CC BY
114
Section 11. Economics, organization and management of enterprises, branches, complexes
DOI: http://dx.doi.org/10.20534/EJEMS-17-2-88-101
Strike Mbulawa, Samuel Chingoiro
Faculty of Business and Accountancy, Botho University, Gaborone, Botswana
E-mail: [email protected]
A test for nonlinearity between fiscal policy and economic performance in Botswana: An Autoregressive Distributed Lag approach
Abstract: The study employs the autoregressive distributed lag (ARDL), bounds testing approach, pair-wise granger causality and annual time series data for the period 1975-2014 to: examine the short and long run connection, test for non linearity and causality between fiscal policy variables (expenditure on education and taxation) and economic performance. Findings, in both the long and short run, show that there is a non linear, hump-shaped, relationship between expenditure on education and economic performance. Taxation on products has a significant negative impact on economic performance while causality flows from the former to the later. It is imperative that the government increases the flow of non tax incomes (like export revenues, loans at concessionary interest rates) into the treasury. This reduces the distortionary effect of taxes on economic performance, enhance productivity and free up investment funds. A targeted expenditure approach and monitoring on education is essential to improve efficiency so as to avoid its distortionary effects after reaching an optimal level. This should be complemented by increasing spending to develop skills as opposed meeting only operating costs.
JEL Classification numbers: E62, F43, H25, I25.
Keywords: Botswana, Economic performance, fiscal policy, ARDL.
1. Introduction and Background may improve the economy. In the United States ofAmer-
Fiscal policy has traditionally been associated with ica (USA) the ESP, introduced in 2009, was meant to using government expenditure and taxation to influence bring the economy out of the 2008 recession, save jobs the level of economic activity. Government spending, and boost economic growth but evidence shows that the through budgets, is a deliberate attempt to influence USA economy contracted by 2.8% in 2009. However, economic variables in a particular direction like in- some 640 329 jobs were saved in 2009 but this was also creasing growth and employment creation. One way attributed to expansionary monetary policy and active of reinvigorating the economy is through the use of an emerging markets. The ESP failed to reduce unemploy-economic stimulus package (ESP). The aim of such a fis- ment to the desired 9% level and debt also increased [2]. cal policy initiative is to take the economy out of reces- Similarly, the Kenyan government introduced the ESP in sion, boost employment and spending. This is rooted in 2009/2010 fiscal year, following a growth rate of 1.7% in Keynesian economics which advocates for increasing 2008, to restore economic growth, expand economic op-government spending to reduce the impact of a reces- portunities and create employment. The economy regission. [4] argue that an ESP is needed when the econo- tered a 5.2% growth in 2010 with good growth prospects my is weak and there is high likelihood for further future but most of the projects were still incomplete [55]. deterioration. It is further argued that the ESP may not Botswana has witnessed high and consistent growth,
be sufficient to correct the economic imbalance but it within the Southern Africa region, since independence in
1966. The discovery of diamonds in 1967 has positively contributed to the country's development and attaining the middle income status. The government has managed to put in place sound macroeconomic policies and strong financial management. Successive national development plans have reiterated the government's role as a promoter, rather than a participant, of economic growth. This may remove doubt on the likelihood of success of current fiscal initiatives by the government [21]. The ESP, in the context of Botswana, was a conscious government decision announced during a state of the nation address by the President of the Republic, His Excellence Lieutenant General Seretse Khama Ian Khama. It was put in place against the backdrop of global economic challenges like persistent droughts and greater market volatility leading to a fall in mineral prices. Botswana has been hard hit by low mineral prices, like diamonds and gold, which form the country's huge revenue base. This created the need to come up with an ESP to stimulate growth, increase the pace of public service delivery, create employment and promote economic diversification. Specifically, the ESP focuses on the following: increased land servicing, manufacturing, road construction, enhance food security, enhance economic activity in the rural areas, improved education and healthcare facilities and information computer technology. The government has, as a sign to show greater commitment to this endeavor, given the Vice President (VP) of the Republic of Botswana the task to coordinate the implementation of the ESP. The VP is helped by the Cabinet Sub-Committee, Technical Committee and District Development Committees [42]. Initially the government decided to fund the ESP by running down part of foreign currency reserves which stood at 88.1 billion Pula in July 2015 [14]. It has now turned out that this will be funded using funds created within the country's budget [48]. Statistics Botswana report shows that the growth rate of the economy has slowed down being 10.8% (2010), 6.1% (2011), 4.2% (2012), 9.3% (2013), 3.2% (2014) and 1.0% (2015). Inflation fell from 3.8%
(2014) to 3.1% (2015) but it is still within the Bank of Botswana's target of between 3 and 6%. Government expenditure [35] has been maintained at around 30% of Gross Domestic Product (GDP) being 36.6% (2011), 34.9% (2012), 32.8% (2013), 29.9% (2014) and 31%
(2015). Tax revenues have remained within the same region being 28.1% (2011), 32.4% (2012), 31.3% (2013), 32.1% (2014) and 31.7% (2015). Outstanding debt as a percentage of GDP remained within the 40% ceiling being manageable as follows: 22% (2010), 20% (2011), 19.5% (2012), 21.8% (2013), 23% (2014) and 24% (2015).
Argues that public spending and saving strategies are important in supporting economic growth in Botswana considering that most of the diamond revenue accrues to the government [22]. The success of government spending initiatives is dependent on honesty and efficiency in the tendering process which needs stringent technical scrutiny. Botswana is credited with maintaining fiscal discipline over the years which contributed to her success. The government has implemented, in the past, other fiscal initiatives directly or indirectly targeting the poor. The government has implemented programmes like Financial Assistance Policy, Micro and Medium enterprises and Citizen Entrepreneurial Development which aimed at improving production and creating more jobs. Initiatives like Arable Lands Development Programme, Accelerated Rainfed Arable Programme and Drought Relief Programme have targeted the rural populace. Other initiatives targeting those who are not economically active and those without sources of income include the Destitute Programme and Old age pension scheme. Such programmes have been accepted as beneficial to the economy but they lacked the drive to diversify the economy [69]. On the other hand the advent of the ESP can be taken as an attempt by the government to make up for the shortfalls of the National Development (NDP) 10 which stretched until the end of 2016. NDP 10 contained goals which are still part of the ESP like attaining sustainable rapid economic growth, having a well developed and reliable infrastructure, eradicate absolute poverty, affordable and quality health care [25].
