0

Medium 34.4 60.5

Small 4.1 23.1

An increasing number of firms in Malawi turn to generators as a source of complementary energy.

74.0 percent of the large scale enterprises and 60.5 percent of medium scale enterprises reported owning or

sharing a generator compared to only 46.0 and 34.4 percent in 2008. A lesser share of small-scale enterprises

own generators, at 23.1 percent in 2014 (up from 4.1 percent in 2008).

Medium size firms seem to be affected more from electricity outages than large scale and small scale firms. The medium sized firms reported losing 7.3 percent of annual sales due to electricity outages in 2014, compared to 4.6 percent of annual sales for small scale enterprises and 3.2 percent for large scale enterprises.

TABLE 13: POWER OUTAGES IN A TYPICAL YEAR IN 2008 AND 2014

Number of electrical

outages in a typical month 2008

Number of electrical

outages in a typical month 2014

Firm Size Small 0.7 5.7

Medium 0.9 7.5

Large 1.0 9.4

Firm Type


Service/retail 0.8 5.5

Type

Exporter 0.8 8.0

Non exporter 0.8 6.2

Ownership Type

Male Top Manager 0.7 7.1

Female Top Manager 1.1 4.8

Domestic 0.9 6.4

Foreign 0.6 7.8

Location

Blantyre 5.9

Mangochi 10.6

Lilongwe 7.8

Mzimba 6.7

Zomba 8.2


Perceptions with Respect to Business Sector

Manufacturing firms are affected more strongly by the electricity outages in Malawi. Firms engaging

in manufacturing reported in the survey that they experienced 7.7 electricity outages in a typical month,

while those engaged in retails and general service sector reported an average of 5.5 outages and 6.6 outages

in a typical month, respectively.

Prospects for increased manufacturing growth in the Malawi economy are evidently undermined by

the outages, and partly by high usage of generators. As a result, manufacturing firms reported more

losses due to this challenge than service and retail firms. Manufacturing firms reported losses of 6.1 percent

of annual sales, while service firms reported 4.7 percent, and retail firms reported 4.3 percent losses of

annual sales. 47.2 percent of manufacturing firms reported that they owned or shared a generator in 2014

54

and 27.8 percent of their electricity was from the use of generators. This is compared to 39.1 percent of

service firms and 35.2 percent of retail firms that owned or shared a generator.

Perceptions with Respect to Location

Electricity outages seem to be a challenge in both the rural and urban centers. Electricity outages were

reported at between 5.9 and 10.6 in a typical month across the surveyed districts. These outages were on

average between 3.8 and 4.6 hours in the major cities of Blantyre, Zomba and Lilongwe.

Losses due to electricity outages were slightly lower in the major cities. Blantyre firms reported losses

of 3.6 percent of annual sales, while Lilongwe reported 6.2 percent. Meanwhile, firms in Zomba reported

losses of 13.8 percent of annual sales, followed by Mangochi (12.9 percent) and Mzimba (11.8 percent).

Firms in the capital city seem to be more constrained than their Blantyre counterparts. 51.6 percent

of firms in Lilongwe reported owning or sharing a generator, and that 35 percent of their electricity came

from generators. Perhaps due to the lower frequency and shorter durations of outages, only 31.1 percent of

Blantyre firms reported to own or share a generator, and that only 18.9 percent of their electricity comes

from generators.

5.3 CORRUPTION

Corruption is seen as the third most significant obstacle to doing business in Malawi. The 2014

Enterprise Survey was conducted at the time of revelations of ‘Cashgate’ – a financial scandal that involved

systematic syphoning of public resources through unperformed contracts. Increased corruption – bribery in

particular – was validated as an obstacle to business during the 2014 enterprise survey, with almost a third

(30.1 per cent) of firms identifying it as a major constraint. This chapter analyzes this obstacle in detail.

55

TABLE 14: CORRUPTION AND COURTS AS MAJOR CONSTRAINTS


FIGURE 27: FIRMS IDENTIFYING CORRUPTION AS A MAJOR

CONSTRAINT (%)

A third of firms in Malawi identified corruption as a major obstacle to enterprise development,

reported mostly by largescale firms and those located in cities. 30.1 percent of enterprises that were

surveyed in 2014 identified corruption as a major constraint obstacle, compared to 12.8 percent in 2008.

18.5 percent who identified this as a major obstacle were large-scale enterprises against 30.5 percent

medium scale enterprises, and 33.1 percent small-scale enterprises. There were no significant differences

in the perceptions between firms engaged in manufacturing and those in services sector. A significant

percentage of firms in the major cities reported this as a huge constraint.

Corruption is perceived to be a serious challenge in Malawi, although somewhat better than in some

comparators countries. Corruption seems to be a widespread problem in the surrounding countries, with

over a third of enterprises in most countries identifying it as a major constraint. Although the perception

among enterprises of corruption as a major constraint is relatively better in Malawi (30.1 percent) than in

the Sub-Saharan Africa region (41.3 percent), the country compares favorably only with Tanzania (47.2

percent) and Zimbabwe (38.3 percent). It compares worse than most of the comparator countries such as

Zambia (29.8 percent), Mozambique (25.4 percent), Kenya (21.3 percent), and South Africa (16.9 percent).

The court system in Malawi is reported to be a major constraint mostly for large enterprises. 7.3

percent of businesses identified the court system as a major constraint to business development, which is a

deterioration from 4.1 percent in 2008. 14.0 percent of large scale enterprises identified this as a major

constraint, against 6.3 percent for medium scale and only 1.1 percent for small-scale enterprises.

Bribery

57

Tanzania

Zimbabwe

Malawi

Zambia

Mozambique

Kenya

South Africa


47.2

38.3

30.1

29.8

25.4

21.3

16.9

41.3

10 20 30 40 50

Percent of firms identifying the courts system as a major constraint Percent of firms identifying corruption as a major constraint

Corruption as

major

constraint

Court as major

constraint

2008 2014 2008 2014

Courts 4.1 7.3

Corruption 12.8 30.1

Firm Size

Small 10.0 33.1 1.1 8.9

Medium 15.1 30.5 6.3 5.4

Large 20.8 18.5 14.0 4.9

Firm Type

Manufacturing 18.4 28.5 11.1 6.3

Service/retail 10.8 38.4 1.8 7.8

Location

Blantyre 29.4 5.1

Lilongwe 30.9 10.3

Mangochi 15.2 0

Mzimba 39.6 19.3

Zomba 29.2 5.5

Bribery incidence30 appears to be increasing in Malawi. In the 2014 Enterprise Survey, Malawi firms

reported experiencing high levels of bribery incidences, as the percentage of firms experiencing at least one

bribe payment request within a year rose from 13.7 in 2008 to 24.2 percent in 2014. The average for Sub-

Saharan countries was at 22.7 percent.

Similarly, bribery is deepening31. Firms reported that in 20.2 percent of public transactions, a gift or an

informal payment was requested in 2014, compared to 16.7 percent in Kenya and 15.7 percent in Tanzania.

South Africa, Zambia and Zimbabwe reported 3.0 percent, 9.7 percent and 9.4 percent respectively. The

depth of bribery in Malawi is below the average for Sub-Sahara (25.0 percent), although it worsened

significantly compared to 8.5 per cent in 2008.

TABLE 15: FIRMS IDENTIFYING CORRUPTION AS MAJOR CONSTRAINT


FIGURE 28: BRIBERY RATES

Firms reported facing challenges getting services such as permits and licenses in the course of doing

their business as well as payment of taxes. Comparing results from the 2008 and 2014 enterprise surveys,

there are growing proportions of firms expected to give gifts in order to meet tax officials, to secure

government contracts, to get business operating licenses, and import licenses, to obtain construction

permits, and to get connected to an electricity grid or water supply. The percentage of firms reporting these

ranged between 16.6 and 34.5 percent (see table 14 below), up from as low as 0 to 12.8 percent in 2008.

Striking examples of worsening situation between the two surveys is that 33 percent of firms interviewed

30 Bribery incidence refers to percentage of firms experiencing at least one bribe payment request 31 Bribery deepening refers to percentage of public transactions where a gift or informal payment was requested

58

35

30

25

20

15

10

2008 2014

to g

et a

n o

per

atin

g lic

ense

in m

eeti

ng

wit

h t

ax o

ffic

ials

to g

et e

lect

ric

con

nec

tio

n

to g

et im

po

rt li

cen

se

to g

et w

ater

co

nn

ecti

on

to s

ecu

re g

ove

rnm

ent

con

trac

t

to g

et c

on

stru

ctio

n p

erm

it

2008 2014

Those identifying corruption as major constraint

12.8 30.1

Those identifying court system as major constraint

4.1 7.3

Bribery Incidence 13.7 24.2


Services 15.1 28.1

Small 14.5 25.7

Medium 12.6 27.2

Large 12.0 15.2

Bribery Depth 8.5 20.2


Services 9.4 22.4

Small 8.6 21.4

Medium 8.1 24.2

Large 8.7 10.4

Firms expected to give gifts

in meeting with tax officials 11.4 18.3

to secure government contract 21.7 33.0

to get an operating license 3.5 16.6

to get import license 0 26.7

to get construction permit 4.9 34.5

to get electric connection 12.6 25.3

to get water connection na 27.3

in the 2014 survey (compared to 21.7 percent in 2008) reported that they were expected to give gifts to

secure a government contract, and 34.5 percent of firms (compared to 4.9 percent in 2008) were expected

to give gifts to get a construction permit. According to the 2014 Enterprise Survey, Malawi was one of the

top three worst performers amongst comparable countries in the region with high corruption levels (in form

of ‘gifts’) when enterprises get services from public institutions, as noted in the table below.

TABLE 16: CORRUPTION LEVELS – COUNTRY COMPARISON

A B C D E F G H I J

Malawi 16.6 (3) 16.6 (2) 26.7(1) 34.5 (2) 25.3 (2) 27.3 (3) 20.2(1) 33.0 18.3(1) 24.2(2)

Kenya 28.2 (1) 15.6 (3) 17.0(2) 34.6 (1) 17.6 (3) 30.9 (2) 16.7(2) 33.4(3) 17.4(2) 26.4(1)

Tanzania 20.0 (2) 17.0 (1) 5.1 31.4(3) 25.3 (1) 20.4 15.7(3) 66.2(1) 14.6(3) 20.8(3)

Mozambique 14.8 6.9 10.6(3) 3.9 14.6 16.2 9.7 32.1 9.8 12.4

Zambia 9.5 7.6 6.7 13.6 14.8 43.3 (1) 9.7 27.5 9.0 15.8

Zimbabwe 14.4 9.0 7.6 9.2 32.7 … 12.3 21.1 12.6 12.3

South Africa 15.1 0 2.7 0 6.7 4 3 34.2(2) 3.1 4.2

Sub-Sahara 25.9 16.1 16.8 25.9 22.5 23.3 17.4 33.8 17.2 22.7

Key

Shaded cells = top three worst performing countries represented in the table

A = Percent of firms expected to give gifts to public officials “to get things done”

B = Percent of firms expected to give gifts to get an operating license

C = Percent of firms expected to give gifts to get an import license

D = Percent of firms expected to give gifts to get a construction permit

E = Percent of firms expected to give gifts to get an electrical connection

F = Percent of firms expected to give gifts to get a water connection

G = % of public transactions where gifts or informal payment was requested

H = % of firms expected to give gifts to secure government contract

I = % of firms expected to give gifts in meetings with tax officials

J = % of firms experiencing at least one bribe payment request


59

CHAPTER 6: OTHER RISK MEASURES

This chapter looks at a number of other risk measures affecting private sector enterprises in Malawi.

