Abstract
Pension funds pools’ investments have an impact on its growth. These investments can be either in equity stock, bonds, deposits, or in other miscellaneous assets that can generate different results with the involvement of some endogenous factors such as rate of return, inflation etc. To bring out the core investment factors determining pension fund growth, a stepwise regression technique was used on a dynamic panel data model. Moreover, to check the individual significance of the included variables in the model progressively, R2-change was observed. This study has found that the investment factors behave positively in high growth-oriented OECD economies and have a negative impact in low growth-oriented countries. Moreover, pension funds growth is slower due to market volatility in low-growth oriented economies. The study helps to know the utilization or investment factors that support the large asset-holding of financial-sector of OECD economies.
Key Words
Pension Funds’ Growth, Investment in Equity, Deposits, Investment on Bonds, Rate of Return, Inflation.
Introduction
Social security plays a
vital role in the smooth functioning of social life of citizens specially the
older segment of the society (Martí-Ballester,
2020).
People wanted to have a surety of smooth, regular income at their old ages or
in any risky situations particularly, when they are retired or out of work
force (Shen, Zhu, Wu, & Chen, 2019). A number of
studies have been observed in the past regarding the performance of pension
funds and their governance (Bohl,
Lischewski, & Voronkova, 2011; Singh & Mehta, 2015).
However, the current study aims to highlight the key factors behind the pension
funds’ growth in relation to their utilization.
OECD (Organization for Economic Co-operation and Development) pension
funds outlook only describes through the descriptive analysis for the
utilization of pension funds (Mellor,
2018).
But it does not cover their individual significant impacts (such as investment
in equities, bills and bonds, cash and deposits, infrastructure and in real
estate etc.) on the growth of pension funds (Ooi, 2020).
Pension funds
are an institutional form of investors that collect, invest and pool funds
contributed by the fund sponsors and its beneficiaries to provide the future
pension to entitled beneficiaries (Bashir, Khan, &
Urooge, 2020; Davis, 1995). It will not be
wrong to say that these are the institutions that help a worker to save in his
active working life for his retirement (Bonizzi, & Guevara, 2019). In most of
the countries it is not allowed to withdraw the pension funds on early stages
which create a long run holding of the funds by the pension firms and open
doors for a number of investment opportunities for investments in order to
yield higher returns (Prammer, & Reiss, 2015).
Thus, the
pension firms utilize their funds in corporate equities, real estate, government
bonds, corporate debt (loans or bonds), securitized loans, money market
instruments, foreign holdings instruments, and deposits in form of liquidity (Kumara, & Pfau, 2012). The perspective of
pension funds cannot be ignored when viewing the OECD economies (Marcinkiewicz, & Chybalski, 2019). The pension
funds’ assets weighted average has reached up to 89% in OECD economies (OECD,
2017). It is significant to consider the method of governance and identify the
determinants of their growth. The problem of aging in OECD economies makes it
even more important to look at the management of pension funds (Mellor, 2018).
This study tries
to analyze and explain the factors that may have an effect on the growth of
pension funds in the selected OECD countries. For the purpose of analyzing the
utilization sources, and also for macro-level factors, this study takes into
account a panel data of 24 selected OECD countries for last 43 years. This
study divides the OECD countries into two parts on the basis of their pension
funds’ assets to GDP ratio. The countries above the median ratio are considered
as higher-growth-oriented (HGO) and countries below median are called as
lower-growth-oriented (LGO) countries.
There are
numerous studies discussed the utilization factors of pension funds in selected-OECD
countries. A brief description of them is depicted in table 1.
Table 1. Summary of
Supporting Theories and Literature for Determinants of Utilization of Pension
Funds
Symbol |
Variable |
Expected Relation |
Literature Review |
Supporting Theory |
PFG |
Pension Funds Growth |
Dependent Variable |
Alonso, et al. (2010); Roce, Kaminker, & Stewart (2011); Casey, (2014); Singh & Mehta, (2015); Aç?kgöz, Uygurtürk,
& Korkmaz, (2015). |
Utility Theory+ Institutionists Approach+ Theory
of Immunization |
IEQ |
Investments in Equity |
+/- |
Jones, (1950); Levy, & Gunthorpe, (1993); Diamond, & Geanakoplos, (2003); Diamond, &
Geanakoplos, (1999); Chen et al. (2010). |
Theory of Pooling + Theory of Immunization+
Theory of Intermediation |
IBB |
Investments in Bills and Bonds |
+ |
Feldstein, (1981); Heller, (2013); Kumara, & Pfau, (2012);
Udoka et al. (2012); Das, (2014). |
Theory of Pooling + Theory of Immunization+
Theory of Intermediation |
ICD |
Investments in Cash Deposits |
+/- |
Smith et al. (2003); Coleman, Esho, &
Wong, (2006). |
Theory of Pooling + Theory of Immunization+
Theory of Intermediation |
IMIS |
Investments in Miscellaneous |
+/- |
Vittas, Impavido, &
O’Connor, (2008); Croce,
(2011); Stewart, & Yermo,
(2012); Sievänen, Rita, &
Scholtens, (2013); Gulland, (2016). |
Theory of Pooling + Theory of Immunization+
Theory of Intermediation |
OE |
Operating Expenses |
- |
Obinata, (2000); Collins,
(2003); Bateman, & Mitchell,
(2004); Jiang, (2011); Militaru, (2015); Khan, Nasir, & Arslan, (2020). |
Accounting Profitability Theory+ Absolute
Matching Concept |
NUMPF |
Number of Pension Funds |
+ |
Gökçen, & Yalç?n, (2015); Bradley, Pantzalis, & Yuan, 2016); Thomas, & Spataro, (2016); Sievänen, Rita,
& Scholtens, (2017); Andonov, Hochberg, &
Rauh, (2018). |
Theory of Pooling |
CPI |
Consumer Price Index |
+/- |
Budd, & Seiders, (1971); Thompson, (1978); Heller,
(1980); Pensions Commission.