Other stakeholders [17, 11] view the ESP as an initiative by the ruling Botswana Democratic Party to benefit its members and not the ordinary citizens. However, [51] argue that it aims to eradicate a backlog of outstanding projects over the years making it a national project. [51] argue that the ESP will help diversify the economy and the private sector is expected to play its part as well. [10] argue that the previous fiscal stimulus packages which aimed at alleviating the effects of the 2008 economic recession were not used in a conceited and synchronized way to improve growth. The initiatives have failed to yield the desired outcomes. While it is still early to analyze the impact of ESP programme on economic performance, this study aims to assess how the later has been impacted by expenditure on education and taxation, as a proxy for fiscal policy. This study is important for several reasons: previous studies have failed to agree on the impact of fiscal policy on growth and current studies have not explained the impact of fiscal initiatives in the context of Botswana. The government has to make
critical decisions to diversify the economy and improve performance. The government of Botswana is open for dialogue and to receive guidance on policy which this paper seeks to provide. Previous approaches evaluating the effectiveness of fiscal stimulus packages for Botswana produced mixed results. This study guides on the likelihood of the government of attaining the much coveted rate of growth by conducting fiscal activities. The study employs a different econometric approach, autoregressive distributed lag, to examine the link between economic growth and fiscal initiatives. While agreeing with [66] that economic growth in Botswana is state led, we argue that there is a need to identify and break down the causal relationship between fiscal initiatives and growth in both short and long run. Further studies are not clear on whether or not there is a linear or non linear relationship between fiscal policy variables and economic performance which is important in streamlining policy recommendations.
The study shows that, in both long and short run, there is a non linear, hump-shaped, relationship between expenditure on education and economic performance. Net taxes on products have a significant negative impact on economic performance. Pairwise Granger Causality tests show that the direction of causality flows from taxation to economic performance. On the other hand provision of physical capital has positive and negative effects on economic performance in the short and long run respectively while foreign direct investment positively contributes to long run economic performance. The rest of the study is organized as follows: section 2 reviews both theoretical and empirical literature, section 3 discusses the data and econometric procedure, section 4 discusses the results and section 5 provides conclusions and recommendations.
2. Literature Review
2.1: Theoretical review
The effect of government intervention through fiscal initiatives has been taken as important in achieving stability in the macro economy. Empirical work on the usefulness of fiscal policy was conducted as far back as [31] who examined the responsiveness of price level to economic activity. The failure to settle the debate on whether or not fiscal policy initiatives promote growth has opened further discussions in recent years. Studies still fail to agree on the role of government intervention in promoting economic growth. Other studies [25; 56] are of the view that government involvement in any form retards growth by creating bureaucracy. This view follows the neoclassical growth model which
shows that government policy does not affect the rate of growth but only the output level. However, other studies [49; 30; 12] are of the view that government intervention enhances the growth potential of the country. In this case government intervention helps in resource allocation and regulation of the market. This is supported by the endogenous theory of growth which postulates that the government involvement promotes growth, in the long and short run, through supporting research and development initiatives, investment in physical and human capital and bringing discipline in the economy. The endogenous growth model shows that an increase in government spending raises the steady state rate of growth as a result of spillover effects on investment in both human and physical capital [39]. This study is motivated by the endogenous growth theory which supports the use of fiscal policy initiatives to promote growth.
2.2: Empirical review
This section reviews previous studies linking economic growth and fiscal policy in different economic settings using different approaches. The aim, in this case, is to bring out the key discussions in previous studies and identify areas that have not been fully addressed to justify the discussions in this study.
[13] decomposed public spending and tax revenue into their various components to determine their impact on economic growth. The study found a non-linear relationship between growth and government spending on education, fuel and health. The relationship between growth and expenditure on housing, social security and transport communication is U-shaped. Budget surplus has a positive effect on growth, expenditure on education and social amenities have a stronger positive effect on growth in poorer countries while expenditure on health has a weaker effect. This is supported by [37] who showed that economic growth is enhanced by expenditures on employment, goods and non tax income. Other studies [28; 41; 16; 15] show that, among other forms of government expenditure, education positively contributes to growth. They show that education is a key sector in which resources are to be channeled to promote growth. According to [70] public expenditure on education has direct and indirect effects on growth. Public recurrent expenditure on education has greater positive effect on growth while capital expenditure has effect on education attainment. [53] argue that while government expenditure on education is positively related to growth it does not contribute significantly to the rate of growth. On the other hand several studies [43; 54; 24] argue that expenditure on education adversely affects the rate of economic
growth. This may be explained by increased corruption, bureaucracy and underinvestment in education in developing countries. In a recent study [32] education was found not to have any effect on economic growth.
Evidence [66; 57; 39; 52; 50; 43] also show that government consumption expenditure, foreign direct investment, gross fixed capital formation, exports and tax receipts have a positive effect on growth while budget deficit had no effect. However, [27] argue that fiscal deficits and government consumption expenditure have negative effect on growth and others [52; 43] show that, specifically, capital expenditure by government has a negative effect on growth. Previous studies [39; 23] further argue that the positive relationship between government expenditure and growth is long term in nature. The existence of a long run relationship is further supported by [36], in their recent study, which show that capital expenditure and recurrent expenditure have significant and positive effect on growth. Evidence from Jordan shows a positive effect of current expenses and tax revenue on economic development. Current expenditure exhibits a strong effect than tax revenue [3].
Employed time series data for Kenya and found that unproductive government expenditure and non distor-tionary tax revenue had a neutral effect on growth which confirms economic theory [46]. Productive expenditure had a strong and negative effect on growth and distortion-ary tax revenue had no effect. Investment by government has a positive long run effect on growth. However, these findings were dismissed by [9] using Nigerian data who found that productive expenditure had a positive impact on economic growth over the long run. This is supported by other researchers [12; 60; 71] who found that growth is enhanced through the use of non distortionary taxation and productive government expenditures while the effect of distortionary taxation is neutral. Previous studies [20; 47] suggest that an increase in the rate oftax reduces the rate of economic growth. This is supported by [23] who showed that total tax revenues have a negative impact on growth. However other studies [68; 33] support increasing taxes to enhance economic growth [7] used a sample of eighteen European Union countries and found that fiscal consolidation drags economic growth in the short run. The study finds that expenditure based adjustments are less harmful than revenue based adjustments. However reductions in government investment and consumption were found to reduce economic growth.
Examined a causal relationship between money supply, fiscal deficit, exports and economic growth using time
series data [8]. The study finds a significant causal relationship between fiscal policy and all variables employed. Several studies [43; 23; 67] found a positive unidirectional causal relationship moving from economic growth to fiscal policy variables. However results by [18] suggest a negative causal relationship between economic growth and fiscal revenues. Furthermore other studies [58; 40; 65; 19] found a unidirectional relationship from government expenditure and taxation (fiscal policy) to economic growth which supports the Keynesian hypothesis.
