Financial sector and manufacturing sector performance: evidence from Nigeria

  • Received February 20, 2018;
    Accepted May 24, 2018;
    Published July 6, 2018
  • Author(s)
    Abiola John Asaleye
    ORCID ,
    Joseph Ibrahim Adama
    ORCID , Joseph Olufemi Ogunjobi
  • DOI
    http://dx.doi.org/10.21511/imfi.15(3).2018.03
  • Article Info
    Volume 15 2018, Issue #3, pp. 35-48
  • TO CITE АНОТАЦІЯ
  • Cited by
    21 articles
  • 2309 Views
  • 327 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Nigerian economy depends on oil as the major source of revenue, failure to diversify the revenue base has raised questions about its sustainability and implication on the economy. This study uses market capitalization, broad money stock, credit to private sector, prime interest rate and deposit liability as proxies for the financial sector, while output in the manufacturing sector and manufacturing employment are used as proxies for manufacturing performance. The study examines the causal effects, shock effect and long-run impact using Granger Non-Causality, Vector Error Correction Model, and Dynamic Ordinary Least Square method, respectively. The results showed unidirectional causality, confirming the hypothesis of the ‘supply-leading view’ and ‘demand-following view’ except for market capitalization and output in the manufacturing sector, where independence was observed. The variance decomposition shows that the forecast error shock of credit to private sector and prime interest rate show more variations in manufacturing sector performance than other financial indicators. The long-run result using output in manufacturing sector as dependent variable shows a positive significant relationship with other financial sector indicators, except for broad money stock and deposit liability. This study recommended credit channel for transmission of monetary policy using interest rate to improve the performance of manufacturing sector, among others.

