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The Effect of Bank Credit on the Economic Growth of Tanzania

Elizabeth Joseph

Department of Finance, University of Dar- es- Salaam Business School, Dar-es-Salaam, Tanzania

Abstract: The purpose of this study is to examine the causal relationship between bank credit and economic growth. Specifically, the study examined the causal relationship between bank credit by borrowing sector and economic growth and between overall financial intermediation and economic growth. Time series data for the period 1993-2017 is used. Causality test and vector error correction is applied. Results show that there is no causal relationship between bank credit and economic growth and between economic growth and bank credit. In the long run, bank credit has a significant positive effect on economic growth. Policies towards enhancing growth of financial sector should be emphasized to enable increase in credit provision and promote economic growth through investment in different sectors of the economy. The positive long run effect of financial intermediation means that enhancing deposit mobilization still remain paramount towards credit provision by banking financial institutions.

Keywords: banking sector, credit, economic sector, economic growth, financial intermediation

Received August 17, 2020; Revised September 20, 2020; Accepted September 29, 2020

Citation: Elizabeth Joseph, The Effect of Bank Credit on the Economic Growth of Tanzania. Journal of Finance and Economics, 8 (5). 211-221. January 2020. doi:10.12691/jfe-8-5-2

JEL Classification: E50, E51, O40

1. Introduction

Economic growth remains paramount particularly in developing economies. This is because economic growth enable a country’s improvement in the living standards of its people [1]. It can enable people to move out of poverty and create jobs hence employment opportunities. This later enhances the income levels. This is achieved through production of goods and services in various sectors of the economy. However, it is noted that progress towards higher living standards has stalled for many countries with modest acceleration in GDP growth in many developing regions. In East Africa and East Asia it is expected to exhibit rapid income growth [1]. In Tanzania, economic growth has averaged between 6.8 percent and 7.0 percent respectively for 2013 and 2018. The key contribution of notable sectors of tourism, mining, construction, agriculture and manufacturing has varied. The value added growth rates by agriculture, forestry and fishing was 2.8 percent in 2013 and 5.3 percent in 2018; the industry and construction value added was 10.5 percent in 2013 and 9.3 percent in 2018. The services value added growth rate was 5.1 percent in 2013 and 6.3 percent in 2018 [2]. Furthermore, the share of economic activity in GDP in 2018 was led by services (40.2 percent), followed by Agriculture, forestry and fishing (30.7 percent) and lastly industry and construction (29.1 percent).

Structural shifts have been witnessed between agriculture, services and industry with employment in agriculture declining while in industry and service has slightly increased [3]. Challenges still remain in different sectors. For instance, low level of productivity in the agriculture sector. Furthermore, the agriculture sector has the lowest mean monthly income [4]. The manufacturing sector has small GDP share [5].

Different sectoractivities in the economy are stimulated by private sector activity including financial services in particular access to finance. Credit can contribute to economic growth through capital accumulation and productivity growth [6]. However, poor access to finance is among the largest barriers to doing business and enhancing productivity. The improvement in infrastructure is likely to drive demand for credit to various sectors of the economy. Lending to private sector has increased mainly benefiting the trade, building and construction, real estate, agriculture and personal activities in Tanzania [7]. Despite this, the banking sector is dependent on core deposits for funding hence high levels of loans to deposit ratio that stood at 86 percent in March 2017 but declined to 81.1 percent in December 2017 [8]. Such decline is potentially due to reduction in lending. However, empirical evidence on the effect of bank credit on economic growth is limited in Tanzania.

Literature asserts the positive long run effect of bank credit on economic growth [9,10]. In the short run a negative relationship is observed [9,11]. Thus literature show variation in feedback effects [10] with observed effects from GDP to private sector lending and not from the private sector lending to GDP growth. Thus a lack of consensus on the effect of bank credit on economic growth. This is attributed to cross-country regressions that may not accurately reflect individual country circumstances [12]. Furthermore, expansion of financial sector, increasing stock traded, sophistication of the financial system, quality of financial system have positive impact on economic growth but is sensitive to characteristics of individual countries [13]. In addition, different channels such as corporate credit and consumer credit [14] and also across different sectors examined across different areas such as urban, rural and semi-urban [15] have potential for economic growth.

