UNIVERSITY OF ZIMBABWE FACULTY OF SOCIAL STUDIES ECONOMICS NAME

UNIVERSITY OF ZIMBABWE

FACULTY OF SOCIAL STUDIES

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ECONOMICS

NAME: MUSIYA NATSAI W

REGISTRATION NUMBER: R156862W

COURSE: ECON 370

LECTURER: MR PINDIRIRI

YEAR: 2017
TITLE: THE IMPACT OF FINANCIAL SECTOR DEVELOPMENT ON POVERTY IN ZIMBABWE 1980 TO 2015

CHAPTER ONE
INTRODUCTION AND BACKGROUND
1.0 introduction
The linkage between financial development and poverty reduction has not been the subject of much empirical work. Most studies that have been carried out concentrate on the relationship between financial development and growth (McKinnon, 1973; Goldsmith, 1969; Gurley & Shaw, 1955 and Schumpeter, 1911). Poverty is one of the most prominent problems in the world, regardless of numerous measures which have been taken at both macro and micro level to combat poverty. Since poverty is a burning issue of not only the developing countries but also of the developed countries, financial sector development can be used to alleviate poverty hence leading to the achievement of the first Sustainable development Goal (SDG) which is to end poverty in all forms everywhere.
According to the World Bank (2017), the poverty rate slightly increased from 70.9% in 2001 to 72.3% in 2011/2012, reflecting the structural nature in poverty. United Nation (2015) found out that poverty Zimbabwe is more prevalent in rural areas compared to urban areas with about 76% of the rural households considered poor compared to 38.2% of urban households. However, the financial sector faced problems in its diversification as the country faced a fall in the number of banks from forty to twenty six (Kanyeze et al., 2011). Also the financial sector's performance has been poor as measured by the domestic credit to the private sector which has been falling since 2002 from 103.63% to 10.2% in 2008 (World Bank, 2010).

Both theoretical and empirical studies have shown that there are two channels in which financial sector development (FSD) can impact poverty that is directly and indirectly. The indirect channel is the one in which the financial sector development supports economic growth, whilst the direct channel refers to the one in which financial sector development contribute to poverty reduction by providing or broadening the poor’s access to financial services through the McKinnon conduit effect (Zhuang et al., 2009). The causality between financial sector development and poverty in most studies has been found to have a short run bidirectional causal relationship (Uddin et al., 2012 and Ho ; Odhiambo, 2011). These studies usually apply the autoregressive distributed lag (ARDL).
This study is focusing on the relationship between financial development and poverty in Zimbabwe. The OLS approach will be applied, using annual data for the period 1980 to 2015. Also the granger causality technique will be applied in order to test the causality between financial sector development and poverty.
1.1 Background of study
At independence in 1980, the Government of Zimbabwe inherited the best banking systems in Africa excluding South Africa, at a time when the majority black population had a strong sense of having been excluded from access to modern services, including credit (Brownbridge and Harvey, 1998). The country deregulated the banking sector in the early 1990s which resulted in black owned banking institutions coming up to compete with the traditional institutions (Kanyenze et al., 2011).

Figure 1: GDPPC and Household final consumption per capita

Source: World Bank (2017)
Figure 1 above shows the changes in household final consumption per capita and GDPPC over the period 1980 to 2015. Evidence from the figure below shows that after independence (1980-1983) both GDPPC and household final consumption per capita increased and then declined in 1984 and then the trend followed an inconsistent path between the periods 1988 to 2016. This was influenced by factors such as the increase in population for the past three decades from 7.5 million in 1982 to 13.1 million in 2012 and an estimated economic growth of 1.2 percent in 2016. Unemployment is another factor that is threatening poverty in the country as most people are jobless which constrains household consumption expenditure therefore affecting poverty. In December 2017, 5.3% of the labour force of the country was unemployed (World Bank, 2017).
Figure 2: Domestic credit to private sector as a percentage of GDP and Broad money as a percentage of GDP
Source: World Bank (2017)
From the figure above it can be noted that during the period 2000 to 2002 there was an increase in the ratio which might imply that the higher ratio, the larger the size of the financial sector development. The period 2000 to 2002 lies in the third distinct period known as the financial reform reversal according to Chigumira ; Makochekanwa (2014). This period was characterized by reversal of reforms and its main outcome was currency rationing. From the figure above the domestic credit to private sector ratio to GDP dropped from 14.5% in 2014 to 10.2% (World Bank, 2017). This was due to the challenging macroeconomic environment, thus has constrained credit creation by banks resulting to the lending on short term despite the demand for long term loans to support capital projects (NECF, 2015). As the long term loans are scarce the capital projects close down thereby resulting in unemployment thereby hindering poverty.

According to the Reserve Bank of Zimbabwe (2017) the various types of institutions include 13 commercial banks, 5 building societies, 1 savings bank, 178 microfinance institutions and 2 development financial institutions. Kanyeze et al (2011) stated that although the post reform era saw a flurry of entrepreneurial activity in the financial sector, the indigenous banks were granted licences, the banking crisis began early 2004 which resulted in a number of banks placed under curatorship or closing, quarantining all accounts that were held with them and the number of banks fell from forty in 2002 to twenty six by 2011.

1.2 Problem statement
Poverty reduction is recognizes as one of the pillars of the sustainable development goals, despite the measures put forward by the country to reduce poverty such as the interim poverty reduction strategy (IPRS), the level of poverty is increasing due to the increase in population and income inequality. The household final consumption per capita proxy for poverty has been increasing as shown by figure 1 as the highest value obtained was $900 in 2015. However, domestic credit to private sector as a percentage of GDP proxy for financial sector development is decreasing as it can be denoted from figure 1 that it declined from 14.5% in 2014 to 10.17% in 2015 with a highest of 103.6% in 2002(World bank, 2017). The financial sector of the country has been unstable over the past years ending up in many financial institutions such as banks shutting down. Stakeholders lost confidence in the banking sector due to the hyperinflation which struck the economy in the year 2008 which saw many individuals and businesses losing as people kept some of their wealth in the form of money. Due to the shutting down of financial institutions there is an effect of retrenchment and reduction to financial access thus leading to the effect of increasing poverty. Thereby leads to the need to carry out the study in order to examine if financial sector development causes poverty or poverty causes financial sector development
1.3 Objectives of study
The general objective is to examine the relationship between Financial Sector Development (FSD) and poverty. Whereas the specific objectives are:
To ascertain the direction of causality between financial sector development and poverty reduction in Zimbabwe.

To find out the effect of financial sector development on poverty reduction in Zimbabwe.

1.4 Research Questions
What direction of causality exists between financial sector development and poverty reduction in Zimbabwe?
What is the effect of financial sector development on poverty in Zimbabwe? In particular does an increase in financial sector development lead to a decrease in poverty?
1.5 Justification of study
This study is very important to policy makers, government and others interested in the financial sector (stakeholders). Once the relationship has been determined, it is very important to understand the effect of financial sector development on poverty reduction. According to Honohan (2004), found out that a 10 percentage point increase in private credit to Gross Domestic Product (GDP) reduces poverty ratio by 2.5 to 3 percentage point. With this, the causality of direction will help to recommend for reforms in financial sector development which would help out to support poverty reduction. The Sustained Development Goals (SDGs) also supports the financial sector development as it act as a lubricant engine that drives away poverty reduction, thus fulfilling the SDG number one of no poverty.

1.6 Hypothesis
To achieve the set objectives, the research tests the hypothesis below
There is a positive relationship between financial sector development and poverty.

There is bi direction causality between financial sector development and poverty.

1.7 Organisation of the rest of the study
The study is divided into different chapters that are: Chapter two reviews both empirical and theoretical literature, Chapter three contains the methodology and estimation technique, whilst chapter four presents the estimation of the model and interpretation of results. Finally, chapter five gives the conclusion of the study and policy recommendations.

CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
In this chapter, both empirical and theoretical literature relating to the impact of financial sector development on poverty reduction are reviewed, and also to have an overview of what theory says in relation to the relationship between FSD and poverty reduction. The theory is divided into two segments as the impact of FSD is in two channels namely the direct and the indirect channel.

2.1 Theoretical literature review
The theory on financial sector development as emphasized today, in developing countries, goes back to Schumpeter (1934) when he stresses the role of banking sector as a financier of productive investments and in that way as an accelerator of economic growth. Modern growth theory however identifies two specific channels which are direct and indirect.

