Economic Modelling 32 (2013) 524–531

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Economic Modelling

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Determinants of household saving: Cointegrated evidence fromPakistan (1975–2011)

Aisha Ismail ⁎, Kashif Rashid ⁎⁎Department of Management Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan

⁎ Corresponding author. Tel.: +92 333 5059269 (cell⁎⁎ Corresponding author.

E-mail addresses: aishamalik47@yahoo.com (A. Isma(K. Rashid).

0264-9993/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.econmod.2013.02.004

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 7 February 2013

JEL classification:C22D11D12

Keywords:Household savingCointegrationError Correction ModelPakistan

The purpose of this study is two-fold: firstly, to analyze the long run relationship between household saving andvarious socio-economic and demographic variables and secondly, to determine the short run and the long runimpact of various socio-economic and demographic variables on the household saving rate in Pakistan. The rela-tionship between household saving and various socio-economic and demographic variables is analyzed by ap-plying Johansen cointegration analysis. Furthermore, Error Correction Model is also estimated in order to findout the convergence of themodel towards equilibrium. The results show that there exists a long run relationshipbetween household saving and the variables used in the study, while the result of Error CorrectionModel revealsthat about 45% convergence towards equilibrium takes place every year.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Household sector saving is defined as a percent of householdsector disposable income that is not consumed. Household savingrate is dependent upon the number of variables. This study analyzesthe impact of various social, economic and demographic variableson household saving in Pakistan for the time period between 1975and 2011. Particularly, the impact of GDP per capita, GDP per capitagrowth, inflation, old dependency ratio and public saving is exam-ined. This section consists of background of the problem, review ofexisting literature, and objectives and organization of the study.

1.1. Background

For attaining a high level of development inside the economy, na-tional saving is an important factor among many other factors. Highersavings lead to less consumption expenditures by the households andbring more opportunities of investment. A high rate of investmentleads to more industrial growth, high employment opportunities,improvement in the quality of diverse range of products among manyother beneficial impacts and thus causes higher economic growth. Theshare of household saving in national savings is large in both developedand developing countries.

).

il), mkrashid@ciit.net.pk

rights reserved.

The role of household saving in enhancing economic growth of theeconomy is significant and has been documented in many areas ofdevelopment economics. The saving performance of Pakistan duringthe last two decades was not incredibly impressive as compared toother developing countries in the region (Burney and Khan, 1992).There are many factors ranging from socio-economic to demographicbehind this poor performance of saving rate. The important contribu-tory factors include elevated inclination for apparent expenditures,amplified availability of new products, more attention on the produc-tion of consumer goods and negative real returns to financial savings.Despite this poor performance, the household saving performancehas shown an increasing trend. It has 80% share in the domesticsavings and hence positively influences total savings (Ahmad et al.,2006).

Determinants of household saving are well documented in earlierstudies. For example, the determinants are GDP per capita (Agrawal,2001; Ahmad and Asghar, 2004; Bautista and Lamberte, 1990; Brata,1999; Burney and Khan, 1992; Carroll and Weil, 1994; Collins, 1989;Khan, 1988; Khan et al., 1992; Loayza and Shankar, 2000a, 2000b;Loayza et al., 2000a, 2000b; Ozcan et al., 2003; Salam and Kulsum,2000; Siddiqui and Siddiqui, 1993; Wakabayashi and Mackellar,1999; Wen and Ishida, 2001), GDP per capita growth rate (Agarwal,2000, 2001; Athukorala and Sen, 2003; Deaton and Paxson, 2000;Haque et al., 1999; Kazmi, 1993; Kraay, 2000; Modigliani, 1970;Modigliani and Cao, 2004; Nasir and Khalid, 2004; Qureshi, 1981;Solow, 1956), inflation (Ahmad et al., 2006; Gupta, 1987; Hasnainet al., 2006; Horioka andWan, 2007; Kazmi, 1993; Khan, 1988; Koskelaand Viren, 1985; Loayza et al., 2000a, 2000b; Modigliani and Cao, 2004;

525A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

Ozcan et al., 2003; Qureshi, 1981), public savings (Agarwal, 2000;Athukorala and Tsai, 2003; Balassa, 1992; Dayal-Ghulati andThimann, 1997; Edwards, 1996; Liu and Woo, 1994; Masson et al.,1998; Muradoglu and Taskin, 1996) and old dependency ratio(Agarwal, 2000; Athukorala and Tsai, 2003; Burney and Khan,1992; Kraay, 2000; Loayza et al., 2000a, 2000b; Modigliani and Cao,2004; Wakabayashi and Mackellar, 1999).

