STATISTICAL ANALYSIS OF APPROVED CAPITAL EXPENDITURE IN ECONOMIC AND SOCIAL SECTORS(A CASE STUDYOF KADUNA STATE MINISTRYOF ECONOMIC PLANNING FROM 2002-2017)

CHAPTER ONE

1.0 INTRODUCTION

Statistics is defined as the science of collecting, organizing, summarizing, presenting, analyzing, and interpretation of numerical data as well as drawing a valid conclusion and making a reasonable decision on the basis of such analysis. Statistics can be applied in any field in which there is extension of numerical data. Examples include engineering, sciences, and medical, accounting, business administration, public administration and economics. Ministry of economic planning that is formally known as development planning involves the deliberate efforts on the parts of government to speed up the process of social and economic development. It is the government intervening directly and extensively in the lives of the people. In other words, development planning is the organization of resources for growth. The need for development planning whether if the centralized control, or mixed economic type arises largely from the fact that the productive resources are scarce relative to the demand for them. Had been resources are limited there would be no need for development planning. On the other hand, economic and planning was a bureau under Kaduna state government which deals with the economic and planning system of Kaduna state budget and other activities relating to economic.

STRUCTURE AND FUNCTION OF MINISTRY OF ECONOMIC PLANNING

The ministry of economic planning is responsible for coordinating development planning, budget, man power planning and all forms of foreign assistance, collection and compilation of statistical data in the state. The ministry has four departments such as;

Department of planning

Department of budget

Department of international cooperation and man power

Department of research and statistics

STRUCTURE AND FUNCTION OF THE DEPARTMENTS

Department of Planning

The functions of this department include the following;

Coordinating of local government annual capital programmed.

Advising the honorable commissioner on capital funds and capital payments.

Monitoring the implementation of state and local government capital programmed.

Collaboration with the national planning commission on planned preparation project monitoring and related matters.

Joint secretary, with budget department of the estimate committee.

Department of Budget

The function of this department includes the following;

Formulation of fiscal and budgetary policies of the state government.

Preparation of annual and supplementary estimates of the revenue and recurrent expenditure of the state government.

Issuance of guidelines for implementation, budget interpretation, and performance monitoring.

Secretariat of the state estimate committee.

Department Of International Cooperation And Man Power

The function of this department includes the following;

Coordinating of development assistance to the state from all sources.

Serving as secretariat of the state programmes coordinating committee (SPCC) which coordinates the implementation of united nation (UN) agencies programmed in the states.

Keeping up to date record of federal regulations governing the acquisition of external and local loans and making appropriate policy recommendation to the state government.

Taking necessary steps to make sure that adequate provision is made for debt servicing in the annual budget.

Coordinating issues related to man power planning and develop0menmt in the state.

Department Of Research And Statistics

The function of this department includes the following;

Collection and compilation of statistical latter and production of statistical publication for dissemination to prospective users.

Organizing and coordinating data collection activities in government ministries and departments.

Collaboration with federal office of statistic and other national and international statistics agencies.

Serving as joint secretary (with the international cooperation departments) of the national management of social economic development program.

1.3 STATEMENT OF THE PROBLEM

In every project work there is a problem and the aim is to identify the problems so as to proffer possible solution to such problems. This project is to work on comparative analysis of approved capital estimate (capital expenditure in economic and social sector).

AIM AND OBJECTIVES

The aim of the study is to apply relevant statistical techniques to analyze available data on approved capital estimate(capital expenditure)in economic and social sectors from 2002-2017, which leads to the following objectives;

To determine the relationship that exists between the two sectors (economic and social sectors).

To test if there is significant difference between the means of the two sectors (economic and social sectors).

To test time series model for the on the data.

SIGNIFICANCE OF THE STUDY

Due to the poor development in economic and social sector of the state, this study was carried out to shed more light on the money sent on both sector (economic and social sectors) so as to test if there is significant differences between the means of the three sectors and the relationship between the budget of the two sectors economic and social.

SCOPE AND LIMITATION

The focus of this project will however be restricted to approve capital estimate/expenditure respectively for both the economic, social and regional development systems. However the report will cover the period of fifteen years that is from 2002-2017.

