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Monetary Policy Transmission and Industrial Sector Growth: Empirical Evidence From Nigeria:

Monetary Policy Transmission and Industrial Sector Growth: Empirical Evidence From Nigeria: The goal of this study is to assess the industry effects of monetary policy transmission channels in Nigeria within the period 1981-2014. Techniques of analysis employed in the study are the Johansen cointegration and the error correction model (ECM). Our regression estimates reveal that the private sector credit, interest rate, and exchange rate channels have negative effects on real output growth, both in the long run and in the short run. The results further show that, relatively, the degrees of the established effects are higher in the long run than in the short run. We employed the Johansen cointegration approach to determine the nature of relationship that exists between our dependent variable and the independent variables. The results show that, in the Nigerian case, monetary policy transmission channels jointly have a long-run relationship with real output growth of the industrial sector, and disequilibrium in the system is corrected at the speed of 72.2% annually. Keywords industrial output, monetary policy, credit channel, interest rate channel, Johansen cointegration, error correction model could at same time escalate. Regarding achieving price sta- Introduction bility, Bernanke, Laubach, Mishkin, and Posen (1999) Monetary policy action is the prerogative of the central bank emphasize that an inflation targeting country may not wring and monetary authorities. It involves the process of control- inflation out of their economies without laying itself open to ling the cost, value, and availability of money and credit in costs in lost output and rising unemployment. Moreover, other to achieve the desired level of prices, employment, out- monetary growth target might lead to achieving price stabil- put, and other economic objectives. Monetary policy can be ity but might not result in the attainment ofaccelerated used to influence economic activities and achieve economic growth. It is a herculean task finding a balance or trade-off objectives of a country. The stance of monetary policy, how- between set goals (Khan & Jacobson, 1989). Epstein (2007) ever, can be either expansionary or contractionary. asserts that limiting monetary policy exclusively to price sta- Expansionary monetary policy stance is a situation whereby bilization cannot guarantee improved economic growth the monetary authority increases the supply of money in the because low inflation does not necessarily give rise to high economy with the aim of reducing the cost of money (inter- and steady economic growth. est rate) and stimulating economic activities. However, a Over and above, the primary goal of monetary policy is to contractionary policy entails the reduction in money supply ensure that money supply is at a level that is consistent with which potentially increases the cost of money and slows the the desired growth rate. pace of economic activities. Although no economy is self-sufficient, most economies The choice of any stance to be adopted by the central bank of the world strive for self-reliance. The successful ones are depends on the state of the economy at any time and the pol- often those that have a deepened economy with soaring icy target of the government. But, finding a trade-off between the attainment of price stability and growth is often difficult. University of Nigeria, Nsukka, Nigeria By setting annual monetary growth targets, the central bank 2 Enugu State University of Science and Technology, Nigeria intends to assert its commitment to price stability or inflation Corresponding Author: control. However, in reality, money growth might be consis- Hillary Chijindu Ezeaku, University of Nigeria, 27 Ichida Street, Federal tent with economic growth but not with the stability of prices; Housing Estate, Trans-Ekulu, Enugu, Nigeria, Nsukka 400001, Nigeria. in other words, although the economy is stimulated, inflation Email: gijindu@gmail.com Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open economies of scale, which can take an economy steps farther Monetary Policy Transition Mechanism from economic growth to economic development. Good Interest rate channel (INT) and credit channel (CRDT) are infrastructural development combined with favorable busi- considered in some literature as the key propagation and ness and political environment supports the creation of value strengthening mechanisms of monetary policy changes. Both and increase in the production of goods and services. And, types of transmission channels hold the prediction that any certainly, the monetary authorities are always there to har- variation in bank lending is dependent on monetary policy ness the overall mechanism by formulating policies that will actions. In other words, a change in bank lending is predicted promote growth and stability in the economy. Although eco- to be in response to change in monetary policy stance. nomic theory and quite a few empirical analyses support the Because monetary policy hinges chiefly on the supply of fact that monetary policy influences output, the efficient con- money, it will be remiss and abnormal to ignore the role of duct of monetary policy is crucial (Ćorić, Perović, & Šimić, banks, especially in the money creation process. Hence, the 2012). CRDT perspective portends that monetary policy induces Nigeria is a mono-cultural economy, depending on oil movements in bank lending vis-à-vis changes in bank loan as the mainstay of the economy and foreign exchange supply, whereas shifts in the demand for a bank loan is earner. Most studies that evaluated monetary policy effects explained by the INT (Arnold, Kool, & Raabe, 2006). The usually equate policy measures against the overall econ- Nigerian industrial sector faces insurmountable challenges omy. Formulating future policies based on aggregated per- ranging from infrastructural woes to highly unstable busi- spective on the economy might not be entirely misguided ness environment. Also, the cyclical nature of industrial out- but would ultimately hide vital details that could have put equally intensifies the need for external financing. given a more stable and target-oriented policy designs. Bridging the funding gap depends mainly on both availabil- Therefore, in a country that chimes mantra for economic ity and cost of fund, which is largely determined by money diversification, it is necessary to disaggregate the economy supply through monetary policy action. and assess the effect of key monetary policy channels on Writing on monetary policy transmission mechanism, the “preferred” industrial sector. Most of the existing stud- Friedman and Schwartz (1963) argue that when the central ies have examined transmission effect vis-à-vis the aggre- bank pursues an expansive open market operation, money gate economy hence creating a gap in the frontiers of stock will increase thereby leaving the deposit money banks knowledge regarding the sectoral effects of monetary pol- with fat reserves and enhance their ability to create credit and icy transmission. There is, however, dearth of literature to extend loans and advances, which will increase the money explain the real output response, from a disaggregated supply. Besides the sale and repurchase of financial instru- point of view to monetary policy transmission in Nigeria. ments like treasury bills to regulate the quantity of money in To this end, the need to fill such knowledge gaps informed circulation, the central bank may also decide to use other the choice for this study which examines the linkage monetary policy instruments such as rediscount rate or the between monetary policy transmission and industrial sec- reserve requirements (liquidity and cash ratio) to achieve the tor growth in Nigeria. desired economic objectives of output growth, stable price level, and full employment. The industrial sector and other Review of Related Literature activity sectors stand to benefit from expansionary policy According to Toby and Peterside (2014), a monetary pol- measures (for instance, increase in money supply and reduc- icy shift tends to transmit a change for the future in the tion of rediscount rate). Although this will promote produc- projected behavior of macroeconomic variables. The tradi- tion through cheaper cost of fund (interest rates), it could tional economic analysis considers the response of mone- turn quite inimical to achieving price stability. On the con- tary policy makers as exogenous. As explained by this trary, a stringent policy, using any appropriate instrument, system, money is unbiased in its effects on the economy. can help to attain a stable price level but could lead to a Thus, in the classical theory, transmission mechanism recession. reacts directly and indirectly. The direct mechanism is Economists established the general relationship between based on the demand for and supply for money, whereas real output and monetary policy transmissions. From the the indirect mechanism has linkage with the banking sys- Keynesian point of view, an unrestricted change in money tem and operates through money and interest rate. The stock influences real output by bringing down the interest Keynesian theory explains that a change in money supply rate, which by efficient utilization of capital will stimulate has effects on total expenditure and output level through investment and the real output growth (Athukorala, 1998). the changes in interest rate. Hence, the system operates Some macroeconomists, however, have a different opinion. indirectly. The monetarists affirm that although monetary They