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Abstract
This study provides a framework for comparison and benchmarking of administrative expenditures of public and private social security programs. The paper presents the genesis of the inquiries into the subject, reviewing some of the most relevant literature on administrative expenditures and the costs of mandatory programs produced over the past two decades. The quantitative analysis builds on the extensive body of literature, but our framework evolved considerably from earlier studies. Our dataset includes over 100 observations and a broad set of explanatory variables. We developed and compared a number of standardized cost indices discussing their advantages and limitations. We also discuss major cost components and their shares in total program costs. The analysis explains over 90 percent of variation in administrative expenditures. It confirms some of the hypotheses expressed in the earlier studies and presents new evidence of driving factors for costs. We developed three different specifications for statistical analysis. The first set looks at the impact of design of a program on total costs. The second group of specifications assesses differences in costs of managing pension liabilities between the public and private mandatory pension schemes. Finally, on the basis of the third model we generate benchmarks for staffing levels and for the total administrative expenditures. We compare those to the actual indicators and develop standard performance ratios, providing insights into design variations and performance of the programs. We conclude with a discussion of data limitations and implications of our findings.
Defining, Measuring, and Benchmarking Administrative
Expenditures of Mandatory Social Security Programs
Oleksiy Sluchynsky
D I S C U S S I O N P A P E R NO. 1501
© 2013 International Bank for Reconstruction and Development / The World Bank
About this series...
Social Protection & Labor Discussion Papers are published to communicate the results of The World Bank’s work to the development community with the least possible delay. This paper therefore has not been prepared in accordance with the procedures appropriate for formally edited texts.
The findings, interpretations, and conclusions expressed herein are those of the author(s), and do not necessarily reflect the views of the International Bank for Reconstruction and Development/The World Bank and its affiliated organizations, or those of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.
For more information, please contact the Social Protection Advisory Service, The World Bank, 1818 H Street, N.W., Room G7-803, Washington, DC 20433 USA. Telephone: (202) 458-5267, Fax: (202) 614-0471, E-mail: [email protected] or visit us on-line at www.worldbank.org/spl.
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Defining, Measuring, and Benchmarking
Administrative Expenditures of Mandatory Social
Security Programs
Oleksiy Sluchynsky*
February 2015
* Oleksiy Sluchynsky is a Senior Economist with the World Bank. Correspondence should be
sent to the World Bank, 1818 H St NW, Washington DC 20433;
e-mail: [email protected]
The author is especially grateful to Raluca Golumbeanu for assistance in data collection,
Robert Palacios for very valuable comments and input, and other colleagues from the World
Bank for their advice and support in conducting this research.
i
Abstract
This study provides a framework for comparison and benchmarking of administrative
expenditures of public and private social security programs. The paper presents the
genesis of the inquiries into the subject, reviewing some of the most relevant
literature on administrative expenditures and the costs of mandatory programs
produced over the past two decades. The quantitative analysis builds on the extensive
body of literature, but our framework evolved considerably from earlier studies. Our
dataset includes over 100 observations and a broad set of explanatory variables. We
developed and compared a number of standardized cost indices discussing their
advantages and limitations. We also discuss major cost components and their shares
in total program costs. The analysis explains over 90 percent of variation in
administrative expenditures. It confirms some of the hypotheses expressed in the
earlier studies and presents new evidence of driving factors for costs. We developed
three different specifications for statistical analysis. The first set looks at the impact of
design of a program on total costs. The second group of specifications assesses
differences in costs of managing pension liabilities between the public and private
mandatory pension schemes. Finally, on the basis of the third model we generate
benchmarks for staffing levels and for the total administrative expenditures. We
compare those to the actual indicators and develop standard performance ratios,
providing insights into design variations and performance of the programs. We
conclude with a discussion of data limitations and implications of our findings.
JEL Classification: H55, H83, G23
Keywords: Administrative Costs; Public Pension; Social Security; Public Administration
ii
Table of Contents
Executive Summary ............................................................................................................. 1
I. Introduction ................................................................................................................. 5
II. Formulating the Research Question ............................................................................ 9
III. Scope of Analysis ....................................................................................................... 17
IV. Our Data and Structure of Costs ................................................................................ 22
4. 1. Institutional organization and total expenditures ............................................. 23
4. 2. Key elements of the cost structure ..................................................................... 25
4. 3. Functional analysis: contribution collection and benefit payment .................... 27
V. Cost Normalization .................................................................................................... 29
5. 1. Uses of national income, revenues, and expenditures in cost normalization .... 31
5. 2. Administrative costs and pension liabilities ....................................................... 31
5. 3. Per-member costs ............................................................................................... 33
VI. Data Analysis and Cost Benchmarking ...................................................................... 37
6. 1. Administrative expenditures and program design ............................................. 37
6. 2. Administrative expenditures and pension liabilities .......................................... 40
6. 3. Administrative expenditures and institutional organization ............................. 42
6. 4. Performance against benchmarks ..................................................................... 45
6. 5. Implications for choice of cost indices ................................................................ 47
6. 6. Global benchmarks ............................................................................................. 48
VII. Quality Aspects in Cost Measurement: What is Left to Residual .............................. 52
VIII. Conclusions ................................................................................................................ 53
References ........................................................................................................................ 55
Annex 1: List of Public Pension Programs and Abbreviations Used ................................. 58
Annex 2: Key Institutional and Operational Indicators ..................................................... 60
Annex 3: Benchmarking Performance of Public Pension Programs ................................. 62
Annex 4: Benchmarking Costs Performance .................................................................... 64
iii
Tables
Table 1: Summary of Literature .............................................................................................. 16
Table 2: Classification of the Public Social Security Administration ....................................... 24
Table 3: Activity of Pension Accounts (Thousands of Contributing Members) ...................... 34
Table 4: Choice of Denominator in Cost Indices and Associated Biases ................................ 36
Table 5: Administrative Expenditures and Program Design ................................................... 38
Table 6: Factors Affecting the Cost of Managing Pension Liabilities ...................................... 40
Table 7: Staffing Requirements for Pension Administration .................................................. 42
Table 8: Key Factors Affecting Costs of Public Pension Programs .......................................... 44
Table 9: Choice of Denominator for Cost Index and Correlation with Cost Benchmark ........ 48
Figures
Figure 1: U.S. SSA Staffing and Cost per Beneficiary (1978–1998) ........................................... 6
Figure 2: Administrative Costs as Share of the Imputed Covered Wage................................ 18
Figure 3: Agency Rank and Median Administrative Expenditures (Income Adjusted) ........... 25
Figure 4: Costs of Managing Pension Assets .......................................................................... 26
Figure 5: Share of Benefit Payments in Banks by National Social Security Agencies ............. 27
Figure 6: Allocation of Labor Resources within the Social Security Agencies ........................ 28
Figure 7: Contribution Rate and Administrative Costs ........................................................... 30
Figure 8: Costs of Managing Pension Liabilities (Percentage of Total Assets or IPDs) ........... 32
Figure 9: Economies of Scale in Administrative Expenditures................................................ 49
Figure 10: Per-Beneficiary Cost Spreads for a Midsize Operation (Nominal US$) ................. 50
Figure 11: Economies of Scale in Staffing Requirements ....................................................... 50
Figure 12: Beneficiary per Staff Ratios for a Midsize Operation ............................................ 51
Figure 13: Quality Cost Tradeoffs ........................................................................................... 52
1
Executive Summary
This study was motivated by an interest toward determinants of the operating costs of
public social security programs and implications of policy reform for the institutions that
administer them. A simple comparison of the administrative expenditures of the different
types of schemes may often be misleading, and cost differentials do not always imply
inefficiencies. A comprehensive framework is needed to address various biases and make a
meaningful comparison of programs of different types, sizes, and organizations.
There also is significant interest toward comparing performance of publicly versus privately
managed pension schemes. The wave of reforms with partial or full privatization of the
national social security programs in the 1990s and early 2000s along with the perception of
excessive charges imposed by the private providers generated a considerable body of
literature focusing on the private defined contribution (DC) plans. Yet, that type of research
generated few implications for the public schemes and institutions. While members of the
publicly managed programs do not bear the costs of administration directly, such schemes
have their own risks. Public programs are prone to agency problems, often resulting in
overstaffing, over-resourcing, or under-provision of quality services. Policymakers and
administrators often face the same operational choices and challenges under public or
private management, with cost implications. By focusing on the performance of systems
and institutions rather than on cost incidence, this paper offers a generic approach to the
cost analysis with some emerging recommendations relevant to schemes of all types.
Our analysis builds on the extensive body of literature for both public and private pension
schemes. It summarizes key findings, lays out a new systematic framework for quantitative
analysis, and develops program-specific performance benchmarks for both labor resources
and operating costs. The framework evolved considerably from earlier studies. Our dataset
includes over 100 observations and allows for greater confidence of statistical inferences.
Our data has a broader set of explanatory variables and allows zooming in on functional
accounting of costs. Remarkably, our analysis explains over 90 percent of variation in
administrative expenditures among the observations of our sample. We confirm some of
the hypotheses expressed in the earlier studies and present new evidence of driving factors
for costs.
Administration of mandatory social security programs is a complex operation. There are
significant systemic, institutional, and operational differences among the schemes.
Sometimes, the same agency operates multiple schemes that are very diverse in nature.
Often, one program can be managed by multiple agencies. We discuss several important
challenges in defining comparable cost measures and propose a set of guiding principles.
Availability and quality of the data is a major constraint as the data differs dramatically from
country to country and from institution to institution. There are significant heterogeneities
in how social security agencies report their operational and expenditure information. One
2
clear recommendation emerging from this work is a need to promote standardized
reporting of operating costs, including functional accounting wherever possible.
This paper consists of several parts. We first perform a structural analysis of costs and
review elements associated with various aspects of operations and their contribution to the
overall cost function. We also review several conventional cost indices, exposing
weaknesses associated with each type of cost normalization (including uses of gross
domestic product (GDP), revenues, expenditures, members, and so on). We present an
alternative index in which pension liabilities serve to normalize costs. We further discuss
common biases of normalization and summarize their impact on ten conventional cost
indices. Our key finding from this analysis is that the best normalization for comparative
analysis is achieved when using the number of members (or even better, beneficiaries only)
adjusted for the level of national income (for example, GDP per capita). With that measure,
we observed a group of countries with exceptionally high administrative expenditures.
Notably, from 21 institutions in Sub-Saharan Africa in our sample, 14 agencies were in this
outlier ategor . In all specifications of our regression analysis, this category was coded as
a separate qualitative variable and came out as highly significant.
Among the key findings for both structural and regression analyses is unequivocal evidence
that the variable cost of benefit management is much greater than the cost of contribution
collection. Mere recordkeeping of the contributors does not seem to affect the staffing
requirements or overall costs in a significant way. However, the operation of contribution
collection and provision of additional services does, implying the importance of fixed costs
over variable costs for that line of business. This may be due to the fact that agencies do not
really provide direct service to contributors and mostly interact with their employers, so
statistical association with the number of contributors is loose. On the other hand, the
number of beneficiaries alone explains over 80 percent of the variation in staffing levels
(and hence, significantly, the total costs). This has important implications for the debate
over the proper institutional home for the contribution collection function. For a well-
established internal collection function, the argument for outsourcing and consolidation
with tax collection is weak on the basis of cost reduction alone. It does not mean that other
potential systemic improvements could not be achieved by such reforms (for example,
reduced administrative burden, possibly improved compliance, or overall improvements in
economic efficiency due to reduced informality). Yet, where significant investments are
required to establish or modernize a collection function, both tax and social security
contributions systems could benefit from a well-coordinated effort.
On the benefit-management side, given significant variable costs, considerable economies
of scope may exist. This may argue in favor of consolidating various benefit programs under
a unified administration (for example, universal basic pensions and earnings-related
pensions, retirement benefits, and various short-term or other special benefits, especially
where these cover mostly the same groups of beneficiaries).
3
In our quantitative analysis, we develop three different regression specifications. The first
two are equivalent to cost normalization by members and by pension liabilities,
respectively. We investigate the effects of various elements of program design (such as
private versus public management, in-house collection, and special schemes) as well as the
level of economic development and the quality of institutions on current administrative
expenditures. These are the main observations:
In line with other studies, there are clear economies of scale (expressed either in terms
of scheme members or pension liabilities). Yet, there are dramatic differences in how
functions of managing services for beneficiaries versus contributors add to the overall
cost function. The difference can be up to one order of magnitude.
The evidence of cost differentials of in-house versus outsourced collection is weak. One
possible explanation is that the modes of organizing collection function vary
significantly, so capturing such a variation under one categorical variable constitutes a
measurement challenge. At the same time, managing special supplementary programs
and benefits (such as health, unemployment, and member loans) produces notable
increments in operating costs.
While the evidence of cost differentials between defined benefit (DB) and defined
contribution (DC) schemes (public or private) is weak, the results show the strong effect
of private management on the costs of pension plans. However, there are indications
that this effect may reflect differences in the maturity and coverage of the schemes and
thus fade in the longer term. We also observe that schemes that require the
management of financial assets (DB or DC) produce incremental costs, indicating
advanced complementary resources (both skills and systems).
The level of economic development has a strong impact on costs, suggesting that more
developed countries can manage pension schemes more efficiently, possibly taking
advantage of better technologies, infrastructure, and institutions. However, using the
Government Effectiveness Index, we find that as technologies spread over time they
may become less important in explaining cost differences, and what may ultimately
matter is the quality of governance. We further find that as economies develop and as
new technologies become available they lead to the substitution of capital for labor in
managing social security programs.
We then proceed to our third specification that is used for benchmarking operational
performance. That specification consists of two steps. The first step is to benchmark optimal
uses of labor resources in program operation. The second step is to benchmark levels of
current administrative expenditure. Notably, the spread between low and high estimates
for programs of the same size and same economic environment can be four-fold and is
driven by parameters of design and operation (for example, asset management function, in-
house collection, or operation of special supplementary schemes). This suggests that
inferences about the level of administrative expenditures should always be done keeping in
mind the institutional context for each program.
4
We also produce individual benchmarks (in both labor resources and overall operating
expenditures) for each of the programs in our sample considering the nature of their
operation and their institutional context. We estimate the degrees of deviation from the
individual benchmarks and develop three performance categories: categor A is for the programs that perform at or close to the benchmark; categor B is for the progra s that moderately deviate from the benchmark; and ategor C is for the programs where
performance coefficients are more than double the predicted levels. Most of the programs
i ategor C are suspects for operational inefficiencies, especially those in which the
benchmark coefficients are multiples of the predicted levels. Out of the 11 programs where
the expenditure-to-benchmark ratio exceeds 5, 8 are located in Sub-Saharan Africa. For
programs in Uganda, Kenya, and Ghana, the ratios are 26, 15, and 11, respectively. It may
be easy to overspend when operating significant surpluses, which all three happen to have,
but excessive administrative costs certainly cannot be sustained as schemes mature.