view full abstract hide full abstract
    • Table 1. Long-run relationship using MGDP as dependent variable
    • Table 2. Long run-relationship using MEMP as dependent variable
    • Table 3. Causality result
    • Table 4. Variance decomposition of FCP
    • Table 5. Variance decomposition of FMI
    • Table A1. Summary of the ADF unit root test of the series
    • Table A2. Summary of PP unit root test of the series
    • Table A3. Variance decomposition of FDL
    • Table A4. Variance decomposition of MCP
    • Table A5. Variance decomposition of FPI
    • Acemoglu, D., Philippe, A., & Fabrizio, Z. (2006). Distance to Frontier, Selection, and Economic Growth. Journal of The European Economic Association, 4(1), 37-74.
    • Ali, M., & Hassan, A. F. M. (2008). Dynamics of Financial Development in Co-integrated Error Correction Mechanism (ECM): Evidence from Bangladesh. Studies in Business and Economics, 14(2), 5-24.
    • Allen, F., Carletti, E., Cull, R., Qian, Q. J., Senbet, L. W., & Valenzuela, P. (2016). Resolving the African financial development gap: Cross-country comparisons and a within-country study of Kenya, In S. Edwards, S. Johnson, D. Weil (Eds.). African Successes: Modernization and Development. University of Chicago Press.
    • Ang, J., & Mckibbin, W. (2007). Financial Liberalization, Financial Sector Development and Growth: Evidence from Malaysia. Journal of Development Economics, 84(1), 215-233.
    • Apergis, N., Filippidis, I., & Economidou, C. (2007). Financial Deepening and Economic Growth Linkages: A Panel Data Analysis. Review of World Economics, 143(1), 179-198.
    • 6.Arizala, F., Cavallo, E., & Galindo, A. (2009). Financial development and TFP growth: Cross-country and industry-level evidence (Working paper, Inter-American Development Bank, Research Department, No. 682).
    • Asaleye, A. J., Okodua, H., Oloni, E. F., & Ogunjobi, J. O. (2017). Trade Openness and Employment: Evidence from Nigeria. Journal of Applied Economic Sciences, XII, 4(50), 1194-1209.
    • Asaleye, A. J., Olurinola, I., Oloni, E. F, & Ogunjobi, O. (2017). Productivity growth, wages and employment nexus: Evidence from Nigeria. Journal of Applied Economic Sciences, XII, 5(51), 1362-1376.
    • Bencivenga, V. R., & Bruce, D. S. (1991). Financial Intermediation and Endogenous Growth. Review of Economic Studies, 58, 195-209.
    • Campbell, O., & Asaleye, A. J. (2016). Financial Sector Reforms and Output Growth in Manufacturing: Empirical Evidence from Nigeria. American International Journal of Contemporary Research, 6(3), 112-125.
    • CBN (2017). Central Bank of Nigeria Statistical Bulletin, 2016.
    • Desbordes, R., & Wei, S. (2017). The Effect of Financial Development on Foreign Direct Investment. Journal of Development Economics, 127, 153-168.
    • Diamond, D. W., & Dybvig, H. P. (1983). Banking Runs, Deposit Insurance, and Liquidity. The Journal of Political Economy, 91, 401-419.
    • Ductor, L., & Grechvna, D. (2015). Financial development, real sector and economic growth. International Review of Economics and Finance, 37, 393-405.
    • Economic Recovery and Growth Plan EGRP (2017). Economic Recovery and Growth Plan 2017– 2020, Federal Republic and Growth Plan 2017–2020. Federal Republic of Nigeria, Ministry of Budget and National Planning.
    • Eichengreen, B., Gullapalli, R., & Panizza, U. (2009). Capital Account Liberalization, Financial Development and Industry Growth: A Synthetic View (Mimeo, University of California At Berkeley, Working Paper (No. 144)).
    • Engle, R. F., & Granger, C. W. J. (1987). Cointegration and Error Correction: Representation, Estimation and Testing. Econometric, 55, 251-276.
    • Fashina, O. A., Asaleye, A. J., Ogunjobi, J. O., & Adedoyin, L. I. (2018). Foreign Aid, Human Capital and Economic Growth Nexus: Evidence from Nigeria. Journal of International Studies, 11(2), 104-117.
    • Ghali, K. H. (1999). Government Size and Economic Growth: Evidence from a Multivariate Cointegration Analysis. Applied Economics, 31(1), 975-987.
    • Graff, M. (1999). Financial Development and Economic Growth – A new empirical analysis (Dresden Discussion Papers in Economics (No. 5/99)).
    • Green, C. J. (2013). Financial Reform and Financial Development in Africa (Commissioned Proposal prepared for AERC).
    • Greenwood, J., & Jovanovic, B. (1990). Financial Development Growth and the Distribution of Income. Journal of Political Economy, 98(5), 1076-1107.
    • Grossman, S. J., & Stiglitz, J. (1980). On the Impossibility of Informationally E-Client Markets. American Economic Review, 70, 393-408.
    • Hill, E., & Perez-Reyna, D. (2017). Financial Development and Occupational Choice. Journal of Macroeconomics, 54(PB), 393-409.
    • Huang, H., & Yeh, C. (2017). Level, Structure and Volatility of Financial Development and Inflation Targeting. Journal of Empirical Finance, 44, 108-124.
    • Hung, F. (2003). Financial Development and Economic Growth. International Review of Economics and Finance, 12, 45-67.
    • Ibrahim, M., & Alagidede, P. (2017). Financial Sector Development, Economic Volatility and Shocks in sub-Saharan Africa. Physica A, 484, 66-81.
    • Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.
    • Levine, R. (1997). Financial Development and Economic Growth: Views and Agenda. Journal of Economic Literature, 35(2), 688-726.
    • Levine, R. (2005). Finance and Growth: Theory and Evidence. In P. Aghion & S. Durlauf (Eds.), Handbook of Economic Growth. Amsterdam: North-Holland Elsevier Publishers 1, Part 1 (pp. 865-934).
    • Lucas, R. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22, 3-42.
    • Lutkepohl, H., & Saikkonen, P. (2000). Maximum Eigenvalue versus Trace Tests for Cointegrating Rank of a VAR Process. University of Helsinks and Humbildt.
    • Manganelli, S., & Popov, A. (2015). Financial Development Sectoral Reallocation and Volatility: International Evidence. Journal of International Economics, 95, 323-337.
    • Mishkin, F. S. (1995). Symposium of the Monetary Transmission Mechanism. The Journal of Economic Perspectives, 9(4), 3-10.
    • National Bureau of Statistics, NBS (2017). Unemployment/Under-employment report 2016.
    • Ogun, T. P., & Akinlo, A. E. (2011). Financial Sector Reforms and the Performance of the Nigerian Economy. The Review of Finance and Banking, 3(1), 47-60.
    • Okun, A. M. (1962). Potential GNP: Its Measurement and Significance. In Proceedings of the Business and Economics Statistics Section, American Statistical Association. Washington D.C.: American Statistical Association (pp. 98-103).
    • Park, C., & Mercado, Jr., R. V. (2015). Financial Inclusion, Poverty, and Income Inequality in Developing Asia, In ADB Economics Working Paper Series (No. 426).
    • Park, J. Y. (1992). Canonical Cointegrating Regressions. Econometrica, 60, 119-144.
    • Phillips, P. C. B., & Hansen, B. E. (1990). Statistical Inference in Instrumental Variables Regression with 1(1) Processes. Review of Economic Studies, 57, 99-125.
    • Phillips, P. C. B., & Loretan, M. (1991). Estimating Long-Run Economic Equilibria. The Review of Economic Studies, 58, 407-436.
    • Rodrik, D. (2003). ‘Growth Strategies’ NBER Working paper series (No. wp10050).
    • Saikkonen, P. (1991). Asymptotically Efficient Estimation of Cointegrating Regressions. Econometric Theory, 7, 1-21.
    • Senbet, L. W., & Otchere, I. (2010). African Stock Markets in African Finance in the 21st Century, IMF volume Palgrav; Eds. Marc Quintyn and Genevieve Verdier.
    • Shahbaz, M, Hoang, T. H. V, Mahahk, M. K., & Rouband, D. (2017). Energy consumption, financial development and economic growth in India: New evidence from the nonlinear and asymmetric analysis. Energy Economics, 63, 1999-2012.
    • Shaikh, A. A., Glavee-Geo, R., & Karjaluoto (2017). Exploring the Nexus between financial sector reforms and the Emergence of Digital Banking Culture: Evidence from a Developing Country. Research in International Business and Finance, 42, 1030-1039.
    • Singh, A., Stone S., & Suda, J. (2015). Monetary Policy and Financial Sector. Economic Letters, 132, 82-86.
    • Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-321.
    • Stock, J., & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated System. Econometrica, 61(4), 783-820.
    • Szirmal, A. (2015). Socio-Economic Development, Second Education Cambridge University Press, Cambridge.
    • Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector Autoregression with possibly integrated process. Journal of Econometrics, 66, 225-250.
Related Articles