Previous studies have produced mixed results on the relationship between fiscal policy variables and economic performance. The causal relationship was found to be unidirectional moving from fiscal policy to economic growth and vice versa. There are no conclusive results on whether the relationship between the two variables is short or long term in nature. There is no consensus on the effect of fiscal policy on economic growth which has been found to be both positive and negative. Results have been influenced, mainly, by the differences in methodologies, measurement of variables, sample selection and country settings or environment. This leaves the need to verify the relationship in the case of Botswana which has made a decision to spend more funds aiming to influence the future growth patterns.
3. Data and Methodology
Data employed in this research was obtained from [72] for thirty nine (39) years from 1975-2014. The model captured important variables that the government can target in order to improve the performance of the economy. The general model is as follows:
gdppercapitat = f (fiscalpolicy, gfcf, fdi) (1)
The study used economic performance as dependent variable being measured as percentage annual rate of growth of Gross Domestic Product per capita (gdpper-capita) based on constant local currency divided by the midyear population. It is the gross value added by all resident producers in the economy including net taxes (taxes minus subsidies) that have not been included in the value of products. Fiscal policy is the key dependent variable which has been broken down into two components as follows: education expenditure (eduexp) as a percentage ofGDP which is used as proxy for human capital development. It is expected to have a positive impact on economic performance since it increases the skills base for the work force. It includes current operating expenditures in education which includes remuneration for employees and excludes capital expenditure. The square of education expenditure is represented as eduexp2 and tax revenue (tax) measured as net taxes on products as a percentage of
GDP. The study employs two control variables: physical capital (gfcf) as a percentage of GDP consisted of outlays on additions to fixed assets plus equity capital, reinvestment earnings, short and long term capital. A higher gfcf increases the capacity of the economy to produce hence is expected to have a positive effect on economic performance and foreign direct investment (fdi) is measured as net inflows as a percentage of GDP. It is the sum of equity capital, reinvestment of earnings, other long term capital and short term capital as shown in the balance of payments. It is expected to have a positive impact on economic performance. Thus the economic performance production function is of the form:
Gdppc = (tax, eduexp, eduexp2, fdi, gfcf) (2) The econometric relationship among variables used is as follows:
gdPPc, = P0 + PM +P2edu expt +
+P3edu exp2t + P4taxt + P5 fdit + ¡¡t Where, P0 is a constant, Pl, ..., Ps are coefficients of the dependent variables explaining the effect of the fiscal policy variable on growth and x is an error term. Econometric Procedure
This study employs the Autoregressive Distributed Lag (ARDL) approach and Bounds testing to examine both the short and long run dynamic relationship among the variables. This model has been employed in previous studies [62; 59; 5; 45] to examine the link among economic variables. The study uses time series data which require some preliminary tests to be conducted as follows: stationarity tests were done to avoid using non stationary data which may give spurious results. This is achieved using the Augmented Dickey Fuller (ADF) Tests and the selection of the lag length was done automatically in e-views and tests for heteroskedasticity were done using Breusch-Pagan-Godfrey test.
The Autoregressive Distributed Lag (ARDL) model The ARDL model has been employed for several decades to examine the connection between economic variables. The model shows that the cointegration of nonstationary variables is the same as what is involved in the error correction process. The existence of a long run relationship among variables is assessed using the error correction representation by employing the bounds testing procedure which does not require knowledge of the order of integration of variables [62]. Earlier studies show that the ARDL models were used to analyze the long run relationship when data was integrated of order one. Recent studies have employed the model even where series have different levels of integration [26; 61]. The ARDL approach has several advantages: it can be estimated by
ordinary least squares (OLS) once the order of the model has been identified which differs from the method by [38], using the bounds testing procedure, an extension of the ARDL framework which use F and t-statistics, the long run relationship among variables can be examined even where the order of integration is both I(0) and I(1), it can be applied where sample size is small and it is useful even where variables are both stationary and non-stationary [62]. The bounds test approach is, however, not applicable where some of the variables are integrated of order two. Following [5; 64; 6] the estimated ARDL model (5) is modeled as a general vector autoregressive model of order (p) in zt as follows:
= Po+Qt + X+vt t= 1, 2, 3, ..., t (4)
m=1
Where: is a (k+1) vector of intercept and 0 is a (k+1) vector of trend coefficients
A vector error correction model (VECM) of the following form is derived
Azt =p0 +et + nzt-i + XAzt-m + nt t=1, 2, 3, ... T (5)
m=1
P
Where n = Ik+1 and = - £ 0m = 1, 2, ...,
j=m+1
p-1 contains the long run multiplier and short term dynamic coefficients of the VECM. Zt becomes a vector of variables yt being the dependent variable (gdp per capita) and xt is a vector of I (0) and I (1) independent variables (tax, eduexp, fdi, gfcf). The VECM with unrestricted intercepts, unrestricted trends and the error
correction term was employed as follows:
Agdppercapitat = P0 +Qt + P1gdppercapitat -1 + P2taxt -1 +
+p3edu expt +p4edu exp2t +p5gfcft - +p6 fdit - +
p q1 q2 (6)
Agdppercapitat-m Ataxt _c + Aedu expt (6)
m=1 c=1 d=1
q 3 q 4 q 5
+X® AeduexP2t-g + Agfcft-h + Afdi-l + yDt + ¡dt
g=1 h=1 l=1
where are the long run multipliers, j80 is the intercept, t is the time trend, represents white noise and the short run parameters are represented by coefficients (p,x,h,a>,n,S) of the first difference variables. The ARDL model is the basis upon which the bounds test is done. In conducting the bounds test procedure equation (6) is estimated using OLS to test for the existence of the long term relationship among variables. The null and alternative hypotheses are as follows:
H0 = Pl = P2 =... = P6 = 0. (no long-run relationship) Against the alternative hypothesis Hl ^ Pl ^ P2 ^... ^ P6 ^ 0 (a long-run relationship exists)
The determination of whether or not a long run relationship exists is done by evaluating the value of the
m=l
F-statistic against the critical values in Table 1 as obtained from [62].