These include: taxation; practices of the informal sector; access to land; political instability; regulatory

compliance; crime, theft and disorder; water and transportation.

FIGURE 29: TOP TEN BIGGEST OBSTACLES TO ENTERPRISES IN MALAWI, 2008 AND 2014


6.1 TAX RATES AND ADMINISTRATION

In the 2014 enterprise survey, tax rates were listed as number four on the list of obstacles to enterprise

development in the country. In response to a question of what managers consider as the biggest obstacle

to their establishments, 10 percent indicated that it is tax rates. This compares unfavorably with the

perception in 2008 survey when only 7.0 percent considered this as biggest obstacle. When asked to what

extent this is an obstacle, 35.6 percent identified this as a major constraint compared to 15.6 percent in 2008.

In the 2008 survey, most of the complaints about tax rates seem to have come from large scale enterprises

in the services sector, but in 2014, it was mostly from medium and small-scale enterprises.

60

Access to finance Electricity

Corruption

Tax rates Practices of the informal sector

Access to landPolitical instability

Business licensing and permits

Crime, theft and disorder

Transportation

0.0 20.0 40.0 60.0

Malawi 2014 Malawi 2008

TABLE 17: TAX RATES AS A MAJOR CONSTRAINT

___________________________________

FIGURE 30: LICENSING, TAXATION AND TAX ADMINISTRATION


Most of the complaints about tax rates in 2014 were from the medium scale retail firms. 40.6 percent

of medium scale firms in Malawi identified tax rates as a major constraint. This was followed by 37.4

percent of small firms, and 20.9 percent of large firms. 40.5 percent retail firms and 36.4 percent from

manufacturing firms reported this as major constraint. In terms of location, firms that reported tax rates as

an obstacle were mostly from the country’s major cities in Lilongwe, Blantyre and Zomba (compared to

Mangochi and Mzimba).

On the other hand, tax administration as an obstacle was cited by a significantly larger proportion of

firms in 2014 (21.1 percent) compared to 2008 (9.0 percent). Most of the complaints about this challenge

came from the medium and smaller sized enterprises, and equally from the retail and the manufacturing sectors. It was identified as a major constraint by 24.1 percent of the medium enterprise firms, 22.6 percent

of the small-scale firms and 11.4 percent of the large-scale firms.

On average, 2 visits were required to hold meetings with tax officials in any given month in 2014,

down from 2.6 visits in 2008. While this is better and below the average for Sub-Saharan countries at 2.2

visits, it is above the average for other comparable countries such as Tanzania, Mozambique and Zambia.

There are a number of countries in SSA where average visits are much less. For example, in Mauritius, the

average number of visits is reported at 0.5 days, Namibia (0.6 days), and Madagascar (0.8 days). Kenya

managed to reduce the required number of visits from 6.7 days in 2007 to 1.5 days in 2013.

6.2 PRACTICES OF THE INFORMAL SECTOR

Practices of competitors in the informal sector are ranked as the fifth biggest obstacle in the business

environment in Malawi. The survey suggests that 90.3 percent of firms in the country formally registered

their firms at the start of their operations, up from 78.6 percent in 2008. While this was the case, 71.7 percent

reported competition with informal players as a challenge, down from 77.8 percent in 2008, but still higher

than the Sub Saharan Africa average of 67.4 percent. It was reported as such across all categories, but was

highest for manufacturing firms, followed by retail and other services. When asked to

61

2008 2014

Percent of firms identifying business

licensing and permits as a major constraint

Percent of firms identifying tax

administration as a major constraint

9.0

11.2

9.0

21.1

Percent of firms identifying tax rates as a

15.6

35.6

0.0 10.0 20.0 30.0 40.0

2008 2014

Firms identifying tax

rates as a major constraint

15.6 35.6

Firm Size

Small 5.6 37.4

Medium 29.4 40.6

Large 30.2 20.9

Firm Type


Retail/services 12.7 40.5

Location

Blantyre 34.4

Lilongwe 37.6

Mangochi 26.9

Mzimba 33.5

Zomba 38.4

Number of visits with tax officials

2.6 2.0

what degree this is a constraint in the 2014 enterprise survey, 30.1 percent (against 26.4 percent in 2008)

identified this as a major obstacle. 36.3 percent were small scale, followed by 27 percent from the medium

scale firms and 13.2 percent from the large scale firms. 34.6 percent were non-exporters while 5.6 were

exporters.

TABLE 18: INFORMAL SECTOR COMPETITION AS MAJOR CONSTRAINT


FIGURE 31: COMPETITION FROM INFORMAL

PLAYERS

Practices of competitors in the informal sector seem to be a challenge across the comparable countries

in the region. The percentage of those that identified this as a major constraint for Malawi was 30.1 percent,

against 49.6 percent in Mozambique, 48.5 percent in Zambia, 47.0 percent in Zimbabwe and 47.4 percent

in Tanzania. Kenya and South Africa have lower percentages than Malawi. The average for Sub- Saharan

Africa was at 38.5 percent.

6.3 ACCESS TO LAND AND POLITICAL INSTABILITY

In past years, the processes for acquiring land have not been investment-friendly, as they have been

characterized by long, prohibitive and non-transparent processes. Easing access to land for industrial

and commercial purposes is one of the key targets of recent land law reforms. The reforms in land

governance have since commenced with the review and adoption of 10 land laws in 2016. Implementation

of these new laws is expected to ease the process of acquiring land for commercial and industrial purposes

through the facilitation of the Malawi Investment and Trade Centre. Acquiring land will also be facilitated

by the establishment of an integrated land information system that is being developed with support

from the World Bank.

62

80.0

70.0

60.0

67.4 71.772.673.9

59.3

78.375.4

50.0 38.5

40.0

45.3 45.047.448.549.6

26.930.1

20.0 11.3

10.0

0.0

Sub−

Saha

ra

Sou

th A

fric

a

Ken

ya

Mal

awi

Tan

zan

ia

Zim

bab

we

Zam

bia

Mo

zam

biq

ue

2014 Firms

competing

against

unregistered

or informal

firms (%)

Firms

formally

registered

when they

started

operations in

the country

(%)

Number of

years firm

operated

without

formal

registration

Firms

identifying

practices of

competitors in

the informal

sector as a

major

constraint (%)

2008 77.8 78.6 0.6 35.7

2014 71.7 90.3 0.7 30.1

Firm Size

Small 70.7 87.9 0.3 36.3

Medium 75.2 92.7 1.7 27.0

Large 69.3 95.3 0.6 13.2

Firm Type

Manufacturing 76.5 89.7 0.6 29.9

Service 69.2 90.7 0.8 30.1

Type

Exporter 63.8 68.7 4.7 5.6

Non exporter 73.1 92.5 0.5 34.6

Ownership

Male Top Manager

72.6 89.4 0.8 27.9

Female Top Manager

68.3 95.7 0.1 43.9

18.9

9.8

4.7 5.2 5.1 4.7

0.5 0.6

2.7

1.1 1.1 0.3

Political instability Access to land

Results from the 2008 and 2014 enterprise surveys indicate that access to land has been a growing

challenge to private sector enterprises in Malawi. Only 1.7 percent of the firms reported that access to

land is the biggest constraint that they faced in 2008, but this proportion rose to 7.1 percent in 2014. This is

significantly higher than the average for Sub-Saharan Africa (4.7 percent) and neighboring countries

Tanzania (5.1), and Mozambique (5.2). Access to land proved more of a constraint for firms with a female

top manager than those with male top managers (see table 19 and Figure 32).

TABLE 19: ACCESS TO LAND AND POLITICAL INSTABILITY

Proportion of

firms citing

access to land

as biggest

obstacle (%)

Proportion

citing

political

instability as

biggest

obstacle (%)

2008 2014 2008 2014

Access to Land 1.7 7.1

Political Instability 0.5 4.2

Firm Size

Small 0 6.1 0 4.1

Medium 3.8 10.2 0 4.2

Large 4.4 6.0 4.5 4.6

Firm Type

Manufacturing 6.4 6.0 0 3.1

Retail/services 0 9.3 0.7 1.0

Direct exporters 13.6 0

Non exporters 0 3.9

Location

Blantyre 6.1 5.3

Lilongwe 7.9 2.8

Mangochi 19.7 0

Mzimba 20.7 0

Zomba 2.8 4.9

Female Top manager

11.8 0 5.5

Male Top manager 6.4 0.6 4.0


FIGURE 32: LAND AND POLITICAL INSTABILITY AS OBSTACLES - COUNTRY COMPARISON

20

18

16

14

12 10.6

10 8.6

8

Concerns of political instability also appear to have increased in 2014 compared to the results from

the survey in 2008. 4.2 percent cited this as the biggest obstacle according to results from the 2014

enterprise survey compared to only 0.5 percent in the 2008 survey. Evidence suggests that political

instability constraints were high in the Sub-Saharan African countries including some of the comparable

countries such as Zimbabwe and Kenya. It was certainly more of a concern for enterprises in Malawi than

it was for those in neighboring countries of Zambia, Tanzania, South Africa and Mozambique (Figure 32)

6.4 BUSINESS LICENSING AND PERMITS

Challenges associated with obtaining business licensing and permits are ranked as the eighth biggest obstacle to enterprises in Malawi. 3.6 percent of the respondents to the 2014 enterprise survey identified

this as an obstacle (no change compared to perceptions in 2008). When asked to what degree this is a

63

Sub

−Sah

aran

Afr

ica

Zam

bia

Mo

zam

biq

ue

Sou

th A

fric

a

Tan

zan

ia

Mal

awi

Ken

ya

Zim

bab

we

challenge 11.2 percent identified this as a major obstacle. A bigger percentage was from the small scale

enterprise compared to medium or largescale enterprises and from firms with female top managers.

Government bureaucracy is another challenge cited by private sector. Firms in the survey reported that

5.0 percent of senior management’s time is spent dealing with fulfilling requirements of government

regulations. This is better than the average for sub-Saharan countries and for countries such as Kenya, South

Africa and Zambia. It is however, still very high, and worsened compared to 2008.

TABLE 20: BUSINESS LICENSING AND PERMIT

FIGURE 33: FIRMS IDENTIFYING BUSINESS LICENSING

AND PERMITS AS MAJOR CONSTRAINTS (%)


While being within the average levels for Sub-Saharan countries, significant amounts of time

resources are spent to obtain necessary documentation to do business in Malawi. Evidence from the

2014 Enterprise Survey suggests that it takes 19 days on average to obtain an operating business license in

Malawi. This compares favorably with Mozambique, South Africa, and Zambia and is similar to the average

for Sub-Saharan countries, but worse than countries such as Kenya, Tanzania and Zimbabwe. Malawi firms

reported 36.2 days to obtain construction related permits, one of the best in the region and much better than

the average for Sub-Saharan countries. Except for Mozambique, Malawi records the least number of days

to obtain an import license.

