Private investment is a powerful mean for innovation, economic growth and poverty reduction. Countries with wider and deeper private-sector investments demonstrate accelerated growth (Majeed M.T and Khan S, 2008). Motivated by the concern on the persistent decline in private investment in Zimbabwe since 1980, this study empirically investigated the determinants of private investment in Zimbabwe using time series data for the period 1980-2013. The study employed the OLS estimation criteria. Results indicate that public investment; FDI and GDP are statistically significant in explaining the determinants of private investment in Zimbabwe, while interest rates and inflation are statistically insignificant. The study basically recommends that the government of Zimbabwe, especially through ZimAsset, should promote PPPs, initiate collaborative partnerships between foreign and local investors and create a conducive macroeconomic environment to boost private sector investment.
Table of Contents
APROVAL FORM
DECLARATION FORM
DEDICATION
ABSTRACT
ACKNOWLEDGEMENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDICES
LIST OF ACRONYMS
CHAPTER I INTRODUCTION
1. Introduction
1.1 Background
1.1.0 Private investment trends and economic policies in Zimbabwe since 1980
1.2 Statement of the problem
1.3 Research hypothesis
1.4 Statement of objectives
1.5 Research questions
1.6 Assumptions of the study
1.7 Significance of study
1.8 Delimitations
1.9 Limitations
1.10 Definition of terms
1.11 Summary
CHAPTER II LITERATURE REVIEW
2.0 Introduction
2.1 Theoretical Literature Review
2.1.0 The Accelerator Theory
2.1.1 The Tobin q Theory
2.1.2 The Neoclassical Theory
2.1.3 The Adjustment Cost Approach
2.1.4 The Stock Adjustment theory
2.1.5 Credit Rationing
2.1.6 The Neoliberal Approach
2.1.7 The Uncertainty Theory
2.1.8 The Irreversible Investment Theory
2.1.9 The Dis-equilibrium Approach
2.1.10 The Profit Theory
2.2 Empirical Literature Review
2.3 Summary
CHAPTER III METHODOLOGY
3.0 Introduction
3.1 Theoretical Model
3.2 Empirical Model
3.3 Reasons for using OLS
3.4 Assumptions of the model
3.5 Estimation Procedure
3.6 Justification of variables
3.6.0 Private investment as a percentage of GDP (PI)
3.6.1 GDP growth (GDP)
3.6.2 Public investment as a percentage of GDP (PUI)
3.6.3 Inflation rate (INFL)
3.6.4 Interest rates (INTR)
3.6.5 FDI as a percentage of GDP (FDI)
3.7 Diagnostic Tests
3.7.0 Multicollinearity test
3.7.1 Goodness of Fit test
3.7.2 F-Test
3.7.3 Durbin Watson (DW) test
3.7.4 White test
3.7.5 ARCH LM test
3.7.6 Stationarity test
3.7.7 Normality test
3.8 Data sources
3.9 Summary
CHAPTER IV ESTIMATION, PRESENTATION AND INTERPRETATION OF RESULTS
4.0 Introduction
4.1 Descriptive Statistics
4.2 Correlation Matrix
4.3 ARCH LM test
4.4 White test
4.5 Normality test
4.6 Unit Root tests (ADF test: in levels)
4.7 Regression results
4.8 Misspecification test
4.9 Testing for the significance of the whole model
4.10 Interpretation and discussion of the results on regression:
4.10.0 PUI (as a percentage of GDP)
4.10.1 FDI (as a percentage of GDP)
4.10.2 GDP growth
4.11 Summary
CHAPTER V SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.0 Introduction
5.1 Summary
5.2 Conclusion
5.3 Policy recommendations
5.4 Areas for further study
REFERENCES
Appendices
LIST OF TABLES
Table 4.0 Descriptive statistics
Table 4.1 Correlation matrix
Table 4.2 ARCH LM test
Table 4.3 White test
Table 4.4 ADF test (in levels)
Table 4.5 ADF test (in 1st difference)
Table 4.6 Regression results
LIST OF FIGURES
Figure 1.0 trends in private investment
Figure 1.1 trends in private investment
Figure 1.2 trends in private investment
Figure 1.3 trends in private investment
Figure 1.4 trends in private investment
Figure 4.0 normality of μi
LIST OF APPENDICES
Appendix 1: data used in the research
Appendix 2: regression results
Appendix 3: correlation matrix
Appendix 4: descriptive statistics
Appendix 5: ARCH LM test
Appendix 6: White test
Appendix 7: Unit root tests
LIST OF ACRONYMS
illustration not visible in this excerpt
APROVAL FORM
TITLE:
DETERMINANTS OF PRIVATE INVESTMENT IN ZIMBABWE (1980-2013)
A: To be completed by the Student.
I certify that this dissertation meets the preparation guidelines as presented in the Faculty Guide and Instructions for Typing Dissertations.
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Signature of Student Date
B: To be completed by the Supervisor.
I certify that;
(a) This dissertation is suitable for submission to the Faculty.
(b) This dissertation has been checked for conformity with the Faculty guidelines.
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Signature of Supervisor Date
C: To be completed by the Chairperson of Department.
I certify to the best of my knowledge that the required procedures have been followed and the preparation criteria have been met for this dissertation.
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Signature of Chairperson Date
DECLARATION FORM
I, NYONI THABANI.,declare that this project is an original copy of my own work and has not been published before or submitted to any other institution or university.
Signed.. Date././.
DEDICATION
I dedicate this piece of work: to God Almighty without whom nothing is possible; to my lovely mother, Mrs S Nyoni; who brought me up and taught me the value of hard work and love, and whose prayers and encouragements remain my major source of inspiration; and finally to my bothers Mr M Nyoni and Dr S.P Nyoni for their unrivalled and unending support upon which I concede that my academic accomplishments are a product that owes much to their contributions.
ABSTRACT
Private investment is a powerful mean for innovation, economic growth andpoverty reduction. Countries with wider and deeper private-sector investmentsdemonstrate accelerated growth (Majeed M.T and Khan S, 2008).Motivated by the concern on the persistent decline in private investment in Zimbabwe since 1980, this study empirically investigated the determinants of private investment in Zimbabwe using time series data for the period 1980-2013. The study employed the OLS estimation criteria. Results indicate that public investment; FDI and GDP are statistically significant in explaining the determinants of private investment in Zimbabwe, while interest rates and inflation are statistically insignificant. The study basically recommends that the government of Zimbabwe, especially through ZimAsset, should promote PPPs, initiate collaborative partnerships between foreign and local investors and create a conducive macroeconomic environment to boost private sector investment.
ACKNOWLEDGEMENTS
I would like to thank Bindura University of Science Education for according me an opportunity to study Economics with their reputable institution. I would also like to express my heart-felt gratitude to my supervisor Mr Chigusiwa for his assistance and guidance during my research project. His wisdom greatly helped me build my resilience and character which motivated me despite the seemingly insurmountable challenges. His unconditional inspiration and support went a long way in making this dissertation a success. Thank you Sir! May God bless you.
I am also grateful, particularly; to Mr Damiyano and Mr Tafireyi for their unconditional knowledge and material support they gave me. Their wisdom went a long way in making this dissertation a success. Thank you! This is without forgetting all the other lecturers, who imparted their knowledge in me and encouraged me to soldier on through the degree programme. Thank you! I would also like to extend my grateful acknowledgements to the ZimStats management and staff at the Harare offices for giving me access to national accounts data. May God bless you.
To my lovely mother, Mrs S Nyoni; brothers, Mr M Nyoni and Dr S.P Nyoni; sisters, Mrs S.L Mafa and Mrs F Makoni; and Mr T Dube I do not have enough words to describe my appreciation for the support that you gave me. I could not have done without your unconditional financial, moral and emotional support, a special thank you. May God bless you.
I also acknowledge the pioneers of this work whose works have guided me in writing this research paper. They did a great job indeed, without them this dissertation would not have been possible. Finally, to my fellow students and friends, Talkmore Magora and Jonathan Shoko; your assistance is greatly acknowledged and I am very grateful and will remain indebted to you all for the assistance you gave me. May God bless you.
CHAPTER I INTRODUCTION
1. Introduction
The determinants of private investment and its impact on economic growth is still a subject matter of considerable debate in economic theory and policy. Majeed and Khan (2008) point out that some components of public investment maybe complementary to private investment and so would be beneficial for growth, while others maybe substitutes and have a less positive, or even negative, effect on growth. According to Frimpong and Marbuah (2010), private investment is a crucial pre-requisite for economic growth. Ayeni (2014) argues that the private sector contributes more meaningfully to economic growth than the public sector. Seruvatu and Jayaraman (2001) attribute this to less corruption in the private sector investment compared to the public sector investment.
Private sector led growth has a stronger positive impact on economic growth than public investment, Khan and Reinhart (1990).This is because private investment is relatively more efficient than public sector investment (Serven and Solimano 1990; Countinho and Gallo, 1991). More importantly, in developing countries, private investment plays a greater role than public investment in determining economic growth (Oshikoya, 1994; Naqvi, 2002). As a result, a number of studies have investigated the determinants of private investment in developing countries (Atukeren, 2005). The researcher believes that the expansion of private sector investment in Zimbabwe should be the main impetus for economic growth and development.
1.1 Background
1.1.0 Private investment trends and economic policies in Zimbabwe since 1980
At independence in 1980, Zimbabwe initiated development planning as an instrumentfor achieving rapid socio-economic development. The first two development plans, theTransitional National Development Plan (1981-1983) and the First Five Year NationalDevelopment Plan (1985-1990) were formulated in the context of a command economy where government controls were the order of the day (Chingarande, 2012). Development planning was targeted at raising investment to a sustainable level in order to improve the living standards ofpeople among other goals. The implementation of development plans was faced by many problems andplanned targets were not achieved. In the private sector, the government used incentives as an instrument for implementing development plans. According to the World Bank (1995) the decade of the 1980s witnessed a decline in investment in developing countries. This assertion is also true for Zimbabwe as confirmed by the graph below:
Abbildung in dieser Leseprobe nicht enthalten
Data Source: World Bank online database
Figure 1.0
As shown in the graph above, private investment for the prescribed period kicked off in 1980 at 12.31% and the following year it increased to 15.18%. Private investment performance gradually deteriorated thereafter until it reached 12.2% in 1985. During the period 1985 to 1987, private investment, as a percentage of GDP, exhibited some sign of recovery as it slightly increased to 13.7% in 1987. Unfortunately, private investment dropped since then until it reached 10.76% by 1989. The disturbing trend in terms of the decline in private investment which began in 1981 and ended in 1990, resulted in a marked decline in overall investment.
When it became clear that the economy was not generating sufficient jobs, especially in the context of depressed investment, government adopted a more market driven reform programme, the Economic Structural Adjustment Programme (ESAP) in 1991. The key targets of ESAP were to: achieve GDP growth of 5% during 1991-95; raise savings to 25% of GDP; raise investment to 25% of GDP; achieve export growth of 9% per annum; reduce the budget deficit from over 10% of GDP to 5% by 1995 and reduce inflation from 17.7% to 10% by 1995. To achieve these objectives, government set out to liberalise all markets, in line with the prescriptions of the World Bank and IMF (Kanyenze, 2006).
The performance of the economy under ESAP was not impressive. Private investment remained very low, despite the fact that there were some slight improvements as shown below:
Abbildung in dieser Leseprobe nicht enthalten
Data Source: World Bank online database
Figure 1.1
Generally, private investment signalled some element of improvement as shown above for the ESAP era as compared to the first decade after independence, pointing to the positive impact that ESAP had on private investment. There was a gradual increase from 1990 where private investment started off with 14.82% to 1993 where it reached 19.93%, which was followed by a slight decrease in 1994 where it dropped to 18.27%. The final year of ESAP saw a 3.41% increase in private investment as it reached 21.68% in 1995. It is clear from the graph above that ESAP failed where it counted most because it did not lead to a substantial and sustained increase in private investment despite the fact that it constituted radical economic reforms that were supposed to successfully necessitate and support macroeconomic stability such as trade liberalisation and the consolidation of the financial system. Owing to the failure of ESAP, socio-economic problems such as poverty and unemployment continued to precipitate in Zimbabwe. There are various reasons why ESAP failed. Kanyenze (2006), points out that the performance of the economy during the period 1991-95 cannot be wholly attributed to ESAP (there was a severe drought in 1992 and lack of fiscal discipline on the part of government), much of the outcomes can be traced to the policy prescriptions of the programme.
Following the end of ESAP in 1995, there was indecision in terms of policy. It was not until April 1998 that a successor economic policy, the Zimbabwe Programme for Economic and Social Transformation (ZIMPREST) was launched, yet the programme was supposed to run from 1996-2000. This implies that when the programme was launched, it was already two years behind schedule. ZIMPREST sought to achieve a target average annual growth rate of GDP of 6%, create 42,200 new jobs in the formal sector per annum, per capita income growth of 3.4% and consumption growth of 4.4%. To achieve the minimum target growth, government was expected to reduce the budget deficit from 10% to under 5% of GDP, reduce inflation from over 20% to a single digit level by the year 2000, achieve higher levels of savings and investment (an average of at least 23% of GDP), export growth of at least 9% per annum in US$, and raise the health budget from the average allocation of 2% of GDP to at least 2.5% (Kanyenze, 2006).
The performance of private investment during the ZIMPREST period was also not impressive as shown below:
Abbildung in dieser Leseprobe nicht enthalten
Data Source: World Bank online database
Figure 1.2
Private investment dropped from 21.68% in 1995 to 15.73% in 1996. By the end of 1997, private investment had slightly continued to deteriorate by 0.47%. For the first time in the ZIMPREST era, private investment increased and reached a peak of 18.89% in 1998. Unfortunately, the following year private investment surprisingly dropped to an unbelievable -0.2%; after which it increased by 11.27% and reached 11.07% by the end of the year 2000.