Two views on the relationship between economic growth and financial sector are put by [16]. These are the supply leading hypothesis and the demand following hypothesis. The demand following hypothesis explains that, as the real economy grows, the increasing demand for financial services tends to induce expansion in the financial sector. On the other hand, the supply leading hypothesis explains that supply of financial services leads to economic growth. In that regard, availability of funds from financial institutions enable efficient entrepreneurs to assume a greater debt and to engage in larger amount of productive investments. Thus enhancing deposit mobilizationand lending are important financial intermediation components towards enabling investment activities. Financial intermediation augments the economy’s production potential. This is achieved through collecting deposits from agents that wish to invest money. Subsequently, financial intermediaries like banks are able to fund agents that are seeking capital.

The objective of this paper is twofold; first it examine the causal relationship between economic growth and borrowing by sector. Secondly, the study examine the extent to which financial intermediation affects economic growth in Tanzania using the ratio of bank credit to bank deposit to measure financial intermediation. The study contributes to understanding the long run effect of bank credit on economic growth of Tanzania. That is how credit provision stimulates the growth of the economy and the direction of causality. This is important because sectorial activities can trigger changes in the credit allocation to different sectors of the economy. Furthermore, this can help in policy and strategy implementation given the causality between sectorial borrowing and economic growth.

The results of the study show no short run causal relationship between bank credit and economic growth for both borrowing by sector and financial intermediation. In the long run, bank credit has positive significant effect on economic growth. The results have implications for policy such that access to credit should be enhanced. This should be in tandem with efforts towards enhancing financial intermediation so as to increase investment and productivity in different sectors of the economy.

The rest of the paper is structured as follows; section one is the introduction, section two reviews related literature, section three explains the methodology. Section four presents the empirical results. Section five presents the conclusions of the study.

2. Literature Review

2.1. Domestic Commercial Banks Lending in Tanzania

The financial sector in Tanzania is dominated by commercial banks [17]. However, it is not without challenges in the provision of credit. [18] noted that the formal financial system is critical for business sector but it remains behind in its ability to mobilize savings and provide access to affordable credit to the real economy.

Credit provision has fluctuated over the period 1993-2016 as depicted under Figure 1. During the period 1993 to 2004, domestic credit provided by the financial sector declined to 7.45 percent in 2004 from 32.5 percent in 1993 then later increased reaching 20.25 percent in 2016. Domestic credit to private sector by banks declined to 8.54 percent in 2004 from 10.8 percent in 1993 then later increased reaching 14.24 percent in 2016 slightly lower than 15.03 recorded in 2015.

The adoption of financial sector reforms of 1991/92 to 1993/94 were critical to the improvement in credit provision. Furthermore, other initiatives that involved deepening of financial sector with steps to develop money and capital markets also contributed to enhancement of credit provision. The financial sector adjustment program through privatization, parastatal restructuring and the elimination of directed credit policies were crucial to development of credit to the private sector [19]. The ratio of bank credit to bank deposits in the banking sector, has increased since 2005 where it stood at 47.2 percent, later increased to 57.7 and 79.2 percent respectively for 2010 and 2017.

Bank lending is concentrated in the corporate sector and in a few economic sectors mainly trade, construction and real estate, and manufacturing [17]. Industrialization is crucial in the efforts towards transforming Tanzania to a middle income country. The Sustainable Industrial Development (SIDP) Policy is geared towards sustainable economic growth. It emphasize development of industrialization in Tanzania to contribute to economic transformation towards achieving economic growth [20]. Sustainable development of the industrial sector can enhance productivity.This policy establishment is in tandem with Tanzania vision 2025 that aims “To transform the economy from a least developed country to a middle income country, transform the economy from a low productivity agricultural economy to a semi industrialized economy led by modernized and highly productive agricultural activities integrated and supported by industrial and services activities in urban and rural areas”.