The decision is made as follows: if the computed F-statistic is lower than the lower bound values then the null is accepted which shows that there is no long run relationship; if the value falls between the lower and upper bounds then results are inconclusive and if the value is greater than the upper bound then there is a long run relationship among variables. After establish-
ing the existence of long run relationship the ARDL
(p, q1, q2, ••• q5) model is estimated as follows:
p
Agdppercapitat = +dt + ^ ¡i1gdppercapitat-m +
q1 q 2 q 3
"É ß2taXt-m + É ß3edU eXPt-m + É ß4edU eXP 2t-m
m=0 m=0 m=0
+ É ß5gfcft-m +jt ßßh-m + YD + ^
(7)
Table 1. - Bounds Test for Cointegration Analysis
m=1
m=0
m = 0
Critical value Lower Bound Value Upper Bound Value
1% 3.74 5.06
5% 2.86 4.01
10% 2.45 3.52
The lag structure ofARDL model (7) is selected using Akaike's Information Criterion (AIC), [1], which avoids under fitting the model. The model with lowest AIC is selected and used for estimations. It is also important for errors of the ARDL model to be serially independent otherwise parameters will be inconsistent. This can be verified by extracting the correlogram - Q-statistics. The p-values should be insignificant in the absence of autocorrelation in the model's residuals. The last step is to obtain the short run dynamic parameter which is done by extracting the error correction term (ecm) associated with long run
parameters. The estimations are done using:
p
kgdppercapitat = /30 +dt + Z9m Agdppercapitat-m +
m=1
q1 q 2 q 3
+Zx ^tax,-c + Z4,Aedu expt_d + ZaAedu exp2t_g + (8)
c=1 d=1 g=1
q 4 q 5
+Zv„ -h+Z5, Afdit -+%ecm,-1 +
h=1 1=1
Where % is the speed of adjustment which should be negative and significant in the presence of a long run connection. In estimating model (8) there is need to extract the cointegration and long run form ofthe model. This will give short run parameters, error correction term and long run coefficients or cointegrating equation.
4. Results and Discussion
The overall statistics, Table 2, show that expenditure on physical capital as a percentage of GDP was the highest at 43.41% followed by per capita GDP at 15.83% while expenditure on education was the lowest at 0.015%. The rate of growth of GDP per capita suggests an improvement in welfare which was supported by an acquisition of physical capital. High variability was noticed in physical capital as the government made efforts to improve the capital base and the stock of fixed assets. All variables are positively skewed except for the rate of growth of per-capita GDP and physical capital (gfcf) and all variables are normally distributed except for taxation and gfcf.
Table 2. - Summary statistics
GDP_PERC... D (EDUEXP) D (EDUEXP2) D (TAX) GFCF FDI
Mean 4.622403 0.001677 0.000207 - 0.011507 31.41076 3.604639
Median 4.636350 0.001969 0.000248 - 0.015106 30.75319 2.805231
Maximum 15.83210 0.015184 0.002539 0.163448 43.40568 15.59413
Minimum - 9.462539 - 0.009154 - 0.001465 - 0.180209 15.50083 - 6.897680
Std. Dev. 4.194007 0.005779 0.000763 0.094470 6.176753 3.661533
Skewness - 0.566076 0.161548 0.458023 0.043180 - 0.235420 0.526867
Kurtosis 5.438831 3.000964 4.251587 2.088489 2.663823 5.660464
Jarque-Bera 11.74820 0.169637 3.909115 1.362253 0.543895 13.30619
Probability 0.002811 0.918679 0.141627 0.506046 0.761894 0.001290
Sum 180.2737 0.065394 0.008069 - 0.448777 1225.020 140.5809
Sum Sq. Dev. 668.4083 0.001269 2.21 E-05 0.339137 1449.786 509.4593
Observations 39 39 39 39 39 39
The study tested the null hypothesis for unit root using ADF tests and results (Table 3) show that the null hypothesis was rejected at levels for GDP per capita, foreign direct investment net inflows (fdi) and physical capital (gfcf) at levels of significance of 1%, 1% and 5% respectively.
The null hypothesis was rejected at 1% for expenditure on education and taxation after first differencing. This means that variables were stationary at different levels, which is ideal for using ARDL model. Since some ofthe variables follow an I(1) series it suggests that they are cointegrated.
Table 3. - Unit root test
Variable Levels First di ïerence
Test statistic p-value Test statistic p-value
Gdp percapita - 4.593 0.0007
Eduexp2 - 0.2813 0.9186 - 6.5218 0.0000
Eduexp - 0.747 0.8228 - 7.050 0.0000
Tax - 0.2456 0.1339 - 4.856 0.0003
fdi - 4.7628 0.0004
Gfcf - 3.074 0.0370
The test for autocorrelation is important to avoid us- tion in the residuals because all the p-values were insignifi-ing variables that are highly correlated in the model. Results cant. Thus none ofthe variables are highly correlated hence (Table 4) show that there was no evidence for autocorrela- they can be included in the same model for estimations.
Table 4. - Test for autocorrelation in Residuals
Autocorrelation Partial Correlation AC PAC Q-Stat Prob*
■ d 1 1 -d ' 1 - 0.205 - 0.205 1.6350 0.201
i cj i ■ , 2 - 0.165 - 0.216 2.7292 0.255
' i ■ H 1 3 - 0.074 - 0.174 2.9576 0.398
' ¡3 ' ' 1 ' 4 0.084 - 0.017 3.2608 0.515
5 0.190 0.182 4.8456 0.435
' d ' ' i ■ 6 - 0.140 - 0.039 5.7384 0.453
(=j : f=! ' 7 - 0.314 - 0.323 10.391 0.167
' * ' ' Cj : 8 0.055 - 0.151 10.537 0.229
' tu ■ ' Î ' 9 0.175 0.016 12.093 0.208
i cj i 10 - 0.160 - 0.238 13.436 0.200
1 tu. : 11 0.190 0.243 15.414 0.164
, d , , [] , 12 - 0.109 0.087 16.096 0.187
1 i ■ i i i 13 0.030 - 0.053 16.149 0.241
i i . 1 14 - 0.062 - 0.252 16.392 0.290
1 i 1 1 i ' 15 0.028 - 0.037 16.441 0.353
1 1 1 1 ' i ' 16 0.014 - 0.095 16.455 0.422
*Probabilities may not be valid for this equation specification
The variance of error terms should be constant to avoid spurious results. This was done by testing the null hypothesis that error terms have constant variance. Results, p-value of 0.5981, in Table 5 shows that there was not enough evidence to reject the null hypothesis. This shows that heteroskedasticity is not a problem in the model.
Table 5. - Tests for heteroskedasticity Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.879894 Prob. F(16,19) 0.5981
Obs*R-squared 15.32180 Prob. Chi-Square(16) 0.5012
Scaled explained SS 3.711467 Prob. Chi-Square(16) 0.9993
The results ofbounds test for cointegration (Table 6) show that the null hypothesis of no long run relationship among the variables is being rejected at 1% level of significance since the computed F-statistic of 5.25 is
higher than all the upper bound critical values in Tables 1 and 6. The results give strong evidence of the existence of long connection between economic performance and fiscal policy variables.
