Volume 5, Issue 1, 2020 42 Quality of Governance and Economic wellbeing: The Nigerian Experience Richard, O. Ojike1 Nkechinyere, R. Uwajumogu1 Marius Ikpe1 Sunday, V. Agu2 Joseph, I. Amuka3 1Department of Economics and Development Studies, Alex Ekwueme Federal University,Ndufu-Alike Ikwo, Ebonyi State, Nigeria. 2Department of Economics, Enugu State University of Science and Technology, Enugu. 3Department of Economics, University of Nigeria, Nsukka. Abstract Governance can be good or bad. Good governance promotes economic well being, while bad governance undermines it through harsh policies and poor implementation of the good policies. With the experience of the developed countries of the world, democracy is associated with good governance. However, with more than 20 years of democratic experience in Nigeria, the poor state of the economic indicators of wellbeing continued to go down. Based on the foregoing, this study examined the impact of governance on economic wellbeing in Nigeria for the period 1996q1-2018q4 using autoregressive distributed lag model (ARDL) bounds test approach. Per capita GDP growth was used as a proxy for economic wellbeing while regulatory quality index was used as proxy for quality of governance. The cointegration test result showed that there is a long run relationship between quality of governance and economic wellbeing. The study findings show that quality of governance has a negative significant impact on economic wellbeing in both short- and long-run. Based on the findings and the definition of regulatory quality by the worldwide governance indicators as perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development; the study recommends for an improved oversight function by the legislative arm of the government to ensure that public policies and regulations are properly implemented to promote private sector development and the economic wellbeing of the Nigerian populace. Keywords: Governance, ARDL, Economic wellbeing, Regulatory quality index, Nigeria. Corresponding Author: Richard O. Ojike Department of Economics and Development Studies, Alex Ekwueme Federal University Ndufu-Alike Ikwo, PMB 1010, Abakaliki, Ebonyi state, Nigeria Email: ricvivojaiyke@yahoo.com Phone No: +2348030888231 1. Introduction Governance is a broad concept which describes ways which the public sector excises its powers in its bid to manage economic and social components (World Bank, 1994). mailto:ricvivojaiyke@yahoo.com Volume 5, Issue 1, 2020 43 Exercise of such powers by the state determines level and direction of economic performance as measured by various indices of performance. In recent times, scholars are beginning to realize the useful role of governance institution in the process of growth, development and wellbeing (Mauro 1995; Knack and Keefer 1995; Alesina 1998; Przeworski et al 2001; Feng 2003). Governance can be good or bad. Good governance promotes economic well being, while bad governance undermines it through harsh policies and poor implementation of the good policies. With the experience of the developed countries of the world, democracy is associated with good governance. However, with more than 20 years of democratic experience in Nigeria, the poor state of the economic indicators of wellbeing continued to go down. For instance, the total employment to population ratio for fifteen years and above dropped from 57.8% in 1999 to 48.6% in 2019(World development indicators, 2020), the GDP growth rate dropped from 15.3% in 2002 to 2.2% in 2019(World development indicators, 2020), the inflation rate increased from 6.6% in 1999 to 16.5% in 2017(CBN Statistical Bulletin, 2018) etc. Worldwide governance indicators utilized six indicators- control of corruption, government effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law, voice and accountability- to measure government performance. All the six indicators ranked Nigeria negative from 1996 to 2018 in a scale of -2.5(weak performance) to 2.5(strong performance), showing that governance in Nigeria is bad in all measures. The figure 1 below shows the Nigeria governance performance ranking