Economic growth has improved with annual GDP growth rate increasing slightly as indicated in Figure 1. By 1993 it was as low as 1.21 percent, and increased from 6 percent in 2001 to 8 percent in 2005 and 7.1 in 2017. On the overall, domestic credit has steadily remained well above the GDP growth.The sectorial distribution of commercial banks’ lending show that credit provision varies as depicted in Figure 2. Of the ten sectors summarized, trade is the leading borrowing sector followed by mining, quarrying and manufacturing, then personal and other services and lastly, is borrowing by agriculture sector. Lending to trade sector proportion was 22.57 percent in 1993, then reached 33.11 percent in 2001 and later was 20.38 percent in 2017. This reflects the importance of trade activities in the economy. This is also observed for the manufacturing sector which can be attributed by the increased emphasis on industrialization to transform the economy thus credit largely increasing towards this sector. [5] showed that manufacturing sector has remained to be significant for the growth of Tanzania’s economy despite its small GDP share relative to other sectors like agriculture and service.

Figure 1. Domestic credit and GDP growth Trend

Agriculture sector employs a majority of the population. There are different initiatives that have been undertaken to ensure the growth of the agriculture sector. The Agriculture Sector Development Strategy of 2015/2016 to 2024/2025 notes various achievements in pursuit of development of the sector. This include various public financial service interventions. These are the Tanzania Investment Bank-Agricultural Window that offers concessional loans and an agricultural input trust fund (AGTIF) offering short term loans, Tanzania Agriculture Development Bank. In addition, commercial banks have enabled expansion of agricultural finance services through increasing lending though gradual [21].

Borrowing by agriculture sector ranged between 17.12 percent and 10.01 percent as a proportion of commercial banks’ lending in 2002 and 2013 respectively. However, since 2014, it declined below 10 percent and reached 7.52 percent in 2017. The decline in credit to agriculture sector can be attributed to changes of credit to the mining, quarrying and manufacturing sector which during the same period recorded a slight improvement. The proportion of mining, quarrying and manufacturing was 18.69 percent in 1993 while in 2001 it was 33.56 percent and reached 12.76 percent in 2017.

Personal and other services was 5.27 percent in 1993, then in 2001 it was 5.26 percent, however, it has varied over time and in 2017 it was 29.05 percent. Low level of credit by personal and other services sector can be attributed to formal banking system serving small group of individuals and companies. Furthermore, lending rates have steadily remained high despite decline in trends. According to World Development Indicators, lending interest rate was 31 percent in 1993, however it declined reaching 14.14 percent in 2004 but it started increasing slightly and by 2017 lending interest rate was 17.62 percent [22]. Furthermore, [18] noted that small and medium enterprises are under served and this can limit their capacity to propel job creation. However there has been increase in credit to personal and other services indicating the growing importance of credit to personal and other services towards contribution to economic activities.

Despite the four categories discussed earlier, other sectorial credit are also crucial despite their borrowing being low. The proportion of borrowing by these sectors ranged from 1.30 percent to 10.25 percent over the period 1993 to 2017. However, transport, communication and storage relatively recorded higher proportions ranging from 3.88 percent in 1993 to 13.47 percent in 2000 and later slightly declined to 6.08 in 2017. On the other hand, borrowing by tourism, hotels & restaurants sector has remained low with an average proportion of 3.12 percent for the period 1993 to 2017. Building, construction and real estate proportion experienced an increase in the proportion of borrowing. In 1993 it was 2.42 percent increased to 5.18 percent in 2007 and later reached 10.25 percent in 2017. This reflects increase in demand for construction activities in the economy.

Figure 2. Commercial Banks Lending by Sector

2.2. Empirical Literature Review

There are divergent theoretical views on the relationship between finance and economic growth. Two views are explained by [16]. The demand following hypothesis explains that, as the real economy grows, the increasing demand for financial services tends to induce expansion in the financial sector. On the other hand, the supply leading hypothesis explains that supply of financial services leads to economic growth. Availability of funds from financial institutions enable efficient entrepreneurs to assume a greater debt and to engage in larger amount of productive investments. Thus this enhances the creation of employment opportunities and strengthens the competitiveness of the economy. The supply leading hypothesis thus supports bank credits to affect such aspects as industrial production of sub-sectors hence causing economic growth [23].

Empirical literature as varied on the direction of effect of financial development and economic growth. Real sector activity was found to be a less important determinant of financial development [24]. Instead, [24] found that financial development was a driving, causal force behind the rapid industrial transformations that was experienced by five leading economies of US, UK, Canada, Norway and Sweden. Their results showed that financial intermediation granger-cause real output, but with little evidence of feedback from output to intermediation despite the variation among the countries. Similarly, [25] views from developing and advanced countries suggested that financial intermediation through the efficiency of investment led to higher rate of growth per capita. The results are in line with those from South East Asia where it was found that the causality runs from ﬁnancial structure to economic development [26].

