The last 30 years the rate of private saving has changed in many European countries. Inparticular, in some European countries the average propensity to save has decreased while inothers has showed a marginal increased or remained constant (Table 1). In countries such asGermany, Greece, Italy, Sweden and U.K. saving rates have declined while in others such asAustria, Belgium, Denmark, France, Finland, Netherlands, Portugal and Spain saving rateshave increased slightly or remained constant.1
Many economists argue that there are systematic reasons for these developments. Sincesaving are related to future consumption, if European households underestimate future con-sumption and risks they decrease saving. Contrary, if the economic agents are not myopic theyincrease saving to buy security for future risks. Additional reasoning relates family relation-ships, demographic developments and the existing social security system with the evolution
Source: European Commission, European Economy, Annex, Spring 2000. Luxembourg is not included.
of European saving ratio. In Europe almost all countries experience population ageing thatexercises further economic pressure on the finance of the social security systems and henceinfluences saving. Finally, the globalization of capital markets and the release of liquidityconstraints, in many European countries, have improved consumer welfare by enabling moreintertemporal substitution2 and have influenced the European household saving behavior. Thatis, the evolution of a “new financial landscape” in Europe, mainly since the introduction of Euro,have increased the number and the sophistication of financial means increasing substantiallytotal household loans as percentage of GDP.3
Various studies have analyzed the possible determinants of private saving rates mainly forindividual countries. These studies have estimated the effects of economic and demographicvariables on private saving. Some studies comprise of industrial and developing economies(Attanasio, Picci, & Scorcu, 2000; Edwards, 1996; Loayza, Schmidt-Hebbel, & Serven,2000; Masson, Bayoumi, & Samiei, 1995), other of developing economies (Bandiera, Caprio,Honohan, & Schiantarelli, 2000; Corbo & Schmidt-Hebbel, 1991) and some of country-specificcases (Ostry & Levy, 1995, for France; Cardenas & Escobar, 1998; Corbo & Schmidt-Hebbel,1991, for Colombia; Morande, 1998, for Chile; Lopez Murphy & Navajas, 1998, for Argentina).These studies attempt to isolate the key determinants of the private saving rate across alarge number of industrialized and developing economies. However, the econometric find-ings of the studies have not offered clear evidence regarding the determinants of private savingbehavior, which creates an obvious deficiency that may affect applied research and policymaking.
The paper analyzes, for the first time, the determinants of private saving in a panel of13 European countries. Given the amount of empirical research on this issue and the widerange of empirical results, mainly on individual countries, there appears to be no clearconsensus among research on this issue. Methodologically, the statistical inference of thisresearch relies on univariate time series data of single country over a prolonged period
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of time. Panel studies offer a number of advantages over time series and cross-sectionalanalysis. Having multiple years of data increases the sample size and may lead to morereliable estimates. Also, having multiple observations for each country enables researchersto include country-specific fixed effects, thereby controlling for a wide range of time-invariant country characteristics4 whose omission might otherwise bias the estimated savingfunction.
In light of this, the study extends this line of research to a sample of European countrieson a country-by-country basis and to panel data modeling to examine the determinants ofprivate saving, such as demographic changes, government budget, liquidity constraint, realdisposable income changes, real interest rates and inflation. In addition, since our data involvesnonstationarity, a panel cointegration method is applied as an alternative to traditional timeseries and cross-sectional regressions.5
The purpose of this paper is to examine empirically the main determinants of private savingand to extend our understanding of the impact of these factors on private saving determination.Second, the existence of private saving function is investigated for 13 European countries.Third, the paper applies the empirical framework on a country-by-country and panel coin-tegration modeling utilizing the recently developed estimation procedure of fully modifiedordinary least squares (FMOLS) for heterogeneous panels, proposed by Pedroni (1997, 2000).The utilization of the most recently developed technique of cointegration and FMOLS estima-tion in heterogeneous panel, has several advantages over the other methods used in the past byvarious researchers, since this type of multivariate analysis can clearly estimate heterogeneouscointegrating relationships in country-by-country and panel bases.