using the regulatory quality index. Figure 1 Nigeria governance performance ranking index with regulatory quality measure Source: Authors’ plot using Worldwide governance indicator (2020) data -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 Volume 5, Issue 1, 2020 44 In scale of -2.5(weak performance) to 2.5(strong performance), Nigeria’s governance regulatory quality index shows weak performance for the study period. It is worthy to note that Nigeria had her highest index in 2013 and the lowest in 2004. The implication is that government policy formulation and implementation that promotes private sector development were worst during the Obasanjo led government in 2004 while the best was during Jonathan’s regime in 2013. Evidences from the reviewed empirical literature shows that none of the Nigeria studies used specifically regulatory quality index to measure quality of governance. None of the studies also utilized the ARDL bound test approach to examine the short- and long-run effect of quality of governance on economic wellbeing. In other to fill the identified gaps, the objectives of this study are; (1). To examine the impact of quality of governance on economic wellbeing using regulation quality index to measure the governance performance. (2). To use the ARDL bound test approach in examining both short- and long-run effect of quality of governance on economic wellbeing. 2. Literature Review There has been no consensus regarding whether or not governance promotes or hinders growth and wellbeing. In the argument, the first school of thought (The conflict school) argue that by creating consumption pressures, instigation of distributional conflicts, and equally by discouragement of capital accumulation, democracy hinders the process of growth among developing countries. However, empirical work by Przeworskiet et al. (2000) failed to provide support for this as their findings provided evidence of non- existing tradeoffs between democracy and development. The second school (Compatibility theorists) posits that governance exerts positive influence on growth. They argue that, presence of fundamental human rights, as well as political rights creates the right social environment conducive for economic development. This has been empirically supported by a number of studies (Knack and Keefer 1995; Mauro 1995; Alesina 1998). The third school (The Skeptical School) argues that there is absence of systematic relation between governance and economic development. This non – systematic relation according to Olson (1996) and Knack (2003) has been offered as reasons behind observed anomalies inherent in both old and new growth theories, which were unable to explain factors that explain development in most countries. Volume 5, Issue 1, 2020 45 Empirical evidence from both regional (cross – country) studies and country specific investigations support positive effect of governance on economic growth as an aspect of wellbeing. For instance, in a study of effect of governance on economic performance among sub Saharan Africa, Habtumu (2008) for the period 1996 – 2005, using both difference and system dynamic Generalized Methods of Moments (GMM), provided evidence that governance positively affects economic growth and general wellbeing. This evidence was equally supported by a good number of other studies with similar geographical scope (Fayisa and Nsiah, 2010; Kaufman and Kraay 2002; Cooray, 2009; Emara and Chin 2016; Bayar 2016; Tarek and Ahmed 2013, Lahouij, 2017). On the other hand, in a study of the Rwandan economy, while adopting an estimation strategy that allows separation of correlation into two components, Habyarimana, and Dushimayezu (2018) provided evidence in support of positive effect of governance on growth. Similar outcome in the context of country – specific study was also arrived at by Alomaisi et al. (2016) for Yemen, Ramadhan, (2019) for Indonesia and a number of Nigerian specific studies (Uda and Ayara 2014; Ovat and Bassey 2014; Adenuga and Avbuomwan 2012). On the contrary, Sikod and Teke, (2012) found a negative effect of governance on growth in their