2.1.1Direct Channel
One of the main reasons that explain persistent poverty is due to lack of access to financial services (Levine, 2008). Most theory proposes that access to finance permits the poor to better investments and education (Jacoby ; Skousfias, 1997 and Beegle et al., 2003).
Jalilian and Kirkpatrick (2007) postulated that an increase in the access to financial services to the poor will increase their income growth, thus resulting in the direct channel. Whilst the availability of credit can strengthen the productive assets of the poor by enabling them to invest in productivity, thus enhancing new technologies for instance new and better tools, equipment and fertilizers or to invest in education and health which could provide for a higher income in future. Addressing situations such as financial market failures such as information asymmetry (moral hazard and adverse selection) and the high fixed cost of lending to small scale borrowers the financial sector development can improve the opportunities for the poor to access formal finance (Stiglitz, 1998; Jalilian and Kirkpatrick, 2001). Thus financial development can directly contribute to poverty reduction by improving the opportunities for the poor to access formal finance and enables them to achieve a sustainable livelihood.

Fields (2001) argued that a lot would be gained by developing credit and finance given that an underdeveloped credit markets contributes to continued poverty, increase in income inequality and stagnant economic growth. He then further emphasized that through the better access to credit, the poor are given the opportunity to take part in more productive activities, which results in increases in their incomes and also financial incomes thereby enabling the poor to respond better to economic or health-related shocks thus reducing the likelihood of falling into poverty when such shocks occur.

Deaton (1991) argued that access to credit and other financial services is likely to decrease the proportion of low risk, low return assets held by poor households for precautionary purposes (such as jewellery), and enable them to invest in potentially higher risk but higher return assets, (such as education, or a rickshaw), with overall long term income enhancing impacts. Eswaran and Kotwal (1990) postulated that just the knowledge that credit will be available to cushion consumption against income shocks if a potentially profitable but risky investment should turn out badly, can make the household more willing to adopt more risky technologies. The behaviour will increase the use of modern technologies with productivity increasing, and hence income enhancing benefits. In the same vein, insurance can offer protection against certain types of shocks. These facilities can reduce the vulnerability of the poor and minimize the negative impacts that shocks can sometimes have on long-run income prospects. Thus the value of financial services in helping the poorest to cope with risks can be as or more important than the expected financial return (DFID, 2004).

Mckinnon (1973) and Shaw (1973) provided the theoretical underpinning for financial liberalization, emphasizing the influence of real interest rates on savings, investment and growth. They argued that financial deepening increases the rate of domestic savings and this lowers the cost of borrowing and thus stimulating investment. The major core of this argument was the claim that developing countries suffer from financial repression, this therefore postulated to that the liberalization of these countries from repressive conditions would induce savings, investment and growth. In this view investment is positively related to real interest rates which are in contrast to the neoclassical theory. This then lead to the “conduit effect” as the rise in interest rates increases the volume of financial savings through the financial intermediaries and as such increases investible funds.

2.1.2 Indirect Channel
This is whereby the financial sector development can also help to reduce poverty indirectly by stimulating economic growth through its impact on capital accumulation and the rate of technological progress (De Gregorio, 1996).

Growth may be denoted as a sufficient condition for sustained poverty reduction. However, there are different views of growth-poverty nexus which include the popular Kuznets inverted U hypothesis (Kuznets, 1955). The theory introduced an increase interest in the contribution that financial deepening can make to income distribution in developing countries. Kuznets suggested that the extent that financial sector development facilitates more migration from low-income but more egalitarian agricultural higher income but more unequal modern (industrial and services) sector it may be expected to increase inequality. The view suggests that economic growth may increase income inequality at the stage of industrialization as the asset-rich classes who have easy access to finance would reap the early harvest of industrialization and thus garner a higher share of economic pie, leaving the poor oppressed. On the other hand, the “trickle down” theory postulated that economic growth would either trickle down to the poor through job creation and other economic opportunities or create the necessary conditions for the wider distribution of the economic and social benefits of growth (Todaro 1997).

Datt ; Ravallion (1992) and Kakwani (2000) attempted to explain changes in poverty in terms of a “growth effect”, stemming from a change in average income, and a “distribution effect”, caused by shifts in the Lorenz curve holding average income constant. They found the growth effect to explain the largest part of observed changes in poverty. Fields (2001) postulated that the extent of the impact of growth on poverty alleviation depends on the growth rate itself and the level of inequality.

2.2 Empirical Literature
On the empirical front, a growing body of literature has examined the interaction between financial development and poverty reduction. The empirical evidence from this literature is however ranked in descending order and controversial across countries, data and methodologies.

Uddin et al. (2014) examined the relationship between financial development and poverty reduction in the case of Bangladesh using quarter frequency data over the period of 1975–2011. Applying autoregressive distributed lag (ARDL) bounds testing approach to cointegration, they found short-run bidirectional causality between the development of the financial sector and poverty reduction. Using the similar estimation technique with structural breaks, Uddin et al. (2012) reached the same finding for Bangladesh over the period of 1975–2010. Also they found out that economic growth is accelerated by financial development and poverty reduction.

Ho and Odhiambo (2011) explored the relationship between financial development and poverty reduction in China for the period 1978 to 2008. The results are sensitive to the finance variable. When the credit ratio is used, a feedback effect exists between financial development and poverty reduction in the short run. However, results for broad money supply ratio show bidirectional causal flow in the short run but poverty reduction causes financial development in the long run.

Honohan (2004a) shows a robust effect of financial depth (measured as the ratio of private credit to GDP) on headcount poverty incidence. The regression results suggest that a 10 percentage point increase in the ratio of private credit to GDP would lead to a 2.5–3.0 percentage point reduction in poverty incidence. Given that per capita GDP is controlled in the analysis, the results suggest that a direct relationship between financial development and poverty reduction exists independent of the indirect effect through growth. Similarly, using data for 58 developing countries over the period 1980 to 2000, Beck, Demirgüç-Kunt, and Levine (2004) discovered that countries with better-developed financial intermediaries (measured as the ratio of private credit to GDP) experience faster declines in both poverty and income inequality by disproportionately boosting the incomes of the poor. Their results are robust to controlling for potential reverse causality. They also hold even when controlling for the average rate of economic growth, which suggests that financial development alleviates poverty beyond its effect on aggregate growth.

Regarding Sub-Saharan African countries:
Zahonogo (2016) investigated how financial development affects poverty indicators in developing countries. Using data from 42 Sub-Saharan countries African countries and covering the period 1980-2012 Zahonogo applied generalized method of moment (GMM) at is appropriate to control country specific effects and the possible endogeneity. The evidence then points an inverted U curve type response and the findings are robust to changes in poverty measures and to alternative model specifications, suggesting thus the non-fragility of the linkage between financial development and poverty for sub-Saharan African countries.

Dauda and Makinde (2014) used vector autoregressive model (VAR) to examine the nexus between financial sector development and poverty reduction in Nigeria over the period 1980-2010. The results showed that economic growth exerts the strongest influence on poverty reduction in the short run but could be detrimental to the poor in the long run due to the adverse effect of income inequality. Furthermore, financial deepening proxied by broad money supply (M2) is negatively related to poverty. Hence, the McKinnon Hence, the McKinnon conduit effect is the likely main transmission channel through which the poor benefit from the financial sector development in the long run. However, contrary to the general belief, credits to private sector do not cause a reduction in the incidence of poverty. The authors attributed this result to the wrong attitude of financial intermediaries that have not adequately channelled savings to the pro-poor sectors of the economy.

Aye (2013) used the Johansen cointegration to examine the dynamic causal relationship between financial deepening, economic growth and poverty in Nigeria over the period 1960-2011.The short and long run causality between these variables is tested using a modified Hsaio-Granger causality within a Vector Autoregressive (VAR) and Vector Error Correction Model (VECM) framework. The results indicate no evidence of long run equilibrium relationship between finance, economic growth and poverty. Therefore, we focus on short-run causality. Our results show a short-run unidirectional causality from growth to poverty conditional on finance. This supports the indirect channel through which finance affects poverty via growth. Thus supporting the indirect channel through which finance affects poverty via growth.