(2004); Aamir, Qayyum, Nasir, Hussain, Khan & Butt, (2011); Prammer, & Reiss, (2015); Bivens, (2015). |
Theory of Immunization |
RR |
Rate of Return |
+/- |
Tepper, (1981); Reisen, (1997); Amir, & Benartzi, (1998); Antolin, Schich, &
Yermo, (2011). |
Theory of Immunization |
FC |
Financial Crisis |
- |
Ebbinghaus, (2015); Naczyk, & Domonkos,
(2016); Mellor, (2018) |
Keynesian Paradigm |
Theoretical
framework describes the conceptual model of the study that includes the
purposeful linkage among included variables of the study and their
consequential impact on dependent variable (variables). Figure-1 describes the
factors for utilization of pension funds and their impact on the growth of
pension funds in the selected-OECD countries. This model is build based on the
literature and theories relevant to the utilization of pension fund growth.
Figure 1
Conceptual Model for Determinants of Utilization of Pension Funds and Their Impact on the Growth of Pension Funds.
Data and Methodology
Data Collection
For the purpose of empirical analysis of this study, data of the concerned variables (such as investments in equity, bonds, banks, other assets, inflation, rate of return, number of pension funds) were collected from different sources such as OECD data repository, OECD pension at a glance, statistical profile of each individual country, and from world bank (WB) data repository. There are total of 36 countries which are members of OECD at the end of 2018. Data were collected for the year end observations as on December of each year. After scrutiny, only 24 countries were selected that were became members of OECD before the year of 1975. It makes a panel of 24 selected OECD countries ranging data from 1975 to 2017 comprising of 43 years.
The total observations are 1032 in numbers of this balanced panel data. Among total of 36 OECD countries, 12 countries were dropped due to their delay in membership after 1975. The selected countries are then divided into two streams on the basis of median value of their pension funds to GDP ratio. For this purpose, first of all an average was calculated for each individual country’s pension funds to GDP ratio. In the next step, median value was calculated for these averages of 24 countries. 50% of the countries having their PF/GDP ratio above the median value are considered as HGO countries and whereas rest of the 50% of total countries having their median value below the threshold level are considered as LGO countries.
Figure 2a:
Pension Funds in HGO Countries
Pension Funds in HGO Countries
Pension Funds in HGO Countries
Figure 3
Pension Funds in HGO Countries
Econometric Model
In the literature, researchers have used different techniques for analyzing the effect of different utilization factors on pension funds sustainability and growth (Aç?kgöz et al., 2015; Chen et al., 2010; Heller, 2013). This study will use the dynamic panel data technique along with forward step-wise regression for making a comparative analysis of high-growth-oriented (those countries which have higher ratio of PFG from median value) countries with LGO (those countries which have lower ratio of PFG from median value) countries of observed variables of selected-OECD countries on the financial sustainability of pension funds.
The following models provide the general economic functions for this study. Equation (1) describes the pension funds growth of a country as a function of utilization-factors that may have an influence on the growth of pension funds. Moreover, these country-specific factors are further elaborated in equation (2) which describes that these factors are pension funds’ growth of last year (PFGt-1); investments in equity markets (IEQ), investments in bills and bonds (IBB), investments in cash and bank deposits (ICD), investments in miscellaneous assets (IMISC), operating, administrative and legal expenses (OE), number of pension funds (NUMPF), inflation in the country (INF), rate of return (RR), financial crisis of 2008 (FC).
PFG=f(Utilization Factors of PF ) ----- (1)
PFG=(?PFG?_(t-1),IEQ,IBB,ICD,IMISC,OE,NUMPF,INF,RR,FC) --- (2)
On the basis of the above described model, dynamic panel data approach was used and the following six equations show the econometric models for this study i.e. equation (3, 4, 5, and 6) shows the effect of individual investments (i.e. equity, bills and bonds, cash and bank deposits, and other miscellaneous assets) along with the previous year’s PFG on current year’s PFG. Moreover, equation (7) and equation (8) are aggregated and extended models for this study respectively. Equation (7) testifies the effect of combined investments (i.e. equity, bills and bonds, cash and bank deposits, and other miscellaneous assets) on current year’s PFG. In addition to that, equation (8) extended the above equation (7) and also includes some extraneous factors that can also impact the growth of pension funds i.e. operating expenses incurred for the management of pension funds, number of pension funds in each country, inflation, rate of return, and financial crisis of 2008 that hit the financial markets of the world.
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?2 (IEQ) it + ? it _____ (3)
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?3 (IBB) it + ? it _____ (4)
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?4 (ICD) it + ? it _____ (5)
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?5 (IMIS) it + ? it _____ (6)
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?2 (IEQ) it + ?3 (IBB) it + ?4 (ICD) it + ?5 (IMIS) it + ? it _____ (7)
PFG (OECD) it = ?0 + ?1 (PFG) i, t-1 + ?2 (IEQ) it + ?3 (IBB) it + ?4 (ICD) it + ?5 (IMIS) it + ?6 (OE) it + ?7 (NUMPF) it + ?8 (CPI) it + ?9 (RR) it + ?10 (FC) it + ? it _____ (8)
Where PFG = Pension Funds Growth; IEQ = Investment in Equity Market; IBB = Investment in Bonds and Bills Market; ICD = Investments in Deposits; IMIS = Investments in other companies or securities or infrastructure etc.; OE = Operating, administrative and legal expenses; NUMPF = Number of pension funds registered in a country; CPI = Consumer price index; RR = Rate of return; FC = Financial Crisis Dummy (Assign "1" for 2008 year or "0" otherwise); ? = the error term. The measurement for determinants of utilization of pension funds is provided in Appendix A.