1.7 DEFINITION OF TERMS

Capital: means many things its specific definition depend on the context in which it is used in general, capital refers to financial resources available for the;

Budget: budget is the main financial plans for the formulation and use of the state wide fund of money resources. It is also as instrument for achieving control of the economy during the coming year.

Expenditure: it is the total amount of money that a government or person spends.

Estimates: to give or form a general idea about the size or cost of something etc.

Approved: to believe that something or someone is good or acceptable or to officially accept an idea, action, plan etc.

Economic sector: this comprises of ministries of agriculture, livestock, fishery, forestry, manufacturing, power, commerce, and finance.

Cooperating and supply:

Social sectors: this comprises of ministries of education, health, information and social development.

CHAPTER TWO

LITERATURE REVIEW

INTRODUCTION.

It is of paramount importance for every project to indicate and outline the related literature. This helps to provide a basis and guideline for further study. This part of project normally includes discussion on related literature concerning the study on the topic. As far as this project is concern, we could not lay our hands on any particular project work that is exactly the same as the topic been discussed in the project.

Public expenditure represent the total government spending in attain the predetermined macroeconomic objectives. Government has recorded a continuous increase over time in almost every country despite the fact that there is a continuous increase in government expenditure and in spite of its growing role and importance in national economies. The area of public expenditure remains relatively unexplored utilizing the attention on the theory of taxation. However, the classical have unfavorable thinking towards increasing public expenditure, pointing that government lack capacity to decide and judge economic interest on behalf of others; hence it should limit its spending.

The following are the kind of public expenditure;

The nature of investment which helps the economy in improving its productive capacity. While the unproductive versions are those that are committed to incur and maintain social overheads. The expenditures on administration, defense, justice, law and order and maintenance of state are unproductive (Bhatia, 2002).

Government expenditure is usually classified into Capital and Recurrent Expenditure: - recurrent expenditure is the expenditure that is incurred yearly for implementation of the various functions of government. It includes general administrative expenses on defense, social and economic services. Capital expenditure refers to the expenditure earmarked for specific projects that can last for many years. It includes investment in buildings, roads, airport, and petrochemical project e.t.c.

Transfer and non-transfer expenditures: pigou champions this classification. Transfer expenditure is a payment without corresponding receipt of goods and services by the state e.g. interest payment on the acquired dept, old age pension, unemployment benefit and benefit/allowance paid to the disaster displaced people e.t.c. the non transfer expenditure is that by which the states pay for its purchase or use of goods and services. Expenditures on defense, education, agriculture, transportation and communication and such like are all of non transfer expenditure.

It is noteworthy to observe that government expenditure on various sectors seem to have contributed to the economic growth at different rates in Nigeria. Owing to the diverse feelings on the above, the argument has been inconclusive on whether or not these critical sectors contribute significantly to the economic growth of Nigeria. To the best of researcher’s knowledge, the analysis of the sectoral impact of public expenditures on economic growth has much documentation. This is to say, it has been receiving attentions of the researchers and scholars, the studies that focuses on the discourse limit their variables of study to one variable. For example, each one focuses on agricultural expenditure and defense expenditure performance. While, those that focuses on the impact of the sectoral public expenditure performance on economic growth do not include in their model the capital which is the fundamental determinant of economic growth. The omission of the conventional variable makes the study to suffer the methodological problem of variable omission bias in a multivariate study like Egbetunde & Fasanya, (2013), Nworji (2012); Ogbulu (2012); Ebiringa & Chalse-Anyaogu et al (2012); Okoro (2013); Chude &Chude (2013); Shengen & Saukar (2010); Adesoye (2010); Adewara & Oloni (2012); Darma (2013), Usman (2011), Adesoye (2013), Abu & Abdullahi (2010), Ehiaiamusoe (2012) and Anyawu et al (2013).