Using annual data over the period 1969–2006, Odhiambo (2010b) investigated causality between financial development and poverty in the case of Zambia. The causality analysis reported that when the ratio of broad money (M2) to nominal GDP is used as an indicator of financial development, poverty reduction proxied by private per capita consumption causes the development of the financial sector. However, when domestic credit to private sector as share of GDP is used, financial development Granger causes poverty reduction. Odhiambo (2009) examined the relationship between finance, growth and poverty reduction in South Africa over the period from 1960 to 2006. Using the Johansen cointegration tests, he found that an increase in economic growth leads to an increase in financial development as measured by broad money ratio to GDP. He also reported that both financial development and economic growth Granger cause poverty reduction in South Africa in the short and long run.

Working with the annual data for Pakistan, Shahbaz (2009) investigated the impact of financial development and financial instability on poverty reduction using the autoregressive distributed lag model (ARDL) for long run relationship between the variables by controlling for economic growth, inflation, agricultural growth, manufacturing and trade openness. The results indicated that all the variables are co-integrated for long run relationship and also found that financial development is negatively related with poverty while financial instability increases poverty. In addition, Agriculture growth, manufacturing and trade openness seem to reduce poverty reduction in Pakistan.

Quartey (2005) investigated the interrelationship between financial sector development and poverty in Ghana. This was done using World Development Indicators over the period 1970 to 2001. Quartey tested causality using Granger causality and found out that financial sector development does not granger cause savings mobilization, it induces poverty and secondly that savings do granger cause poverty. He also obtained the effect of financial sector development on poverty to be positive but insignificant. This was due to the fact that financial intermediaries in Ghana had not adequately channelled savings to the pro-poor sectors of the economy because of government deficit financing, high default rate, lack of collateral and lack of proper business proposals. Another interesting finding is that there is a long-run cointegration relationship between financial sector development and poverty reduction.

2.3 Conclusion
The chapter has provided vital information about the determinants of poverty. The major determinants are economic growth, inflation, agricultural growth, manufacturing and trade openness. The study proxied financial sector development with domestic credit as a percentage of GDP.

CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter presents the methodology used in achieving the objectives of the study. This encompasses model specification (theoretical and empirical framework), definition and justification of variables, data sources and finally the model diagnostic test. Methodology is guided from literature in the previous chapter. The study makes use of the model used by Shabhaz (2009) and some necessary alterations are made to the model to suit Zimbabwe’s situation.

3.1 Model specification
3.1.1 Theoretical framework
Most theory from the indirect channel postulates growth to have a positive impact on reducing poverty prominently from the “growth effect” and “trickledown” (Field, 2001; Kakwani, 2000; Datt & Ravallion, 1992 and Kuznets, 1955). These theories lead to economic growth being one of the factors that affect poverty indirectly.

Povt=fy…………. (1)
Where yis economic growth
3.1.2 Empirical Model
From literature review Dauda & Makinde (2014), Shahbaz (2009) and Odhiambo (2009) carried out related studies in different countries and successfully analyzed variables of the model with an error term to show a stochastic relationship as clearly defined by Gujarati (2004).

Shabhaz (2009) used the following model in their study:
LGR=?1+?2LFD+?3FNS+?iCV+vt ………….. (2)
In order to make the model suitable for the study the measure Income growth of bottom 20 percent population (GR) for poverty is changed to household final consumption per capita as used by Odhiambo (2009) and financial instability (FNS) is removed due to that it is not of relevance to this study. Control variables (CV) include economic growth (GDPPC), agriculture as a share of GDP, manufacturing as a share of GDP, trade openness, investment as a share of GDP and inflation proxy for monetary instability.
Therefore the model becomes
LPOVt=?1+?2LFDt+?iCVt+?t
Where:
POVt is poverty in year t, FDt is financial sector development and CVt are control variables which are economic growth measured by GDPPC is gross domestic product per capita, agriculture as a share of GDP, manufacturing as a share of GDP, trade openness, investment as a share of GDP and inflation proxy for monetary instability and ?t is the white noise error term.

Expected signs all explanatory variables are expected to have positive influence on poverty except inflation which as a negative expected sign.
3.2 Definition and Justification of variable
Poverty
This is the dependent variable of this study measured as household final consumption per capita as used in the studies of Odhiambo (2009), Aye (2013) and Uddin (2014). It is found that consumption expenditures reveals not only what a household is able to command based on its current income, but also whether that household can access credit markets or household savings, Hentschel and Lanjouw (1996). It was previously known as Private per capita consumption expenditure now it is known as household final consumption per capita.

Where household final consumption per capita:
householdfinalconsumptionpercapita=householdexpendituretpopulationtGross Domestic Product Per Capita
This captures the indirect effect as the financial sector development can also help to reduce poverty indirectly by stimulating economic growth which is measured by GDP.Inflation
Mankiw (2004) defined inflation as the increase in the overall levels of prices in the economy. Inflation is measured by GDP deflator. This captures the macroeconomic instability were high and unpredictable inflation is thought to have disproportionally negative impact on poverty because the poor have relatively limited access to financial instruments that hedge against inflation and are more likely to have larger share of cash in their small portfolios (Holden & Prokopenko, 2001).

Financial sector development
Financial development refers to improvement in the quality, quantity or efficiency of the financial systems that are comprised of financial markets, banks and other financial intermediaries (Maskay, 2012). Domestic credit to the private sector (DCPS) is the proxy for FD and it refers to the financial resources that are provided to the private sector by financial intermediaries such as banks.
Trade openness
This is an independent variable measured by the sum of exports and imports as a share of GDP. Since international trade brings in better methods and new ideas, moreover trade openness exposes developing countries to tough competition from developed financial sector of other countries.

Manufacturing as percentage of GDP
Manufacturing value-added increases income for the poor people as it creates employment, generating activities which in turn increase income distribution along with rise in income. Employment opportunities for both skilled and unskilled labor are generated through investment activities. This situation raises the aggregate income and hence improves the economic position of poor segment of population.

Agriculture as a percentage of GDP
Agriculture sector is providing more employment which will increase income of lower segments of population and it will also enhance its share to GDP. This variable was used in a related study by Shabhaz (2009
Investment as a percentage of GDP
This indicator refers to the total share of investment in total production as a share of GDP. It is measured by gross capital formation as a percentage of GDP (GCF_GDP).
3.3 Estimation technique
The study adopts (OLS) Ordinary Least Squares (OLS) methodology because of its effectiveness in the estimation procedure of the Classical Linear Regression Model (CLRM) and also its ability to produce Best Linear Unbiased Estimators (BLUE) (Gujarati, 2004).

3.3.1Unit root test
The unit root test was developed by Dickey- Fuller in 1979, whereas a number of studies postulated that the unit root test has shown that using classical estimation methods, such as the OLS, to estimate relationships with unit root variables gives misleading inferences (Gujarat, 2008; New Bold & Granger, 1974). The presence of non-stationarity might lead to what is known as spurious regression. A spurious regression usually has a high R-squared, and the t-statistics appears to be significant whereas the results have no economic meaning. This test is usually done to detect the order of integration of the variables before estimation. Illustrating the econometric model yt=?+?yt?1+et the ADF test the hypothesis that;
H0: ?=1; H1:?>1 If the null hypothesis is rejected then the series will be stationary at I(0). If not then the differencing method for making them stationary will be applied Gujarati (2008).

3.3.2 Granger-causality
The Granger causality test is used to examine the causality between financial sector development and poverty. The test is chosen for this study because it is suitable technique since it is favourable for both large and small samples (Odhiambo, 2008). This test involves trying to detect if FSD caused poverty reduction or poverty reduction causes poverty. It states tat if poverty causes FSD and FSD does not cause poverty it is uni-directional causality however if poverty causes FSD and FSD causes poverty this implies bi-directional causality.

The hypotheses that are going to be tested are as follows:
H01: FSD does not Granger cause poverty
H02: poverty does not Granger cause FSD
Alternative hypotheses:
H11: FSD Granger causes poverty
H12: poverty Granger causes FSD
Granger causality is sensitive to number of lags included, for lag selection Akaike Information Criterion (AIC) is used to determine the lag length.