Despite its importance in total savings, few studies have beenconducted to analyze the performance of household saving in Pakistan.Mostly, studies are conducted for cross sectional and panel data(Attanasio and Szekely, 2000; Callen and Thimann, 1997; Horioka andWan, 2007; Loayza et al., 2000a, 2000b; Muradoglu and Taskin, 1996;Schmidt-Hebbel et al., 1996). The time series data analyses are alsoconducted for specific countries like China (Aaberge and Zhu, 2001;Chamon and Prasad, 2006, 2010; Chamon et al., 2010), Ghana(Issahaku, 2011), Kenya (Kibet et al., 2009), Taiwan (Athukorala andTsai, 2003), India (Athukorala and Sen, 2003; Buragohain, 2009; Sinhaand Sinha, 2007), Indonesia (Kelley andWilliamson, 1968), Philippines(Bautista and Lamberte, 1990; Orbeta, 2006), Austria (Dirschmid andGlatzer, 2004) and Morocco (Khalek et al., 2009). Some studies haveexamined private sector savings (Loayza and Shankar, 2000a, 2000b;Loayza et al., 2000a, 2000b; Ozcan et al., 2003), etc.

Few studies are conducted in Pakistan for determining the behaviorof household saving in specific regions like Multan district (Rehman etal., 2010, 2011), but only few have specifically paid attention to house-hold saving and the factors influencing household saving. For example,the studies conducted by Burney and Khan (1992) and Ahmad et al.(2006) analyzed the behavior of household saving and the impact ofvarious social, economic and demographic variables on it. The studyby Burney and Khan (1992) was conducted with the help of primarydata source and they employed regression analysis technique. Onthe contrary, the study by Ahmad et al. (2006) was conducted for thetime series data over the time period between 1972 and 2001. Theconclusion of the above mentioned discussion is as follows:

a) no researcher has previously attempted to assess the overall rela-tionship between household saving and various social, economicand demographic variables; and

b) no researcher has previously estimated the long-run and short-runeffects of various social, economic and demographic variables onhousehold saving in Pakistan.

The present study fills the gap in the literature by applying avariety of econometric techniques in order to validate the results inthe context of Pakistani environment. The results reveal that in theshort run, inflation, old dependency ratio and public saving are criti-cal in determining the household saving while in the long run, infla-tion, old dependency ratio, GDP per capita, GDP per capita growthand public saving are important in influencing the household savingbehavior.

The present study has some important policy implications in orderto improve the household saving rate as household saving shares a larg-er portion in the total saving of the country. Growth of any countrydepends heavily on the level of savings so the present study providesuseful directions in order to improve the socio-economic conditions aswell as saving rates.

Following the Introduction section, the rest of the paper is struc-tured as follows: Section 2 discusses the existing literature reviewand hypotheses development. Section 3 presents the data and meth-odological framework. The results and discussion are presented inSection 4. Finally, Section 5 concludes the study.

2. Literature review

There have been many previous empirical analyses of the impactof various growth, government policy, financial, macroeconomicstability and demographic variables on the household saving rates

by using cross sectional, panel cross-country and time series data. Fewimportant studies are performed by Modigliani (1970), Feldstein(1980), Modigliani and Sterling (1983), Horioka (1989), Edwards(1996), Dayal-Ghulati and Thimann (1997), Bailliu and Reisen (1998),Higgins (1998), Loayza et al. (2000a, 2000b), Chinn and Prasad(2003), Luhrman (2003), Bosworth and Chodorow-Reich (2007), Itoand Chinn (2007), Kim and Lee (2008), Park and Shin (2009), andHorioka and Yin (2010).

2.1. GDP per capita and household saving

Economic growth is enhanced by an increase in the saving andconsumption activities. Increase in the income level and resultingincrease in the living standard are the indicators of economic growth.Carroll and Weil (1994) showed in their study that higher householdsaving rates are the outcome of increased GDP per capita. Similarresults are found by Loayza and Shankar (2000a, 2000b) and Ozcanet al. (2003).