64

34.2 35.0 30.0

25.0

20.0

15.0

10.0

5.0

0.0

17.8 17.4 18.7

9.4 11.2 13.7

3.0 Su

b−S

ahar

an A

fric

a

Sou

th A

fric

a

Zam

bia

Mal

awi

Mo

zam

biq

ue

Zim

bab

we

Ken

ya

Tan

zan

ia

2014 Percent of firms

identifying

business

licensing and

permits as a

major constraint

(11.2%)

Senior

management

time spent

dealing with

the

requirements

of government

regulation (5.0%)

Firm Size

Small 13.0 4.0

Medium 9.1 6.8

Large 8.2 5.8

Firm Type


Service/retail 10.8 4.2

Type

Exporter 13.0 5.6

Non exporter 11.8 5.1

Male Top Manager

11.2 5.2

Female Top Manager

7.6 3.9

FIGURE 34: NUMBER OF DAYS TO GET LICENSES AND PERMITS


6.5 CRIME, THEFT AND DISORDER

Though still high, there was a slight improvement in the perception by firms in Malawi on the extent

to which crime, theft and disorder was a constraint in 2014 in comparison with the 2008 findings. The

proportion of firms that identified crime, theft and disorder as a major constraint in the country was 20.7

percent in 2014 a slight improvement from 22.8 percent in 2008. In terms of size of firms, 20.0 percent of

small scale enterprises and 29.1 percent of medium scale enterprises reported this as a major constraint

compared to 9.2 percent of large scale enterprises. A higher percentage of retail firms also reported this

compared to those engaged in manufacturing. Crime, theft and disorder was reported by 38 percent of the

enterprises in South Africa, 33.6 percent in Mozambique, 21.2 percent in Kenya, 21.1 percent in Tanzania,

10.5 percent in Zambia, and 6.6 percent in Zimbabwe. The Sub-Sahara average was 17.1 percent.

65

192.2

150.0

50.0

50.6

22.7

36.2

19.0

38.6

22.0

41.7

35.2 41.3

18.8

41.3

55.0

8.1

0.0

Days to obtain a construction−related permit

Days to obtain an operating license

Days to obtain an import license

Sub

−Sah

aran

Afr

ica

Mal

awi

Zam

bia

Mo

zam

biq

ue

Tan

zan

ia

Ken

ya

Sou

th A

fric

a

Zim

bab

we

TABLE 21: LOSSES DUE TO THEFT, CRIME, VANDALISM


FIGURE 35: CRIME, THEFT AND DISORDER - COUNTRY COMPARISON

Compared to 2008, the percentage of firms experiencing losses due to theft and vandalism also

reduced in 2014. 33.5 percent of the firms reported experiencing revenue losses due to this challenge, down

from 50.1 percent in 2008. The large and the medium scale enterprises and the manufacturing firms

experienced heavier losses compared to the small-scale enterprises, as well as those engaged in the retail

services. To cope with theft challenges and related losses, the enterprises reported getting services of private

security firms. 70 percent of firms reported paying for security, again, an improvement over 2008 when

90.4 percent spent money for services of private security firms due to these challenges.

Nonetheless, theft and vandalism cause significant losses to enterprises in Malawi. 1.5 percent of

product value being shipped to supply domestic markets is normally lost due to theft according to the 2014

survey. Malawi and Zambia firms for instance reported highest losses due to theft and vandalism at 2.2

percent of annual sales and 2.3 percent of annual sales, respectively. These two countries also registered

highest security cost (as percentage of annual sales), at 4.9 percent and 6.9 percent, respectively. The

average for Sub-Saharan Africa is 6.5 percent. In general, theft and vandalism levels in Malawi improved

significantly from 2008, but do not compare favorably with other countries in the region.

More foreign firms pay for security services compared to the domestic firms, costing 5.5 percent,

compared to 4.6 percent of their annual sales. Consequently, foreign firms experienced lesser losses than

domestic firms at 6.1 percent versus 9.1 percent, respectively. 0.9 percent loss of products shipped to

domestic markets were reported by foreign firms, compared to 1.9 percent by domestic firms. Overall, there

is a significant difference in the extent to which crime, theft and disorder is experienced as a challenge, with

only 8.5 percent of foreign firms citing it as a major constraint, compared to 22.5 percent of domestic firms.

66

80.0 70.0 60.0

40.0 30.0 20.0 10.0

0.0

Sub

−Sah

aran

Afr

ica

Zim

bab

we

Zam

bia

Mal

awi

Tan

zan

ia

Ken

ya

Mo

zam

biq

ue

Sou

th A

fric

a

Firms

payin

g for

securit

y (%)

Securit

y costs

(% of

annual

sales)

Firms

experien

cing

losses

due to

theft

and

vandalis

m (%)

Losses

due to

theft

and

vandalis

m

against

the firm

(% of

annual

sales)

Products

shipped

to supply

domestic

markets

that were

lost due

to theft

(% of

product value)*

Percent of

firms

identifyin

g crime,

theft and

disorder

as a major

constraint

2008 90.4 6.6 50.1 11.7 22.8 2014 70.1 3.2 33.5 2.2 1.5 20.7 Firm Size

Small 59.1 2.5 29.6 2.0 1.9 20.0 Medium 81.7 3.8 36.9 3.0 1.2 29.1 Large 90.2 4.3 41.6 1.8 1.1 9.2 Business Sector

Manufactur ing

67.3 3.6 34.3 2.4 17.2

Retail 65.1 2.3 28.2 1.7 24.6 Other service

75.2 3.3 36.0 2.4 21.2

Trucks awaiting entry at the Dedza Boeder

36.7

21.6 23.0

16.4 17.1

6.6 TRANSPORT AND LOGISTICS

The transport sector in Malawi faces a number of challenges. Producers in the country face high transport

costs in sourcing raw materials and in delivering outputs to the domestic, regional, and global markets.

Road transport accounts for 70 percent of all freight, and 99 percent of all passengers. However, the overall

quality of the road network, particularly at the secondary and tertiary level, is poor. The rail sector is also

not fully functional. It was also noted that the road and rail infrastructure were particularly in bad shape

around 2008.

TABLE 22: TRANSPORT CHALLENGES


FIGURE 36: TRANSPORT AS A MAJOR CONSTRAINT -COUNTRY

COMPARISON

40.0

35.0

30.0 26.1

25.0

20.0 15.7

15.0

10.0 3.9

5.0

0.0

a a i

According to the results of the enterprise surveys, there has been some improvement in 2014 in the

perception of business persons over transport challenges compared to 2008, but none-the-less, this

remains a significant challenge to business enterprises. This obstacle ranked 10th among the biggest

obstacles to enterprise development in the 2014 enterprise survey. It was ranked 2nd biggest obstacle, after

access to finance, in the 2008 enterprise survey, but has been replaced in this position by electricity

constraints. This points at some improvements that have happened in between, but could also point at the

increase in electricity constraints relative to those related to transport in the operations of firms.

In the 2014 survey, only 2.7 percent of top managers and enterprise owners cited this as the top most

obstacle, an improvement from 11.4 percent that reported the same in 2008. This was slightly above the average for Sub-Saharan Africa. When asked to what extent transportation is a challenge, 15.7 percent

of the firms identified this as a major constraint, again, an improvement from the 2008 perception when

24.6 percent of firms identified this as a major problem. While this is lower than other comparator countries

in the region, the impact of transport and logistics challenges for Malawi are more pronounced, considering

that Malawi is a landlocked country. The small-scale enterprises seem to be more concerned by this

challenge, compared to the medium and the large-scale enterprises, more especially in the retail subsector.

68

Sub

−Sah

aran

Afr

ic

Sou

th A

fric

Mal

aw

Zam

bia

Zim

bab

we

Ken

ya

Mo

zam

biq

ue

Tan

zan

ia

2008 2014

Firms identifying transportation

as biggest obstacle 11.4 2.7

Firms identifying transport as a

major constraint (%) 24.6 15.7





Retail n.a 18.5

Services 21.1 15.2

Exporter (portion of sales are exports)

34.2 15.3

Domestic only firm (no exports 24.4 15.7



Firm has no foreign ownership 24.4 17.0

Firm has at least 10% foreign

ownership 25.4 12.9

Lilongwe 29.0

Blantyre 25.8

Mangochi 9.2

Mzimba 22.4

6.7 WATER SHORTAGES

Earlier on in Chapter 4, it was highlighted that Malawi faces challenges of water unavailability to

industry and agriculture production. Some of the key issues that were identified as causes are: (i)

increasing climatic and hydrological variability, and limited resilience to floods and droughts; (ii) limited

stock of water storage and irrigation infrastructure; (iii) degradation of watersheds leading to increased soil

erosion and sedimentation; (iv) inefficient water utilities that lack autonomy and accountability; and (v)

limited maintenance of distribution infrastructure resulting in significantly high leakage rates. It was also

noted that the supply of water in the country is growing slower than the demands of a growing population.

The 2014 enterprise survey confirmed that water shortages and disruption pose challenges to Malawi

enterprises. In the 2014 survey, firms reported experiencing on average 5.3 insufficiencies of water in a

typical month, a concerning deterioration from 0.5 in 2008. This is the highest estimate amongst comparable

countries in the region, and almost triple the average number of days of water disruptions in Sub-Saharan

African countries (see figure 37). Water outages were reported more prominent by enterprises in Blantyre

city at 6.4 insufficiencies in a typical month, compared to 4.1 in Lilongwe. Large and small scale enterprises

identified themselves with this problem more than the medium enterprises, with more effects noted on

manufacturing firms.

TABLE 23: WATER INSUFFICIENCIES

Malawi 2008 2014

Number of Water

Insufficiencies in a typical month

0.5 5.3

Firm Size

Small 0.5 5.6

Medium 0.4 3.9

Large 0.5 5.8

Firm Type


Retail/services 0.5 n.a.

Location

Blantyre 6.4

Lilongwe 4.1


FIGURE 37: WATER INSUFFICIENCIES IN A TYPICAL MONTH

69

Sub

-Sah

aran

…

Sou

th A

fric

a

Mo

zam

biq

ue

Zim

bab

we

Tan

zan

ia

Ken

ya

Zam

bia

Mal

awi

CHAPTER 7: CONCLUSIONS FOR POLICY ON INVESTMENT CLIMATE IN ORDER TO

IMPROVE PRODUCTIVITY

The foregoing chapters have analyzed the constraints that firms in Malawi’s current investment climate

face. A few issues have come out requiring policy interventions that can improve the investment climate

and enhance the productivity of firms in the country. From the analysis it is clear that policymakers face

one distinct pathway, namely, the reinforcement in the approach to private sector development in which

case, given all the challenges the country faces, the pace of reform for Malawi needs to be focused on

critical areas, be more rapid, deep and consistent. This is necessary if Malawi is to enhance and maintain

its competitiveness in a fast moving world. This paper proposes that the focus should be in four main areas

as follows:

(a) Increase the productivity of firms, particularly medium sized ones: Advantage must be taken

of Malawi’s higher levels of labor productivity and lower unit cost levels than neighboring countries

to attract investment into labor-intensive industries. While doing this, labor productivity of medium

firms needs to be improved, as does their use of capital. An increase in labor-intensive industries

can create jobs that through increasing incomes, can positively reinforce capital utilization for

domestic markets. Market access to regional and global markets can also enhance capital utilization

as more products are produced for those markets. Further to these partial productivity measures,

total factor productivity needs to be improved inter alia through development of an innovation

ecosystem and frameworks that facilitate high levels of technology diffusion; and research and

development, education and skills development that promote agricultural production and feed into

the labor needs of emerging manufacturing sub-sectors.

(b) Improve access to finance: Access to finance was the biggest obstacle to enterprise development

in 2014 and skewed in disfavor of poor local and rural enterprises. A typical firm owned by local

Malawian entrepreneurs in rural areas, especially from the firms headed by women, and those that

are involved in production for the domestic market seemed to be more concerned about this

challenge. The proportion of loans requiring collateral and the average value of collateral

requirements are very high in Malawi. As a result, only a small proportion of working capital in

Malawi is financed by banks, and this is skewed in favor of large scale enterprises, those engaged

in the manufacturing, those engaged in export trade and the firms that are foreign owned. Most of

the firms depend heavily on funding their working capital and investments through own funds.