study for Cameroon, while Yerrabati and Hawkes (2015) provided a mixed evidence on the effect of governance on growth. Nigeria specific studies differ in methodology and measurement of governance. Adenuga and Avbuomwan (2012) added up the six indicators to measure governance in an error correction model. Uda and Ayara (2014) included five out of the six indices in a single Ordinary Least Squares (OLS) model. Ovat and Bassey (2014) used descriptive statistics to analyze the six indicators relationship with economic growth. Dates of the Nigeria specific studies also show that recent studies are lacking in literature. The available empirical literature shows that none of the Nigeria studies used specifically the regulation quality index to measure the short- and long-run impact of governance on economic wellbeing in an ARDL bounds test approach. This study is set to fill the above identified gaps. Volume 5, Issue 1, 2020 46 3. Methodology 3.1 Theoretical Framework The theoretical framework adopted in this study is the Solow growth model, which can be stated thus: Y(t) = f (K (t), A(t) L(t) …………………………………………………………. (1) Where Y is output, K is capital, L is labour, A is knowledge and t is time. A and L enter multiplicatively and is referred to as effective labour (Romer, 1996). Governance as an institution that manages the economy enters the model through her policy formulation and implementation to increase economic growth and general wellbeing of the citizens. Public policy if properly implemented will improve the output through its effect on capital and the effective labour inputs. 3.2 Model specification Solow growth model will be modified to incorporate the governance variable and other economic variables. Model of the study is specified in functional form thus: PCGDPG = f( GFCF, LFPR, GOVRN, FRED, EXCHR, INFLR, BRKPCGDPG) ………… (2) The econometric form of the model is stated as follows: PCGDPGt = β0 + β1GFCFt + β2LFPRt + β3GOVRNt + β4FREDt + β5EXCHRt + β6INFLRt + β7BRKPCGDPGt + Єt …………………………………………………………………… (3) Where PCGDPG is per capita GDP growth (proxy for economic wellbeing), GFCF is gross fixed capital formation ( proxy for capital inputs), LFPR is labour participation rate ( proxy for effective labour), GOVRN is quality of governance (measured with regulatory quality index), FRED is federal government recurrent expenditure on education (proxy for government investment in education), EXCHR is naira official exchange rate with US dollar, INFLR is inflation rate, BRKPCGDPG is per capita GDP growth structural break dummy, Є is the error term, and t is time. β0 is the intercept and β1 - β7 are the slope coefficients of the variables. The a priori expectations are; β1 > 0, β2 > 0, β3 > 0 or <0, β4 > 0, β5 < 0, β6 < 0, β7 > 0. Based on the results of the unit root test in table 1 below, the study adopted Pesaran, Shin and Smith (2001) Autoregressive Distributed Lag Model (ARDL) bounds test approach for cointegration test. The bound test approach is applicable when the regressors are integrated of order zero (I(0)) or one (I(1)) or are mutually cointegrated (Pesaran and Volume 5, Issue 1, 2020 47 Shin (1999). It is also efficient when the sample size is small. The basic ARDL (p, q) model of the study can be stated thus: PCGDPGt = β0 + β1PCGDPGt-1 +...+ βpPCGDPGt-p + α0GFCF + α1GFCFt-1 + …+ αqGFCFt-q + δ0LFPR + δ1LFPRt-1 + …+ δqLFPRt-q + λ0GOVRN + λ1GOVRNt-1 + …+ λqGOVRNt-q + η0FRED + η1FREDt-1 + …+ ηqFREDt-q + φ0EXCHR + φ1EXCHRt-1 + …+ φqEXCHRt-q + υ0INFLR + υ1INFLRt-1 + …+ υqINFLRt-q + ψ0BRKPCGDPGt + Єt ……………… (4) Where the variables remain as defined in equation (3). p, and q are the lag orders that will be determined through Akaike information criterion (AIC).. Following Pesaran et al (2001), the bound test equation is stated thus: ΔPCGDPG = β0 +   p i 1 βiΔPCGDPGt-i +   q j 0 αjΔGFCFt-j +  q k 0 δkΔLFPRt-k +  q l 0 λlΔGOVRNt-l +   q m 0 ηmΔFREDt-m +  q n 0 φnΔEXCHRt-n +  q o 0 υoΔINFLRt-o + ψ0BRKPCGDPGt + Ω0PCGDPGt-1 + Ω1GFCFt-1 + Ω2LFPRt-1 + Ω3GOVRNt-1 + Ω4FREDt-1+ Ω5EXCHRt-1 + Ω6INFLRt-1 + 𝑒t .. (5) Where; Δ represents difference. The bounds test for cointegration