3.4 Diagnostic test
3.4.1Multicollinearity
Various diagnostic tests were carried out starting with multicollinearity. To detect the presence of multicollinearity the study used the pairwise correlation test where a partial correlation coefficient exceeding absolute 0.8 indicates the presence of high multicollinearity between variables.
3.4.2 Normality test
Testing for normality is also important in regression analysis. Non normality of errors causes bias in the construction of confidence intervals and significance test (Greene, 2003). The JacqueBera formal test was used to test for normality.
3.4.3 Heteroskedasticity
Heteroskedasticity causes estimators to no longer have the minimum variance. To test the presence of heteroskedasticity the Breusch Pagan Godfrey test was used.
3.4.4 Autocorrelation
In the presence of autocorrelation the OLS estimators remain unbiased, consistent and asymptotically normally distributed, but they are no longer efficient (Ibid, 2004). The study used the Breusch Godfrey Serial Correlation LM test in detecting autocorrelation.

3.4.5 Ramsey RESET test
Ramsey’s Regression Specification Error Test (RESET) was used in order to test for the model specification error. Gujarati (2008) stated that if the model was incorrectly specified the researcher would have encounter model misspecification error.

3.5 Data sources and collection
The study used time series data for Zimbabwe to investigate the impact of financial sector development on poverty for the period 1980 to 2016. Data on financial sector development is obtained from the World Bank. Whilst data for inflation, poverty, trade openness, agriculture as a share of GDP, manufacturing as a share of GDP, trade openness, investment as a share of GDP and gross domestic product per capita is obtained from the World Bank.
3.6 Conclusion
This chapter presented the methodology adopted in this study and various tests carried out to purify the data. Data sources were also provided.

CHAPTER FOUR
PRESENTATION AND INTERPRETATION OF RESULTS
4.0 Introduction
This chapter presents application of the OLS method and diagnostic procedures. Descriptive statistics are presented first while stationarity, model diagnostic and regression results follow respectively. This chapter also gives an interpretation of the results obtained from the regressions carried out. And the last section provides an overview of whether the hypothesis stated in Chapter One are rejected or accepted.

4.1 Descriptive statistics
Table 1: Descriptive Statistics results
HFPC AV_GDP GCF_GDP GDP_DEFLATOR GDPPC PRIVCRE TOT MVA_GDP
Mean  524.6010  16.13843  14.67408  2.153850  1083.545  26.19803 66.71309 18.22576
Median  428.3380  16.25094  15.98748  1.956202  1169.694  24.74932 69.37150 17.44822
Maximum  900.7536  22.67357  23.72906  74.29818  1347.972  103.6323 109.5216 29.53704
Minimum  327.9399  7.413793  1.525177 -27.04865  593.1272  7.476843 35.91686 9.831452
Std. Dev.  174.6733  3.648627  6.040022  14.94728  219.8828  17.61774 19.08893 4.943309
Skewness  0.894086 -0.234857 -0.679745  2.880900 -0.726938  2.492208 0.2372904 0.161845
Kurtosis  2.466571  2.522200  2.527560  16.60458  2.199465  11.54024 2.372904 2.304485
Jarque-Bera  5.223159  0.673386  3.107120  327.4245  4.131917  146.6702 0.927631 0.882767
Probability  0.073418  0.714128  0.211494  0.000000  0.126697  0.000000 0.628880 0.643146
Sum  18885.64  580.9835  528.2668  77.53859  39007.62  943.1289 2401.671 656.1273
Sum Sq. Dev.  1067876.  465.9368  1276.865  7819.737  1692196.  10863.46 12753.55 855.2705
Observations  36  36  36  36  36  36 36 36

Table 1 provides summary of the variables used in the study. The variations in GDPPC are relatively higher than that of the other variables with 219.8828, followed by HFCP with 174.6733, then TOT with 19.08893, PRIVCRE with 17.61774, GDP deflator with 14.94728, GCF_GDP with 6.040022, MVA_GDP with 4.943309 and AV_GDP with 3.648627 as shown by the standard deviations. Measures of skewness show that stock GDP deflator, HFCP, TOT, PRIVCRE and MVA_GDP are positively skewed whereas GDPPC, GCF_GDP and AV_GDP are negatively skewed. None of the variables has a kurtosis closer to three. The Jarque-Bera null hypotheses are not rejected and hence there is normality in all the variables.
4.2 Stationarity tests
Table 2: Stationarity test results
Variable ADF Prob level ADF Prob First Difference ADF Prob Second Difference Order of Integration Level of Stationarity
Log(HFCP) 0.8509 0.0000 – I(1) ***
Log(PRIVCRE) 0.1947 0.0000 – I(1) ***
AV_GDP 0.1086 0.0000 – I(1) ***
MVA_GDP 0.356 0.0000 – I(1) ***
TOT 0.1225 0.0000 – I(1) ***
GDP deflator 0.0005 – – I(0) ***
GDPPC 0.6704 0.0001 – I(1) ***
GCF_GDP 0.4902 0.0000 – I(1) ***
Where *** implies stationary at 1 %, ** stationary at 5% and * stationary at 10
The Augmented Dicky-Fuller (ADF) unit root test has been employed to test for stationarity. It was discovered that GDP deflator is stationary at its level meaning integrated of order zero. Variables HFCP, GDPPC, GCF_GDP, AV_GDP, MVA_GDP, PRIVCRE and TOT are not stationary and they have a unit root, after being differenced once they became stationary thus integrated of order one.

4.3 Diagnostic test results
4.3.1 Multicollinearity
The pairwise correlation test was carried out to check the multicollinearity between independent variable. The results show that the explanatory partial correlation coefficient are less than the absolute 0.8 implying that there is no serious multicollinearity, thus all variables are linearly independent.

4.3.2 Normality test results
The Jacque-Bera was found to 1.751901 with a probability value of 0.416466, the probability is greater than 0.05 therefore we fail to reject null hypothesis that the residuals are normally distributed at 5% level of significance.
4.3.3 Heteroscedasticity test results
Breusch Pagan Godfrey show that the probability value 0.7496 is less than 0.8008 the model’s probability value, thus we fail to reject the null hypothesis that the error variance is homoskedastic and conclude that at 5% level of significance the variances are equal.

4.3.4 Autocorrelation test results
Applying the Breusch Godfrey Serial correlation LM results show that the probability value of 0.9655 is less than 0.9752 the model’s probability value. This implies that we may fail to reject the null hypothesis of no autocorrelation since residuals in one period are not correlated to residuals in periods before.

4.3.5 Model Specification test results
Results from the RAMSEY RESET test show that the probability value of the F-statistic is 0.0805 which is greater than 0.05, thus supporting the null hypothesis that the model is correctly specified. Thereby the results are considered reliable for reporting and interpretation as they have passed most of the diagnostic tests.

4.4 Regression results
Table 3: Regression results
-71755635Dependent Variable: DLHFPC
Variable Coefficient Std. Error t-Statistic Prob.  
C -0.006052 0.014351 -0.421695 0.6766
DGDPPC 0.000974 0.000208 4.688549 0.0001
DGCF_GDP -0.021428 0.004268 -5.021022 0.0000
GDP_DEFLATOR 0.011473 0.001414 8.112985 0.0000
DLPRIVCRE 0.031427 0.029801 1.054569 0.3010
DMVA_GDP 0.010804 0.008756 1.233886 0.2279
DAV_GDP 0.005006 0.005404 0.926398 0.3624
DTOT 0.003967 0.001683 2.356898 0.0259
Dependent Variable: DLHFPC
Variable Coefficient Std. Error t-Statistic Prob.  
C -0.006052 0.014351 -0.421695 0.6766
DGDPPC 0.000974 0.000208 4.688549 0.0001
DGCF_GDP -0.021428 0.004268 -5.021022 0.0000
GDP_DEFLATOR 0.011473 0.001414 8.112985 0.0000
DLPRIVCRE 0.031427 0.029801 1.054569 0.3010
DMVA_GDP 0.010804 0.008756 1.233886 0.2279
DAV_GDP 0.005006 0.005404 0.926398 0.3624
DTOT 0.003967 0.001683 2.356898 0.0259

R squared=0.832407 F-statisic=19.15783
Adjusted R squared=0.788957 Prob( F-statistic)=0.00000
Durbin Watson stat=1.913509
All the significant variables have the expected signs except for gross capital formation as a percentage of GDP which has a negative sign and GDP deflator which exhibits positive value. DGDPPC, DGCF_GDP and GDP_DEFLATOR are significant at 1% level while DTOT is significant at 5% level and DLPRIVCRE, DMVA_GDP and DAV_GDP are not *statistically significant at all levels. Both the R2 and adjusted R2 are greater than 0.5 with values 0.832407 and 0.788957 respectively. This implies that R2 is a reliable measure of goodness fit as about 83% variations in poverty are explained by combination of explanatory variables. Also the F-statistic probability is 0.0000000006 (614591e-09) which is less than 0.01 implying that the model is significant at 1%.