Despite, the directional causality from financial intermediation to economic growth, a bi-directional causality is also observed in China [27] with a Vector Auto regression (VAR). Furthermore, the impact of ﬁnancial development on economic growth was the second force after the contribution from labor input, in leading to economic growth.

Finance and economic development also have a weak relationship as observed using domestic credit to the economy, liquid liability and liquid reserves [28]. Using a VAR on first differences, [28] found that in all but a few countries, results indicated a weak causal relationship between ﬁnance and economic development and between economic development and ﬁnance. Thus growth did not cause credit and vice versa except for some countries like Mauritania and Sierra Leone where credit was a source of growth. However, the results sensitivity to individual characteristics of the countries is also evidenced by [13]. Expansion in the level of financial sector, increasing stock traded, net foreign assets, sophistication of the financial system, quality of financial system had a positive impact on economic growth however domestic monetary and credit expansion had negative impact on economic growth using least squares estimation, LSDV and random effects analysis [13].

In the long run, a positive significant relationship between financial development and economic growth is observed. [9] used three indicators of financial development being broad money, deposit/GDP and domestic credit to private sector in Cameroon and the Auto Regressive Distributive Lag (ARDL). They found a short-run positive relationship between monetary money, government expenditure and economic growth, a short run negative relationship between bank deposits, private investment and economic growth. In the long run, financial development had a positive and significant impact on economic growth. In contrast, [11] used the Fully-Modified Ordinary Least Squares (FMOLS), Error Correction and the Generalized Method of Moments (GMM) in Ghana from 1971 to 2010. It was found that domestic credit to GDP ratio as well as broad money supply as a ratio of GDP did hamper economic growth both in the short run and in the long run, credit to private sector as a share of GDP ratio had a positive but insignificant relationship with economic growth. Similarly, [29] used error correction model from 1980 to 2009 in Nigeria and found a positive effect of financial sector development on economic growth. However, credits to private sector and financial sector depth were ineffective and fail to accelerate growth.

In particular to bank credit, considering the lending behavior of different bank ownership and efficiency, [30] in the Central East and South Eastern European countries found that banking sector conditions do not play a signiﬁcant role in improving economic growth. Furthermore, bank credit could be an important source of economic growth to the extent that banks owned by development banks are involved in the process of providing credit to the private sector. However, credit has remained crucial to enhancing production. [23] found that bank credit was more effective than loan rates on industrial production of sub-sectors using ARDL bound co-integration test.

[31] examined the relationship between credit market development and economic growth in Spain from 1976 to 2007 using Vector Error Correction Model (VECM). The short-run and long-run relationship between bank lending, gross domestic product and inflation rate using Johansen co-integration and it was found that, in the short run, increase in economic growth induced an increase in bank lending while increase in the inflation rate led to a decrease in bank lending. Furthermore, increase in investment rate increased bank credit. Hence economic growth and investment have a positive effect on credit market development, while inflation rate has a negative effect.

Similarly, [10] examined the effect of bank credit to the private on the economic growth of Nepal from 1975 to 2013 applying Johansen co-integration and error correction model. It was found that bank credit had a positive effect on the economic growth only in the long run. However, there was a feedback effect from economic growth to private sector credit in the short run. In Romania, [32] analyzed the relationship between credit and economic growth at regional level from 2005 to 2014 using the random effects. The results indicated that credit had a significant influence on the evolution of gross domestic product.

In emerging market economies, [14] examined the contribution of credit growth and the composition of credit portfolio being corporate, consumer, and housing credit to economic growth. Using panel regression, they found a significant impact of credit growth on real GDP growth, and the magnitude and transmission channel of the impact of credit on real activity depended on the specific type of credit. Corporate credit shocks influenced GDP growth mainly through investment, while consumer credit shocks were associated with private consumption. [15] examined the effect of bank credit on the economic growth of North India categorizing bank credit into various sectors and how they affected different regions; urban, rural and semi-urban areas. Using Pooled Fixed effect model, it was found that on the overall, bank credit had no much impact on economic growth however it had the potential for economic growth in the future.