From 201432, there have been positive developments aimed at improving access to finance. For

example, the Credit Reference Bureau Act was amended in 2016, and the Collateral Registry was

launched in the same year, opening up prospects for responsible reduced risk in lending and

movable-asset lending respectively. Implementation of the two reforms is in its early stages but

shows some promising results. A Warehouse Receipts Bill has been drafted, and is scheduled for

submission to parliament soon. The payments system infrastructure continues to be developed and

other innovations in electronic payments are mushrooming. Thoughts around the setting up of

institutions for long term development financing are featuring in the dialogue on several fora

including the Public Private Dialogue (PPD) meetings. Government seems to be committed to this,

but implementation of these plans needs to be expedited. On a broader level, efforts on

macroeconomic fundamentals are bearing fruit, with the Reserve Bank of Malawi’s policy rate

declining to 22 per cent in 2017 and inflation reducing to 14.2 percent in May 2017.

32 Establishment of Export Development Fund (EDF) is one of the earlier initiatives to increase access to finance by firms

engaging in export.

70

Concerted efforts need to be engaged to ensure the following:

(i) That the macro economy continues to stabilize, interest rates lower, and sufficient space is

created for the private sector to thrive including through settlement of arrears to ensure

private sector players are able to finance their operational costs and implement expansion

plans.

(ii) That the improvement of credit reporting infrastructure, collateral registry, warehouse

receipts as well as commodity exchange frameworks are fully developed and implemented

and that ensure that these respective systems are functioning effectively.

(iii) That the country develops a long term financing institution to provide concessional lending

to the private sector and hence ensure sustainable expansion of their operations.

(iv) That new financial instruments are introduced, especially those targeting the

disadvantaged. Such efforts need to ensure increased access to finance by small and

medium-scale enterprises with attention to the disadvantaged identified across the different

categories. In particular, the gender disparities noted in access to finance need to be

interrogated further and addressed. Examples include leasing, and factoring, which can be

done within the framework of the Collateral Registry.

(v) To support the missing middle, effective channeling of rapidly accumulating pension funds

needs to be given priority including through improvements to the Malawi Stock Exchange

so that more companies are publicly listed.

It is recommended that IFC should strengthen its support on the access to finance agenda, such as to provide

funds for agriculture financing and for FIs in Malawi to develop new and innovative lending instruments

such as for specific investments or specific industries.

The Financial Sector Technical Assistance Project financed from the World Bank is due to close in August,

2017, but a number of activities on that project are yet to be concluded. Some of the critical elements include

digital solutions to strengthen micro finance institutions and SACCOs (including those in remote rural

areas) and integration of mobile network operators to the national switch infrastructure. It is recommended

that these activities be seen to their conclusion.

RBM is developing a leasing framework together with a diagnostic study examining the applicability of a

long term finance guarantee scheme. It is recommended that this work be expedited along with efforts to

establish long term financing institutions, such as a development bank in Malawi. The Export Development

Fund operations need to be improved and that the institution should be made more visible and able to reach

out to more clients that require financing for their enterprise operation. IFC could be engaged to support

these areas.

There is need to review the credit culture in Malawi to facilitate the flow of credit. In theory, this could be

a result of low levels of financial literacy, limited ability to manage collateralized loan portfolio by financial

institutions, and lack of consequences as a result of limited enforcement action. Development partners

including the World Bank should support Government to deal with these issues, including the

implementation of robust financial literacy programs, public awareness of various opportunities, reviewing

legal framework to protect FI in cases of default, identification of necessary adjustments to the legal and

regulatory environment and development of disclosure frameworks for banks and MFIs products.

71

The Malawi Resilience Development Policy Financing (DPO) has so far helped to support Government

efforts to implement good policies which promote favorable macroeconomic environment, that in turn

translate into conducive business environment. While asking government to ensure that there should be

long lasting progress in promoting macroeconomic stability, follow up World Bank support to the DPO

should aim at strengthening this aspect, which ultimately will lead to a much more favorable business

environment through sustained low inflation and interest rates.

(c) Focusing finite public sector resources where they can achieve the greatest impact:

Improvements in Infrastructure: Malawi needs to focus finite public sector resources where they

can achieve the greatest impact. The evidence of this analysis points to the poor supply of utilities,

particularly electricity and water, as major binding constraint to growth. In the 2014 enterprise

survey, 24.8 percent of firms identified electricity as a major obstacle to their businesses. This was

the second biggest obstacle overall, but was first for largescale enterprises in the manufacturing

subsector. Firms expressed concerns about challenges experienced to get connected to the

electricity grid. Infrastructure improvements in this area and transport, therefore, should be

prioritized, to ensure that enterprises have the basic requirements to operate efficiently.

It is imperative that the legal and policy reforms that have commenced in the energy sector, are

effectively implemented to lead to the sound regulation of the sector that will translate to substantial

investment. Within this, it is essential that independent power producers be effectively brought on

stream utilizing all alternative sources of renewable energy to ensure adequate supply of electricity

in both rural and urban areas. The national grid needs to continue to be expanded to uptake and

efficiently transmit the resultant increased generation of electricity. Efforts to implement

interconnectors with Malawi’s neighbors such as Mozambique and Zambia must also be reinforced

to ensure that Malawi participates in electricity trade through the Southern African Power Pool

(SAPP). These efforts should go along deeper efforts to improve the governance and efficiency of

utility suppliers

Alongside the upgrading of electricity, the government should also prioritize investments into water

and transport infrastructure. On transportation, the Government should prioritize efforts to develop

the Nacala Corridor, and improve railway connectivity as well as road networks in order to reduce

domestic and international trade costs and make Malawi’s products more competitive.

(d) Address policy and regulatory challenges to increase predictability and transparency and

curb corruption. Corruption is increasingly seen as a big challenge to business activities in

Malawi. This is experienced more significantly in the areas where businesses interact with

Government (G2B services) which are also key factors for successful setting up and operations of

business enterprises, such as on getting permits for construction, getting electricity connected,

obtaining import licenses and acquiring land. Alongside corruption, Malawi faces challenges of

lack of predictability in implementation of policy measures, which would otherwise encourage and

facilitate businesses.

Measures need to be taken, especially in Government institutions, to ensure transparency and

accountability, as well as to improve the provision of services that facilitate businesses. In this way,

private sector would not lose resources and time to get services that support growth of their

enterprises. Strong measures are required to curb corruption in all its forms. A number of measures

are proposed as follows: -

(i) Ensure simplification and automation of G2B systems such as licensing and business

permit systems. With time there is also a need to automate the majority of payment

transactions with Government institutions such as for taxes. These would play a big part

72

in curbing corruption in such areas of interaction between the private sector and

Government. This should be preceded by building and implementing necessary literacy

programs. Lack of clarity is a major enabler of corruption and demands of informal

payments by government officials. Much greater efforts are needed to make existing

regulations, including tax and licensing requirements, simpler, more accessible and easier

to implement.

(ii) Malawi should ensure highly sound public financial management system and processes.

Improving the IFMIS infrastructure, and the reductions of cash transactions in favor of e-

payments for Government services are some of the important steps that Government should

expedite on.

(iii) The Anti-Corruption Bureau and other governance institutions should be adequately

financed and be provided with the necessary independence to effectively execute their

mandates. Implementation of reforms in land governance (new set of land laws) that have

commenced should be effectively done to ensure predictability and ease in acquiring land

by investors. Automation of land registration should be prioritized to ensure improvement

on transparency. Government institutions mandated to provide land to investors, such as

the Malawi Investment and Trade Centre, should be empowered to undertake their

functions effectively. Some of the old protective laws such as the Control of Goods Act

and the Special Crops Act should be reviewed to allow for transparency and predictability

in investment decisions and trade across the country’s borders particularly for agriculture

and agribusiness. Deeper efforts will be required to both ensure adequate consultation in

policy changes, build in mechanisms that prevent ad hoc policy shifts, and address

inconsistencies between the practices of different regulatory bodies. Apart from the

automation of tax processes highlighted above, a comprehensive tax review needs to be

prioritized, with due regard to feasibility assessments of spatial zones, which could

potentially provide special tax incentives as an inherent feature of their design.

73

BIBLIOGRAPHY

Clarke G. R. G. (2010). Are Managers' Perceptions of Constraints to Growth Reliable? Evidence from a

Natural Experiment in South Africa. Texas: A&M International University.

Clarke, G. R. G. (2011). Assessing How the Investment Climate Affects Firm Performance in Africa:

Evidence from the World Bank’s Enterprise Surveys, World Background paper IV, Washington DC: World

Bank.

Clarke, G. R. G. (2012). Manufacturing Firms in Africa: Some Stylized Facts about Wages and

Productivity, Texas: A&M International University.

Clarke, G. R. G. (2014). Firm Performance in the West Bank and Gaza. Journal of International Finance

Studies 14(3).

Clarke, G. R. G. (2015). The Impact of Different Aspects of the Investment Climate on Total

FactorProductivity: Evidence from the World Bank's Enterprise Surveys. Texas: A&M International

University.

Clarke, G. R. G., Qiang, C. Z., and Xu, L. C. (2015). The internet as a general-purpose technology: Firm-

level evidence from around the world. Economics Letters.

Dollar, D., Hallward-Driemeier, M., and Mengistae, T. (2005). Investment climate and firm performance

in developing countries. Economic Development and Cultural Change 54(1).

Escribano, A., and Guasch, J. L. (2005) Assessing the impact of the investment climate on productivity using

firm-level data: Methodology and the cases of Guatemala, Honduras and Nicaragua. Policy Research

Working Paper No. 3621.Washington DC: World Bank.

Escribano, A., Guasch, J. L., Pena, J., and de Orte, M. (2005) Investment climate assessment on productivity

and wages: Analysis based on firm level data from selected South East Asian countries. Washington DC:

The World Bank.

Felipe, J. (2008). What Policy Makers Should Know About Total Factor Productivity. Malaysian Journal

of Economic Studies, Vol. 45 No. 1, 2008.

Fisman, R., and Svensson, J. (2007) Are corruption and taxes really harmful to growth? Firm level evidence.

Journal of Development Economics 83(1).

Gatti, R., and Love, I. (2008) Does access to credit improve productivity? Evidence from Bulgaria.

Economics of Transition 16(3): 445-465.

Gonzelez, A., Lopez-Gordova, J.E., Valladares, E.E. (2007). The Incidence of Graft on Developing Country

Firms, World Bank Policy Research Working Paper, Washington DC: World Bank.

Government of Malawi (various years), Annual Economic Reports, Lilongwe, Ministry of Finance,

Economic Planning and Development

Government of Malawi. (2011). Malawi Growth and Development Strategy II. Lilongwe: Ministry of

Finance, Economic Planning and Development

74

Greene, W. (2002) Econometric analysis. 5th edition. New Jersey: Prentice-Hall.

Halvorsen, R., and Palmquist, R. (1980). The interpretation of dummy variables in semilogarithmic

equations. American Economic Review.

Harrison, A. E., Lin, J. Y., and Xu, L. C. (2014). Explaining africa's (dis)advantage. World Development

63(1): 59-77.