4.5 Granger Causality test results
Table 3: Granger causality results
Pairwise Granger Causality Tests
Date: 04/06/18 Time: 12:24
Sample: 1980 2015
Lags: 2
 Null Hypothesis: Obs F-Statistic Prob. 
 DLPRIVCRE does not Granger Cause DLHFPC  33  2.69602 0.0850
 DLHFPC does not Granger Cause DLPRIVCRE  0.29977 0.7433
There is no variable that granger causes other variable since the first null hypothesis probability is 0.0850 is greater than 0.05 and the second null hypothesis probability of 0.7433 is greater than 0.05. Thus leading to the rejection of first and second hypotheses and concluding that there is no bi directional causality between FSD and poverty.

4.6 Interpretation of results
4.6.1 Financial Sector Development
The coefficient of financial development was found to be positive with value 0.031427 which is statistically insignificant at all conventional levels. Moreover it leads to the rejections of the hypothesis that there is a positive relationship between FSD and poverty since the variable is statistically insignificant.

4.6.2 Economic growth
The coefficient of GDPPC was found to be to be positive with value 0.000974 which is statistically significant at 1% level of significance. This means a unit increase in GDPPC is approximately 0.0974% increase in poverty reduction. This is in line with the sign expectations.
4.6.3 Investment as a percentage of GDP
In this case, the coefficient of GCF_GDP has been observed to have a negative value which is statistically significant 5% of and the coefficient is -0.021428. This means a unit increase in GCF_GDP leads to a 2.1428% increase in poverty reduction meaning that the level of poverty would have increased as there is a negative constant value.

4.6.4 Manufacturing as a share of GDP
The coefficient of MVA_GDP was found to be positive with a value of 0.010804. However, this value was statistically insignificant.
4.6.6 Agriculture as a percentage of GDP
The coefficient AV_GDP was found to have a positive value 0.000506 and the coefficient was statistically insignificant. Meaning that the variable as no economic impact on poverty.

4.6.7 Inflation
The coefficient GDP deflator was found to have a positive value of 0.011473, whereas it contradicts with the theory expectations as it is expected to have a negative value. This means that a unit increase in GDP deflator results in approximately 1.15 percent increase in Poverty reduction.

4.7 Conclusion
The present chapter has presented the regression results after the model had passed most of the diagnostic tests. In addition an interpretation of the results was given. Guided by these results, the next chapter presents policy conclusions and recommendations.

CHAPTER FIVE
CONCLUSIONS AND POLICY RECOMMENDATIONS
5.0 Introduction
This chapter provides a summary of the key findings the study. The chapter is outlined as follows policy recommendations and areas of further study.

5.1 Summary
The main objective of this study is to examine the relationship between financial sector development and poverty using time series data for the period 1980 to 2015. Domestic credit as a percentage of GDP is used as the indicator for financial sector development, the coefficient was found to be statistically insignificant which lead to rejection of the second objective of the study implying that financial sector development does not have an effect to poverty. Most of the independent variable where statistically significant except for manufacturing value added as a percentage of GDP and agriculture value added as a percentage of GDP which are statistically insignificant at all conventional levels. As the study passed all of the all diagnostic tests the model produced parsimonious results. Additionally the coefficient of determination R2 and the adjusted R2 are greater than 0.5 indicating that the estimated model is of good fit. Applying the granger causality none of the variables influenced another variable since both probabilities where less than 0.05 thus resulting in rejection of the null hypothesis of a bi directional causality.

5.2 Policy Recommendations
Financial sector development coefficient is found to be statistically insignificant at all conventional levels one of the problems faced in this sector is that it faces lack of confidence in the economy due to issues such as the hyperinflation faced in the country in 2008 and the current liquidity crisis in the country. In order for the country to overcome this government should restore confidence of the financial sector by implementing the move to resuscitate RBZ’s role as the lender of last resort. With this function banks can definitely benefit and probably the risk of bank closures would reduce. Additionally the lender of last resort function would lead to the expectation of easing liquidity challenges through improved interbank market which leads to the prospects of increasing lending to the key productive sectors and export sectors of te economy and thus result in poverty reduction.

As a result from the indirect channel economic growth (GDPPC) is statistically significant at 1% level of significance. Since economic growth is sustained through measures and policies that may develop the financial system. Such policies may influence innovation in the sector. Through government intervention it is advocated to encourage financial partnerships with Sub-Suharan countries or other international countries in order to benefit the sector through increase in the information regarding ways/methods to increase financial services and innovation so as to increase access of financial systems to the poor. Also it is imperative for government to encourage for opening of new financial markets as it is likely to go a long way in enhancing competition in the financial sector which should in turn lead to increased investment levels.

Furthermore Zimbabwe’s majority population of the poor people reside in rural areas and mostly they are unskilled labour force with agriculture being their main occupation. The formulation of government policies at both macro and micro level make agricultural reforms favouring the local people and the poor should not be excluded from various development programs rather undertaken on priority basis. Thus both the agriculture and agri-based manufacturing sectors should continue to be labour intensive as they have the potential for employment as well as income generating of the unskilled labour.
5.3 Areas of Further Study
Future studies can use another variable to measure financial sector development such as Broad money as a percentage of GDP in order to determine the relationship between FSD and poverty. Also the study can use different methodologies especially to determine short and long run relationships thus use of ARDL and VECM. Also since financial sector development does not poverty there is need to know what determines financial development so that we may target those variables in order to enhance the prospects of poverty in Zimbabwe.

Reference
Schumpeter, J. 1911. The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle. Cambridge: Harvard University Press.

Zhuang, J. et al. (2009).Financial Sector Development, Economic Growth, and Poverty Reduction: A Literature Review.ADB Economics Working Paper Series No. 173,Economics and Research Department, Asian Development Bank, Manila.

McKinnon, R. I. (1973). Money and Capital in Economic Development. Brookings Institution, Washington, DC.

Goldsmith, R. W. (1969).Financial Structure and Development. New Haven, CT: Yale University Press.

Brownbridge, M, and Harvey, C. (1998). Banking in Africa: The Impact of Financial Sector Reform Since Independence. Africa World Press, USA.

Kanyenze, G., Kondo, T., Chitambira, P., and Martens, J. (2011). Beyond the Enclave,
Ministry of Finance, (2011). 2012 Budget Strategy Paper: Building on Our Priorities. Harare, Zimbabwe.

Ministry of Finance, (2015), 2016 Budget Strategy Paper: Building on Our Priorities. Harare, Zimbabwe.

Honohan, Patrick.. 2004. Financial Sector Policy and the Poor: Selected Findings and Issues.World Bank Working Paper No. 43. Washington, D.C.: The World Bank.

Chigumira, G and Makochekanwa, A. (2014). Financial Liberalization and Crisis: Experience and Lessons for Zimbabwe, ZEPARU, Zimbabwe.

Uddin, G. S, Kyophilavong, P., ; Sydee, N. (2012). The causal nexus between financial sector development and poverty reduction in Bangladesh. International Journal of Economics and Financial Issues, 2(3), 304-311.

Uddin, G. S., Shahbaz, M., Arouri, M., ; Teulon, F. (2014). Financial development and poverty reduction nexus: a cointegration and causality analysis in Bangladesh. Economic Modelling, 36, 405-412.

Shahbaz, M. (2009). Financial performance and earnings of poor people: a case study of Pakistan. Journal of Yasar University, 4, 2557-2572.

Odhiambo, N. M. (2009). Finance–growth–poverty nexus in South Africa: a dynamic causality linkages. Journal of Socio-Economics, 38, 320-325.
Odhiambo, N. M. (2010a). Is financial development a spur to poverty reduction? Kenya;s experience. Journal of Economic Studies, 37(3), 343-353.
Odhiambo, N. M. (2010b). Financial deepening and poverty reduction in Zambia: an empirical investigation. International Journal of Social Economics, 37(1), 41-53.