Results Analysis and Discussion
In appendix, Table-2
represents the descriptive or summary statistics of the data used in the
sample. Moreover, Appendix B shows the correlation matrix along with VIF analysis
of the data.
Table 2: Summary
Statistics of Variables
Variable |
All-OECD Countries |
HGO-Countries |
LGO-Countries |
|||
Mean |
Std. Dev. |
Mean |
Std. Dev. |
Mean |
Std. Dev. |
|
TPF (Bill. US $) |
526.68 |
1860.91 |
631.34 |
2130.11 |
344.56 |
1214.22 |
TI/GDP (%) |
32.52 |
39.76 |
37.78 |
30.82 |
25.18 |
44.37 |
IEQ/TI (%) |
53.54 |
14.93 |
51.25 |
33.66 |
23.12 |
23.76 |
IBB/TI (%) |
22.86 |
19.81 |
25.12 |
71.01 |
34.54 |
59.66 |
ICD/TI (%) |
8.35 |
10.26 |
10.17 |
13.95 |
27.15 |
83.65 |
IMIS/TI (%) |
15.25 |
13.44 |
13.46 |
17.21 |
15.19 |
9.24 |
RR (%) |
2.612 |
7.81 |
4.08 |
8.17 |
8.35 |
11.07 |
CPI (%) |
2.413 |
2.35 |
3.38 |
4.18 |
6.69 |
3.46 |
NUMPF (Total) |
29677.71 |
139000 |
24321.54 |
87000 |
15356.17 |
52000 |
OE/TPF (%) |
2.683 |
1.178 |
5.44 |
3.59 |
8.23 |
5.77 |
Table-3, table-4
and table-5 show a comparative analysis of Whole-sample, HGO-countries and
LGO-countries by applying DPD-Hierarchical regression analysis of the observed
data for this study. Each analysis was further sub-divided into six models i.e.
model-1 to model-6 based on the earlier mentioned six equations i.e. equation-3
to equation-8 respectively. Table-3, table-4 and table-5 also describe the
diagnostic tests for checking the heteroskedasticity and serial correlation
along with the goodness-of-fit for data and the model-fitness by using R-square
and F-test respectively. Pension funds’ growth is taken as the observed
dependent variable for this study, whereas, lagged value of PFG, IEQ, IBB, ICD,
IMIS, OE, NUMPF, CPI, and RR are taken as independent variables on the basis of
the prior literature and theories.
Table 3. Forward-Step
Wise DPD Regression Analysis for Whole-Sample
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
PFG |
PFG |
PFG |
PFG |
PFG |
PFG |
|
PFG (L1) |
0.46* |
0.11*** |
0.73** |
0.77** |
0.71** |
0.65** |
(0.656) |
(0.525) |
(0.042) |
(0.882) |
(0.304) |
(0.144) |
|
IEQ |
0.387*** |
0.245** |
0.179** |
|||
(0.0768) |
(0.0624) |
(0.0445) |
||||
IBB |
0.830*** |
0.562*** |
0.299** |
|||
(0.217) |
(0.209) |
(0.142) |
||||
ICD |
0.372** |
0.476** |
0.120*** |
|||
(0.0707) |
(0.0594) |
(0.0325) |
||||
IMIS. |
0.689** |
0.490* |
0.313** |
|||
(0.0743) |
(0.071) |
(0.031) |
||||
OE |
-0.706*** |
|||||
(0.0314) |
||||||
NUMPF |
0.152* |
|||||
(0.03) |
||||||
CPI |
0.0826** |
|||||
(0.149) |
||||||
RR |
0.141* |
|||||
(0.16) |
||||||
FC (Dummy) |
-0.134** |
|||||
(0.155) |
||||||
Constant |
9.880*** |
18.00*** |
11.43*** |
9.515*** |
15.99*** |
11.148*** |
(0.238) |
(0.85) |
(0.158) |
(0.19) |
(0.924) |
(0.737) |
|
Observations |
1032 |
1033 |
1034 |
1035 |
1036 |
1037 |
Groups |
24 |
24 |
24 |
24 |
24 |
24 |
R-squared |
0.072 |
0.177 |
0.078 |
0.207 |
0.444 |
0.641 |
F-Test |
67.94*** |
56.74*** |
56.03*** |
55.04*** |
70.74*** |
98.12*** |
Hetero (P-value) |
0.0719 |
0.111 |
0.107 |
0.0831 |
0.108 |
0.183 |
Serial Auto (P-value) |
0.08628 |
0.1332 |
0.1284 |
0.09972 |
0.1296 |
0.2196 |
Hausman-Test |
FE |
FE |
FE |
FE |
FE |
FE |
AIC |
263.52 |
159.84 |
154.08 |
119.664 |
155.52 |
103.536 |
BIC |
259.128 |
157.176 |
151.512 |
117.6696 |
152.928 |
101.8104 |
Firstly, in the table-3,
for the whole-sample, R-squared values of the six models explain the change in
growth of pension funds by using observed the regresses as 7.2%, 17.7%, 7.8%,
20.7%, 44.4%, and 64.1% respectively. Here, the F-values in all six models show
that each of the used models is significant at a 1% level of significance with
the values of 67.94, 56.74, 56.03, 55.04, 70.74, and 98.12 respectively. Secondly,
in table-4, for HGO-Countries sample, R-squared values of the six models
explain the change in growth of pension funds by using observed regresses as
19.5%, 16.5%, 9.1%, 22.6%, 51.1%, and 71.1% respectively. Here, the F-test
values in all six models show that each of the used models is significant at a
1% level of significance with the values of 60.31, 51.22, 53.33, 54.89, 72.07,
and 95.88 respectively. Thirdly, in table-5, for the LGO-Countries
sample, R-squared values of the three models explains the change in growth of
pension funds by using observed regresses as 5.1%, 23.2%, 12.7%, 26.5%, 48.7%,
and 62.2% respectively. Here, the F-test values in all six models show that
each of the used models is significant at a 1% level of significance with the
values of 61.32, 57.14, 57.17, 68.22, 74.78, and 92.65 respectively.