This is accomplished in four steps. First, the stationarity properties of the data and the orderof integration are tested on country-by-country and in panel modeling. The Im, Pesaran, andShin (2003), Levin and Lin (1993) and Hadri (2000) test for stationarity in heterogeneouspanel data are applied. Second, the Johansen maximum likelihood technique is applied tosearch for cointegration among saving ratio, demographic variables and economic variables.The Johansen technique controls for endogeneity and focuses on long-run relationships (cointe-gration) among nonstationary variables. Third, the Pedroni technique for heterogeneous paneldata is applied to search for the existence of cointegration in the panel setting. Finally, theFMOLS for heterogeneous panel technique is applied to estimate the long-run estimates incountry-by-country basis and in panel modeling (Pedroni, 1997, 2000).
The paper proceeds as follows. Section 2 reviews empirical and theoretical work on esti-mating saving function. Section 3 deals with methodological issues and the data used in theempirical analysis. Section 4 presents the empirical results. In Section 5 the conclusions of theanalysis are summarized and the policy implications are discussed.
2. Determinants of private saving
There is a vast literature, both empirical and theoretical, on the determinants of private savingrates. Various researchers have completed comprehensive surveys on private saving behavior(Balassa, 1990; Deaton, 1995; Fry, 1995; Gersovitz, 1988; Loayza et al., 2000). This sectiondeals briefly with the determinants of private sector saving behavior.
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2.1. Demographic influences on private saving
According to the lifecycle hypothesis (Modigliani, 1966, 1970) the age structure of thepopulation can have an influence on the private saving behavior. In societies with a highproportion of population in the working age, a high rate of private saving should exist aspeople save for their retirement. When this working group reaches the retirement age it dissavesto maintain consumption, causing a decline in the saving ratio. Graham (1987) and Masson,Bayoumi, and Samiei (1998) find that higher proportions of the young and elderly in relation topersons of working age are associated with lower saving rates. However, Hurd (1990), Carrolland Summers (1991) and Haque, Pesaran, and Sarma (1999) question the robustness of thedemographic effects on private saving behavior. They argue that elderly people may not dissaveto the extent that the lifecycle model predicts. Bequests and unpredictable expenses may alterthe saving pattern of the elderly. As supported by Attanasio et al. (2000) “if the negative savingof the young is large enough at the aggregate level, a strong productivity growth might leadto a negative correlation between saving rates and growth rates. The precise sign of the long-run equilibria correlation among saving and growth in a lifecycle model depends upon theprecise shape of the utility function, the demographic structure and other factors.” So higherproportions of the dependents to persons of working age may be associated either with higheror lower saving ratio.
2.2. Interest rates and private saving
The effects of interest rate on consumption and consequently saving are ambiguous. Becauseof the wealth, intertemporal substitution effects and user cost of durable goods, there is nopresumption as to the direction of the aggregate saving response to an exogenous interest-ratechange. A positive sign in real interest rate indicates an incentive to increase savings whenreal interest rate increases that is the substitution and human-wealth effects are greater than theincome effect. Recent reviews, by Balassa (1990) and Fry (1995), conclude that most studieshave found a positive interest elasticity of saving than a negative one, but the coefficients havegenerally been small and often insignificant. Bosworth (1993) finds a positive interest-ratecoefficient for a sample of countries, but a negative coefficient in a panel set of countries.Ogaki, Ostry, and Reinhart (1996) report positive interest-rate effects developing economiesthat vary with income but are small in magnitude. Masson et al. (1998) report positive, but lessrobust, effects of interest rates on private saving. Bandiera et al. (2000) find that there is nostrong interest-rate effect on saving. When data are pooled, they report a significant, positive,interest-rate effect on saving, and even then the effect is small.6
2.3. Financial liberalization and saving
Many researchers have stressed the importance of liquidity constraints on the saving behaviorof the individuals. In the simplest specification, they identify eras before and after liberalizationwith dummy variables. Jappelli and Pagano (1994) set out a three-period model to analyze therole of liquidity constraints in national private saving behavior. To proxy the liberalizationconstraints they have used the volume of consumer credit. They apply their model to a panel of
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19 OECD countries and find that liquidity constraints have a significant impact on net nationalsaving. Also, Ostry and Levy (1995) have employed the volume of consumer credit as a proxyfor financial liberalization and concluded that financial liberalization had lowered saving inFrance.7 Bandiera et al. (2000), using principal components, construct 25-year time seriesindices of financial liberalization for eight developing countries. They support the idea thatliberalization overall may be associated with a fall in saving. Loayza et al. (2000) have usedthe ratio of M2 to GNP as an indicator of financial depth and the private credit flow relativeto income to capture consumers’ access to borrowing. For a sample of 20 industrial and 49developing economies they conclude that credit availability reduces the private saving rate andthat larger financial depth does not raise saving.