is based on an asymptotic non-standard F-test on the lagged level variables in equation 2. In this regards, two bounds critical values are generated. The upper bounds critical value serves as a bench mark for I(1) variables while the lower bounds critical value is the bench mark for I(0) variables. Null hypothesis of no cointegration; H0: Ω0 = Ω1 = Ω2 = Ω3= Ω4 = Ω5 = Ω6 = 0 is tested against the alternative hypothesis of presence of conintegration: H1: Ω0 ≠ Ω1 ≠ Ω2 ≠ Ω3 ≠ Ω4 ≠ Ω5 ≠ Ω6 ≠ 0. Null hypothesis of no cointegration is rejected if the computed F-statistics exceeds the upper bounds critical value. If the calculated F-statistics is lower than the lower bounds critical value, the null hypothesis cannot be rejected. The test is inconclusive when the calculated F-statistics is between lower and upper bounds critical values. Volume 5, Issue 1, 2020 48 3.2 Data The study utilized an annual data that spanned from 1996 to 2018. Eviews 9.0 was used to interpolate the annual data to quarterly series (1996q1-2018q4). GDP per capita growth, inflation rate, and exchange rate were sourced from World Development Indicators (2020). Federal government recurrent expenditure on education and gross fixed capital formation were sourced from CBN Statistical Bulletin (2018). Regulation quality index that measured the quality of governance was sourced from Worldwide Governance Indicators (2020). Regulation quality index is measured in a scale of -2.5 to 2.5 (-2.5 = weak performance, 2.5 = strong performance). The data publication started in 1996 but gapped 1997, 1999 and 2001. Average of 1996 and 1998 was used to fill 1997; average of 1998 and 2000 was used to fill 1999, while average of 2000 and 2002 was used to fill 2001. 4. Results Presentation and Discussion 4.1 Unit root test Following Perron (2006) assertion that conventional unit root test approaches like Augmented Dickey–Fuller (ADF; Dickey and Fuller 1979, 1981), Philip Perron (PP; Phillips and Perron 1988), Kwiatkowski–Phillips–Schmidt–Shin (KPSS; Kwiatkowski et al. 1992) and Ng and Perron (2001) give biased results due to their low explanatory powers to identify unknown structural breaks in time series, the study adopted Zivot and Andrews (1992) unit root test approach that incorporates unknown single structural break in its unit root test. Volume 5, Issue 1, 2020 49 Table 1; Zivot-Andrews Unit Root Test Variable Level form First difference Order of integration Brake date t-statistic Brake date t-statistic PCGDPG GFCF LFPR GOVRN FRED EXCHR INFLR 2000q1 2015q2 2012q1 2005q1 2011q1 2015q1 2006q1 -4.3478 -1.9776 -7.1975*** -4.5849 -6.2882*** -3.2191 -4.5400 2000q1 2007q1 2012q4 2005q1 2011q3 2015q1 2005q4 -15.8632*** -7.7208*** -11.0071*** -9.90845*** -10.1884*** -10.7398*** -7.2705*** I(1) I(1) I(0) I(1) I(0) I(1) I(1) Source: Authors’ computation using sourced data Note: **indicates significance at 5%, ***indicates significance at 1% Summary of the unit root test result in table 1 shows that per capita GDP growth, gross fixed capital formation, quality of governance, exchange rate and inflation rate are stationary at first difference while labour force participation rate and federal government recurrent expenditure on education are stationary at level form. Since none of the variables is integrated of order two, Pesaran et al (2001) ARDL bound test technique is appropriate for the cointegration test. 4.2: ARDL Model Selection result Figure 2: Akaike information Criteria model selection 3.655 3.660 3.665 3.670 3.675 3.680 3.685 3.690 AR D L( 4, 0 , 0 , 1 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 0 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 1 , 0 , 0 , 0 ) AR D L( 4, 0 , 0 , 1 , 0 , 1 , 1 ) AR D L( 4, 0 , 0 , 0 , 0 , 0 , 0 ) AR D L( 4, 0 , 0 , 1 , 0 , 0 , 2 ) AR D L( 4, 0 , 0 , 1 , 0 , 1 , 0 ) AR D L( 4, 0 , 0 , 0 , 0 , 1 , 1 ) AR D L( 4, 0 , 0 , 0 , 0 , 0 , 2 ) AR D L( 4, 1 , 0 , 1 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 2 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 1 , 1 , 0 , 1 ) AR D L( 4, 1 , 0 , 0 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 0 , 0 , 1 , 0 ) AR D L( 4, 0 , 1 , 1 , 0 , 0 , 1 ) AR D L( 4, 0 , 0 , 1 , 0 , 0 , 3 ) AR D L( 4, 0 , 0 , 1 , 1 , 0 , 0 ) AR D L( 4, 0 , 0 , 1 , 1 , 1 , 0 ) AR D L( 4, 0 , 0 , 0 , 1 , 0 , 1 ) AR D L( 4, 0 , 0 , 1 , 0 , 1 , 2 ) Akaike Information Criteria (top 20 models) Source: Authors’ plot from the ARDL model Volume 5, Issue 1, 2020 50 Figure 2 above shows that using Akaike information criteria (AIC), ARDL (4, 0, 0, 1, 0, 0, 1) has the lowest value and was used for the study. The R-squared (0.8563) and the Adjusted R-squared (0.8311) of the ARDL (4, 0, 0, 1, 0, 0, 1) regression result in the appendix showed that the model has a good fit. F-statistic of 33.9226 with a p-value of 0.0000 showed that the model is statistically significant at 1% level. 4.3: Cointegration test Table 2: Bounds test result Null Hypothesis: No long run relationships exist Calculated F-statistic = 3.95 Level of significance Critical bound value Lower bound I(0) Upper bound I(1) 10% 5% 2.5% 1% 2.12 2.45 2.75 3.15 3.23 3.61 3.99 4.43 Source: Authors’ computation from the ARDL model The cointegration test result in table 2 above shows that the F-statistic is higher than the upper critical bound of 5% level of significance, indicating the presence of long run relationship between the dependent variable and the independent variables in the model. 4.4 Diagnostic tests for the model 4.4.1 Serial autocorrelation test Table 3: Correlogram-Q-Statistic Q-statistic probabilities adjusted for 4 dynamic regressors Autocorrelation Partial Correlation AC PAC Q-Stat Prob* . |*. | . |*. | 1 0.098 0.098 0.8773 0.349 . | . | . | . | 2 0.012 0.002 0.8902 0.641 .*| . | .*| . | 3 -0.074 -0.077 1.4071 0.704 **| . | **| . | 4 -0.230 -0.218 6.3858 0.172 . | . | . | . | 5 0.001 0.045 6.3859 0.270 . | . | . | . | 6 -0.035 -0.040 6.5041 0.369 .*| . | .*| . | 7 -0.071 -0.101 6.9939 0.430 .*| . | **| . | 8 -0.185 -0.234 10.389 0.239 . | . | . | . | 9 -0.053 -0.016 10.669 0.299 . | . | . | . | 10 -0.016 -0.041 10.696 0.382 . | . | . | . | 11 0.020 -0.050 10.738 0.465 .*| . | **| . | 12 -0.092 -0.230 11.619 0.477 . | . | . | . | 13 0.016 0.009 11.645 0.557 . | . | . | . | 14 0.027 -0.018 11.720 0.629 . | . | . | . | 15 0.038 -0.038 11.873 0.689 . | . | .*| . | 16 0.063 -0.103 12.304 0.723 *Probabilities may not be valid for this equation specification. Source: Authors’ computation from the ARDL model . Volume 5, Issue 1, 2020 51 Result of the correlogram-Q-statistics in table 2 shows that none of the lags is statistically significant at 5% level, indicating the absence of serial autocorrelation in the model. 4.4.2: Stability test Figure 3: Recursive Estimates (OLS Only) – CUSUM Test -30 -20 -10 0 10 20 30 05 06 07 08 09 10 11 12 13 14 15 16 17 18 CUSUM 5% Significance Source: Authors’ computation from the ARDL model CUSUM test result in figure 3 above shows that the coefficients of the model are stable, since the plot of the CUSUM statistics remained within 5% level of significance. 4.4.3 Normality, Heteroskedasticity, and Specification tests Table 4: Table 4: Jarque-Bera Normality test, Breusch-Pagan-Godfrey Heteroskedasticity test, and Ramsey RESET Specification test Test Statistic Prob. Jarque-Bera Normality Jarque-Bera 0.9592 0.6190 Breusch-Pagan-Godfrey Heteroskedasticity Obs*R-squared 19.7964 0.1004 Ramsey RESET Specification F-statistic 1.0431 0.3105 Source: Authors’ computation from the ARDL model The non-significant of the normality test, heteroskedasticity, and the specification test shows that the model is normally distributed, homoskedastic, and well specified. Volume 5, Issue 1, 2020 52 4.5 Short run and Long run results Table 6: Results of the short run and long run models Short run model result Long run model result Dependent variable: D(PCGDPG) Dependent variable: PCGDPG Variable Coeffecien t t-Statistic Variable Coeffecien t t-Statistic D(GFCF) D(LFPR) D(GOVRN) D(FRED) D(EXCHR) D(INFLR) D(BRKPCGDPG) CointEq(-1) 0.00008 0.3099 -6.2636 0.0032 -0.0194 -0.0559 3.7492 -0.5194 0.7705 2.1250** -3.1257*** 0.9355 -2.2669** -0.8336 4.4630*** -5.6343*** GFCF LFPR GOVRN FRED EXCHR INFLR BRKPCGDP C 0.00015 0.5967 -6.6520 0.0062 -0.0373 0.0459 7.2182 -41.7300 0.791676 2.3011** -2.9760*** 0.9593 -2.6421** 0.4380 5.9508*** -2.5203** Source: Authors’ computation from the ARDL model Note: **indicates significance at 5%, ***indicates significance at 1% The short- and long-run results show that quality of governance (GOVRN) has a negative significant impact on economic wellbeing. Since the regulatory quality index used to measure quality of governance is scaled from -2.5(weak) to 2.5(strong) and Nigeria had negative indices throughout the study period, the quality of governance in Nigeria by the measure is bad. From the above assertion therefore, the negative coefficient of the quality of governance conforms to a priori expectation. The finding is not consistent with the works of Uda and Ayara (2014), Ovat and Bassey (2014), and Adenuya and Avbuomwan (2012). It is consistent with the work of Sikod and Teke (2012) for Cameroon. An increase in the quality of governance (bad governance i.e., reduction in regulatory quality) by one unit on average, leads to 6.26 units and 6.65 units reduction in economic wellbeing of Nigerians in short- and long- run respectively. On the other way round, a unit reduction in bad governance (improvement in regulatory quality) will increase the economic wellbeing by the same units in both short- and long-run. This implies that the more government fails to formulate sound policies and implement the good policies very well; the poverty level of Nigerians will increase by 6.26 units and 6.65 units in both short-run and long-run. The finding attributes the Brookings Institute report (2018) that Nigeria is the poverty capital of the world to high level of bad governance. The report asserts that 87 million Nigerians earn less than 1.9 US dollars (N684.00) in a day and six persons go into extreme poverty in every minute. Following the Worldwide Governance Indicators definition of regulation quality index as perceptions of the ability of the government to Volume 5, Issue 1, 2020 53 formulate and implement sound policies and regulations that permit and promote private sector development; the implication of the finding is that the various regimes of government in Nigeria within the study period failed to implement to the latter policies that will promote private sector development; which resulted to the downward trend in most of the indicators of economic wellbeing. Gross fixed capital formation (GFCF) conforms to a priori expectation in both short-run and long-run but has non-significant impact on economic wellbeing. This can be attributed to the anti-business policies that make the investment environment unfavourable to investors. To buttress the above point, Nigeria’s ranking of 131 in 2019 World Bank ease of doing business index out of 190 countries was regarded as a huge improvement from 2018 ranking of 146 (Salaudeen, 2019).This shows how poor the ranking has been over the years if a position of 131 out of 190 economies is a huge achievement. The labour force participation rate (LFPR) conforms to a priori expectation in both short run and long run. It exhibited a positive significant impact on economic wellbeing. A unit increase in labour force participation rate improves the economic wellbeing by 0.31 unit and 0.60 unit in the short- and long-run respectively. The finding shows that the economy’s workforce engages actively in the labour market, indicating that the size of labour supply available to engage in the production of goods and services are adequate. Federal government recurrent expenditure on education (FRED) has a non- significant impact on economic wellbeing in the short- and long-run. This can be attributed to the poor funding of the education sector in Nigeria. Budgetary allocation to the education sector over the years has remained below 26% of the total annual budget recommended by the UNESCO. The short run and long run results show that exchange rate (EXCHR) has statistically negative significant impact on economic wellbeing in both short run and long run. A unit increase in exchange rate will reduce the economic wellbeing by 0.0194 unit and 0.0373 unit