promoted the theory of financial liberalization and expansions affect output and employment in the short argued that if the market forces prompt interest rate to rise, term, interest rate and prices are influenced in the long run savings would be channeled to the productive investments (Chaudhry, Qamber, & Farooq, 2012). thus stimulating growth in real output (Mehdi & Reza, 2011). Ezeaku et al. 3 Figure 1. Trend analysis of money supply growth and industrial sector growth relationships in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. M2GR = broad money growth; INDGR = industrial sector growth. In the overall process, the banking system stands as the key rate was a paltry 0.44%, which could be attributed to a conduit of monetary policy transmission, which draws atten- decline in money growth from 29.7% in 2003 to 9.2% in tion to the CRDT effects of monetary policy in Nigeria. In 2004. The industrial sector had a relative positive response to light of any expansionary policy, therefore, it is expected that money growth over the period under study except in 2001, it will have indirect positive effects on industrial activities as where an increase in money supply from 39.7% in 2000 to the cost of borrowing (to finance production or expansion) 44.5% in 2001 resulted in −19.0% decline in the industrial will fall. Ceteris paribus, the theoretical expectation is that sector growth in 2001. the low cost of fund prompts the makings for an increase in We observe from the Figure 2 that industrial sector contri- national income via the stimulation in economic activities. butions to the Nigerian economy was consistently significant and trended with relative stability in the 1980s until it dipped by 10.5% in 1990, from 48.7% in 1989 to 38.2% in 1990. Trend Analysis Since rising to an all-time high of 54.9% in 1991, the trend A cursory look at the money supply growth and the industrial fluctuated visibly but averaged 40.3% between 2004 and sector growth in Nigeria from 1981 to 2014 reveals undulat- 2014. ing movements over the years, where on average, the money To avoid a general assumption based on the performance supply growth and the industrial growth rate were 48.08% of the industrial sector, we have disaggregated this sector and 26.67%, respectively. Because the industrial sector is into various subsectors that constitute the industrial sector. made up of some subsectors, we shall use a few charts to The sum of the contributions of each of these sectors in any analyze the trends of the money supply, and the characteris- given year equals the input of the industrial sector for that tics of the industrial sector as well as the various subsectors particular year. Therefore, breaking it down further will help it comprises. us understand what the exact contribution of each subsector Figure 1 reveals unstable trends and indicates that money to the gross domestic product (GDP) is. These are explained supply, descriptively, does have a significant influence on the in Figures 3 to 5. pace of industrial advancement. Substantial decreases in Figure 3 reveals that the manufacturing subsector fared money stock have at some years found to have an adverse well in the 1980s and was the highest contributor to indus- impact on the growth of the industrial sector, whereas at trial sector performance during this period. It is worthy to other points increases in the money supply do not produce a emphasize that the subsector recorded 33.6%, 36.4%, 33.0%, corresponding commensurate increase in the growth of the and 35.1% in 1981, 1982, 1983, 1984, and 1985, respec- industrial sector. From the chart above, it can be observed, tively. However, as the economy began to depend on crude for instance, that money supply decreased from 10.9% in oil and natural gas, activity in the manufacturing sector 1985 to 6.8% in 1986 resulting in a proportionate decline in dwindled, and Nigerian economy changed from an export- industrial growth from 22.1% in 1985 to −4.9%. Money based economy to an import-dependent economy. As a result, growth fell from19.7% in 1989 to 15.2% in 1990 equally the impact of the manufacturing subsector on the economy lead to negative growth in 1990 from 47.5% in 1989 to fell consistently, without meaningful recovery, from 12.4% −32.4% in 1990. Moreover, such other years (1997, 1998, in 1990 to 1.9% in 2014. 2001, 2004, 2009, and 2013) that recorded significant cuts in Figure 4 demonstrates similar trend over the years as broad money supply all had corresponding negative growths Figure 3. However, in absolute terms, as can be observed from of the industrial sector except for 2004. In 2004, the growth the scaling, the solid mineral subsector contributions to GDP 4 SAGE Open Figure 2. Trend analysis of the industrial sector contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 3. Trend analysis of the manufacturing subsector contributions to GDP in Nigeria from 1981 to 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 4. Trend analysis of the solid mineral subsector contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. have been quite negligible and has never made up for a 2% solid mineral averaged 0.13%. This further explains the input to the composite industrial sector contribution and the neglect of this subsector as oil exploration gained momentum GDP in any particular year. In fact, its best influence was a and “oil-money” crowded out the attention given to other sub- 1.9% input in 1982. Between 1989 and 2014, the impact of sectors that used to be the mainstay of the Nigerian economy. Ezeaku et al. 5 Figure 5. Trend analysis of crude petroleum and natural gas contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 5 depicts that the crude petroleum and the natural that both bank lending and monetary policy have a strong gas trend has a reverse characteristic compared with other influence on industrial growth Olorunfemi and Dotun (2008) subsectors analyzed earlier. The lowest contributions were in used simple regression to assess the impact of monetary policy the 1980s, contrary to the manufacturing and solid mineral on the economic performance in Nigeria. The results indicate subsectors, and trended highly and positively from 1990 to that both interest rate and inflation have a negative relation- 2014. The reason is not farfetched. As oil revenue trickled in ship with GDP. the late 1980s, the nonoil subsectors, which hitherto used to Ćorić et al. (2012) explored the effects of a monetary pol- be the highest contributor and foreign exchange earner, were icy shock on output and prices. Results of the structural vec- neglected, hence the downward trend observed from the tor autoregression (VAR) model suggested that economic early 1990s to date. size and industry size (share of industry in GDP) are among Figure 6 descriptively explains the responsiveness of that factors critical for the effects of a monetary policy shock. monetary policy transmission channels to industrial sector Peersman and Smets (2002) estimated the effects of mon- size (INDSZE), which is measured as the percentage of etary policy change on output growth in seven euro area industrial sector real output to GDP at current basic prices, countries between 1980 and1998. The results revealed that where EXR is the exchange rate channel, MPR is the mone- the adverse impact of an interest rate tightening has greater tary policy rate, LTR is the commercial banks lending rate, negative and significant effect on output in times of reces- TBR is the treasury bill rate, CPSGDP is the ratio of private sion than in booms. However, the study underscored the sector credit to GDP, M2GDP is the ratio of broad money overall impact of cross-industry heterogeneity as well as supply to GDP, and INDSZE is the industrial size. From the information asymmetry vis-à-vis overall policy effects. graph, it appears that a change in industrial sector output Yakubu, Barfour, and Shehu (2013) investigated the (which is real value added [RVA]) is a function of changes in effectiveness of monetary-fiscal policies on price and output some transmission channels. The relationship between RVA growth in Nigeria. Variance decomposition and impulse and each of the channels of transmission seems to be relative response function for Vector Error Correction Model other than absolute. (VECM) captured the correlation among variables. The results suggested that money supply and government reve- nue have a greater positive influence on prices and output in Empirical Reviews the long run. A look at existing empirical studies will provide further Chaudhry et al. (2012) investigated the relationships insights into the dynamics of monetary policy and growth. between monetary policy, inflation, and economic growth in The effectiveness of the various monetary policy channels Pakistan over the period of 1972-2010 using co-integration in stimulating economic growth remains debatable. Owolabi and causality analysis. The results show that credit to private and Adegbite (2014) used multiple regression to examine sector, real exchange rate, and budget deficit are significant the impact of monetary policy on industrial growth in the variables that influence the real GDP in Pakistan. The pair- Nigerian economy, covering the period of 1970-2010. The wise Granger causality results suggest a bidirectional causal- study found that rediscount rate and deposit have significant ity between real GDP and real exchange rate, whereas positive effect on industrial output, whereas treasury bills do unidirectional causality run from real GDP to money supply, not have a positive impact on industrial output. domestic credit, and budget deficit. Arnold et al. (2006) studied the industry effects of bank Ghosh (2009) used a VAR model