The impact of GDP per capita on household saving is significantand positive. An increase in GDP per capita increases the income ofthe people thus raising the ability of the people to save more. Severalearlier studies verified the positive relationship betweenGDPper capitaand household saving (Abdul-Malik and Baharumshah, 2007; Ahmad etal., 2006; Bosworth and Chodorow-Reich, 2007; Choudhury, 2005;Fasoranti, 2007; Hasnain et al., 2006; Khalek et al., 2009; Newman etal., 2008; Orbeta, 2006; Sajid and Sarfraz, 2008). Next the impact ofGDP per capita growth on household saving is analyzed in light of theexisting studies.

2.2. GDP per capita growth and household saving

The results of various studies indicate that GDP per capita growthrate positively affects the household saving. A strong positive associa-tion between household saving ratios and real GDP per capita growthhas been documented in the empirical studies performed by Ahmad etal. (2006), Hasnain et al. (2006), Newman et al. (2008), Abdul-Malikand Baharumshah (2007), Verma and Wilson (2005), Verma (2007),Bosworth and Chodorow-Reich (2007), Horioka and Wan (2007),Sinha and Sinha (2007) and Park and Shin (2009).

2.3. Household saving and inflation

Household saving and inflation rate are interconnected. Thereexists a positive as well as a negative relationship between inflationand household saving as documented in the earlier studies. A positiveassociation between household saving and inflation prevails becausehigher inflation reveals higher income and saving. Inflation can havea positive effect on saving rates as an insecurity about future assetvalues and future real incomes in an inflationary environment canpromote saving in order to continue future consumption levels.Many empirical studies have noted a very high correlation betweeninflation and household saving rates. The important contributionsare made by Ahmad et al. (2006), Hasnain et al. (2006) and Horiokaand Wan (2007).

Conversely, the relationship between inflation rate and householdsaving may be negative as uncertainty about future increases. Theother reason for this negative relationship is that mostly peoplewant to maintain real level of consumption thus higher spendingtoday results in lower level of household saving. A higher rate of infla-tion leads to insecurity about financial returns and results in lowerrate of saving (Hondroyiannis, 2004). The empirical work of Corboand Schmidt-Hebbel (1991), Iqbal (1993), Masson et al. (1998),Haque et al. (1999), Athukorala and Tsai (2003) and Ozcan et al.(2003) revealed that inflation and household saving are negativelycorrelated.

526 A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

2.4. Public saving and household saving

Public saving has a significant relevance to household saving. Thepositive attitude of the government towards public saving may have avery large and positive impact on the household saving. Severalimportant studies are performed by Verma and Wilson (2005), Ahmadet al. (2006), Horioka and Wan (2007), Sinha and Sinha (2007) andVerma (2007).

2.5. Old dependency ratio and household saving

Old dependency ratio is an important dominant factor influencingthe household saving rates. Leff (1969) showed a negative associationbetween the old dependency ratio and household saving rate. Thepopulation structure has been identified as a factor affecting savingbehavior in less developed countries. Changes in the old dependencyratio affect saving behavior, not only in the form of changed demandfor health expenditure, human capital formation, and retirement con-sumption levels, but this ratio can also affect government expendi-ture. Several important studies performed previously have showedthe significant impact of old dependency ratio on household saving.Important contributions are made by Ahmad et al. (2006), Hasnainet al. (2006), Kibet et al. (2009) and Park and Shin (2009).

As shown in Fig. 1, the dependent variable used in this study ishousehold saving. On the other hand, the independent variables inthis study are GDP per capita growth, GDP per capita, inflation, publicsaving and old dependency ratio.

2.6. Hypotheses development

The hypotheses relevant for this study are presented as follows:

H1. The long run and short run relationship between householdsaving and GDP per capita is significant and positive.

H2. The long run and short run relationship between householdsaving and GDP per capita growth is significant and positive.

H3. The long run and short run relationship between householdsaving and inflation rate is significant and negative.

H4. The long run and short run relationship between householdsaving and public saving is significant and positive.

H5. The long run and short run relationship between householdsaving and old dependency ratio is significant and negative.

INDEPENDENTVARIABLES

DEPENDENTVARIABLE GDP PER CAPITA

GROWTH

GDP PER CAPITA

INFLATION

PUBLIC SAVING

HOUSEHOLD SAVING

OLD DEPENDENCY RATIO

Fig. 1. Conceptual framework.

3. Methodology

In this study, the empirical work is based on the secondary data forthe time period between 1975 and 2011. The data is collected from var-ious sources. A time series data analysis for Pakistan is conducted withthe help of various statistical techniques. Long run relationship betweenhousehold saving and various independent variables is analyzed byapplying Johansen cointegration analysis developed by Johansen in1991.

