1. Thorsten Leo Beck (World Bank) M. Habibur Rahman (Bangladesh Bank)
Financial Development—Economic Growth Nexus: A Case Study of Bangladesh By
Thorsten Leo Beck (World Bank)
and
M. Habibur Rahman (Bangladesh Bank)
***Preliminary Draft***
***Comments and Suggestions are Welcome***
Finance-Growth
6-Dec-18

2. Plan of the Presentation
Two parts
One: Existing literature on Finance-Growth debate followed by a sophisticated econometric analysis to establish the view that financial development is an important factor for economic growth in Bangladesh
Two: Analysis of figures and facts identifying the causative factors behind financial development in Bangladesh
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3. Motivation (Finance-Growth Debate)
Why some countries are developed and some are not is a ‘MYSTERIOUS’ question for development economists
Better infrastructure, institutions, technology, more capital could be the possible answer
But again one can pose another question: why they are better?
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4. Motivation……..cont.……
The Role of Financial Intermediation that facilitate most of the ingredients for economic growth seems to be very important factors in newly emerging economies
The intention, therefore, is to investigate the role of ‘Financial Development’ on capital formation and economic growth in light of Bangladesh economy
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5. From Market Frictions to Economic Growth: A Theoretical Approach to Finance and Growth
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6. Finance-Growth
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7. Literature Finance to Growth
Goldsmith’s (1969) paper on 35 countries is the first empirical study that investigates finance-growth link
King-Levine (1993a), Levine (1997 & 1999), Levine-Zervos (1998), Rajan-Zingales (1998), Beck-Levine-Loayza (2000)
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8. Literature Finance to Growth
Theoretical papers, such as Bencivenga-Smith (1991), Diamond (1984), and Williamson (1996 & 1998) explain various channels through which financial development could contribute positively to economic growth
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9. Literature Finance to Growth
Studies based on time series technique, such as Demetriades-Hussein (1996), Hansson-Jonung (1997), Luintel-Khan (1999), and Shan et al. (2001) are dominated with the evidence of bi-directional causality.
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10. Literature Economic growth to financial development
Other studies, such as Deveraux-Smith (1994), Jappelli-Pagano (1994), Singh (1997), Arestis-Demetriades (1997) and Singh-Weisse (1998) including Robinson (1952) argue that financial development may not always promote economic growth.
They show that depending on the stage of development economic growth may promote financial development. To the contrary of the previous literature they argue that economic development generates additional demand for financial services and hence establishes a more developed financial sector. According to their view economic growth leads and financial development follows.
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11. Literature Finance-Growth Joint Evaluation
Some other papers, however, including Gurley-Shaw (1955), Greenwood-Jovanovic (1990), Galetovic (1996), Geenwood-Smith (1997), and Bencivenga-Smith (1998) observe inextricable link between financial development and economic growth. They experience both way causality between financial development and economic growth. They predict joint evolution of the real and financial sectors during the growth process. They argue that at the initial stage of economic development ‘finance follows economy’. After a certain threshold level when financial intermediaries emerge, economy starts to get benefit from the financial sectors.
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12. Objective
The main objective of this study is to investigate the causal relationship between financial development and economic growth in Bangladesh, particularly the long-run impact of financial development on capital formation and per capita income.
A system of equations based on the hypothesis that financial development has long-run impact on investment and per capita income is specified and estimated using Blanchard-Quah’s (1989) technique of structural vector autoregressions (SVARs).
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13. Objective
To examine the short-run dynamics among the variables in the system, however, the impulse response functions (IRFs) and variance decomposition (VDCs) are computed based on Cholesky factorization where the standard errors for VDCs are computed through 1000 Monte Carlo simulations.
To substantiate the causal link among the various indicators of financial development, investment and income per capita a graphical presentation has also been used.
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14. An Overview of Financial Development in Bangladesh
As “financial development” lacks any precise definitions, following the practice of existing literature [King-Levine (1993a and 1993b), Levine (1997 and 1999), and Levine-Zervos (1998)] some indicators of financial development may be used for effective policy formulation, implementation and evaluation.
Accordingly, three alternative indicators of financial development, such as domestic credit to the private sector by banks to GDP ratio, total deposits to GDP ratio and broad money (M2) to GDP ratio for Bangladesh economy have been used.
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15. An Overview of Financial Development
Domestic credit to the private sector as a percent of GDP (denoted by cr_y) is one of the popular indicators of financial development.
The second indicator of financial development is total deposits (demand plus time) as a percent of GDP (denoted by dep_y) which is relatively broader measure of financial development as it includes all the liquid liabilities of the financial system excluding currency.
A third indicator, broad money as a percent of GDP (denoted by m2_y) is basically the liquid liabilities of the financial system in Bangladesh that includes currency plus demand and interest-bearing liabilities of financial intermediaries.
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16. An Overview of Financial Development
Period lr
cr_y
dep_y
m2_y
i_y
y_pcap
11.09
6.59
14.86
19.03
10.44
160.0
13.68
13.67
20.23
24.54
10.51
192.0
14.71
19.08
24.75
28.67
13.87
242.0
13.90
16.58
23.07
26.68
17.93
283.0
13.83
23.17
26.7
31.01
21.51
353.0
12.33
28.83
35.08
40.02
22.63
395.0
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17. An Overview of Financial Development