to the interlinkage lending in Germany. The dynamic panel data models indicated between a monetary policy shock and real industry output in 6 SAGE Open Figure 6. Trend analysis of monetary policy transmission channels and industry size in Nigeria, 1981-2014. Source. Authors’ computations from Central Bank of Nigeria Bulletins (various years). Note. EXR = exchange rate channel; MPR = monetary policy rate; LTR = commercial banks lending rate; TBR = treasury bill rate; CPSGDP = ratio of private sector credit to GDP; GDP = gross domestic product; M2GDP = ratio of broad money supply to GDP; INDSZE = industrial size. India. The findings indicate that industries show differential MS-models showed that the magnitude or significance of response to a monetary tightening and both interest rate and these empirical relationships is more under a “higher response financial accelerator variables appear to be central in explain- regime.” Similarly, Partachi and Mija (2015) emphasized that ing the differential response. MPR influences the direction of the national economy and Using an equilibrium-correction model is estimated, the stimulates interest for refinancing banks as they require more findings in Ryan-Collins, Werner, and Castle, (2016) sug- loans, used for lending to the economy, thereby stimulating gested short-term and long-term interest rates and broad economic activities and fostering sustainable growth (see money do not appear to influence nominal GDP significantly. Donath, Veronica, & Oprea, 2015; Jain-Chandra & Unsal, VAR estimate revealed that the real economy credit growth 2014; Lerskullawat, 2017; Matemilola, Bany-Ariffin, & variable is strongly exogenous to nominal GDP growth (see Muhtar, 2015; Rosoiu, 2015b; Wulandari, 2012). also Grigoli, Herman, Swiston, & Bella, 2015; Kandil, 2014; Kalu (2017) analyzed the nature of the relationship Rosoiu, 2015a). between monetary policy and private sector credit in Nigeria. Afonso, Araújo, and Fajardo (2016) argue that the pro- The cointegrating regression results revealed evidence of a cesses involved—from the formulation of then monetary long-run relationship between monetary policy and credit to instruments to their implementation and consolidation in private sector. The long-run parameter estimate stability tests developing countries—are often carried out in response to a support cointegration in the presence of structural breaks. On succession of internal and, principally, external crises. the contrary, error correction model (ECM) results showed Besides, Leith, Moldovan, and Rossi (2015) contend that in that changes in credit have positive and significant short- developing economies, the presence of deep habits at empiri- term influence on changes in monetary policy. The findings cally estimated levels can imply large externalities that sig- further indicate unidirectional causality running from credit nificantly affect the conduct of monetary policy. to monetary policy. Moreira, Chaiboonsri, and Chaitip (2016) applied the Fu and Liu (2015) investigated the monetary policy Markov-switching models and a Bayesian VAR to verify effects on corporate investment adjustment, using a sample empirical linkage between expected and effective short-term of China’s A-share listed firms within the period 2005 and interest rates in Brazil. The main findings support the theo- 2012. The results showed that corporate investment adjust- retical idea which argues that Central Bank can smooth ment is faster in expansionary than contractionary monetary adjustments of effective short-term interest rates, given that policy periods. The study showed that an increase in the these last ones have effects on expected short-term rates, growth rate of money supply or credit accelerates adjust- thereby influencing long-term interest rates, which are essen- ment. The monetary channel was also found to have signifi- tial for controlling output activity and price changes. Also, the cant asymmetry, whereas the CRDT has none. Ezeaku et al. 7 where denotes change; ∆ i and j are lag lengths; n is number Data and Method of lags; δ is the error correction term (ECT) (and speed of t−1 The research design for this study is ex post facto, because adjustment), which is integrated at Order 0, 1(0); β is the the events we are studying had already taken place. This constant term; β –β are coefficients; and μ is the error term. 1 6 t design can also be referred to as comparative research, The Johansen cointegration will be employed to test for the which is applicable for studies geared toward ascertain- cointegrating relationship among the variables given the fact ing the cause–effect association between the independent that all our series are stationary after first differencing (i.e., at and dependent variables (Onwumere, Onodugo, & Ibe, Order 1). However, in a situation where some of the variables 2014). Determining cause–effect relationships among our have unit root, we will have to develop the Autoregressive selected variables is the major aim of this study; hence, Distributed Lag (ARDL) or Bound Test model for co-integration our data are of secondary nature, collated from the Central which merges the two steps suggested by Engel and Granger Bank of Nigeria (CBN) Statistical bulletins for various (1987) into a one-step function. The model substitutes in t −1 years, covering the period 1981-2014. The annualized Equation 2 for lagged variables of the dependent variable (C ) time series data will be analyzed using the ECM, whereas and the independent variables (C –C ). The ARDL model is 8 10 the Johansen cointegration approach will be employed to therefore specified in Equation 3: test for the long-run relationship among the series. In other words, the underlying assumption is that all vari- n1 n2 ables are integrated of Order 1 or I(1). The speed of ΔΔ YC =+ CY + CC Δ RDT tj ,, 0 ∑∑ 11 ij tj − ,, 21 ij tj − , i=1 i=0 adjustment will be ascertained based on the ECM and n3 n4 will be able to tell us the rate at which the previous period + C ΔΔ INTC + EXR ∑ 3iij ,, tj −1 ∑ 41 ij ,, tj − disequilibrium is adjusted toward equilibrium path on an i=0 i=0 (3) annual basis. n5 n6 ++ CT Δ BR CF Δ DIR ∑ 51 ij ,, tj − ∑ 61 ij ,, tj − i=0 i=0 0 ++ CY CCRDTC + INT 71 tj −− ,, 8 tj 191 tj − , Model Specification + CEXR R + μ . 10 tj −1, t It is our aim to derive the output effect of monetary policy transmission channels. To achieve this, we estimate for the Equation 3 is arrived at by solving for δ in Equation 1 and industry the linear regression equation: t then lagging the outcome by one period. The result is substi- tuted for ä in Equation 2 to arrive at Equation 3. t−1 YC =+ ββ RDTI ++ ββ NT EXR + μ , tt 01 23 (1) Results and Analysis where Y is the real output (measured as annualized percent- Unit Root Test age contribution of the industrial sector to GDP), CRDT is the credit channel (measured as the ratio of private sector The result in Table 1 indicates that the variables attained sta- credit to GDP), INT is the interest rate channel (this is the tionarity at the same orders of integration. Y, CRDT, INT, real lending rate), and EXR is the exchange rate channel. and EXR are stationary at first difference, I(1). When the Equation 1 is our baseline long-run model for determining order of integration among variables are all I(1), the appro- the industry effects of monetary policy transmission in priate technique for cointegration testing is the Johansen Nigeria. cointegration test; otherwise, the ARDL would apply. Hence, It has been vastly buttressed in recent literature of finan- we use this approach to ascertain a long-run relationship cial econometrics that upon the establishment of a long-run among our variables. relationship, there is need to integrate a model which accom- modates for short-run dynamic adjustment process, which is Model Estimations the speed of adjustment from short-run disequilibrium to long-run equilibrium. Based on this, we developed ECM by The results reported in Table 2 reveals that CRDT, INT, and modifying Equation 1 as follows: EXR jointly have a long-run relationship with the real indus- trial output (Y). We observe that there are four cointegrating n1 n2 equations. For our purpose, we are interested in the variables ΔΔ YY =+ ββ + β ΔCRDT tj ,, 0 ∑∑ 11 ij tj − ,, 21 ij tj − , moving together, hence jointly in the long run. Given this i=1 i=0 n3 n4 finding, we shall estimate the long-run industry effect of + β ΔΔ INT + β EXR ∑ 3iij ,, tj −1 ∑ 41 ij ,, tj − monetary policy based on our baseline model and, after that, (2) i=0 i=0 apply the ECM to determine the speed of adjustment toward ++ δμ , tj −1, t long-run equilibrium. 8 SAGE Open Table 1. ADF Unit Root Test Result. Variable ADF test statistic 5% critical value Order of integration Inference Y −9.141847 −2.963972 I(1) Stationary CRDT −5.738119 −2.960411 I(1) Stationary INT −6.407172 −2.960411 I(1) Stationary EXR −5.392317 −2.957110 I(1) Stationary Source. Kalu (2017) and Lerskullawat (2017). Note. ADF = Augmented Dickey-Fuller; Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel. Table 2. Result of Johansen Cointegration Test for a Long-Run Relationship. Series: D(Y) D(CRDT) D(INTR) D(EXR) Unrestricted Cointegration Rank Test (Trace) Hypothesized no. of CE(s) Eigenvalue Trace statistic .05 critical value Probability** None* 0.776572 105.0338 47.85613 .0000 At most 1* 0.612075 58.57509 29.79707 .0000 At most 2* 0.503775 29.21987 15.49471 .0002 At most 3* 0.214827 7.497382 3.841466 .0062 Hypothesized no. of CE(s) Eigenvalue Max-Eigen statistic .05 critical value Probability** None* 0.776572 46.45871 27.58434 .0001 At most 1* 0.612075 29.35522 21.13162 .0028 At most 2* 0.503775 21.72248 14.26460 .0028 At most 3* 0.214827 7.497382 3.841466 .0062 Note. Trace and Max-eigenvalue tests indicate four cointegrating equations at the .05 level. Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; CE(s) = cointegrating equations. *Denotes rejection of the hypothesis at the .05 level. **MacKinnon–Haug–Michelis (1999) p values. Table 3. Estimate of Long-Run Effect Based on Baseline Model. Variable Coefficient SE t Statistic Probability CRDT −0.240861 0.189979 −1.267826 .2146 INT −0.372674 0.168747 −2.208474 .0350 EXR −0.039157 0.020520 −1.908202 .0660 Intercept 55.92576 4.084098 13.69354 .0000 Source. Kalu (2017) and Lerskullawat (2017). 2 2 Note. Prob(F statistic) = .000232, R = .86, Adjusted R = .74, DW = 1.796801. CRDT = credit channel (measured as the ratio of private sector credit to GDP); GDP = gross domestic product; INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; DW = Durbin–Watson. The results in Table 3 indicate that CRDT, INT, and supports one of the theoretical foundations of this dis- EXR have a negative influence on real industrial output course that interest rate is negatively related to real out- (Y). A unit change in credit to the private sector leads to put. However, the evidence on private sector credit does 24.1% decline in real output (Y). When INT rises by 1%, not support the a priori expectation which states that credit real industrial output falls by 37.2%. Therefore, an to the private sector has a direct influence on real output. increase in interest rate is expected to raise the cost of Exchange rate equally has a negative but nonsignificant funds and slow down economic activities. This outcome effect on Y. Ezeaku et al. 9 Table 4. Short-Run Estimate Based on Error Correction Model. Variable Coefficient SE t Statistic Probability C 0.078322 1.035439 0.075641 .9404 D(CRDT) −0.240673 0.254001 −0.947530 .3532 D(INTR) −0.002448 0.393904 −0.006215 .9951 D(EXR) −0.120997 0.083473 −1.449526 .1607 ECT(−1) −0.721949 0.225152 −5.160732 .0000 R .610214 Prob(F statistic) 0.000685 Adjusted R .508531 DW 1.688665 Note. Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; ECT = error correction term; DW = Durbin–Watson. Table 5. Serial Correlation and Heteroskedasticity Tests. 2 2 Test Observations × R Probability χ F Statistic (probability) Breusch–Godfrey Serial Correlation LM Test 3.17638 .1526 0.724351 (.4418) Heteroskedasticity Test: Breusch–Pagan–Godfrey 15.97382 .4473 0.814360 (.6063) Source. Kalu (2017) and Lerskullawat (2017). Table 6. Ramsey RESET Test. Specification: Y C CRDT INTR EXR Value df Probability t Statistic 0.070324 24 .9445 F Statistic 0.004946 (1, 24) .9445 Likelihood ratio 0.006387 1 .9363 Source. Kalu (2017) and Lerskullawat (2017). Note. RESET = Regression Equation Specification Error Test; Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel. The regression estimate in Table 4 reveals that all the regres- Figure 7. Recursive estimate’s CUSUM test. sors exert negative influence on industrial sector output. This Note. CUSUM = Cumulative Sum Control Chart. result shows that monetary policy has relatively the same effect on output both in the long run and the short run. It is worthy to model has no serial correlation. This confirms the result of note that the negative influence of INT on Y is of less magni- Durbin–Watson (DW) result in Table 3 which shows the tude in the short run—a unit change in leads to 0.24% decline same result. The second test for heteroskedasticity reveals in real output, but the effects get more significant in the long that our model is homoskedastic. These results are desirable run at 37.2%. The ECT has the right sign and is significant at and confirm that our overall results are not spurious hence 5%. The ECT indicated the speed of adjustment toward long- reliable. run equilibrium. The result, therefore, shows that 72.2% of the The p values of the t test, F statistics, and the Likelihood ratio deviation from equilibrium path is corrected on an annual basis. in Table 6 show that the null hypothesis that the model is cor- rectly specified is accepted. This also indicates that the model does not have any specification error. This result is confirmed Validity and Stability Tests by the Cumulative Sum Control Chart (CUSUM) test below. In the results reported in Table 5, Breusch–Godfrey Serial The graph in Figure 7 shows that our model is stable. Its Correlation Lagrange multiplier (LM) Test indicates that our stability is explained with the blue line within the upper and 10 SAGE Open Figure 8. Histogram normality test. lower bound red lines. The above plot remaining within the a speed of adjustment of 72.2%. This implies that disequi- critical bounds of the 5% significance level also reveals that librium in the system is corrected at the rate of 72.2% annu- our model is correctly specified. ally. Based on the findings, we recommend that credit The normality test result is presented in Figure 8. The should be made available to the productive sectors of the result shows that the series are normally distributed as indi- economy at a competitive rate with adequate monitoring. cated by the p value of the JB statistic. Concerted efforts toward effective guidelines and supervi- sion are also recommended to ensure that credit is not diverted to unproductive sector of the economy. The key Conclusion and Recommendations implication is that private sector credit, interest rate, and exchange rate are effective channels for monetary policy Variation in monetary policy stance is expected to have an transmission in Nigeria, and a policy action should be put in effect on industrial sector productivity. Determining the place to effectively harness these key channels to stimulate appropriate policy of choice remains the role of the central the real sector of the economy and boost economic activi- bank that sets policy targets and determines transmission ties. It is therefore important that the monetary authorities channels that will produce expected result. This study was consider these variables as major channel for monetary pol- aimed at ascertaining the industry effects of monetary policy icy implementations. Another critical policy implication is and find out whether monetary policy transmission channels that rising interest rate stifle growth and the policy makers jointly have a long-run relationship with industrial output must take policy actions to stabilize MPRs and implement growth in Nigeria. Our baseline regression result reveals effective foreign exchange policy aimed at stimulating real that the private sector CRDT, INT, and the EXR all have a output growth in the economy. negative effect on real output growth (Y). The ECM esti- mate relatively corroborates the above findings. However, Declaration of Conflicting Interests the degree of responsiveness of Y to changes in the mone- tary policy was found to be higher (or more significant) in The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. the long-run compared with the short-run dynamics. For instance, 1% change in INT caused Y to fall by 0.24% in the Funding short run and by 37.2% in the long run. Same unit change in EXR leads to 3.9% decline in Y in the short run and 12.9% The author(s) received no financial support for the research, author- fall in the long run. The magnitude of effect of CRDT on Y ship, and/or publication of this article. appears stable both in the long run and short run at 24.07% and 24.09%, respectively. Moreover, the Johansen cointe- References gration test results indicate that monetary policy transmis- Afonso, J. R., Araújo, C. E., & Fajardo, B. G. (2016). The role sion channels jointly have a long-run relationship with of fiscal and monetary policies in the Brazilian economy: industrial output growth. This finding was confirmed by Understanding recent institutional reforms and economic both the Trace and the Max-Eingen tests. The ECM result changes. The Quarterly Review of Economics and Finance, 62, further confirmed the cointegrating association and revealed 41-55. Ezeaku et al. 11 Arnold, I. J. M., Kool, C. J. M., & Raabe, K. (2006). Industries Lerskullawat, A. (2017). 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Bank_Management_and_Real_Sector_Finance_in_Nigeria_ doi:10.1016/j.bir.2014.04.001 Who_is_to_Blame Khan, G. A., & Jacobson, A. (1989). Lessons from West German Wulandari, R. (2012). Do credit channel and interest rate chan- monetary policy. Economic Review, 74, 18-35. nel play important role in monetary transmission mechanism Leith, C., Moldovan, I., & Rossi, R. (2015). Monetary and fiscal in Indonesia? A Structural Vector Autoregression Model. policy under deep habits. Journal of Economic Dynamics & Procedia —Social and Behavioral Sciences, 65, 557-563. Control, 52, 55-74. doi:10.1016/j.jedc.2014.11.005 doi:10.1016/j.sbspro.2012.11.165 12 SAGE Open Yakubu, I. M., Barfour, K. A., & Shehu, U. G. (2013). Effect of private and public universities in Nigeria and is an associate mem- monetary-fiscal policies interaction on price and output growth ber of the Chartered Institute of Bankers of Nigeria (CIBN). in Nigeria. CBN Journal of Applied Statistics, 4, 55-74. Uche Boniface Ugwuanyi is currently a senior lecturer in the Department of Accountancy. He has broad experience in the field of accounting and finance. Author Biographies Hillary Chijindu Ezeaku is a doctoral student of banking and N. J. Modebe is a currently a senior lecturer in the Department of finance, University of Nigeria. His research interests are in interna- Banking and Finance. She has long years of corporate and academic tional Finance, econometrics modeling, macroeconomics, and experience both in Nigeria and in the United States. development finance. Emmanuel Kalu Agbaeze is currently a senior lecturer in the Imo Godwin Ibe is a lecturer in the Department of Banking and Department of Management. His vast knowledge and research Finance, University of Nigeria. He has lectured in a number of experience cuts across several disciplines. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png SAGE Open SAGE