in short run and long run respectively. This shows that the continuous devaluation and depreciation of naira is reducing economic wellbeing instead of promoting export and the associated capital inflow. The inflation rate (INFLR) conforms to a priori expectation in the short run but does conform to a priori expectation in the long-run. It exhibited an insignificant impact on economic wellbeing in both short- and Volume 5, Issue 1, 2020 54 long-run. This shows that the level of inflation is not discouraging economic activities that enhance the standard of living of Nigerians. The positive significant outcome of the per capita GDP growth structural break variable (BRKPCGDPG) in both short-and long-run results show that the average economic wellbeing of Nigerians increased as a result of the new public policy that resulted to the structural break. This implies that public policy that resulted to the structural break has a significant effect on the mean economic wellbeing of Nigerians. CointEq(-1) that captures the speed of adjustment to any disequilibrium in the model is negative and statistically significant with an adjustment speed of 51.94%. This shows that 51.94% of the difference between long run and short run economic wellbeing is corrected within a quarter. 5. Conclusion Following the findings, the study concludes that bad governance in Nigeria due to poor policies formulation and implementation that discourages private sector development affects economic wellbeing of the citizenry negatively. The study attributed the Brooking Institute (2018) report that six Nigerians go into extreme poverty in every minute due to bad governance. The study also concludes that labour force participation rate affects the economic wellbeing positively, while exchange rate affects the economic wellbeing negatively in both short- and long-run. Based on the findings, the study recommends for an improved oversight function by the legislative arm of the government to ensure that public policies and regulations are properly implemented to promote private sector development and the economic wellbeing of the Nigerian populace. Volume 5, Issue 1, 2020 55 References Adenuga, A.O, and Evbuomwan, O. 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Advances in Economics and Business, Vol. 3(1) pp: 1-21 https://edition.cnn.com/2019/10/24/africa/nigeria-improves-in-world-bank-anking/index.htm https://edition.cnn.com/2019/10/24/africa/nigeria-improves-in-world-bank-anking/index.htm http://ibimapublishing.com/articles/JESR/2013/390231/ https://databank.worldbank.org/source/worldwide-governance-indicators Volume 5, Issue 1, 2020 58 Appendix: ARDL model result Dependent Variable: PCGDPG Method: ARDL Date: 07/09/20 Time: 11:05 Sample (adjusted): 1997Q1 2018Q4 Included observations: 88 after adjustments Maximum dependent lags: 4 (Automatic selection) Model selection method: Akaike info criterion (AIC) Dynamic regressors (4 lags, automatic): GFCF LFPR GOVRN FRED EXCHR INFLR Fixed regressors: BRKPCGDPG C Number of models evalulated: 62500 Selected Model: ARDL(4, 0, 0, 1, 0, 0, 1) Variable Coefficient Std. Error t-Statistic Prob.* PCGDPG(-1) 0.681226 0.107230 6.352939 0.0000 PCGDPG(-2) 1.10E-14 0.129967 8.47E-14 1.0000 PCGDPG(-3) -1.28E-14 0.129967 -9.86E-14 1.0000 PCGDPG(-4) -0.200636 0.100578 -1.994828 0.0497 GFCF 7.77E-05 0.000101 0.770484 0.4435 LFPR 0.309943 0.145854 2.125026 0.0369 GOVRN -6.263550 2.003911 -3.125663 0.0025 GOVRN(-1) 2.808451 2.026677 1.385742 0.1700 FRED 0.003222 0.003444 0.935541 0.3526 EXCHR -0.019385 0.008551 -2.266881 0.0263 INFLR -0.055850 0.066998 -0.833602 0.4072 INFLR(-1) 0.079676 0.050761 1.569606 0.1208 BRKPCGDPG 3.749216 0.840059 4.463037 0.0000 C -21.67501 9.478833 -2.286675 0.0251 R-squared 0.856309 Mean dependent var 2.626429 Adjusted R-squared 0.831066 S.D. dependent var 3.413289 S.E. of regression 1.402916 Akaike info criterion 3.659892 Sum squared resid 145.6447 Schwarz criterion 4.054014 Log likelihood -147.0353 Hannan-Quinn criter. 3.818674 F-statistic 33.92262 Durbin-Watson stat 1.771210 Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model selection.