2.4. Government dissaving and saving
The relationship between budget deficits and saving can be broadly classified into twocategories. The first view suggests that if the public does not value government consumptionthen saving will decline when the government spending increases independently on the way thatthis spending is financed. On the contrary, when the public value public goods, the effect of theirincrease depends on the degree of substitutability between public and private goods. Thus, thesecond view suggests that higher budget deficits will lower national saving, lower investmentand cause a trade deficit. An increase in the government deficit as a result of lower taxes or highergovernment spending can boost consumption and can suggest a coefficient between 0 and −1for the private saving ratio variable. An extension of this approach is Barro’s view (Barro,1974) based on the theory of Ricardian Equivalence (RE), which argues that federal deficitsare irrelevant to the level of national saving because increases in private saving will neutralizefederal budget deficits. If Ricardian Equivalence holds, private saving will rise when publicsaving fall, implying a negative association between private saving and the government outcometo GDP ratio. If complete RE holds, then a coefficient of −1 should be expected. Corbo andSchmidt-Hebbel (1991) use a sample of 13 developing countries to analyze the macroeconomicconsequences of higher public saving. They find a 0.47–0.50% offset on private saving ofchanges in government saving which suggests that government saving crowd out private saving.The magnitude of this effect is below the one-to-one relationship suggested by the simpleRicardian Equivalence doctrine. According to Bernheim (1987) evidence from industrializedcountries suggests that a unit increase in the government deficit would be associated with adecrease in consumption of 0.5–0.6. Masson et al. (1995) find for 21 industrial countries a0.40–0.53 offset coefficient. Finally, Dalamagas (1992a, 1992b) presents empirical evidencefrom 52 countries. He found that in countries with high debt-GDP ratios there is absence of debtillusion. In countries with low-debt ratio there is a positive relationship between consumptionand government deficit indicating that when deficit increases, savings drop.
2.5. Income and private saving
Based on the lifecycle hypothesis, Modigliani (1966, 1970) relates aggregate saving behaviorto the income growth. He argues that a higher growth rate, caused by a population or productivitygrowth, would raise aggregate saving as the aggregate income of those having a job relative to
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those not working would increase. Attanasio et al. (2000) correlate saving behavior and growthin a lifecycle model. In their model, if wealth is accumulated in the first part of the lifecycleand decumulated during retirement, then population and/or productivity growth might lead tohigher aggregate saving if the saving of the young exceeds the dissaving of the old. Carrolland Weil (1994) and Edwards (1996) find statistical evidence supporting a positive associationbetween income and saving behavior. Masson et al. (1998) find a direct positive associationbetween output growth and private saving for most of the specifications examined for a sampleof developing countries. Attanasio et al. (2000) show that growth and saving are mutuallyand positively related, but the results are sensitive to the additional controls introduced in thevariable system. Sarantis and Stewart (2001) demonstrate that income growth exerts a positiveinfluence on saving for some of the OECD countries examined.
2.6. Inflation and private saving behavior
The effects of the inflation rate on private saving are ambiguous and can be associatedwith higher or lower saving rates. Higher inflation rates are associated with higher nominalinterest rates and consequently higher measured household income and saving. When inflationincreases, consumers attempt to maintain a target real wealth relative to income by reducinginflation. Thus inflation is affecting savings through its impact on real wealth. In addition,higher inflation may lead to higher saving on precautionary grounds to decrease uncertainty infuture income streams. On the contrary, in some economies where income prospects are lessuncertain inflation may be associated with may lower saving. Empirical research has producednegative or zero coefficients of inflation (Corbo & Schmidt-Hebbel, 1991; Haque et al., 1999;Masson et al., 1998).