APPENDIX LIST
Appendix 1: Descriptive statistics

HFPC AV_GDP GCF_GDP GDP_DEFLATOR GDPPC PRIVCRE TOT
 Mean  524.6010  16.13843  14.67408  2.153850  1083.545  26.19803 66.71309
 Median  428.3380  16.25094  15.98748  1.956202  1169.694  24.74932 69.37150
 Maximum  900.7536  22.67357  23.72906  74.29818  1347.972  103.6323 109.5216
 Minimum  327.9399  7.413793  1.525177 -27.04865  593.1272  7.476843 35.91686
 Std. Dev.  174.6733  3.648627  6.040022  14.94728  219.8828  17.61774 19.08893
 Skewness  0.894086 -0.234857 -0.679745  2.880900 -0.726938  2.492208 0.2372904
 Kurtosis  2.466571  2.522200  2.527560  16.60458  2.199465  11.54024 2.372904

 Jarque-Bera  5.223159  0.673386  3.107120  327.4245  4.131917  146.6702 0.927631
 Probability  0.073418  0.714128  0.211494  0.000000  0.126697  0.000000 0.628880

 Sum  18885.64  580.9835  528.2668  77.53859  39007.62  943.1289 2401.671
 Sum Sq. Dev.  1067876.  465.9368  1276.865  7819.737  1692196.  10863.46 12753.55

 Observations  36  36  36  36  36  36 36

Appendix B: Correlation matrix

AV_GDP GCF_GDP GDP_DEFLATOR GDPPC PRIVCRE TOT MVA_GDP
AV_GDP  1.000000 -0.149487 -0.100146  0.148241  0.128590  0.090144  0.036710
GCF_GDP -0.149487  1.000000 -0.147222  0.549109 -0.255357 -0.271429  0.463574
GDP_DEFLATOR -0.100146 -0.147222  1.000000 -0.405403 -0.101286  0.053018 -0.260668
GDPPC  0.148241  0.549109 -0.405403  1.000000  0.185244 -0.585773  0.556157
PRIVCRE  0.128590 -0.255357 -0.101286  0.185244  1.000000  0.028521 -0.003929
TOT  0.090144 -0.271429  0.053018 -0.585773  0.028521  1.000000 -0.610347
MVA_GDP  0.036710  0.463574 -0.260668  0.556157 -0.003929 -0.610347  1.000000
Apendix C: Stationarity tests
Variable HFPC
Null Hypothesis: LHFPC has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -1.374741  0.8509
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LHFPC)
Method: Least Squares
Date: 04/03/18 Time: 10:00
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

LHFPC(-1) -0.135021 0.098215 -1.374741 0.1788
C 0.765068 0.608141 1.258045 0.2175
@TREND(;1980;) 0.004512 0.002831 1.593713 0.1208

R-squared 0.115084     Mean dependent var 0.009536
Adjusted R-squared 0.059777     S.D. dependent var 0.174095
S.E. of regression 0.168811     Akaike info criterion -0.638258
Sum squared resid 0.911909     Schwarz criterion -0.504942
Log likelihood 14.16951     Hannan-Quinn criter. -0.592237
F-statistic 2.080815     Durbin-Watson stat 1.849327
Prob(F-statistic) 0.141394

Variable D(LHFPC)
0635Null Hypothesis: D(LHFPC) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -5.553387  0.0000
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LHFPC,2)
Method: Least Squares
Date: 04/03/18 Time: 10:15
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(LHFPC(-1)) -0.953552 0.171706 -5.553387 0.0000

R-squared 0.483014     Mean dependent var -0.002758
Adjusted R-squared 0.483014     S.D. dependent var 0.241858
S.E. of regression 0.173900     Akaike info criterion -0.631705
Sum squared resid 0.997957     Schwarz criterion -0.586812
Log likelihood 11.73898     Hannan-Quinn criter. -0.616395
Durbin-Watson stat 1.980307

Null Hypothesis: D(LHFPC) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -5.553387  0.0000
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LHFPC,2)
Method: Least Squares
Date: 04/03/18 Time: 10:15
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(LHFPC(-1)) -0.953552 0.171706 -5.553387 0.0000

R-squared 0.483014     Mean dependent var -0.002758
Adjusted R-squared 0.483014     S.D. dependent var 0.241858
S.E. of regression 0.173900     Akaike info criterion -0.631705
Sum squared resid 0.997957     Schwarz criterion -0.586812
Log likelihood 11.73898     Hannan-Quinn criter. -0.616395
Durbin-Watson stat 1.980307

Variable PRIVCRE
Null Hypothesis: LPRIVCRE has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -2.836192  0.1947
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LPRIVCRE)
Method: Least Squares
Date: 04/03/18 Time: 10:01
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

LPRIVCRE(-1) -0.379742 0.133892 -2.836192 0.0079
C 1.324908 0.435822 3.040017 0.0047
@TREND(;1980;) -0.007442 0.007542 -0.986702 0.3312

R-squared 0.228775     Mean dependent var 0.006966
Adjusted R-squared 0.180574     S.D. dependent var 0.496397
S.E. of regression 0.449350     Akaike info criterion 1.319785
Sum squared resid 6.461281     Schwarz criterion 1.453101
Log likelihood -20.09624     Hannan-Quinn criter. 1.365806
F-statistic 4.746228     Durbin-Watson stat 2.059081
Prob(F-statistic) 0.015664

Variable D(PRIVCRE)
Null Hypothesis: D(LPRIVCRE) has a unit root
Exogenous: None
Lag Length: 1 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -6.148211  0.0000
Test critical values: 1% level -2.636901
5% level -1.951332
10% level -1.610747

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LPRIVCRE,2)
Method: Least Squares
Date: 04/03/18 Time: 10:02
Sample (adjusted): 1983 2015
Included observations: 33 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(LPRIVCRE(-1)) -1.586563 0.258053 -6.148211 0.0000
D(LPRIVCRE(-1),2) 0.356738 0.168960 2.111379 0.0429

R-squared 0.634632     Mean dependent var -0.005856
Adjusted R-squared 0.622846     S.D. dependent var 0.778400
S.E. of regression 0.478038     Akaike info criterion 1.420438
Sum squared resid 7.084123     Schwarz criterion 1.511135
Log likelihood -21.43722     Hannan-Quinn criter. 1.450955
Durbin-Watson stat 2.075656

Variable GDPPC
Null Hypothesis: GDPPC has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 1 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -1.812753  0.6764
Test critical values: 1% level -4.252879
5% level -3.548490
10% level -3.207094

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDPPC)
Method: Least Squares
Date: 04/03/18 Time: 10:06
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

GDPPC(-1) -0.139887 0.077168 -1.812753 0.0799
D(GDPPC(-1)) 0.387895 0.161664 2.399395 0.0228
C 179.6459 109.7959 1.636180 0.1123
@TREND(;1980;) -1.901686 1.712281 -1.110615 0.2756

R-squared 0.201632     Mean dependent var -10.03471
Adjusted R-squared 0.121796     S.D. dependent var 71.75142
S.E. of regression 67.24009     Akaike info criterion 11.36455
Sum squared resid 135636.9     Schwarz criterion 11.54412
Log likelihood -189.1973     Hannan-Quinn criter. 11.42579
F-statistic 2.525558     Durbin-Watson stat 2.043098
Prob(F-statistic) 0.076343

Variable D(GDPPC)
Null Hypothesis: D(GDPPC) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -4.190570  0.0001
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDPPC,2)
Method: Least Squares
Date: 04/03/18 Time: 10:07
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(GDPPC(-1)) -0.666567 0.159064 -4.190570 0.0002

R-squared 0.346403     Mean dependent var -3.112504
Adjusted R-squared 0.346403     S.D. dependent var 84.20985
S.E. of regression 68.07976     Akaike info criterion 11.30821
Sum squared resid 152950.2     Schwarz criterion 11.35310
Log likelihood -191.2395     Hannan-Quinn criter. 11.32352
Durbin-Watson stat 1.955252

Variable AV_GDP
Null Hypothesis: AV_GDP has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -3.161611  0.1086
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(AV_GDP)
Method: Least Squares
Date: 04/03/18 Time: 10:09
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