Table 6. - Tests for cointegration
Test Statistic Value K
F-statistic 5.251525 5
Critical Value Bounds
Significance 10 Bound 11 Bound
10% 2.08 3
5% 2.39 3.38
2.5% 2.7 3.73
1% 3.06 4.15
The estimation results for the ARDL model (Table 7) show the goodness of fit of the model is fairly high at 0.741 and a significant F-statistic at 1%. All the diagnostic tests (autocorrelation, heteroskedasticity, Durbin Watson) have the desired econometric properties. The results are valid for giving econometric inference since there is no problem of serial autocorrelation and heteroskedasticity. The ARDL combines both short and long run effects of variables. Generally the results (Table 7) show that economic performance is affected by expenditure on education and taxation, which are the fiscal policy variables, while physical capital and foreign direct investment have no significant effect. Findings show that economic performance is positively
Table 7. - The
affected by expenditure on education in current period and the square of expenditure on education variable is negative and significant suggesting a nonlinear relationship. The model shows that taxation in the current period and that of the last two periods has a negative effect. This suggests that taxation has a distortionary effect on economic performance. However, the ARDL model can be understood better by decomposing the impact of variables into short and long term components. This was achieved by estimating error correction model (see Table 8) which shows how the short and long run behavior of variables are reconciled. The same table shows the error correction term which is reported together with short run parameters. ARDL model
Variable Coefficient Std. Error t - Statistic Prob*
GDP PERCAPITA(- 1) 0.190145 0.186111 1.021672 0.3198
GDP P E RCAP ITA(- 2) - 0.241996 0.180512 - 1.340612 0.1959
D(EDUEXP) 1206.521 412.4888 2.924980 0.0087
D(EDUEXP2) - 11025.55 3004.809 - 3.669303 0.0016
DCTAX) - 14.66696 7.933349 - 1.848773 0.0801
D(TAX(- 1)) 6.566382 9.243827 0.710353 0.4861
D(TAX(- 2)) - 17.65032 7.202951 - 2.450429 0.0241
GFCF - 0.188865 0.119083 - 1.585987 0.1292
GFCF(- 1) 0.263846 0.169849 1.553414 0.1368
GFCF(- 2) - 0.226991 0.159089 - 1.426816 0.1699
GFCF(- 3) - 0.102242 0.112831 - 0.906151 0.3762
FDI 0.066900 0.211533 0.316263 0.7553
FDI(- 1) 0.295590 0.220097 1.342998 0.1951
FDI(- 2) 0.053227 0.198550 0.268080 0.7915
FDI(- 3) 0.198901 0.218228 0.911437 0.3735
FDI(- 4) 0.088376 0.171491 0.515337 0.6123
C 10.06371 3.976099 2.531052 0.0204
R - squared 0.741395 Mean dependent var 4.335696
Adjusted R-squared 0.523622 S.D. dependent var 4.222217
S.E. of regression 2.914179 Akaike info criterion 5.282418
Sum squared resid 161.3564 Schwarz criterion 6.030191
Log likelihood - 78.08352 Hannan-Quinn criter. 5.543411
F-statistic 3.404446 Durbin-Watson stat 2.351894
Prob(F-statistic) 0.006223
Selected Model: ARDL(2, 0, 0, 2, 3, 4) Note: final equation 5ample is larger than 5election 5ample
The results (Table 8) show that the error correction term (-0.9392) is negative and significant which suggests that there is dynamic adjustment in economic performance whenever fiscal initiatives are undertaken. All the fiscal policy variables are important in explaining economic performance in the short run. Economic performance is positively and negatively affected by expenditure on education and taxation in the current period respectively. For example a 1% increase in net taxes on products will lead to a 12.76% fall in the rate of growth of economic performance. This finding is consistent with previous studies [12, 20, 47] who showed that taxation may have distor-tionary effects on benefits associated with growth. When taxation is increased it reduces the funds available for reinvestment for firms which reduces the capacity to produce and capital tends to flow out ofthe country hence a reduc-
tion in rate of growth. The short run relationship suggests a non linear relationship between economic performance and expenditure on education. The result show a hump-shaped relationship in which economic performance increases up to a certain level beyond which expenditure on education results in a fall in economic performance. In the context of Botswana education is mainly financed by the government and forms part of the investment in human capital. The result suggests that expenditure on education improves the productivity of labour which enhances the rate of growth of the economy. It is vital for enhancing economic growth which is consistent with previous studies [63, 33, 16]. Physical capital in the previous two periods turns out to have a positive impact on economic performance while foreign direct investment remains insignificant.
Table 8. - Results for the cointegration and Long form model Cointegrating Form
Variable Coefficient Std. Error t-Statistic Prob.
D(GDP_ PERCAPITA(- 1)) - 0.174735 0.170335 - 1.025829 0.3179
D(EDUEXP) 846.187567 469.218778 1.803397 0.0872
D(EDUEXP2) - 8187.27... 3436.018023 - 2.382779 0.0278
D(TAX, 2) - 12.762410 7.076872 - 1.803397 0.0872
D(TAX(- 1), 2) 8.217907 6.811616 1.206455 0.2425
D(GFCF) - 0.115360 0.118998 - 0.969429 0.3445
D(GFCF(- 1)) 0.183199 0.138769 1.320175 0.2025
D(GFCF(- 2)) 0.208613 0.120419 1.732402 0.0994
D(FDI) 0.180326 0.193285 0.932955 0.3625
D(FDI(- 1)) - 0.355305 0.208005 - 1.708156 0.1039
D(FDI(- 2)) - 0.145151 0.186981 - 0.776284 0.4471
D(FDI(- 3)) - 0.067006 0.166212 - 0.403136 0.6913
CointEq(- l) - 0.939203 0.207629 - 4.523469 0.0002
Cointeq = GDP_PERCAPITA - (1147.0450*D(EDUEXP) -10482.0400 *D(EDUEXP2) -24.4815*D(TAX) -0.2417*GFCF + 0.6683*FDI + 9.5676 )
Long Run Coefficients
Variable Coefficient Std. Error t-Statistic Prob.