3.1. Sources of data

The secondary data for the period between 1975 and 2011 iscollected by using the websites of Economic Survey of Pakistan, variousyears, Islamabad, World Development Index (WDI), Key Indicators ofLabor Market (KILM), Penn World Tables and Pakistan Institute ofDevelopment Economics (PIDE) Islamabad.

3.2. Econometric methodology

Johansen's cointegration technique is employed to find a long-runrelationship between GDP per capita, GDP per capita growth, inflation,public policy, old dependency ratio and household saving. The follow-ing model will be estimated.

D Ln HSð Þ ¼ β0 þ β1D Ln GDPPCð Þ þ β2D Ln GDPPCGð Þ þ β3D Ln CPIð Þþ β4DLn PSð Þ þ β5D Ln ODRð Þ þ β6 Ln HS −1ð Þð Þþ β7 Ln GDPPC −1ð Þð Þ þ β8 Ln GDPPCG −1ð Þð Þþβ9 Ln CPI −1ð Þð Þ þ β10 Ln PS −1ð Þð Þ þ β11 Ln ODR −1ð Þð Þ þ έ

ð1Þ

where:

D first differenceLn natural logarithmHS household savingGDPPC GDP per capitaGDPPCG GDP per capita growthCPI consumer price indexPS public savingODR old dependency ratioέ error term

In testing cointegration evidence, first step in the analysis is toestablish the order of integration of each variable. If all the variablesare stationary at levels then cointegration analysis is no more valid.It means for cointegration analysis all the variables should be inte-grated to higher orders. If the variables are not stationary then testsof nonstationarity (that is, unit root) are the usual practice today.Engle and Granger (1987) defined a nonstationary time series to anintegrated of order ‘d’ if it becomes stationary after being differentiated‘d’ in time. This notion is normally denoted by I (d). The AugmentedDickey Fuller (ADF) test is a popular unit root test and is used to testthe order of integration of the series. This test is performedwith variousspecifications to check whether the series is stationary at levels or attheir first difference and whether these statistics are calculated witha constant, and a constant plus time trend. These tests have a nullhypothesis of nonstationarity against an alternative of stationarity.The ADF test to check the stationary series is based on the followingequation:

Δyt ¼ β1 þ β2tþ δyt−1 þαi

Xmt¼1

Δyt−1 þ εt ð2Þ

527A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

where ΔYt shows the change in Y at period t. Y is a dependent variableat time t.α is the intercept of the equation.β is the slope of the time var-iable t. δ and γ are the parameters of Yt − 1 and ΔYt − 1. Furthermore,Yt − 1 is the lag value of the dependent variable, where t varies from 1up to m. ∑ shows summation of Δy for t − 1 time period and εtis a pure white noise error term. Finally, ΔYt = Yt − Yt − 1 andΔYt − 1 = Yt − 1 − Yt − 2, etc.

It is an empirical fact that many macroeconomic variables appearto be an integrated of order ‘d’ [or I(d) in the terminology of Engleand Granger (1987)] so that their changes are stationary. Hence, ifall the variables used in the study are each I(d), then it may be truethat any linear combination of these variables will also be I(d).When it is established that all of these variables are I(d), the studywill then proceed to determine the order of integration of the seriesfor the analysis of the long-run relationships between the dependentvariables. To examine this long-run relationship among the variables,they must be cointegrated. Two or more variables are said to becointegrated if their linear combination is integrated to any orderless than ‘d’. The cointegration test provides the basis for tracing thelong-run relationship. Two tests for cointegration have been givenin the literature review (Engle and Granger, 1987; Johansen andJuselius, 1990).

In the multivariate case, if the I(1) variables are linked by morethan one cointegrating vector; the Engle–Granger procedure is notapplicable. The test for cointegration used here is the likelihood ratioput forward by Johansen and Juselius (1990), indicating that the maxi-mum likelihood method is more appropriate in a multivariate system.Therefore, this method is used in the current study to identify thenumber of cointegrated vectors in the model.

The Johansen and Juselius method has been developed in part bythe literature available in the field and reduced rank regression, andthe cointegrating vector ‘r’ which is defined by Johansen as the max-imum Eigen-value and trace test. There are ‘r’ or more cointegratingvectors. Johansen and Juselius (1990) and Johansen (1991) proposethat the multivariate cointegration methodology can be defined as:

Ln HStð Þ ¼ Ln GDPPC;GDPCG;CPI; PS and ODRð Þ ð3Þ

which is a vector of P = 5 elements. Considering the followingautoregressive representation:

HSt ¼ α0 þHSt−1 þ μ t where t ¼ 1…k and i ¼ 1…n: ð4Þ

In Eq. (4), HSt is the household saving at time t.α0 is the intercept.HSt−1 is the household saving at time t − 1. Finally, μ tis the errorterm.