Table 4. Forward-Step
Wise DPD Regression Analysis for HGO Countries
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
PFG |
PFG |
PFG |
PFG |
PFG |
PFG |
|
PFG (L1) |
0.54* |
0.20*** |
0.82** |
0.86** |
0.79** |
0.74** |
(0.11) |
(0.02) |
(0.02) |
(0.02) |
(0.13) |
(0.03) |
|
IEQ |
0.456** |
0.314** |
0.241*** |
|||
(0.065) |
(0.062) |
(0.104) |
||||
IBB |
0.223** |
0.344*** |
0.263** |
|||
(0.30) |
(0.20) |
(0.041) |
||||
ICD |
-0.082** |
-0.077** |
-0.090** |
|||
(0.062) |
(0.05) |
(0.022) |
||||
IMIS. |
-0.078** |
-0.054** |
-0.086*** |
|||
(0.054) |
(0.07) |
(0.051) |
||||
OE |
-0.221*** |
|||||
(0.045) |
||||||
NUMPF |
0.243** |
|||||
(0.023) |
||||||
CPI |
0.183** |
|||||
(0.261) |
||||||
RR |
0.422* |
|||||
(0.619) |
||||||
FC Dummy |
-0.066** |
|||||
(0.132) |
||||||
Constant |
5.751*** |
5.006*** |
4.879*** |
3.721** |
5.744** |
5.272*** |
(0.21) |
(0.32) |
(0.33) |
(0.76) |
(0.13) |
(0.45) |
|
Observations |
516 |
516 |
516 |
516 |
516 |
516 |
Groups |
12 |
12 |
12 |
12 |
12 |
12 |
R-squared |
0.195 |
0.165 |
0.091 |
0.226 |
0.511 |
0.711 |
F-Test |
60.31*** |
51.22*** |
53.33*** |
54.89*** |
72.07*** |
95.88*** |
Hetero (P-value) |
0.0712 |
0.0732 |
0.0633 |
0.082 |
0.192 |
0.1871 |
Serial Auto (P-value) |
0.08544 |
0.08784 |
0.07596 |
0.0984 |
0.2304 |
0.22452 |
Hausman-Test |
FE |
FE |
FE |
FE |
FE |
FE |
AIC |
102.528 |
105.408 |
269.424 |
118.08 |
276.48 |
91.152 |
BIC |
100.8192 |
103.6512 |
264.9336 |
116.112 |
271.872 |
89.6328 |
Moreover, in
tables 3, 4 and 5, the test for heteroskedasticity and serial-correlation were
also performed and did not find any evidence of having any type of such
problems in the data. The null-hypothesis for heteroskedasticity is that there
is no heteroskedasticity (means data is homoscedastic) against the alternative
hypothesis of data has heteroskedasity. The results of p-values do not reject
the null-hypothesis and shows that there is no problem of hetero. Moreover, the
null-hypothesis for auto-correlation is that data has no-serial correlation
against the hypothesis that data has serial correlation. By observing the
p-values which are greater than 5% and hence we cannot reject the
null-hypothesis and therefore data has no serial correlation problem. Hausman
test was applied to check whether fixed effect or random effect is better and
all models show the priority of fixed effect model. Moreover, Akike and Beysian
information criterion were applied for the best model selection criteria and
both criteria (AIC and BIC) support the preference of model-6 over all other
models.
In table-3,
table-4, and table-5, in all six models PFG for last year shows positive and
significant relationship with the current year’s PFG (with different levels of
significance i.e. at 1%, 5%, and 10%) for whole-sample, HGO, and LGO countries
respectively. In table-3, for the whole sample, it can be described as if last
year’s PFG will be increased by 1%, the current year’s PFG will also be
increased by 0.46%, 0.11%, 0.73%, 0.77%, 0.71%, and 0.65% in six models
respectively. In table-4, for HGO-Countries, it can be described as if last
year’s PFG will be increased by 1%, the current year’s PFG will also be
increased by 0.54%, 0.20%, 0.82%, 0.86%, 0.79%, and 0.74% in six models respectively.
In table-5, for the HGO-Countries, it can be described as if last year’s PFG
will be increased by 1%, the current year’s PFG will also be increased by
0.38%, 0.07%, 0.65%, 0.69%, 0.83%, and 0.57% in six models respectively. By
comparing HGO and LGO countries, it is clear that the coefficients of HGO
countries are comparatively higher as compared to LGO countries. Hence, we can
say that in LGO countries, the last year’s pension growth rate is affecting the
current year’s pension growth but at a slower pace.