In an attempt to solve the socio-economic problems that ESAP and ZIMPREST had failed to address, government launched its 18-month programme, the Millennium Economic Recovery Programme (MERP) early in 2000. MERP built on the fiscal policy adjustment targets under ESAP, ZIMPREST and the Millennium Budget announced on 21 October 1999. MERP was presented as a continuation of the commitments and targets of ESAP and ZIMPREST. The target of MERP, as that of the 2000 budget, was to reduce the budget deficit to 3.8% of GDP. MERP sought to allocate at least 25% of total expenditures to capital projects. However, in the millennium budget, the capital budget was allocated 8% of total expenditures, down from 11% in 1999. The capital budget amounted to 4% of total expenditures in 2001, 8.1% in 2002 and 11.7% in 2003 (Kanyenze, 2006).
In August 2001 government started implementing yet another new strategy, the Ten-Point Plan; which was agriculturally driven. According to Kanyenze (2006) the Ten Point Plan remained a mystery, with then Minister of Finance and Economic Development (Simba Makoni) insisting that MERP was still alive. In February 2003 the National Economic Revival Programme (NERP) was launched to provide, inter alia, humanitarian support in the face of a long term drought. The consequent failure of these policies (MERP, Ten Point Plan and NERP) had a detrimental gross effect on private investment in Zimbabwe.
The policy framework on the indigenisation of the economy was first put in place by government in 1998 and was later revised and adopted in 2004 to become the foundation for the Indigenisation and Economic Empowerment (IEE) Act [Chapter 14:33] of 2007 (Chowa and Mukuvare, 2013). According to the IEE Act of 2007, the rationale behind the promulgation of the indigenisation policy is to empower black populations which were disadvantaged in the colonial era; to give them a chance to partake in the national economy through owning businesses and generally increasing their stake in the corporate sector.
However, controversies have risen from the very contents of the policy, as well as the implications thereof. For instance, under Section 15 of the Act, the Minister establishes a database of people who want indigenous Zimbabweans to acquire shares in their businesses, and of indigenous Zimbabweans who wish to partner such people. The problem with this Section is that it gives the Minister much leeway to impose politically acceptable partners upon reluctant businesses, which altogether will make a marriage of the unwilling, where partners are not chosen on agreement or suitability but political merit.
Furthermore, another obvious but more serious negative implication of the policy is its impediment of the investment drive. Zimbabwe is currently looking for investment especially FDI and this Act is seen as a stumbling block to foreign investment in Zimbabwe (Mzumara, 2012). Therefore, the policy makes the country an undesirable investment destination in the sense that investors have become increasingly discriminating and show a marked preference for countries with sound policies. The condition of surrendering 51% to locals is an exorbitant price to pay, making the whole exercise a disempowerment of the investors.
The performance of private investment during the period 2000 to 2008 is the worst ever in the history of private investment in Zimbabwe as shown below:
Abbildung in dieser Leseprobe nicht enthalten
Data Source: World Bank online database
Figure 1.3
The new millennium started off with private investment which was already low that is at 11.07%. Private investment continued to deteriorate until it reached 8.08% in 2002. For the first time in the period 2000 to 2008, private investment increased to 11.73% in 2003. Unfortunately, private investment suddenly dropped to 0% in 2004. It is unpleasant to note that private investment remained at 0% for the next two consecutive years that is 2005 and 2006. The year 2007 saw an increase of private investment to 3.76%, which was followed by a decrease of 0.8% to 2.96% in 2008. The period 2000 to 2008 in Zimbabwe was a period of economic recession which saw economic policies such as those discussed above (MERP, Ten-Point Plan and NERP), formulated and implemented with failure. It is important to recognise that the failure of these economic policies had a detrimental effect on private investment in the sense that the successful mobilisation of private investment depends on sound economic policies.
The inclusive government formally took power on February 11, 2009 (Noko, 2011). Thereafter, the inclusive government took office in the context of an economy that had many challenges: unprecedented levels of hyper-inflation, sustained period of negative Gross Domestic Product (GDP) growth rates, massive devaluation of the currency, low productive capacity, loss of jobs, food shortages, poverty, massive de-industrialisation only to mention, but a few. As part of its obligation to address such economic ills, the government came up with the Short Term Emergency Recovery Programme (STERP I: February – December 2009).
STERP I was an emergency short term stabilisation programme, whose key goals were to stabilise the macro and micro-economy, recover the levels of savings, investment and growth, and lay the basis of a more transformative mid-term to long term economic programme that was supposed to turn Zimbabwe into a progressive developmental state (Government of Zimbabwe, 2009). STERP was part of implementation of the (GPA) and set to address the key issues of economic stabilisation and national healing, whilst at the same time laying the foundation of a more comprehensive and developmentalist economic framework to succeed the same.
In February 2009, under STERP, the government consented to transactions in foreign currency and to the full dollarization of Zimbabwe, though without any formal agreements with the United States (US), (Noko, 2011). Consequently, the Zimbabwean dollar was automatically damped just like that. Now, the event of dollarization in Zimbabwe cannot be ignored because it has some important implications on private investment in Zimbabwe since it brought stability in the economy. Due to the hyperinflation that took centre stage at the climax of the 2008 economic recession, savings were wiped out and as a result individuals could not save. While some individuals may have been able to salvage their savings by converting to foreign currency, those hardest hit were businesses and people reliant on pensions because all businesses were subject to strict foreign exchange laws. Therefore the stability of the US dollar has allowed people to save which can only help the economy if these savings are loaned out to businesses to invest.
Just like any other previous economic policies STERP I did not achieve its targets as set. There are many reasons why STERP I failed. Mzumara (2012) points out that the failure by the government to raise money to enable the implementation of the programme resulted into its failure and that the programme did not have clear strategies how the goals would be achieved. The launching of STERP II (2010-2012) with a medium term outlook was a clear admission that the problems are huge and were not solved in STERP I and require a longer period than 9 months to solve them. Since the launch of STERP II, there is very little implementation taking place. The programme faces the same fate like STERP I, it is also being shun away by international donors (Mzumara, 2012).
The Medium Term Plan (MTP: 2011-2015) was launched in 2011 by the Ministry of Economic Planning and Investment Promotion yet it was supposed to have been launched in March 2010 but it took time to be approved by Cabinet. It was only launched in July 2011 a year behind the original intended start. The objectives of MTP are intralia: macroeconomic stability; good governance, maintenance of political stability; diversified economy with very high growth rates; access to social services by all; acceleration of rural development; equal opportunities for all; development and utilization of modern science and technology; achieve vibrant and dynamic culture; and sustainable utilization and management (Government of Zimbabwe, 2010). The programme suffers the same fate like STERP I and STERP II in that it doesn’t have very clear strategies how the noble objectives will be achieved (Mzumara, 2012).
The performance of private investment in Zimbabwe in the period 2008 to 2013 can be summarised graphically as shown below:
Abbildung in dieser Leseprobe nicht enthalten
Data Source: World Bank online database
Figure 1.4
As shown in the graph above, private investment in Zimbabwe was as low as 2.96% by 2008 after which it increased to 9.97% in 2009. Private investment dropped to 8.41% in 2010 and slightly increased by 0.38% to 8.79% in 2011 after which it further dropped by 1.01% and recorded a 7.78% contribution to GDP. The year 2008 is historic in the Zimbabwean economy in the sense that this is the year which saw the climax and sudden end of the hyperinflationary situation which had become the talk of the day. In 2009 the economy of Zimbabwe was officially dollarized and since then Zimbabwe is still enjoying a somewhat stable economy, compared to the pre-multi-currency era. Unfortunately, Zimbabwe has seen little economic activity from private investment activity as revealed by its poor contribution to GDP. Therefore, it is important to examine what determines private investment in Zimbabwe, in order to uncover the critical macroeconomic indicators that can be stimulated to revive and restore the much awaited role of the private sector as an engine of growth.
In pursuit of a new trajectory of accelerated economic growth and wealth creation, a new plan known as the Zimbabwe Agenda for Sustainable Socio-Economic Transformation (Zim Asset: October 2013 - December 2018) was recently crafted to achieve sustainable development and social equity anchored on indigenization, empowerment and employment creation. The policy was crafted around four strategic clusters that are expected to enable Zimbabwe to achieve economic growth and reposition the country as one of the strongest economies in the region and Africa and the four strategic clusters are: food security and nutrition; social services and poverty eradication; infrastructure and utilities; and value addition and beneficiation (Government of Zimbabwe, 2013).
The current economic policy ZimAsset is expected to propel the economy forward in terms of growth. It is important to note that the policy is still at its early stages of implementation and as such it is too early to pinpoint its own successes and failures. ZimAsset has some implications and effects on private investment in Zimbabwe. It is therefore important to identify the major determinants of private investment in Zimbabwe especially in light of this current economic policy ZimAsset. This will assist in identifying and analysing the major macroeconomic indicators that can be stimulated in order to make the policy become more fruitful.
1.2 Statement of the problem
Private investment in Zimbabwe, as a percentage of GDP has been falling over the years underpinned by both economic and non-economic factors despite government’s effort to encourage and support private sector investment. In 1980 private investment as a percentage of GDP was 12.31%, by 1989 it had dropped to 10.76% as shown in Figure 1.0 above and in 1999 it was -0.2% as shown in Figure 1.2 above. Private investment continued to decline with the years 2004, 2005 and 2006 witnessing a 0% contribution to GDP as shown in Figure 1.3 above. Even in the multicurrency era (2009 to date) private investment contribution to GDP in Zimbabwe has continued to decrease from 9.97% in 2009 to 7.78% in 2013 as shown in Figure 1.4 above.
According to Mlambo and Oshikoya (2001) declining investment ratios and levels are a problem, firstly because investment matters for growth, and secondly because low investment increase vulnerability in the economy. Therefore, the major challenge facing the country is to come up with policies that would help revive and promote the private sector in order to raise private investment, stimulate and sustain economic growth. With a view to drawing appropriate policy conclusions and recommendations from the findings, especially in light of the current economic policy ZimAsset; it is therefore important to analyze the determinants of private investment in Zimbabwe.
1.3 Research hypothesis
The main hypotheses to be tested in this study are:
- GDP growth (annual percentage) results in an increase in private investment.
- An increase in the contribution of public investment to GDP results in an increase in private investment.
- An increase in the contribution of FDI to GDP results in an increase in private investment.
1.4 Statement of objectives
- The main objective of this study is to find out the determinants of private investment in Zimbabwe.
Specific objectives are:
- To determine whether GDP growth (annual percentage) affect private investment in Zimbabwe.
- To find out whether public investment (as a percentage of GDP) affect private investment in Zimbabwe.
- To examine whether FDI (as percentage of GDP) affect private investment in Zimbabwe.
- To investigate whether interest rates (commercial lending rates) affect private investment in Zimbabwe.
- To analyse whether inflation rate (annual percentage changes) affect private investment in Zimbabwe.
- To draw policy implications from the research findings, especially in light of ZimAsset.
1.5 Research questions
- What are the determinants of private investment in broad terms?
- Does GDP growth affect private investment in Zimbabwe?
- Doespublic investment (as a percentage of GDP) affect private investment in Zimbabwe?
- Does FDI (as a percentage of GDP) affect private investment in Zimbabwe?
- Do interest rates (commercial lending rates) affect private investment in Zimbabwe?
- Does inflation rate (annual percentage changes) affect private investment in Zimbabwe?
- What policy implications can be drawn from the research findings, especially in light of ZimAsset?
1.6 Assumptions of the study
The research is based on a number of assumptions but however the most important which are worth mentioning are listed below:
- Data collected is accurate, complete, relevant and reliable.
- E-views 3.1 econometrics package gives a true reflection of the practical situation the study tries to explore.
- The study will have enough time and resources to carry out all the necessary steps up to completion.
1.7 Significance of study
This is a timely study in view of the current public debate on the need to mobilise all resources of development finance in pursuit of a new trajectory of accelerated economic growth and wealth creation through a new policy known as ZimAsset. Therefore, the study seeks to analyse the determinants of private investment in Zimbabwe in order to identify major macroeconomic indicators that can be stimulated to make ZimAsset become more fruitful.
To the extent that private investment is at the helm of sustainable economic growth; the desire and anxiety to carry out a comprehensive analysis of what determines private investment in Zimbabwe is completely unstoppable, especially with a view to analysing the importance of private investment to the growth and development strategy of Zimbabwe and subsequently drawing appropriate policy conclusions and recommendations for Zimbabwe.
Literature has it that, private investment still occupies a central position in solving economic problems such as poverty and unemployment especially for developing countries (Reinhart, 1989; Ghura and Hadjimichael, 1996). With a view to addressing such economic ills, the study will play a pivotal role in emphasising on the development of the private sector. The researcher believes that successful mobilization of private investment will go a long way in encouraging and supporting the implementation of investment vehicles especially Public Private Partnerships (PPPs) particularly in the Special Economic Zones as enshrined in the ZimAsset economic blue print.
The findings of this study will provide a useful contribution to the empirical basis needed for proper management of private investment in Zimbabwe in order to materialise the much awaited role of the private sector as an engine of growth. Finally,knowledge of the determinants of private investment in Zimbabwe is paramount not only to the policy makers but also to academics and other interested stakeholders such as the corporate sector.
1.8 Delimitations
Investment is a broad topic. However, this study intends to focus on the determinants of private investment in Zimbabwe from 1980-2013 based on the time series data for the period under study. An Ordinary Least Squares (OLS) econometrics methodology will be used for estimation in determining the major determinants of private investment in this country.
1.9 Limitations
Zimbabwe data continues to suffer from incomplete coverage and this may mean loss of credibility of the results of the study. However, the researcher will collect data from reliable sources such as the World Bank and ZIMSTATS.
1.10 Definition of terms
Investment: consists of goods bought for future use. Investment is also divided into three subcategories: business fixed investment, residential fixed investment, and inventory investment. Business fixed investment is the purchase of new plant and equipment by firms. Residential investment is the purchase of new housing by households and landlords. Inventory investment is the increase in firms’ inventories of goods (if inventories are falling, inventory investment is negative), (Mankiw, 2004).
Private Investment: is investment which is made by privately owned business firms on new buildings, plants and equipment that are used in the production of goods and services, (Chibber etal, 1993).