We conclude with remarks on the implications of data limitations, especially in the quality
of services provided by different programs. To properly interpret the results of comparative
cost studies, we point to the need to look beyond our results and use special operational
and beneficiary surveys to capture information on the performance and satisfaction of
various stakeholders with the administration of programs (including information on
processing times, compliance costs and various overheads, and overall perception of service
quality).
This study provides a framework for analyzing the operational efficiency of public social
security programs. It also helps guide complex organizational transformations that involve
the reallocation of resources between functions, adopting new technologies, developing
synergies between multiple agencies, and outsourcing. Decisions on optimal investments in
systems, processes, and people require clear understanding of the key factors that affect
the costs of operating schemes of various types and scope.
5
I. Introduction
Mandatory social security programs play an important role in the lives of millions of
individuals by securing a stable income over their lifespan. The administration of these
programs is a complex operation defined by their objectives and design. For example,
retirement income programs that provide only flat benefits will not require the extensive
machinery of contribution collection and recordkeeping. In contrast, earnings related
schemes, especially individual retirement savings, will require not only elaborate
mechanisms of contribution collection but also provisions for individual accounts and the
management of assets.
There is growing pressure to improve the efficiency of public social security programs and
constrain their costs. There also is significant interest in comparing the performance of
publicly and privately managed pension schemes. The objective of this study is to present a
framework for comparative analysis of operational efficiency for mandatory pension
programs and develop program-specific performance benchmarks.
A good illustration of efficiency improvements at work is an experience of continuous
administrative transformations within the Social Security Administration (SSA) of the United
States. Figure 1 shows how over a period of 20 years (from 1978 to 1998), the agency was
able to achieve significant unit cost reduction along with a 25 percent cut in the total staff in
the context of a 30 percent expansion of the beneficiary base over the same 20-year period.
The agency implemented a series of adjustments for technical efficiency and cost efficiency.
That is, the agency attempted to produce greater output with the same or reduced
resources and attempted to alter the combination of labor and capital in the pursuit of
further cost reductions by adopting new technologies. Other examples of similar efficiency
improvements include the Marshall Islands Social Security Administration and the Swaziland
National Provident Fund. These agencies recently implemented dramatic reforms, resulting
in efficiency gains and a 30 percent reduction in their staff.
6
Figure 1: U.S. SSA Staffing and Cost per Beneficiary (1978–1998)
Source: Author’s al ulatio s ased o data fro the U.S. SSA.
Note: Inflation adjusted.
Conceptually, there are three types of questions concerned with efficiency: (1) for a given
level of resources, is the output maximized (technical efficiency); (2) is the combination of
resources the most optimal (cost efficiency); and (3) does the output represent the optimal
product for the society (allocative efficiency or effectiveness)?
Several studies have attempted to measure the technical efficiency of public pension
programs (that is, if members are serviced in the most cost-efficient manner and if public
transfers operate at the optimal cost). Our study builds on that analysis but also attempts at
benchmarking cost efficiency in the utilization of labor resources for given types of
programs and technology.
The focus of allocative efficiency is on whether systems offer services that best fit the needs
of society. There are several categories of studies of this sort. Some discuss alternative
designs or organizational modes (for example, whether contribution collection or
management of assets should be centralized, with specific focus on identifying economies
of scale and scope). Others look at the tradeoffs in spending resources on improving
services for current beneficiaries versus expanding coverage of existing programs to new
members. Yet others investigate dynamic allocative efficiency in benchmarking the optimal
packages of services over time as economies develop.1
Finally, there are aspects of equity in the allocation of the total cost of operating social
security among different groups of plan members and general public. Those are studies that
1 For example, Robalino et al. (2008) and Palacios et al. (forthcoming).
$40
$45
$50
$55
65,000 70,000 75,000 80,000 85,000 90,000
Co
st p
er
be
ne
fici
ary
Total Staff of SSA
7
look at the incidence of costs across participants with different incomes, demographics, or
participation profiles.
The focus of quantitative analysis presented in this paper is on technical and cost efficiency.
Allocative efficiency or equity is outside the scope of our study (although we do develop
and suggest cost estimates that could guide the allocative efficiency analysis). The fact that
different countries adopted different types or mixes of products complicates the task of
comparative analysis as an efficient combination of resources for one type of program may
be suboptimal for another type of program. Therefore, a simple comparison of the costs of
widely differing types of schemes may be misleading. Additionally, more complex programs
allow for greater variation in the quality and types of services, so cost differentials should
not always imply inefficiencies. Even within the same class of programs, benchmarks of
operational efficiency are difficult to obtain. Box 1 raises questions on the consistency of
policy advice in the absence of such a framework.
Costs may vary over time within the same program or as the program undergoes systemic
changes. They also vary across countries often for the same program types even after
adjusting for the size of the schemes and other important factors. In this paper, we use
tools of quantitative analysis and data on administrative expenditures and operational
setup to develop a framework and assess the technical and cost efficiency of institutions in
charge of public retirement programs. High administrative expenditures may be a symptom
of inefficiencies in some systems, but in other systems, these high administrative
expenditures may simply indicate public choice for systems of more diverse and high-quality
services that come with high costs. Factors of quality are very difficult to quantify (see
discussion in section 7). Those include, for example, better accessibility and greater variety
Box 1. Reforms of the Croatian Pension Insurance Institute
The World Bank was involved with providing support to the government of Croatia
since the early stages of reform for its national pension system. In 2002, in the
do u e t Croatia: Pe sio “ ste I est e t Proje t, a World Bank team noted
that the [a]dministrative costs of the pension system in Croatia are unjustifiably
high, and reflect significant inefficiencies and overstaffing in the Pension Institute.
Currently, these costs run to 3.7 percent of total benefits, while 2 percent is a typical
share based on international experience. Reducing these costs to regular levels
ould sa e . per e t of GDP a uall , ithout sig ifi a t loss of effe ti e ess.
However, by 2006, in a follow-up do u e t e titled Proje t Paper on Restructuring
Pe sio “ ste I est e t Proje t for the Repu li of Croatia, a e tea recommended that [ ]y the end of 2007 CIPI should reduce its administrative costs
from 1.8 to 1.2 percent of pension expenditures and improve its productivity by 20– per e t. The reader should note that while over the five-year period the cost
index did drop below the originally suggested benchmark, the suggested additional
30 percent reduction lacks sufficient justification. While the agency was undergoing
some structural changes around that time, it is exactly this lack of a consistent
quantitative framework that produces such ambiguity in defining a reference point.
8
of services, more competent staff and higher responsiveness of administration, greater
individual choice and more transparent systems, and effective enforcement and reduced
fraud. Better and more complex services require investments in systems and people.
For example, a substantial part of the debate around reform of Social Security in the United
States has been focused on the types of services, efficiency, and costs of the reformed
administration. A lot of that discussion is around benchmarking and costing of different
bundles of services compared to the current set provided by the SSA.2 We provide a
summary of one of such studies in box 2.
At the same time, there are some fundamental institutional differences across countries
that may create cost differentials for similar types of programs. As James et al. (2001)
indicate with reference to experiences of setting up individual account systems, [p]robably
the least-cost alternatives and trade-offs are available for industrialized rather than for
developing countries. Industrialized countries have access to existing financial institutions,
lo er tradi g osts, passi e i est e t opportu ities, a d ore effe ti e go er a e. […] In developing and transitional countries, particularly those with small contribution and
assets bases, investment costs are likely to be higher and the opportunities for reducing
fees lo er. In fact, we confirm this statement in our analysis and show that for less
developed countries, a substantial institutional cost premium may be unavoidable. Hence,
high costs may not represent a problem in itself but rather point in the direction of further
inquiries on a case-by-case basis.
Important factors responsible for cost differences are scheme coverage, benefit generosity,
maturity of the program, and others. We discuss all of them and their effects in the context
of the data available for this analysis. We have collected data on operational organization
and various components of administrative expenditures for over 100 public programs
internationally (see annexes 1 and 2 for details), which is the largest sample among similar
studies to date. Our objective is to standardize presentation of administrative expenditures,
ensuring consistency in comparing schemes of different types and capturing some obvious
deviations from the expected performance of the various programs as projected by our
simulations. We also are interested in the composition of total expenditures and cost
components associated with various functions. While analysis is perhaps less conclusive
here, given the differences in cost accounting and gaps in information, it is instructive in
terms of the magnitude of various factors as they enter the total cost function. The analysis
also suggests considerable scope for standardization of accounting and reporting of costs
across these institutions.
2 See Genetski (1999) for a review of a possible decentralized model versus Hart et al. (2001) for options for a
centralized system.
9
The remainder of the paper is organized as follows. First, we present the genesis of the
inquiries into the subject, reviewing some of the most relevant literature on the
administrative expenditures and costs of mandatory programs produced over the past two
decades. Our primary objective is to review the methodologies used, although each paper
comes with a rich set of findings and recommendations. We then present our set of
administrative data on the public programs and develop a number of standardized cost
indices, discussing their advantages and limitations. We also discuss various major cost
components and their shares in total costs. Finally, we proceed with simulations on the
basis of our data and develop three different models. The first model looks at the impact of
design of a program on its total costs. Our second group of model specifications assesses
differences in costs of managing pension liabilities between the public and private
mandatory pension schemes. Finally, on the basis of the third model we generate
benchmarks for staffing levels and for the total administrative expenditures. We compare
those to the actual indicators and develop standard performance ratios, providing insights
into the differences in the performance of various programs.
II. Formulating the Research Question
The first notable generation of comprehensive research inquiries into the subject of
administrative expenditures and efficiency of mandatory pension programs internationally
was produced in the early-to-mid 1990s. The focus was primarily on exploring
administrative inefficiencies and on benchmarking operational performance by comparing
expenditures of the public and private pension plans with centralized versus decentralized
modes of organization. Two types of approaches emerged: one in which cost indices were
constructed on the basis of recurrent program expenditures or revenues and another in
which costs were measured as applied to individual members (either as one-off charges or
as cumulative costs over the period of plan participation). The latter measure emphasized
the incidence aspect, bringing analysis from the macro-level down to micro-level.
Box 2. Cost Analysis of Reform Options for the U.S. Social Security Program
In a discussion of the possible centralized organization of the Individual Accounts (IA)
of the reformed Social Security, Hart et al. (2001) look at two hypothetical models
with basic and advanced levels of services. The higher-service program is intended to
represent an IA program that would provide participants with as many features and
services as those offered today by leading private providers of financial services and
by employers who offer defined contribution plans, such as a 401(k). It, therefore,
would require more extensive new information systems and processes. The authors
note that for additional functions, the SSA would require an estimated 7,000 to
33,000 additional employees under the basic- and higher-service IA examples,
respectively. The range of additional operational costs is defined between US$0.7
and US$3 billion (or the equivalent of an additional US$3 to US$15 per member,
including both covered employees and active beneficiaries). These are significant
increases compared to the current mode of operation, and hence, such cost analysis
clearly cannot be ignored in the process of reform discussions.
10
One of the most comprehensive early studies that adopted macro-methodology is found in
Mitchell et al. (1993). Their sample of the costs of managing national social security
systems, including retirement programs, dates back to 1986 and includes 25 countries of
Latin America and the Caribbean and 24 countries of the Organisation for Economic Co-
operation and Development (OECD). The costs were defined quite broadly as expenditures
borne by the state to provide certain inputs in exchange for services of the social security
system. They did not differentiate between particular types of retirement programs or cost
incidences in different schemes. To explain cross-country differences, the cost function
included the explanatory variables of technological and infrastructural characteristics,
program organization, and the level of national income as a proxy for input prices. The
studies show that administrative expenditures of social security systems exhibit
considerable economies of scale and cannot be explained simply by technological
differences in the production of such services across countries.
James and Palacios (1995) point to fundamental difficulties in measuring and comparing the
administrative costs of mandatory systems. They explain some of the differences by quality
differentials, subsidized operation, and the non risk-taking nature of the public sector
provisions, concluding that pu li l a aged old-age programs tend to understate their
true administrative costs and overstate their efficiency relative to privately managed plans.
They also indicate biases of simplified cost ratios, particularly evident in immature or small
and poor systems (which we discuss in greater detail below). They propose a measure that
would better reflect various internal and external factors that affect costs as administrative
cost per member over income per capita, although recognizing that it is only a crude
adjustment for the higher input prices and the higher-quality services. This study perhaps
also is the first effort to compare individual systems to their corresponding benchmarks.
Statistical analysis on the basis of a sample of 50 countries indicated that national schemes
in Austria, Chile, Finland, and Kuwait cost more to administer than predicted by the model,
while schemes in Canada, Denmark, and Mauritius that feature universal flat benefits cost
less than predicted.3
Bebczuk and Musalem (2008) also made an attempt at benchmarking operational efficiency
of public pension programs, although on the basis of simple cost ratios. They find that the
group with the most inefficient programs includes Belize, Sierra Leone, Fiji, Tanzania,
Philippines, Costa Rica, and South Africa. They exhibit a ratio of operating expenses to gross
income between 24 percent and 5.5 percent. Their intermediate group includes Jersey, New
Zealand, Ghana, Egypt, France, the United States, Japan, and Ireland. The ratio of operating
expenses to gross income here ranges between 3.3 percent and 1 percent. Finally, they find
that the most efficient countries are Denmark, Sweden, Ecuador, Guatemala, Singapore,
3 Our analysis that follows confirms these findings for Canada, Denmark, and Mauritius while also indicating
that Finland operates close to its benchmark. For corresponding programs in Australia, Chile, and Kuwait we
did not have data.
11
Korea, Finland, Sri Lanka, and Malaysia, where the operating expense ratio is below 1
percent of gross income. These findings should be interpreted with care given serious
limitations in the simple cost indices. We further discuss biases of these measures.