2.1 DEFINITION OF ECONOMIC PLANNING

There are various definitions of economic planning. In general, economic planning is the process of coordinating development planning, budgeting, manpower planning, all forms of foreign assistance and collecting and compilation of statistical data. According to Adam Smith (1776), defines economic as “an inquiry into the nature as cause of wealth nations” in particular as: a branch of the science of a statesman or legislator with two fold objectives of providing a plentiful revenue or subsistence for the people and supply the states services. Jhingan B. Say (1803) distinguish the subject from its public-policy and defines it as the science of production, distribution, and consumption of wealth. On the satirical side, Thomas Caaryle (1849) coined the dismissal science as an apple for classical economics, of Maltus (1789). John S.N. (1844) defines economics as the science with traces the law of such the phenomena of society as aroused from the combined operations of making for the production of wealth.

IMPORTANCE OF ECONOMIC PLANNING

To increase the national income: the objectives of planning are to utilize the resources of the state/country in such a manner that it should increase the size of national income in the developing countries/states. Planning is very useful for increasing the production of the country.

Balanced economy: through planning, resources of the country/state can be allocated in such a manner that it provides balance for the economy.

Improvement in the balance of payment: the balance of payment of developed countries/ state remains defeated. It adversely affects the rate of economic growth, through planning government can reduce imports and increase exports.

2.2 DEFINITION OF SOCIAL PLANNING

There are various definitions of social planning. In general, social planning is the process by which policy makers, legislators, government agencies, planners and often founders try to solve community problems or conditions in the community by devising and implementing policies results. But according to, Menzies (2004) defines social as building (structure) providing physical resources that are used substantially for community activities or adequate storage facilities. Similarly, Brisbane city council (1999) defines social as informal or formal places and spaces providing physical resources that are used substantially for community activities and services.

IMPORTANCE OF SOCIAL PLANNING

It helps to know the well-being of people in a country/state.

It helps in planning on how to distribute health services within the state.

It helps in protection of people and properties e.t.c

CHAPTER THREE

METHODOLOGY

3.0 INTRODUCTION

Statistical data could be gathered by data collection which is very important stage in any statistical investigation. The soundness of the methods applied in collection of statistical data determines the great extent of the source of the study. Therefore the source of the data used in this project work is secondary data from a secondary source, which is obtained from Kaduna state ministry of economic planning.

3.1 METHOD OF DATA COLLECTION

Documentary method of data collection was used to obtain the data for this project. Therefore the data is said to be secondary data, from a secondary source (the data used is obtained from Kaduna state ministry of economic planning).

3.2 SOURCES OF DATA COLLECTION

There are basically two sources of data collection which are primary and secondary source.

3.2.1 PRIMARY SOURCE

They are the most original and authentic because they are collected directly through interview, questionnaire, observation, or experiments. Data obtained from this source are called primary data.

3.2.2 SECONDARY SOURCE

These set of data’s are obtained from existing records, this source includes, magazines, journals, library, e.t.c. data obtained from this sources are called secondary data.

However, the data for this project is secondary data, which is obtained from existing record of Kaduna State Ministry of economic planning.

3.3 STATISTICAL TOOLS USED

Statistical tools used in this analysis are;

Correlation analysis (simple correlation).

T-test (independent sample t-test).

ANOVA (one way ANOVA).

Time series Analysis.

3.4 CORRELATION

Correlation is defined as the extent or measure of degree and direction of linear relationship that exist between two or more variables. When we have only two variables, we talk of simple correlation, and when we have only two variable we talk of multiple correlation can be positive, negative, or zero correlation coefficient (r) the correlation coefficient ranges from -1 to +, the closer the value of (r) is to 1, the higher the extent of the relationship between the variable and for a value of (r) closer to zero It shows that the relationship is insignificant.

3.4.1 MEASURE OF CORRELATION

Karl Pearson moment product correlation

Spearman brown correlation

In this project work we are using Karl Pearson moment product correlation.

3.4.2 FORMULA FOR CALCULATING KARL PEARSON CORRELATION

Where , and

=

3.4.3 ASSUMPTIONS OF CORRELATION

1. Level of measurement: this means that each variable should be continuous.

2. Related pairs: this means each participant or observation should have a pair’s value.

3. Absence of outliers: this refers to not having outliers in either variables.

4. Linearity: this means that a straight line relationship between the variables should be formed.

5. Normality of variables: the variables should be randomly selected.

3.5 T-TEST OF DIFFERENCE OF SAMPLE MEANS

The independent sample t-test is a statistical tool used to measure the significant difference between the mean of two groups. In independent sample t-test, the data to be tested from both the two groups should be homogenous (for just the same variables) but categorized into two groups (usually distinguish by another nominal variable).