D(EDUEXP) 1147.044... 444.120624 2.582733 0.0182
D(EDUEXP2) - 10482.0... 3452.103337 - 3.036421 0.0068
DCTAX) - 24.481488 10.477947 - 2.336478 0.0306
GFCF - 0.241718 0.128239 - 1.884900 0.0748
FDI 0.668339 0.254138 2.629827 0.0165
C 9.567616 3.656449 2.616642 0.0170
In the long run all fiscal policy and control variables are significant and help to explain economic performance. Consistent with short run relationship, the long term model shows that both taxation and expenditure on education have a negative and positive impact on
economic performance. The two fiscal policy variables have maintained their signs as explained using the short run model. The long run model still confirms the existence of a non linear relationship between education expenditure and economic performance. However,
the adverse impact of taxation is more severe in the long than in the short term. The results shows that a 1% increase in taxation results in 24.48% fall in the annual rate of growth gdppercapita. Consistent with [50, 43] the study shows that the net inflows of foreign direct investment have a positive effect on economic performance in the long term while the effect of physical capital is negative. This means that the country benefits more from foreign direct investment inflows in the long term as opposed to increasing physical capital which has a positive contribution in the short term.
Table 9. - Pairwise Granger Causality tests
Findings (Table 9) on causality confirm that there is causation between taxation and the rate of growth per capita GDP. Spefically the study rejects the null hypothesis of lack of causality at 10% level of significance. Consistent with previous studies [65, 19] the study shows that there is unidirectional causality flowing from taxation to the measure of economic performance employed. This shows that taxation is important in explaining the rate of economic performance in the short term.
Null Hypothesis: Obs F-Statistic Prob.
D(EDUEXP) does not Granger Cause GDP_PERCAPITA GDP_ PERCAPITA does not Granger Cause D(EDUEXP) 37 1.06315 0.59014 0.3573 0.5602
D(EDUEXP2) does not Granger Cause GDP_PERCAPITA GDP_ PERCAPITA does not Granger Cause D(EDUEXP2) 37 1.57764 0.29924 0.2221 0.7434
D(TAX) does not Granger Cause GDP_PERCAPITA GDP_PERCAPITA does not Granger Cause D(TAX) 37 2.79385 0.99261 0.0761 0.3817
5. Conclusion and Recommendations
The study aimed to assess fiscal policy variables on economic performance in the context of Botswana using annual time series data for the period 1975-2014. The study employs the autoregressive distributed lag, Bounds testing approach and pairwise granger causality to examine the possibility of any short and long run connection between economic growth and fiscal initiatives. Furthermore the study tests the possibility of non linear relationship between fiscal variables and economic performance. It is also possible that other forms of government expenditure may affect economic performance. However, the study employed two fiscal policy variables: expenditure on education and taxation. The study, in both the long and short run, shows that there is a non linear, hump-shaped, relationship between expenditure on education and economic performance. Also it shows that net taxes on products have a significant negative impact on economic performance. Results using pair wise granger causality tests show that the direction of causality flows from taxation to economic performance. Provision of physical capital has positive and negative effects on economic performance in the short and long run respectively while foreign direct investment positively contributes to long run economic performance. The study supports the endogenous growth model which shows that an increase in government spending raises the steady state rate of growth as a result of spillover effects on investment in both human and physical capital.
In view of these findings it is imperative that the government increases the flow of non tax incomes (like export revenues, loans at concessionary interest rates) into the treasury to influence the rate of economic performance. This reduces the reliance on taxes which may have a distortionary effect on performance. Well designed policies on taxation and spending will increase economic performance. Reduction in tax rates on labour increases the incentive to work and hence productivity. The same policy on income tax would reduce the distortionary effects and stimulate private investments and excess funds would be availed to other critical areas like research and development. It is important for the government to widen the tax base to increase revenue flows which helps to reduce distortionary effect. The government should use a targeted approach as it spends money on education to improve efficiency and monitoring to avoid distortionary effects after reaching a certain level. More money should be targeted towards developing the skills base, as opposed to only meeting operating costs, within the education sector to improve future efficiency levels. Reaching the optimal expenditure levels, considering the existence ofpositive externalities of education, is possible as both government and private sector work together. The impact of taxation on human capital accumulation can be reduced by giving credits linked to educational expenses. This has an effect of increasing the stock of human capital and improving future economic performance.
References:
1. Akaike H. (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory, ed. B. N. Petrov and F. Csaki, - 267-281. - Budapest: Akailseoniai-Kiudo.
2. Amadeo K. (2016). What was Obama's stimulus Package? Retrieved from: URL: http://useconomy.about. com/od/candidatesandtheeconomy/a/Obama_Stimulus.htm (Accessed 04 June - 2016).
3. Al-Shatti A. S. (2014). The effect of fiscal policy on economic development in Jordan, International Business Research, - 7 (12): 67-76.
4. Appelbaum E., Baker D., and Schmitt J. (2008). The need for economic stimulus package, Center for economic and policy research. Retrieved from: URL: http://cepr.net/documents/publications/ceprcww_stimulus.pdf (Accessed 04 June 2016).
5. Asiedu M. K. (2013). Trade liberalization and growth: The Ghanaian Experience, Journal of economics and Sustainable development, - 4 (5): 125-135.
6. Atif R. M., Jadoon A., Zaman K., Ismail A. and Seemab R. (2010). Trade liberalization, Financial Development and Economic growth: Evidence from Pakistan (1980-2009), Journal of International Academic Research - 10 (2): 30-37.
7. Attinasi M. A. and Klemm A. (2014). The growth impact of discretionary fiscal policy measures, European Central Bank, Working paper series - No. 1697. - 2014.
8. Audu N. P. (2012). The impact of fiscal policy on the Nigerian economy, International Review of Social sciences and Humanities, - 4 (1): 142-150.
9. Babalola S. J. and Aminu U. (2012). Fiscal policy and economic growth relationship in Nigeria, International Journal of Business and Social science, - 2 (17): 244-249.
10. Bakwena M. (2012). The recent economic reforms in Botswana: A critical assessment, PULA: Botswana Journal of African Studies, - 26 (1), 39-50.
11. Balise J. (2015). Economists cast doubt over Khama's economic stimulus package, Sunday Standard, 14 October. Available at: URL: http://www.sundaystandard.info/economists-cast-doubt-over-khama%E2%80%99s-economic-stimulus-package (Accessed 05 June 2016)
12. Barro R. J. (1990). Government Spending in a Simple Model of Endogenous Growth. Journal of Political Economy, - 98 (5): 103-125.
13. Benos N. (2004). Fiscal policy and economic growth: Empirical evidence from OECD countries, University of Cyprus. Available at: URL: http://www.aueb.gr/crete2004/docs/Benos.pdf (Accessed 08 June - 2016).
