Pension funds play a key part in the healthy functioning of people's social lives, especially senior individuals who seek a factor of confidence against unsafe situations, i.e. when they are unemployed. Pension fund performance and governance have been studied. The current study attempts to highlight the important growth and use characteristics of pension funds. OECD pension funds forecast only presents the descriptive study for pension fund utilization,

not their particular major implications.

These institutions assist workers to save for retirement during their working lives. In most countries, pension funds cannot be withdrawn early, which creates long-term holdings by pension corporations and opens doors for investment opportunities to provide higher returns. When analysing OECD economies, pension funds must be considered. OECD pension funds' average asset weight is 89%. (OECD, 2020). Considering governance and growth determinants of pension funds is a must. Moreover, the ageing factor in OECD economies makes pension fund management

increasingly crucial.

A number of past studies were surveyed to form the current study and support its formation. Table 1. Describes them briefly.

. Accumulation-Factors of Pension Funds in Selected-OECD Countries

. Depicts the relationship of each of the above hypotheses with pension fund growth.

Figure 2 describes the factors of accumulation i.e. firstly; three main contributing factors are occupational pension funds (OPA), private pension funds (PPF), and pension insurance contract assets (PIC). Secondly, occupational pension funds are then further divided into defined benefits (DB), defined contributions (DC), employees' contributions (EMPSC), and employers' contributions (EMPRC). Thirdly, DB and DC are further subdivided into defined benefit mixed or hybrid (DBM) plus traditional defined benefit pension assets (TDB) and protected defined contributions (PDC) plus unprotected defined contribution pension funds (UDC) respectively. The purpose of this part of the study is to analyze the significant impact (along with the magnitude and direction) of each of the above-mentioned described factors on the growth of pension funds. Moreover, from the literature review, it is observed that administrative and legal expenses also play a vital role in the growth of pension funds. Hence, this factor is also considered important to be a part of this analysis.

Data were obtained from many sources, including the OECD and the profile of each country. The Organization for Economic Co-operation and Development (OECD) had 36 members by the end of 2018. Only 24 countries that joined the OECD before 1975 were included. It includes statistics from 1975 to 2017 from 24 selected OECD nations, totalling 43 years. This balanced panel data has 1032 total observations. Twelve nations were deleted from the 36-country OECD because of a delay in joining after 1975.

The countries selected are separated into two groups on the basis of their median GDP growth. For this reason, an average of each country's pension funds to GDP ratio was calculated. These averages of 24 countries were then used to calculate the median value. As a rule of thumb, countries with a PF/GDP ratio over the median level are regarded higher-growth countries, while those with a PF/GDP ratio below the threshold level are deemed lower-growth nations.

Using dynamic panel data and forward step-wise regression, the study compares high-growth-oriented nations with low-growth-oriented countries based on the observed characteristics of selected OECD countries.

General economic functions are provided by the models listed below. With the use of accumulation factors, the growth of a country's pension funds can be modelled mathematically using Equation 1. In addition, these country-based characteristics are expanded upon in (2), which states that they are occupational pension assets (OPA), private pension funds' assets (PPFA), private insurance contract assets (PICA), defined benefit pension funds (DB), defined benefit traditional funds (DBT), defined benefit mixed funds (DBM), defined contributions (DC), defined contributions protected funds (DCP), defined contributions unprotected funds (DCU), employees' contributions (ESTC), employers' contributions (ERTC), and operating and administrative expenses (OE) which are incurred to generate revenue.

PFG=f(Accumulation Factors of PF )..(1)

PFG=(?PFG?_(t-1),OPA,PPFA,PICA,DB,DBT,DBM,DC,DCP,DCU,ESTC,ERTC,OE) --- (2)

On the basis of the model presented above, a dynamic panel data technique was employed, and the three equations that follow i.e.; model-1 show the effect of three main accumulation factors on PFG, i.e., OPA, PPFA and PICA. This model is extended in equation (4) and OPA was subdivided into DB and DC funds and the other two variables PPFA and PICA remain the same in the model. Moreover, equation (5) further segregates the model and subdivides the DB funds into DBT & DBM and DC funds into DCP & DCU respectively.