3. Methodological issues and data
Following a modified specification of the lifecycle model of saving behavior proposed byModigliani and extended by Jappelli and Pagano (1994) a linear saving function is estimatedemploying as explanatory variables economic and demographic variables. Therefore, in theempirical study the following specification for the long-run saving behavior is employed:
where i is the country i, SADYt the saving to disposable income ratio at time t, DEPt thedependency ratio at time t, DPRt the old-age dependency ratio at time t, CLAIMSt the liquidityconstraint at time t, GBt the government balance to GDP ratio at time t, �LDYt the realdisposable income growth at time t, RINTER the real interest rate at time t, �LCPIt the inflationrate at time t and ut is an error term. The first two independent variables, dependency and old-age dependency ratios are used to determine the demographic characteristics of the country.Inflation is used in the specification as proxy for macroeconomic uncertainty. A positive signsuggests that an increase in macroeconomic uncertainty (for example changes in nominal
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incomes and future economic policies) will induce people to save a higher fraction of theirincome.
The empirical analysis has been carried out using annual data for the period 1961–1998 for13 European countries. The 13 European countries are: Austria, Belgium, Denmark, Finland,France, Germany, Greece, Ireland, Italy, Netherlands, Spain, Sweden and United Kingdom.8
DEPt is the dependency ratio (i.e., the number of young individual aged between 0 and 19 yearsto the working population aged between 20 and 65 years). DPRt is the old-age dependency ratiodefined as the ratio of elderly (population above 65 years old) to the working age population(those aged between 20 and 65 years). RINTERt is the real deposit interest rate defined asthe nominal interest rate minus inflation. CLAIMSt is the liquidity constraint defined as theprivate sector domestic credit as percent of nominal GDP. GBt is the central governmentfiscal surplus(+)/deficit(−) as percent of nominal GDP. �LDYt is the growth rate of realdisposable income and �LCPIt is the rate of inflation. All data except for the two demographicvariables are obtained from International Financial Statistics cd-rom and AMECO cd-rom.The demographic data are collected from the World Development Indicators cd-rom publishedby the World Bank.
In the empirical analysis we test for the existence of a long-run relationship among thevariables (estimation of Eq. (1)). Testing for the existence of statistical relationship among thevariables is done in four steps. The first step is to verify the order of integration of the variablesof the individual country since the cointegration tests are valid only if the variables have thesame order of integration. Standard tests for the presence of a unit root based on the work ofDickey and Fuller (1979, 1981), Perron (1988), Phillips (1987), Phillips and Perron (1988)9
and Kwiatkowski, Phillips, Schmidt, and Shin (1992)10 are used to investigate the degree ofintegration of the variables used in the empirical analysis.
The next panel unit root tests are employed to examine the order of integration of thevariables in the panel data setting. The Levin and Lin (LL) (1993), Im et al. (IPS) (2003) andHadri (2000) tests for the presence of a unit root in panel data are employed. The LL testallows for heterogeneity in the constant term while the IPS test allows for heterogeneity inboth the constant and slope terms of the ADF test. Both of them examine the null hypothesisthat there is a unit root against the alternative hypothesis that a unit root does not exists inpanel data. The Hadri (2000) test is panel equivalent to the Kwiatkowski et al. (1992) statisticsfor the time series case and tests the null hypothesis of stationarity against the alternative ofnonstationarity. The test is distributed as N(0, 1) and is appropriate for panel data with high T(number of periods) and relatively small N (cross-section units).
The second step involves testing for cointegration in the individual country and in the paneldata. The Johansen maximum likelihood approach (Johansen, 1988; Johansen & Juselius, 1990,1992) for the multivariate model is used for each individual country. The Johansen-Juseliusestimation method is based on the error-correction representation of the VAR Model withGaussian errors.
Next panel cointegration tests are employed. Kao (1999), Kao and Chiang (2000) andPedroni (1997, 1999, 2000) developed several test to examine the existence of cointegration ina multivariate framework. The proposed statistics test the null hypothesis of no-cointegrationversus the alternative of cointegration. However, pooling time series has resulted in a substantialsacrifice in terms of the permissible heterogeneity of the individual time series. It is important
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in the process of pooling time series data to permit as much heterogeneity as possible amongindividual time series. Testing for cointegration among the variables should permit for as muchheterogeneity as possible among the individual countries of the panel. If pooled results rely onhomogeneous panel cointegration theory then common slope coefficients are imposed. Pesaranand Smith (1995) show that if a common estimator is used when there are differences amongthe individual countries then the variables are not cointegrated.