14. Bloomberg (2015). Botswana to use some of its reserves for economic stimulus plan. Available at: URL: http://www.bloomberg.com/news/articles/2015-10-12/botswana-to-use-some-of-its-reserves-for-economic-stimulus-plan (Accessed 06 June - 2016).
15. Bojanic A. N. (2013). The composition of government expenditure and economic growth in Bolivia, Latin American Journal of Economics, - 50 (1): 83-105.
16. Bose P. Hague N., Osborn D. R. (2007). Public expenditure and economic growth. A disaggregated analysis for developing countries. The Manchester school, - 75 (5): 533-556.
17. Botswana Chronicles (2015). The story of two economic stimulus packages, Botswana Chronicles, 04 December. Available at: URL: http://botswanachronicles.com/article/the-story-of-two-economic-stimulus-packages (Accessed 06 June - 2016).
18. Brasoveanu L. O. and Brasoveanu I. (2008). The correlation between Fiscal policy and economic growth, Theoretical and Applied Economics, Academy of Economic Studies, Bucharest. Available at: URL: https://ideas. repec.org/a/agr/journl/v7 (524)y2008i7 (524)p19-26.html (Accessed 02 June 2008).
19. Chigbu E. E., Eze A. L. and Ebimodowei A. (2012). An empirical study on the causality between economic growth and taxation in Nigeria, Current research journal of economic theory, - 4 (2): 29-38.
20. Christina and David R., (2007). The Macroeconomic Effects of Tax Changes: Estimates Based on New Measure of Fiscal Shocks, NBER Working Paper 13264.
21. Clover J. (2003) Botswana: Future prospects and the need for broad-based development, Institute for Security Studies. Available at: URL: https://issafrica.org/AF/current/botswanasep03.pdf (Accessed 05 June - 2016)
22. Collier P. (2007). Growth Strategies for Africa, a paper prepared for the Spence Commission on Economic Growth, Center for the Study of African Economies, Department of Economics, Oxford University.
23. Djelloul B., Rouaski K., Toumache R. and Talbi B. (2014). The impact of fiscal policy on economic growth: Empirical evidence from panel data estimation. The 2014 WEI International Academic conference proceedings, New Orleans, USA, - P. 49-56.
24. Eggoh J., Houeninvo H., and Sossou G. A. (2015). Education, Health and economic growth in African Countries, Journal of economic development, - 40 (1): 93-111.
25. Enache C. (2009). Fiscal Policy and Economic Growth in Romania. Annales Universitatis Apulensis Series Oeconomica, - 11 (1): 502-12.
26. Engle R. F. (1984), Handbook of econometrics, Elsevier Science Publishers BV, - 2:776-825.
27. Ezeabasili V. N., Tsegba I. N., and Ezi-Herbert W. (2012). Economic growth and fiscal deficits, Economics and Finance Review, - 2 (6): 85-96.
28. Fan S., and Rao N. (2003). Public Spending in Developing Countries: Trend, Determination and Impact. EPTD Discussion Paper - No. 99, Washington, D. C.
29. Faye I., and Olanrewaju S. (2009). Program: Economic diversification support loan, African Development Bank. Available at: URL: http://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-and-Operations/Bo-tswana_-_Economic_Diversification_Support_Loan_-_Appraisal_Report.pdf (Accessed 30 May - 2016).
30. Folster S., and Henrekson M. (2001). Growth Effects of Government Expenditure and Taxation in rich Countries', European Journal of Political Economy, - 45: 1501 -1520.
31. Friedman M., and Meiselman D. (1963). The Relative Stability of Monetary Velocity and the Investment Multiplier in the United States, - 1897-1958. In Commission on Money and Credit: Stabilization Policies. Englewood Cliffs, NJ: Prentice-Hall: - 165-268.
32. Gisore N., Kiprop S., Kalio A., Ochieng J., and Kibet L. (2014). Effect of government expenditure on economic growth in East Africa: A disaggregated model, European Journal of Business and social sciences, - 3 (8): 289-304.
33. Glomm G. and Ravikumar B. (1997). Productive government expenditures and long run growth, Journal of Economic Dynamics and Control, - 21 (1997): 183-204.
34. Glomm G., and Ravikumar B., (1998). Taxes government spending on education and growth. Review of Economic Dynamics - 1, 306-325.
35. Honde G. J. (2015). Botswana - 2015 report. African Economic Outlook. Retrieved from: URL: http://www. afriacaneconomicoutlook.org (Accessed 01 June - 2016).
36. Igwe A., Emmanuel E. C., and Ukpere W. I. (2015). Impact of fiscal policy variables on economic growth in Nigeria (1970-2012): a managerial economic perspective, Investment management and financial innovations, - 12 (2): 169-179.
37. Ismal R. (2011). Assessing economic growth and fiscal policy in Indonesia, East-West Journal of Economics and Business, - XIV - (1): 53-71.
38. Johansen S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control -12: 231-254.
39. Joharji G. A., and Starr M. A. (2010). Fiscal policy and growth in Saudi Arabia. Available at: URL: https://ameri-can.edu/cas/economics/pdf/upload/2010-7.pdf (Accessed 08 June - 2016).
40. Kakar Z. K. (2011). Impact of fiscal policy on economic development of Pakistan, Romanian Journal of Fiscal Policy, - 2 (2): 1-10.
41. Kalio A. M. (2000). Government Expenditure and Economic Growth in Kenya. Egerton Journal, - Volume 3, - 35-43.
42. Khama S. K. I., (2015). State of the nation address. Daily News, - 11 November, - P. 18.
43. Khan M. A., Khan M. Z., and Zaman K. (2012). Measuring the impact of fiscal variables on Economic growth in Pakistan: New Light on an old problem, Journal of Economic and Social research, - 14 (2): 53-82.
44. Korman J., and Bratimasrene T. (2007). The relationship between Government Expenditure and Economic Growth in Thailand, Journal of Economic Education, - Vol. 14. - 234-246.
45. Kumo W. L. (2012). Infrastructure investment and economic growth in South Africa: A granger causality analysis, Africa Development Bank Group, Working Paper Series - No. 160, - 2012.
46. MAmanja D., and Morrisey O. (2006). Fiscal policy and economic growth in Kenya, CREDIT research paper -No. 05/06.
47. Mahdavi S., (2008). The level and composition of tax revenue in developing countries: Evidence from unbalanced panel data. International Review of Economics and Finance - 17, 607-617.
48. Matambo O. K. (2016). Budget speech. Ministry of Finance and Development Planning, Botswana. Available at: URL: http://www.finance.gov.bw (Accessed on 21 November - 2016).