Iarossi G., and Clarke, G. (2011). Nigeria 2011: An Assessment of the Investment Climate in 26 States,

Washington D.C., World Bank

Kaplan, D. S., and Pathania, V. (2010). What influences firms' perceptions? Journal of Comparative

Economics 38(4): 419-431.

Kumbhakar, S. C., and Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge: Cambridge

University Press.

Levinsohn, J. (2008). Comments on methodology used by escribano, guasch, and co-authors in analyzing

World Bank investment climate surveys. Ann Arbor: University of Michigan.

Levinsohn, J., and Petrin, A. (2003). Estimating production functions using inputs to control for

unobservables. Review of Economic Studies 70(2): 317-341.

Malomo, F. (2013). Factors influencing the propensity to bribe and size of bribe payments: Evidence from

formal manufacturing firms in west Africa. Brighton: University of Sussex.

Malawi Confederation of Chambers of Commerce and Industry (2015), Malawi Business Climate Survey.

Blantyre: MCCCI

Malawi Confederation of Chambers of Commerce and Industry (2016), Malawi Business Climate Survey.

Blantyre: MCCCI

Olley, S., and Pakes, A. (1996) The dynamics of productivity in the telecommunications equipment

industry. Econometrica 64(6): 1263-1298.

Pakes, A. (2008) Comments on methodology used in analyzing World Bank investment climate survey.

Cambridge: Harvard University.

Record, R., Clarke, G.H., Chilima, E. (2016). Malawi Undersized Private Sector, What are the Constraint

to Higher productivity and Increased Competitiveness? Malawi Country Economic Memorandum 2016

Background Paper Series.

Reserve Bank of Malawi. (2014a), Financial Institutions Supervison, Annual Report 2014. Lilongwe:

Reserve Bank of Malawi.

Reserve Bank of Malawi. (2014b), Financial Stability Report June 2014. Lilongwe: Reserve Bank of

Malawi.

Reserve Bank of Malawi (2016), Financial Stability Report December 2016, Lilongwe: Reserve Bank of

Malawi.

Deraniyagala, S. and Kaluwa, B. (2011) Macroeconomic policy for employment creation: The case of

Malawi, ILO Employment Working paper. Geneva: International Labor Organization.

75

Svensson, J. (2003). Who must pay bribes and how much? Evidence from a cross section of firms. Quarterly

Journal of Economics 118(1): 207-230.

Tybout, J. R. (2003). Plant -and firm- level evidence on 'new' trade theories. In E. K. Choi & J. Harrigan

eds., Handbook of international trade (pp. 389-415). Malden, Mass.: Blackwell Publishers.

Van Ark, B. (2014). Total Factor Productivity: Lessons from the past and directions for the future. [Online].

Available at: https://www.nbb.be/doc/ts/publications/wp/wp271en.pdf

Verhoogen, E. (2008). Comments on escribano-guasch methodology for estimating the effects of the

investment climate on productivity. New York: Columbia University.

World Bank. 2007. Turkey - Investment climate assessment: from crisis to private sector led growth.

Washington D.C.: World Bank.

World Bank. (2006). Malawi Investment Climate Assessment. Washington D.C.: World Bank.

World Bank. (2014). Lao PDR Investment Climate Assessment: Policy Uncertainty in the Midst of

Natural Resource Boom. Washington D.C.: The World Bank.

World Bank. (2016a). An Assessment of the Investment Climate in Nigeria, The Challenges of Nigeria’s

Private Sector, Washington, D.C.: World Bank.

World Bank. (2017a). Malawi Economic Monitor – Harnessing the Urban Economy, Washignton D.C.:

World Bank

World Bank. (2017a). Doing Business Indicators. Washington, D.C.: World Bank.

World Bank. (2017b). World development indicators. Washington, D.C.: World Bank

World Bank. (2017c). Enterprise Survey Online Database. Washington D.C.: World Bank Group

Xu, L. C. (2011). The effects of business environments on development: Surveying new firm-level

evidence. World Bank Research Observer 26(2): 310-340.

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http://www.nbb.be/doc/ts/publications/wp/wp271en.pdf

ANNEX 1: MEASURING FIRM PERFORMANCE USING ENTERPRISE SURVEY DATA

The analysis presented in this background paper focuses on several measures of firm productivity. These

are calculated in a uniform way in all countries with available Enterprise Survey data from between 2006

and 2015.The accounting data is generally lagged one year from the year of the survey. So, for example,

the accounting data for a 2015 survey will be for fiscal year 2014.

The World Bank Enterprise Survey collect financial data in the local currency in the country being surveyed.

To compare firm performance across countries, there is need to convert financial data into a common

currency in a single year (i.e., to control for inflation and exchange rate differences). For surveys conducted

between 2006 and 2010, data are inflated to 2010 values in local currency using GDP deflator33. For surveys

conducted after 2011, the values are deflated for a different year. The values are then converted into US$

using 2010 exchange rates34.

However, it is important to note that since most firms in the sample sell their products primarily in local

markets, exchange rates have to be close to their equilibrium values in 2010 for these comparisons to be

very accurate. If the exchange rate in a given country is over- or under-valued, the comparisons will under-

or overstate firm performance for that country. Note that this proviso only applies to performance measures

that are measured in U.S. dollars. For measures that are ratios such as capital productivity or unit labor

costs, the exchange rates will cancel out during the calculations. As a result, these measures are mostly

unaffected by exchange rate fluctuations.

The individual measures are constructed in the following way;

Value-added. Value-added is value of the goods and services that the firm produces less the cost of the raw

materials (such as iron or wood) and intermediate inputs (such as engine parts or textiles) used to produce

the output. Output is measured in local currency not in physical units. The cost of raw materials,

intermediate inputs, electricity and fuel are subtracted from output to get value-added. Firms report

electricity and fuel costs separately from raw materials and intermediate inputs. Firms that do not report

sales or raw materials and intermediate inputs are dropped from the analysis. Electricity and fuel costs are

treated as if they are zero for firms that do not report electricity or fuel costs (i.e., the firms are not

dropped).This is done because dropping firms that do not report electricity or fuel costs would have a

significant impact on sample size and because electricity and fuel costs are small relative to sales and raw

materials.

Number of workers. The number of workers is the number of permanent and temporary full-time workers.

Temporary workers are weighted by the average length of employment for these workers. So, for example,

if the average length of employment for a temporary worker was 6 months, the weight for temporary

workers would be ½. If this is not done, it would result in very small sample estimates. Data on part-time

workers is not collected in most countries (outside of Sub-Saharan Africa) and so part-time workers are

omitted for reasons of comparability. In practice, for countries with data on part-time workers, including

these workers do not have a large effect on relative rankings. Firms that do not report permanent or

temporary workers are dropped for measures that use workers (e.g., value-added per worker).

33 GDP deflators is used rather than sector specific deflators because sector specific deflators are not

available for most countries. 34 The 2010 values are deflated using a common year’s exchange rate because of concerns that fluctuations in the value of the dollar might otherwise make comparisons difficult.

77

Labor Productivity. Value-added per worker is the basic measure of labor productivity used in this paper.

It is value-added divided by the number of full-time workers in the firm (see above). Firms that produce

more output with less raw material and fewer workers have higher labor productivity.

Capital Intensity. There are two measures of capital in the Enterprise Survey. The first measure is the book

value of capital. For firms that keep detailed financial accounts, this measure should be the value of capital

taken from those accounts. For other firms, it will either be omitted or estimated by the manager. In the

World Bank’s survey implementation manual, this variable is described as follows: “The net book value

represents the actual cost of assets at the time they were acquired, including all costs incurred in making the

assets usable (such as transportation and installation) minus depreciation accumulated since the date of

purchase.”

The second measure is the sales value of capital. The manager is asked to estimate the value of the capital

if sold in its current condition. Although this is probably closer to the true value of the capital, it has some

shortcomings. In particular, when markets for capital equipment are thin, it might be difficult for the

manager to give an accurate estimate. The implementation manual notes: “Ask the manager to estimate the

market value if all of the equipment, land and buildings were sold on the open market. If the respondent

states that there is no market, ask how much the respondent would be willing to pay for the capital, knowing

what it can produce in its current condition. Estimate how much it would cost to buy machinery in the

current market which is similar in terms of age and characteristics.”

In the empirical analysis, focus is placed on the sales value of capital. This is done because it is closer to

the economic concept of capital. Capital intensity is capital per worker. This is calculated by dividing capital

by the number of workers. Firms that do not report these measures have to be dropped when calculating

total factor productivity.

Capital productivity. This is the ratio of value-added to capital. It can be constructed either using the book

value or sales value of capital. When a firm produces a lot of output with little capital, capital productivity

will be high. Firms that do not provide information on capital or enough information to calculate value-

added are dropped.

Total factor productivity/technical efficiency. This measure of productivity takes both labor and capital use

into account. The methodology is described in detail in Annex 3.

Labor costs per worker. The cost of labor is the cost of wages, salaries, bonuses, other benefits, and social

payments for workers at the firm divided by the number of workers. The data is taken from the firms’

accounts. It includes wages and salaries paid to all workers and managers – not just production workers.

We divide this by the number of workers to get labor costs per worker. Firms are only dropped from these

averages when they do not report labor costs or workers.

Unit labor costs. This measure is labor costs divided by value-added. Although it is an approximation to

true unit labor costs (i.e., it measures output in dollars rather than as physical measure of production), it can

be calculated using information from the Enterprise Surveys. Unit labor costs are higher when higher labor

costs are not fully reflected in higher productivity.

Employment and sales growth. Firms are asked about employment in the most recent fiscal year and two

years earlier (e.g., for 2015 surveys, firms report 2013 and 2014 employment). Because the question for

lagged employment only asks about full-time permanent employees, we use this as the measure of

employment. That is, we do not include temporary or part-time workers for either the initial or final year.

78

The formula that we use is:

Employment Growth 1

2

Employmentt Employmentt 2

(1)

Rather than using the initial value of employment as the denominator when calculating the growth rate, we

use the approach used in Davis and Haltiwanger (1992). This measure is preferable to a measure that divides

by initial employment because it does not become very large when initial sales or employment are small.

We divide it by the growth rate by two to get an annual growth rate.

It is important to note that initial employment is reported retrospectively. So, for example, for a 2015 survey,

the manager reports employment in both 2013 and 2014 during the same 2015 interview. That is, this

calculation is based on recall data reported in 2015 not on data from separate surveys in 2013 and 2014.

We use an analogous measure for sales growth. Sales in both years are deflated into 2010 local currency

units to get real, rather than nominal sales growth. See the description above for information on deflators.

79

ANNEX 2: CROSS COUNTRY COMPARISON USING ENTERPRISE SURVEY DATA

In this annex, graphs are presented that plot each of the partial productivity measures discussed in the main

body of the report and in annex 1 for countries where Enterprise Surveys was conducted, against per capita

GDP35. The regression line comes from a simple linear regression of the partial productivity measure on per

capita income and a constant. Within countries, the partial productivity measures are weighted medians or

means. The regressions are based on unweighted country-level regressions. This is done to avoid giving

large countries greater weight in the regressions. Although the graph only includes countries with per capita

GDP less than US$ 12,000, these countries are excluded only for presentational purposes—they are

included when calculating the regression line. If a point is above the regression line, the partial productivity

measure is greater than would be expected given the county’s per capita income. If it is below the regression

line, the opposite is true.