In Nigeria, bank credit has a significant effect on economic growth. [33] examined the relationship between banking sector credit and economic growth from 1970-2008. Granger causality test and two stage least square indicated that private sector credit impacts positively on economic growth but the lending rate impedes growth. Furthermore, [34] used ordinary least square from 1992 to 2012 and found that commercial bank credit has a significant effect on the economic growth. Similarly, [35] found that credit granger caused output. In addition, oil exports were negatively related to credit however non-oil exports had positive relationship with credit. In Cameroon, [36] considered domestic credit to the private sector by banks and bank deposits as proxies for bank credit development and gross domestic product per capita for economic growth from 1969 to 2013 and applied Augmented Dickey-Fuller (ADF), Johansen multivariate co-integration test and Vector Error Correction Model (VECM). It was found that there is a unidirectional causal relationship that flows from bank credit to economic growth.

Taking into consideration structural break analysis, [37,38] examined economic growth with finance. Results showed that, there is a difference among countries in terms financial development and economic growth. [37] used Gregory Hansen and VECM and found that there is cointegration among financial development proxy by broad money supply, trade openness and economic growth with structural break. Furthermore, there was significant and negative relationship between financial development and economic growth. This is similar to [38] who found some country with financial development measured as credit to private sector to GDP to negatively impact real GDP. However, it is also observed that other countries had positive and significant long run effect on GDP.

Literature shows a lack of consensus on the effect of bank credit on economic growth. To further understand, the effect of bank credit on the economic growth, this study uses the approach on bank credit category following [14] and [15], however the study takes a time series approach using a bivariate analysis between each credit category by borrowing sector and economic growth. In addition, the study also considers an overall financial intermediation effect on economic growth using the ratio of bank credit to bank deposits which less has been considered in the previous literature on financial intermediation.

3. Methodology

Data was collected for the period 1993 to 2017. The main variables of the study were annual GDP from World Development Indicators (WDI) and commercial banks’ lending by sectors from the Bank of Tanzania (BOT). The ratio of bank credit to bank deposit was extracted from the World Bank Financial Structure Database as a proxy of overall financial intermediation. Bank credit by borrowing sector was the independent variable and the dependent variable is the economic growth measured as the natural logarithm of GDP (lnGDP). Credit by borrowing sectors were six selected based on sample period availability of data. The bank credit sector categories were; agriculture (lnBC_AGRIC), second was mining, quarrying and manufacturing (lnBC_MQMANU), the third was building, construction and real estate (lnBC_BUCORES), fourth was trade (lnBC_TRADE), fifth was Tourism, Hospitality and Restaurant (lnBC_TOHORES), and sixth was transport, communication and storage (lnBC_TRCOMSTORE).

The study used time series data and therefore check of stationarity was crucial. It is important that the data is stationary that is absence of unit root problem. The presence of unit root problem in economic growth and bank credit category can cast doubt on the validity of causality tests between the variables. To test for stationarity, the DF-GLS test for unit root test is used [39]. The DF-GLS performs a modified DF test for a unit root in which the series has been transformed by a generalized least squares regression. This test has a significantly greater power than the augmented Dickey-Fuller test. In addition, the unit root test with structural break is also examined. To test stationarity taking into consideration structural breaks, the Zivot Andrews test has been used. However, it is noted to have weaknesses such that it is limited as it takes into account only one break hence unable to handle the evolution of the series [39]. In that regard, the Clemente, Montanes and Reyes approach to unit root with structural break is used [39].

Testing for co-integration is important as it allows to test for co-integration allows to assess the existence of a vector of co-integration [40]. The Johansen co-integration test was used. Furthermore, to take into account structural break, the Gregory Hansen test was used to check for structural break [41]. The Gregory Hansen test allows to test for structural break in cointegration by examining the null hypothesis of no cointegration against the alternative hypothesis of cointegration with structural breaks based on the extension of the ADF, Zα and Zt tests.

The Gregory Hansen test is superior to the Engle and Granger approach to testing for cointegration that tends to under-reject the null hypothesis of no cointegration if there is a cointegration relationship that has changed at some unkown time during the sample period. In order to test for cointegration with structural break, three altrenative models are proposed under the Gregory Hansen test approach; the level shif, level shift with trend and both level shift and slope coefficients can change [41,42]. The Gregory Hansen has also been supported to improve significance of results in VECM by accounting for structural break [37].