49. Mauro Paolo (1995). Corruption and Growth. Quarterly Journal of Economics, - 110: 681-712.
50. Milova O., and Vokshi-Abazi A. (2014). Empirical evidence of fiscal policy impact on endogenous models of economic growth - the case ofAlbania, Journal of Knowledge Management, Economics and Information Technology, - IV (1): 1-13.
51. Motsamai M. (2015). Economic stimulus package for backlog. Daily News, - 15 October. Available at: URL: http://www.dailynews.gov.bw/news-details.php?nid=23784 (Accessed 06 June 2016).
52. Munongo S. (2012). Effectiveness of fiscal policy in economic growth: The case of Zimbabwe, International Journal of Economic Research, - 3 (6): 93-99.
53. Muthui J. N., Maingi J., and Thuku K. G. (2013).The impact of public expenditure components on economic growth in Kenya (1964-2011), International Journal of Business and Social science, - 4 (4): 233-253.
54. Niloy B., Emranul H., and Osborn D. (2003). Public Expenditure and Economic Growth: Journal of Social sciences, Manchester.
55. Njiraini J. (2011). The economic stimulus package- did it really stimulate? Standard Media, - 5 April - 2011. Avialable at: URL: http://www.standardmedia.co.ke/business/article/2000032568/the-economic-stimulus-package-did-it-really-stimulate (Accessed 05 June 2016).
56. Nurudeen A., & Usman A. (2010). Government Expenditure and Economic Growth in Nigeria, - 1970-2008: A Disaggregated Analysis. Business and Economics Journal, - Volume - 2010: BEJ-4. URL: http://astonjournals. com/bej
57. Ocran M. K. (2009). Fiscal policy and Economic in South Africa, Paper presented at the Center for the study of African Economies, St Catherine's College, Oxford University, UK, 22-24 March - 2009.
58. Ogbole O. F., Amadi S. N., and Essi I. D. (2011). Fiscal policy and economic growth in Nigeria: A granger causality analysis, American Journal of Social and Management sciences, - 2 (4): 356-359. - Doi:10.5251 /ajsms.2011.2.4.356.359.
59. Omoniyi L. G. and Olawale A. N. (2015). An application ofARDL Bounds testing procedure to the estimation of level relationship between exchange rate, crude oil and Inflation rate in Nigeria, International Journal of statistics and Applications - 5 (2): 81-90.
60. Ortigueira S. (1998). Fiscal Policy in an Endogenous Growth Model with Human Capital Accumulation.Journal of Monetary Economics, - 42, 323-355.
61. Pesaran M. H., and Shin Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis, chapter 11in Econometrics and Economic Theory in the 20th century the Ragnar Frisch Centennial symposium, Strom S. (ed.) Cambridge University Press Cambridge.
62. Pesaran M. H., Shin Y., and Smith R.J. (2001). Bounds Testing Approaches to the Analysis of level relationships", Journal ofApplied Econometrics, - 16: 289-332.
63. Rebelo S. (1991). Long-run policy analysis and long-run growth, Journal of Political Economy - 99, 50&521.
64. Samimi A. J., Ghaderi S., and Sanginabadi B. (2011). Openness and inflation in Iran, International Journal of Economics and Management Engineering, - 1 (1): 42-49.
65. Sevitenyi L. N. (2012). Government expenditure and economic growth in Nigeria: An empirical investigation, Journal of Economic Analysis, - 3 (1): 38-51.
66. Siphambe H. K. (2007). Growth and employment dynamics in Botswana: A case study of policy coherence, International Labour Office, Geneva, Working paper - No. 82.
67. Shihub R. A. (2014). The causal relationship between fiscal policy and economic growth in Jordan, International Journal of Business and Social Science, - 5 (3): 203-208.
68. Uhliga H., and Yanagawa N. (1999). Increasing the capital income tax may lead to faster growth. European Economic Review, - 40, 1521-1540.
69. United Nations Development Programme (2005). Poverty status report for Botswana: Incidence, trends and dynamics, International Poverty Centre. Available at: URL: http://www.ipc-undp.org/publications/reports/Bo-tswana.pdf (Accessed 01 June - 2016).
70. Urhie E. S. (2013). Public education expenditure and economic growth in Nigeria: -1970-2010, (PhD thesis), Covenant University, Nigeria.
71. Wambui T. V. (2013). Effects of government expenditure, taxes and inflation on economic growth in Kenya: A disaggregated time series analysis (Masters thesis), University of Nairobi, Kenya.
72. World Bank (2016). World Development Indicators, World Bank. Available at: URL: http://data.worldbank. org/data-catalog/world-development-indicators (Accessed 09 June - 2016).
DOI: http://dx.doi.org/10.20534/EJEMS-17-2-101-107
Federico De Andreis, Professor E-mail: [email protected] with the contribution of Sara Petruzzo, D.ssa E-mail: [email protected] Universita degli Studi "Giustino Fortunato",
Benevento. Italy
Environment and conflict management for research and development support
Abstract: A situational examination of research and development, with particular reference to public intervention and to the conflict management.
The research will focus on the contribution of the public sector in the innovation and research and on the necessity to avoid conflict in the working groups, in order to develop skills and attitudes useful for the company's success.
Keywords: environment, internal environment, conflict management, research, development.
1. Research and development: corporate objec- a company, or by the fact that companies consider the tive and need
In economics, the term market failure refers to the situation where the allocation of goods and services, made through the free market, is not efficient or rather, for the same situation, another solution exists, i. e. another imaginable outcome in which an individual can be made richer without damaging another.
A market therefore is definable economically efficient when it reaches the perfect allocation of inputs and outputs, i. e. balances of Pareto efficiency are reached.
On the contrary, when there are imperfections that prevent from achieving an efficient allocation of resources, and therefore the maximum social welfare, for economists becomes acceptable the state intervention.
These deficiencies may result from the unwillingness of private companies to invest in this area, since the eventual investment could not ensure profits; the risk of some investments that turned out to be too high for
negative externalities resulting from production.
Market failures are often associated with temporal preferences, non-competitive markets, inconsistencies and asymmetries but, whatever the cause, are often the reason why corporate self-regulatory organizations, national Governments or supranational institutions decide to intervene in a particular market, undertaking studies on the causes of the failure of the market itself and the possible means of correction of public policy.
The State can intervene with different modes, for example policies based on research, tax relief or finance for infrastructure projects, taking on a strategic role in support of business. From the perspective of welfare economics, the determination of optimal allocation of resources for invention will depend on the characteristics and on the nature of the invention process technological market knowledge.