Pedroni (1997, 1999, 2000) developed several tests for no-cointegration in dynamic panelallowing for heterogeneity among the individual countries. The estimated tests permit for het-erogeneity in cointegrating vectors and the dynamics of the underlying error process acrossthe cross-sectional units and are estimated as residuals tests. Seven tests are used to examinewhether the error process of the estimated equation is stationary (Table 4). The first four statis-tics are based on pooling along within-dimension. The null hypothesis associated with the firstfour statistics is that ρi = 1 against the alternative that ρi ≤ 1 for all cross-sectional units (homo-geneous panel).11 Specifically, the four statistics test the null hypothesis of no-cointegrationfor all cross-sectional units versus the alternative of the existence of cointegration for all cross-sectional units. The next three statistics are based on pooling along between-dimension. Thenull hypothesis tested is the same as in the previous case while the alternative is equal to ρi < 1for all i (existence of cointegration) so it permits distinct slope values (heterogeneous panel).
Evidence of cointegration rules out the possibility that the estimated relationship isspurious.12 So long as the four variables have common trend, causality must exist in at leastone direction and information for the endogeneity of the variables is revealed.
The final step of the analysis is to estimate the cointegrating vectors. The cointegratingvectors for each individual country and for the dynamic panel are estimated following the fullymodified OLS estimation technique for heterogeneous panels (Pedroni, 1997, 2000). Pedroni(2000) follows a semiparametric correction to the OLS estimator for panel data as developedby Phillips and Hansen (1990) for time series data. This semiparametric correction eliminatesthe second-order bias caused by the fact that the independent variables are endogenous.
4. Empirical results
Initially, the ADF, PP and KPSS tests examine the nonstationarity for the eight variables,saving ratio, dependency ratio, age dependency ratio, private sector domestic credit as percentof disposable income, budget balance as percent of GDP, real disposable income growth, realinterest rate and inflation used in the analysis in levels and first differences on individual countrybasis.13 The combined results from all the tests (ADF, PP, KPSS) suggest that all the seriesexcept for real disposable income growth, real interest rate and inflation under considerationappear to be I(1) processes while real disposable income growth, real interest rate and inflationare I(0).14
Next, panel unit roots are used to examine if the series are I(1). The IPS, Levin–Lin andHadri unit root tests for each variable for the panel data are estimated. The results are presentedin Table 2. The null hypothesis of nonstationarity (IPS and LL tests) is not rejected at differentlevels of significance for all series except for real disposable income growth, real interest rateand inflation. Employing the Hadri test, the null hypothesis of stationarity is rejected for all
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Table 2Panel unit root tests
Variable Unit root tests
IPS Levin–Lin ρ-statistic Hadri
Private savings ratio (SADY) 0.42 −1.75 2.90***
Dependency ratio (DEP) −0.27 −1.42 4.94***
Old-age dependency ratio (DPR) 0.81 −0.62 5.57***
Liquidity constraint (CLAIMS) 0.44 1.05 4.56***
Government deficit as percentage of GDP (GB) −0.33 2.21 2.78***
Real interest rate (RINTER) −2.66*** −6.46*** 1.56Real disposable income growth (�LDY) −7.56*** −47.73*** 1.69Inflation (�LCPI) −1.75** −7.81*** 0.37
Notes. ** and *** indicate rejection of the null hypothesis at 5% and 1% levels of significance, respectively.
variables except for real disposable income growth, real interest rate and inflation. Therefore,the results from the three tests suggest that all series except real disposable income growth,real interest rate and inflation appear to be nonstationary in the panel data set.
Since all the variables except for real disposable income growth, real interest rate andinflation in the individual country basis are integrated of the same order, it is appropriate tolook for a relationship among the variables. Table 3 summarizes the results of cointegrationanalysis among the variables using the Johansen maximum likelihood approach employingthe trace statistic. In the VAR estimation, real disposable income growth, real interest rateand inflation are treated as stationary variables. To determine the lag length of the VAR, threeversions of the system were initially estimated: a four-, a three- and a two-lag version. Then, anAkaike Information criterion (AIC), a Schwarz Bayesian Criterion (SBC) and a likelihood ratio
Table 3Johansen and Juselius cointegration test based on trace of the stochastic matrix (savings ratio, dependency ratio, olddependency ratio, claims, budget balance, net disposable income growth, real interest rate and inflation) (sample:13 European countries during 1961–1998)
Notes. r indicates the number of cointegrating relationships. Maximum eigenvalue and trace test statistics arecompared with the critical values from Johansen and Juselius (1990). The 5% critical values are 68.5 for r = 0, 47.2for r = 1, 29.7 for r = 2, 15.4 for r = 3 and 3.8 for r = 4. ** indicates rejection of the null hypothesis at 95% criticalvalue.