1.11 Summary
The research focuses on the determinants of private investment in Zimbabwe. The chapter has introduced the research topic and uncovered the following: background to the study, statement of the problem, research hypothesis, statement of objectives, research questions, assumptions, significance of study, delimitations, limitations, definition of terms and the summary thus setting a tone for the next chapters. The second chapter is a review of both empirical and theoretical evidence related to the topic. Chapter three will particularly look at the methodology that will be employed in the study and will also set the platform upon which the presentation, interpretation and analysis of the data will commensurate, which is the subject matter of chapter four. Last but not least, chapter five will make conclusions, recommendations and suggestions based on the findings of the study.
CHAPTER II LITERATURE REVIEW
2.0 Introduction
This chapter provides a discussion and a review of the theoretical literature and the empirical studies that have attempted to investigate the determinants of private investment in both developing and developed countries.
2.1 Theoretical Literature Review
The theoretical literature on private investment is quite rich and diverse (Karagoz, 2010; Yin 2011). In general, the literature on investment tries to explain how firms, given a set of motives, select their optimal capital stock and how the selected optimal capital stock is affected by variations in the degree of uncertainty about future prospects, (Malumisa, 2013). A number of theories explaining investment behaviour exist in the literature. The major ones are the accelerator theory, Tobin’s q theory,neoclassical theory, the adjustment cost approach, stock adjustment approach and credit rationing theory. Other theories of investment include the neoliberal approach, uncertainty theory,irreversible investment theory,the dis-equilibrium approach and the profits theory.
2.1.0 The Accelerator Theory
The Keynesian accelerator model was coined by Keynes (1936). In its simple version propounded by Clark (1917), the model conjectures that there exists an optimum quantity of real capital for a given level of output. Consequently, larger stocks of capital held by firms are necessitated by high demand. In this model net investment expenditures equal the change in the level of real capital and thus net investment is proportional to the expected change in output. In this model gross investment requires the incorporation of replacement capital or depreciation.
Further, owing to the fact that adjustment to the desired level of capital is not instantaneous but rather long and variable, the concept of a flexible accelerator becomes apparently useful. The fundamental argument of the flexible-accelerator principle is that when the gap between the existing capital stock and the desired capital stock is substantial, the firms’ rate of investment will be high. According to Chirinko (1993), the hypothesis is that firms plan to close a fraction of the gap between the desired capital stock and actual capital stock in each period. In this model, output, internal funds, and cost of external financing may be important factors in affecting investment.
According to the accelerator model, investment is determined from the difference between the desired level of capital and the capital that survives from the past. The capital that survives from the past is constant proportion of past capital. The accelerator model is based on an assumption of a stable (or fixed) capital to output ratio. It stresses that planned investment is demand induced. That is, the demand for new plant and machinery comes from the demand for final goods and services. If expected demand (output) is higher than the present capacity of the firm then additional plant and equipment may be required. Thus investment is a function of the rate of change in national income. The underlying assumption is that there is a close association between output changes and investment spending.
However, in its simplest form, the accelerator theory of investment postulates that the desired capital stock K*t is proportional to the level of expected output Y*t. This gives us the equation:
K*t=αY*t (2.0)
Where K*t is the capital stock that the private sector desires to have in period t, α is the ratio of the desired level of output (a constant fraction) and Y*t is the expected level of output in period t.
However, the accelerator theory is criticised on the basis that α which is assumed to be constant may not be constant. According to Sachs and Larraine (1993), α is constant if the user cost of capital is fixed. On the other hand, if the cost of capital changes, either because of changes in the market interest rate or changes in tax laws regarding investment, then α would change as well, at least in the medium term.
The accelerator theory assumes that investment is always sufficient to keep the actual capital stock equal to the desired capital stock, period to period. According to Sachs and Larraine (1993) the assumption is unrealistic. Because of the cost of adjusting the capital stock and inevitable lags in installation of capital, it is more likely that the capital stock will adjust only gradually to the desired level. Also, since next period’s output will usually not be known with certainty, investment must be based on expectations of next period’s desired level, and such expectation may turn out to be faulty, (Sachs and Larraine, 1993).
Inspite of these limitations, the accelerator model in its simple form accurately describes much about the movement of investment. Quite surprisingly to many economists, the accelerator theory generally outperforms many other more complicated theories in explaining and predicting actual investment patterns, (Sachs and Larraine, 1993).
In short, the accelerator theory of investment identifies output (GDP) as the major variable determining investment. However, the same theory also considers other factors that affect investment such as internal funds and external cost of financing. In Zimbabwe, private investment is also affected by these factors.
2.1.1 The Tobin q Theory
The q theory was propounded by Tobin and Brainard (1968) but however the use of the letter “q” did not appear until Tobin’s 1969 article “A general equilibrium approach to monetary theory”. Tobin hypothesized that the combined market value of all the companies on the stock market should be equal to their replacement costs. In the Tobin q theory of investment, the ratio of the market value of the existing capital stock to its replacement cost (the q ratio) is the main force driving investment (Chirinko, 1993; Ghura and Goodwin, 2000). That is to say, enterprises will want to invest if the increase in the market value of an additional unit exceeds the replacement cost (Ajide and Lawanson, 2012).
Tobin’s famous q theory of investment starts from the idea that the stock market value of a firm helps to measure the gap between K (actual capital stock) and K*+1 (desired capital in the next period). The variable q is defined as the stock market value of the firm divided by the replacement cost of the capital of the firm.The “replacement cost of capital” refers to the cost that one would have to pay to purchase the plant and equipment of the firm in the output market. If the firm sells for $USD150 million on the stock market, and the replacement cost of capital of the firm is equal to $USD100, then q would be equal to 1.5. Thus, q is the ratio of the cost of acquiring the firm through the financial market versus the cost of purchasing the firm’s capital in the output market (Sachs and Larraine, 1993).
Tobin and his followers have shown conditions under which q is good indicator of the profitability of new investment spending. Specifically, when q is greater than 1, it tends to mean that K*-1 (previous capital stock) is greater than K, so that investment should be high. Similarly, when q is less than 1, the market is indicating that K*+1 is less than K, so that investment should be low (Sachs and Larraine, 1993).
In the simplest theoretical setting, the value of q for an enterprise equals the discounted value of future dividends paid by the firm per unit of capital of the firm. Suppose that the capital stock is constant, that the MPK (Marginal Productivity of Capital) is constant, and that depreciation occurs at rate d. In this case, as outlined by Sachs and Larraine (1993), the dividend each period per unit of capital equals MPK – d, and the value of q equals:
illustration not visible in this excerpt
Where r is the cost of borrowing and d is the true rate of depreciation (different from the legal rate).
According to Sachs and Larraine (1993), in this simple setting in which the MPK is the same in each future period, the expression for q can be written as:
illustration not visible in this excerpt
We can see that q will tend to be greater than 1 if MPK is greater than (r - d) in future periods and q will tend to be less than 1 if MPK is less than (1 + d) in future periods. If q is greater than 1, it means that the price per share of capital on the stock exchange is greater than the physical cost of capital. A firm would then issue new shares, use the money to undertake the physical investment, and will still have some extra earnings left over for the benefit of the shareholders. Thus q is greater than 1 can signal directly that by selling shares, the firm can profitably finance a new investment project (Sachs and Larraine, 1993).
The q theory of investment is relatively easy to test because the value of q can be directly computed, and we can observe whether fluctuations in investment are closely linked to movements in q (Sachs and Larraine, 1993).
However, movements in q do not explain much of the observed fluctuation in investment. It is clear that other variables in addition to q, such as changes in output and cash flow of the firm, also help to account for fluctuations in aggregate investment spending (Sachs and Larraine, 1993).
In short, the q theory of investment basically identifies interest rates as the major determinant of investment. Interest rates affect investment in a negative manner according to this theory in the sense that a rise in interest rates results in the increased user cost of capital, hence reduced investment. In Zimbabwe, interest rates may also have a bearing on private investment in the like manner.
2.1.2 The Neoclassical Theory
The neoclassical theory which is a version of the accelerator theory was developed by Jorgenson (1971) and it proposes that the desired or optimal capital stock is proportional to output and the user cost of capital which in turn depends on the price of capital goods, the real interest rate, the rate of depreciation and the tax structure (Chirinko, 1993; Asante, 2000).
Gupta (2004), points out that the neoclassical theory provides yet another rationale for the output as a positive factor in the investment function. Under this, every firm tends to maximise its production, subject to the budget (cost) constraint (or minimise the production cost subject to a given output constraint) as follows:
Production function: Y = f (K, L) (2.3)
Total cost equation: C = RK + WL (2.4)
Where Y is output, K is capital, L is labour, C is total production cost, R is rental capital (nominal) and W is wage rate (nominal).
The above constrained optimisation problem can be solved through the Langrangian multiplier technique as follows:
L = f (K, L) + λ (C – RK - WL)
The partial derivatives of the above function with respect to each of K and L, and equating each results to zero give:
MPPk = λR and MPP1 = λW
Solving the above two equations we get:
MPPk/R = MPP1/W = λ = 1/MC.. (2.5)
Where MPPk and MPP1 are marginal physical productivity of capital and labour respectively, MC is the marginal cost.
As shown above, the partial derivative of the Langrangian term (L = output) with respect to total cost equals λ and thus, the inverse of λ equals marginal cost.
Solving equation 2.3 and equation 2.5 we get the capital demand function:
K = f (Y, R/W). (2.6)
As shown in the above equation K is a positive function of Y and a negative function of R/W. This, according to Gupta (2004), leads investment to vary directly with a change in output.
However, in perfect competition, MC = product price (P). Substituting this in the select part of equation 2.5 above would give:
MPPk/R = 1/P
The solution gives:
MPPk = R/P.. (2.7)
Thus, a maximum profit seeking production firm would invest up to the point where its MPP of capital equals the real capital rental. Since MPPk declines as capital increases, more investment would be undertaken at the lower capital rental, and vice versa. The rental capital is the charge on the use of capital by the production firm and this would be determined by the optimum behaviour of the rental firm (Gupta, 2004).
This theory, as shown above, invokes the microeconomic model of a firm’s production function and profit-maximising behaviour to relate the desired capital and investment to product prices (demand) and interest rates. The investment equation results from the gap between desired capital and the actual capital stock. However, a particular drawback of the neo-classical model is that it does not rationalize the rate of investment towards the capital stock (Ajide and Lawanson, 2012).
In short, the neoclassical theory of investment identifies output, depreciation, interest rates and tax structure as basically the main factors affecting investment. These factors also have some effects on specifically private investment.
2.1.3 The Adjustment Cost Approach
The adjustment cost approach model is theory of investment that argues that changing prices is a complex and costly process. The theory seeks to address some limitations of the accelerator model. It actually states that actual and desired levels of stocks are not always equal.
In general, a firm might require a considerable amount of time to calculate and install the “desired” level of capital. For any given investment proposal, there must be feasibility studies, marketing analysis and financial negotiations. Once an investment decision is made, it takes time to construct a new factory, to install the new machines, and to train workers to operate the new facilities, (Sachs and Larraine, 1993).
Moreover, overall investment costs tend to rise if the company rushes to finish its investment project in a very short period of time. Thus, it is not technical constraints but also profit maximisation that leads a firm to make gradual changes in the levels of its capital stock (Sachs and Larraine, 1993). According to Clark (1917), cited by Sachs and Larraine (1993), some studies have concluded that no more than one-third of the gap between actual and desired capital is closed by investment within a given year.
The simplest amendment to the accelerator model, as noted by Sachs and Larraine (1993), was to specify a partial adjustment mechanism, which describes the gradual adjustment of K to the desired level K*:
J – K – 1 – K = g (K*+ 1 – K). (2.8)
Where g is a parameter known as the coefficient of partial adjustment, with 0<g<1. J is the net investment, K is the actual stock of capital and K – 1 is the stock of capital in the previous period. When g = 1, we have the accelerator model. When g <1, K adjusts only gradually through the gap between actual and desired capital. The lower, the g, the slower the adjustment. Thus, g measures the speed at which the actual capital stock approaches the optimal desired capital stock.
Basically this theory is hinged on cost adjustments as the major determinants of investment. These are the costs of installing for example a new factory, new machines and training workers. This theory also applicable in Zimbabwe especially given that firms acquire technologically advanced machinery and hence the need to train workers on how to operate them.
2.1.4 The Stock Adjustment theory
The essential properties of the stock adjustment model, as noted in Meyer (1980), are that the desired capital stock depends on long-run considerations (the factors that determine the internal rate of return, r or the present value, PV) and that net investment is determined by a distributed lag function of the arguments the demand function for capital. The model can allow for wide range of variables to influence the desired stock of capital (K*). The simplest specification, according to Meyer (1980), is one in which net investment (IN) is proportional to the gap between the desired and actual (beginning-of-period) capital stocks:
IN = β (K* - K).. (2.9)
Where β is the speed of adjustment coefficient, the proportion of the gap between the desired and actual capital stock that firms plan to close in a given period. However, the determinants of the speed of adjustment are not explicit in approach.
Slow adjustment may reflect the implementation lag of the investing firm and or the production lag in the capital goods supplying firms. Once firms have decided on their desired capital stock, they still must decide where to purchase it and how to finance it (Meyer, 1980).
The desired capital stock depends on the internal rate of return, i and market rate of interest rate, r. Given that i is not observable, the desired capital stock is usually related to r and the factors that determine i; given that the probability of any investment depends crucially on the overall level of economic activity, the main additional argument is generally the current and expected future levels of aggregate demand (output) in the economy (Meyer, 1980). The stock adjustment model identifies output and the internal rate of return or the present value as the main determinants of investment.
2.1.5 Credit Rationing
Some theories hinge on credit rationing. Credit rationing refers to the situation where lenders limit the supply of additional credit to borrowers who demand funds, even if the latter are willing to pay higher interest rates.
In practice, however, firms and households might be unable to obtain the credit necessary to carry out an investment project even when the project passes the test of profitability. If firms are credit rationed, the rate of investment will depend not only on the market interest rate and the profitability of investment, but also on the availability of investible funds, which in turn will depend on the cash flow of the enterprise considering the investment project, (Sachs and Larraine, 1993).