PFG (OECD) it = ?0 + ?i D it + ?1 (PFG) i, t-1 + ?2 (OPA) it + ?3 (PPFA) it + ?4 (PICA) it + ? it ____ (3)

PFG (OECD) it = ?0 + ?i D it + ?1 (PFG) i, t-1 + ?2 (DBA) it + ?3 (DCA) it + ?4 (PPFA) it + ?5 (PICA) it + ? it ____ (4)

PFG (OECD) it = ?0 + ?i D it + ?1 (PFG) i, t-1 + ?2 (DBT) it + ?3 (DBM) it + ?4 (DCP) it + ?5 (DCU) it + ?6 (ESTC) it + ?7 (ESRC) it + ?8 (OE) it + ? it ____ (5)

Where:

• PFG = Pension Funds Growth

• OPA = Occupational Pension Funds’ Assets

• PPFA = Personal Pension Funds’ Assets

• PICA = Pension Insurance Contracts’ Assets

• DB = Defined Benefit Funds

• DBT = Defined Benefits Traditional

• DBM = Defined Benefit Mixed/Hybrid

• DC = Defined Contribution Funds

• DCP = Protected Defined Contributions

• DCU = Unprotected Defined Contributions

• ESTC = Employees’ Contributions

• ERTC = Employers’ Contributions

• OE = Operating and Administrative Expenses

• ? = the error term

Table 3 shows the summary and there is also a correlation matrix and VIF analysis of the data in tables 4, 5, and 6. Using DPD-Hierarchical regression analysis, Table 7 presents a comparison of the Whole-sample, HGO-Countries, and LGO-Countries. Three models were created for each analysis based on the already mentioned three equations i.e., 3, 4 and 5: model 1, model 2, and model 3. For this study, the growth of pension funds is used as the observed dependent variable, while the lagged values of PFG, OPA, PPFA, PICA, DB, DC, DBT, DBM, DCP, DCU, ESTC, ERTC, and OE are used as independent variables based on the literature.

The OPF is showing a momentous relationship through the development of pension funds in selected OECD countries at 1%, 5% and 5% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the OPF of a country is increased by one per cent, the PFG will also be increased by 0.620%, 0.740% and 0.698% in whole-sample, HGO-Countries and LGO-Countries respectively. This connection is also reinforced in the works by (Lamla, & Coppola, 2013; Haukenes, 2011; Ahola, 2009). It also provides sustenance to the theory of pooling and the Welfarist approach in the sense that occupational pension funds are a major source of pooling funds. Hence, pooling makes it easier for pension firms to invest in a huge amount of different types of assets to generate a steady return over an extended time. In sum, it provides strong support to the first developed hypothesis for this study i.e., H1 which mentions a strong positive association between OPA and PFG. It is obvious that the coefficient of HGO countries (0.740) is higher as compared to LGO countries (0.698). Hence, it can be said that occupational pension funds are contributing more in HGO countries as compared to LGO countries.

While talking about the model (1), the PPF depicts a positive relationship with the growth of pension funds in selected OECD countries at 1%, 5% and 5% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the PPF of a country is increased by one per cent, the PFG will also be increased by 0.625%, 0.433% and 0.539% in whole-sample, HGO-Countries and LGO-Countries respectively. Moreover, model (2) and model (3) are also depicting the same type of relationship between PPF and PFG. This relationship is also supported in the literature by (Poterba, Venti, & Wise, 2001; Perozek, & Reinsdorf, 2002; Konstantin, 2007). It also provides backing to the theory of pooling according to which private pooling of current resources can give them support when they will be older in the future. It provides strong support to the second developed hypothesis for this study i.e., H2 which mentions a strong positive association between PPA and PFG. By comparing HGO and LGO countries, the coefficients of LGO countries (0.539 and 0.744) are larger as compared to HGO countries (0.433 and 0.130) in model-1 and model-2 respectively. It means the private pension funds are growing faster in LGO countries and more and more people are motivated to have a private pension plan for their future.