TEST STATISTICS

=

Where, =

And, =

=

3.5.1 ASSUMPTION OF T-TEST

The data must be approximately normally distributed.

The data must be measurable.

The sample size n must be less than or equal to 30.

Homogeneity of variance: must have equal variance (the degree to which the distributions are spread out is approximately equal)

3.5.2 PROCEDURE FOR TESTING T-TEST

Hypothesis:

(There is significant difference between the means of the two sectors [economic and social])

LEVEL OF SIGNIFICANCE

Î± = 0.05

Test Statistic

DECISION CRITERIA

Reject: if >, otherwise accept.

3.6 ANOVA (ONE WAY ANALYSIS OF VARIANCE)

The one way ANOVA is similar to the t-test of independent samples, it is used when the number of the groups (sample) is more than two.

3.6.1 ASSUMPTIONS OF ANOVA

Each sample is an independent random sample.

The distribution of the response variable follows a normal distribution.

The population variances are equal across response for the group levels.

3.6.2 PROCEDURE FOR ANOVA TEST

State the hypothesis i.e.

: = =

: ≠ ≠

Level of significance i.e. a=1% or 5%

Test Statistic

=

DECISION CRITERIA

Reject: if >, otherwise accept.

3.7 TIME SERIES

We define time series as an orderly arrangement of data collected, recorded, or observed at successive intervals of time. When we observed numerical data at different points of time, the set of observation is known as time series.

3.7.1 COMPONENTS OF TIME SERIES

The fluctuation of time series is classified into four (4) basic components or types of variations which user imposed and acting all together for changes in the time series over a period of time.

The four types of components are;

Secular trend.

Seasonal variations.

Cyclical variations.

Irregular variations.

Secular trend

The secular trend represents a general rise or fall occurring in a time series data over a long period of time. It is a smooth, steady, regular and a broad movement of the series in the same direction generally covering a minimum of ten years.

Seasonal variation

These are the changes that occur in time series data that can be attributed to seasonal effect with fairly regular period (usually a year) a seasonal variation shows identical or almost identical pattern over the period. Season effects could be observed within a day, a week, or a quarter of a year depending on the nature of the data being observed.

Cyclical variations

This refers to recurrent u and down wavelike variations or oscillation about a trend line. They are often described as ‘swings from prosperity, through recession, depression, recovery and back again to prosperity’. A circle is said to be completed when beginning with a peak, the falling curve reaches a minimum point and then rising again reaches the next peak. Cyclical movement may or may not follow exactly similar pattern after equal intervals of time, and they occur over several years. A business cycle is a typical example of cyclical movements.

Irregular variations

These are random or sporadic movement of time series due to chance or unpredictable events such as floods, strikes, election, fires, war, earthquakes e.t.c. this movements are sometime referred to as residual, erratic or accidental variations, which cannot be ascribed to cyclical or seasonal influences.

3.7.2 TIME SERIES MODEL

In the analysis of time series data, it is assumed that there is a multiplicative relationship between the four components, that is;

This is called the multiplicative model,

Where:-

= the value of the observed series for a given time period

= trend, a long-term growth factor.

= the cyclical factor.

= the seasonal factor.

= the irregular factor.

Another approach is to treat each observation of a time series as the sum of these four components.

This is called the additive model. The multiplicative model is mostly accepted because the factors are viewed as amplifying each other rather than acting separately as assumed by the additive model. This implies that the factors are not independent of each other.

3.7.3 MEASUREMENT OF SECULAR TREND

There are four (4) methods of measuring trends, they are;

The freehand method

The semi average method

The moving average method

The method of least squares

The freehand method

The procedure is to plot the time series data on a graph with the given variable on the y axis and time period on the x axis and fix a straight line through the plotted points by mere inspection.

The semi average method

This method involves splitting the time series data into two 2 equal parts. For odd number of periods, the middle observation is ignored. The arithmetic mean of each part is obtained and placed against the midpoint of each part. These two points are plotted on the original graph and a straight line is drawn to join the two points. This line gives the required trend line which is either extended downward or upwards to predict future values.