For the firm that faces credit rationing, investment spending might depend on the discounted marginal productivity of capital. The phenomenon of credit rationing has two principal causes: disequilibrium interest rates and differential risks in the face of uncertainty. Disequilibrium interest rates arise when government authorities impose interest rate ceilings on lending institutions, resulting in interest rates held below the disequilibrium level. With interest rates kept artificially low, investment demand tends to exceed the supply of savings and firms that want to borrow to make investments are rationed, (Sachs and Larraine, 1993).
The problem of credit rationing resulting from artificially low interest rates has been acute in many developing countries, particularly in situations of high inflation. Interest rate ceilings are typically set in nominal terms, so that as inflation rate increase, the real interest rate ceilings fall, often to negative rates. Credit rationing might also arise when lenders are unable to assess the risks of lending a particular borrower. In general, investment spending is risky: the returns to a particular project can be estimated, but cannot be known with certainty, (Sachs and Larraine, 1993).
Therefore before a loan is made to finance an investment project, the lender must evaluate the credit risk involved and decide how likely it is that the borrower will be able to repay the loan. In practice it is very difficult for banks to assess the risk of particular borrowers. Many of the specific risks of an investment project are not easily observable, (Sachs and Larraine, 1993).
The bank might have to rely on a few observable chacteristics of a borrower, even though signs do not address all the risk of the particular loan. The size of the business is one such characteristic, and small companies are much less likely to get loans than large companies. Lenders also tend to discriminate among borrowers on the basis of net worth. The higher the value of total equity of a given firm, the less likely that it would find its credit rationed. The crucial implication of the credit rationing theory of investment is that internal resources of the firm acquire a fundamental importance in determining the overall level of investment, (Sachs and Larraine, 1993).
However, when firms cannot simply borrow at the market interest rate their ability to finance investment projects depends on their retained earnings and their future generation of cash flow. Under these circumstances, the capital stock will not adjust in every period to its optimal level as determined by the market interest rate and the marginal productivity of capital. Thus, credit rationing provides another reason, along with cost of adjustment, for the slow movement of the capital stock to the desired level.
2.1.6 The Neoliberal Approach
The neoliberal approach, associated with McKinnon (1973) and Shaw (1973) is another theory that attempts to explain investment behaviour. The approach posits that developing countries suffer from financial repression and if they were liberated from this problem, saving would be induced, and eventually, growth. Liberalisation is crucial in this theory. With liberalisation, both savings and loanable funds will increase, resulting in more efficient allocation of funds with potential contribution to higher economic growth, (Asante, 2000).
Unlike the neoclassical theory, in this theory investment is positively related to the real rate of interest.The reason for this is that a rise in interest rates increases the volume of financial savings through financial intermediaries and thereby raises investible funds, a phenomenon that McKinnon (1973) calls the “conduit effect”. Thus, while it may be true that demand for investment declines with the rise in the real rate of interest, realized investment actually increases because of the greater availability of funds. This conclusion applies only when the capital market is in disequilibrium with the demand for funds exceeding supply, (Asante, 2000).
In short, this theory identifies interest rates as the main determinant of investment. According to this theory interest rates have a positive effect on investment, however this is in contrast with both the q theory and the neoclassical theory of investment that suggest a negative effect of interest rates on investment.
2.1.7 The Uncertainty Theory
Some have extended the traditional investment theory to introduce uncertainty (Pindyck, 1991; Rodrik, 1991). Uncertainty takes two forms: uncertainty due to irreversible investment; and policy uncertainty. The basic argument is that since capital goods are often firm-specific, with a low resale value, disinvestment is more costly than positive investment. Pindyck (1991) argues that the net present value rule: “invest when the value of a unit of capital is at least as large as its cost”, must be modified when there is an irreversible investment because when an investment is made, the firm cannot disinvest should market conditions change adversely. This lost option value is an opportunity cost that must be included as part of the cost. Accordingly, the value of the unit must exceed the purchase and installation cost, by an amount equal to the value of keeping the investment option active (Pindyck, 1991).
In terms of policy uncertainty, Rodrik (1991) argues that when policy reform is introduced, it is very unlikely that the private sector will see it as one hundred percent sustainable. A number of reasons may be adduced, among them the expectation that the political-economic configuration that supported the earlier policies may resurface. There is also the fear that unexpected consequences may lead to a reversal. Investors must respond to the signals generated by the reform for it to be successful. However, rational behaviour calls for withholding investment until much of the uncertainty regarding the eventual success of the reform is eliminated, (Asante, 2000).
This theory identifies policy reform and the irreversibility nature of investment as the main determinants of investment. In Zimbabwe, private investment has also been affected by policy uncertainty in the context of political-economic configuration. Unfortunately, policy uncertainty scares away private investment.
2.1.8 The Irreversible Investment Theory
The irreversible investment theory has been extended to the real options theory which views an investment opportunity as an option to purchase an asset at different points in time (Serven, 1997). In this way, the real options theory of investment interprets a firm as consisting of a portfolio of options (Busari and Omoke, 2008). The optimal investment policy balances the value of waiting for new information with the cost of postponing the investment in terms of foregone returns.
When a firm makes irreversible investment expenditure, it kills its option to wait for new information that might affect the desirability of the new investment. To take account of this fact, the standard net-present value investment rule must be modified: the anticipated return must exceed the purchase and installation cost by an amount equal to the value of keeping the option alive (Serven, 1997).
2.1.9 The Dis-equilibrium Approach
According to Meyer (1980), the firm simultaneously chooses desired values for output, labour input, and capital, given the cost of capital, the wage rate and the price level as in the following equation in which the demand for capital depends on the production, the cost of capital, the wage rate and the price of output:
K* = K (c/P, W/P).. (2.13)
Where c is the marginal cost of capital, W is the wage rate and P is the price
If it is assumed, on the other hand, that firms are limited in the amount they can sell at the prevailing price level, as must be the case when there is excess supply in the output market, then the capital stock decision becomes output constrained (Meyer, 1980). In this case, the demand for capital depends on the input prices and the demand for output:
K = K (c/P, W/P, X).. (2.14)
Where X is the demand for output
In addition, the dis-equilibrium approach, according to Chirinko (1993), views investment as a function of both profitability and demand for output. In this instance, investment decisions have two stages: first is the decision to expand the level of productive capacity, and second, is the decision about the capital intensity of the additional capacity (Serven etal, 1992).
However, the first decision depends on the expected degree of capacity utilization in the economy, which provides an indicator of demand conditions, while the second decision depends on relative prices such as the cost of capital and labour. The investment decision takes place in a setting in which firms may be facing current and expected future sales constraints (Serven etal, 1992).
Therefore, investment depends both on profitability and on the prevailing sales constraints, which determine the rate of capacity utilisation (Serven etal, 1992). Criticism of the models (dis-equilibrium models) arises because the models are not clear on the role of cash flow, (Ajide and Lawanson, 2012).
2.1.10 The Profit Theory
Zebib and Muoghalu (1998) recognize the profit theory of investment. They note that the expected profit hypothesis puts emphasis on profits earned by business units and industries instead of output. Investment is affected by current profits, the amount of retained profits and by other variables such as output, prices and sales, which reflect profits. In this model, the greater the gross profits, the greater the internally generated funds and the greater the investment (Zebib and Muoghalu, 1998).
According the profits theory, firms invest more when their profits rise and vice versa. This is so because part of the profit is retained in the business, which adds to the internal funds. Internal funds definitely provide an incentive as well as a source of funds for investments, thereby encourage investments. Since the level of profits usually varies directly with the level of output, investments are influenced positively by the output (Gupta, 2004).
The profit theory, according to Meyer (1980), is one basis for the view that investment is related to the level of income. The level of profits (II) is closely related to the level of aggregate demand or output (X):
II = II (X).. (2.10)
Therefore, the profits theory
I = I (II).. (2.11)
implies that the investment function can be expressed as
I = I (X) where I is investment and IX>0. . (2.12)
Assuming that firms are profit maximisers, investment is undertaken if it is expected to be profitable. Therefore, the optimal capital stock depends on the expected profitability of further additions, not on actual profits. One rational for the role of the actual level of profits is that current profits may be used as a guide to expected profits from additions to the capital stock (Meyer, 1980).
In short, the profits theory of investment identifies profits as the major determinant of investment. Output is outlined in this theory as closely related to investment. It is important to note that it is expected profit that is being referred to here not actual profit.
2.2 Empirical Literature Review
Akpalu (2002) modelled the determinants of private investment in Ghana using time series data from 1970-1994 and employed the Engle-Granger two step approaches and the Johansen multivariate approach. The study revealed that in relative terms, private investment in the short run responds more to real per capita income growth, credit availability and public investment. There was a significant positive relationship between real GDP and private investment both in the short run and long run models; this is in line with the accelerator theory of investment in Ghana, as confirmed by Syed etal (2003) who analyzed the importance of government policies in determining private investment in Pakistan by expressing the coefficients of adjustment as a function of cyclical factors and monetary and fiscal policy variables. Syed etal (2003) also found that public sector investment, changes in bank credit to the private sector and degree of capacity in the economy are playing an important role in the determination of private investment.
Sundararajan and Thakur (1980) examined the relationships between public and private investment in a developing country. For this purpose, a dynamic model of public investment, private investment, savings and growth was postulated and applied to India and Korea. The results of the study reveal that public investment exerts a short-term crowding-out effect on private investment thus agreeing with the findings of Maganga and Edriss (2012) who carried out an empirical test of the macroeconomic variables that can potentially affect private investment decisions in Malawi in a short and long run perspective using time series data for the period 1979-2009 and employing the co-integration and error correction models. Maganga (2012) also found that investment decisions seem to be determined by bank credit to the private sector and the real interest rate in the short run and in the long run, the capital accumulation path seems to be closely dependent on both GDP growth and real exchange rates.
Islam and Wetzel (1991), in a World Bank Study empirically examined the link between real private investment and real public investment/GDP, corporate tax revenues/GDP, credit to the private sector /GDP and real rate of interest in Ghana. Employing Ordinary Least Squares (OLS), they find a negative public-private relationship in the case of Ghana thus confirming the findings of Akpalu (2002) but contrast that of Asante (2000) where public investment was found to crowd-in private investment in Ghana. The study also established a positive relationship between corporate tax revenue and flow of credit to the private sector with all the variables having significant coefficients. However, real interest rate was found not have a substantial effect on private investment even though it has the expected negative sign.
Khan and Khan (2007) analyzed the determinants of private investment in Pakistan over the period 1972-2005. The ARDL co integration approach was employed. The results of their analysis show a partial support for the accelerator principle. The same authors also found out the crowding-out effect of public investment on private investment in the case of Pakistan, agreeing with the results of Majeed and Khan (2008) who analyzed the factors that play an important role in determining private investment in Pakistan using annual data for the period 1970-2006. Majeed and Khan (2008) also found out that private sector output, net capital inflows to the private sector, total sources of funds and past capital stock are significant determinants of private investment rates, while changes in the volume of bank credit also has a positive effect on private investment.
Ahmed and Qayyum (2007) examined the role of macroeconomic uncertainty and public expenditure in determining private fixed investment applying unit roots, co integration and Error Correction Mechanism (ECM) in Pakistan. It is found that there is a long-run relationship between private fixed investments, public consumption expenditure, public development expenditure, and market activities. It is revealed that public development expenditure stimulates private investment, whereas public consumption expenditure is detrimental to private investment. The preferred dynamic private fixed investment function confirms that in the short run, public development expenditure enhances private investment. Macroeconomic instability and uncertainty depresses private investment in Pakistan as confirmed by Naqvi (2002) who examined the relationship between economic growth, public investment and private investment employing the Co-integrating VAR based method using 37 years of the annual data for Pakistan. Naqvi (2002) also found that public investment appears to have a positive effect on private investment; that the growth in the economy generates investment of both types and that investment by itself doesn’t seem to be the igniting source of economic growth.
Jecheche (2011) looked at the determinants of private investment in Zimbabwe from 1990-2008 using an unrestricted Error Correction Model (ECM). The results indicated that public investment crowds-in private investment thus confirming the results of Tadeu and Silva (2013), Muyambiri etal (2012) and Ouattara (2004). Using time series data for the period 1967 to 2004 and employing co integration and VECmodels, Muyambiri etal (2012) examined the causal relationship between private and public investment in Zimbabwe. Ouattara (2004) investigated the determinants of private investment in Senegal over the period of 1970-2000 employing theDickey-Fuller test, the Ng-Perron test, Johansen co integration techniques and the bounds test approach proposed by Pesaran etal (2001). In line with the findings of Ouattara (2004), Jecheche (2011) also found out that real income and foreign aid flows affected positively private investment whilst the impact of credit to private sector and terms of trade was negative. Using a cross section econometric analysis and a Monte Carlo Simulation for data analysis, Tadeu and Silva (2013) upon examining the determinants of long term private investment in Brazil also found that political and economic instabilities were harmful to private investment in Brazil.
In a closely related study, Bayai and Nyangara (2013) analyzed the determinants of private investment post the introduction of the multi-currency system in Zimbabwe (2009-2011) and employed correlation analysis and multiple regressions. In identifying political risk as a key determinant (among other factors) of private investment over the study period, Bayai and Nyangara (2013) partially confirm the findings of Jenkins (1998) who studied the determinants of private investment in Zimbabwe from the period (1970-1990) using the Engle-Granger two step approach and found that the uncertainty about political developments also discouraged investment. Bayai and Nyangara (2013) also identified interest rate, GDP, debt servicing and trade terms as key determinants of private investment over their study period (2009-2011). This however, is in contrast with the other findings by Jenkins (1998) who identified retained profits as the long run determinant of investment and that foreign exchange shortages and external debt to GDP ratio also discourage private capital formation in Zimbabwe.
Mbanga (2002) investigated the impact of external debt on private investment in Cameroon from 1970-1999 using time series data. The study found out the investment accelerator in existence (since a significant positive real GDP- private investment relationship was found), a crowding-in effect of public investment on private investment and the detrimental effect of overvalued exchange rate, thus confirming the results of Asante (2000) who employed the Ordinary Least Square (OLS) approach to model private investment using time series data over the period 1970-1992 in Ghana. Mbanga (2002) also found there was a crowding –out effect of debt service ratio, a positive and significant relationship between credit expansion and private investment and that deteriorating terms of trade had negative effects on private investment. Asante (2000) also established that lack of foreign exchange, corruption and erratic import licensing and rent seeking activities on private investment were also key factors over the study period and that the political dummy representing political stability was highly significant and negative in all trials.