Valdes-Prieto (1994) zooms in on four national programs and compares the costs of public
and privately operated schemes in Chile, the United States (including both public and
voluntary private schemes), Malaysia, and Zambia, focusing specifically on different types of
services offered by each system. This is one of the earliest studies that adopted a micro-
approach that captured and converted all lifetime member costs (before or after
retirement) to the equivalent charge ratios and subsequently to the annual absolute cost
per member. It also is one of the few studies that explicitly accounted for the costs incurred
to the beneficiary after retirement.4
By the late 1990s, in the United States, the discussions of privatization of the U.S. Social
Security program were at their height, generating a significant body of literature on the
organization and costs of various alternative provisions. Diamond (1998) was one of the
most influential studies on the topic, where issues on the cost measurement of privately
managed pension plans were summarized. Around the same time, the interest in comparing
the operations of publicly and privately managed programs intensified. This occurred as
reforms of public pension schemes unfolded in a number of countries with the shifting
mandate for retirement income provisions from the public to private sector. Systemic
reforms resulted in significant changes in the machinery of administration with partial or full
privatization. Other countries were closely watching and contemplating similar reforms.5
This prompted the second generation of studies of the costs of mandatory pension plans.
However, the focus this time noticeably shifted toward privately managed schemes and to
the cost incidence with analysis of the effects of various charges levied on plan members.6
Much of the literature on the subject was generated from Latin America and transition
economies of the former socialist block. The new schemes were fully funded and resulted in
4 Using the early experiences of the Chilean insurance industry providing annuity products, the study shows
that under reasonable assumptions up to 50 percent of all the costs of individual pensions could be incurred
after becoming a pensioner. Since the time the study was published, however, the insurance industry has
significantly evolved, premiums have substantially decreased, and products have grown in diversity.
Mackenzie (2002), for example, indicates that today most annuitants in many OECD countries can expect to be
subject to costs between 5 and 10 percent for converting lump sums into an annuity. Furthermore, Rocha and
Thorburn (2006) conclude that today Chilean annuitants have a deal that is even better than annuitants in
other countries, which is in part explained by the large supply of indexed instruments in Chile. 5 Where no national mandatory schemes existed or where coverage was limited, the focus of discussions was
often on reforms of the civil service pension programs as well as sustainable and cost-effective initiatives of
e pa sio of the o erage to e populatio groups, like i I dia, for e a ple see The Proje t OA“I“ Report. Submitted by the Expert Committee for Devising a Pension System for India. January 2000). 6 Valdes-Prieto (1994) suggested several reasons for differences in costs and charges; for example, due to
implicit subsidies of publicly managed programs and profit margins and indirect taxes by private pension
providers.
12
accumulation of assets under the management of private providers. Typically, under such
settings, operations are no longer subsidized and costs get passed on to members in the
form of implicit or explicit charges. The charges were of different types and applied at
different times throughout the accumulation and payment phases. When combined and
compounded over time, such charges can consume a significant part of future benefits.
Hence, the authorities recognized the need to analyze and regulate costs to protect plan
members against excessive charges.
In line with Valdes-Prieto (1994), a number of studies began to emphasize a lifetime
approach to measuring the cost incidence of charges in both voluntary and mandatory
pension schemes. For example, Murthi et al. (1999) indicate that various fees accumulated
over a lifetime could consume over 40 percent of individual pension account value in the
U.K. Mitchell (1999) produced standardized presentation of costs with simulations on the
basis of data from the new mandatory pension program in Mexico and found that
depending on the assumptions, over the long term, on average between 30 to 40 percent of
contributions could go to fund commissions. (While not significantly different from
aggregate commission loads in neighboring countries that introduced similar reforms, the
author discusses several factors that still would work both to increase and to reduce
charges in the new Mexican system in the medium-to-long run).
Given the variety of types and rates of charges, such analysis requires standardization.
Building on Diamond (1998) and reflecting on the experiences of private pension industries
in OECD countries and approaches found in other studies, Whitehouse (2000) presents a
formal framework of interrelations between various measures of charges, including those
with equivalent effects on contributions (reduction in premium), on earnings (reduction in
yield), and on resulting accumulations (charge ratio).7 These indices provide for aggregation
of various types of charges over the lifetime of plan members and allow for consistent
comparison of fees across different plans, bringing multiple forms of charges to a set of
comparable indicators.8 However, as box 3 indicates, policy implications of these ratios in
the multi-pillar program context are not straightforward.
With that framework, Dobronogov and Murthi (2005) surveyed early experiences of
reforms in Poland, Kazakhstan, Croatia, and Hungary. On the basis of available data, they
observed that over a i di idual’s lifetime, the charges could result in an average of 1
percent reduction in yield or 19 percent reduction in assets of mandatory programs. Their
study also made an attempt at functional accounting of costs and investigated connections
between the charges and actual costs. While observing deficiencies in reporting of costs by
7 As one particularly useful observation, the analysis suggested a rule of thumb that under reasonable
assumptions and continuous plan participation over the long-term, a 1 percent charge against pension assets
is equivalent to a 20 percent charge on contributions. 8 A related challenge is the disclosure of fees to plan members. For a comprehensive discussion of the issues
and a review of the situation in selected OECD countries, see Turneri and Witte (2008).
13
pension providers, their simulations suggest that for private individual accounts to be
viable, they should be funded by a minimum contribution of 4–6 percent of wages. This is
the only benchmark of that kind of which we are aware. In many countries, however, the
rates of supplementary mandatory or voluntary pension contribution remain quite low,
which raises questions regarding the financial viability of such policies.
Palacios (2005) also attempts to determine the relationship between costs and charges and
finds a high correlation between the two. Furthermore, the author uses a sample of 49
pension fund managers from 8 countries of Latin America and finds significant economies of
scale (on both the cost per contributor and per affiliate basis). He discusses the implications
of an alternative organization of systems by centralizing the management of funds and
critiques that approach.
Corvera et al. (2006) investigate the performance of the pension industry in Latin America
and apply the same methodology to 67 pension managers in 10 countries that operate
mandatory private pension programs in the region. They find significant dispersion of
charges both across and within the countries and produce a ranking of providers according
to the equivalent lifetime cost effect on members. While some of the cross-country
variations can be explained by differences in the services provided, the limited competition
as well as the presence of state-owned managers is blamed for the differences within a
country.
Tapia and Yermo (2008) adopted a simplified methodology to compare the effects of
various fees and charges. Their indicator of equivalent annual reduction in yield is the sum
of all annual charges in U.S. dollar terms divided by total assets, without any extrapolation.9
The small size of their sample (18 countries) did not allow for a regression analysis of
various factors; hence, they looked at some of the key factors independent of each other.
As a result, the discussion remains largely inconclusive. Apart from an evident relationship
between the adopted measure and the maturity of the systems,10 the results did not
provide clear evidence of the impact of size or concentration of the industry on charges.
However, they point to the potential effects of those factors along with the nature of the
collection system, composition of the investment portfolio, and others.
9 Diamond (2011) uses these estimates to debate alternative modes of institutional organization of the
national DC pension plans and implications for costs to members, arguing for a centralized system with
wholesale interactions with fund managers. 10
Results suggest two different groups of countries: a set of countries from Latin America and a set of
countries from Central and Eastern Europe that are more recent reformers.
14
Box 3. Cost Incidence and Multi-pillar Pension Programs
Application of indices proposed in Whitehouse (2000) is quite common, including in the
context where new DC schemes have recently been introduced on top of legacy DB
programs. Such indices are often used to scrutinize the cost efficiency of new private
pension schemes compared to legacy plans. One serious limitation of such measures and
of all similar indices, however, is that they do not capture differences in generosity of
various plans (see discussion in section 5). Thus, for example, a low charge against high
contribution (relative to wages a eat up ore of a i di idual’s resour es o pared to a high charge against a lower contribution rate. An individual will simply pay more in
absolute dollar terms to administrators for the former plan. We used sample data from
21 countries in Gómez Hernández and Stewart (2008) to calculate and confirm high
negative correlation between the 40-year charge ratios and corresponding contribution
rates. So, small DC schemes often will be seen as expensive. Yet, this does not necessarily
imply their inefficiencies if put in the context of multi-pillar programs.
Let us consider three alternative scenarios of a multi-pillar program in figure B3. Each
case is presented by an average absolute contribution amount to the legacy DB
component I (C-I) and new DC component II (C-II). The grey areas are average costs per
member in absolute terms. Case A represents the larger DC component, while cases B
and C have a smaller DC component. Keeping average costs per member constant, simply
by the design of the scheme and not any other intrinsic differences, plan B will show a
less favorable value for the reduction in premium for the DC component. However,
comparison between cases A and B would be incomplete without incorporating
administrative costs of the legacy DB component. When it is added, the result indicates
that in both cases the individual pays the same for the overall program administration.
So, purely from a cost perspective, it is hard to argue for either option. In plan C, the
legacy plan has quite high costs per member. While shares of the costs may be very
similar in both the legacy DB and new DC plans, the argument from the cost perspective
may well be for further expansion of a more efficient DC component.
Figure B3: Contribution Space Within Multi-pillar Pension Programs
Source: Author’s desig .
A B C
C-I cost
C-II cost
C-I
co
ntr
ibu
tio
ns
C-I
I
co
ntr
ibu
tio
ns
15
One immediate implication from that body of research is a need for a more detailed
analysis of the impact of organization of various systems on their administrative costs. Some
studies specifically focus on assessing the advantages of consolidated collection of pension
contributions with other social insurance contributions and income taxes.11 A report funded
by the International Federation of Pension Fund Administrators (FIAP) (2006) surveyed the
processes and costs of collecting pension contributions in 11 countries that implemented
mandatory defined contribution pension schemes, comparing them in the context of
multiple organizational modes of the collection.12 If anything, the study reflects the complex
nature of cost definition and measurement, given the multiplicity of agencies associated
with the collection function and varying incidence of costs.
From a simplified binary framework adopted by FIAP’s report, Anusic’s (2005) work is a
serious improvement in terms of organizing the knowledge about the collection function
and assessing its cost impact.13 In response to inquiries into the efficiencies of unifying tax
and social insurance contribution collection, the study emphasizes a continuum of options.
It defines five specific modes of administration depending on the responsibilities of
different agencies over managing the flows of money and information on social insurance
contributions. It also captures operational costs of institutions that are directly responsible
for social insurance as well as the cost of functions performed by other agencies for social
insurance institutions. The author uses social insurance revenues, total social insurance
expenditures, and GDP as denominators to construct simple cost indices for a set of over 30
European countries.14
There also have been a number of country-specific studies of administrative performance
and efficiency of the national mandatory social insurance programs.15 We provide a
summary of literature on the cross-country studies and select country-focused studies in
table 1.
11
See for example, Barrand et al. (2004). 12
This study follows a framework defined in Demarco and Rofman (1999). 13
For a more detailed analysis of the contribution collection process, see Fultz and Stanovnik (2004). 14
Interestingly, the study finds no clear association between the mode of organization of the collection
function and administrative costs. Furthermore, the study does not confirm the hypothesis that a
consolidation of social insurance administrations implies lower administrative costs, suggesting considerable
lags in administrative savings as a result of such reforms, possibly due to political factors that slow down the
reform process. There are important dynamic problems, however, in measuring the cost impact of collection
integration overtime. First, the denominator changes over time (as a result of compliance improvements).
Second, moving functions from one organization to another implies a need for aggregation of costs pre- and
post- impact from all agencies involved in such a transfer. 15
See, for example, Chlo o Pola d a d Gru išić a d Nuši o ić o Croatia. Yoo (2002) applied
a very innovative approach to the Korean Public Pension Schemes with the uses of the stochastic cost frontier
function model and a decade worth of panel data from the national pension agencies. The major observation
is that, on average, the Korean system could produce the same outputs at half of the current costs, suggesting
reforms with restructuring and management innovations, including operational integration of public pension
schemes.
16
Table 1: Summary of Literature
Publications
(in chronological order)
Number of
Countries/Region
Type of Indices
Mitchell et al. (1993) 49/OECD & LAC AE over GDP
Valdes-Prieto (1994) 4/World Lifetime charges per member
James and Palacios (1995) 50/World AE per member (over national income)
Mitchell (1999) Mexico Lifetime charges per member
Murthi et al. (1999) U.K. Lifetime charges per member
Whitehouse (2000) 13/World Multiple indicators
Hart et al. (2001) U.S. AE per member
James et al. (2001) LAC & U.S. Multiple indicators
Szilágyi (2004) 8/ECA & LAC Annual charges per member
Anusic (2005) 30/Europe AE over expenditures, revenues, GDP
Dobronogov and Murthi (2005) 4/ECA Lifetime charges per member
Palacios (2005) 8/LAC AE and charges per member
Corvera et al. (2006) 10/LAC Lifetime charges per member
FIAP (2006) 11/World Collection costs over contributions
Chłoń-Do iń zak et al. (2007) n.a./World Multiple indicators
Bebczuk and Musalem (2008) 24/World AE over expenditures
Gómez, Hernández, and Stewart (2008) 21/World Lifetime charges per member
Tapia and Yermo (2008) 18/World Annual charges per member
Note: ECA = Europe and Central Asia region; LAC = Latin America and Caribbean region; AE = Administrative Expenditures
(current annual).
Finally, a significant body of literature exists on the operations of occupational and
voluntary plans.16 Research covers the span of issues from market organization of the sector
to benchmarking the performance of specific plans. A Toronto-based group CEM
Benchmarking Incorporated specializes in benchmarking the cost and performance of
investments and the administration of pension funds, foundations, and sovereign wealth
funds. It maintains one of the most extensive global databases for the private sector and
uses data to generate analysis and research with performance comparisons and insights
into best practices.17 These types of studies are quite relevant to public sector programs, as
the private sector is an excellent incubator for ideas and cost-optimizing solutions. Such
16
For example, some earlier studies by Caswell (1976) and Mitchell and Andrews (1981) provide evidence of
economies of scale and explore the effects of other factors on the administrative costs of private pension
plans. 17
Bauer et al. (2010) use that database (with observations from 463 DB and 248 DC funds over a period
beginning 1990) to study the performance of the U.S. pension industry. One interesting conclusion of that
study is that the DB plans may be better cost watchers compared to the DC plans, given incentives. An earlier
study on the basis of the same database by Lum (2006) indicates that adjusting for asset mix, size, and style,
Ca adia fu ds are the orld’s lo est ost fu ds.
17
analysis generally provides direction on more efficient modes of organization for public
sector programs, especially those of small size or narrow statutory coverage.