Note: All data points are for the median firm on each measure of performance. For presentational purposes the chart is shown

only for countries with per capita GDP between $0 and $12,000. Countries with GDP per capita over this amount are, however,

included when we calculate the linear projection

35 We do this because many, although not all of the measures, appear to vary consistently with per capita GDP 36 Note for Figure 40: For presentational purposes the chart is shown only for countries with per capita GDP between $0 and $12,000. Countries

with GDP per capita over this amount are included when we calculate the linear projection. The country average estimates are from the LAD

regressions, although the results are virtually identical when coefficients from the OLS and frontier regressions are used instead. For example,

labor productivity, capital intensity, and per worker labor costs all appear to increase as per capita GDP increases. In contrast, other measures

such as unit labor costs do not appear to increase consistently with per capita income (Clarke, 2012).

80

TFP Malawi Linear (TFP)

FIGURE 41: TFP IS HIGHER IN MALAWI THAN COUNTRIES AT

SIMILAR LEVEL OF DEVELOPMENT36

TFP relative to Malawi (Malawi=100%)

480%

400%

320%

240%

160%

80%

0%

Malawi Linear Capital Productivity Per capita GDP (2011 PPP $)

(Capital Productivity)

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

400%

350%

300%

250%

200%

150%

100%

50%

0%

FIGURE 40: MALAWI'S LABOR PRODUCTIVITY AND CAPITAL INTENSITY

ARE HIGH BUT CAPITAL PRODUCTIVITY IS LOW

FIGURE 39: MALAWI CAPITAL INTENSITY - HIGHER THAN WOULD BE

CONSIDERING THE COUNTRY'S LEVEL OF DEVELOPMENT

$20,000

$17,500

$15,000

$12,500

$10,000

$7,500

$5,000

$2,500

$0

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

Per capita GDP (2011 PPP $) Capital Intensity Malawi Linear (Capital Intensity)

FIGURE 38: MALAWI LABOR PRODUCTIVITY - HIGHER THAN WOULD BE

EXPECTED GIVEN THE LEVEL OF DEVELOPMENT

$22,500 $20,000 $17,500 $15,000 $12,500 $10,000

$7,500 $5,000 $2,500

$0 $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

Per capita GDP (2011 PPP $)

Labor Productivity Malawi Linear (Labor Productivity)

Val

ue-

added

per

wo

rker

(2009 U

S$)

Val

ue

added

div

ided

by c

apit

al

Cap

ital

per

work

er (

sale

s val

ue,

2009 U

S$)

TF

P (

rela

tive

to M

ala

wi)

FIGURE 42: DESPITE HIGH LABOR COSTS, UNIT COST IN MALAWI

REMAINS REASONABLE

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

Per capita GDP (2011 PPP $) Per worker labor costs

FIGURE 44: FEW FIRMS IN MALAWI PROVIDE WORKERS WITH FORMAL

TRAINING

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Per capita GDP (2011 PPP $) % of firms with training program

Malawi

Linear (% of firms with training program)

Graphs below present plot of unit labor costs, employment growth over a period of three years, and

provision for formal training. Like in the analysis above, if a point is above the regression line, the partial

productivity measure is greater than would be expected given the county’s per capita income. If it is below

the regression line, the opposite is true.


81

NOTES:

Per capita GDP (2011 PPP $)

Ave. growth rate

Linear (Ave. growth rate)

FIGURE 43: OVER THE PAST THREE YEARS, EMPLOYMENT GROWTH

WAS SLOW AMONG MANUFACTURING FIRMS

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

% o

f fi

rms

wit

h f

orm

al t

rain

ing p

rogra

ms

Unit

lab

or

cost

s (%

of

val

ue-

add

ed)

Em

plo

ym

ent

gro

wth

(ave.

an

nu

al

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ANNEX 3: ESTIMATING TOTAL FACTOR PRODUCTIVITY USING ENTERPRISE

SURVEY DATA

This annex 3 describes how total factor productive (TFP) for the cross-country TFP comparisons are

estimated and how the within-Malawi-comparisons for different types of firms are estimated. It also

describes how changes to the investment climate that might improve TFP in the country are estimated.

A. Methodology to Estimate Total Factor Productivity

Total factor productivity (TFP), or Technical Efficiency (TE), is the residual from a regression of the log

of output (either value-added or revenue) on labor, capital, and other intermediate inputs. Following Caves

and Barton (1990), this annex uses value-added rather than sales as the dependent variable. The estimation

assumes a Cobb-Douglas Production function:37

Y A KL (2)

i i i i

Y is value-added for firm i, K is a measure of capital (e.g., the book value or replacement value of capital),

L is the number of workers and A is TFP or TE. Because constant returns to scale are not imposed,

productivity can differ by firm size. When A is larger, the firm produces more output with the same amount

of capital and labor.

Taking natural logs of both sides implies:

ln Yi ln Ki ln Li i

(3)

Where:

A evi e

i

That is, firm i’s productivity is equal to a constant, μ, and a firm-specific measure of productivity, εi. It is

easy to generalize this into a more general ‘augmented’ production function where the error term is:

vi FCi ICi i

FCiis a vector of variables representing the characteristics of firm i and ICi represents the investment climate that firm i faces. This implies that:

ln Yi ln Ki ln Li FCi ICi i

(4)

(5)

To allow productivity to be different in different countries, the regressions include a vector of country-year

dummies ( c ).The country-year dummies control for country-level differences that might affect

productivity.38 For example, firms in some countries might be more productive if infrastructure is better

quality, the rule of law is stronger, workers are more highly skilled or better educated, or the country is

more economically or politically stable than in other countries. Although some of these differences in parts

of the empirical analysis are controlled when investment climate variables in the regressions are included,

it is difficult to control for all aspects of the investment climate. By comparing the coefficients on the

country dummies, average levels TFP in Malawi with TFP in other countries39 can be compared. This

implies:

37 It is possible to make other assumptions about the functional form of the production function (e.g., to assume a trans-log

production function), although this does not appear to have a significant impact on the results in most cases. See, for example,

Clarke (2014). 38 That is, for countries with multiple surveys we include separate dummies for each survey. This allows macroeconomic factors

that might affect TFP to differ over time. For brevity, we will usually refer to these as ‘country dummies’ rather than ‘country-

year’ dummies unless we want to explicitly remind the reader that they are country-year dummies. 39 For the LAD regressions, the coefficients are better thought of as median levels of productivity than average levels.

82

ln Yi c ln Ki ln Li FCi ICi i

(6)

Equation (5) can be estimated by Ordinary Least Squares (OLS) as long as the basic assumptions of the

linear model are met. One important assumption is that capital and labor must be uncorrelated with the error

term. This implies any shock that affects productivity must be uncorrelated with the firms’ capital and labor

choices. This would be violated if, for example, managers were aware of something that affected

productivity and decided to hire or fire workers or invest in new machinery and equipment to take advantage

of a temporary or permanent increase in productivity. Managers might do this, for example, if they received

technical advice from one of their suppliers or buyers that improved their firm’s productivity and then the

manager decided to hire more workers to take advantage of this improved know-how.

Characteristics of the firm and the investment climate can also be directly included in the OLS regression

as long as these characteristics are exogenous.40If becoming an exporter makes a firm more productive (e.g.,

through exposure to foreign markets or greater competition) then a dummy variable indicating that the firm

is an exporter could be included in the regression if productivity did not affect the firm’s decision to become

an exporter. This would be violated, however, if, for example, a firm became more productive and decided

that this meant that it could start exporting.41 Similarly, an investment climate variable could be included

representing how much the firm pays in bribes if productivity did not affect how much the firm pays. If, for

example, corrupt bureaucrats target firms that are especially productive—perhaps because productive firms

are more profitable—then this assumption would be violated.42

Rather than including firm or investment climate characteristics directly in the model, it is possible to first

estimate equation (2) through OLS or another more robust estimation method, obtain estimates of TFP by

calculating ε for each firm from equation (2) and then regress the residuals on the firm and investment

climate characteristics (e.g., estimating equation (3)). An advantage of this approach is that if panel data is

available it might be possible to estimate equation (2) using a robust technique such as the method suggested

by Levinsohn and Petrin (2003) and then use something such as 2SLS in the second stage if one of the firm

or investment climate characteristics were thought to be endogenous.43

The drawback of this second approach is that if the firm level or investment climate characteristics are

correlated with the amount of labor and capital that the firm uses (i.e., if the manager’s knowledge about

the investment climate affects the firms use of labor or capital) then estimates of the coefficients in equation

(2) will be biased.44 As a result, TFP (i.e., the error terms) will be estimated incorrectly and the coefficients

from the second stage will also be biased. It seems this will often be the case. Escribano and Guasch (2005),

argue that “this is almost always the case since the inputs are correlated with the Investment Climate (IC)

variables and least squares estimators of [equation 2] are inconsistent and biased. ”For this reason,

estimation is done in a single step in this report.

Another concern about OLS is that outliers can have a significant effect on OLS estimates. If some firms

misreport their sales, capital, or workers, this can affect estimation considerable. Given that many of the

firms in the analysis do not report data directly from company accounts—and that many small firms in

developing countries might not even keep detailed accounts—this can be a serious problem. In the

40 This also assumes that there are no omitted variables correlated with both the characteristics of the firm or investment climate

and productivity. 41 There is a large literature on whether exporting improves performance (learning-by-exporting hypothesis) or whether only

productive firms can export (self-selectivity hypothesis). The large literature on this topic is summarized in Tybout (2003) and

Bernard and others (2007). 42 Svensson (2003) find more profitable firms pay more in bribes than other firms do. 43 Gatti and Love (2008) do this allowing access to credit to be endogenous in the second step. 44 This is due to omitted variable bias. It is discussed in more detail in Chapter 7 in Kumbhakar and Lovell (2000) and in

Escribano and Guasch (2005).

83

Enterprise Survey for Malawi, for example, only about 50 percent of firms report that they have audited

accounts and only about 29 percent of firms always reported numbers directly from their accounts.45

Outliers can be dealt with in several ways. One way is the estimate the equation with a robust estimation

method such as a Least Absolute Deviations (LAD) estimator, which weights outliers less heavily than OLS

does.46 Another approach is to drop outliers. In OLS and frontier estimation, which are less robust to

outliers, we exclude outliers that report value-added per worker or capital per worker more than three

standard deviations from the mean for that country. In most countries, this drops about 10 percent of the

sample. In Malawi, about 10 percent of firms are dropped.47 As discussed below, however, the main results

are similar in the LAD regressions, which include outliers, and the other regressions, which exclude them.

In addition to concerns about outliers, other concerns have been discussed in the literature. These include

the functional form of the error term (ε) and whether the error term is correlated with capital or labor. There

are several methods that have been proposed regarding the functional form of the error term. Stochastic

frontier analysis allows for two error terms, a one sided term assumed to have a half normal distribution, ν,

representing technical efficiency and a two-sided normally-distributed error term, , representing temporary

shocks to productivity and measurement error. The frontier model is estimated using maximum likelihood

estimation. In the analysis, we use the LAD estimators as our base estimator. We also estimate the model

using standard OLS estimators and stochastic frontier analysis as robustness check.

In practice, the results are similar in the two models. As discussed below, the coefficients on the investment

climate and firm characteristic variables are similar in terms of size and statistical significance in the three

models. Further, the estimates of average TFP in different countries are also not sensitive to the way that

we estimate the production function. The correlations between the country-level estimates of productivity

from the three methods are between 0.966 and 0.981.48

That is, countries that appear more productive using one estimation method also appear more productive

using other methods. In the text, we therefore focus on results using the LAD estimator. The TFP measures

are also very highly correlated with labor productive with correlations between 0.913 and 0.952, indicating

that sectoral differences and differences in capital intensity explain relatively modest differences in labor

productivity.