The PICF highlights a positive relationship with the PFG in selected OECD countries at 1%, 5% and 5% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the PICF of a country is increased by one per cent, the PFG will also be increased by 0.101%, 0.120% and 0.213% in whole-sample, HGO-Countries and LGO-Countries respectively. Moreover, model (2) and model (3) are also depicting the same type of relationship between PICF and PFG. This relationship is also supported in the literature by (Guo et al. 2016; Ha, 2017; Cheng, 2015). It also provides sustenance to the theory of pooling i.e.; insurance companies provide a platform for pooling funds and investing in different types of investments for the purpose of generating a stable return in the long run. It provides strong support to the third developed hypothesis for this study i.e., H3 which mentions a strong positive association between PICA and PFG. By comparing HGO and LGO countries, the coefficients of LGO countries (0.213 and 0.251) are larger as compared to HGO countries (0.120 and 0.235) in model-1 and model-2 respectively. It means the pension insurance funds are growing faster in LGO countries and more and more people are motivated to have private pension insurance plans for their future.

Defined benefit funds (DBF) is presenting a significant relationship with the growth of pension funds whereas it is showing a significant negative relationship at a 10% level of significance in LGO-Countries. It describes that if the DBF of a country is increased by one per cent, its PFG will also be increased by 0.240% and 0.140% in whole-sample and HGO-Countries respectively. On the other hand, a 1% increase in DBF will decrease the PFG by 0.242% in LGO-Countries. This relationship is also supported in the literature by (Copeland, & VanDerhei 2009; Poterba et al. 2007; Pesando, 2008). It also provides sustenance to the theory of pooling, life cycle theory, and human capital spill-over theory. According to life cycle theory and human capital spill-over theory, current generations should save to feed the elder ones and with the passage of time, when they will be older, their children will feed them in a defined benefit scheme under the PAYG system. In sum, it provides strong support to the fourth developed hypothesis for this study i.e. H4 which mentions a strong positive association between DBF and PFG. By comparing HGO and LGO countries, the coefficient of LGO countries (-0.242) is showing a negative relationship and the coefficient of HGO (0.140) is showing a positive relationship with PFG. It means that with the passage of time, the DBF schemes are becoming fewer in LGO countries and they are moving towards DC schemes. It points out an important phenomenon where the countries are trying to shift their DB schemes towards DC schemes.

Defined contribution funds (DCF) is portraying a relationship with the PFG in selected OECD countries at 1%, 10% and 5% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the DCF of a country is increased by one per cent, the PFG will also be increased by 0.271%, 0.224% and 0.308% in whole-sample, HGO-Countries and LGO-Countries respectively. This relationship is also supported in the literature by (Benartzi, & Thaler, 2001; Holzmann, & Palmer, 2006; Choi et al. 2002; Cairns, Blake, & Dowd, 2006; Huberman, Iyengar, & Jiang, 2007). It also provides sustenance to the theory of pooling and prodigal father probability theory in the sense that every generating should save for its future. In sum, it provides strong support to the first developed hypothesis for this study i.e. H7 which mentions a strong positive association between DCF and PFG. By comparing HGO and LGO countries, we come to know that HGO countries have a lower coefficient (0.224) as linked to LGO countries (0.308). It indicates that in LGO countries the defined contribution schemes are growing faster as compared to HGO countries.

Employees' contributions (ESTC) have a significant and positive relationship with the growth of pension funds in selected OECD countries at 5%, 5% and 10% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the ESTC of a country is increased by one per cent, the PFG will also be increased by 0.237%, 0.266% and 0.166% in whole-sample, HGO-Countries and LGO-Countries respectively. This relationship is also supported in the literature by (Byrne, 2007; Toshiki, 2009; Zhang, 2011). It also provides sustenance to the theory of pooling because employees pool together to save for their future. In sum, it provides strong support to the 8th developed hypothesis for this study i.e. H8 which mentions a strong positive association between ESTC and PFG. By comparing HGO and LGO countries, the coefficients of ESTC (0.266) in HGO countries are larger as compared to (0.166) in LGO countries. It means that employees are contributing much more in HGO countries to fund their retirement plans as compared to LGO countries.