Table 1ADF unit root test on household saving and its determinants.

Variables ADF test statistic

Levels

Constant(critical value)

Constant a(critical va

Household saving −1.123(−3.635)

−3.376(−4.251)

GDP per capita −1.781(−3.635)

−1.663(−4.251)

GDP per capita growth −2.875(−3.635)

−2.880(−4.251)

Inflation −2.443(−3.635)

−2.279(−4.251)

Public saving −1.623(−3.635)

−3.199(−4.251)

Old dependency ratio 0.132(−3.635)

1.555(−4.251)

⁎ Shows the variable is significant at 1% level.

Johansen's method involves the estimation of the above equationby the maximum likelihood technique and the testing of the hypoth-esis H0: π ¼ Ψξð Þ of ‘r’ cointegrating relationships, where ‘r’ is therank or the matrix π 0∠r∠Ρð Þ;Ψ is the matrix of weights with whichthe variable enters cointegrating relationships and ξ is the matrixof cointegrating vectors. The null hypothesis of no cointegrationamong variables is rejected when the estimated likelihood test statistic

ϕi

�¼ −n

Xpt¼rþ1

ln 1−λ̂i

��exceeds its critical value. Given estimates of

the Eigen-value λ̂ i

� �, the Eigen-vector (ξi) and the weights (Ψi), one

can find out whether or not the variables in the vector (HSt) arecointegrated in one ormore long-run relationships among the indepen-dent variables.

If the time series are I(1), then one could run regressions in theirfirst differences. However, by taking first differences, the long-runrelationship that is stored in the data is lost. This implies that oneneeds to use variables in levels as well. The advantage of the ErrorCorrection Model (ECM) is that it incorporates variables both at levelsand at first differences. In doing this, ECM captures the short-run dis-equilibrium situations as well as the long run equilibrium adjust-ments between the variables. An ECM term having a negative (−)sign and a value between 0 and 1 indicates a convergence of themodel towards a long-run equilibrium and shows howmuch percent-age adjustment takes place every year.

4. Results and discussion

This section consists of the determination of the long run relationshipbetween household saving, public policy, demography and growth. Thisrelationship is determined byusing the technique knownas cointegrationanalysis which was developed by Johansen in 1991.

4.1. Application of unit root test

In order to check the order of integration of various variableswhich are involved in the study, unit root test is applied. The nullhypothesis is that variables are not stationary. On the contrary, thealternative hypothesis suggests that variables used in this study arestationary.

The results indicate that all the variables are not stationary in theirrespective levels but are stationary at their first difference. Hence thenull hypothesis of non-stationary for all the variables is rejected atfirst difference at the particular significance level explained by thecritical values inside the parenthesis. Consequently, all the variablesare integrated of order one (Table 1).

1st difference

nd trendlue)

Constant(critical value)

Constant and trend(critical value)

−5.857⁎

(−3.642)−5.865⁎

(−4.261)−4.436⁎

(−3.635)−4.724⁎

(−4.251)−5.757⁎

(−3.642)−5.698⁎

(−4.261)−4.890⁎

(−3.642)−4.977⁎

(−4.261)−5.104⁎

(−3.642)−4.945⁎

(−4.261)1.912(−3.642)

−5.298⁎

(−4.261)

Table2

Cointegrationtest

resu

lts.

Mod

els

Mod

el2

Mod

el3

Mod

el4

App

roache

sIntercep

t(n

otren

d)in

CE,n

ointercep

tor

tren

din

VAR

Intercep

tin

CEan

dVAR,

notren

din

CEan

dVAR

Intercep

tin

CEan

dVAR,

linea

rtren

din

CE,n

otren

din

VAR

H0

H1

Test

statistics

λtrace

5%critical

value

1%critical

value

Test

statistics

λtrace

5%critical

value

1%critical

value

Test

statistics

λtrace

5%critical

value

1%critical

value

r=

0r>

023

8.17

9b10

2.14

111.01

188.82

8b94

.15

103.18

227.37

3b11

4.9

124.75

r≤

1r>

114

9.98

0b76

.07

84.45

112.85

0b68

.52

76.07

150.57

5b87

.31

96.58

r≤

2r>

281

.563

b53

.12

60.16

55.152

b47

.21

54.46

92.356

b62

.99

70.05

r≤

3r>

340

.933

a34

.91

41.07

22.434

29.68

35.65

49.669

b42

.44

48.45

r≤

4r>

420

.504

a19

.96

24.6

7.70

315

.41

20.04

16.957

25.32

30.45

r≤

5r>

55.82

19.24

12.97

1.75

343.76

6.65

3.39

012

.25

16.26

rsh

owstheco

integrationrelation

ship

amon

gtheva

riab

les.