Table 5. Forward-Step
Wise DPD Regression Analysis for LGO Countries
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
PFG |
PFG |
PFG |
PFG |
PFG |
PFG |
|
PFG (L1) |
0.38* |
0.07* |
0.65** |
0.69** |
0.83** |
0.57** |
(0.42) |
(0.15) |
(0.22) |
(0.16) |
(0.43) |
(0.27) |
|
IEQ |
-0.291** |
-0.153* |
-0.181** |
|||
(0.217) |
(0.071) |
(0.031) |
||||
IBB |
0.751** |
0.420** |
0.345* |
|||
(0.0768) |
(0.0594) |
(0.0325) |
||||
ICD |
0.222*** |
0.413*** |
0.098** |
|||
(0.0743) |
(0.209) |
(0.142) |
||||
IMIS. |
0.429*** |
0.478** |
0.198*** |
|||
(0.0707) |
(0.0624) |
(0.0445) |
||||
OE |
-0.379*** |
|||||
(0.525) |
||||||
NUMPF |
0.314* |
|||||
(0.042) |
||||||
CPI |
-0.432** |
|||||
(0.882) |
||||||
RR |
-0.741*** |
|||||
(0.304) |
||||||
FC Dummy |
-0.335** |
|||||
(0.144) |
||||||
Constant |
3.721** |
5.744** |
5.272*** |
2.232*** |
4.690** |
4.842*** |
(0.24) |
(0.54) |
(0.31) |
(0.35) |
(0.25) |
(0.76) |
|
Observations |
516 |
516 |
516 |
516 |
516 |
516 |
Groups |
12 |
12 |
12 |
12 |
12 |
12 |
R-squared |
0.051 |
0.232 |
0.127 |
0.265 |
0.487 |
0.622 |
F-Test |
61.32*** |
57.14*** |
57.17*** |
68.22*** |
74.78*** |
92.65*** |
Hetero (P-value) |
0.0634 |
0.178 |
0.185 |
0.112 |
0.132 |
0.163 |
Serial Auto (P-value) |
0.07608 |
0.2136 |
0.222 |
0.1344 |
0.1584 |
0.1956 |
Hausman-Test |
FE |
FE |
FE |
FE |
FE |
FE |
AIC |
234.72 |
256.32 |
266.4 |
161.28 |
190.08 |
91.296 |
BIC |
230.808 |
252.048 |
261.96 |
158.592 |
186.912 |
89.7744 |
In table-3 and
table-4 for the whole sample, and for HGO countries IEQ shows a significant and
positive relationship. However, in table-5 for LGO-Countries, IEQ shows a
significant and negative relationship with the growth of pension funds at5%,
10%, and 5% levels of significance in model-1, 5 and 6 respectively. By
comparing HGO and LGO countries, it is clear that the coefficients of HGO
countries are showing positive results as compared to LGO countries. Hence, we
can say that in LGO countries, the investment in equity is decreasing with the
passage of time. In addition to that, because of more volatility in equity
markets of LGO countries, pension funds management companies are declining
their funds’ investments in equity markets.
In table-3,
table-4, and table-5 for the whole sample; for HGO countries; and for LGO
countries IBB shows a significant and positive relationship with the growth of
pension funds. By comparing HGO and LGO countries, it can be deduced that the
coefficients of IBB for LGO countries are higher as compared to HGO countries.
It depicts that LGO countries have a more tendency towards investing in
treasury bills and bonds as compared to equity markets because of their less or
no volatility in the markets. This type of investment generates stable return
over the longer period of time. In table-3 and table-5, for the whole sample
and for LGO countries, ICD shows a significant and positive relationship with
the growth of pension funds. However, in table-4 for the HGO-Countries, ICD
shows a significant and negative relationship with the growth of pension funds.
By comparing HGO and LGO countries, it is clear that HGO countries are showing
negative relationship between ICD and PFG. It describes that the developed or
HGO countries are decreasing their pension funds’ investments in cash and bank
deposits as compared to LGO countries.
In table-3 and
table-5 for the whole sample and for LGO countries, IMIS shows a significant
and positive relationship with the growth of pension funds. In table-4 for the
HGO-countries, IMIS shows a significant and negative relationship with the
growth of pension funds. In table-5 for LGO-Countries, IMIS shows a significant
and negative relationship with the growth of pension funds. By comparing HGO
and LGO countries, it is clear that HGO countries are showing negative
relationship between IMIS and PFG. It describes that developed or HGO countries
are decreasing their pension funds’ investments in miscellaneous assets as
compared to LGO countries. It may due to the reason that after financial crisis
of 2008, investors become reluctant to invest in property or infrastructure.
In table-3,
table-4, and table-5 for the whole sample, for HGO and LGO countries, operating
expenses (OE) shows a significant and negative relationship with the growth of
pension funds. By comparing HGO and LGO countries, it is clear that the
coefficient of LGO countries is greater than that of HGO countries. It means
that in LGO countries, OE are more due to high operational, legal, and administrative
expenses and are affecting more to the PFG as compared to HGO countries. By
looking at number of pension funds (NUMPF), it is clear that the coefficient of
LGO countries is greater than that of HGO countries. It means that in LGO
countries, NUMPF are affecting more to the PFG as compared to HGO countries. It
may be due to the reason that in LGO countries, there is still a very huge
market to cover and, therefore, by introducing new pension schemes, more and
more population can come under the umbrella of pension funds for getting
retirement benefits.
Moreover,
inflation has a positive relationship with PFG in HGO countries whereas it
shows a negative sign in LGO countries. This may be due to the reason that in
HGO countries the average inflation rate is no so high and it can be adjusted
easily with pension benefits. On the other hand, in LGO countries, where the
average inflation is comparatively higher and easily cannot be adjusted to the
pension funds. Therefore, it shows it shows a negative sign in LGO countries.
Real rate of return (RR) shows a positive sign in HGO and negative sign in LGO
countries. It is due to the reason that in LGO countries combining with the
inflation effect; weak monetary and fiscal policies; and volatile markets, the
rate of return on pension funds is not growing comparatively faster as compared
to HGO
countries. These findings
are in line with the literature such as (Antolin, Schich, & Yermo, 2011).
In table-3 for
the whole sample, FC shows a significant and negative relationship with the
growth of pension funds at 5% level of significance in model-6. It describes
that a 1% increase in FC situation will decrease the PFG by 0.134%. In table-4
for the HGO-Countries, FC shows a significant and negative relationship with
the growth of pension funds at 5% level of significance in model-6. It
describes that a 1% increase in FC situation will decrease the PFG by 0.066%.
In table-5 for LGO-Countries, FC shows a significant and negative relationship
with the growth of pension funds at 5% level of significance in model-6. It
describes that a 1% increase in FC situation will decrease the PFG by 0.335%.