  • Selection of the right proxy market portfolio for CAPM

    Rashmi ChaudharyORCID , Priti BakhshiORCID doi: http://dx.doi.org/10.21511/imfi.18(3).2021.02
    Investment Management and Financial Innovations Volume 18, 2021         Issue #3         pp. 16-26 Views: 2590 Downloads: 1502 TO CITE АНОТАЦІЯ

    The purpose of the paper is to select the right market proxy for calculating the expected return, since critically evaluating proxies or selecting the correct proxy market portfolio is essential for portfolio management because the change in the market portfolio proxy affects returns. In this study, monthly data of equity indices are evaluated to find out the better market proxy. The indices taken are BSE 30 (Sensex), Nifty 50, BSE 100, BSE 200, and BSE 500. The macroeconomic variables used in the study are industrial production index (IIP), consumer price index (CPI), money supply (M1), and exchange rate in India. To avoid the influence of COVID-19, the research period was from January 2013 to December 2019 to critically evaluate these proxies in order to find the most appropriate market proxy. This paper reveals a noteworthy relationship between stock market returns and macroeconomic factors, while suggesting that the BSE 500 is a better choice for all equity indices, as the index also shows a significant relationship with all macroeconomic variables. BSE500 is a composite index comprising all sectors with low, mid and large cap securities, therefore it reflects the impact of macroeconomic factors most efficiently, taking it as a market proxy. This study was carried out in the context of India and can be replicated for other countries.

  • The impact of strategic planning on enhancing the strategic performance of banks: evidence from Bahrain

    Abdul Aziz A. Abdul Rahman doi: http://dx.doi.org/10.21511/bbs.14(2).2019.12
    Banks and Bank Systems Volume 14, 2019         Issue #2         pp. 140-151 Views: 1597 Downloads: 985 TO CITE АНОТАЦІЯ

    This study aims to test the impact of strategic planning on enhancing the strategic performance of Bahraini banks. The study has two variables. The strategic planning as an independent variable includes a group of four perspectives, namely environmental scanning, strategy formulation, strategy implementation, and strategy evaluation. The strategic performance as a dependent variable also includes a group of four perspectives: financial, customer, internal business process, and learning and growth. The sample of the study consists of the employees of 10 of all the 12 commercial banks in Bahrain. A questionnaire was developed to gather the data. Five questionnaires were distributed to each bank totaling 50 questionnaires. The results showed that the strategic planning has a good statistically significant impact on financial, customer, and learning and growth. The results also showed a medium statistically significant impact of strategic planning on internal business process. The research recommended focusing on all aspects of the strategic performance in the banks under study, specifically the internal business process perspective.

  • The impact of cash flow statement components on stock volatility: Evidence from Qatar

    Mo’taz Kamel Al ZobiORCID , Othman Hel Al-DhaimeshORCID doi: http://dx.doi.org/10.21511/imfi.18(2).2021.29
    Investment Management and Financial Innovations Volume 18, 2021         Issue #2         pp. 365-373 Views: 1486 Downloads: 1146 TO CITE АНОТАЦІЯ

    The published financial statements are considered one of the most important sources of information that investors rely on in forecasting stock performance or even judging the organization’s ability to cover short-run liabilities. Cash flows play a core role in maintaining a high market value for its shares. Hence, this study came to analyze the explanatory value of the cash flow statement in explaining stock volatility (SV) in the Qatar financial market. Study data were collected using published financial statements from a sample of 44 Qatari-listed companies throughout 2013–2019. A panel cross-sectional data technique using the E-views program was used to analyze the data. The study results show there is a positive and significant impact of cash flows from operating CFO activities on SV, indicating that the higher change in CFO increases stock volatility. This means that operating cash flows give significant information to investors, and it is reflected in the stock price movements directly. Also, the cash flow from CFF financing activities has a positive and significant effect on SV. This means that CFF affects stock prices, causing greater changes and fluctuation in stock returns. This is because one of the major components of CFF is dividends, which affect directly stock prices and stock returns. In contrast, there is an insignificant effect of CFI on SV, which may indicate that investors do not build their investment decisions based on CFI. Accordingly, the cash flow from investing activities failed to explain the stock volatility of the listed Qatari companies.

Download Preview
Close
Investment Management and Financial Innovations
Investment Management and Financial Innovations