TABLE 24: CORRELATION BETWEEN COUNTRY LEVEL ESTIMATES OF TECHNICAL EFFICIENCY

Median OLS Frontier Labor Productivity

Median 1.000 0.981 0.966 0.926

OLS 1.000 0.967 0.952

Frontier 1.000 0.913

Labor Productivity 1.000 Source: Author’s calculations based on data from the WBES.

45 An additional 34 percent reported some numbers from their accounts. 46 Due to concerns about outliers, LAD estimators are often used when estimating production functions. See, for example, Greene

(2002, pp. 449-450). 47 This does not include firms that refused to report productivity data (i.e., it is the percent of firms that reported enough data to

calculate total factor productivity that were more than 3 standard deviations away on those two dimensions for firms that reported

data). 48 Using Enterprise Survey data from a slightly smaller group of countries, Clarke (2014) shows that the coefficients on the

country dummies are very similar under a broad range of different assumptions. In addition to using OLS, LAD, and frontier

estimation, Clarke (2014) also estimates translog production functions, includes additional sector dummies and interaction terms,

and estimates a restricted model.

84

Note: Based on calculations from regressions with country dummies, sector dummies, and sector specific

factor intensities but no investment climate or firm characteristics included.

A broader problem is that things that affect productivity might affect firm managers’ choices regarding

capital and labor. If this is the case, OLS, stochastic frontier estimation and LAD estimation will all produce

biased estimates of the coefficients. Although several methods have been proposed to deal with this, they

require panel data (Levinsohn and Petrin, 2003; Olley and Pakes, 1996)—something that is not available

for most Enterprise Surveys.

A final concern is the analysis includes firms from many different areas of manufacturing. The production

function in equation 7 assumes all firms in all different areas use the same production technologies. A more

flexible estimation technique that allows firms in different sector to use different production technologies

might be preferable to a technique that assumes all firms use the same production technologies. This can be

done mechanically by including sector dummies in the regression and interacting the dummies with labor and

capital to allow different labor and capital intensities across subsectors of manufacturing.

The augmented production function then becomes:

ln Yij c j j log Kij j log Lij FCij ij ij j

(7)

The coefficients on labor and capital, β and γ, are assumed to be different in different subsectors. Subsector

dummies, α, are also included to allow for systematic differences in productivity.49 Models that include the

additional dummies and the interaction terms are sometimes referred to as ‘unrestricted models’ while the

models that assume identical production technologies are referred to as ‘restricted models.’ In practice,

including the extra terms does not appear to have a significant effect on the estimates of average TFP across

countries (Clarke, 2014).

B. General Methodological Issues

There are some well-known problems with this methodology. These include the following:

1. To make cross-country comparisons of TFP, value-added and capital have to be denominated in a

common currency (e.g., US dollars in these examples). Because these two variables are

denominated in local currency in the survey, cross-country comparisons of TFP are vulnerable to

exchange rate fluctuations. If the exchange rate is overvalued relative to its long-run equilibrium

then TFP might look artificially high. Although this can make it difficult to interpret differences in

TFP between countries, this does not affect coefficients on the investment climate or firm-level

variables. That is, because the estimation uses natural logs of value-added and capital, the country-

year dummies will control for exchange rate fluctuations.

2. The model assumes firms in different countries in the same sector use similar technologies. That is,

it assumes that garment firms in Bangladesh have the same production function as garment firms in

Slovenia. It also assumes that firms within broad sectors use similar technologies (e.g., that firms that

make t-shirts have similar production functions to firms that make dresses). In practice, the results

for the country averages appear robust to including additional sector dummies and interaction

terms.50

49 The analysis in this paper includes 12 sub−sector dummies and the related interaction terms. The sub−sectors are garments;

textiles; food and beverages; chemicals; metals and machinery; electronics; non−metal minerals; wood and furniture; paper and publishing; plastics; automobiles and parts; and other manufacturing. As noted below, the results are not highly sensitive to using more detailed breakdowns. 50 Clarke (2014), in particular, includes 176 subsector dummies, measured at the 4−figure ISIC classification level, and interacts the dummies with labor and capital (i.e., allowing different capital and labor intensities in each subsector). The results are similar to when 12 subsector dummies are included.

85

3. Capital is more difficult to measure than labor for both theoretical and practical reasons. In addition

to the standard problem of dealing with depreciation, few firms keep audited accounts and few

managers report figures directly from their accounts. Most firms will have purchased their

machinery and equipment over a long period—and sometimes in the distant past—meaning that

managers find it harder to estimate the value of their machinery and equipment than they do to

estimate things such as sales or expenditures over the past year. Because of this, capital is probably

measured less accurately than value-added or the number of workers the firm has. Even if the

mismeasurement is random, the coefficients will be biased. Because the coefficients are biased, TE,

which is the residual from the regression, will also be mismeasured when capital is mismeasured.

4. Ideally the measure of output would be a physical measure of output. In practice, because it is

difficult to obtain physical measures of output, most TFP analyses, including those using Enterprise

Survey data, use sales (i.e., output multiplied by unit price) as the dependent variable. Because

production is affected by price as well as quantity, these functions are sometimes referred to as

sales generating functions rather than production functions.51 With firms producing heterogeneous

products, this can be a problem if some have market power. That is, a firm with market power that

charges high prices (e.g., monopolists) would appear more productive than a similar firm in a

competitive market that charges lower prices.52

5. Because estimates are calculated in a regression framework, it is less straightforward to calculate

TFP than labor productivity. One issue is that estimates of TFP for groups of firms do not have

natural units. For cross country comparisons, TFP is shown as percent of TFP in Malawi. For other

groups (e.g., exporters) differences are presented in terms of a base category (e.g., non-exporters).

6. As noted by Escribano and Guasch (2005), there is no single accepted approach to estimating TFP.

For this reason, following Escribano and Guasch (2005), we estimated the model in several different

ways to check the robustness of results. We therefore estimate the model in various ways making

different assumptions about the error terms (i.e., we present estimates from stochastic frontier, OLS,

and LAD models.

7. Recent studies have noted that inputs in the production function (labor and capital) are endogenous

(Levinsohn and Petrin, 2003; Olley and Pakes, 1996). This can affect the estimation of TFP. With

panel data, it is possible to control for this using sophisticated econometric techniques,

instrumenting for inputs with intermediate inputs or investment. In this case, without a long panel,

these methodologies cannot be implemented.

C. Methodology Issues Related to Investment Climate Variables

As noted earlier, investment climate variable can be directly included in the productivity regressions as

long as they are exogenous. That is, they can be included as long as productivity does not affect the measure

51 See, for example, the discussion in Pakes (2008). 52 See, for example, the discussion by Levinsohn (2008) on the Escribano-Guasch methodology (Escribano and Guasch, 2005;

Escribano and others, 2008; Escribano and others, 2005).

86

of the investment climate (exogeneity) and as long as there are no omitted variables that are correlated with

both productivity and the investment climate (no omitted variables).

To avoid the omitted variable problems, the regressions include many investment climate variables. These

include variables to control for the burden of regulation, petty corruption, the cost of crime and security,

the reliability of electricity supply, the availability of bank credit, the ease of Internet access, access to

foreign technologies, and the quality of workers. The regressions also include country dummies, which

should help control for other omitted investment climate variables that might affect firm performance.

All investment climate variables included in the analysis are objective. Although the Enterprise Surveys

also include subjective questions about managers’ perceptions about the investment climate, these variables

are not included in the regressions because these variables might not be comparable across firms, regions,

or countries. Various biases have been shown to affect subjective questions in ways that might affect the

reliability of regressions coefficients. For example, Kaplan and Pathania (2010) show that macroeconomic

performance affects managers’ perceptions about unrelated aspects of the investment climate. Similarly,

Clarke (2010) shows that a power crisis in South Africa appears to have affected managers’ perceptions

about unrelated areas of the investment climate. If macroeconomic stability or other things that affect

perceptions also affect firm performance, this could bias coefficients on subjective variables.

In addition to concerns about omitted variables, it is also possible that some investment climate variables

are endogenous. For example, better performing firms might be more able to pay for training programs or

internet access and find it easier to get bank loans. If these variables are included in productivity regressions,

their coefficients—and coefficients on others—would be biased. Even if productivity does not affect the

investment climate variables directly, omitted firm-level variables such as the manager’s ability might be

correlated with firm productivity and with aspects of the investment climate such as how well the firm

handles power outages or corruption. Because of these concerns, it is difficult to include investment climate

variables directly in firm-level productivity regressions.

Because of concerns about endogeneity, we do not include the investment climate variables directly in the

productivity regressions. Instead, we average the investment climate variables across similar firms in the

same city and include the averages in the regression. The averages are taken over all firms of similar size

and in the same sector as the firm in each city. These city-sector-size averages are called as ‘local averages’

in the analysis. That is, we use other firms’ experiences in each area of the investment climate as proxies

for the firm’s experiences. If similar firms in the same location tend to have similar experiences, this might

be a reasonable proxy. In addition, because a single firm’s responses are unlikely to have a large impact on

the average responses of all similar firms in the city, the local average will be less affected by that firm’s

performance than is the firm’s own response.

We include the averages directly in the equation rather than using them as instruments. There are three

reasons for this. First, in many cases, the local average might be a more appropriate measure of the

investment climate than the firm’s own response. For example, firms that refuse to pay bribes will be

affected by corruption if it means they fail to get needed licenses or lose government contracts. That is, the

overall level of corruption in a region will affect the firm’s performance whether or not the firm pays bribes.

Second, because the regressions include many investment climate variables, 2SLS might be problematic

due to weak instruments and other problems. Finally, using 2SLS would greatly reduce sample size. Firms

often refuse or fail to answer the investment climate variables. When 2SLS is used, any firm that failed to

answer any question needs to be dropped. In contrast, local averages can be calculated for almost all firms

in the sample.53 Given that there are so many investment climate variables in the regression, many

observations would end up being dropped. his approach, replacing the firm’s own values with the local

53 To avoid sample loss, when no firm in the city-size-sector group answers the question, we use the local average for all firms in

the same city and sector. Similarly, if no firms in the city-sector group answer the question, we use the city average.

87

averages, is standard in the literature—many studies that have looked at how the investment climate affects

firm performance have taken this approach.54

A final issue related to the investment climate variables relates to a final group of questions where managers

were asked to estimate losses due to investment climate problems: losses due to crime and security costs;

bribe payments; and losses due to power outages. The questions that these three variables are based on

questions that allow the managers to answer either as a percent of sales or in monetary terms. Although this

should not matter in principles, earlier studies have shown that managers who answer in monetary terms

report significantly lower losses than managers who answer as a percent of sales (Clarke, 2012; Malomo,

2013). Moreover, these differences do not appear to be due to observable or unobservable differences

between firms whose managers answer as a percent of sales and firms whose managers answer in monetary

terms (Clarke, 2012). Although it is not clear whether managers who report as a percent of sales over-report

losses or managers who report in monetary terms under-report, it is important to ensure that managers’

responses are comparable. To do this, we regress the reported losses on a complete set of country dummies

and a dummy indicating the manager answered in monetary terms. We then calculate responses assuming

the manager answered in monetary terms.

D. Total Factor Productivity Related to Other Countries

As a first exercise, we estimate equation 8 without any investment climate variable or firm characteristics.

These regressions, however, include country dummies that we can use to compare average levels of

productivity across countries. To calculate average TFP, we look at the coefficients on the country dummies.