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Notes. All statistics are from Pedroni (1999). *** indicates rejection of the null hypothesis of no-cointegration at1% level of significance.
test (Sims’ test) were used to test whether all three specifications are statistically equivalent.All tests reject the null hypothesis that all the specifications are equivalent. In particular, thetests suggest a different lag structure for each country. In the most cases the appropriate lagstructure was equal to 2.
The tests provide evidence to reject the null of zero cointegrating vectors in favor of onecointegrating vector at 5% level for all the countries.15 On the basis of the results, the long-runrelationship among the variables and therefore the existence of private saving function findsstatistical support in each individual country over the period under examination.
Next, the panel tests are estimated. Table 4 summarizes the results of cointegration analysisamong the variables using the Pedroni statistics. All the tests strongly reject the null hypothesisof no-cointegration using both the panel and group versions of the Phillips-Perron and ADFtests. Thus, all the statistics provide evidence of cointegration to support the existence ofsaving function in the panel. In particular, all the country-by-country and panel cointegrationtest results suggest that there is a cointegrating relationship among the variables in the sampleof 13 European countries.16 Therefore, we can conclude that Eq. (1) finds statistical supporton a country-by-country basis and in the panel.
Having established that the variables are structurally related the long-run saving equationsare estimated using the fully modified OLS (FMOLS) for heterogeneous cointegrated panels(Pedroni, 1997, 2000).17 The results are presented in Table 5. From the estimated equations, sixmain points can be concluded. First the demographic variables are significant and have positivesign in the panel and in most of the countries. Therefore, we can conclude that an upward shockin fertility and in old dependency ratio will increase private saving. This result goes againstthe lifecycle hypothesis. The positive effect of demographic variables can be explained alongwith the precautionary-saving motive. In most of the countries in the European Union, theestablished social security systems are under financial pressure. The level of reimbursementfrom the existing social security systems does not satisfy the individuals, and they realize theneed for more own provision by private saving. This result coincides, for some countries, withthe findings of Haque et al. (1999), Attanasio et al. (2000) and Sarantis and Stewart (2001)who report similar findings for the demographic variables.
Second, an increase in the liquidity constraints has a negative effect on private saving, sug-gesting that the relaxation of the credit constraints leads to a decrease in private saving rate.The liquidity constraint is statistically significant for about half of the countries and in the
Notes. Figures in brackets are t-statistics. ** and *** indicate significance at 5% and 1% levels, respectively.
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panel. Jappelli and Pagano (1994) and Loayza et al. (2000) report similar results of the influ-ence of credit constraints on private saving. Third, an increase in government surplus leadsto a decrease in private saving ratio in four countries while in the rest and in the panel anincrease in government surplus increases private saving ratio. This finding is similar to that ofDalamagas (1992a, 1992b). He showed that in countries with high debt to GDP ratio there isa negative relationship between surplus and private saving, while the Ricardian Equivalencein the most of the cases does not apply. In other countries where debt to GDP is historicallylow, there is a positive relationship between surplus and private saving since individuals arerather myopic and have debt illusion. This value is statistically less than 1 for all the countriesand in the overall panel. Fourth, the real interest rate has a statistically significant positiveeffect on the private saving rate in the panel. This suggests that the intertemporal substitutioneffect is greater than the wealth effect. Fifth, growth of real disposable income has a statis-tically significant positive effect on the private saving rate for most of the countries and inthe panel. This result suggests that as individuals’ incomes grow faster or individual agentsbecome richer, their private saving rate increases. Finally, for the most of the countries andin the panel, inflation, which is a proxy for macroeconomic uncertainty, has a statisticallysignificant positive effect on private saving behavior. This result suggests that in periods ofhigh inflation, individuals prefer to save a larger fraction of their income for precautionarymotives.18
In short, the estimated private saving function implies that in the panel of 13 countries sav-ing function is affected positively by changes in demographic developments, budget balance,growth of real disposable income, real interest rate and inflation and negatively by changesin liquidity constraint. The coefficients are not the same in magnitude across the panel andsometimes have different sign. However, the panel estimation provides more precise esti-mates of the parameters of private saving function in this sample of 13 European countries(Baltagi, 2001). Table 6 briefly summarizes the determinants of private savings and lists thesigns.