aDen

otes

rejectionof

thehy

pothesis

at5%

sign

ificanc

eleve

l.b

Den

otes

rejectionof

thehy

pothesis

at1%

sign

ificanc

eleve

l.

Table 3Pantula principle test.

r n − r Model 2 Model 3 Model 4

0 8 238.1789b 188.8284b 227.3734b

1 7 149.9794b 112.8497b 150.5749b

2 6 81.5628b 55.15182b 92.35586b

3 5 40.93254a 22.43386 49.66856b

4 4 20.50355a 7.703427 16.95655 3 5.820693 1.753383 3.390208

r shows the cointegration relationship among the variables.a Denotes rejection of the hypothesis at 5% significance level.b Denotes rejection of the hypothesis at 1% significance level.

528 A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

4.2. Johansen cointegration analyses

As shown by the above results that all the variables satisfied thecriteria for applying the cointegration analysis developed by Johansen.The results are presented in Table 2.

This study starts with the null hypothesis of no cointegration(r = 0) among the variables. The criterion for the rejection of null hy-pothesis is that trace value should be greater than 95% or 99% criticalvalues (Johansen, 1991). If the trace value is less than the said criticalvalue then the null hypothesis cannot be rejected.

As presented in Table 2, it is found that in model 2, there are fivecointegration relationships among the variables. In model 3, thereare three and in model 4, the cointegration relationships are fouramong the dependent and independent variables. Next Pantula testis applied in order to determine the appropriate model withmaximum amount of cointegration relationship from the abovethree estimated models proposed by Johansen.

4.3. Pantula test

In the next step, the trace statistics for all the three models arecombined together. The results of this test are presented in Table 3.

From the above results it is proved that in model 2, there are fivecointegration relationships among the variables. In model 3, there arethree and in model 4, the cointegration relationships are four. It isobvious from the above results that model 2 is appropriate becausethere are greater numbers of cointegrating vectors compared to theresults of the other model. Next, in order to check the stability ofthe long-run relationship between the household saving and the in-dependent variables, the Error Correction Model (ECM) is estimatedwith the short run and long run elasticities.

4.4. Stability of Johansen cointegration analysis

The results of ECM show that it has a negative sign and its valuelies between 0 and 1. This value shows the convergence of themodel towards equilibrium and its stability. Short run elasticitiesare calculated in order to know the changes in the variables overthe time period. These elasticities are calculated at a first difference.First difference means the difference of the variable at period t andt − 1. Furthermore, the long run elasticities are calculated in orderto know the values of the variables at their own lags. These elasticitiesare calculated at the first lag.

Table 4Error correction model.

ECM −0.603R-squared 0.727Adjusted R-squared 0.570Durbin–Watson stat 2.201F-statistic 4.652Prob(F-statistic) 0.001

Table 5Short run and long run elasticities dependent variable: household saving.

Variables Short run elasticities Long run elasticities

Coefficients Prob. Coefficients Prob.

Inflation −0.293⁎ (0.0011) −0.443⁎ (0.0088)GDP per capita 0.694 (0.7667) 3.978⁎ (0.0447)Public savings 0.082⁎⁎ (0.1029) 0.221⁎ (0.0022)Old dependency ratio −0.550⁎⁎ (0.0672) −0.38⁎ (0.0508)GDP per capita growth −0.062 (0.1775) 0.141⁎ (0.0304)

⁎ Shows that certain variable is significant at 5% level.⁎⁎ Indicates the significance of variables at 10% level based on thementioned probabilityvalues.

529A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

4.4.1. Error Correction Model (ECM)The ECM results are shown in Table 4. The ECM estimation reveals

that 60% of the disequilibrium in household saving produced by inde-pendent variables would be adjusted every year.