AV_GDP(-1) -0.488500 0.154510 -3.161611 0.0034
C 8.651967 2.858132 3.027141 0.0048
@TREND(;1980;) -0.045672 0.054534 -0.837504 0.4085

R-squared 0.241905     Mean dependent var -0.117142
Adjusted R-squared 0.194524     S.D. dependent var 3.604692
S.E. of regression 3.235150     Akaike info criterion 5.267844
Sum squared resid 334.9183     Schwarz criterion 5.401160
Log likelihood -89.18727     Hannan-Quinn criter. 5.313865
F-statistic 5.105532     Durbin-Watson stat 1.950946
Prob(F-statistic) 0.011901

Variable D(AV_GDP)
Null Hypothesis: D(AV_GDP) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -7.226290  0.0000
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(AV_GDP,2)
Method: Least Squares
Date: 04/03/18 Time: 10:09
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(AV_GDP(-1)) -1.221133 0.168985 -7.226290 0.0000

R-squared 0.612693     Mean dependent var -0.075791
Adjusted R-squared 0.612693     S.D. dependent var 5.708495
S.E. of regression 3.552623     Akaike info criterion 5.402220
Sum squared resid 416.4974     Schwarz criterion 5.447113
Log likelihood -90.83774     Hannan-Quinn criter. 5.417530
Durbin-Watson stat 2.139692

Variable GCF_GDP

Null Hypothesis: GCF_GDP has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -2.170986  0.4902
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GCF_GDP)
Method: Least Squares
Date: 04/03/18 Time: 10:10
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

GCF_GDP(-1) -0.256390 0.118098 -2.170986 0.0375
C 5.003960 2.680321 1.866926 0.0711
@TREND(;1980;) -0.075391 0.070467 -1.069875 0.2927

R-squared 0.128381     Mean dependent var -0.132844
Adjusted R-squared 0.073905     S.D. dependent var 3.817016
S.E. of regression 3.673261     Akaike info criterion 5.521853
Sum squared resid 431.7711     Schwarz criterion 5.655169
Log likelihood -93.63243     Hannan-Quinn criter. 5.567874
F-statistic 2.356642     Durbin-Watson stat 1.715612
Prob(F-statistic) 0.110976

Variable D(GCF_GDP)
Null Hypothesis: D(GCF_GDP) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -5.724293  0.0000
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GCF_GDP,2)
Method: Least Squares
Date: 04/03/18 Time: 10:12
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(GCF_GDP(-1)) -0.981274 0.171423 -5.724293 0.0000

R-squared 0.498053     Mean dependent var -0.100503
Adjusted R-squared 0.498053     S.D. dependent var 5.387412
S.E. of regression 3.816887     Akaike info criterion 5.545718
Sum squared resid 480.7646     Schwarz criterion 5.590611
Log likelihood -93.27720     Hannan-Quinn criter. 5.561028
Durbin-Watson stat 1.957980

Variable GDP _deflator
Null Hypothesis: GDP_DEFLATOR has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -5.432800  0.0005
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GDP_DEFLATOR)
Method: Least Squares
Date: 04/03/18 Time: 10:25
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

GDP_DEFLATOR(-1) -0.942469 0.173478 -5.432800 0.0000
C -5.433836 5.187703 -1.047446 0.3027
@TREND(;1980;) 0.397735 0.256720 1.549290 0.1311

R-squared 0.480107     Mean dependent var -0.338368
Adjusted R-squared 0.447614     S.D. dependent var 19.97522
S.E. of regression 14.84612     Akaike info criterion 8.315170
Sum squared resid 7053.032     Schwarz criterion 8.448486
Log likelihood -142.5155     Hannan-Quinn criter. 8.361191
F-statistic 14.77558     Durbin-Watson stat 2.019630
Prob(F-statistic) 0.000028

Variable MVA_GDP
Null Hypothesis: MVA_GDP has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -2.432419  0.3576
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(MVA_GDP)
Method: Least Squares
Date: 04/03/18 Time: 10:29
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

MVA_GDP(-1) -0.289224 0.118904 -2.432419 0.0208
C 7.457892 3.087387 2.415600 0.0216
@TREND(;1980;) -0.136270 0.055678 -2.447461 0.0201

R-squared 0.169865     Mean dependent var -0.335672
Adjusted R-squared 0.117982     S.D. dependent var 2.034819
S.E. of regression 1.911017     Akaike info criterion 4.214965
Sum squared resid 116.8636     Schwarz criterion 4.348280
Log likelihood -70.76188     Hannan-Quinn criter. 4.260985
F-statistic 3.273974     Durbin-Watson stat 2.067620
Prob(F-statistic) 0.050860

Variable D(MVA_GDP)
Null Hypothesis: D(MVA_GDP) has a unit root
Exogenous: None
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -6.468006  0.0000
Test critical values: 1% level -2.634731
5% level -1.951000
10% level -1.610907

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(MVA_GDP,2)
Method: Least Squares
Date: 04/03/18 Time: 10:30
Sample (adjusted): 1982 2015
Included observations: 34 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

D(MVA_GDP(-1)) -1.118765 0.172969 -6.468006 0.0000

R-squared 0.559012     Mean dependent var -0.019905
Adjusted R-squared 0.559012     S.D. dependent var 3.130841
S.E. of regression 2.079095     Akaike info criterion 4.330713
Sum squared resid 142.6470     Schwarz criterion 4.375606
Log likelihood -72.62212     Hannan-Quinn criter. 4.346023
Durbin-Watson stat 2.018321

Variable TOT
Null Hypothesis: TOT has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -3.097679  0.1225
Test critical values: 1% level -4.243644
5% level -3.544284
10% level -3.204699

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(TOT)
Method: Least Squares
Date: 04/03/18 Time: 10:31
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

TOT(-1) -0.513751 0.165850 -3.097679 0.0040
C 21.71849 7.403997 2.933347 0.0062
@TREND(;1980;) 0.726365 0.313374 2.317881 0.0270

R-squared 0.233582     Mean dependent var 0.559764
Adjusted R-squared 0.185681     S.D. dependent var 11.83856
S.E. of regression 10.68307     Akaike info criterion 7.657014
Sum squared resid 3652.096     Schwarz criterion 7.790330
Log likelihood -130.9977     Hannan-Quinn criter. 7.703035
F-statistic 4.876338     Durbin-Watson stat 2.090722
Prob(F-statistic) 0.014173

Appendix D: Regression results
Dependent Variable: DLHFPC
Method: Least Squares
Date: 04/03/18 Time: 10:47
Sample (adjusted): 1981 2015
Included observations: 35 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.  

C -0.006052 0.014351 -0.421695 0.6766
DGDPPC 0.000974 0.000208 4.688549 0.0001
DGCF_GDP -0.021428 0.004268 -5.021022 0.0000
GDP_DEFLATOR 0.011473 0.001414 8.112985 0.0000
DLPRIVCRE 0.031427 0.029801 1.054569 0.3010
DMVA_GDP 0.010804 0.008756 1.233886 0.2279
DAV_GDP 0.005006 0.005404 0.926398 0.3624
DTOT 0.003967 0.001683 2.356898 0.0259

R-squared 0.832407     Mean dependent var 0.009536
Adjusted R-squared 0.788957     S.D. dependent var 0.174095
S.E. of regression 0.079978     Akaike info criterion -2.016499
Sum squared resid 0.172705     Schwarz criterion -1.660991
Log likelihood 43.28873     Hannan-Quinn criter. -1.893778
F-statistic 19.15783     Durbin-Watson stat 1.913509
Prob(F-statistic) 0.000000

Appendix E: Heteroccedasticity results
Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.534309     Prob. F(7,27) 0.8008
Obs*R-squared 4.258457     Prob. Chi-Square(7) 0.7496
Scaled explained SS 1.244740     Prob. Chi-Square(7) 0.9899

Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 04/03/18 Time: 10:52
Sample: 1981 2015
Included observations: 35

Variable Coefficient Std. Error t-Statistic Prob.  