Even numbers of years: - when there are even numbers of years like 2, 4, and 6 and so on, equal parts can easily be formed and an average of each parts obtained. However, when the average is to be centered there would be some problems in case the number of years 8, 12 and so on. Example: if the data relates to 2010, 2012, and 2013, this would be the middle year, in such a case the average will be centered corresponding to 1st July, 2011, that is middle of 2011 and 2012.

The following example shall illustrate the points:-

Moving average method

This is a technique of smoothening out erratic fluctuation in the time series, supposed we have time series data, a 3 year moving average would be gotten by averaging the values first of period 1 to 3 , period 2 to 4, period 3 to 5 and so on. These averages are assumed to represent the trend values. Each average is placed in the middle of the period to relate to. For instance, the 3 year average for period 1 to 3 is centered at period 2, that of 2 to 4 at period 3 e.t.c.

in cases where periods being averaged have even number of terms, say, four year moving average, it is necessary to compute another 2 periods moving averages to center the moving averages at periods rather than have them between periods.

To determine the order (or period) of the moving average, all you need to do is to observe the data over the years, if it is found, e.g. that the figures seems to come peak every 3 years, then a three year moving average would be appropriate. If however the figure seems to have peak at every five years, then a five year moving average would be used, the longer the length of the period, the smoother the resulting series. However, the longer the length of the period the more information is lost at the beginning and the end of the series. Also, moving averages can be used if the trend is linear.

The method of least squares

This method is mostly widely used in practice. It is a mathematical method which helps in fitting a trend line to the data in such a manner that the following 2 conditions are satisfied;

= 0, that is the sum of deviations of the actual value of y and the computed values of y is zero.

Is least, that is the sum of the squares of the deviations of the actual and computed value is minimum from the line and hence, the name method of least squares. The line obtained by this method is known as “the line of best fit”.

The method of least square may be used either to fit a straight line trend or parabolic trend.

The straight line trend is represented by the equation;

= a + bx.

Where:-

= the estimated trend value for a given time period (c).

a= the trend line value when, x= 0

b= the gradient or slope of thee trend line, i.e., the change in () per unit of time.

x= thee time unit.

The estimate of the parameters of the trend equations are, a and b and they are obtained by solving the following normal equations:-

…………………………………….. (1)

…………………………………….. (2)

Where n represents of item s(months, quarters, or years) under consideration. The variable x (time] can be measured from any point as origin. The mathematical rigors involved are simplified if the origin is placed exactly in the middle of the series. For an odd number of periods the origin will coincide with a particular period. However, the origin for even number of periods is placed midway between the two middle periods. In any case, we shall have 0. The two equations becomes:-

…………………………………….. (3)

…………………………………….. (4)

From equation 3 and 4

a = and b =

The constant “a” is simply equal to the mean of y values and the constant “b” gives the rate of change.

It’s should be noted that in case of odd number of years, when the deviations are taken from the middle year will be zero, provided there is no gap in the given data. However, in case of even number of years also will be zero if the origin is placed midway between the two middle years. E.g., if the years are 2012, 2014, 2015, 2016, and 2017, we can take deviations from the middle year 2013.5. The deviations would be -2.5, -0.5, 0.5, 1.5, 2.5 for the various years and the total would be zero. Hence both in odd as well as in even number of years, we can use the simple procedure of determining the values of the constants a and b.

CHAPTER FOUR

DATA PRESENTATION, ANALYSIS AND CONCLUSION

4.0 INTRODUCTION

This chapter is made to partially use four different statistical tools namely time series, one sample t-test, and ANOVA and correlation analysis. The main purpose is to see the trend from time plot, to determine the difference amongst the mean of two set of data and check whether there is a relationship between economic and social sector respectively.

4.1 DATA PRESENTATION

The table below shows the data presentation of Kaduna state approved capital estimate (capital expenditure) in two sectors (economic and social) from 2002 to 2017.