By comparing HGO and LGO countries, it is obvious that financial crisis
significantly affected both HGO and LGO countries negatively. However, by
looking at the coefficient’s values of LGO and HGO countries, it is clear that
2008 financial crisis hit the LGO countries more badly as compared to HGO
countries in terms of pension funds growth.
Conclusion
This study was conducted to analyze the importance of utilization-factors of pension funds that have an influence on the growth of pension funds. For the purpose of empirical analysis of this study, data were collected from different sources such as OECD data repository, OECD pension at a glance, and statistical profile of each individual country. The selected OECD countries are divided into two streams as HGO countries and LGO countries. A dynamic panel data regression model was applied by using fixed effect technique. Results indicate that PFG (lagged value); IBB, and NUMPF are showing positive and significant results with the growth of pension funds in HGO and LGO countries. However, IEQ, ICD, IMIS, CPI, and RR show mixed (positive and negative) and significant results i.e. IEQ, CPI, RR and (ICD, IMIS) are showing a positive (negative) and significant behavior in HGO countries but are negative (positive) and significant in LGO countries. It depicts that LGO countries have volatile equity markets, higher inflation rates and that’s why investors demand high rate of return on their investments. Operating, legal and administrative expenses are also higher in LGO countries.
The results support the pre-established theories such as theory of pooling, utility theory, and Institutions approach, theory of immunization, accounting profitability theory and absolute matching concept. In sum, we can say that the above mentioned and described pension funds’ utilization or investment factors have a significant and contributory effect on the growth of pension funds in the selected OECD countries. Moreover, the coefficients for HGO countries differ from the coefficients of LGO countries.
Conclusion
This study was conducted to analyze the importance of utilization-factors of pension funds that have an influence on the growth of pension funds. For the purpose of empirical analysis of this study, data were collected from different sources such as OECD data repository, OECD pension at a glance, and statistical profile of each individual country. The selected OECD countries are divided into two streams as HGO countries and LGO countries. A dynamic panel data regression model was applied by using fixed effect technique. Results indicate that PFG (lagged value); IBB, and NUMPF are showing positive and significant results with the growth of pension funds in HGO and LGO countries. However, IEQ, ICD, IMIS, CPI, and RR show mixed (positive and negative) and significant results i.e. IEQ, CPI, RR and (ICD, IMIS) are showing a positive (negative) and significant behavior in HGO countries but are negative (positive) and significant in LGO countries. It depicts that LGO countries have volatile equity markets, higher inflation rates and that’s why investors demand high rate of return on their investments. Operating, legal and administrative expenses are also higher in LGO countries.
The results support the pre-established theories such as theory of pooling, utility theory, and Institutions approach, theory of immunization, accounting profitability theory and absolute matching concept. In sum, we can say that the above mentioned and described pension funds’ utilization or investment factors have a significant and contributory effect on the growth of pension funds in the selected OECD countries. Moreover, the coefficients for HGO countries differ from the coefficients of LGO countries.
Research Implication or Contribution
This study contributes to the literature in the following manner that OECD countries were divided into HGO and LGO countries on the basis of their pension funds’ growth pattern. LGO countries show a different behavior in the case of investments in equities, bonds, deposits, and miscellaneous investments as compared to HGO countries. Operating expenses (OE) for LGO countries are larger as compared to HGO countries which shows LGO have more legal and administrative charges for controlling of pension funds. All the possible utilization or investment factors were included in the research model by using DPD-hierarchical regression that was missing in the literature.
This study provides some practical implications as follows: OECD analytical officers can use this study for identifying the patterns of pension growth in HGO and LGO countries. OECD pension outlook can report these results to show the different behavior of LGO countries for IEQ, IBB, ICD, IMIS, CPI, RR and OE as compared to HGO countries. Pension funds authorities can observe the change patterns of investments of pension funds in LGO countries and can suggest further policy recommendations to pension funds management companies for the benefit of the retired employees.
Research Limitations and Recommendations
It covers the data from 1975-2017 of 24 selected OECD countries. However, it can be extended to
more years or by collecting semi-annual observations. This study divided OECD countries on the basis of pension funds growth; however, they can also be divided on the basis of other measures such as income equality by using Gini-coefficient. Further research can be conducted to analyze the difference in investment patterns of pension funds by incorporating legal reforms of the pension systems for LGO and HGO countries with respect to their causes and outcomes.