It has been observed from the Table that the average credit, deposit and broad money to GDP ratios increased substantially respectively from 6.6 percent, 14.9 percent and 19.0 percent in to respectively 28.8 percent percent and 40.0 percent in
Investment as a percent of GDP and per capita income (in current USD) also display a similar pattern and move broadly together reflecting a close association among financial development, investment and per capita income during the period
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18. An Overview of Financial Development
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19. An Overview of Financial Development
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20. An Overview of Financial Development
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21. An Overview of Financial Development
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22. An Overview of Financial Development
The scatter-plots of the three indicators of financial development vis-à-vis investment as well as per capita income strongly supports the co-movement of financial development and economic activity.
Besides, almost a linear relationship is also observed in a scatter-plots between investment-GDP ratio and per capita income.
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23. Methodology
Structural macroeconometric models, such as the Klein interwar model, the Brooking model, the BEA model, the St. Louis model and the Taylor model that are based on hundreds of equations are replaced by the vector autoregressions (VARs). The problem of identification and endogeneity are associated with these structural macroeconometric models which can easily be overcome by the VARs approach (Sims’s 1980)
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24. Methodology
Because it does not impose any a priori restrictions and is based on reduced form equations, it is difficult to reconcile VARs with economic theory and to provide any meaningful interpretations of the estimated parameters
In order to overcome the above difficulties with the standard unrestricted VARs some studies, such as Bernanke (1986), Blanchard-Watson (1986) and Sims (1986) come up with a structural VARs (SVARs) model that allows contemporaneous structural restrictions
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25. Methodology
As the objective of this paper is to investigate long-run relationship between financial development and economic growth in Bangladesh, a Blanchard-Quah (1989) type of long-run structural model is estimated
To examine the short-run dynamics among the variables in the system, however, the impulse response functions (IRFs) and variance decomposition (VDCs) are computed based on Cholesky factorization
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26. Methodology
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27. Methodology
The restrictions stated in previous slide have some interesting implications regarding financial development-economic growth relationship
it asserts financial development has long-run effect on investment and per capita income
Income per capita, on the other hand, has no long-run effect on financial development.
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28. Preliminary data analysis
Variables (in natural log)
without trend
with trend
Decision DF PP
KPSS
Rate
Lending rate (lr) φ
Lending rate at 1st difference (dlr) φ
I(1)
I(0)
Financial development
Domestic credit to the private
sector as a percent of GDP (cr_y)
Total deposit as a percent of GDP (dep_y)
Broad money as a percent of GDP (m2_y)
Investment
Per capita gross fixed capital
formation as a percent of GDP (i_y)
Income
Per capita GDP at current USD (y_pcap)
Notes:
φ = without log, I(1) = unit-root and I(0) = stationary.
Lag length for ADF tests are decided based on Akaike’s information criterion (AIC).
Maximum Bandwidth for PP and KPSS test are decided based on Newey-West (1994).
All the tests are performed on the basis of 5% significance level.
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29. Empirical Results
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30. Empirical Results
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31. Variance Decompositions of Financial Development Financial Development
Empirical Results
Variance Decompositions of Financial Development
Time Horizon
(Year)
Explained by shocks in
Lending Rate
Financial Development
Investment
Income per Capita 4 8 12 16 20
27.61
(-16.84)
52.89**
(-17.81)
63.72**
(-18.80)
70.36**
(-19.54)
70.72**
(-19.77)
40.57**
21.60
(-16.48)
18.84
(-17.16)
12.72
(-17.10)
14.63
(-17.72)
31.30
(-17.74)
20.17
(-16.43)
12.40
(-16.36)
11.15
(-16.54)
8.64
(-16.90)
0.52
(-3.75)
5.33
-(6.28)
5.04
(-6.15)
5.77
(-6.26)
6.01
(-5.92)
Finance-Growth
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32. 2. Variance Decompositions of Investment
Empirical Results
2. Variance Decompositions of Investment 4 8 12 16 20
31.86
(-16.95)
43.29**
(-17.84)
50.09**
(-18.92)
62.32**
(-19.59)
57.43**
(-19.89)
2.02
(-14.64)
5.95
(-16.17)
14.15
(-16.75)
15.56
(-17.01)
28.07
61.57**
(-19.07)
45.68**
(-18.21)
31.19
(-17.60)
17.73
(-17.41)
10.56
(-17.38)
4.54
(-5.01)
5.08
(-5.91)
4.57
(-5.50)
4.40
(-5.71)
3.94
(-5.73)
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33. 3. Variance Decompositions of Income per Capita
Empirical Results
3. Variance Decompositions of Income per Capita 4 8 12 16 20
13.92
(-16.54)
41.35
(-18.80)
66.24**
(-19.64)
70.69**
(-19.67)
65.60**
(-20.14)
38.56**
(-17.96)
28.39
(-17.04)
11.94
(-17.24)
14.13
(-17.32)
20.35
(-17.57)
33.56**
(-16.22
19.56
(-16.14
15.14
(-16.46
8.94
(-16.86
8.34
(-17.00
13.96**
(-6.90)
10.70
(-6.53)
6.68
(-5.94)
6.24
(-5.99)
5.71
(-5.92)
Finance-Growth
6-Dec-18

34. Summary and Conclusion
The graphical presentation as well as estimated coefficients of the long-run response matrix indicates that various indicators of financial development and investment have long-run impact on per capita income
The estimated results also support the argument that in the long-run financial development stimulates investment activities. The estimated coefficients of the long-run response matrix, however, do not provide any statistical evidence that the lending rate has any impact on financial development, investment or on per capita income
Finance-Growth
6-Dec-18

35. Summary and Conclusion
Regarding the short-run dynamics among the variables in the system, the results from IRFs indicate that both the financial development and investment have short-run impact on per capita income at the immediate year of initial shocks
The results from VDCs, on other hand, imply that all the variables in the system, such as lending rate, indicator of financial development and investment contain very useful information in predicting the future path of per capita income
Finance-Growth
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36. Thank You
Finance-Growth
6-Dec-18