In closing this section, we again need to emphasize that it always is important to
differentiate the objectives of various cost studies and the questions that they help to
answer. While some studies help to access the effectiveness of a particular program, they
may not be very helpful in addressing the issue of efficiency. For example, using simple cost
indices may show that a program spends as much on benefits as on administration, which
may raise questions on the effectiveness of that particular program; at the same time, it
may operate efficiently from a technical perspective, that is, producing services or outputs
with the most efficient use of available resources. Alternatively, some mature programs
with broad coverage and generous benefits may look impressive in terms of the relative
share of expenditures that go into administration; however, such programs may be
overstaffed or overspending on other inputs.18
III. Scope of Analysis
Cost esti ati g is […] diffi ult i the est of ir u sta es. It requires both science and judgment. And, since
answers are seldom—if ever—pre ise, the goal is to fi d a reaso a le a s er. (U.S. Government
Accountability Office [GAO], 2007)
The incidence of costs of public pension programs will vary across countries. Some will be
directly or indirectly borne by the members of the program (active or inactive), while others
will be addressed with the general budget. Figure 2 indicates that the cost of running a
public pension program can constitute a substantive share of the covered wage bill, with
the median at 1 percent for a sample of 70 country observations. On the high end of the
spectrum, there are CNSS of Burkina Faso, NSSF of Kenya, GIPF of Namibia, NASSIT of Sierra
Leone, POPF of Botswana, SSNIT of Ghana, SNPF of Swaziland, and NPF of the Solomon
Islands—all with operational costs above 3 percent of the covered wage19.
18
James and Palacios (1995) find that Indonesia and Kenya spend 30 and 72 percent of contributions,
respectively, on administrative costs, which is more than they pay out in benefits. At the same time, Japan and
the United States spend 1 percent or less of benefits and contributions on operating expenses. Their statistical
analysis, however, indicates that both sets of countries are spending approximately what would be expected,
given their per capita incomes and the numbers of covered workers and pensioners. So they note that those
ratios raise questions about the overall wisdom of starting old-age security programs in small, poor countries,
but they do not tell much about the internal efficiency of those schemes. 19
See annex 1 for abbreviations.
18
Figure 2: Administrative Costs as Share of the Imputed Covered Wage
Source: Author’s al ulatio s.
Note: Data labels presented only for select countries. Agency abbreviations listed in annex 1.
Ultimately, whether or not subsidized from public funds, most of the costs will be shared
among economically active individuals.
There are issues of whether such total cost allocations are regressive and whether they
represent the most dynamically equitable outcomes. We do not directly deal with those
issues. Keeping that in mind and unless we explicitly mention it, we refer to the costs in this
paper as total expenditures associated with operating a public pension program. Clarity is
required, however, on what needs to be captured in the cost measure. In what follows, we
discuss several important challenges and propose a set of guiding principles. We do admit,
however, that in many cases the ultimate and accurate measures will be impossible to
obtain and the best we can do is rely on approximations.
There are significant systemic, institutional, and operational differences in how public
pension programs are designed and managed. Often the same agency operates multiple
programs or the administration of one particular program can be distributed across multiple
agencies. We, therefore, identify three approaches to constructing the cost measures:
Programmatic. With the programmatic approach, the focus is on the overall
administration of one particular scheme. So, when administration functions are
shared among multiple institutions, all such related expenses get captured, and
when one agency operates multiple programs, the costs of other programs get
excluded.
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
19
Institutional. Under the institutional approach, the measure is constructed around
one institution that manages one or multiple programs (or parts of such programs).
Functional. The functional approach, in principle, allows for an across-the-border
comparison of different institutional or operational elements but requires diving into
the intricate details of functional organization of each program.20 Anusic (2005) is an
example of a functional approach. Such an approach also is useful as it allows for a
broader analytical context of the comparison exercise for pension programs, for
example, by bringing in the collection side of tax administration or the benefit side
of various mass payment systems.
There are several expense categories of administrative systems that should be considered.
All administrations have to bear regular operational expenses in the form of labor cost,
office maintenance, supplies, utilities, and so on. In addition, they may incur significant
capital expenditures. They sometimes bear expenses that are not directly related to the
core benefit administration (for example, the SSA in the United States provides certain tax
processing services to the Internal Revenue Service [IRS]; similarly, some public pension
agencies have corporate mandates imposed on them by the state to manage certain
publicly owned businesses or assets, for example, state-owned recreation facilities). Some
in-kind benefits, such as rehabilitation services, can arguably be treated as both benefits
and costs. Other expenses are never incurred directly and come in the form of implicit
subsidies (for example, use of public assets such as office premises or other infrastructure)
or as opportunity costs (when office buildings form part of the pension assets under the
real-estate investment schemes for pension reserves). There also are expenses not borne by
the public administration but incurred by the participants (for example, in the form of bank
charges for contribution remittances or benefit payments).
Whether it is a programmatic, institutional, or functional approach, all direct and indirect
current operational expenses related to the program administration should ideally be
included in constructing a consistent cost measure. Capital expenditures should ideally be
averaged out (or amortized) over several years; alternatively, they could be completely
excluded (which is the approach that we follow in our comparative analysis). Operational
expenses of various unrelated functions (for example, rehabilitation services) should be
excluded. We also recommend excluding all expenses for providing in-kind services (on both
benefits and costs).
Formal or informal costs—external to the program administration—are conceptually of
three types. First, bank charges and the like, while easier to capture or estimate, are often a
function of the overall efficiency of the national infrastructure and hence, are outside the
control of the pension administration. It would be ideal, therefore, to assess the efficiency
20
Proper functional accounting of costs in public plans is very rare. The only cases we are aware of include the
United States, Canada, and New Zealand.
20
of the services provided by the financial intermediaries separately and exclude the
associated costs from the cross-country comparison. However, this is not always possible,
and in most cases, such costs remain part of the total expenditures presented in this
analysis. We further discuss the magnitude of these costs.
Second, there often are numerous informal costs borne by the program members (for
example, fee for benefit claim facilitation service or postman fees). Informal fees are
interesting because they compensate for services that are otherwise inadequate or
unavailable and hence, arguably constitute an extension of conventional administration.
From this perspective, such facilitation fees transform conventional administration from a
low-cost or low-services scheme to a high-cost or high-services program, although altering
the incidence of cost in a less transparent manner. When there is a void in some areas of
formal administration, it gets quickly filled with alternative provisions. There are plenty of
examples of such arrangements. Experience shows that often a whole formal or informal
industry could emerge and fill the void of services, such as increased awareness or
understanding of scheme rules, contribution collection services,21 benefit facilitation,
complaints processing, and legal representation.22 All those direct costs to beneficiaries
often remain unaccounted. In the end, low-quality formal services often translate into
additional transaction costs for the beneficiaries.23 However, as long as we treat all service
altering arrangements as different and discrete packages, the issues of associated cost
differences pertain to allocative efficiency and equity and are not the focus of our analysis.
Box 4 discusses some other approaches to optimizing benefit packages from a cost
perspective.
21
In Chile, in order to perform electronic collection and support the payment of social security contributions
through the Internet, the pension fund administrators (AFPs) created Previred, an agency that has captured
the large companies market (FIAP 2006). The portal allows those who employ workers, both businesses and
home-helpers, and self-employed workers to make their monthly payments in an easy and secure manner.
The service is free for employers. 22
In the United States, the National Organization of Social Security Claimants' Representatives is an
association of over 4,000 attorneys and other advocates who represent social security and supplemental
security income claimants. The members provide representation services for claimants (for example,
advocating change in the disability determination and settlement process). 23
See discussion in Palacios (forthcoming).
21
Box 4. Limiting Program Coverage as a Cost-Controlling Strategy
There are important tradeoffs between coverage, the quality of services, and costs.
Palacios and Pallarès-Miralles (2000) indicate that coverage is a function of the level
of national income. This can be explained by several factors, including infrastructure
and technological constraints. The costs associated with reaching out to individuals
employed in less formal jobs or living in more remote communities could be
excessive. Thus, limiting coverage of mandatory contributory programs often is an
efficient mechanism of cost controls. Mandate often is defined on a sectoral basis
with some professions or industries excluded due to the anticipated high compliance
and enforcement costs.* Alternatively, some sectors set limits on minimum earnings,
so that contributions would not be required from individuals with very low incomes,
often providing services informally. Finally, there often are minimum participatory
requirements for firms in terms of their size. As countries develop, they reduce these
floors and accommodate greater number of beneficiaries at a lower cost.
Therefore, holding everything else constant, it will be inappropriate to compare the
costs of delivering social security, say in India, with its current 10 percent coverage
of formal contributory schemes and with a hypothetic 40 percent coverage by the
same schemes. Those are two different products.
Note: *In the United States, participation in social security for various groups of
workers was mandated only gradually over time. It was not until 15 years after the
introduction of the social security system that the non-farming self-employed could
join the system; the farming self-employed joined after an additional 4 years.
Third, there are conventional costs of compliance with a contributory mandate in the form
of time and effort of employers spent on keeping records, preparing and filing returns,
making payments, preparing for audits, getting trained in new rules and procedures,
following up on various inquiries, and so on. There is a separate body of literature on
organization and costs of compliance with revenue collection (usually in the context of tax
payments). Contribution collection can be organized in many different ways. For example in
Egypt, employers send information on wage changes only once a year and to a single
agency, while in Chile, employers send detailed reports to multiple providers each month.
This implies differences in burden in different countries. Furthermore, if the collection of
data is not synchronized between various components of the mandatory program, it further
increases compliance costs. Slemrod and Yitzhaki (2002) based on their literature review for
the United States and a group of OECD countries o lude that [i] al ost all ases the private compliance costs dwarf the public ad i istrati e osts of olle ti g ta es. Thus, by
minimizing public administration costs (that is, by not providing adequate services of
counseling or electronic submission), the costs are simply pushed out to the domain of
22
nontransparent compliance costs perhaps producing a heavier burden for smaller
businesses and the self-employed than for medium-sized and large firms.24
In closing this section, it is interesting to note that different collection and recordkeeping
models may not necessarily produce differences in administrative expenditures but rather
differences in the distribution of resources in the cycle of benefit administration. Some
countries still have quite a weak collection and recordkeeping systems that do not even
provide for centralized or electronic facilities and rather shift the burden of recordkeeping
on employers who would have to support the employee claim of pension rights at the time
of retirement with valid records of earnings and contributions. This implies pushing costs to
the end of the administrative cycle of the program, that is, putting fewer resources in the
collection effort and more resources in claims verification and processing. So, the structure
of cost could be revealing of the maturity of the administrative systems. Arguably, the
legacy contributory schemes will have more resources in benefits processing, while more
advanced systems likely will spend more effort on contribution collection and in-house
record maintenance. As systems mature administratively, they will be strengthening the
collection side. The trend further is reinforced when new DC schemes get introduced with
their significant upfront data quality requirements.
IV. Our Data and Structure of Costs
Availability and quality of data differs dramatically from country to country and from
institution to institution. There are significant heterogeneities in how pension agencies
report their budgets and operational information. The truth is that those reports were
never meant to be standardized across countries. In collecting data for this research, we
adopt a set of standard definitions of cost categories. However, reporting norms and
practices often do not conform to our definitions. For example, we are not always able to
differentiate between labor and non-labor costs or segregate capital expenditures from the
current operating expenses. In a good number of cases, however, we are able to obtain
those details, which allow us to assess potential biases in cases where such breakdown is
not possible.
We collected data from over 100 public social security programs around the world (see
annexes 1 and 2 for data description). Those programs vary in size. The smallest in our
sample is the Falkland Islands Pension Scheme with 600 contributing members. The largest
scheme is the Old-Age, Survivors, and Disability Insurance (OASDI) program in the United
States that covers over 160 million active contributors and some 50 million beneficiaries.
The nature of the operation and institutional organization of the programs in our sample
24
As a cost-control measure in such circumstances, various simplified contributory regimes could be a good
alternative where compliance and collection costs are unreasonably high, if such are seen as a major deterrent
to coverage expansion. Again, in our study, we view these issues as pertaining to allocative efficiency and
simply control for them in our model without analyzing them in detail.
23
also vary. We were able to obtain separate data on at least 10 noncontributory pension
schemes. On the other end of the spectrum, there are a significant number of social security
institutions that offer a broad range of benefits, including maternity, child allowance,
unemployment, sickness insurance, and others.
We used a combination of methods to obtain data. In most cases, the information was
obtained directly from annual statements and reports of the social security agencies or
other offi ial statisti s a aila le o the age ies’ e portals. We also obtained data from
around 25 agencies using a detailed questionnaire. Our objective was to collect information
on each agency as a whole with all its multiple programs. In some cases, we were able to
obtain data on separate programs or on institutions operating at the subnational levels (for
separate states, autonomous regions, or overseas territories), which increased the number
of observations for our analysis. The data includes the nature of the schemes operated by
each agency, coverage in terms of the contributors and beneficiaries, financial flows,
accumulated assets, staffing and number of offices, and the level and details of
administrative expenditures.
Our main data set is presented in annex 1 and it includes both raw data expressed in the
national currencies and various indices constructed on the basis of that data (which we
discuss in the following section).
To make our data set consistent, we report data on labor resources and administrative costs
related to the associated functions. Thus, where certain functions of an agency were
excluded from the analysis, we had to prorate both staffing numbers and cost allocations
associated with such functions (as we did, for example, with Medicare-related and
reimbursable services provided by the SSA in the United States). Conversely, if we combined
functions performed by various agencies under one observation pertaining to a particular
program, we combined both labor resources and total costs (like in the case of the Sri
Lankan Employees’ Provident Fund that uses collection and other services provided by the
Department of Labor).
4. 1. Institutional organization and total expenditures
To better visualize our data, we developed a typology of the administration of public
schemes reflecting varying degrees of institutional complexity, assigning higher ranks to
more complex organizational structures, as summarized in table 2.
24
Table 2: Classification of the Public Social Security Administration
Agency
Rank Benefit Programs Managed by Social Security Agencies
No. of Observations
in Our Sample
1 Plain (universal) Basic Pension (BP) 3
2
Means tested BP and/or disability BP, or earnings related DB
schemes with no in-house service records (for example, some civil
service schemes)
9
3 Earnings related DB/DC/Provident Fund schemes (possibly with an
associated small non-pension scheme, like housing loans) 49
4 Multiple in-house pension schemes and/or additional extensive
non-pension schemes (assistance, health, etc.) 58
Source: Author’s desig .
Figure 3 shows median levels of administrative expenditure for each type of program in our
sample. We normalized expenditures by the total members25 and by beneficiaries only. We
further adjusted expenditures by the ratio of national incomes per capita in each country
and in the United States, which implies equivalent costs of running the same operation in
the United States. In constructing median values, we excluded a group of schemes that we
considered as outliers in terms of excessive costs per member adjusted for income
differences.26 From the 21 Sub-Saharan African institutions in our sample, 14 are in this
category.