Frimpong and Marbuah (2010) examined the determinants of private sector investment in Ghana and employed modern time series econometric techniques namely unit root tests, co integration and error correction techniques within an ARDL framework. The study revealed that private investment is determined in the short-run by public investment, inflation, real interest rate, openness, real exchange rate and a regime of constitutional rule, while real output, inflation, external debt, real interest rate, openness and real exchange rate significantly influenced private investment response in the long-run, confirming the findings of Lesotlho (2006) who investigated the determinants of private investment in Botswana for the period 1976-2003 and employed the regression analysis based on the co-integration and Error Correction Model (ECM).
Naa-Idar etal (2012), examined the determinants of private investment in Ghana employing a time series analysis of private investment in Ghana covering annual data set from 1960-2010. The study employed the techniques ofco-integration and error correction modelling. The study provides evidence that inflation, exchange rate, public investment, GDP, trade openness, aid, credit and external debt both in a short run and long run significantly affect the level of private investment, confirming the results of Karagoz (2010) who examined the determinants of private investment in Turkey employing the bounds test (ARDL) approach to co integration in estimating the long-run private investment equation. These results are also consistent with the findings of Ajide and Lawanson (2012) who modelled the long run determinants of domestic private investment in Nigeria over the period 1970-2010, employing theadvanced econometric technique of Auto-Regressive Distributed Lag (ARDL) bounds testing approach. Also applying the general to specific approach to error correction model, Naa-Idar (2012) found the existence of stable long run co-integrating relationships between macroeconomic and other variables and private investment.
Al-awad (2005) examined the linkage between inflation rate and private investment in developing countries using panel Co integration approach and a variance decomposition. The result of the study was a negative relationship between inflation rate and private investment confirming the results of Ayeni (2014) who investigated the determinants of private investment in Nigeria applying the ARDL (Autoregressive Distributed Lag) Co- integration approach. Ayeni (2014) also found out that GDP, real interest rate, real exchange rate and credit to private sector have not been able to contribute effectively or boost private investment in Nigeria. The results of these two studies are consistent with the findings of Ikhide (2004) who did empirical studies on external shocks, savings and investment in Nigeria. Ikhide (2004) also found out that growth of real income, increase in public expenditure and exchange rate, openness of the economy and high savings have positive effect on private investment in Nigeria.
Kehinde etal (2012) examined the determinants domestic private investment in Nigeria for the period 1970 – 2010 using time series data and employing the Philip-Peron unit root test and the Johansen (1988) technique. Empirical investigations showed that growth in private investment is best explained by changes in political situations. These results are almost similar to those of Anfofun (2005) who investigated the macroeconomic determinants of private investment in Nigeria and discovered that political crises, external debt burden, inflation and exchange rate negatively affect private investment.
The results of the studies done by Anfofun (2005) and Kehinde etal (2012) are partially confirmed by the findings of Le (2004), who examined the political and economic determinants of private investment in 25 developing countries over 21 years and employed a private investment equation based on the portfolio choice model of asset allocation. The following results were found: socio-political instability characterized by nonviolent protests promotes private investment while violent uprisings hinder private investment; regime change instability characterized by constitutional government change promotes private investment while unconstitutional government change hinders private investment; and policy uncertainty characterized by variability of contract enforcement rights promotes private investment while variability of government political capacity hinders private investment.
Jude (2014) empirically tested the hypothesis of FDI led capital accumulation in Central and Eastern European Countries (CEEC) by sampling 10 CEEC over the period 1990-2010. They found FDI to crowd out domestic investment, while the effect decreases with time. These results are consistent with the findings of Apergis etal (2006) as well as Udomkerdmongkol and Morrissey (2008). However, Apergis etal (2006) analyzed the dynamic relationship between FDI inflows and domestic investment for a panel of 30 selected countries including Egypt, Tunisia, South Africa and Morocco among others using a multivariate model in a panel co-integration approach. Udomkerdmongkol and Morrissey (2008) investigated the effect of FDI on private investment controlling for political regime for developing regions classified into different regions. Jude (2014) also found that greenfield FDI may develop long run complementaries with domestic investment, while mergers and acquisitions do not prove any significant effect on domestic investment and that financial development seems to foster a certain crowding-in effect. Udomkerdmongkol and Morrissey (2008) sampled 13 Latin American countries, 8 Caribbean countries, 8 Asian countries, 10 European transition countries and 5 African countries. Their study also reveals that FDI crowd out effect is greater in countries with high governance scores and lower in Latin America compared to Asia, Europe and Africa.
In contrast to the above findings, Tang etal (2008), investigated the causal link between foreign direct investment (FDI), domestic investment and economic growth in China for the period 1988-2003 using a multivariate VAR system with error correction model (ECM) and the innovation accounting (variance decomposition and impulse response function analysis) techniques. The results show that while there is a bi-directional causality between domestic investment and economic growth, there is only a single-directional causality from FDI to domestic investment and to economic growth. Rather than crowding out domestic investment, FDI is found to be complementary with domestic investment. Thus, FDI has not only assisted in overcoming shortage of capital, it has also stimulated economic growth through complementing domestic investment in China.
In line with the above findings, Borensztein etal (1998) investigated the effect of foreign direct investment (FDI) on economic growth in a cross-country regression framework, utilizing data on FDI flows from industrial countries to 69 developing countries. Their results suggest that FDI is an important vehicle for the transfer of technology, contributing relatively more to growth than domestic investment. However, the higher productivity of FDI holds only when the host country has a minimum threshold stock of human capital. Thus, FDI contributes to economic growth only when a sufficient absorptive capability of the advanced technologies is available in the host economy.
In short, the empirical literature review identified a lot factors as the determinants of private investment. Some of the major factors are output or GDP, inflation, interest rates, public investment, private sector credit, FDI, political instability, exchange rates, terms of trade, savings and external debt. This study will, however, concentrate on a few variables as mentioned earlier on in chapter one. The variables to be studied are namely public investment, FDI, GDP, inflation and interest rates.
2.3 Summary
It can be noted that in Zimbabwe few empirical studies on the determinants of private investment have been done. In as much as the researcher is aware only Jenkins (1998), Jecheche (2011) and Bayai and Nyangara (2013) have written on determinants of private investment in Zimbabwe. It can also be noted from the above empirical literature review, that most studies on the determinants of private investment have been done in developing countries as compared to developed countries. The reason behind this as given by (Oshikoya, 1994; and Naqvi, 2002) is that in developing countries, private investment plays a greater role than public investment in determining economic growth.
To the extent that it is important for the author to further studies on the determinants of private investment in Zimbabwe (since it is a developing country), especially in view of the current public debate on the need to mobilise all resources of development finance in pursuit of a new trajectory of accelerated economic growth and wealth creation through a new policy known as ZimAsset. The main determinants from the literature review are, generally; the variables of the accelerator model of investment.
CHAPTER III METHODOLOGY
3.0 Introduction
This study seeks to analyse the determinants of private investment in Zimbabwe based on time series data for the country from 1980-2013. This chapter will highlight the methodology and specify the model that will be used in the study.
3.1 Theoretical Model
The study follows the leads of the accelerator theory of investment. This is because according to Oshikoya (1994); and Ghura and Goodwin (2000), investment function for developing countries is difficult to estimate due to the lack of adequate data on capital stock and returns on capital. Zimbabwe is a developing country that also is still facing similar problems of inadequacy of data on macroeconomic variables. The study follows the accelerator theory because the variables of the accelerator theory such as GDP growth, public investment (as a percentage of GDP), FDI (as percentage of GDP), inflation (annual percentage changes) and interest rates (commercial lending rates) can be accessed adequately in comparison to other macroeconomic economic variables that affect private investment.
The accelerator theory will be used to come up with the regression model and additional factors that determine private investment will be added subsequently. The accelerator theory of investment postulates that the desired capital stock K*t is proportional to the level of expected output Y*t. This gives us the equation:
K*t=αY*t (3.0)
Where K*t is the capital stock that the private sector desires to have in period t and Y*t is the expected level of output in period t.
3.2 Empirical Model
In order to analyse the determinants of private investment in Zimbabwe, the researcher will modify the accelerator model and estimate it using the Ordinary Least Squares (OLS) econometrics methodology. The study follows Ayeni (2014) who used a modified accelerator model for Nigeria and estimated it using OLS. The choice to follow Ayeni (2014) is based on the fact that Nigeria, just like Zimbabwe; is a developing country. Therefore there is relevance in terms of the need to boost private investment in both countries.
However, in specifying his model, Ayeni (2014) made private investment (PIV) a function of five explanatory variables selected to proxy the following macroeconomic conditions: aggregate demand,competitive condition, liquidity constraint, uncertainty or instability. Ayeni (2014) specified his model in functional form as follows:
PIV = f (RGDP, RIR, RER, INFR, CRPS). (3.1)
Where PIV is Private Investment (change in capital stock), RGDP is RealGross Domestic Product (proxy for thedemand condition in the economy), RIRisReal Interest Rate, RER is Real Exchange Rate, INFRisInflation Rate, CRPS is Credit to Private Sector.
Ayeni (2014) then wrote his econometric model as follows:
PIV = β0 + β1RGDP+β2RIR+ β3RER+ β4INFR+ β5CRPS+ μt.. (3.2)
Where β0 is the constant intercept; β1 ... β5 are coefficients of explanatory variables (RGDP, RIR, RER, INFR and CRPS respectively); μt is the error term; t represents time.
However in specifying the empirical model in this research, the study also assumes private investment (PIV) is a function of five explanatory variables and these are public investment, foreign direct investment, GDP, inflation and interest rates and this results in the multiple regression equation explicitly specified in functional form as follows:
PIV= f (PUIV, FDIV, GDP, INFL, INTR).. (3.3)
Where PIV is Private Investment, PUIV is public investment, FDIV is foreign direct investment, GDP is gross domestic product growth rate, INFL is the inflation rate and INTR is the interest rate.
The study dropped real exchange rates (RER) and credit to the private sector (CRPS) used by Ayeni (2014) because data on these variables is relatively scarce in comparison to other variables. These variables are replaced by PUIV and FDIV whose data is readily accessible and they give us approximately the same information. This can be specifically expressed in linear form as:
PIV = β0 + β1PUIV + β2 FDIV + β3 GDP + β4INFL + β5INTR... (3.4)
Econometrically, to include the stochastic or random error term the model is expressed as:
PIV = β0 + β1PUIV + β2 FDIV + β3 GDP + β4INFL +β5INTR + μt. (3.5) In order to put all the variables on the same wave length so that all variables are measured in percentages; we divide PIV, PUIV and FDIV by GDP. Another advantage of doing so as put forward by Gujarati (1995) is that transforming variables by a weight reduces the chances of facing the problem of heteroscedasticity in the variables. The final model becomes:
PI= β0 + β1PUI + β2 FDI + β3 GDP + β4INFL +β5INTR + μt. (3.5)
where
PI = private investment as a percentage of GDP
PUI = public investment as a percentage of GDP
FDI = foreign direct investment as a percentage of GDP
GDP = gross domestic product growth rate
INFL = inflation rate (annual percentage changes)
INTR = interest rate (commercial lending rates)
μt = stochastic or random error term (with usual properties of zero mean and non serial correlation) representing any other determinants of PI that are not captured in the specified model because they are considered trivial;
t = time.
β0 = the constant intercept.
β1..β5 = coefficients of the explanatory variables.
3.3Reasons for using OLS
The choice of the OLS econometrics methodology is based on the Gauss–Markov Theorem (GMT) of a Classical Linear Regression Model (CLRM), named after Carl Friedrich Gauss and Andrey Markov, which states that in a linear regression model in which the errors have expectation zero and are uncorrelated and have equal variances, the Best Linear UnbiasedEstimator(BLUE) of the coefficients is given by the OLS estimator. This means that upon satisfaction of the GMT assumptions of a CLRM, the OLS econometrics methodology produces consistent, efficient and unbiased estimates of the parameters.
3.4 Assumptions of the model
The OLS econometrics methodology adopted in this study is based on the Gauss-Markov assumptions of a CLRM.
3.5 Estimation Procedure
In this study private investment as a percentage of GDP (PI) is a function of GDP growth, FDI as a percentage of GDP, inflation rate (annual percentage changes), interest rates (commercial lending rates) and public investment as a percentage of GDP. The estimation of this model will be done using data for the Zimbabwe economy for the period 1980 – 2013. The determinants of private investment in Zimbabwe are estimated using the CLRM OLS econometrics methodology. Below is an explanation of the estimation procedure, especially how the OLS method is used to estimate the parameters.
Given a two variable Population Regression Function(PRF):
Yi= β1+ β2Xi+ μi... (3.6)
Where Yi is the dependant variable, Xi is the explanatory variable, β1 and β2 are parameters and μi is the error term.
The PRF is not directly observable (Gujarati, 1995). Therefore, we estimate it from the Sample Regression Function (SRF):
Yi= β1+ β2Xi+ μi.. (3.7)
= Y + μi
Where Y is the estimated (conditional mean) value of Yi.
ˆ ˆ
To determine the SRF:
ˆ
μi= Yi–Yi.( 3.8)
ˆ ˆ
= Yi–β1–β2Xiwhich shows that the μi(the residuals) are simply the differences betweenthe actual and estimated Y values.
ˆ
Given npairs of observations on Y and X, researcher would like to determine the SRF in such a manner that it is as close as possible to the actual Y. Tothis end, we may adopt the following criterion: Choose the SRF in such away that the sum of the residuals∑μi= ∑ (Yi–Yi) is as small as possible. In this regard we say the OLS method minimises the sum of squared errors.