The empirical results presented in the table above are very good interms of the usual diagnostics. The value for the R-squared indicatesthat 72.7% variation in dependent variable is explained by the indepen-dent variables of the model. F value is higher than its critical valuesuggesting a good overall significance of the estimated model. There-fore, fitness of the model is empirically acceptable. The Durbin Watsonvalue shows that there is no problem of serial correlation in the model.

4.4.2. Short run and long run elasticitiesBy looking at the short run and long run elasticities presented in

Table 5, it is shown that in short run inflation, public saving and olddependency ratio are significant while in the long run GDP per capita,inflation, public saving, old dependency ratio and GDP per capitagrowth are statistically significant.

The result reveals that a percent increase in inflation causeshousehold saving to decrease by 29% in the short run and 44% inthe long run. The result of the negative impact of inflation on house-hold saving is consistent with Corbo and Schmidt-Hebbel (1991),Iqbal (1993), Masson et al. (1998), Haque et al. (1999), Athukoralaand Tsai (2003) and Ozcan et al. (2003). Similarly, the coefficientvalue of old dependency ratio reveals that a 1% increase in old depen-dency ratio will cause household saving to decrease by 55% in theshort run and 38% in the long run. The findings concerning the impactof old dependency ratio on household saving rate are consistentwith the recent empirical studies (Abdul-Malik and Baharumshah,

Table 6Hypotheses testing.

S. no Variable Hypotheses

Short run elasticities1 GDP per capita The short run relationship between househ

is significant and positive.2 GDP per capita growth The short run relationship between househ

growth is significant and positive.3 Inflation The short run relationship between househ

significant and negative.4 Public saving The short run relationship between househ

significant and positive.5 Old dependency ratio The short run relationship between househ

ratio is significant and negative.

Long run elasticities1 GDP per capita The long run relationship between househ

significant and positive.2 GDP per capita growth The long run relationship between househ

growth is significant and positive.3 Inflation The long run relationship between househ

significant and negative.4 Public saving The long run relationship between househ

significant and positive.5 Old dependency ratio The long run relationship between househ

ratio is significant and negative.

2007; Ahmad et al., 2006; Bosworth and Chodorow-Reich, 2007;Choudhury, 2005; Fasoranti, 2007; Hasnain et al., 2006; Khalek etal., 2009; Newman et al., 2008; Orbeta, 2006; Park and Shin, 2009;Sajid and Sarfraz, 2008). Furthermore, the coefficient of public savingreveals that a percent increase in public saving will cause householdsaving to increase by 8% in the short run while 2% in the long run.The findings regarding the positive relationship between publicsaving and household saving are in line with Verma and Wilson(2005), Ahmad et al. (2006), Horioka and Wan (2007), Sinha andSinha (2007) and Verma (2007). It is indicated by the coefficient ofthe GDP per capita and GDP per capita growth that a 1% increase in GDPper capita and its growth will cause household saving to increase by 4%and 14% respectively. The study strengthens the earlier evidence regard-ing the positive impact of GDP per capita and its growth on householdsaving provided by Horioka and Wan (2007), Sinha and Sinha (2007),Newman et al. (2008), Sajid and Sarfraz (2008), Khalek et al. (2009)and Park and Shin (2009). By looking at the short run and long run elas-ticities, the next section discusses the hypotheses testing.

4.5. Hypotheses testing

In the short run, only inflation, public saving and old dependencyratio are significant while GDP per capita and GDP per capita growthare insignificant. Similarly, in the long run all the variables become sig-nificant and followed the expected signs. The results of the hypothesestesting are presented in Table 6.

In the short run, GDP per capita and GDP per capita growth are insig-nificant. These results reject the null Hypotheses 1 and 2. Moreover, in-flation, public saving and old dependency ratio are significant. Theseresults validate Hypotheses 3, 4 and 5. Similarly, in the long run all thevariables become significant and followed the expected signs. These re-sults support and accept all the hypotheses of the study. The next sectionconsists of various tests to check the reliability of the estimated model.

4.6. Reliability of the estimated model

The model is satisfactory in terms of the results. In order to checkwhether the model satisfies the assumptions of classical linear regres-sion model (CLRM), which are assumptions of normality of residuals,homoskedasticity, no autocorrelation, the correct specification and nomulticollinearity, the following tests as presented in Table 7 are appliedto the model.