To examine causality, the VAR allow to investigate whether one variable is useful in predicting another variable. A variable X is said to granger cause a variably Y if given the past values of Y, the past values of X are useful for predicting Y. For the case of granger causality, a system of equations was used for each bank credit by borrowing sector. The same causality equation was also used for overall financial intermediation as displayed by equations below.

[picture] | (1)

[picture] | (2)

From the above equations, and are the values of bank credit by borrowing sector and economic growth respectively for each regression that is tested. Furthermore, the values of δ, α, β and γ are the parameter estimates to be estimated, t and t-i are the current and lagged values of the variables.

A test of the regression models (1) and (2) with variables being non-stationary and co-integrated would make the models specified not appropriate. Hence the relationship is also modeled using the first difference of the variables. The estimation of the vector error correction model with first difference variables to test the causality between economic growth and each bank credit by sector is specified as follows;

[picture] | (3)

[picture] | (4)

The term in both equation (3) and (4) is the error correction term formed with elements of the co-integrating vector and lagged one period. The λ term is the short run coefficient of the error correction term. The sign and size of the coefficient on the error correction term is the direction and the speed of adjustment. A significant coefficient of the error correction term implies that past equilibrium errors play a role in determining current outcomes.

4. Empirical Results

4.1. Stationarity and Co-integration

The DF-GLS test for unit root test is used. The results are reported in Table 1 for level variables and Table 2 for first difference variables. The null hypothesis examined is that there is a random walk possibly with a drift and the alternative hypothesis is that it is stationary. Results on level variables show that the null hypothesis could not be rejected and thus level variables were not stationary.

The DF-GLS test results (Table 1) on first difference indicated that all first difference variables were stationary as summarized in Table 2. The null hypothesis which examine whether variables contain unit root are rejected at the 5 percent significance. Hence, the variables were integrated of order one I (1).

The unit root test with structural break using the Clemente - Montanes - Reyes test of unit root with two structural breaks is also applied. The null hypothesis test is there is a unit root. Accordingly, if the t-statistic is smaller in absolute values than the 5% critical value then fail to reject the null hypothesis of unit root. The results are summarized in Table 3. Results show that all first difference variable series for GDP, borrowing by AGRIC, MQMANU, BUCORES, TRADE and financial intermediation measure of BCREDIT_BDEPOSIT did not have unit root at first differences. However, the variable series borrowing by TOHORES and TRCOMSTORE had unit root given their t-statistic being less than the 5% critical value. Thus results show that despite the structural break for the first difference of GDP, borrowing by AGRIC, MQMANU, BUCORES, TRADE and BCREDIT_BDEPOSIT, there was no unit root at first difference.

The unit root test with structural break for first difference variables indicated that the break points were significant except for the case of GDP and TOHORES which were not significant during the second break period as indicated by the p-values in Table 3. For each of the variables, the break period is shown in the brackets as follows; ∆lnGDP (1997;2004), ∆lnBC_AGRIC (1999;2007), ∆lnBC_MQMANU (1997; 2008), ∆lnBC_BUCORES (1998; 2012), ∆lnBC_TRADE (1997;1999), ∆lnBC_TOHORES (1998;2008), ∆lnBC_TRCOMSTORE (2000;2005) and the financial intermediation measure of ∆lnBC_BCREDIT_BDEPOSIT (1996; 2000).