From the empirical analysis, four conclusions can be drawn. First, all the country-by-countryand panel cointegration estimations suggests that there is a long-run saving function. Second,an upward shock in demographic variables will increase private saving implying the need ofindividuals for their own provision by private saving due to the existing financial pressureon the social security systems. Third, a decrease of liquidity constraints, resulting from anincrease of financial liberalization decreases private savings. Fourth, an increase in growth of
Table 6Summary results: determinants of private savings
Explanatory variable Variable category Sign
Dependency ratio (DEP) Demographics +Old-age dependency ratio (DPR) Demographics +Liquidity constraint (CLAIMS) Financial depth −Government deficit as percentage of GDP (GB) Fiscal policy +Real interest rate (RINTER) Rates of return +Real disposable income growth (�LDY) Income +Inflation (�LCPI) Uncertainty +
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real disposable income and real interest rate and inflation increase private saving while the signof government constraint depends on the magnitude of debt to GDP ratio.
5. Conclusions
This paper provides an empirical model that estimates the private saving function using panelcointegration techniques for 13 European countries. The private saving function is estimatedfor 13 European countries, namely Austria, Belgium, Denmark, Finland, France, Germany,Greece, Ireland, Italy, Netherlands, Spain Sweden and U.K. on a country-by-country basis andusing panel data modeling for the 1961–1998 period.
The study examines the stationarity properties of the data, employing individual along withpanel unit root tests. Multivariate Johansen cointegration tests are employed along with panelcointegration tests to ensure that problems of power in finite samples do not distort Johansen’stests. Finally, the private saving function is estimated employing the recently developed esti-mation procedure of the fully modified OLS for heterogeneous panels (Pedroni, 1997, 2000).
The analysis supports the findings of a private saving function which depends on demo-graphic and economic variables, for each country individually and for the panel as a whole.The empirical results suggest that for the whole panel, private saving should be considered asendogenous variable affected positively by changes in dependency ratio, old-age dependencyratio, government budget constraint, growth of real disposable income, real interest rate andinflation and negatively by the liquidity constraint. The results suggest that deregulation of thecapital markets resulted in a decrease of private saving while the existing financial pressureon social security systems resulted in an increase of private saving. This last result may implythat private saving make up public pensions in the provision of retirement income. In thefuture, it is possible that there is need for private saving to complement public pension in theprovision of retirement income since today social security systems are hardly sustainable andpopulation ageing is on an upward trend in most European countries. Pension systems reformwill change this trend, but it is possible that there will be side effects on capital markets andbudget surplus. However, social security system reform resulting in a decrease in governmentsurplus will have different effects on private saving among the European countries. Theempirical results indicate that a decrease in government balance will decrease the savingratio in economies with low debt to GDP ratio. Contrary, in the debt-ridden countries such asGreece and Belgium, a decrease of budget surplus will increase private saving.
Recent developments, that is the total liberalization and development of European financialmarkets coupled with the currency changeover to Euro, are expected to further affect thedevelopment of credit markets, mortgage markets and consumer credit arrangements. Theadoption of the single currency has reduced transaction costs, leading to a restructuring ofthe financial sector across countries. The major changes, since the introduction of Euro, infiscal and financial policies may give more opportunities to consumers to reallocate theirportfolio decisions cross-border and to reduce home bias in portfolios inducing further savings.Social security restructuring combined with pension reforms are key fiscal policy issues forContinental Europe, which may affect future savings. In addition, unified tax treatment ofdifferent assets is one of the most important issues, which has to be solved to implement
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further financial integration among the European countries. Financial integration is importantin an ageing Europe, since the existence of new financial products, may increase, decrease orleave unchanged, savings.19 Consumers may increase or substitute for existing savings sincethey are able to enter capital markets directly or indirectly through institutional investors,buying new products or changing the composition of their portfolio.
Another implication of the empirical analysis is that policies that spur development canbe effective in raising private saving rates in Europe. Inflation has a positive influence onprivate saving. However, it can not be argued, that inflation reduction could have adverseeffects on private saving as lower inflation might raise growth and this might have positiveeffect on private saving. In a Europe of low inflation, high economic growth, well-financedsocial security systems and fiscal stability, the evolution of “new financial landscape” permitsconsumers to enter capital markets, provide more opportunities for financial investments, altersignificantly the composition of their portfolio of risky and nonrisky assets and hence, in thefuture, might increase savings.