There is no particular pattern emerging from this analysis. For schemes that do not involve
recordkeeping of earnings, programs with DB (rank 2) require more resources compared to
flat-benefit schemes. The result for ranks 3 and 4 depends on the denominator used in
normalization. However, overall it is consistent: there is a significant increase in the per-
beneficiary measure, indicating extra costs associated with the functions of employee
record management and contribution collection. The drop in per-member costs for rank 3
indicates that average recordkeeping costs per member is much lower than the average
benefits management costs per beneficiary. This is entirely consistent with all other findings
in this paper. The drop in average costs of the most complex programs (rank 4) is a
challenge to explain. We associate it with important biases of this simple measure we use,
including program size, generosity, level of economic development, and so on. We discuss
them in greater detail in section V. In the regression analysis that follows, we did not find
categorical variables for different ranks of the programs consistently significant, except for
basic pension. However, certain elements associated with the nature of operational
organization of the programs did play a role in explaining cost differences.
25
Members are defined as the total number of beneficiaries of all cash programs and contributors/insured for
whom records are kept in-house. 26
Those institutions include BWA-POPF, BFA-CNSS, GHA-SSNIT, KEN-NSSF, KEN-LAPT, MLI-NSII, NAM-GIPF,
PHL-GSIS, RWA-RSSB, SEN-SII, SLE-NASSIT, SWZ-PSPF, TZA-GEPF, TZA-PPF, UGA-NSSF (see annex 1 for
definitions).
25
Figure 3: Agency Rank and Median Administrative Expenditures (Income Adjusted)
Source: Author’s al ulatio s.
4. 2. Key elements of the cost structure
In what follows, we discuss key observations from the cost structures of various institutions.
For 71 observations in our sample, in which we separately provided information on
expenditures on capital investments and depreciation, we found that the median for such
costs is only around 5 percent of the total administrative expenses. In some exceptional
cases, however, capital expenditures are up to one-third of the total operational budget (for
example, in the case of Maldives, which was in the process of establishing a new agency to
run a new national contributory program at the time of collecting this data).
For 74 observations, the median share of the direct labor cost in current expenditures is 57
percent, although the variation is extremely broad from 6 to 90 percent, in part due to
reporting differences, with the lowest share of labor cost reported for the Swedish national
DC program and the U.S. Thrift Savings Plan for public sector employees (two programs
similar in nature of operation). We also note that for the 27 countries in which data on both
direct labor costs and asset management expenses are available, the correlation between
the sizes of those two cost components (as a share of total current expenditures) is negative
81 percent, implying that as systems accumulate and actively begin to manage considerable
financial assets, direct labor costs become insignificant in explaining total cost differentials.
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
1 2 3 4
Cu
rre
nt
ad
min
exp
pe
r m
em
be
r (U
S e
qv
), U
S$
Agency rank
Members
Beneficiaries
26
For 39 programs with available data on pension asset management expenses, such expenses
constitute 25 percent, as a median, of the total current expenses. (Only one-third of the
programs in this subset were DC schemes.) We find no direct association between the
volume of assets and the share of reported asset management expenses in total current
expenditures. At the same time, larger pools of assets are clearly less expensive to manage
on per unit basis, while for the smaller portfolios there is a significant dispersion in asset
management costs.27
Figure 4: Costs of Managing Pension Assets
Source: Author’s al ulatio s.
With 31 available observations on office rent expenditures, the median reported amount
constitutes 1.3 percent of the total current expenses. In the Netherlands, Northern Ireland,
Kosovo, and the Maldives, however, where accounting recognizes these costs more
explicitly, office rent expenditures reach a 10 percent share, which perhaps is closer to the
actual situation with such costs borne by the pension agencies.28
The median for benefit delivery costs for 30 countries in which such data is available stands
at around 5 percent of the total current administrative expenditures of the agency. While
bank charges seem to be part of those costs, more analysis is required on the classification
27
We note, however, that such direct comparison does not take into account the composition of the portfolios
and the nature of the management practices. 28
In Kosovo, the new pension agency that was established to manage a national DC scheme selected and
leased premises for its main office on a commercial basis. In Northern Ireland, assessment of the rent which
would be payable on an open market basis for most buildings occupied by the Social Security Agency is
charged to the age ’s operati g ost, as part of otio al osts.
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%
0 50,000 100,000 150,000 200,000 250,000 300,000
Ass
et
ma
na
ge
me
nt
exp
en
ses
(% t
ota
l ass
ets
)
Total assets (USD, millions)
27
of expenditures in that category on a case-by-case basis. In Romania, for example, where
such costs are reported at 54 percent of the total administrative expenditures, the benefit
delivery services have been outsourced. Another interesting observation is that the next
three most expensive delivery services are in neighboring Georgia, Azerbaijan, and Armenia
(in the range of 40 to 50 percent of total administrative expenses). At the same time, in all
four countries, those costs as a share of the total benefit expenditures still are relatively
small (between 1 and 2 percent).
We do not have data on the cost differences associated with various types of benefit
delivery, but as evidenced by our data presented in figure 5, as countries develop, they tend
to process a greater share of social security benefit payments through the banking system.
This perhaps is a function of the efficiency of the financial sector, costs of these services,
and program coverage.
Figure 5: Share of Benefit Payments in Banks by National Social Security Agencies
Source: Author’s al ulatio s.
Finally, for five countries where we have information on the delivery of individual account
statements, the associated costs are small and range from less than one to five percent of
the total current administrative expenditures.
4. 3. Functional analysis: contribution collection and benefit payment
We also investigated resource allocation between the key administrative functions of
contribution collection and benefit payments. Explicit functional cost accounting has been
adopted so far only in a handful of countries, including the United States, Canada, and New
0%
20%
40%
60%
80%
100%
120%
$- $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000
Ba
nk
pa
ym
en
ts
GDP per capita
28
Zealand. We conducted a small survey with select public social security agencies and
requested, through special questionnaires, information on staff associated with the
functions of contribution collection, benefit payments, and all other staff (IT support and
maintenance and general management). For the nine agencies that operate in-house
contribution collection and for which we received such data, figure 6 (a) and (b) suggest
that benefit administration is much more resource-intensive than the process of
contribution collection on a per head basis. The difference between the presentations in (a)
and (b) is that in the former, one observation depicts one institution (with its collection and
benefit functions) while the latter expands the sample, incorporating additional agencies
that do not operate in-house collection function. Based on these observations, the
beneficiary payment side may require between 3 and 10 times more staff per member
serviced compared to the contribution collection side. We will further validate these
findings in our regression analysis.
Figure 6: Allocation of Labor Resources within the Social Security Agencies
Source: Author’s al ulatio s.
Note: * Members ratio: active contributors over beneficiaries; Staff ratio: estimated staff involved in
contribution collection over estimated staff involved in benefit payments.
**Members: active contributors and beneficiaries; Members over Staff: ratio of total members over
staff of the agency.
This finding has important implications for understanding member accounting in the cost
analyses. Specifically, for the same membership size, the programs that do only benefit
payments will always look more expensive on a per-member basis compared to the
programs that collect contributions and pay benefits. Therefore, for the same 100,000
members, it may seem to be more expensive to run a basic pension-type program relative
to a contributory scheme as measured on a per-member basis. Hence, it is important to
recognize that bias in any comparative analysis.
Finally, some agencies operating multiple programs produce programmatic accounting of
costs (for example, in Canada, St. Kitts and Nevis, Sweden, and the United States). In fact, in
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Me
mb
ers
ra
tio
*
Staff ratio*
(a)
4
6
8
10
4 6 8 10 12 14 16 18 20
LN (
Me
mb
ers
ov
er
Sta
ff*
*)
LN (Members**)
Contribution Collection Benefit Payments
(b)
29
some of those cases, given the different nature of the programs, we included separate
observations on those programs in our data set.
V. Cost Normalization
Having all current administrative costs aggregated and converted to a common currency
does not make them directly comparable given significant underlying heterogeneities in
program design, size, and institutional and operational setup. Some normalization is
required. For the purposes of reporting operational efficiency, we favor the measure
suggested by James and Palacios (1995), which is income-adjusted annual current cost per
member,29 and in this paper, we offer a more intuitive variation of it (see annex 2). At the
same time, justice needs to be done to other measures, and we now discuss the advantages
and limitations of various alternative indices broadly used in the literature.30
As important
caveats of such analysis, we need first to consider several common biases:
Maturity bias. This is evident when newer earnings-related schemes with very few
beneficiaries and payouts turn out more expensive as compared to older and stabilized
schemes.
Financing bias. This implies that noncontributory schemes or contributory schemes with
significant budget subsidies cannot be compared with financially balanced contributory
schemes, if contribution revenues are used as the denominator. (To address this, the
sum of contributions and benefits can be used instead.)
Generosity bias. This bias reveals itself when the schemes of the same organization and
coverage do not look the same when differences in the rules of accruals (or in
contribution rates) are significant. Figure 7 indicates that the share of program revenues
that finance administrative costs generally increases as the contributory mandate
shrinks; however, the variation remains quite significant. (To remedy this problem,
some use GDP as the denominator.)
Coverage bias. This bias will disqualify GDP as a useful denominator if various sector-
specific schemes or schemes with very narrow coverage (by design or implementation
outcomes) need to be compared.
Technology bias. Several studies point to the fact that more advanced technologies and
better infrastructure are available in more developed countries, hence availing more
cost-efficient solutions. This information is not easy to reflect in any of the cost indices.
29
Based on their regression analysis, the authors note that the administrative costs rise at a much lower rate
than per capita income, owing to higher productivity of more developed countries. So, such income
adjustment overcorrects. This is in interesting contrast to Mitchell et al. (1993) that constructed proxies to
capture cross-country differences in production technologies but found them insignificant explanatory
variables in explaining cost differences of Social Security. 30
Valdes-Prieto (1994) offers a related discussion of alternative indices with their limitations.
30
Operational bias. Biases of all sorts exist when resources are shared with other
programs or functions to sustain operational synergies (for example, in contribution
collection or benefit administration).31
Size and membership biases. These biases are quite a few and diverse. First, fixed costs
imply that smaller schemes will be costlier to manage. Second, we point above to the
implications of the composition of the membership bases (beneficiaries versus
contributors) for per-member cost accounting. Third, we present empirical evidence
that the number of inactive members may pose important implications for any measure
used. Fourth, some programs provide service to special groups of beneficiaries, such as
widows and the disabled, where additional administrative resources presumably would
be required to assess eligibility. More generally, some programs require complex
categorical or resource eligibility checks for potential beneficiaries.
Figure 7: Contribution Rate and Administrative Costs
Sources: Author’s al ulatio s. Note: The sample includes only programs with the collection function operated largely in-house. The costs of
managing pension assets are excluded.
The choice of the denominator for comparable cost measures is important. It needs to
relate to something that the social security system produces, such as delivery of a general
public good, administration of contributions and benefits, management of pension
liabilities, or servicing various members and beneficiaries. The following sections discuss
corresponding candidates for the denominator.
31
For example, it is common for the civil service pension schemes to rely on the personnel and payroll units of
various government branches and agencies to conduct public information and contribution collection.
0%
10%
20%
30%
40%
50%
60%
70%
0% 5% 10% 15% 20% 25% 30% 35% 40%
Ad
min
co
sts
ov
er
con
trib
uti
on
re
ve
nu
es
Statutory contribution rate (% of wage)
31
5. 1. Uses of national income, revenues, and expenditures in cost normalization
Some studies use GDP as a denominator to normalize costs, which for the same coverage
and program type is sufficient. The problem, however, is that coverage is not the same
across countries or even across schemes within the same country. So, the coverage bias is a
major deficiency of this index.
When the focus is on financial flows, conventional indices are composed on the basis of
contributions collected or benefits paid. We do report those indices in our table in annex 2.
However, these measures have the greatest number of biases associated with them, which
we report in table 4. At the same time, just as with other indices, comparison across
programs of similar size and design can be fair.
5. 2. Administrative costs and pension liabilities
All retirement schemes, whether funded or not, are in the business of liability management.
If such liabilities can be clearly defined and measured, one could compare the cost
efficiency of various types of schemes on that basis. In figure 8, we use the estimated
Implicit Pension Debt (IPD) of selected unfunded or partially funded mandatory DB schemes
and reported assets of DC schemes (including provident funds) to normalize administrative
expenditures. For comparison, we also present data on combined equivalent annual
charges from private mandatory pension programs.32 Out of 52 observations for which we
had information on either total pension assets or IPD, 30 schemes are DC, including 12
schemes that are privately managed (see data in annex 2).
32
The numerator is the actual current expenditures of public programs and combined annual member charges
in private schemes. Hence, we ignore the difference between the costs and expenditures of operating private
programs assuming that profit margins at the national level are insignificant. In fact, Palacios (2005) reports
high correlation between the costs and charges in private plans.
32
Figure 8: Costs of Managing Pension Liabilities (Percentage of Total Assets or IPDs)
Sources: Author’s al ulatio s. For private schemes, data from Tapia and Yermo (2008).
Note: Agency abbreviations listed in annex 1.
The impressions that we get from figure 8 are as follows:
i. There is significant dispersion in simple cost indices among DC schemes. It also appears
that centralized publicly managed programs are necessarily the least costly
arrangements compared to privately managed decentralized DC schemes, perhaps in
part due to the following attributes of the public DC schemes in our sample: (a) some of
them operate in less developed countries with relatively more expensive financial sector
infrastructure, (b) additional costs of in-house contribution collection, and (c) smaller
size of some of those schemes.
ii. Unfunded DB programs seem much less expensive to manage. This is not a reflection of
their greater efficiency but rather an indication that management of individual funded
liabilities requires much greater effort, providing a public good of a completely different
nature. For five countries that operate two parallel mandatory programs, the contrast in
costs of managing funded versus unfunded liabilities is particularly striking (although
given the discussion in box 3, not necessarily conclusive). We also note that the DB
schemes in the sample are larger and more mature.
iii. Contribution collection seems to be associated with some additional costs of program
management. Nevertheless, the impact does not seem highly significant.
These observations are not without shortfalls, as the simple measure we use is subject to
biases of maturity, generosity, and size. We further validate these findings with regression
analysis in section 6.
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33
5. 3. Per-member costs
Using the number of program members as a means of normalization of costs seems
obvious. Unfortunately, the methods are not that straightforward and deserve a detailed
discussion. Our approach to membership accounting is based on the principle of liability
management. In broad terms, the business of pension administration is to (i) accumulate,
(ii) manage, and (iii) discharge the member pension liabilities. Hence, all those to whom
such liabilities relate should be generally considered members of the program. We discuss
these three components of pension administration and how we propose to count the
associated members under each component in greater detail.
Under contributory regimes, the business of accumulating pension rights is encapsulated in
the contribution collection and processing function. Contributing individuals become direct
beneficiaries of administration services, and we consider them as members. There are
inactive members, or those without contributions, who also accumulate pension liabilities.