Monetary Policy Transmission and Industrial Sector Growth: Empirical Evidence From Nigeria:

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Abstract

The goal of this study is to assess the industry effects of monetary policy transmission channels in Nigeria within the period 1981-2014. Techniques of analysis employed in the study are the Johansen cointegration and the error correction model (ECM). Our regression estimates reveal that the private sector credit, interest rate, and exchange rate channels have negative effects on real output growth, both in the long run and in the short run. The results further show that, relatively, the degrees of the established effects are higher in the long run than in the short run. We employed the Johansen cointegration approach to determine the nature of relationship that exists between our dependent variable and the independent variables. The results show that, in the Nigerian case, monetary policy transmission channels jointly have a long-run relationship with real output growth of the industrial sector, and disequilibrium in the system is corrected at the speed of 72.2% annually. Keywords industrial output, monetary policy, credit channel, interest rate channel, Johansen cointegration, error correction model could at same time escalate. Regarding achieving price sta- Introduction bility, Bernanke, Laubach, Mishkin, and Posen (1999) Monetary policy action is the prerogative of the central bank emphasize that an inflation targeting country may not wring and monetary authorities. It involves the process of control- inflation out of their economies without laying itself open to ling the cost, value, and availability of money and credit in costs in lost output and rising unemployment. Moreover, other to achieve the desired level of prices, employment, out- monetary growth target might lead to achieving price stabil- put, and other economic objectives. Monetary policy can be ity but might not result in the attainment ofaccelerated used to influence economic activities and achieve economic growth. It is a herculean task finding a balance or trade-off objectives of a country. The stance of monetary policy, how- between set goals (Khan & Jacobson, 1989). Epstein (2007) ever, can be either expansionary or contractionary. asserts that limiting monetary policy exclusively to price sta- Expansionary monetary policy stance is a situation whereby bilization cannot guarantee improved economic growth the monetary authority increases the supply of money in the because low inflation does not necessarily give rise to high economy with the aim of reducing the cost of money (inter- and steady economic growth. est rate) and stimulating economic activities. However, a Over and above, the primary goal of monetary policy is to contractionary policy entails the reduction in money supply ensure that money supply is at a level that is consistent with which potentially increases the cost of money and slows the the desired growth rate. pace of economic activities. Although no economy is self-sufficient, most economies The choice of any stance to be adopted by the central bank of the world strive for self-reliance. The successful ones are depends on the state of the economy at any time and the pol- often those that have a deepened economy with soaring icy target of the government. But, finding a trade-off between the attainment of price stability and growth is often difficult. University of Nigeria, Nsukka, Nigeria By setting annual monetary growth targets, the central bank 2 Enugu State University of Science and Technology, Nigeria intends to assert its commitment to price stability or inflation Corresponding Author: control. However, in reality, money growth might be consis- Hillary Chijindu Ezeaku, University of Nigeria, 27 Ichida Street, Federal tent with economic growth but not with the stability of prices; Housing Estate, Trans-Ekulu, Enugu, Nigeria, Nsukka 400001, Nigeria. in other words, although the economy is stimulated, inflation Email: gijindu@gmail.com Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open economies of scale, which can take an economy steps farther Monetary Policy Transition Mechanism from economic growth to economic development. Good Interest rate channel (INT) and credit channel (CRDT) are infrastructural development combined with favorable busi- considered in some literature as the key propagation and ness and political environment supports the creation of value strengthening mechanisms of monetary policy changes. Both and increase in the production of goods and services. And, types of transmission channels hold the prediction that any certainly, the monetary authorities are always there to har- variation in bank lending is dependent on monetary policy ness the overall mechanism by formulating policies that will actions. In other words, a change in bank lending is predicted promote growth and stability in the economy. Although eco- to be in response to change in monetary policy stance. nomic theory and quite a few empirical analyses support the Because monetary policy hinges chiefly on the supply of fact that monetary policy influences output, the efficient con- money, it will be remiss and abnormal to ignore the role of duct of monetary policy is crucial (Ćorić, Perović, & Šimić, banks, especially in the money creation process. Hence, the 2012). CRDT perspective portends that monetary policy induces Nigeria is a mono-cultural economy, depending on oil movements in bank lending vis-à-vis changes in bank loan as the mainstay of the economy and foreign exchange supply, whereas shifts in the demand for a bank loan is earner. Most studies that evaluated monetary policy effects explained by the INT (Arnold, Kool, & Raabe, 2006). The usually equate policy measures against the overall econ- Nigerian industrial sector faces insurmountable challenges omy. Formulating future policies based on aggregated per- ranging from infrastructural woes to highly unstable busi- spective on the economy might not be entirely misguided ness environment. Also, the cyclical nature of industrial out- but would ultimately hide vital details that could have put equally intensifies the need for external financing. given a more stable and target-oriented policy designs. Bridging the funding gap depends mainly on both availabil- Therefore, in a country that chimes mantra for economic ity and cost of fund, which is largely determined by money diversification, it is necessary to disaggregate the economy supply through monetary policy action. and assess the effect of key monetary policy channels on Writing on monetary policy transmission mechanism, the “preferred” industrial sector. Most of the existing stud- Friedman and Schwartz (1963) argue that when the central ies have examined transmission effect vis-à-vis the aggre- bank pursues an expansive open market operation, money gate economy hence creating a gap in the frontiers of stock will increase thereby leaving the deposit money banks knowledge regarding the sectoral effects of monetary pol- with fat reserves and enhance their ability to create credit and icy transmission. There is, however, dearth of literature to extend loans and advances, which will increase the money explain the real output response, from a disaggregated supply. Besides the sale and repurchase of financial instru- point of view to monetary policy transmission in Nigeria. ments like treasury bills to regulate the quantity of money in To this end, the need to fill such knowledge gaps informed circulation, the central bank may also decide to use other the choice for this study which examines the linkage monetary policy instruments such as rediscount rate or the between monetary policy transmission and industrial sec- reserve requirements (liquidity and cash ratio) to achieve the tor growth in Nigeria. desired economic objectives of output growth, stable price level, and full employment. The industrial sector and other Review of Related Literature activity sectors stand to benefit from expansionary policy According to Toby and Peterside (2014), a monetary pol- measures (for instance, increase in money supply and reduc- icy shift tends to transmit a change for the future in the tion of rediscount rate). Although this will promote produc- projected behavior of macroeconomic variables. The tradi- tion through cheaper cost of fund (interest rates), it could tional economic analysis considers the response of mone- turn quite inimical to achieving price stability. On the con- tary policy makers as exogenous. As explained by this trary, a stringent policy, using any appropriate instrument, system, money is unbiased in its effects on the economy. can help to attain a stable price level but could lead to a Thus, in the classical theory, transmission mechanism recession. reacts directly and indirectly. The direct mechanism is Economists established the general relationship between based on the demand for and supply for money, whereas real output and monetary policy transmissions. From the the indirect mechanism has linkage with the banking sys- Keynesian point of view, an unrestricted change in money tem and operates through money and interest rate. The stock influences real output by bringing down the interest Keynesian theory explains that a change in money supply rate, which by efficient utilization of capital will stimulate has effects on total expenditure and output level through investment and the real output growth (Athukorala, 1998). the