Notes
1. For an extensive discussion over savings in Europe, see, among others, Khaled andThirlwall (1999) and Borsch-Supan and Brugiavini (2001). Data, not presented in Table 1due to space limitations, show that savings ratio rises at a decreasing rate as per capitaincome increases and levels off at around 20–25% of national income.
2. Intertemporal substitution is the process of maximizing consumer’s utility by allocatingresources across time.
3. Total households’ loans, that is consumption and housing loans, as percentage of GDPin the Euro area were increased from 30.5% in 1997 to 36.8% in 2001 and 38% in 2002.
4. Specific country characteristics, which do not alter over time, such as religion, education,etc. might affect savings.
5. Panel cointegration modeling is applied to estimate the long-run relationship betweensavings and the other macroeconomic variables. For more details see Section 3.
6. Many empirical studies report that the response of savings to interest rate is not differentto zero for developing countries where the population may not be able to borrow evenat black market rates. In addition, this result may imply that interest rate is an effectivepolicy tool, only in conjunction with financial markets.
7. The main deregulation and liberalization of financial system occurred in France in thesecond half of the 1980s to improve the efficiency of the allocation of financial resources.The creation of new financial instruments provided the private sector access to a broaderrange of financing, while the unification of the credit market favored the expansion ofthe French financial system.
8. Luxembourg and Portugal are excluded since there are no available data for the totalperiod.
9. This version of the test is an extension of the Dickey-Fuller test, which makes a semi-parametric correction for autocorrelation and is more robust in the case of weaklyautocorrelated and heteroskedastic regression residuals.
G. Hondroyiannis / The Social Science Journal 43 (2006) 553–569 567
10. The KPSS procedure assumes the univariate series can be decomposed into the sumof a deterministic trend, random walk and stationary I(0) disturbance and is based on aLagrange Multiplier Score Testing Principle. This test reverses the null and the alternativehypothesis. A finding favorable to a unit root in this case requires strong evidence againstthe null hypothesis of stationarity.
11. The following equation is estimated: uit = ρui(t − 1) + eit where uit are the estimated resid-uals.
12. When the regression analysis reveals the existence of a relationship, otherwise notexpected, then such regression is called spurious.
13. A time series is stationary if its mean and variance do not vary systematically over time.14. The unit root tests are not reported but are available from the author upon request.15. In some countries there is more than one cointegrating vectors. From theory it seems
likely that there will be additional long-run relationships between the demographicand economic variables. In addition, the Larsson, Lyhagen, and Lothgren (2001) testfor panel cointegration is employed. The test is obtained by estimating the averageof the N individual trace statistics and then standardizing. The test follows standardnormal distribution. The test indicates the existence of three cointegrating vectors.However the other long-run relationships for the individual countries and for thepanel are not examined since the purpose of the study is to estimate a private savingfunction.
16. The Pedroni (1999) panel cointegration statistics are calculated using Chiang and Kao(2002) NPT 1.3 program.
17. The FMOLS is estimated using a routine kindly provided by Pedroni.18. Although, it may be concluded that inflation stabilization could have an adverse effect on
saving, it should be emphasized that price stabilization affects saving through indirectchannels that are likely to more than compensate for any negative direct effect of inflation.In this context, lower inflation raises growth and this might in turn increase saving. Inaddition, the macroeconomic stability implied from a fiscal discipline policy will havea positive effect on national saving (Loayza et al., 2000).
19. Private savings as percentage of GDP, since the currency changeover to Euro, remainedalmost constant. In particular, provisional figures indicate that the private savings to GDPratio for the Euro area (excluding Luxembourg) increased to 20.8% in 2004 comparedto 20.1% in 2002 and 19.6 in 2001.
Acknowledgments
The views expressed in this paper are those of the author and not of the Bank of Greece.The author wishes to thank two anonymous referees for their helpful comments in a previousversion of this paper; Chiang and Kao who offer the NPT 1.3 as public program and PeterPedroni for providing his program for the estimation of FMOLS. The author also wishes toacknowledge useful comments and suggestions by Stephen Hall. All errors and deficienciesare the responsibility of the author.
568 G. Hondroyiannis / The Social Science Journal 43 (2006) 553–569
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