According to Gujarati (1995) all the residuals receive equal importance no matter how close or how widely scattered the individual observations are from the SRF. A consequence of this is that it is quite possible that the algebraic sum of the Uiis small (even zero) although the Uiare widely scattered about the SRF. The least-squares criterion, as noted in Gujarati (1995), states that the SRF can be fixed in such a way that:
illustration not visible in this excerpt
is as small as possible, where μiare the squared residuals. Under the minimumcriterion, the sum can be small even though the Ui are widely spread aboutthe SRF. But this is not possible under the least-squares procedure, for thelarger the ∑μi (in absolute value), the larger the ∑μi.A further justificationfor the least-squares method lies in the fact that the estimators obtained byit have some very desirable statistical properties (Gujarati, 1995).
illustration not visible in this excerpt
The principle or the method of least squares chooses β1 and β2 in such a manner that, for a given sample or set of data, ∑μiis as small as possible. In other words, for a given sample, the method of least squares provides us with unique estimates ofβ1 and β2 that give the smallest possible value of ∑μi. Theprocess of differentiation yields the following normal equations for estimating β1andβ2:
illustration not visible in this excerpt
Where n is the sample size.
These simultaneous equations popularly known as normal equations, can be solved simultaneously to obtain the parameters β1 and β2:
illustration not visible in this excerpt
Where X and Y are the sample means of X and Y and where we define xi = ∑(Xi - X) and yi = (Yi - Y)
Henceforth we adopt the convention of letting the lowercase letters denote deviations from mean values:
illustration not visible in this excerpt
Hence the OLS estimators β1, β2 . βn are known as least squares estimators because they are derived from the least squares principles as shown above.
However, the study will employ a similar estimation procedure for a multiple regression function of the form:
PI = f (PUI, FDI, GDP, INFL, INTR) where. (3.12)
Where PI is the dependant variable and PUI, FDI, GDP, INFL and INTR are explanatory or independent variables as previously defined. For simplicity, a mathematical economist as noted in Gujarati (1995) might suggest the following form of the function:
PI = β0 + β1PUI + β2FDI + β3GDP + β4INFL + β5INTR... (3.13)
where β0 β5 are coefficients of the explanatory variables.
Now, according to Gujarati (1995), the purely mathematical model (such as the one above) is of limited interest to the econometrician because it assumes that there is an exact or deterministic relationship between PI (the dependant variable) and the explanatory variables. But, according to Gujarati (1995), the relationships between economic variables are inexact.
To allow for the inexact relationships between economic variables, the researcher will modify the deterministic function of the form above into an explicitly estimable econometric form as follows:
PI = β0 + β1PUI + β2FDI + β3GDP + β4INFL + β5INTR + μi (3.14) Whereaccording to Gujarati (1995),μi, known as the disturbance or error term, is a random (stochastic) variable that has very well defined probabilistic properties and represents all the factors that affect PI but are not taken into account explicitly.
Under certain assumptions (the Gauss-Markov assumptions of a Classical Linear Regression Model) the OLS method has some very attractive statistical properties that have made it one of the most powerful and popular methods of regression analysis (Gujarati, 1995). To estimate the econometric model of the form above, that is to find the numerical values of β0..β5, data is needed and this is basically the subject matter of the next chapter.
3.6 Justification of variables
3.6.0 Private investment as a percentage of GDP (PI)
This is the dependant variable. The paper will adopt gross capital formation, private sector (as a percentage of GDP) in measuring private investment in Zimbabwe.
3.6.1 GDP growth (GDP)
The paper will adopt GDP growth rate (annual percentage) as a measure of GDP growth. Changes in output (GDP) are the most important determinant of private investment (Oshikoya, 1994). Neoclassical investment theory suggests that the growth rate of real output is positively related to investment because it indicates changes in aggregate demand for output that investors seek to meet (Chirinko, 1993; Ndikumana, 2000). This relationship can also be readily derived from a flexible accelerator model with the assumption that the underlying production function has a fixed relationship between the desired capital stock and the level of real output (Mlambo and Oshikoya, 2001). The expected sign in this study is positive in relation to investment.
3.6.2 Public investment as a percentage of GDP (PUI)
In this study, public investment as a percentage of GDP (gross capital formation, public sector) will be adopted as a proxy for public investment. The relationship between public and private investment is ambiguous in the sense that public investment may either be crowding out or in private investment. Crowding out may occur through what is referred to as the Ricardian Equivalence Theorem (RET). According to Ghura and Goodwin (2000) this situation may occur if additional government borrowing raises domestic interest rates and the future tax burden. In this case the expected sign of public investment in relation to private investment is negative.
Crowding in is also possible via boosting of private investment by increasing private returns through the provision of infrastructures (communication, transport, energy and so on). Public investment on social and physical infrastructure by raising private and social rate of return can boost private investment. Most of the developing countries have a large component of public investment concentrated on infrastructure projects, which may be complementary to private investment (Oshikoya, 1994). In this case public investment is expected to complement private investment thus giving a positive sign.
3.6.3 Inflation rate (INFL)
In this study inflation is measured in annual percentage changes. Inflation affects private investment but its impact is ambiguous in the sense that it may either be positive or negative. Higher expected inflation lowers the real interest rate, causing portfolio adjustments away from real money balances to real capital thereby raising real investment (Ghura and Goodwin, 2000). In this case the expected sign of inflation in relation to private investment is positive.
However, anticipated high inflation raises the cost of acquiring capital and thus lowers capital accumulation (Rossiter, 2002). Also, high inflation rates are an indicator of macroeconomic instability, which can have adverse impact on private investment (Oshikoya, 1994). In this case the expected sign of inflation in relation to private investment is negative.
3.6.4 Interest rates (INTR)
The paper will adopt commercial lending rate (measured in percentages) as a measure of interest rates. To determine whether to invest in a particular project a firm must compare the rate of return with the interest rate. Thus the net private investment depends on the total volume of investment projects where the expected rate of return exceeds the interest rate. The effect of real interest rate on private investment is ambiguous in the sense that it may either be positive or negative. The sign of the real interest rate is an empirical issue and depends on whether the data supports the McKinnon-Shaw hypothesis or the neoclassical view (Ndikumana, 2000). The McKinnon-Shaw hypothesis states that interest rates affect private investment positively (Agrawal, 2001). In this case the expected sign of interest rate in relation to private investment is positive.
However, the neoclassical view is that real interest rate are expected to affect private investment negatively since higher interest rates raise the user cost of capital and therefore reduce investment (Ndikumana, 2000). According to the Tobin’s q theory of investment, interest rates are negatively related to investment. In this case the expected sign of interest rate in relation to private investment is negative.
3.6.5 FDI as a percentage of GDP (FDI)
The study adopts percentage contribution to GDP of foreign direct investment as a measure of FDI.The presence of FDI in a host country, according to economic theory may either lead to the crowd in effects or crowd out effects of private investment; thus the impact of FDI on private investment is ambiguous. Crowding in effects occur through different mechanisms such as competition effectsbetween foreign and domestic firms leading to increased productivity. Other mechanisms may include the imitation effects. As domestic firms observe the actions of their foreign competitors, they learn about new technologies, which can apply locally hence increased domestic productivity (Aitken et al, 1997). After noticing a product innovation from the MNCs, domestic firms observe and learn the successes and failures of foreign firms. Another mechanism is the labour mobility effects . In this channel, workers and managers originally employed and trained by the foreign firms or participating in joint ventures may later either move to local firms or establish own businesses in similar fields taking with them their upgraded human capital leading to increased productivity (Fosfuri etal, 2001). I n this case the expected sign of FDI in relation to private investment is positive.
Crowding out effect may occur according to Aitken and Harrison (1999), if the presence of FDI in a host country destabilizes the market equilibrium by forcing the domestic firms to produce less output and reduce their market shares. The presence of foreign firms would mean that the workers employed by Multi-National Companies (MNCs) may not leave those firms to establish their own firms because foreign firms would do all it takes to prevent the loss of the trained personnel by paying the workers higher wages (Aitken et al, 1997). In addition, Nguyen and Xing (2006) argues that domestic firms that receive labour mobility may not be able to provide appropriate working conditions for those workers, thus their abilities are unable to be fully utilized. In this case the expected sign of FDI in relation to private investment is negative.
3.7 Diagnostic Tests
The OLS model estimates the parameters and their signs which are used in the interpretation where the significance is tested using either probability values (p-values), the t- tests or both. Below is a discussion of the tests that will be carried out:
3.7.0 Multicollinearity test
Multicollinearity is a regression phenomenon that occurs when two or more predictors (explanatory variables) in the model are highly correlated and provide redundant information about the response. According to Gujarati (1995) the OLS assumption of no perfect multicollinearity requires that in the Population Regression Function (PRF) we include only those variables that are not exact linear functions of one or more variables in the model.
Multicollinearity can be as a result of the data collection method employed, (for example, sampling over alimited range of the values taken by the regressors in the population); constraints on the model or in the population being sampled, (forexample, in the regression of electricity consumption on income [X2] andhouse size [X3] there is a physical constraint in the population in that familieswith higher incomes generally have larger homes than families withlower incomes); model specification, (for example, adding polynomial terms to a regressionmodel, especially when the range of the Xvariable is small); and finally an over-determined model, (this happens when the model has moreexplanatory variables than the number of observations).An additional reason for multicollinearity, especially in time series data,may be that the regressors included in the model share a common trend,that is, they all increase or decrease over time, (Gujarati, 1995).
A model suffering from multicollinearity will produce confusing and misleading results in the sense that there will be increased standard errors of the estimates of the βs (that is decreased reliability).The study will use the correlation matrix to test for multicollinearity. The rule of thumb is that the value of the correlation between two different variables should be less than 0.8. In testing for multicollinearity the null hypothesis is that there is perfect multicollinearity while the alternative hypothesis is that there is no perfect multicollinearity. The decision rule is that we reject the null hypothesis when the correlation between two different variables is less than 0.8 for all sets of combinations in the correlation matrix.
3.7.1 Goodness of Fit test
The researcher also looked at the measure of “goodness of fit” that is the coefficient of determination R2 and the adjusted R2 in order to find out how well the sample regression fits the data. According to Gujarati (1995), R2, a coefficient of determination; is a summary measure that tells how well the sample regression line fits the data. The presence of multicollinearity is expected among variables as in real life we cannot have orthogonal variables since most economic magnitudes are interdependent. Problems only emerge where the degree of multicollinearity is high. In cases where we have a high R2, then there will be higher possibility of the multicollinearity prevalence.
In this study, E-views 3.1is used in the estimation of the regression model because it can do unit root tests and co-intergration as compared to other statistical packages like Software Package for Social Scientists (SPSS). The estimated coefficients are subject to some test in order to prove their statistical relevance to the model. It is required that the series in a regression model be stationary. There is need to consider whether the estimated regression is not spurious. The decision reliability of the model rests on the coefficient of determination, R2. Thus R2 is used to test for the specification of the model. According to Gujarati (1995), R2 is also used to test for the overall significance of the whole model. The null hypothesis will be that the model is misspecified while the alternative hypothesis will be that the model is correctly specified. The rule of thumb is that R2 should be greater than 0.6 (that is, 60%). Therefore the decision criterion is that we reject the null hypothesis (H0) and accept the alternative hypothesis (H1) if R2 is greater than 60%.
In addition, this R2 determines the statistical reliability of the whole model, that is, the goodness of fit of the line of regression for the data sampled. It actually shows the predictive power of the model and in other words tells us to what extent do the explanatory variables explain the dependant variable. According to Granger and Newbold (1995), an R2 greater than the DW statistic is a good rule of thumb to suspect that the estimated regression is spurious. If the DW statistic is greater than the R2 then the regression will not be spurious
3.7.2 F-Test
The study will adopt the F-test for testing for the significance of the whole model. The probability of the F-statistic should be very significant, especially at 1% (that is at all levels). This means that the probability of the F-statistic should ideally be zero that is 00000. Basically for one to estimate a model, that model should be significant. There always consequences associated with estimating an insignificant model, these include the unreliability of the results. The null hypothesis is that the model is not significant while the alternative hypothesis is that the model is significant.
3.7.3 Durbin Watson (DW) test
The assumption of the OLS regression analysis requires that there should be no autocorrelation amongst successive explanatory variables. The Durbin Watson d test, which is the ratio of the sum of squared differences in successive residuals (Total Sum of Squares [TSS]) to the residual sum of squares (RSS), is used in this study to test for autocorrelation. It must lie between 0 and 4. If d is equal to 2, there will be no autocorrelation (Granger and Newbold, 1995). A great advantage of the d statistic is that it is based on the estimated residuals, which are routinely computed in regression analysis. Because of this advantage, it is now a common practice to report the Durbin-Watson d along with summary statistics such as R2, adjustedR2 and so on. Therefore as a rule of thumb, if d is found to be 2 in an application, one may assume that there is no first order autocorrelation, either positive or negative. The closer d is to 0, the greater the evidence of positive serial correlation. If d=4 there is perfect negative correlation among successive residuals. The closer d is to 4, the greater the evidence of negative serial correlation (Gujarati, 1995).
The summary of the results of the Durbin Watson test can be shown using a simple diagram below:
illustration not visible in this excerpt
DL is the lower bound and DU is the upper bound. The regions from 0 to DL and 4-DL to 4 are the rejection zones of H0. DL to DU and 4-DU to 4-DL are the indecision zones where there is no conclusion to be drawn. The region DU to 4-DU is the acceptance region because the Durbin Watson statistic is closer to 2.
The Durbin Watson test decision rule can be summarised in a simple as shown below:
illustration not visible in this excerpt
3.7.4 White test
To detect the presence of heteroscedasticity, the researcher will perform the White test. According to Gujarati (1995)the White test can be a test of (pure) heteroscedasticity or specification error or both.It has been argued that ifno cross-product terms are present in the White test procedure, then it is atest of pure heteroscedasticity. If cross-product terms are present, then it isa test of both heteroscedasticity and specification bias (Gujarati, 1995). In this regard, the drive for this test is to check whether there is a systematic relationship between the squared residuals and the explanatory variables.The White test is based on the residuals of the fitted model. To test the assumption of homoscedasticity, one can use auxiliary regression analysis by regressing the squared residuals from the original model on set of original regressors, the cross-products of the regressors and the squared regressors. In the presence of autocorrelation the OLS estimators are unbiased and consistent but not efficient, for example the variance estimated are not minimal.