Years

Economic Sector in(#)

Social Sector in(#)

2002

5533.8

2359.0

2003

11849.4

3128.6

2004

10000.9

5260.1

2005

11133.8

5118.8

2006

11133.3

3997.7

2007

16143.3

1278.1

2008

18533.6

3777.2

2009

19507.6

13549.1

2010

18606.0

15353.0

2011

38461.6

23411.9

2012

49064.5

28270.5

2013

36245.3

17397.4

2014

8396.6

8166.6

2015

4071.5

3829.5

2016

46644.0

34151.6

2017

32548.7

25298.4

Source: Kaduna state ministry of economic planning.

4.2 ANALYSIS ONE: Pearson Correlation

Aim: To determine the degree of relationship that exists between the two sectors (economic and social planning).

TEST STATISTIC

COMPUTATION: consider the following SPSS output analysis.

Economic sector

Social sector

Pearson correlation

Economic sector Sig (2-tail)

Pearson correlation

social sectors Sig (2-tail)

1

16

921

000

16

921

000

16

1

16

CONCLUSION:

The above table shows that the relationship between economic and social sectors is (0.921), and the value (0.00) < Î± – value (0.05) we therefore conclude that there is a strong positive correlation between the two sectors (economic and social).

4.3 ANALYSIS TWO: One-Sample T: Economic Sector, Social Sector

COMPUTATION: Consider the following SPSS output.

Variable N Mean StDev SE Mean 95% CI

Economic Sector 16 21117 14691 3673 (13289, 28945)

Social Sector 16 12147 10638 2659 (6478, 17815)

Both the t-test and ANOVA are purposely used in the same vain, which is to test for the equality between the two sectors and have already concluded that the differences do exist among the sectors.

CONCLUSION

We conclude that in the data element of trend do exist and suggest that to forecast the data other important investigation have to be undergoes. In the correlation analysis one can see clearly that strong positive relationship exist with 0.921. However, the effort to compare the means it was found that there is significance difference between the means of both sectors from their extracted population.

4.4 ANALYSIS THREE: One-way ANOVA on Economic Sector and Social Sector

Aim: To test if there is significant difference between the mean of the sectors (economic and social).

COMPUTATION: Consider the following SPSS output.

Source DF SS MS F P

Factor 1 643744429 643744429 3.91 0.007

Error 30 4934791735 164493058

Total 31 5578536163

S = 12825 R-Sq = 11.54% R-Sq (adj) = 8.59%

CONCLUSION

The analysis above is conducted in other to test the significance differences between both sectors. Hence we are to reject the null hypothesis because Pvalue < Î± at 0.05 level of significance (0.007 < 0.05) and conclude that there is significance difference between the mean of the populations of both the sectors.

4.5 ANALYSIS THREE: Trend Analysis for Time Series

Aim: To test time series model for the data.

4.5.1 Trend analysis plot for economic sector

Figure 4.4.1: Time Plot on economic sector

CONCLUSION: Figure 4.4.1; is a time plot on economic sector and has indicated some elements of trend in it because, the declination behaviour it displayed shows both the mean and variance are not constant over a time.

4.5.2 Trend analysis plot for social sector

4.5.2 Time plot on social sector

CONCLUSION: Figure 4.4.2 is another time plot but on social sector and has indicated some elements of trend in it because, the declination behaviour it displayed shows both the mean and variance are not constant over a time. There transformation or differencing and some other testing criterions are needed when there is need to forecast what might be spend over some period of time in both economic and social sector.

4.6 SUMMARY

In this project research, correlation analysis, T-test (independent sample t-test), One-way ANOVA, and time series analysis were used to achieve the objectives as stated in chapter one. The data used in this project research is a secondary data and was collected by documentary method at the record office of Kaduna state ministry of economic planning. The purpose of this study was to determine the relationship that exist between the two sectors (economic and social), to test if there is a significance difference between the mean of the two sectors and to build time series model for those on the data. Based on the analysis carried out, the result of the correlation analysis shows that there is a strong positive correlation between the two sectors (economic and social). And from the second analysis based on the t-test analysis we discover that there is no significant difference between the means of the two sectors (economic and social). From the third analysis based on One-way ANOVA, we are to reject the null hypothesis because Pvalue < Î± at 0.05 level of significance (0.007 < 0.05) concludes that there is significance difference between the mean of the populations of both the sectors. And finally, from the fourth analysis, the time series analysis shows that the data for economic social sectors are fluctuating throughout the series while from the trend analysis revealed that the year 2011 has the highest capital expenditure in economic sector and 2014 has the lowest. While 2016 has the highest capital expenditure in social sector and 2006 has the lowest.