Employers’ contributions is showing a significant and positive relationship with PFG in selected OECD countries at 1% and 5% levels of significance in whole-sample and HGO-Countries respectively. It is showing a significant negative relationship at a 5% level of significance in LGO-Countries. It describes that if the ERTC of a country is increased by one per cent, the PFG will be increased by 0.038% and 0.069% in whole-sample and HGO-Countries respectively. On the other hand, a 1% increase in ERTC will decrease the PFG by 0.013% in LGO-Countries. This relationship is also supported in the literature by (Chen, & Wu, 2014; Ghilarducci, & Sun, 2006; Pensions, (2005). It also provides sustenance to the theory of pooling and the Welfarist approach in the sense that it is the employer's duty to take care of his employees even after their retirement. That's why he contributes to the pooled funds to feed them in the future. In sum, it provides strong support to the 9th developed hypothesis for this study i.e. H9 which mentions a strong positive association between ERTC and PFG. It is interesting to know that LGO countries have a negative relationship with the growth of pension funds. The reason behind this phenomenon is that in LGO countries, employers are decreasing their contributions towards pooled funds and rather they are trying to put more burdens on the shoulder of employees.

Operating and administrative expenses (OE) is depicting a negative and significant relationship with the PFG at 1%, 5% and 10% levels of significance in whole-sample, HGO-Countries and LGO-Countries respectively. It describes that if the OE of a country is increased by one per cent, the PFG will be decreased by 0.238%, 0.112% and 0.306% in whole-sample, HGO-Countries and LGO-Countries respectively. It also provides sustenance to the theory of accounting profitability theory and absolute matching concept in the sense that expenses must be minimized to generate good profits to meet current and future liabilities. Moreover, the current expenses must be written off from the current revenues to make the correct accounting procedure. Nonetheless, the increasing amount of expenses will lead to a decrease in profits and the pension fund will not grow. In sum, it provides strong support to the 10th developed hypothesis for this study i.e. H10 which mentions a strong negative association between OE and PFG. By comparing HGO and LGO countries, the coefficient 0.306 of for LGO countries is much larger as compared to 0.112 for HGO countries. It means that the relationship between OE and PFG is much stronger in LGO countries as compared to HGO countries.

This study examines the importance of pension fund accumulation elements that affect the growth of pension funds. With the help of the fixed effect technique, we built a dynamic panel data regression model. With the increase of pension funds in HGO and LGO countries, results demonstrate that PFG (lag-1), PPF (lag-2), PICF, DCF, DCP, DCU, and ESTC are displaying good and significant results. There is some evidence to suggest that DB, DBT, and DBM are effective in high-income nations, but that they are less effective in low-income ones, with inconsistent outcomes. It shows that with the passage of time, LGO countries are trying to shift their pension system from the DB system to the DC system. Moreover, OE shows negative and significant behaviour with PFG.

The fixed effect is good on the basis of R-square, F-value, AIC and BIC. Moreover, the Hausman specification test also favours the application of a fixed-effect approach. The p-values for testing the heteroskedasticity and serial correlation do not show their presence in the data by accepting their null hypothesis of having no heteroskedasticity and no serial correlation in the data. The VIF column in the correlation matrix does not show the presence of a multicollinearity problem in the observed variables. As a result, ideas such as utility theories, the theory of pooling, Institutionists' approaches and theory of immunization, life-cycle theory and accounting profitability theory, human capital spillover theory and absolute matching concepts are all supported. All in all, we can conclude that the elements outlined above have a contributory impact on the expansion of pension funds.

This research adds to the body of knowledge by dividing OECD nations into LGO & HGO categories based on the rate of growth of their pension funds. In the case of defined benefit plans, LGO countries behave differently from HGO countries. OE for LGO countries is larger than those for HGO countries, which indicates that LGOs face higher legal and administrative costs in the management of pension funds. Using DPD-hierarchical regression, we were able to add all of the accumulation components that were lacking from past studies.

Researchers at the OECD can make use of this study to discover trends in the rise of pensions in high- and low-income nations. OECD-pension-outlook can publish these statistics to highlight the contrasting behaviour of LGO nations in relation to DB funds, DC funds, employers' contributions, and OE in comparison to HGO countries. OECD pension outlook LGO countries are transitioning from a DB to a DC pension system, and pension fund officials can offer policy proposals to help those who have reached retirement age.

The Gini coefficient can also be used to split OECD countries based on other indicators, such as the growth of pension funds, however, this study focused on the latter. Research into the move from DB to DC systems and its causes and outcomes can be explored further.