changes in interest rate. Hence, the system operates Some macroeconomists, however, have a different opinion. indirectly. The monetarists affirm that although monetary They promoted the theory of financial liberalization and expansions affect output and employment in the short argued that if the market forces prompt interest rate to rise, term, interest rate and prices are influenced in the long run savings would be channeled to the productive investments (Chaudhry, Qamber, & Farooq, 2012). thus stimulating growth in real output (Mehdi & Reza, 2011). Ezeaku et al. 3 Figure 1. Trend analysis of money supply growth and industrial sector growth relationships in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. M2GR = broad money growth; INDGR = industrial sector growth. In the overall process, the banking system stands as the key rate was a paltry 0.44%, which could be attributed to a conduit of monetary policy transmission, which draws atten- decline in money growth from 29.7% in 2003 to 9.2% in tion to the CRDT effects of monetary policy in Nigeria. In 2004. The industrial sector had a relative positive response to light of any expansionary policy, therefore, it is expected that money growth over the period under study except in 2001, it will have indirect positive effects on industrial activities as where an increase in money supply from 39.7% in 2000 to the cost of borrowing (to finance production or expansion) 44.5% in 2001 resulted in −19.0% decline in the industrial will fall. Ceteris paribus, the theoretical expectation is that sector growth in 2001. the low cost of fund prompts the makings for an increase in We observe from the Figure 2 that industrial sector contri- national income via the stimulation in economic activities. butions to the Nigerian economy was consistently significant and trended with relative stability in the 1980s until it dipped by 10.5% in 1990, from 48.7% in 1989 to 38.2% in 1990. Trend Analysis Since rising to an all-time high of 54.9% in 1991, the trend A cursory look at the money supply growth and the industrial fluctuated visibly but averaged 40.3% between 2004 and sector growth in Nigeria from 1981 to 2014 reveals undulat- 2014. ing movements over the years, where on average, the money To avoid a general assumption based on the performance supply growth and the industrial growth rate were 48.08% of the industrial sector, we have disaggregated this sector and 26.67%, respectively. Because the industrial sector is into various subsectors that constitute the industrial sector. made up of some subsectors, we shall use a few charts to The sum of the contributions of each of these sectors in any analyze the trends of the money supply, and the characteris- given year equals the input of the industrial sector for that tics of the industrial sector as well as the various subsectors particular year. Therefore, breaking it down further will help it comprises. us understand what the exact contribution of each subsector Figure 1 reveals unstable trends and indicates that money to the gross domestic product (GDP) is. These are explained supply, descriptively, does have a significant influence on the in Figures 3 to 5. pace of industrial advancement. Substantial decreases in Figure 3 reveals that the manufacturing subsector fared money stock have at some years found to have an adverse well in the 1980s and was the highest contributor to indus- impact on the growth of the industrial sector, whereas at trial sector performance during this period. It is worthy to other points increases in the money supply do not produce a emphasize that the subsector recorded 33.6%, 36.4%, 33.0%, corresponding commensurate increase in the growth of the and 35.1% in 1981, 1982, 1983, 1984, and 1985, respec- industrial sector. From the chart above, it can be observed, tively. However, as the economy began to depend on crude for instance, that money supply decreased from 10.9% in oil and natural gas, activity in the manufacturing sector 1985 to 6.8% in 1986 resulting in a proportionate decline in dwindled, and Nigerian economy changed from an export- industrial growth from 22.1% in 1985 to −4.9%. Money based economy to an import-dependent economy. As a result, growth fell from19.7% in 1989 to 15.2% in 1990 equally the impact of the manufacturing subsector on the economy lead to negative growth in 1990 from 47.5% in 1989 to fell consistently, without meaningful recovery, from 12.4% −32.4% in 1990. Moreover, such other years (1997, 1998, in 1990 to 1.9% in 2014. 2001, 2004, 2009, and 2013) that recorded significant cuts in Figure 4 demonstrates similar trend over the years as broad money supply all had corresponding negative growths Figure 3. However, in absolute terms, as can be observed from of the industrial sector except for 2004. In 2004, the growth the scaling, the solid mineral subsector contributions to GDP 4 SAGE Open Figure 2. Trend analysis of the industrial sector contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 3. Trend analysis of the manufacturing subsector contributions to GDP in Nigeria from 1981 to 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 4. Trend analysis of the solid mineral subsector contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. have been quite negligible and has never made up for a 2% solid mineral averaged 0.13%. This further explains the input to the composite industrial sector contribution and the neglect of this subsector as oil exploration gained momentum GDP in any particular year. In fact, its best influence was a and “oil-money” crowded out the attention given to other sub- 1.9% input in 1982. Between 1989 and 2014, the impact of sectors that used to be the mainstay of the Nigerian economy. Ezeaku et al. 5 Figure 5. Trend analysis of crude petroleum and natural gas contributions to GDP in Nigeria between 1981 and 2014. Source. Authors’ computations from Central Bank of Nigeria Statistical Bulletins 2014. Note. GDP = gross domestic product. Figure 5 depicts that the crude petroleum and the natural that both bank lending and monetary policy have a strong gas trend has a reverse characteristic compared with other influence on industrial growth Olorunfemi and Dotun (2008) subsectors analyzed earlier. The lowest contributions were in used simple regression to assess the impact of monetary policy the 1980s, contrary to the manufacturing and solid mineral on the economic performance in Nigeria. The results indicate subsectors, and trended highly and positively from 1990 to that both interest rate and inflation have a negative relation- 2014. The reason is not farfetched. As oil revenue trickled in ship with GDP. the late 1980s, the nonoil subsectors, which hitherto used to Ćorić et al. (2012) explored the effects of a monetary pol- be the highest contributor and foreign exchange earner, were icy shock on output and prices. Results of the structural vec- neglected, hence the downward trend observed from the tor autoregression (VAR) model suggested that economic early 1990s to date. size and industry size (share of industry in GDP) are among Figure 6 descriptively explains the responsiveness of that factors critical for the effects of a monetary policy shock. monetary policy transmission channels to industrial sector Peersman and Smets (2002) estimated the effects of mon- size (INDSZE), which is measured as the percentage of etary policy change on output growth in seven euro area industrial sector real output to GDP at current basic prices, countries between 1980 and1998. The results revealed that where EXR is the exchange rate channel, MPR is the mone- the adverse impact of an interest rate tightening has greater tary policy rate, LTR is the commercial banks lending rate, negative and significant effect on output in times of reces- TBR is the treasury bill rate, CPSGDP is the ratio of private sion than in booms. However, the study underscored the sector credit to GDP, M2GDP is the ratio of broad money overall impact of cross-industry heterogeneity as well as supply to GDP, and INDSZE is the industrial size. From the information asymmetry vis-à-vis overall policy effects. graph, it appears that a change in industrial sector output Yakubu, Barfour, and Shehu (2013) investigated the (which is real value added [RVA]) is a function of changes in effectiveness of monetary-fiscal policies on price and output some transmission channels. The relationship between RVA growth in Nigeria. Variance decomposition and impulse and each of the channels of transmission seems to be relative response function for Vector Error Correction Model other than absolute. (VECM) captured the correlation among variables. The results suggested that money supply and government reve- nue have a greater positive influence on prices and output in Empirical Reviews the long run. A look at existing empirical studies will provide further Chaudhry et al. (2012) investigated the relationships insights into the dynamics of monetary policy and growth. between monetary policy, inflation, and economic growth in The effectiveness of the various monetary policy channels Pakistan over the period of 1972-2010 using co-integration in stimulating economic growth remains debatable. Owolabi and causality analysis. The results show that credit to private and Adegbite (2014) used multiple regression to examine sector, real exchange rate, and budget deficit are significant the impact of monetary policy on industrial growth in the variables that influence the real GDP in Pakistan. The pair- Nigerian economy, covering the period of 1970-2010. The wise Granger causality results suggest a bidirectional causal- study found that rediscount rate and deposit have significant ity between real GDP and real exchange rate, whereas