However, the White test is general because it does not rely on the normality assumptions and it is also easy to implement. Because of the generality of the White test, it may also be used to identify the specification error as noted in Gujarati (1995). To test for heteroscedasticity the null hypothesis is that there is heteroscedasticity (that is to say, there is no homoscedasticity) and the alternative is that there is no heteroscedasticity (that is to say, there is homoscedasticity). The decision rule (or criterion) is that we reject H0 if the probability values of the White test are insignificant.
3.7.5 ARCH LM test
An uncorrelated time series can still be serially dependent due to a dynamic conditional variance process. A time series exhibiting conditional heteroscedasticity - or autocorrelation in the squared series - is said to have autoregressive conditional heteroscedastic (ARCH) effects. Engle’s ARCH test is a Lagrange Multiplier (LM) test to assess the significance of ARCH effects (Engle, 1982). To test for autocorrelation the study will use the ARCH LM test. The results of the ARCH LM test will complement the Correlation Matrixthat will also be used to detect autocorrelation. To test for autocorrelation the null hypothesis is that there autocorrelation and the alternative hypothesis that there is no autocorrelation. The decision rule is that we reject H0 if the probability values of the ARCH LM test are insignificant.
3.7.6 Stationarity test
Since the study is using time series data, testing for stationarity is essential. More specifically, testing for stationary helps to avoid spurious results, that is results without any economic meaning. According to Gujarati (1995) if a time series is stationary, its mean, variance, and autocovariance (at various lags)remain the same no matter at what point we measure them; that is, they are time invariant.Such a time series will tendto return to its mean (called mean reversion) and fluctuations around thismean (measured by its variance) will have broadly constant amplitude.
If a time series is not stationary in the sense just defined, it is called anon-stationary time series. In other words, a nonstationary time series will have a timevaryingmean or a time-varying variance or both. Stationary time series are important because if a time series is nonstationary, we can study its behaviour only for the time period under consideration.Each set of time series data will therefore be for a particularepisode. As a consequence, it is not possible to generalize it to other timeperiods. Therefore, for the purpose of forecasting, such (nonstationary) timeseries may be of little practical value (Gujarati, 1995).
Several tests for stationarity have widely been accepted in econometric theory and literature. One of the stationary tests is the unit root test for time series data. When data is non stationary, unit roots are detected and are integrated of order (p). The study will adopt the Augmented Dickey Fuller (ADF) test to test for stationarity. According to Engle and Granger (1987), a non-stationary series is said to be integrated of order nth if it can be made stationary by differencing it nth times. With ADF the unit roots are valid even with the presence of serial correlation of unknown form. The null hypothesis is that there is a unit root (that is, the variables are non-stationary) and the alternative hypothesis is that there is no unit root (that is, the variables are stationary). The decision rule is that we reject the null hypothesis if the ADF statistic calculated is greater than the critical value in absolute terms.
3.7.7 Normality test
The classical normal linear regression model, as noted in Gujarati (1995), assumes that each μi (error term) is distributed normally with:
Mean: E (μi) = 0. (3.15)
[illustration not visible in this excerpt] (3.16)
Covariance: [illustration not visible in this excerpt] (3.17)
Or simply:
μi ̴ N (0, Ϭ2)... (3.18)
However, there are several tests of normality in econometrics literature; amongst the tests are the histogram of residuals, the line graphs of residuals, the probability plot, chi-square, Anderson-Darling and Jarque-Bera tests. The study will adopt the graphical (line graph) test of normality of the error term. The logic behind the choice of the graphical method is that it is much easier to plot and interpret in comparison to other methods.
3.8 Data sources
The study estimates an empirical model using secondary data on real GDP, inflation rate, public investment, real interest rate and FDI for the period 1980-2013. Data on inflation and interest rate were gathered from ZimStats and data on GDP, public investment and FDI were gathered from the World Bank (WB) online database. The choice of the data sources is based on the assumption that ZIMSTATS and WB provide data that are accurate, relevant and reliable.
3.9 Summary
This chapter has covered the methodology of the study. The next chapter will focus on estimation, presentation and interpretation of results.
CHAPTER IV ESTIMATION, PRESENTATION AND INTERPRETATION OF RESULTS
4.0 Introduction
In this chapter the researcher will estimate the model, present and interpret the results. The chapter specifically presents the descriptive statistics, results of the diagnostic tests described in chapter three; regression results and finally the interpretation and discussion of regression results.
4.1 Descriptive Statistics
illustration not visible in this excerpt
Source: E-views 3.1 Software Package
The table above shows descriptive statistics showing measures of central tendency. The maximum and minimum show the existence of outliers in the variables. The large gap between the maximum and the minimum show the existence of outliers, for example INTR has a maximum of 10600 and a minimum of 7.5; this shows that there are outliers in this variable. It can be seen that outliers exist in INTR which also have a very high standard deviation of 1810.334. P, GDP and PUI are negatively skewed while other variables are positively skewed. The rule of thumb for kurtosis is that it should be around 3 for normally distributed variables. Private investment (P) has a kurtosis of 2.56914, public investment has a kurtosis of 2.640612 and GDP has a kurtosis of 3.146182. From mere observation it can be seen that these variables have kurtosis that can generally be approximated to be equal to 3. This shows that it is reasonable to use these variables in this study.
4.2 Correlation Matrix
Table 4.1
illustration not visible in this excerpt
Source: E-views 3.1 Software Package
The correlation matrix is used to test for multicollinearity. If the variables are highly correlated then its means there is the problem of multicollinearity.
H0: there is perfect multicollinearity
H1: there is no perfect multicollinearity
Decision:
We reject the null hypothesis because all the values are less than 0.8 and conclude that there is no perfect multicollinearity.
Therefore the variables used in the study are not highly correlated. Thus there is no problem of multicollinearity. This means that the OLS assumption that requires that there should be no multicollinearity holds.
4.3 ARCH LM test
Table 4.2
illustration not visible in this excerpt
Source: E-views 3.1 Software Package
H0: there is autocorrelation
H1: there is no autocorrelation
Decision:
We reject the null hypothesis that there is autocorrelation because the probability (p-value = 0.387917) is insignificant and conclude that there is no autocorrelation. This means that the OLS assumption of no serial autocorrelation holds. This test also compliments the results of the correlation matrix shown in table a, above.
The study also carried out a test for autocorrelation as shown in table h using the DW test and the DW statistic was found to be 1.799685 which is approximately equal to 2. This implies that there is no autocorrelation complimenting the results in tables 1 and 2.
4.4 White test
Table 4.3
illustration not visible in this excerpt
Source: E-views 3.1 Software Package
H0: there is heteroscedasticity
H1: there is no heteroscedasticity
Decision:
We reject the null hypothesis that there is heteroscedasticity because the p-value (p = 0.629093) is insignificant and conclude that there is no heteroscedasticity. This means that the OLS assumption of homoscedasticity holds.
4.5 Normality test
illustration not visible in this excerpt
Figure 4.0
In the graph above, Ui is the error term (also shown by e). As shown by the graph, the variance of the error term is generally normal. Therefore, the study assumes the OLS normality assumption holds. However, according to Gujarati and Dawn (2009), even without the normality assumption, the Gaus-Markov theorem shows that the OLS estimators are BLUE. With the additional assumption of normality, the OLS estimators are not only best unbiased estimators (BUE) but also follow well-known probability distributions (Gujarati and Dawn, 2009).
4.6 Unit Root tests (ADF test: in levels)
Table 4.4
illustration not visible in this excerpt
Note * , ** and *** means that the variable isstationary at 1%,5% and 10% levels respectively
Source: E-views 3.1 Software Package
As shown in the table above the variables namely GDP, INFL and INTR are stationary in levels. FDI and PUI are not stationary therefore the ADF test was performed in 1st difference and the results obtained are presented below:
ADF in: 1st difference
Table 4.5
illustration not visible in this excerpt
Note * , ** and *** means that the variable isstationary at 1%,5% and 10% levels respectively
Source: E-views 3.1 Software Package
To avoid the spurious regression problem that may arise from regressing a nonstationary time series on one or more stationary time series, we have to transform nonstationary timeseries to make them stationary. Spurious regression problems should be avoided by ensuring that all variables in the regression are stationary since this removes the stochastic trend responsible for the “spurious” result. Nonstationary variables can be transformed to stationarity by differencing, but simply differencing all the variables is not the solution to uncovering the underlying relationship between them (Lewis and Mizen, 2000).
Thus the differencing method is not without problems. According to Gujarati (1995), there is a loss of one observation due to the differencing procedure and therefore the degrees of freedom are reduced by one.Sometimes this remedy might be worse than the disease itself, in the sense that differencing may, in some extreme cases; result in changing the study. Above all,the major problem of non-stationarity is seen in the interpretation of results, therefore the best methods suggested to deal with non-stationarity are the Auto-Regressive Distributed Lag (ARDL) approach and the Vector Auto-Regressive (VAR) approach. However, both approaches are beyond the scope of this study. Therefore we are going to estimate the model in levels and assume away the problem of spurious relationships.
4.7 Regression results
Table 4.6
illustration not visible in this excerpt
Note * , ** and *** means that the variable issignificant at 1%, 5% and 10% levels respectively
Source: E-views 3.1Software Package
illustration not visible in this excerpt
PI = - 1.475511 + 0.931308 PUI - 0.986944 FDI - 0.265513 GDP - 0.029481INFL- 0.000368 INTR + μt
4.8 Misspecification test
R2 is 0.689624 as shown above. This is the coefficient of determination that determines the extent to which the explanatory variables explain the dependant variable. In this case it is approximately 0.69. This means that approximately 69% of what determines private investment in Zimbabwe is explained by the explanatory variables. As mentioned earlier on in the previous chapter, R2 is also used to test for misspecification of the model as shown below:
H0: the model is not correctly specified
H1: the model is correctly specified
Decision:
We reject the null hypothesis and conclude that the model is correctly specified since R2 is greater than the rule of thumb, (that is 0.6).
Therefore the value of R2 (0.689624) show that the model variables are significant in explaining private investment in Zimbabwe because the model has a high explanatory power. As mentioned earlier on, that R2 also tests for the overall significance of the whole; in this case it shows that the model is not only correctly specified but also significant.
4.9 Testing for the significance of the whole model
The F-statistic is 12.44265 and the probability of the F-statistic is 0.000002.
H0: the model is not significant
H1: the model is significant
Decision:
We reject the null hypothesis and conclude that the model is significant since the probability of the F-statistic is 0.000002, meaning that there is an approximately 0 probability of rejecting the model.
The rule of thumb states that the F – statistic should be greater than 5, therefore it indicates a significant relationship between the dependant variable and the explanatory variables. Therefore the model is correctly specified and valid. The model is also valid and correctly specified because the F-statistic is greater than the Prob (F-statistic).
4.10 Interpretation and discussion of the results on regression:
4.10.0 PUI (as a percentage of GDP)
From the results, public investment or PUI (as a percentage of GDP) has a positive sign and is statistically at 1% levelof significance. This means that a 1% increase in public investment in Zimbabwe will lead to approximately 0.93% increase in private investment in Zimbabwe. Economic theory also suggests a positive sign on the relationship of real public investment with private investment through the crowding-in effect.These results also conform to the research hypothesis and the expected sign.
Thecrowding-in effect in Zimbabwe is possible via boosting of private investment (as a percentage of GDP) by increasing private returns through the provision of infrastructures such as communication, transport, energy and so on. The results also imply that public investment on social and physical infrastructure by raising private and social rate of return is boosting private investment in Zimbabwe.
These results are consistent with previous studies done in Zimbabwe by Jecheche (2011) and Muyambiri etal (2012). The results are also similar to studies done in other developing countries such as Asante (2000) in Ghana, Naqvi (2002) in Pakistan, Mbanga (2002) in Cameroon, Ouattara (2004) in Senegal and Tadeu and Silva (2013) in Brazil.
However, these results contradict the other side of the coin in the sense that economic theory, on the other hand also suggests a negative sign on the relationship of real public investment with private investment through the crowding-out effect. Therefore the results of this study are not consistent with other previous studies whose results support the crowding-out hypothesis. These studies are Sundararajan and Thakur (1980) in India and Korea, Akpalu (2002) in Ghana, Khan and Khan (2007) in Pakistan, Majeed and Khan (2008) in Pakistan and Maganga and Edriss (2012) in Malawi.
The crowding-out effect may occur through what is called the Ricardian Equivalence Theorem (RET) when additional government borrowing raises domestic interest rates and the future tax burden.The implication of the RET is that a tax cut financed by higher borrowing would have no impact on increasing aggregate demand because consumers would save the tax cut to pay the future tax increases and as a result investment is compromised in the sense that individuals will save not to invest but to pay for the future tax burden (an increase in tax).
4.10.1 FDI (as a percentage of GDP)
From the results, FDI (as a percentage of GDP) has a negative sign and is statistically significant at 10% level of significance. This means that a 1% increase in FDI (as a percentage of GDP) will lead to an approximately 0.99% decrease in private investment. Economic theory also suggests a negative sign of the relationship between FDI (as a percentage of GDP) and private investment (as a percentage of GDP) through the crowding-out effect. This is not consistent with the research hypothesis and but with the expected sign.
The major implication of these results is that local firms in Zimbabwe are facing stronger direct competition from foreign investments that probably puts them out of the competition. One of the most important explanations of this negative effect, as given by Aitken and Harrison (1999), is that local firms and multinationals that operate in the same sector will compete with each other and the latter have an incentive to prevent technology leakage and spillovers from taking place. This means that local firms will obviously be “kicked out” of competition; in this regard foreign firms are having detrimental effects on private sector growth in Zimbabwe.
Aitken and Harrison (1999) also argue that foreign investment can generate negative spillovers to domestic firms through a reduction in the productivity of domestic firms in the same industry. If foreign firms can produce with lower marginal costs, they are likely to compete with domestic firms by increasing their production. This means that in Zimbabwedomestic firms are therefore losing their market shares to the foreign-owned firms and have to cut the volume of their production which results in a decline in their productivity.