To calculate average TFP in each country relative to TFP in Malawi in 2014, we calculate the following

equation:

TFP expc (8)

Where c is the country dummy for country c.

This formula was proposed by Halvorsen and Palmquist (1980). For the OLS and frontier regressions, this

can be interpreted as the average TFP in the country after controlling for sector and capital and labor

intensities. For the LAD regressions, it can be interpreted as median TFP in the country. Since the omitted

country dummy is for Malawi, the estimates give average or median TFP relative to average or median TFP

in Malawi.

TFP is lower in Malawi than in most of the comparator countries. Although TFP is slightly higher for the

median firm in Malawi than in Mozambique or Tanzania (57 and 77 percent of TFP in Malawi), it is lower

than in the other regional comparator countries. Moreover, although labor productivity in Malawi was

higher than in Zambia, TFP is lower. This suggests that the higher labor productivity is due to firms in

Malawi being more capital intensive than firms in Zambia rather than more productive.

Although total factor productivity appears low in Malawi, this probably partly reflects Malawi’s low per

capita income.55 As noted earlier, per capita income is lower in Malawi than in the comparator countries.

Because total factor productivity tends to be higher in countries where per capita income is greater, it might

not be surprising that TFP is relatively low in Malawi.

To see whether this is the case, we compare TFP in Malawi with TFP in all countries where Enterprise

Surveys have been completed. We then plot TFP against per capita income. The upward sloping regression

54 Dollar and others (2005), Aterido and others (2009), Harrison and others (2014), Fisman and Svensson (2007), and Clarke

and others (2015) take this approach in their analyses. Xu (2011) discusses this approach in more depth. 55 Malawi’s per capita GNI was only $815 in purchasing power parity (PPP) US$ in 2014 (World Bank, 2015). This is lower

than in all but three of the 188 countries where the World Bank has computed per capita GNI.

88

line confirms that TFP is higher in countries where per capita income is higher (see Figure 40 in Annex 2).

TFP is higher in countries above the regression line than would be expected given the country’s per capita

income.

89

ANNEX 4: TFP AND INVESTMENT CLIMATE

Table below shows results from the base quantile regression model in Clarke (2015), which analyzed the

effects of investment climate variables on TFP.56 The coefficients on the investment climate variables are

statistically significant and have the expected signs. Firms are more productive in areas where they spend

less time dealing with regulation, have smaller losses due to power outages and crime, pay less bribes, and

have better access to credit. Further, the coefficients on the percent of firms with their own websites, the

percent of firms with formal training programs, and the percent of firms that license foreign technologies

are positive and significant. These results suggest that productivity is also higher in areas where firms have

easier access to foreign technology, where internet use is higher, and where workers have more human

capital.

These results are mostly robust in the two alternate models, namely the base quantile regression model and

ordinary least squares (OLS).The two main exceptions are that the coefficient on time dealing with

regulation becomes statistically insignificant in the OLS model. It remains about the same size, however,

as in the quantile regression. The other main difference is that the coefficient on bribes becomes statistically

insignificant and smaller in size in the frontier regression.

GENERAL COMMENTS ON THE TFP MEASURE

Apart from the statistical considerations above, some authors have highlighted some potential drawbacks

of the TFP measure. For example, Felipe (2008) identified the following:

• The measure is calculated on the assumptions of profit maximization, competitive markets and constant

returns to scale, which are crude and are not always considered by researchers in calculation of the measure.

• There are challenges with apportioning growth in output to respective factors in growth accounting

exercises due to the fact that growth is the result of the complex interaction of a number of different factors.

• There are interpretation challenges whereby some researchers regressions of TFP as a dependent variable

on many different variables just to identify some statistically significant variables that could explain the

composition of the TFP measure.

• Most TFP estimates are biased downward since the actual factor shares from national accounts are seen

to have been affected by technical progress, rendering the TFP estimates inaccurate.

• Savings rates and population growth do not clearly affect the steady-state growth rates of per capita

output and hence productivity and long-term growth as posited by Solow’s neo-classical growth model,

which depends on a number of very restrictive assumptions.

• The aggregation of factors in the context of aggregate production functions is problematic as it relies on

very stringent conditions which are probably not attained in practice.

• In spite of these suggested challenges, TFP has been an important measure for explaining increases in

productivity and hence long term growth over the years. As put by van Ark (2014), TFP does not suffer

from diminishing returns as homogenous inputs typically do, and it also represents spillovers or externalities

that arise from returns on inputs that go beyond what the investor can internalize. In other words, they relate

more to investors’ operating environment, which is of concern to this paper. Under strict neo-classical

assumptions, growth in TFP measures technological change. In practice, it also measures the impact of other

inputs that are not measured such as research and development (R &D), human capital skills development,

imperfect competition, increasing returns to scale and other externalities related to investment, the

reallocation of market shares across firms and other measurement errors arising from measuring outputs

and inputs in the productivity process. In a nutshell, TFP is seen to be affected by factors such as:

56 is based on the base regression results in Clarke (2015). Clarke (2015) discusses the robustness of the results and presents

results from alternative models.

90

• Intangible investments such as education and skills, R&D, patents, licenses, organizational change and

product marketing;

• Skills, motivation and competencies – in part affected by intangible investments above;

• Innovation and technological change – in part also affected by the intangible investments above; and;

• Markets, institutions and regulations.

91

TABLE 25: BREAKDOWN OF PARTIAL PRODUCTIVITY DATA BY FIRM TYPE

(1) (2) (3)

Value added (natural log) Quantile

regression OLS Frontier

regression

Observations 31,297 28,053 28,053

Sector dummies Yes Yes Yes

Country dummies Yes Yes Yes

Sector-specific factor intensities Yes Yes Yes

Local Averages

Time dealing with regulation (%) -0.002* -0.002 -0.001** (-1.92) (-1.50) (-2.04)

Bribes (% of sales) -0.014*** -0.009*** -0.006 (-2.67) (-10.38) (-1.40)

Losses to crime (% of sales) -0.033*** -0.044*** -0.028*** (-3.83) (-3.01) (-3.75)

Losses due to power outages (% of sales) -0.022** -0.029** -0.031*** (-2.43) (-2.46) (-3.94)

% of firms with bank credit 0.112*** 0.129*** 0.129*** (3.84) (3.26) (5.00)

% of firms with own website 0.308*** 0.326*** 0.313*** (10.28) (7.04) (11.90)

% of firms that license foreign

technologies

0.145*** 0.146*** 0.136***

(3.92) (3.22) (4.13)

% of firms with training programs 0.127*** 0.082*** 0.086*** (4.46) (2.94) (3.45)

Firm Characteristics

Firm is an exporter (dummy) 0.186*** 0.174*** 0.169*** (10.93) (8.12) (11.40)

Firm is foreign-owned (dummy) 0.281*** 0.173*** 0.185*** (10.95) (6.00) (7.85)

Age of firm (nat. log, years) 0.015 0.026** 0.020** (1.63) (1.99) (2.52)

Constant 6.401*** 6.258*** 7.473*** (41.77) (48.57) (54.39)

Adjusted R-Squared --- 0.782 --- Source: Author’s calculations based upon data from the World Bank Enterprise Survey.

***, **, * indicate statistical significance at 1%, 5% and 10% levels

Note: T-statistics in parentheses. All regressions include capital (natural log of sales value), number of workers (natural log), sector dummies, and

country dummies. The sector dummies are interacted with the capital and labor variables to allow capital and labor intensities to vary across sectors

(see equation). The local averages are average values of IC variables for similar firms (see description in text). Standard errors in the OLS regression

are clustered at the country level. Clustering is not possible in the quantile or frontier regressions.


92

ANNEX 5: WHAT IS AN ENTERPRISE SURVEY

An Enterprise Survey is a firm-level survey of a sample of private sector firms in an economy, following

standard methodology and approach, to obtain firm level performance and constraints. The World Bank

undertakes periodic enterprise surveys throughout developing countries, in order to provide policymakers

with a better understanding of how different factors of the business environment are facilitating or

constraining efficiency and productivity of firms and hence a country’s prospects for reaching its potential

with respect to employment, production and welfare. Since 2002, the World Bank has collected this data

from face-to-face interviews with top managers and business owners in over 155,000 companies in 148

economies/countries. Survey data is collected by private contractors on behalf of the World Bank57, and

covers business environment topics including Access to Finance, corruption, infrastructure, crime,

competition, and performance measures.

World Bank enterprise surveys follow a standard format and methodology. The survey covers firm

characteristics, gender participation, Access to Finance, annual sales, costs of inputs/labor, workforce

composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and

permits, taxation, informality, business-government relations, innovation and technology, and performance

measures. The Enterprise Surveys adopt a stratified random sampling technique. The strata include firm

size, business sector, and geographic region within a country. Firm size levels are 5-19 (small), 20-99

(medium), and 100+ employees (large-sized firms). Panel data are usually obtained, by interviewing same

firms across multiple years. This has been a priority in recent Enterprise Surveys.

57 Due to sensitive survey questions addressing business-government relations and bribery-related topics, private contractors,

rather than any government agency or an organization/institution associated with government, are hired by the World Bank to

collect the data

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ANNEX 6: ENTERPRISE SURVEY IN COMPARISON TO DOING BUSINESS

METHODOLOGIES

Enterprise Surveys (ES) and Doing Business (DB) are used to assess the quality of business environment. An ES is a

firm-level survey of a representative sample of the private sector in an economy, covering a range of business

environment topics such as access to finance, corruption, infrastructure, crime, competitions and performance measures.

DB focus on measuring the complexity of business of business regulations and quantifying the ease of doing business

across countries through indicator sets and rankings. The indicators use common transactions such as starting a business

or studies.

Table below provides some key features of the two methods.

Global coverage Covers 139 economies Collects data annually for 190 economies

Data Source Firm level data. Unit of observation is

and enterprise. Business owner or top

manager is usually given face to face

interview. Business surveyed include

manufacturing, retail, construction,

transport, communication and other

services

Collects information through surveys

administered to local experts on the subject

matter or business transaction such as

lawyers, accountants, and architects. The

information is confirmed through the

underlying laws and regulations

Geographical

coverage within an economy

The sample design aims to include the

main cities/regions of economic activity

Collects data only for the main (most

populous business city)

Information

gathered

Objective data on the business

environment as experienced by firm,

performance measures, firm

characteristics, and perceptions

regarding obstacles to growth

Measures 11 business regulation topics:

starting business, dealing with construction

permits, getting electricity, registering

property, getting credit, protecting minority

investors, labor market regulations, paying

taxes, trading across borders, enforcing

contracts, and resolving insolvency.

Data Stratified Random sampling Standardized case-studies that relate to a common business situation

Measurement Measures what happens to existing

firms, such as the actual experiences of a

firm regarding the payment of taxes,

number of meetings with tax officials

and the incidence of bribery. The survey

also asks how much of an obstacle a

challenge is to business growth.

Measures what a standardized firm should

expect if everything was done according to

the official legal requirements and costs in

place.

Assumptions Measures what happens in practice in the

normal course of business.

Assumes firms are aware of and comply

with all formal regulations, waste no time

in collecting information and all regulations are enforced.

Usage Can be useful to identify potential areas

of reform in the business environment as

well as ass the impact of reform on

businesses

Can be used to identify areas for reform

based on bottlenecks or weaknesses in

specific areas of private sector regulation,

gain insights and learn from practices of

other economies around the world.

Source Enterprise Survey (World Bank, 2017c)

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