Table 1. DF-GLS Test on Level Variables

Table 2. DF-GLS Test on First Difference

Table 3. Clement Montanes Reyes Unit Root Test with Structural Break

The structural break points in the initial break points are 1996, 1997, 1998 and 1999. On the other hand, the second break points are 1999, 2000, 2004, 2005, 2007 and 2008. These periods coincide with significant changes in the financial sector. For instance, the financial sector reforms of 1992 to 1994 were crucial towards enhancement of credit in the economy. These reforms contributed to the reviving of the economy which allowed increase in the provision of credit to the private sector. Hence the resultant effect of the financial sector reforms. Furthermore, the break points can potentially be linked to the implementation of various policies and strategies. For instance, break point observed in 2007 for Agriculture is significant which can partly be linked to the increased emphasis on the agricultural sector production and investment. This could potentially be attributed to strategies such as the Agriculture Sector Development Strategy (ASDS) that started in 2001. It has undergone through revisions after the implementation of the ASDP of 2006-2014 and the recent ASDS of 2015/16 to 2024/25. The development strategy aimed at transforming the sector into a commercial, highly productive, resilient and competitive sector. Manufacturing also had a significant break in 2008 for which can be linked to the developmental strategies under industrialization in Tanzania. For instance, the establishment of the Sustainable Industrial Development Policy 1996-2020 (SIDP) that was launched in the second half of the 1990’s. This establishment aimed at allowing the private sector to become the main player in the economy. The resultant effects were in tandem with growth of manufacturing activities between 1996 and 2010 and improvement in annual growth rate of manufacturing [43]. This could have also potentially increased borrowing by the sector.

Financial intermediation significant break is observed in 2000. This can be linked to privatization in Tanzania. The significant break observed under borrowing by building, construction and real estate in 2012. This can be potentially linked to the Tanzania Housing Finance project under Bank of Tanzania that was established in March 2010. This aimed at expansion of affordable housing supply through improving the capacity of banks to provide real estate development finance and promote the use of low cost building materials and technologies to make housing more affordable. This led to a growth in mortgage market with more lender offering mortgage products and also a decline in mortgage loan rates [44]. As a result, this could have potentially increased trends in lending to BUCORES. However, started to decline by 2011 and further declined into 2012 potentially due to high lending rates.Results show that borrowing by TRCOMSTORE did not have a significant break point.

When the clement-Montanes-Reyes results indicate significant structural break with existence of unit root, then results of the unit without structural break are likely to result to misspecification [39]. In that regard, the variable lnBC_TOHORES is excluded from the VAR and VECM analysis.

The Johansen co-integration test for each pair of credit by borrowing sector and economic growth was examined. Results showed that the maximum number of co-integrating equations was one as summarized in Table 4. For each of the bank credit by borrowing sector analyzed, the null hypothesis of no co-integration was strongly rejected and failed to reject the null hypothesis of at most one co-integrating equation. Therefore the hypothesis that there was one co-integrating equation was accepted in the bivariate model between economic growth and each of the variables of trade (lnBC_TRADE), Tourism, hospitality and Restaurant (lnBC_TOHORES) and transport, communication and storage (lnBC_TRCOMSTORE) and mining, quarrying and manufacturing (lnBC_MQMANU). Furthermore, the measure of financial intermediation BCREDIT_BDEPOSIT was also co-integrated. Variables which are co-integrated implicitly showed that there was a long run causal relationship between borrowing by TRADE, TOHORES, TRCOMSTORE and MQMANU with economic growth. Furthermore, there is a long run causal relationship between economic growth and financial intermediation.

Table 4. Johansen Co-integration

Table 5. Gregory Hansen Co-integration Test with structural break

The Gregory Hansen test was also used to test for structural break. The null hypothesis test no co-integration while the alternative hypothesis test for co-integration with structural break of unknown timing [41]. The null hypothesis is rejected if the value of Zt statistic is higher than the 5 percent critical value otherwise do not reject. The results in Table 5 show that taking into account the intercept shift and slope, results indicate that it was only lnBC_TRADE, lnBC_BUCORES, lnBC_MQMANU and BCREDIT_BDEPOSIT that had stable properties in the long run with structural break. On the other hand, results showed thatlnBC_TRCOMSTORE with economic growth exhibited stable properties in the long run with structural break when the case of intercept shift was considered. The break point observed with respect to the test were observed for 2013, 2008, 1997 and 1996 respectively for lnBC_TRCOMSTORE, lnBC_MQMANU, lnBC_BUCORES and lnBC_TRADE and lnBC_BCREDIT_BDEPOSIT.

4.2. Granger Causality and Vector Error Correction

The causality between economic growth and borrowing by sector is examined using VAR. The VAR granger causality results in Table 6 test the null hypothesis that the lagged value does not granger cause economic growth. The results show that for each of the borrowing sectors examined, bank credit by borrowing sector did not granger cause economic growth. Therefore bank credit to the sectors examined did not contribute to explaining economic growth. This result is also augmented by the overall financial intermediation measure where the lagged value of ratio of bank credit to bank deposits did not granger cause economic growth. This is contrary to [35] whereby bank credit granger caused output.