C 0.005128 0.000936 5.476459 0.0000
DGDPPC 1.51E-05 1.36E-05 1.113268 0.2754
DGCF_GDP -4.40E-05 0.000278 -0.158028 0.8756
GDP_DEFLATOR -3.28E-05 9.23E-05 -0.355365 0.7251
DLPRIVCRE 0.002005 0.001945 1.031299 0.3116
DMVA_GDP 0.000168 0.000571 0.294470 0.7707
DAV_GDP 0.000144 0.000353 0.408705 0.6860
DTOT 4.43E-05 0.000110 0.403571 0.6897

R-squared 0.121670     Mean dependent var 0.004934
Adjusted R-squared -0.106045     S.D. dependent var 0.004962
S.E. of regression 0.005219     Akaike info criterion -7.475562
Sum squared resid 0.000735     Schwarz criterion -7.120054
Log likelihood 138.8223     Hannan-Quinn criter. -7.352840
F-statistic 0.534309     Durbin-Watson stat 2.405918
Prob(F-statistic) 0.800782

Appendix F: Autocorrelation
Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.025118     Prob. F(2,25) 0.9752
Obs*R-squared 0.070188     Prob. Chi-Square(2) 0.9655

Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 04/03/18 Time: 10:53
Sample: 1981 2015
Included observations: 35
Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.  

C -0.000184 0.014924 -0.012341 0.9903
DGDPPC -2.03E-05 0.000234 -0.086864 0.9315
DGCF_GDP 0.000329 0.004766 0.069016 0.9455
GDP_DEFLATOR -1.61E-05 0.001493 -0.010778 0.9915
DLPRIVCRE -0.001950 0.032306 -0.060352 0.9524
DMVA_GDP 0.000223 0.009271 0.024027 0.9810
DAV_GDP -0.000106 0.005692 -0.018557 0.9853
DTOT -5.00E-05 0.001762 -0.028362 0.9776
RESID(-1) -0.035947 0.234110 -0.153548 0.8792
RESID(-2) -0.043056 0.236682 -0.181915 0.8571

R-squared 0.002005     Mean dependent var -3.97E-18
Adjusted R-squared -0.357273     S.D. dependent var 0.071271
S.E. of regression 0.083032     Akaike info criterion -1.904221
Sum squared resid 0.172359     Schwarz criterion -1.459836
Log likelihood 43.32386     Hannan-Quinn criter. -1.750819
F-statistic 0.005582     Durbin-Watson stat 1.888531
Prob(F-statistic) 1.000000

Appendix G: Model specification results
Ramsey RESET Test
Equation: EQ01
Specification: DLHFPC C DGDPPC DGCF_GDP GDP_DEFLATOR
        DLPRIVCRE DMVA_GDP DAV_GDP DTOT
Omitted Variables: Squares of fitted values

Value df Probability
t-statistic  1.818887  26  0.0805
F-statistic  3.308350 (1, 26)  0.0805
Likelihood ratio  4.192157  1  0.0406

F-test summary:
Sum of Sq. df Mean Squares
Test SSR  0.019495  1  0.019495
Restricted SSR  0.172705  27  0.006396
Unrestricted SSR  0.153210  26  0.005893

LR test summary:
Value df
Restricted LogL  43.28873  27
Unrestricted LogL  45.38481  26

Unrestricted Test Equation:
Dependent Variable: DLHFPC
Method: Least Squares
Date: 04/03/18 Time: 10:55
Sample: 1981 2015
Included observations: 35

Variable Coefficient Std. Error t-Statistic Prob.  

C 0.010619 0.016545 0.641845 0.5266
DGDPPC 0.000935 0.000201 4.660716 0.0001
DGCF_GDP -0.017404 0.004655 -3.738670 0.0009
GDP_DEFLATOR 0.011709 0.001363 8.587283 0.0000
DLPRIVCRE 0.040842 0.029068 1.405045 0.1718
DMVA_GDP 0.009500 0.008435 1.126213 0.2704
DAV_GDP 0.003192 0.005281 0.604417 0.5508
DTOT 0.002802 0.001738 1.612550 0.1189
FITTED^2 -0.687325 0.377882 -1.818887 0.0805

R-squared 0.851325     Mean dependent var 0.009536
Adjusted R-squared 0.805579     S.D. dependent var 0.174095
S.E. of regression 0.076764     Akaike info criterion -2.079132
Sum squared resid 0.153210     Schwarz criterion -1.679186
Log likelihood 45.38481     Hannan-Quinn criter. -1.941071
F-statistic 18.60980     Durbin-Watson stat 1.972405
Prob(F-statistic) 0.000000

Appendix H: Normality results

Appendix I: Raw Data
Year HFPC GDPPC GDP deflator TOT PRIVCRE GCF/GDP MVA/GDP AV/GDP
1980 645,1463 1175,135 12,7409 49,8904 7,97441 16,937 21,58 15,6971
1981 774,7579 1274,683 6,59908 45,3306 8,82371 20,8159 21,7679 17,7366
1982 771,4266 1259,196 3,85876 39,1453 7,47684 19,0537 20,8819 16,1197
1983 714,4797 1230,394 -10,502 35,9169 11,9813 14,3052 23,0131 11,2377
1984 483,843 1161,406 -16,595 41,3661 20,8859 17,0355 22,6512 14,8583
1985 402,154 1196,635 -17,017 44,2137 24,4871 17,82 19,85 22,6736
1986 407,0306 1178,559 8,02588 45,5704 25,0116 18,0564 21,4468 17,7608
1987 427,5508 1151,447 7,18936 45,2906 29,9427 14,9362 22,6894 14,4075
1988 410,2686 1198,305 7,78512 44,1003 25,8203 18,7017 21,5114 16,3821
1989 541,0039 1222,647 0,79293 45,0625 44,3659 15,038 25,5966 14,9303
1990 544,3438 1272,051 -0,9204 45,6593 23,0399 17,3769 22,7557 16,4763
1991 563,1761 1309,005 -6,7773 51,0515 26,1662 19,1034 27,1562 15,2673
1992 409,9339 1164,254 -14,13 63,7125 28,7709 20,2373 29,537 7,41379
1993 385,2112 1152,45 -3,7911 63,1671 29,8406 22,7749 23,0119 15,0389
1994 381,1789 1234,966 -3,8957 71,1195 28,4084 23,7291 21,167 18,9734
1995 407,2875 1214,693 3,03854 79,1568 33,8378 19,6602 21,7952 15,2352
1996 470,8001 1317,51 8,98438 72,0696 31,2324 18,5419 18,7807 21,7711
1997 535,7287 1330,676 -2,879 82,2051 38,5965 18,1339 18,0075 18,9341
1998 350,926 1347,972 -27,049 88,514 34,7091 20,7505 16,6279 21,7885
1999 363,3399 1317,969 8,00681 70,9227 22,2877 14,3963 16,3525 19,1767
2000 327,9399 1261,163 0,6279 74,0674 27,1112 13,5694 15,6051 18,2616
2001 383,7556 1264,431 -0,1309 67,8979 34,5213 10,2665 14,5585 17,307
2002 406,9653 1139,59 2,71295 66,8074 103,632 5 13,2514 14,029
2003 361,5095 935,9302 8,80128 70,452 57,03 8 13,6472 16,5934
2004 370,7141 871,6671 7,61152 76,0396 18,0247 4,50911 15,1165 19,575
2005 410,1135 811,563 5,1366 76,0437 15,7937 1,52518 16,3831 18,5773
2006 429,1253 772,4726 -2,0177 82,8206 46,4901 1,57116 16,889 20,2818
2007 390,1866 732,77 0,89489 84,1729 27,3012 7,10975 16,4013 21,5979
2008 388,9023 593,1272 1,34922 109,522 10,2381 5,12791 16,659 19,3989
2009 628,7098 652,2887 74,2982 69,2609 17,5438 14,2907 14,2793 13,9065
2010 632,4728 719,9795 4,48786 98,6952 9,629 22,2781 12,5889 13,1382
2011 681,1096 813,834 3,33187 104,282 18,1924 20,2788 12,2638 11,5865
2012 898,0457 913,5306 2,56318 89,1193 16,4203 11,8449 11,3696 11,0252
2013 861,9478 942,0387 2,8057 72,4715 12,7963 11,3785 10,7828 10,0944
2014 823,7967 939,7803 0,70092 67,0724 14,5693 11,8256 10,3202 12,1346
2015 900,7536 933,5033 0,89807 69,4821 10,1762 12,2874 9,83145 11,5971