In addition, foreign firms in Zimbabwe are probably exercising strategies such as protection of their intellectual property, trade secrecy and paying higher wages to local employees. When foreign firms engage in strategies such as intellectual property protection and trade secrecy, it means that they cease to serve their much celebrated role of technology transfer to their host countries. Thus in Zimbabwe, foreign companies are not serving their expected role because they exercise such strategies as trade secrecy so that they remain monopolistic in the market. By so doing, small domestic firms are “kicked out” of competition as they have little or no access to advanced technologies that their counterparts (MNCs) are using in the production and provision of both goods and services.
Aitken etal (1997) argues that foreign firms would do it all to prevent loss of the trained personnel by paying the workers higher wages. In light of this argument, the results imply that people who are employed by foreign companies in Zimbabwe are being highly paid to the extent that they cannot leave these companies to establish their own firms. They are automatically faced with the only option of taking a highly paid job and they work for the MNC probably until retirement. This reduces private investment in Zimbabwe in the sense that if such workers were given a chance they could establish their own companies and private investment would inevitably increase in Zimbabwe.
These results are similar to studies done by Apergis etal (2006) in 30 selected countries (including Egypt, Tunisia, South Africa and Morroco among others); Udomkerdmongkol and Morrissey (2008) in 13 American countries, 8 Caribbean countries, 8 Asian countries, 10 European transition countries and 5 African countries; and Jude (2014) in Central Eastern European Countries.
According to Agosin and Mayer (2000), if FDI crowds out domestic investment or fails to contribute to capital formation, there would be good reasons to question its benefits for recipient developing countries. Thus in Zimbabwe the debate on whether FDI is beneficial or not is still an empirical issue, but however, the results of this study indicate that FDI (as a percentage of GDP) is not beneficial to Zimbabwe’s private sector growth.
According to Jude (2014) if FDI is found to significantly crowd out domestic investment, its benefits for developing countries could be seriously challenged and policies designed to attract FDI could be put into question. Thus these results inevitably lead us into questioning the benefits of FDI (as a percentage of GDP) to Zimbabwe as a recipient developing country. The next chapter will give a policy message in light of these results, especially in the context of the FDI policy in Zimbabwe.
However, these results contradict the other side of the coin in the sense that economic theory, on the other hand also suggests a positive sign on the relationship between FDI and private investment through the crowding-in effect. Therefore the results of this study are not consistent with other previous studies whose results support the crowding-in hypothesis. These studies are Borensztein (1998) in 69 developing countries and Tang etal (2008) in China.
The crowding-in effect, as argued by Wang and Blomstrom (1992), is based on the much celebrated fact that MNCs bring with them sophisticated and cutting-edge technologies from their parent company in order to remain competitive. The stronger the competition, the more technology will be brought into the local market. The argument is that foreign firms will intensify the competition by increasing the number of competitors and introducing new ways to compete (Blomström and Kokko, 2003; Driffield and Love, 2007).
Thus, in order for local firms to compete and raise the competitive advantage, they have to introduce new strategies to respond to the competition (Bowen and Wiersema, 2005). However, failure to come up with new strategies to respond to the competition, may mean that the intended benefits of FDI may turn out to be drawbacks especially in the sense that those local firms may not be able to compete with their foreign counterparts.
4.10.2 GDP growth
From the results, GDP growth has a negative sign and is statistically significant at 5% level of significance. This means that a 1% increase in GDP growth will lead to a decrease in private investment by approximately 0.27%.
The results indicate that local investors are investing outside the country probably because of fear of macroeconomic instability in Zimbabwe especially considering the recent 2008 hyperinflationary situation that arguably scared away investors. This means that even when national output increase, private investment in Zimbabwe will not increase because investors invest their capital outside the country where there are better or conducive macroeconomic environments.
The results also show that there are wide disparities between the poor and the rich in Zimbabwe. Therefore there is a significant gap between the rich and the poor in Zimbabwe. The implication is that in Zimbabwe poor people are getting poorer at the expense of the rich people.
The other implication in light of these results is that in Zimbabwe wealth is concentrated in the hands of the minority rich individuals and such individuals are the only ones who probably have the capacity to save and invest. The majority of Zimbabweans are poor and as a consequence they lack the capacity to save and invest. This implication is based on the argument that most people in Zimbabwe are living below the Poverty Datum Line (PDL), while the minority are living well above the PDL. Those who live well above the PDL (the rich minority) are the ones who can save and invest, the bulk of the population are mere consumers.
These results are however contrary tothe research hypothesis, accelerator theory of investment which suggests a positive sign of GDP growth on private investment through the accelerator effect, previous studies and the expected sign of GDP growth in relation to private investment.
More specifically the results are not consistent with previous studies such as Asante (2000) in Ghana, Akpalu (2002) in Ghana, Mbanga (2002) in Cameroon and Syed etal (2003) in Pakistan.
However, reasons can be put forward to explain why the results on GDP growth are not consistent with economic theory (the accelerator theory and the neoclassical theory) which suggest a positive relationship between GDP and private investment through the accelerator effect and the fact that the growth rate of real output indicates changes in aggregate demand for output that investors seek to meet, respectively.
One of the possible reasons is based on the argument that probably in the Zimbabwean economic scenario, there were some violations of the assumptions of the accelerator theory that resulted in the variable (GDP growth) responding in an unusual manner. Violations could have taken place in Zimbabwe during the early 2000s to the year 2008 during which there was economic recession (and or severe macroeconomic instability) in Zimbabwe that culminated in the 2008 hyperinflationary situation. Such unexpected situations could have caused the variable to behave in such a way. For example, the accelerator theory assumes that the ratio of the desired level of output (referred to as α in equation 2.0) is a constant faction. However, α is affected by changes in the cost of capital (which is not always fixed) due to changes in tax laws and or market interest rate. The economic recession that prevailed in Zimbabwe in the period 2000-2008 probably resulted in some violations of this assumption of a constant α, due to economic changes especially in market interest rates in Zimbabwe during the period of hyperinflation.
However, the study also found out that some variables are not significant in explaining the determinants of private investment in Zimbabwe. These variables are inflation and interest rates. It therefore implies that inflation and interest rates are not important in explaining the determinants of private investment in Zimbabwe.
4.11 Summary
In this chapter, the researcher estimated and interpreted the regression results which indicated that public investment, FDI and GDP are important in explaining the determinants of private investment in Zimbabwe. The results also show that interest and inflation rates are not important in explaining the determinants of private investment in Zimbabwe. The study managed to answer the research questions and achieved its objectives outlined in chapter one. Finally, these findings will be used in the next chapter, that is chapter five (the last chapter of this research) in compiling the overall summary, conclusions and recommendations.
CHAPTER V SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.0 Introduction
This chapter presents the summary, conclusion and recommendations. It is in this chapter that the researcher will also take this opportunity to finally highlight some areas of further study in light of this research.
5.1 Summary
The study investigated the determinants of private investment in Zimbabwe for the period 1980 – 2013. The study was carried out to address the problem thatprivate investment in Zimbabwe, as a percentage of GDP has been falling over the years underpinned by both economic and non-economic factors despite government’s effort to encourage and support private sector investment.The main objective was to find out the determinants of private investment in Zimbabwe. The specific objectives were to determine whether GDP growth, interest rates (commercial lending rates), inflation rate (annual %), public investment (percentage of GDP) and FDI (percentage of GDP) affect private investment in Zimbabwe. The main theories reviewed in the study are the accelerator theory, credit rationing theory, the neoclassical theory, the Tobin’s q theory, the cost adjustment approach and the stock adjustment approach. The study indicates that the main determinants of private investment in Zimbabwe are public investment (as a percentage of GDP), FDI (as a percentage of GDP) and GDP growth.
5.2 Conclusion
Public investment (as a percentage of GDP) was found to be complementary to private investment thus yielding a positive sign in relation to private investment. These results indicate that public investment is essential in stimulating private sector growth in Zimbabwe. FDI was found to crowd-out private investment in Zimbabwe thus yielding a negative sign in relation to private investment in Zimbabwe. These results indicate that a lot needs to be done on policies designed to attract FDI, together with the Indigenisation policy. GDP was found to be negatively related to private investment. Results on GDP basically point to disparities between the rich and the poor in Zimbabwe and arguably the fact that local investors prefer investing in other countries to Zimbabwe. Inflation and interest rates were found to be insignificant in explaining the determinants of private investment in Zimbabwe. The government of Zimbabwe should stimulate public investment and address issues to do with FDI and GDP as per policy recommendations below in order to make ZimAsset become more fruitful.
5.3 Policy recommendations
Policy messages emanating from these results are basically three-fold:
Firstly, since the positive impact of public investment on private investment found in this study is arguably well documented in most existing and recent literature, efforts by the government of Zimbabwe should therefore be geared towards putting in place and spending on necessary public investments. The government can do this by focusing expenditure on public infrastructures like constant electricity supply, good motorways (transport infrastructure), better health delivery system, better communication infrastructure and so on.
In light of the positive sign of public investment, the study also recommends, supports and encourages the acceleration in the implementation of Public Private Partnerships (PPPs) and other private sector driven initiatives not only to make ZimAsset economic become more fruitful but also to create a conducive business environment which will crowd-in private investment as well as making other small and medium scale businesses to thrive.
In this regard, once again in view of the positive impact of public investment on private investment, triggering public sector resources to the end of capital accumulation is a useful channel to boost private sector development in Zimbabwe. In light of these findings, the government of Zimbabwe should bridge the gap between public and private investors through the implementation of PPPs in the Special Economic Zones in order to make the ZimAsset economic policy more successful and to ultimately materialise the much awaited role of the private sector as an engine of growth.
Secondly, since the impact of FDI (as a percentage of GDP) on private investment in Zimbabwe has been found to be negative, Zimbabwe should be keen on protecting private sector investment. The FDI policy in Zimbabwe should reflect this by enforcing regulations such as tariff and non-tariff barriers to FDI in areas where local investors have the capacity to invest (mainly because of relatively low capital outlay needed to start up a business), thereby reducing the size of FDI that flow into the country. One such area or sector in Zimbabwe is arguably the retail sector. This will go a long way in reducing the intensity of competition that MNCs pose on local firms. Tariff and non tariff barriers to FDI, among other regulations; create relative shortages in the domestic market and drive prices up, with total output falling and therefore it is this price that provides an incentive for less efficient domestic firms to increase their output.
However, there are certain areas where local investors do not have the capacity to invest, mainly because of the huge capital outlay that is needed to start up a business.One such sector in Zimbabwe is arguably the tourism sector. The government of Zimbabwe can target promoting FDI in such sectors of the economy in which there is little or less domestic private investment. The government of Zimbabwe can address this strategy through the Cluster Key Result Areas such as tourism research and international tourism cooperation enshrined in the ZimAsset economic policy.
The government of Zimbabwe should, for example; through ZimAsset, promote collaborative partnerships between foreign investors and local investors; this will go a long way in mitigating the crowding-out effect of FDI (as a percentage of GDP) on private investment in the sense that local investors will benefit from the technical know-how and technological skills brought by foreign companies as they will be closely working together. This will go a long way in initiating growth and development of private sector investment in Zimbabwe.
Furthermore, it is recommendable; once again, that Zimbabwe should otherwise stick to its Indigenisation policy to regulate the extent of ownership of local firms by MNCs. This will once again reduce or dilute the monopoly power of foreign firms in Zimbabwe. The main policy message in this regard is that the government of Zimbabwe should continue empowering its people through owning businesses as enshrined in the Indigenisation and Economic Empowerment (IEE) Act, thus increasing their stake in the corporate sector and promoting the development of a competitive domestic private sector that will spearhead economic growth and development.
Thirdly, GDP growth has been found to increase while private investment is decreasing. The policy message following these findings is that the government of Zimbabwe should device measures to improve and promote macroeconomic stability in Zimbabwe in order for local investors to invest in this country instead of investing in the other countries. This will go a long way in attracting local investors to invest in this country as there will be little risk of losing their capital due to macroeconomic instabilities such as the 2008 hyperinflationary situation. The government of Zimbabwe can initiate this recommendation through one of the ZimAsset’s Key Success Factors (KSFs) of continued use of multicurrency regime to consolidate macroeconomic stabilisation.
Finally, the government of Zimbabwe should address the issue of disparities between the rich and the poor especially in terms of access to resources and business opportunities. The government can address this critical issue through one of the strategic clusters highlighted in the ZimAsset economic blue print; in this case the cluster on social services and poverty eradication. Such interventions that seek to address the plight of the poor are recommendable for the purpose of ultimately closing or at least minimising the gap between the poor and the rich in Zimbabwe. In this regard, strategies towards empowerment of the vulnerable should be implemented by the government as soon as possible.
5.4 Areas for further study
These could be the extension of the study in terms of considering the impact of the same variables on private investment in Zimbabwe using more advanced econometric methodologies such as the ARDL and the VAR approaches to cater for non-stationary time series data problems and also to check for long-run relationships as well as short-run dynamics of private investment in Zimbabwe. Extensions of the study can also consider other variables that affect private investment such as savings, political instability and policy uncertainty.
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Appendices
APPENDIX 1: DATA
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Source: Data on INTR and INFL were obtained from ZIMSTATS while data on P, PUI, FDI and GDP were obtained from the World Bank online database. Data on residuals (the error terms-Ui, used in graphically testing for normality) were computed using the software package E-views 3.1
APPENDIX 2: REGRESSION RESULTS
Dependent Variable: P
Method: Least Squares
Date: 10/15/14 Time: 00:11
Sample: 1980 2013
Included observations: 34
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APPENDIX 3: CORRELATION MATRIX
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APPENDIX 4: DESCRIPTIVE STATISTICS
Date: 10/15/14 Time: 00:18
Sample: 1980 2013
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APPENDIX 5: ARCH LM TEST:
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APPENDIX 6: WHITE HETEROSKEDASTICITY TEST:
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APPENDIX 7: UNIT ROOT TESTS (ADF TESTS)IN LEVELS:
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- Quote paper
- Thabani Nyoni (Author), 2014, Determinants of Private Investment in Zimbabwe (1980-2013), Munich, GRIN Verlag, https://www.grin.com/document/299181
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