Results Decision

old saving and GDP per capita Insignificant Rejected

old saving and GDP per capita Insignificant Rejected

old saving and inflation rate is Significant and negative Accepted

old saving and public saving is Significant and positive Accepted

old saving and old dependency Significant and negative Accepted

old saving and GDP per capita is Significant and positive Accepted

old saving and GDP per capita Significant and positive Accepted

old saving and inflation rate is Significant and negative Accepted

old saving and public saving is Significant and positive Accepted

old saving and old dependency Significant and negative Accepted

Table 7Reliability check.

Problem Test name Hypotheses F-Statistics P-Value Null hypothesis testing

Heteroskedasticity White heteroskedasticity residual test H0 = residuals are homoskedasticH1 = residuals are heteroskedastic

2.184 0.112 Accepted

Autocorrelation Breusch–Godfrey serial correlation LM test H0 = no autocorrelation existsH1 = autocorrelation exists

0.90 0.423 Accepted

Correct specification Ramsey regression specification error test H0 = correct specification of modelH1 = incorrect specification of model

1.397 0.251 Accepted

Normality of residuals Jarque–Bera normality test H0 = residuals are normalH1 = residuals are not normal

1.221(Jarque–Bera statistic)

0.543 Accepted

530 A. Ismail, K. Rashid / Economic Modelling 32 (2013) 524–531

From the result, it is indicated that there is no problem of autocor-relation and heteroskedasticity in the model. The model is correctlyspecified and residuals are normal.

4.6.1. Multicollinearity testIn order to check the presence of multicollinearity in the model,

Variance Inflation Factor (VIF) test is applied. The formula to calculatethe VIF test is presented as follows:

VIF ¼ 11−R2 : ð5Þ

In our estimated model, the value for the R-squared is 0.73 and thevalue of VIF becomes 3.7. This indicates that there is nomulticollinearityin the model.

5. Conclusion

This paper empirically investigates the impact of various publicpolicy, demography and growth variables on household saving inPakistan by using Johansen cointegration technique for the timeperiod between 1975 and 2011. The cointegration analysis, whichexamines the existence of the long term relationship between publicpolicy, demography and growth variables and household saving,reveals that there exists a long term relationship between the depen-dent and independent variables. In order to check the stability ofthe long-run relationship between household saving and variousindependent variables, the Error Correction Model is estimated. Theresults reveal that about 60% convergence towards equilibriumtakes place every year.

Similarly, important results related to the short run and long runelasticities estimated under Error Correction Model show that in theshort run inflation, public saving and old dependency ratio are impor-tant and significant variables in determining household saving, whileGDP per capita and GDP per capita growth are insignificant in theshort run. In the long run, all the variables (inflation, public savingand old dependency ratio, GDP per capita and GDP per capita growth)become significant. The impact of inflation on household saving isnegative in the short as well as in the long run. Similarly, the impactof public saving and old dependency ratio on household saving ispositive both in the short run and in the long run. Moreover, the im-pact of GDP per capita on household saving is positive in the long run.Finally, the impact of GDP per capita growth rate on household savingis negative.

Various econometric tests are applied in order to check thereliability of the estimated model. The results are satisfactory and re-veal that there is no problem of misspecification, heteroskedasticity,multicollinearity and autocorrelation in the model. The results alsoshow that the residuals are normal. All of the above mentioned re-sults also satisfy the assumptions of classical linear regression model.

5.1. Policy implications

The results of the study prove that all the variables are importantin influencing household saving rate. Those variables that were insig-nificant in the short run become significant in the long run. Theimpact of inflation on household saving is negative both in the shortas well as in the long run. Inflation is the most important cause ofmacroeconomic instability in Pakistan that should be lowered to putthe country on the path of economic growth. Government shouldsupport various national saving schemes in order to increase thehousehold saving rate. The impact of GDP per capita and GDP percapita growth is positive and significant in the long run. Greaterendeavor to boost economic growth might be a significant policymeasure to encourage household saving rate in Pakistan.

The results show that increase in old dependency ratio will causethe household saving to decrease both in the short as well as in thelong run. The results also highlight the critical role of old dependencyratio in determining the household saving in Pakistan.

5.2. Recommendations

Future research may be taken to investigate the other importantaspects of household saving in Pakistan. The present study only includesfew important determinants of household saving. There are large poolsof other variables that are critical in determining household saving like in-terest rate, young dependency ratio, trade openness, population growthrate, education level, etc. It is suggested that researchers can compare var-ious techniques to analyze the impact of various social, economic and de-mographic variables on household saving. The researchers can alsoanalyze the determinants of household saving for the pool of countries.Thus these aspects should be considered to explore the various dimen-sions of household saving in Pakistan and all over the world.

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