For example, in the context of operation of most European pension programs, certain
inactive member groups can constitute part of the category referred to as insured (for
example, students, military servicemen, or recipients of certain public benefits). In line with
our proposed definition of pension administration services, our approach is to count and
include all members who accrue new rights in a given period, including all members who
earn such rights without contributions.33 Furthermore, we count all members who are
directly supported by the mechanisms of the system, that is, who accumulate pension rights
and for whom the records are kept in-house.34
33
Our definition, however, provides for a minor complication of vesting, or minimum period of contributory
participation in the scheme to earn and in the future benefit from pension rights. For all practical reasons, we
suggest ignoring that issue, as in most cases some form of compensation, usually a lump sum payment, will be
provided to members who fail to earn the required minimum. 34
An alternative approach to counting members would be to include all those who accrue benefit on a
noncontributory basis. While there is no contribution, the service of rights accumulation still is provided. This
applies to both noncontributory earnings-related civil service schemes and universal pension programs. In
some cases, such an accumulation process is literal and gradual and is related to the years of residency (for
example, Denmark) or taxable work (for example, Netherlands and U.K.). This approach helps answer an
important policy question regarding the cost differentials of alternative design options for the same group of
potentially eligible individuals. Hence, such an approach would help make inferences about allocative
efficiency. A pure mechanical measure (focusing only on benefit recipients of the noncontributory schemes)
will lack such insight. Furthermore, there are important caveats in using such indexes for inferences about
allocative efficiency and in the choices between different design options on the basis of costs. One could argue
that direct current beneficiaries of earnings-related schemes that provide survivorship benefit are both
contributing members and their families. Note that some DC schemes price such services separately and
collect a special premium to service survivors of current members. Following this logic, the DB schemes have
an implicit premium for survivorship benefit. If there is a premium, there must be well-defined beneficiaries.
At the same time, plain universal basic pension programs in most cases do not offer survivorship benefits. So,
while the latter schemes could be seen as providing coverage to all working age individuals (without
contributing), the earnings-related schemes with an implicit or explicit survivorship premium cover a smaller
34
The liability management side of pension administration covers all those members of the
program who conceptually have legal claims against the total outstanding stock of pension
liabilities. That includes (i) all those currently contributing (or who otherwise accumulate
new pension rights); (ii) all inactive members who do not accumulate new rights in any form
but whose implicit or explicit rights still constitute part of the total liabilities managed by
the pension agency (often referred to as members with dormant accounts); and (iii) all
those currently in receipt of the regular benefits. While categories (i) and (iii) will be
counted as members under other components of pension administration services, our
approach is to exclude the second category (dormant accounts) from the definition of
members. Thus, this side of the pension business administration should not produce any
additional members for our count.35 Not all workers, however, regularly contribute,36 and
he e, defi itio s of a ti e orker ary from program to program. Valdes-Prieto (1994)
defi es the o ept of effe ti el o ered o tri utors as registered perso s aki g at least one contribution in the last 12 months. Variations of this approach seem common in
how pension agencies report data on their active members. To provide the idea of
differences in the reported numbers of active contributing workers versus total accounts,
we present data from our survey in table 3.
Table 3: Activity of Pension Accounts (Thousands of Contributing Members)
Source: Author’s al ulatio s.
Note: Agency abbreviations listed in annex 1.
In many cases, more than half of all accounts remain passive. The reasons for such
differences are few. In less operationally efficient systems, in which identification means are
weak, some workers end up having multiple accounts when moving from one employer to
group of workers but also indirectly provide services to a broader group (those who can claim the survivorship
benefit). This observation may tend to equalize allocative efficiencies of the earnings-related and universal
schemes. One way of dealing with this issue is to exclude the survivorship element from the contribution rate
and to use adjusted revenues in the denominator for proper comparison. 35
This approach, however, may constitute problems with incorporating closed earnings-related schemes into
the cross-program analysis, but that is a very small group of programs that do not attract significant research
interest. 36
For analysis of contribution density, see Arenas de Mesa et al. (2004) who use household survey data linked
with the Chilean social security records for over 20 years.
Country Bu
rkin
a Fa
so
De
nm
ark
Gh
ana
Gu
yan
a
Ind
on
esi
a
Jord
an
Ke
nya
Mal
aysi
a
Mal
aysi
a
Pak
ista
n
Ph
ilip
pin
es
Sin
gap
ore
Sri L
anka
Uga
nd
a
USA
Van
uat
u
Agency CNSS ATP SSNIT NIS ESS SSC NSSF EPF SSO EOBI SSS CPF EPF NSSF TSP VNPF
Year 2004 2007 2006 2002 2007 2006 2007 2007 2006 2007 2007 2007 2005 2005 2008 2006
Registered 150 3,800 1,200 590 23,700 1,500 3,600 12,000 11,800 2,700 27,000 3,100 11,900 248 3,900 40
Active 73 3,100 850 130 7,900 660 900 5,400 5,500 1,800 7,900 1,500 2,100 138 2,600 28
Activity rate 49% 82% 71% 22% 33% 44% 25% 45% 47% 67% 29% 48% 18% 56% 67% 70%
35
another.37 Other reasons include inactivity of accounts due to prolonged periods of
unemployment, temporary migrants, and members of specialized schemes (for example, for
civil servants) who left their employer and opted for deferred retirement.
On discharge of pension liabilities or the benefit side of pension administration, the
approach may seem straightforward: all those in receipt of pension payments should be
counted as members. However, there are three types of issues here. First, there are lump
sum payments (for example, provident fund type schemes or new DC schemes usually pay
only lump sums or make arrangements with external annuity providers). Our approach is to
include lump sum payments along with regular payments in the count of beneficiaries.38
The second group of issues is associated with common benefits, such as survivor pensions:
some institutions report only family cases, others only beneficiaries (family members), yet
others report both. For our purposes, we recommend using the count of all beneficiaries
who are the actual collectors of the benefits. Finally, many pension agencies administer
benefits other than pensions. Those typically include various short-term benefits (sickness,
maternity, child, family, and unemployment allowances); special schemes (industrial injury
pensions); multi-pillar pension schemes; and several other assistance programs.
Conceptually, the institutional approach will dictate incorporating all the schemes managed
by the same agency and all those benefiting from such schemes into the institutional cost
measure. A programmatic approach focusing on the pension program only would require
some form of cost proration. In either case, on the practical side, while capturing the count
of beneficiaries of special, multi-pillar pension, or assistance schemes is relatively
straightforward, there often are problems with the availability or interpretability of the
count of beneficiaries of certain short-term benefits and other schemes.39 The provision of
such benefits, therefore, should be controlled in the cross-country comparison.
In our analysis, we account for beneficiaries in two different ways. We combine the counts
of regular maternity, children, and family allowances with the numbers of pension
recipients, which constitutes our totals for the beneficiary numbers in all our results and
regressions. We also account for the provision of other benefits, such as sickness,
unemployment, health insurance, and personal loans. However, given the complexity of
measurement and interpretation of these types of benefits, we use a categorical variable
and activate it each time when at least one of these programs is available.
37
A number of countries explicitly have recognized that problem and as part of the system modernization
effort implemented projects of record cleanup and consolidation. 38
Arguably, the level of effort across these cases is comparable. One-off lump sum payments require
extensive regular certification as all first-time claims. Regular (monthly) payments while less resource
intensive are numerous. 39
For example, as is often the case with short-term health benefits, there are risks of double counting when
the same members are covered and benefit from multiple insurance programs.
36
These factors should be carefully considered in the choice of the denominator in universal
indices. Table 4 summarizes various options, including associated biases.
Table 4: Choice of Denominator in Cost Indices and Associated Biases
Cost Index Denominator
Ma
turi
ty B
ias
Fin
an
cin
g B
ias
Ge
ne
rosi
ty B
ias
Co
ve
rag
e B
as
Te
chn
olo
gy
Bia
s
Op
era
tio
na
l B
ias
Siz
e/m
em
be
rsh
ip
Bia
s
1. Contributions + + + + +
2. Benefits + + + + +
3. Contributions + Benefits + + + +
4. GDP + + + +
5. Pensions Assets or IPD + + + + +
6. Covered wages + + + +
7. Contributors (+) + + +
8. Beneficiaries + + + +
9. All members + + +
10. All members (income adjusted) (+) + +
Source: Author’s desig .
Let us illustrate some of these biases at work. There is a maturity bias associated with (2):
younger earnings-related schemes that cannot be compared to some older stabilized
schemes (see the example of Kosovo in the table in the annex). Financing bias is present in
(1): noncontributory schemes or contributory schemes with significant budget subsidies
that cannot be put on the same line of comparison with fiscally balanced contributory
schemes (see the cases of New Zealand and Netherlands). To address these problems,
often, we find that a composite measure (3) is used. However, there is another generic
problem—generosity bias—as schemes with the same organization, coverage, and
operational costs will not look the same when differences in the contribution and/or benefit
rates are significant (for that reason programs in Poland and St. Kitts and Nevis, while very
similar in relative coverage and institutional organization, cannot be directly compared). To
remedy this problem, some would use the administrative costs per GDP ratio (4). This
measure, however, has its own problem, a coverage bias. Consider, for example, the
provident fund for the formal sector employees in India (EPFO), civil service pension plan in
the United States (TSP), and the national pension program in Estonia (managed by SIB). In
these cases, the differences in total cost per GDP measure for SIB and TSP are dramatic
(almost 80 times), reflecting differences in the relative size of those programs in the
economy (and perhaps additional institutional subsidies that TSP receives). However,
income adjusted per member costs are almost the same for SIB and TSP. At the same time,
while the cost per GDP is 3 times smaller in EPFO than in SIB (with labor coverage of that
program 10 times smaller in India), the income adjusted per member cost of EPFO is 7 times
larger, indicating potential inefficiencies.
37
In summary, all these measures are quite informative but should be used only with subsets
of comparable programs. We also note that there may be a progression in the usefulness
and applicability of various indices. Indices in (1), (2), and (3) may be more relevant when a
system is stable and mature. However, when administrative costs devour most revenues or
investment profits, such indices will not reveal much about the health of the program.
Rather, indices in (4), (9), or (10) would be more revealing.
VI. Data Analysis and Cost Benchmarking
We provide three alternative quantitative models for our analysis. We first investigate the
effects of program design on administrative expenditures (for example, private versus
public management). We then study factors explaining the differences in administrative
expenditures in managing pension liabilities. Finally, we proceed with benchmarking
analysis for individual programs.
We account for the program size by using the information on the total number of members.
However, we track separately the active contributing or effectively insured members
(where the pension agency keeps corresponding records of such members in-house) and
the beneficiaries (including recipients of old-age, disability, survivors, work injury, and other
pensions as well as cash benefits like maternity, family, and child allowances). We find
significant differences in how total counts in these two groups affect total costs.
To control for heteroscedasticity, all our quantitative variables are in natural logs.
Corresponding coefficients, therefore, indicate percentage change in the endogenous
variable as a result of the 100 percent increase in the explanatory variables.
6. 1. Administrative expenditures and program design
The first data set is the most comprehensive and includes all observations of our sample,
including 116 publicly managed programs (see annex 1 and annex 2) and 12 additional
observations of the combined administrative charges of privately managed pension
programs.40
The cost function is constructed as follows:
ln EXP = a0 + a1 ln BEN + a2 ln INS + a3 DCSCHEME + a4 PRIVATEMGT + a5 COLLECTION
+ a6 SHUL + a7 BP + a8 ln GDPpc + a9 OUTLIERS + e,
where EXP is the total operating expenses;41 BEN is the total number of beneficiaries
serviced by the program or agency;42 and INS is the total number of active contributors (or
40
Sources include Tapia and Yermo (2008) and FIAP statistics (http://www.fiap.cl). 41
This definition treats expenditures of public programs and total charges imposed under mandatory private
schemes equally. It also includes all costs associated with asset management (in-house or outsourced).
38
insured). The five categorical variables are correspondingly for DC scheme; DC scheme
managed by private agency(ies); schemes where contribution collection is operated largely
in-house; schemes with additional benefits (sickness, health insurance, unemployment
insurance, or loans to members); and basic pension schemes (rank 1 in table 2). GDPpc is
the national income per capita to account for differences in technology and quality of
institutions.
Finally, we define a group of programs that are outliers in terms of excessive costs per
member adjusted for income differences (see section 4.1); in all our regressions this
variable was highly significant. Table 5 contains the results.
Table 5: Administrative Expenditures and Program Design
Independent Variables Ln Total Operating Expenses
(a) (b)
ln BEN 0.45
(11.97)
0.60
(17.15)
ln INS 0.15
(6.07)
0.06
(2.59)
DCSCHEME – 0.44
(1.87)
PRIVATEMGT – 3.33
(8.49)
COLLECTION – 0.05
(0.21)
SHUL – 0.22
(1.16)
BP – -1.44
(-2.46)
ln GDPpc 0.65
(8.08)
0.59
(8.86)
OUTLIERS
1.47
(3.88)
1.84
(6.40)
CONSTANT 4.29
(5.35)
3.69
(4.96)
Observations 128 128
Adjusted R2 0.72 0.84
Note: t-statistic in parentheses.
Our first specification is simply to account for program size and income differences, while
the second specification introduces design elements and generates considerable additional
explanatory power. These are the key observations:
42
This generally includes old-age pensions, disability pensions, survivor’s pensions, work-injury pensions,
other pension benefits, maternity (parental) allowances, family allowances, child allowances, and other
assistance and compensations.
39
In line with other studies, we identify economies of scale as the coefficients by both the
beneficiaries and insured are less than one. At the same time, confirming the
observations of section 4.3, there are significant differences in the effects produced by
beneficiaries versus contributors. This is consistent across all our specifications and
subsets of data. There are significant differences in variable costs between two different
lines of social security operations. We discuss this finding later in the text.
The results show the robust and significant effect of private management of pension
plans. However, as we move to our long-term specifications, we find that the effect is
not robust and may, in part, reflect differences in the maturity and coverage of the
schemes in the shorter term.
The evidence of cost differentials between the DB and DC schemes is weak. In fact,
substituting this variable with the categorical variable for fund management produces
somewhat stronger results. This indicates that the mere fact of managing financial
assets (in either DC or DB schemes) is associated with some additional costs, thereby
reflecting a need for advanced skills and systems.
We expected that bringing the collection function in-house would increase costs.
However, the results do not show robust evidence for such an increase. One possible
explanation is that the modes of organizing the collection function vary significantly (see
Anusic 2005), so capturing such a variation under one categorical variable constitutes a
measurement challenge. We also note that as far as variable costs are concerned, the
function of managing active contributors is not that impactful. Perhaps the collection
function, however loosely defined, is a relatively small add-on in terms of variable costs.