positive effect on industrial output, whereas treasury bills do unidirectional causality run from real GDP to money supply, not have a positive impact on industrial output. domestic credit, and budget deficit. Arnold et al. (2006) studied the industry effects of bank Ghosh (2009) used a VAR model to the interlinkage lending in Germany. The dynamic panel data models indicated between a monetary policy shock and real industry output in 6 SAGE Open Figure 6. Trend analysis of monetary policy transmission channels and industry size in Nigeria, 1981-2014. Source. Authors’ computations from Central Bank of Nigeria Bulletins (various years). Note. EXR = exchange rate channel; MPR = monetary policy rate; LTR = commercial banks lending rate; TBR = treasury bill rate; CPSGDP = ratio of private sector credit to GDP; GDP = gross domestic product; M2GDP = ratio of broad money supply to GDP; INDSZE = industrial size. India. The findings indicate that industries show differential MS-models showed that the magnitude or significance of response to a monetary tightening and both interest rate and these empirical relationships is more under a “higher response financial accelerator variables appear to be central in explain- regime.” Similarly, Partachi and Mija (2015) emphasized that ing the differential response. MPR influences the direction of the national economy and Using an equilibrium-correction model is estimated, the stimulates interest for refinancing banks as they require more findings in Ryan-Collins, Werner, and Castle, (2016) sug- loans, used for lending to the economy, thereby stimulating gested short-term and long-term interest rates and broad economic activities and fostering sustainable growth (see money do not appear to influence nominal GDP significantly. Donath, Veronica, & Oprea, 2015; Jain-Chandra & Unsal, VAR estimate revealed that the real economy credit growth 2014; Lerskullawat, 2017; Matemilola, Bany-Ariffin, & variable is strongly exogenous to nominal GDP growth (see Muhtar, 2015; Rosoiu, 2015b; Wulandari, 2012). also Grigoli, Herman, Swiston, & Bella, 2015; Kandil, 2014; Kalu (2017) analyzed the nature of the relationship Rosoiu, 2015a). between monetary policy and private sector credit in Nigeria. Afonso, Araújo, and Fajardo (2016) argue that the pro- The cointegrating regression results revealed evidence of a cesses involved—from the formulation of then monetary long-run relationship between monetary policy and credit to instruments to their implementation and consolidation in private sector. The long-run parameter estimate stability tests developing countries—are often carried out in response to a support cointegration in the presence of structural breaks. On succession of internal and, principally, external crises. the contrary, error correction model (ECM) results showed Besides, Leith, Moldovan, and Rossi (2015) contend that in that changes in credit have positive and significant short- developing economies, the presence of deep habits at empiri- term influence on changes in monetary policy. The findings cally estimated levels can imply large externalities that sig- further indicate unidirectional causality running from credit nificantly affect the conduct of monetary policy. to monetary policy. Moreira, Chaiboonsri, and Chaitip (2016) applied the Fu and Liu (2015) investigated the monetary policy Markov-switching models and a Bayesian VAR to verify effects on corporate investment adjustment, using a sample empirical linkage between expected and effective short-term of China’s A-share listed firms within the period 2005 and interest rates in Brazil. The main findings support the theo- 2012. The results showed that corporate investment adjust- retical idea which argues that Central Bank can smooth ment is faster in expansionary than contractionary monetary adjustments of effective short-term interest rates, given that policy periods. The study showed that an increase in the these last ones have effects on expected short-term rates, growth rate of money supply or credit accelerates adjust- thereby influencing long-term interest rates, which are essen- ment. The monetary channel was also found to have signifi- tial for controlling output activity and price changes. Also, the cant asymmetry, whereas the CRDT has none. Ezeaku et al. 7 where denotes change; ∆ i and j are lag lengths; n is number Data and Method of lags; δ is the error correction term (ECT) (and speed of t−1 The research design for this study is ex post facto, because adjustment), which is integrated at Order 0, 1(0); β is the the events we are studying had already taken place. This constant term; β –β are coefficients; and μ is the error term. 1 6 t design can also be referred to as comparative research, The Johansen cointegration will be employed to test for the which is applicable for studies geared toward ascertain- cointegrating relationship among the variables given the fact ing the cause–effect association between the independent that all our series are stationary after first differencing (i.e., at and dependent variables (Onwumere, Onodugo, & Ibe, Order 1). However, in a situation where some of the variables 2014). Determining cause–effect relationships among our have unit root, we will have to develop the Autoregressive selected variables is the major aim of this study; hence, Distributed Lag (ARDL) or Bound Test model for co-integration our data are of secondary nature, collated from the Central which merges the two steps suggested by Engel and Granger Bank of Nigeria (CBN) Statistical bulletins for various (1987) into a one-step function. The model substitutes in t −1 years, covering the period 1981-2014. The annualized Equation 2 for lagged variables of the dependent variable (C ) time series data will be analyzed using the ECM, whereas and the independent variables (C –C ). The ARDL model is 8 10 the Johansen cointegration approach will be employed to therefore specified in Equation 3: test for the long-run relationship among the series. In other words, the underlying assumption is that all vari- n1 n2 ables are integrated of Order 1 or I(1). The speed of ΔΔ YC =+ CY + CC Δ RDT tj ,, 0 ∑∑ 11 ij tj − ,, 21 ij tj − , i=1 i=0 adjustment will be ascertained based on the ECM and n3 n4 will be able to tell us the rate at which the previous period + C ΔΔ INTC + EXR ∑ 3iij ,, tj −1 ∑ 41 ij ,, tj − disequilibrium is adjusted toward equilibrium path on an i=0 i=0 (3) annual basis. n5 n6 ++ CT Δ BR CF Δ DIR ∑ 51 ij ,, tj − ∑ 61 ij ,, tj − i=0 i=0 0 ++ CY CCRDTC + INT 71 tj −− ,, 8 tj 191 tj − , Model Specification + CEXR R + μ . 10 tj −1, t It is our aim to derive the output effect of monetary policy transmission channels. To achieve this, we estimate for the Equation 3 is arrived at by solving for δ in Equation 1 and industry the linear regression equation: t then lagging the outcome by one period. The result is substi- tuted for ä in Equation 2 to arrive at Equation 3. t−1 YC =+ ββ RDTI ++ ββ NT EXR + μ , tt 01 23 (1) Results and Analysis where Y is the real output (measured as annualized percent- Unit Root Test age contribution of the industrial sector to GDP), CRDT is the credit channel (measured as the ratio of private sector The result in Table 1 indicates that the variables attained sta- credit to GDP), INT is the interest rate channel (this is the tionarity at the same orders of integration. Y, CRDT, INT, real lending rate), and EXR is the exchange rate channel. and EXR are stationary at first difference, I(1). When the Equation 1 is our baseline long-run model for determining order of integration among variables are all I(1), the appro- the industry effects of monetary policy transmission in priate technique for cointegration testing is the Johansen Nigeria. cointegration test; otherwise, the ARDL would apply. Hence, It has been vastly buttressed in recent literature of finan- we use this approach to ascertain a long-run relationship cial econometrics that upon the establishment of a long-run among our variables. relationship, there is need to integrate a model which accom- modates for short-run dynamic adjustment process, which is Model Estimations the speed of adjustment from short-run disequilibrium to long-run equilibrium. Based on this, we developed ECM by The results reported in Table 2 reveals that CRDT, INT, and modifying Equation 1 as follows: EXR jointly have a long-run relationship with the real indus- trial output (Y). We observe that there are four cointegrating n1 n2 equations. For our purpose, we are interested in the variables ΔΔ YY =+ ββ + β ΔCRDT tj ,, 0 ∑∑ 11 ij tj − ,, 21 ij tj − , moving together, hence jointly in the long run. Given this i=1 i=0 n3 n4 finding, we shall estimate the long-run industry effect of + β ΔΔ INT + β EXR ∑ 3iij ,, tj −1 ∑ 41 ij ,, tj − monetary policy based on our baseline model and, after that, (2) i=0 i=0 apply the ECM to determine the speed of adjustment toward ++ δμ , tj −1, t long-run equilibrium. 8 SAGE Open Table 1. ADF Unit Root Test Result. Variable ADF test statistic 5% critical value Order of integration Inference Y −9.141847 −2.963972 I(1) Stationary CRDT −5.738119 −2.960411 I(1) Stationary INT −6.407172 −2.960411 I(1) Stationary EXR −5.392317 −2.957110 I(1) Stationary Source. Kalu (2017) and Lerskullawat (2017). Note. ADF = Augmented Dickey-Fuller; Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel. Table 2. Result of Johansen Cointegration Test for a Long-Run Relationship. Series: D(Y) D(CRDT) D(INTR) D(EXR) Unrestricted Cointegration Rank Test (Trace) Hypothesized no. of CE(s) Eigenvalue Trace statistic .05 critical value Probability** None* 0.776572 105.0338 47.85613 .0000 At most 1* 0.612075 58.57509 29.79707 .0000 At most 2* 0.503775 29.21987 15.49471 .0002 At most 3* 0.214827 7.497382 3.841466 .0062 Hypothesized no. of CE(s) Eigenvalue Max-Eigen statistic .05 critical value Probability** None* 0.776572 46.45871 27.58434 .0001 At most 1* 0.612075 29.35522 21.13162 .0028 At most 2* 0.503775 21.72248 14.26460 .0028 At most 3* 0.214827 7.497382 3.841466 .0062 Note. Trace and Max-eigenvalue tests indicate four cointegrating equations at the .05 level. Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; CE(s) = cointegrating equations. *Denotes rejection of the hypothesis at the .05 level. **MacKinnon–Haug–Michelis (1999) p values. Table 3. Estimate of Long-Run Effect Based on Baseline Model. Variable Coefficient SE t Statistic Probability CRDT −0.240861 0.189979 −1.267826 .2146 INT −0.372674 0.168747 −2.208474 .0350 EXR −0.039157 0.020520 −1.908202 .0660 Intercept 55.92576 4.084098 13.69354 .0000 Source. Kalu (2017) and Lerskullawat (2017). 2 2 Note. Prob(F statistic) = .000232, R = .86, Adjusted R = .74, DW = 1.796801. CRDT = credit channel (measured as the ratio of private sector credit to GDP); GDP = gross domestic product; INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; DW = Durbin–Watson. The results in Table 3 indicate that CRDT, INT, and supports one of the theoretical foundations of this dis- EXR have a negative influence on real industrial output course that interest rate is negatively related to real out- (Y). A unit change in credit to the private sector leads to put. However, the evidence on private sector credit does 24.1% decline in real output (Y). When INT rises by 1%, not support the a priori expectation which states that credit real industrial output falls by 37.2%. Therefore, an to the private sector has a direct influence on real output. increase in interest rate is expected to raise the cost of Exchange rate equally has a negative but nonsignificant funds and slow down economic activities. This outcome effect on Y. Ezeaku et al. 9 Table 4. Short-Run Estimate Based on Error Correction Model. Variable Coefficient SE t Statistic Probability C 0.078322 1.035439 0.075641 .9404 D(CRDT) −0.240673 0.254001 −0.947530 .3532 D(INTR) −0.002448 0.393904 −0.006215 .9951 D(EXR) −0.120997 0.083473 −1.449526 .1607 ECT(−1) −0.721949 0.225152 −5.160732 .0000 R .610214 Prob(F statistic) 0.000685 Adjusted R .508531 DW 1.688665 Note. Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel; ECT = error correction term; DW = Durbin–Watson. Table 5. Serial Correlation and Heteroskedasticity Tests. 2 2 Test Observations × R Probability χ F Statistic (probability) Breusch–Godfrey Serial Correlation LM Test 3.17638 .1526 0.724351 (.4418) Heteroskedasticity Test: Breusch–Pagan–Godfrey 15.97382 .4473 0.814360 (.6063) Source. Kalu (2017) and Lerskullawat (2017). Table 6. Ramsey RESET Test. Specification: Y C CRDT INTR EXR Value df Probability t Statistic 0.070324 24 .9445 F Statistic 0.004946 (1, 24) .9445 Likelihood ratio 0.006387 1 .9363 Source. Kalu (2017) and Lerskullawat (2017). Note. RESET = Regression Equation Specification Error Test; Y = real output (measured as annualized percentage contribution of the industrial sector to GDP); GDP = gross domestic product; CRDT = credit channel (measured as the ratio of private sector credit to GDP); INT = interest rate channel (this is the real lending rate); EXR = exchange rate channel. The regression estimate in Table 4 reveals that all the regres- Figure 7. Recursive estimate’s CUSUM test. sors exert negative influence on industrial sector output. This Note. CUSUM = Cumulative Sum Control Chart. result shows that monetary policy has relatively the same effect on output both in the long run and the short run. It is worthy to model has no serial correlation. This confirms the result of note that the negative influence of INT on Y is of less magni- Durbin–Watson (DW) result in Table 3 which shows the tude in the short run—a unit change in leads to 0.24% decline same result. The second test for heteroskedasticity reveals in real output, but the effects get more significant in the long that our model is homoskedastic. These results are desirable run at 37.2%. The ECT has the right sign and is significant at and confirm that our overall results are not spurious hence 5%. The ECT indicated the speed of adjustment toward long- reliable. run equilibrium. The result, therefore, shows that 72.2% of the The p values of the t test, F statistics, and the Likelihood ratio deviation from equilibrium path is corrected on an annual basis. in Table 6 show that the null hypothesis that the model is cor- rectly specified is accepted. This also indicates that the model does not have any specification error. This result is confirmed Validity and Stability Tests by the Cumulative Sum Control Chart (CUSUM) test below. In the results reported in Table 5, Breusch–Godfrey Serial The graph in Figure 7 shows that our model is stable. Its Correlation Lagrange multiplier (LM) Test indicates that our stability is explained with the blue line within the upper and 10 SAGE Open Figure 8. Histogram normality test. lower bound red lines. The above plot remaining within the a speed of adjustment of 72.2%. This implies that disequi- critical bounds of the 5% significance level also reveals that librium in the system is corrected at the rate of 72.2% annu- our model is correctly specified. ally. Based on the findings, we recommend that credit The normality test result is presented in Figure 8. The should be made available to the productive sectors of the result shows that the series are normally distributed as indi- economy at a competitive rate with adequate monitoring. cated by the p value of the JB statistic. Concerted efforts toward effective guidelines and supervi- sion are also recommended to ensure that credit is not diverted to unproductive sector of the economy. The key Conclusion and Recommendations implication is that private sector credit, interest rate, and exchange rate are effective channels for monetary policy Variation in monetary policy stance is expected to have an transmission in Nigeria, and a policy action should be put in effect on industrial sector productivity. Determining the place to effectively harness these key channels to stimulate appropriate policy of choice remains the role of the central the real sector of the economy and boost economic activi- bank that sets policy targets and determines transmission ties. It is therefore important that the monetary authorities channels that will produce expected result. This study was consider these variables as major channel for monetary pol- aimed at ascertaining the industry effects of monetary policy icy implementations. Another critical policy implication is and find out whether monetary policy transmission channels that rising interest rate stifle growth and the policy makers jointly have a long-run relationship with industrial output must take policy actions to stabilize MPRs and implement growth in Nigeria. Our baseline regression result reveals effective foreign exchange policy aimed at stimulating real that the private sector CRDT, INT, and the EXR all have a output growth in the economy. negative effect on real output growth (Y). The ECM esti- mate relatively corroborates the above findings. However, Declaration of Conflicting Interests the degree of responsiveness of Y to changes in the mone- tary policy was found to be higher (or more significant) in The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. the long-run compared with the short-run dynamics. For instance, 1% change in INT caused Y to fall by 0.24% in the Funding short run and by 37.2% in the long run. Same unit change in EXR leads to 3.9% decline in Y in the short run and 12.9% The author(s) received no financial support for the research, author- fall in the long run. The magnitude of effect of CRDT on Y ship, and/or publication of this article. appears stable both in the long run and short run at 24.07% and 24.09%, respectively. Moreover, the Johansen cointe- References gration test results indicate that monetary policy transmis- Afonso, J. R., Araújo, C. E., & Fajardo, B. G. (2016). The role sion channels jointly have a long-run relationship with of fiscal and monetary policies in the Brazilian economy: industrial output growth. This finding was confirmed by Understanding recent institutional reforms and economic both the Trace and the Max-Eingen tests. The ECM result changes. The Quarterly Review of Economics and Finance, 62, further confirmed the cointegrating association and revealed 41-55. Ezeaku et al. 11 Arnold, I. J. M., Kool, C. J. M., & Raabe, K. (2006). Industries Lerskullawat, A. (2017). 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Uche Boniface Ugwuanyi is currently a senior lecturer in the Department of Accountancy. He has broad experience in the field of accounting and finance. Author Biographies Hillary Chijindu Ezeaku is a doctoral student of banking and N. J. Modebe is a currently a senior lecturer in the Department of finance, University of Nigeria. His research interests are in interna- Banking and Finance. She has long years of corporate and academic tional Finance, econometrics modeling, macroeconomics, and experience both in Nigeria and in the United States. development finance. Emmanuel Kalu Agbaeze is currently a senior lecturer in the Imo Godwin Ibe is a lecturer in the Department of Banking and Department of Management. His vast knowledge and research Finance, University of Nigeria. He has lectured in a number of experience cuts across several disciplines.

Journal

SAGE Open – SAGE

Published: Apr 13, 2018

Keywords: industrial output; monetary policy; credit channel; interest rate channel; Johansen cointegration; error correction model

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