The results of this study indicate that the supply leading hypothesis was not supported in Tanzania. Therefore the role of credit in economic performance has not attained the intended goal in enhancing economic growth despite the increase in credit provided. This can be explained by the relative low level of financial sector activity in Tanzania.

On the other hand, results showed that only lagged values of economic growth granger caused credit to the transport, communication and storage sector (TRCOMSTORE). Thus growth of the economy contributed to predicting credit to transport, communication and storage sector. Thus growth of the economy has led to the expansion of financial services towards the activities in the transport, communication and storage sector. This result agrees with the demand following hypothesis. This is similar to the short-run results by [31] where short-run economic growth induced an increase of bank lending.

On the overall, economic growth did not granger cause bank credit to sectors of the economy. This result is similar to [28] where growth did not granger cause credit for some of the African countries. The results based on each bank credit category in relation to economic growth are supported by the overall financial intermediation measure where financial intermediation did not granger cause economic growth and neither did economic growth granger cause financial intermediation.

Table 6. VAR Granger Causality

Table 7. Vector Error Correction

Table 8. Vector Error Correction Results

The vector error correction results show that all borrowing by sector had no significant causality on GDP as summarized under Table 7. The null hypothesis tested whether the coefficients β and δ for equation (3) and (4) respectively of the lagged value of each borrowing by sector was equal to zero. The Chi (2) results show that the null hypothesis could not be rejected. Furthermore, the p- values of the bank credit by borrowing sector for each of equations were greater than 0.05 implying that the null hypothesis that there was no short run causality on economic growth could not be rejected. Therefore results show that in the short run, neither economic growth granger causes borrowing by sector nor borrowing by sector granger causes economic growth. This is also augmented by the results on financial intermediation which reflect no causality.

The vector error correction model reveals long run relationship. Results show that bank credit had a positive long run effect on economic growth as summarized in Table 8. The long run relationship between borrowing by sector and economic growth is significant. This is also observed with financial intermediation that had a positive long run effect on economic growth. This shows that in the long run development of financial intermediation along with enhancement of credit remain crucial for economic growth.

The error correction term (ECT) as depicted in Table 8 below show that in the short run, agriculture (lnBC_AGRIC) had a significant adjustment of 11.1 percent to equilibrium level. This meant that the speed of adjustment from short run disequilibrium was 11.1 percent for agriculture. However, all other borrowing by sector did not have significant adjustment from short run disequilibrium.

Tests of the presence of serial correlation for each of the models show that the null hypothesis of no serial correlation cannot be rejected as suggested by the value of the probability of the test statistic. Results are summarized for each of the models under Table 8 for lag order two.

5. Conclusions

The objective of this study was twofold; first to examine the causal relationship between economic growth and borrowing by sector and secondly to examine the extent to which financial intermediation affect economic growth in Tanzania. Results show that there is no causal relationship between bank credit and economic growth. This is observed for both borrowing by sector and also for financial intermediation on the overall in the short run. However, only economic growth granger caused bank credit to transport, communication, and storage sector. In the long run, bank credit has a positive significant effect on economic growth.The results thus did not support the supply leading hypothesis and the demand following hypothesis in the short run. However, in the long run results show that, bank credit has a positive and significant effect on the economic growth of Tanzania.

The results have implications for policy. The role of the financial sector is crucial for economic growth. However, there is still need to enhance the growth of both the financial markets and banking sector as they are key source of credit for the future performance of the economy. This can later enhance investment in various sectors of the economy. The positive long run effect of financial intermediation on economic growth imply that enhancing credit provision has great potential for the growth of the economy.Furthermore, this can be improved through enhancing deposit mobilization. Since deposits are core to bank funding, therefore increasing deposit mobilization is crucial by reducing financial exclusion from financial services access and usage.Thus in support of efforts towards moving the economy to a middle income economy, policy makers need to facilitate establishment of policies to enhance access to credit from the financial system that will support efforts towards industrialization and thus economic growth by enhancing access to credit.

Future studies could examine the effect of bank credit on the productivity of different sectors such as manufacturing to gain further understanding on how credit affects the productivity of various sectors of the economy.

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