Appendix A
Variables and Measurement for Determinants of
Utilization of Pension Funds
Symbol |
Variable |
Unit of Measurement |
Measurement of Variable |
PFG |
Pension Funds Growth |
Percentage |
(Current year
funds-previous years funds)/ Previous year funds*100 from OECD Library Data. Singh & Mehta, (2015); Aç?kgöz, Uygurtürk, & Korkmaz, (2015) |
IEQ |
Investments in Equity |
Percentage |
Total Investments in
Equity markets divided by total investment. Chen et
al. (2010); Levy,
& Gunthorpe, (1993); |
IBB |
Investments in Bills and
Bonds |
Percentage |
Total Investments in
treasury bills and bonds divided by total investment. Udoka et al. (2012); Heller, (2013); |
ICD |
Investments in Cash
Deposits |
Percentage |
Total Investments in cash
and demand bank deposits divided by total investment. Smith et al. (2003); Coleman, Esho, & Wong, (2006); |
IMIS |
Investments in
Miscellaneous |
Percentage |
Total Investments in real
estate, infrastructure, manufacturing etc. divided by total investment. Sievänen, Rita, & Scholtens, (2013); Croce, (2011); |
OE |
Operating Expenses |
Percentage |
Total expenses incurred
to manage pension funds including administrative and legal expenses divided
by total PFs. Obinata, (2000);
Militaru, (2015); |
NUMPF |
Number of Pension Funds |
Numbers |
Total number of penson
funds by selected-OECD Counties. Thomas, & Spataro,
(2016); Gökçen, & Yalç?n, (2015); |
CPI |
Consumer Price Index |
Percentage |
Rate of Inflation in
selected-OECD Countries. Prammer, & Reiss, (2015); Bivens, (2015) |
RR |
Rate of Return |
Percentage |
Rate of Return in
selected-OECD countries. Tepper, (1981); Reisen, (1997) |
FC |
Financial Crisis |
Dummy |
Assign "1" for
2008 year or "0" otherwise. Ebbinghaus,
(2015); Mellor, (2018) |
Appendix B
Correlation Matrix and VIF Values
10 |
||||||||||
Variables |
VIF |
PFG |
IEQ |
IBB |
ICD |
IMISC. |
OE |
NUMPF |
CPI |
RR |
PFG |
1 |
|||||||||
IEQ |
5.66 |
0.461* |
1 |
|||||||
IBB |
5.43 |
0.447* |
-0.383* |
1 |
||||||
ICD |
3.56 |
0.369* |
-0.402* |
-0.176* |
1 |
|||||
IMISC. |
2.22 |
0.353* |
0.056 |
-0.491* |
-0.057 |
1 |
||||
OE |
1.99 |
-0.331* |
0.152* |
-0.064 |
-0.170* |
0.054 |
1 |
|||
NUMPF |
1.43 |
0.270* |
0.149* |
-0.105* |
-0.143* |
0.095* |
0.456* |
1 |
||
CPI |
1.23 |
0.246* |
-0.003* |
0.113* |
-0.041 |
-0.131* |
0.012 |
-0.278* |
1 |
|
RR |
1.11 |
0.230* |
0.411* |
-0.403* |
-0.116* |
0.191* |
-0.015 |
-0.01 |
-0.021 |
1 |
Table B2: Correlation
Matrix of HGO-Countries |
||||||||||
PFG |
|
1 |
|
|
|
|
|
|
|
|
IEQ |
6.43 |
0.769* |
1 |
|
|
|
|
|
|
|
IBB |
5.33 |
0.761* |
-0.252 |
1 |
|
|
|
|
|
|
ICD |
5.29 |
-0.680* |
0.390* |
-0.043 |
1 |
|
|
|
|
|
IMIS |
4.22 |
-0.561* |
-0.113* |
-0.101 |
-0.144* |
1 |
|
|
|
|
OE |
3.54 |
-0.482* |
0.121 |
-0.203 |
0.086* |
-0.302* |
1 |
|
|
|
NUMPF |
3.21 |
0.423* |
-0.412 |
-0.112 |
0.202 |
-0.150* |
0.411 |
1 |
|
|
CPI |
1.67 |
0.375* |
0.324 |
0.209 |
-0.301 |
-0.116 |
-0.585 |
0.431 |
1 |
|
RR |
1.65 |
0.244* |
0.146* |
0.293 |
0.167* |
-0.172* |
0.246* |
0.335* |
0.272 |
1 |
Table B3: Correlation
Matrix of LGO-Countries |
||||||||||
PFG |
|
1 |
|
|
|
|
|
|
|
|
IEQ |
8.65 |
-0.715* |
1 |
|
|
|
|
|
|
|
IBB |
8.32 |
0.641* |
0.526 |
1 |
|
|
|
|
|
|
ICD |
8.12 |
0.624* |
0.306* |
0.002 |
1 |
|
|
|
|
|
IMISC. |
6.44 |
0.522* |
0.178* |
-0.233 |
-0.221* |
1 |
|
|
|
|
OE |
3.22 |
-0.431* |
-0.512 |
-0.101 |
0.331 |
-0.251* |
1 |
|
|
|
NUMPF |
3.04 |
0.426* |
0.150* |
0.202 |
0.485* |
-0.441* |
-0.274 |
1 |
|
|
CPI |
1.22 |
-0.363* |
0.333 |
-0.180* |
-0.505 |
0.545 |
0.603 |
-0.174 |
1 |
|
RR |
1.02 |
-0.231* |
-0.227 |
0.061* |
0.213 |
-0.139 |
-0.111 |
-0.202 |
0.049* |
1 |
* shows significance at
the .1 level |
References
- Aamir, M., Qayyum, A., Nasir, A., Hussain, S., Khan, K. I., & Butt, S. (2011). Determinants of tax revenue: A comparative study of direct taxes and indirect taxes of Pakistan and India. International Journal of Business and Social Science, 2(19), 173-178.
- Acikgoz, E., Uygurturk, H., & Korkmaz, T. (2015). Analysis of factors affecting growth of pension mutual funds in Turkey. International Journal of Economics and Financial Issues, 5(2), 427- 433.
- Adami, R., Gough, O., Mukherjee, S., & Sivaprasad, S. (2014). An empirical analysis of the performance of pension funds: evidence from UK. Studies in Economics and Finance.
- Alonso, J., Bjelic, J., Herrera, C., Ordoñez, I., Romero, C., Tuesta, D., & Ugarte, A. (2010). Projections of the Impact of Pension Funds on Investment in Infrastructure and Growth in Latin America. Ivonne and Romero, Carolina and Tuesta, David Alfredo and Ugarte, Alfonso, Projections of the Impact of Pension Funds on Investment in Infrastructure and Growth in Latin America (January 1, 2010).
- Alptekin, N., & Şiklar, E. (2009). Türk hisse senedi emeklilik yatirim fonlarinin çok kriterli performans değerlendirmesi: Topsis metodu. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (25).
- Ambachtsheer, K., & Bauer, R. (2007). Losing ground: do Canadian mutual funds produce fair value for their customers? Canadian Investment Review, 20(1), 8-14.
- Amir, E., & Benartzi, S. (1998). The expected rate of return on pension funds and asset allocation as predictors of portfolio performance. Accounting Review, 335-352.