4.7 CONCLUSION

Based on the analysis carried out, the result of the correlation analysis shows that there is a strong positive correlation between the two sectors (economic and social). And from the second analysis, based on the t-test analysis we discover that there is no significant difference between the mean of the two sectors (economic and social). For the third analysis, there is significance difference between the mean of the populations of both the sectors. And finally, from the third analysis, the time series analysis shows that the data for economic social sectors are fluctuating throughout. The highest capital expenditure in economic sector is 2011 and 2014 has the lowest, while 2016 has the highest capital expenditure in social sector and 2006 has the lowest. We recommended that the government should try and increase the budget of the two sectors and also try to maintain and improve in the development of the two sectors (economic and social).

4.8 RECOMMENDATION

Based on the analysis carried out, since there is no biasness in the planning of the sectors, we recommend that;

Government should try and increase the budget of the three sectors (economic and social sectors).

They should try to maintain and improve in the development of the two sectors.

Government should also plan more on infrastructure and security of the state.

REFERENCES

A.N. and Abdullahi U (2010). Government Expenditure and Economic Growth in Nigeria, 1970-2008: A Disaggregated Analysis. Business and Economics Journal, Volume 2010: BEJ-4.

Abdulazeez S. A (2012) “Fundamentals of Statistics” published in Kaduna, Nigeria by Eskodhab Ventures (printing and publishing) W4, Ahmadu Bello Way, Kaduna.

Adewara S. O and Oloni E.F. (2012) Composition of Public Expenditure and Economic Growth in Nigeria. Journal of Emerging Trends in economics and management sciences (JETEMS) 3(4): 403-407.

Adesoye A.B., Maku O.E and Atanda A.A. 2010). Dynamic Analysis of Government Spending and Economic Growth in Nigeria, Journal of Management and Society: Vol. 1, No 2, pp. 27-37, December Edition 2010.

Aliyu U. (2010): Statistical Methods for Biometric and Research, Revised Edition.

Millennium Printed and Published Company Limited Kaduna, Nigeria.

Andy F. (2009) “ Discovering Statistics Using SPSS” London Sage Publication Ltd, 1 Olivers Yard, Third Edition.

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Anyanwu S., Egwaikhde C. and Aiyedogbon J. O. (2012). Comparative Regime Analysis of the Trend and Structure of Military Expenditure in Nigeria, 1980-2010.Journal of African Macroeconomic Review, Vol. @, No. 1 (2012)

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Chude N. P. and Chude D. I. (2013). Impact of Government Expenditure on Economic Growth in Nigeria; International Journal of Business and Management Review; Vol.1, No.4, pp.64-71, December 2013.

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Ebiringa O. F. and Chalse-Anyaogu N. B.(2012). Impact of Government Sectoral Expenditure on the Economic European Journal of Business and Management, www.iiste.org ISSN 2222-1905 (paper) ISSN 2222-2839 (online) Vol.7, No.12,2015 184 Growth of Nigeria, Ebiringa, et.al Int.J. Eco. Res., 2012, v3i6, 82-92.

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Specification: Zagreb International Review of Economics & Business, Vol.16, No.1, pp. 79-92, 2013.

Ehigiamusoe U.K. (2012). A Comparative Analysis of Agriculture

APPENDIX

Years

Economic Sector in(#)

Social Sector in(#)

2002

5533.8

2359.0

2003

11849.4

3128.6

2004

10000.9

5260.1

2005

11133.8

5118.8

2006

11133.3

3997.7

2007

16143.3

1278.1

2008

18533.6

3777.2

2009

19507.6

13549.1

2010

18606.0

15353.0

2011

38461.6

23411.9

2012

49064.5

28270.5

2013

36245.3

17397.4

2014

8396.6

8166.6

2015

4071.5

3829.5

2016

46644.0

34151.6

2017

32548.7

25298.4

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