The conclusion is that the argument for consolidation of the collection function cannot
be supported on the basis of cost reduction alone but rather on the basis of other
systemic improvements (for example, reduced administrative burden, improved
compliance, and overall improvements in economic efficiency due to reduced
informality). This finding does not extend to start-up or other fixed costs. Where
significant investments are required to establish or modernize a collection function,
both tax and social security contributions systems could benefit from a well-coordinated
effort.
Administrative costs increase less than proportionately with increases in income per
capita. One possible explanation suggested by earlier studies is that more developed
countries can manage pension schemes more efficiently, taking advantage of better
technologies, infrastructure, and institutions (see Mitchell et al. 1993 and James and
Palacios 1995).
In line with figure 3, plain basic pension schemes that do not require a history of
contributions to establish eligibility are less expensive to manage. At the same time, we
did not find robust evidence that schemes that serve the public sector only or means-
tested pension programs are systematically less expensive on average. This can be in
line with our earlier observation of the disproportional importance of the benefit-
management function relative to contribution management. As long as the agency does
40
benefit calculations and payments, the costs of recordkeeping do not differ much across
different program types.
6. 2. Administrative expenditures and pension liabilities
We now construct a specification that would reveal the long-term effects of various design
factors on costs. We use a measure of pension liabilities and validate preliminary findings
from section 5. 2. To control for maturity and generosity biases, we introduce a measure of
the average member account value.43
The cost function is constructed as follows:
ln EXP = a0 + a1 ln LIABILITY+ a2 DCSCHEME + a3 PRIVATEMGT + a4 COLLECTION
+ a5 ln GDPpc + a6 ln ACCOUNT + a7 OUTLIERS + a8 GOV + a9 SHUL + e
where EXP is the total operating expenses defined as in the previous equation; LIABILITY
equals the total reported assets of the DC schemes or an estimated Implicit Pension Debt
(IPD) for the DB schemes;44 the categorical variables are the same as in the previous
equation; ACCOUNT is the ratio of total DC assets or DB IPD over total members (to
approximate maturity); GOV is the government effectiveness index45.
We use the same observations as in the previous regression; however, the sample is smaller
due to the limited data on the implicit pension debt of public programs. These are the same
observations as figure 6 illustrates. Table 6 contains the results.
Table 6: Factors Affecting the Cost of Managing Pension Liabilities
Independent Variables Ln Total Operating Expenses
(a) (b) (c)
ln LIABILITY 0.84
(16.97)
0.90
(20.08)
0.90
(21.09)
DCSCHEME 1.19
(3.46)
0.40
(1.30)
0.39
(1.40)
PRIVATEMGT 0.98
(2.83)
0.50
(1.67)
0.51
(1.82)
COLLECTION 0.55
(2.09)
0.98
(4.27)
0.87
(3.91)
43
We define members as all contributors and beneficiaries for the DB schemes and as contributors only for
most DC schemes in our sample (recognizing that retiring members in most cases liquidate their balances at
the point of separation). 44
Source of data is Holzmann et al. (2004). 45
Government effectiveness captures perceptions of the quality of public services, public administration,
public infrastructure, the quality of the civil service and the degree of its independence from political
pressures, the quality of policy formulation and implementation, and the credibility of the government's
commitment to such policies (World Bank 2012).
41
Independent Variables Ln Total Operating Expenses
(a) (b) (c)
ln GDPpc –
0.63
(4.39)
1.11
(5.57)
ln ACCOUNT – -0.75
(-5.22)
-0.80
(-6.09)
OUTLIERS – 2.01
(4.75)
2.71
(6.47)
GOV – – -0.72
(-3.22)
SHUL – – 0.51
(2.30)
CONSTANT -3.07
(-2.34)
-3.06
(-2.83)
-6.63
(-4.20)
Observations 52 52 51
Adjusted R2 0.88 0.93 0.94
Note: t-statistic in parentheses.
At first look, our baseline specification reveals costs differences associated with our core set
of design elements. However, as we introduce the adjustment for maturity, the marginal
effects on costs of the scheme design become smaller and less significant. Here are some
more specific observations:
The value of the coefficient by pension liabilities (<1) indicates economies of scale in
management of pension liabilities. The larger, older, and more generous schemes tend
to be less expensive to manage per unit of liability. However, effects of these three
factors are not distinguishable here.
The DC schemes, private sector schemes (all DC in our sample), and schemes that
operate a fund management function (not shown among these results) are all
associated with a cost increment. However, the results are not robust and their
significance depends on choice of specification. As the maturity indicator is added, the
scheme design becomes a less significant explanation of the cost differentials. This may
suggest that in the long run, design largely does not matter and the costs should not be
the key driving factor in policy choices over a particular design type.
The sign of the coefficient of the average account size is as expected and suggests that
unit costs decline as schemes mature. In the long run, this factor may compensate for
the possibly higher overall management costs of the DC schemes, if the same account
over time can generate greater value under the DC arrangements compared to implicit
wealth generated under a DB scheme. So, the policy focus should be on the wealth-
generating patterns of different types of schemes and not just on cost ratios.
This specification indicates that in-house collection of contributions is associated with
higher administrative expenditures.
We find again that the value of the coefficient for national income per capita is positive
but less than one, indicating that more developed countries may have better institutions
and access to better technologies, and so can manage pension liabilities more
42
efficiently. We also experimented with several governance indices and found that index
that captures government effectiveness produces the most robust effects (specification
[c] in table 6).46 Higher levels in that index are associated with lower administrative
costs. Interestingly, as this index captures most of institutional factors, the response to
changes in the levels of national income becomes close to one. These results seem to
suggest that technologies as they spread over time become less important in explaining
cost differences, and what ultimately matters is the quality of governance.
We also note that managing special supplementary programs and benefits produces
increments in operating costs.
6. 3. Administrative expenditures and institutional organization
We now proceed with the third and main model of our analysis, which we also use for
performance benchmarking of mandatory programs. The sample of programs used in the
regression contains only publicly operated programs and only those programs for which we
have information on staffing levels.
We adopted a two-step approach. In the first step, we assess and benchmark technical
efficiency using the data on staffing levels. We then obtain residuals from this step as an
indication for over- or under-staffing and use them in the regression of the second step in
which we look at the cost efficiency of the same programs. In the final results, we can then
distinguish sources of deviations from the benchmarks.
Step I regression equation is constructed as follows:
ln STAFF = a0 + a1 ln BEN [+ a2 (ln BEN)2] + a3 ln GDPpc + a4 COLLECTION + a5 SHUL + e,
where the new variable is STAFF as the total number of staff in the agency.
Table 7: Staffing Requirements for Pension Administration
Independent Variables Ln Total Number of Staff
(a) (b) (c)
ln BEN 0.66
(20.57)
0.72
(25.32)
–
(ln BEN)2 – –
0.03
(27.58)
ln GDPpc – -0.11
(-1.98)
-0.13
(-2.57)
COLLECTION – 0.70
(3.52)
0.67
(3.64)
SHUL – 0.52
(3.26)
0.52
(3.52)
46
The governance indices were not found to be significant in other regressions.
43
Independent Variables Ln Total Number of Staff
(a) (b) (c)
CONSTANT
-1.31
(-3.26)
-1.76
(-3.00)
2.41
(4.85)
Observations 99 99 99
Adjusted R2 0.81 0.88 0.90
Note: t-statistic in parentheses.
Remarkably, the number of beneficiaries (recipients of retirement benefits) alone explains
over 80 percent of the variation in the staffing levels. The level of the coefficient indicates
economies of scale. We experimented with several alternative specifications and found that
the number of the insured or contributors for whom records are kept in-house produced
only a small additional power, and the significance of its coefficient drops to almost zero
when we add various categorical variables. This may be due to the fact that the agencies do
not really provide direct service to contributors and largely interact with their employers, so
association with the contributor numbers is loose. Mere recordkeeping of the contributors
does not seem to significantly affect staffing requirements but contribution collection and
special additional services does, implying the importance of fixed costs over variable costs
for that line of business. We also found that design of the scheme (DC versus DB) or sectoral
affiliation (public only versus private sector) did not produce any systematic differences in
staffing requirements.
In most regressions, we obtained slightly better fitting results when using a squared
function for beneficiaries. It may reflect the fact that the variable costs on top of significant
fixed costs are relatively indistinguishable for smaller plans, so the quadratic function
captures that aspect better. In our benchmarking we used quadratic functions in both
staffing requirements and administrative costs regressions.47
An interesting observation can be made regarding the negative sign of the income per
capita coefficient in the staffing regression. It indicates that as economies develop and new
technologies become available, they tend to substitute capital for labor. This particularly
conforms to the case for the U.S. Social Security Administration depicted in figure 1.
We obtained residuals from (c) and used them as STAFF_RES in the Step II regression:
ln EXP_NAMC = a0 + a1 (ln BEN)2 + a2 (ln INS)2 + a3 ln GDPpc + a4 STAFF_RES
+ a5 COLLECTION + a6 SHUL + a7 FUNDSMNGMT + a8 OUTLIERS + e.
The principal difference in this regression is that here we use the progra ’s total operati g expenses net of explicit direct costs associated with asset management, often external to
the administration (EXP_NAMC). Our reasoning was that practices of managing pension
47
We also observed that the coefficients of linear terms become insignificant in the quadratic specifications.
44
assets vary substantially and so do the associated costs and norms of reporting those costs.
By taking out those costs, we focused on benchmarking only the core operation
mechanisms. We do admit, however, that total and clear segregation of those costs was not
possible in all cases.48 To capture various related costs, we added a categorical variable
FUNDMGT associated with the management of financial assets in either DC or DB schemes.
Table 8: Key Factors Affecting Costs of Public Pension Programs
Independent Variables Ln Total Operating Expenses Net of
Asset Management Costs
(a) (b)
(ln BEN)2 0.03
(20.52)
0.03
(27.01)
(ln INS)2 0.003
(2.70)
–
ln GDPpc 0.46
(9.18)
0.49
(10.15)
STAFF_RES 0.69
(6.43)
0.72
(8.08)
COLLECTION – 0.42
(2.54)
SHUL – 0.45
(3.52)
FUNDSMNGMT – 0.50
(3.21)
OUTLIERS 1.47
(6.08)
1.53
(7.54)
CONSTANT 8.43
(19.39)
7.15
(15.53)
Observations 99 99
Adjusted R2 0.91 0.94
Note: t-statistic in parentheses.
One of the most important and already familiar observations is a striking difference in the
coefficients by beneficiaries versus insured (specification [a]), which consistently reflects
across all our models and specifications (including linear and quadratic). This indicates that
the variable costs are much more important in the management of beneficiaries relative to
the management of contributor accounts. In other words, there are significant economies
of scale in managing active member accounts. Furthermore, variable costs become
insignificant when the categorical variable of in-house collection is added. These findings
have interesting implications for the policy of collection administration. Specifically, as
significant capital investments have been made in the collection function, transferring that
function to an external agency, such as tax administration, may not be a significant cost
48
In most such cases, investment management remains largely in-house and is often restricted to passive,
limited, or illiquid portfolios, including public debt and real estate.
45
saver. Hence, if the capacity of an external collection agency is weak, the advantage of such
outsourcing may be questionable. At the same time, significant up-front cost-saving
advantages could be seized if new schemes can rely on the existing collection infrastructure.
On the beneficiary side, given significant variable costs, there may be considerable
economies of scope, which may argue in favor of consolidating various benefit programs
under a unified administration (for example, universal basic pensions and earnings-related
pensions or retirement benefits and various short-term or other special benefits, especially
where these mostly cover the same groups of beneficiaries).
The significance of the coefficients by GDPpc and by the residual from the staffing
regression again suggests that more developed countries can operate their schemes more
efficiently and that a significant part of variation in costs can be explained by the deviation
of staffing levels from projected benchmarks.
Additional services to active members (benefits of sickness, health, unemployment, or
individual loans) increase the total costs. The significance of the coefficient by funds
management may capture two aspects: (a) the additional costs of managing pension assets
that cannot be distinguished from the total costs are important, and (b) even if costed out
separately, such functions may be associated with a cost premium, for example, for higher
skilled staff.
6. 4. Performance against benchmarks
Annex 3 contains the results of our benchmarking analysis, providing actual and fitted
estimates for both staffing and total administrative expenditures. It also provides
benchmark coefficients: where such coefficients are close to 1, the program operates at its
benchmark.
While we ran regressions only for 99 observations where we had full information on staffing
levels, our methodology allows for producing expenditure benchmarks for the programs
outside of that sample. We treated two specific parameters as anomalies: (a) deviations
from projected staffing levels and (b) outliers with excessive administrative expenditures.
To benchmark the expenditures, we turned both parameters to zero. Effectively, our
expenditure benchmarks assume the benchmark performance on the staffing side. Where
we have information on the actual staffing levels for particular programs, such information
provides additional insight into program performance. Where such additional information is
not available, the benchmark ratio simply indicates how the program performs relative to
the cost benchmark and given staffing performance just at the benchmark. We will illustrate
further how to interpret these results.
We further group programs in three categories. Category A is for progra s that perfor at the benchmark. Expenditure coefficients for this group are within the range of plus and
minus 25 percent of the benchmark to allow for measurement errors and minor
46
inefficiencies. Around 30 percent of the programs in our sample are in this category for the
total expenditure performance.
Categor B is for progra s that de iate fro the e h ark e o d per e t ut ot exceeding 100 percent. This may indicate moderate inefficiencies of under- or over-
resourcing in either staff or other expenditures. Around 50 percent of the programs in our
sample fall under this category and may merit further inquiries into potential operational
optimization.
Programs where performance coefficients are more than double the predicted levels are in
ategor C . Most of them are suspects for operational inefficiencies, especially those
where benchmark coefficients are multiples of the predicted levels. Out of the eleven
programs where the expenditure-to-benchmark ratio exceeds 5, eight are located in Sub-
Saharan Africa. In the cases of Uganda, Kenya, and Ghana the expenditure coefficients are
26, 15, and 11, respectively. It may be easy to overspend when operating significant
surpluses, which all three happen to have, but excessive costs certainly cannot be
affordable as schemes mature.
Where we have information on the staffing levels, we can define several categories in terms
of how staffing affects overall expenditure outcomes.
There is a AA ategor of progra s i hi h oth staffi g a d e pe ditures are at the benchmark (13 programs in our sample).