- Andonov, A., Hochberg, Y. V., & Rauh, J. D. (2018). Political representation and governance: Evidence from the investment decisions of public pension funds. The Journal of Finance, 73(5), 2041- 2086.
- Antolin, P. (2008). Pension fund performance. Available at SSRN 1368816
- Antolin, P., Schich, S., & Yermo, J. (2011). The economic impact of protracted low interest rates on pension funds and insurance companies. OECD Journal: Financial Market Trends, 2011(1), 237-256.
- Bashir, T., Khan, K. I., & Urooge, S. (2020). Assumptions of making good deal with bad person: Empirical evidence on strong form market efficiency, Global Social Sciences Review, 5(1), 154-162.
- Bauer, R., & Kicken, L. (2008). The pension fund advantage: are Canadians overpaying their mutual funds? Rotman International Journal of Pension Management, 1(1).
- Bivens, J. (2015). Gauging the impact of the Fed on inequality during the Great Recession. Hutchins Center Working Papers.
- Blake, D., Lehmann, B. N., & Timmermann, A. (1999). Asset allocation dynamics and pension fund performance. The Journal of Business, 72(4), 429-461.
- Bohl, M. T., Lischewski, J., & Voronkova, S. (2011). Pension funds' performance in strongly regulated industries in Central Europe: Evidence from Poland and Hungary. Emerging Markets Finance and Trade, 47(sup3), 80-94.
- Bonizzi, B., & Guevara, D. (2019). Private pension funds in emerging economies: from broken promises to financialisation. In Finance, Growth and Inequality. Edward Elgar Publishing.
- Bradley, D., Pantzalis, C., & Yuan, X. (2016). The influence of political bias in state pension funds. Journal of Financial Economics, 119(1), 69-91.
- Budd, E. C., & Seiders, D. F. (1971). The impact of inflation on the distribution of income and wealth. The American Economic Review, 61(2), 128-138.
- Cairns, A. J., Blake, D., & Dowd, K. (2006). Stochastic lifestyling: Optimal dynamic asset allocation for defined contribution pension plans. Journal of Economic Dynamics and Control, 30(5), 843- 877.
- Casey, B. H. (2014). From pension funds to piggy banks:(Perverse) consequences of the S tability and G rowth P act since the crisis. International Social Security Review, 67(1), 27-48.
- Chen, C., Comerton-Forde, C., Gallagher, D. R., & Walter, T. S. (2010). Investment manager skill in small-cap equities. Australian Journal of Management, 35(1), 23-49.
- Coleman, A. D., Esho, N., & Wong, M. (2006). The impact of agency costs on the investment performance of Australian pension funds. Journal of Pension Economics & Finance, 5(3), 299-324.
- Collins, S. (2003). The expenses of defined benefit pension plans and mutual funds. Perspective, 9(6), 1-19.
- Das, B. C. Investment in treasury bills and treasury bonds in 2013: A study of Bangladesh.
- Davis, E. P. (1995). Debt, financial fragility, and systemic risk. Oxford University Press.
- Della Croce, R. (2011). Pension funds investment in infrastructure.
- Della Croce, R., Kaminker, C., & Stewart, F. (2011). The role of pension funds in financing green growth initiatives.
- Diamond, P., & Geanakoplos, J. (1999). Social security investment in equities I: Linear case. National Bureau of Economic Research.
- Diamond, P., & Geanakoplos, J. (2003). Social security investment in equities. American Economic Review, 93(4), 1047-1074.
- Ebbinghaus, B. (2015). The privatization and marketization of pensions in Europe: A double transformation facing the crisis. European Policy Analysis, 1(1), 56-73.
- Fanning, D. (1980). The Growth and Development of Occupational Pension Funds. Managerial Finance.
- Feldstein, M. S. (1981). Should private pensions be indexed?
- Foley, B. (1987). Pension Funds in the UK-Danger Ahead? Employee Relations
- Gökçen, U., & Yalçın, A. (2015). The case against active pension funds: Evidence from the Turkish private pension system. Emerging Markets Review, 23, 46-67.
- Gulland, A. (2016). Pension funds: tobacco investment up in smoke. Bmj, 352, i1491.
- Heer, B., & Irmen, A. (2014). Population, pensions, and endogenous economic growth. Journal of Economic Dynamics and Control, 46, 50-72.
Cite this article
-
APA : Qayyum, A., Arslan, A., & Khan, K. I. (2020). Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries. Global Social Sciences Review, V(II), 45-60. https://doi.org/10.31703/gssr.2020(V-II).05
-
CHICAGO : Qayyum, Arslan, Aniqa Arslan, and Kanwal Iqbal Khan. 2020. "Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries." Global Social Sciences Review, V (II): 45-60 doi: 10.31703/gssr.2020(V-II).05
-
HARVARD : QAYYUM, A., ARSLAN, A. & KHAN, K. I. 2020. Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries. Global Social Sciences Review, V, 45-60.
-
MHRA : Qayyum, Arslan, Aniqa Arslan, and Kanwal Iqbal Khan. 2020. "Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries." Global Social Sciences Review, V: 45-60
-
MLA : Qayyum, Arslan, Aniqa Arslan, and Kanwal Iqbal Khan. "Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries." Global Social Sciences Review, V.II (2020): 45-60 Print.
-
OXFORD : Qayyum, Arslan, Arslan, Aniqa, and Khan, Kanwal Iqbal (2020), "Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries", Global Social Sciences Review, V (II), 45-60
-
TURABIAN : Qayyum, Arslan, Aniqa Arslan, and Kanwal Iqbal Khan. "Investment of Pension Funds in Different Streams: Evidence from Low vs. High Growth Oriented OECD Countries." Global Social Sciences Review V, no. II (2020): 45-60. https://doi.org/10.31703/gssr.2020(V-II).05