Where both staffing and expenditure benchmarks are on the higher side, it indicates
that osts are ai l dri e e essi e staffi g. There are progra s i the CC category. In the cases of Norway and U.K.-Northern Ireland, both staffing and
expenditure ratios are of the same magnitude (around 5 and 3, respectively), indicating
that staffing is a key driving factor behind the higher levels of administrative
expenditures. We do note, however, that findings of excessive costs for some European
agencies, including in Norway, Ireland, and U.K.-Northern Ireland, should be taken in the
context of operating multiple programs, including active labor market programs and
various targeted welfare schemes, which our analysis cannot fully capture. The same is
true for the U.S. SSA that has targeting costs to consider. The Canadian public sector
plan falls into this category perhaps due to the additional services it provides to
employers outside of the government, requiring a more extensive compliance
enforcement effort, even though we have classified its collection effort as insignificant
compared to conventional private sector plans. Uganda is an interesting case; in 2008,
the NSSF embarked on the mission to decentralize its operation, and by 2009 the agency
had 4 times the predicted requirements for staffing. The decentralization never took full
effect, but it subsequently was found that many staff were underutilized and roles
duplicated. Thus, a decision was made to downsize and release close to 40 percent of
47
the staff,49 which would be in line with recommendations of our analysis but still far
from the benchmark performance.
At the lower e d of resour e o it e t is the BB ategor . We fi d, for e a ple, that the DC schemes in Denmark, the Maldives, and Singapore, operate with very low
demand on both labor and other resources when the costs of asset management are
excluded, which may reflect efficient operation. At the same time, there are low-end
resource-commitment programs like the public sector pension scheme in Afghanistan,
which is a candidate for suboptimal operation. While serving beneficiaries across
Afghanistan, the operation is concentrated in the capital city, where 90 percent of
beneficiaries have to travel in order to get service. Additionally, payments are made
only once a year. Clearly, low costs come with poor service here.
There is a group of programs in which expenditures are excessive while staffing levels
are at or below the benchmark. For example, Burkina Faso and Tanzania (PPF) are in the
C ategor for e pe diture, a d those also happe to e flagged as outliers in our
regressions. An important point to note is that their staffing levels are on the low side.
We made additional investigation into the overall costs of labor. For the observations
where we had separately reported labor costs, we calculated the ratios of those costs
per staff numbers and adjusted by GDP per capita. We then calculated the median
alues of that i de for e pe diture oeffi ie t ategories B-lo , A , B-high , and
C a d those stood at . , . , . , a d . , respectively. This implies that those
programs that overspend have a tendency to pay premiums relative to the rest of the
economy to their staff.
Finally, there are programs in which total expenses against the benchmark are on the
low side while staffing is at or above the benchmark. The most notable cases are the Sri
Lankan Farmers Pension Scheme and the Syrian GOSI. It seems that those programs
retain inexpensive labor, possibly lack investments in technology and infrastructure, and
operate suboptimally in terms of resource allocation between labor and capital.
6. 5. Implications for choice of cost indices
Our analysis leaves many programs outside the scope of the benchmarking exercise.
However, it can offer guidance on the ultimate choice of a simple index that makes cost
comparisons meaningful. The following table provides estimates of correlation between our
expenditure benchmark ratios and a set of conventional cost indices that are discussed in
section 5.
49
NSSF Business Reorganization Exercise: Rightsizing for Efficiency. 2010.
http://www.nssfug.org/uploads/NSSF%20Business%20reorganisation%20exercise.pdf.
48
Table 9: Choice of Denominator for Cost Index and Correlation with Cost Benchmark
GDP Contribution
Revenues
Benefit
Expenditure
Insured + Beneficiaries
(GDPpc adjusted)
Beneficiaries
(GDPpc adjusted)
11% 43% 56% 69% 80%
Source: Author’s al ulatio s.
The results broadly reflect our earlier discussion of biases associated with various indices.
Expenditure over GDP is not a good predictor of program health. Using program revenues
or benefit expenditures does not produce sufficient explanatory power either. Using the
combined total number of insured and beneficiaries and adjusting for differences in
incomes provides better results among the commonly used indices. Apparently, even better
results can be achieved by using just the number of beneficiaries in the denominator. This
should not be surprising as our regressions show disproportionate importance of the
numbers of beneficiaries compared to the insured in explaining the cost differences.
6. 6. Global benchmarks
To further guide inquiries into the cost indices analysis, we generated a set of benchmarks
on the basis of our model. Annex 4 provides a set of just-on-benchmark estimates of the
index of Administrative Expenditures over Beneficiaries for different program sizes and
different levels of national income. The direct costs of asset management, if any, are
excluded. We provide estimates for five stylized country income categories (GDP per capita
in current US$ indicated): low income (LI: US$500); lower-middle income (LMI: US$2,500);
upper-middle income (UMI: US$8,000); lower-high income (LHI: US$15,000); and upper-
high income (UHI: US$40,000). In each case, we provide 5 schedules: a baseline low
estimate; three estimates which correspondingly are the functions of in-house collection,
fund management, and special supplementary schemes assessed individually; and a high
cost estimate in which all three functions are combined.
Results indicate that with differences in functional organization and services, the spread
between the high and low estimates is almost fourfold. With such a broad benchmark, we
are cautioned against providing advice on the appropriate level of expenditures for a
particular program without detailed inquiry into its operational organization.
We also observe the same schedule of economies of scale across all stylized cases.
Figure 9 depicts such a schedule as a proportion of a midsize plan with 500,000
beneficiaries.
49
Figure 9: Economies of Scale in Administrative Expenditures
Source: Author’s calculations.
Note: Per-beneficiary costs relative to per-beneficiary costs of a plan with 500,000 beneficiaries.
For plans with 100,000 beneficiaries, the premium for their smaller size is 50 percent over
the costs of similar plans with 500,000 beneficiaries. Similarly, larger-sized plans could be 25
percent less expensive (per beneficiary) to manage. One particular application of this
schedule could be the planning of consolidation programs.
This schedule can also be helpful in more flexible benchmarking of program costs. Figure 10
depicts low and high scenarios for per-beneficiary costs of a program with 500,000
beneficiaries for different levels of GDP per capita. As our regression coefficients indicate,
costs increase less then proportionally with increases in income levels.
To illustrate applicability of this method, let us assess the benchmark spread of
expenditures for a plan with 100,000 members in an economy of US$15,000 GDP per capita.
First, we use from figure 10 the spread for the 500,000 plan, which is US$50–200. Then
from figure 9, we find that the size premium for a 100,000 plan is approximately 50 percent.
This means that the benchmark spread we are looking for is US$75–300.
-
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
Beneficiaries
50
Figure 10: Per-Beneficiary Cost Spreads for a Midsize Operation (Nominal US$)
Source: Author’s al ulatio s.
We also developed similar benchmark schedules for staffing levels. For the convenience of
application, we provided workload schedules in which beneficiaries are in the numerator.
Figure 11 indicates economies of scale in staffing requirements to manage programs of
different sizes.
Figure 11: Economies of Scale in Staffing Requirements
Source: Author’s al ulatio s.
Note: Workload relative to workload of a plan with 500,000 beneficiaries.
-
50
100
150
200
250
300
350
400
450
500
550
GDP per capita
-
0.20
0.40
0.60
0.80
1.00
1.20
1.40
Beneficiaries
51
Interpretation of results is similar to the one provided for the costs schedule. Plans with
100,000 beneficiaries require approximately 30 percent more staff per beneficiary
compared to the same type of plan with 500,000 beneficiaries. At the same time, the larger-
sized schemes require 30 percent less of human resources per beneficiary.
Figure 12 depicts low and high scenarios for the workload ratios of a program with 500,000
beneficiaries for different levels of GDP per capita. As our regression coefficients indicate in
table 7, staffing requirements per beneficiary actually decline with increases in income
levels (the reverse of that relationship is presented in the workload schedule). The variation
between the low and high scenarios is defined by adding functions of in-house contribution
collection and provision of special schemes, which we found statistically significant in
explaining cost differences. Note that the curve on top indicates the results of the low
resource requirement, and the curve at the bottom is the high resource estimate (with both
of these additional functions factored in). It is important to emphasize again the wide
spread in the benchmark, which is threefold between the low and high estimates. Hence,
any recommendations on staffing levels should be carefully crafted with incorporating
information on the nature of operational organization or services a particular agency
provides.
Figure 12: Beneficiary per Staff Ratios for a Midsize Operation
Source: Author’s al ulatio s.
-
100
200
300
400
500
600
700
800
900
1,000
1,100
GDP per capita
52
VII. Quality Aspects in Cost Measurement: What is Left to Residual
In this section we discuss the implications of not having full data on the nature and quality
of services for our results. Service quality comes at a cost. Higher operational expenses may
reflect better services (for example, more frequent and direct communications with clients,
faster processing of benefit claims, and more inclusive payment methods). Unfortunately,
differences in the nature of particular bundles of service or variation in their quality remain
uncaptured. Hence, we need to interpret our results and recommendations on
benchmarking with caution. Figure 13 illustrates the limitations of our analysis.
Figure 13: Quality Cost Tradeoffs
Source: Author’s desig .
This diagram introduces an additional dimension to our multivariate regression and
effectively collapses our regression line into one single point, A . Assuming that there is a
relationship between the costs and quality of services offered, our regression generates an
average cost estimate around an average bundle of services that are of average quality.
Thus, if our assessment finds that a particular scheme operates just at its benchmark, it
corresponds to line M in the diagram. However, it does not necessarily imply that the
scheme offers services at an efficient level. In fact, given the cost, the scheme can offer a
service bundle that would place it to the left of poi t A , which is a suboptimal cost and
quality combination. Or it can offer exceptional services, which would put it on the far right
of poi t A , implying an efficiency premium in such a system. So, on-the-benchmark results
of our simulations can only be taken to imply equal probabilities of over- and under-
efficient operation.
However, if the osts e eed our e h ark, it puts su h a s ste o li e H, hi h ea s that ith the opti al operatio arked poi t B there o is a greater pro a ilit that the system operates sub-efficiently. Finally, with the estimate below the benchmark, we are
Quality
Cost
A
L
M
H
B
C
53
o o li e L, a d so, there is a higher pro a ilit that the s ste osts less tha the average cost estimate of the same bundle of services would suggest.
In summary, the tools of conventional analysis with narrow sets of explanatory variables
can only produce very limited inferences about the performance of various programs. In
each case, we need to look beyond our results. Special operational and beneficiary surveys
could help capture information on the performance and satisfaction of various stakeholders
with program administration, including processing times, compliance costs and various
overheads, and overall perception of service quality. At the same time, our methodology
and findings help point in the direction of such additional inquiries.
Furthermore, it is difficult to establish a clear association between the costs and value of
service, even in theory, to support any assumption about the shape of the cost line in figure
13. Even if we could define distinct bundles of services, ranking of their social preferences
and hence, value for society would not be straightforward. While it is beyond the scope of
this paper, we can take a brief look at this challenge. For example, in the context of a DC
plan where individuals may have a choice of an investment option, let us consider three
alternative operational arrangements: (1) there are three investment options with switching
allowed once a month, (2) there are five investment options but with switching allowed no
more than twice a year, and (3) there is only one default investment portfolio. Perhaps both
(1) and (2) are superior to (3) but choosing a superior solution between (1) and (2) is not
clear-cut. In fact, on a society utility plateau, (1) and (2) may be equally preferable but the
costs may considerably and systematically differ. So, it would be difficult to establish a clear
association between the costs and values (quality) of services in this case.50
Thus, the gap in accounting for the variation in service value and quality in the operation of
mandatory social security programs limits conclusiveness of our benchmarking analysis.
VIII. Conclusions
As countries develop and seek to provide coverage to greater segments of their population,
administrative costs become an important aspect of reforms, especially where new
mandate extends to low-income or informal-sector workers. New technologies pave the
way for effective outreach, monitoring, and recordkeeping, while infrastructure
improvements (including financial services) provide for better access.
Comparing and benchmarking administrative expenditures helps assess the efficiency of
different modes of operational organization of public social security programs. It provides
guidance on reform strategy, choice of alternative organizational models, and trade-offs in
50
We also note that all quality improvements with their additional investment costs exhibit diminishing
returns. While (3) is one extreme of the spectrum, at the other end of the same spectrum, we may have an
unlimited number of options with unlimited switching allowed and all instantly available.
54
instituting various new operational elements. Inquiries into operational efficiency often
prompt complex organizational transformations. Among trend-setting practices are cutting
redundant staff, employing more advanced technologies, sharing certain functions with
other public entities, and outsourcing select tasks to other agencies. To decide on optimal
investments in systems, processes, and people, it is important to understand the key factors
that affect the costs of operating various schemes.
First, it is important to recognize economies of scale and scope in managing social security
programs, including their magnitudes in contribution collection and benefit management.
Synergies with existing mechanisms should always be sought. Private management of the
schemes will be more expensive compared to the public option but differences may
disappear over the long term. At the same time, funding of pension liabilities (in either DB
or DC schemes) will always involve cost premium, given advanced complementary resource
requirements. These considerations will involve important policy decisions. Finally, the level
of economic development has a strong impact on costs, suggesting that more developed
countries can manage social security schemes more efficiently, possibly taking advantage of
better technologies, infrastructure, and institutions. As technologies spread over time, they
may become less important in explaining cost differentials. Yet, quality of governance
seems to remain an important indicator of the financial health of any program in both the
short and long run.
55
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58
Annex 1: List of Public Pension Programs and Abbreviations Used
Country Year Population GDP pc, US$ Institution / Program Abbreviation
Afghanistan 2006 27,518,809 300 MOLSA Pension Department AFG-PD
Armenia 2007 3,072,450 3,000 State Service of Social Security ARM-SSSS
Australia 2007-2008 21,072,500 40,660 ComSuper+All other expenses AUS-ComSuper
Azerbaijan 2007 8,581,300 3,850 State Social Protection Fund AZE-SSPF
Bahamas 2007 333,609 21,680 National Insurance Board BHS-NIB
Bahrain 2007 759,560 24,320 General Organisation for Social Insurance BHR-GOSI
Bahrain 2006 743,522 21,320 Pension Fund Commission BHR-PFC
Belize 2005 291,800 3,820 Social Security Board BLZ-SSB
Bhutan 2007/2008 676,040 1,770 National Pension and Provident Fund BTN-NPPF
Bosnia Herzegovina (Federation 2003 3,783,067 2,210 Federal Fund for Pension and Disability Insurance BIH-(F)FPDI
Bosnia Herzegovina (Republika 2003 3,783,067 2,210 Fund for Pension and Disability Insurance of the Republika Srpska BIH-(RS)FPDI
Botswana 1999 1,692,814 3,470 Old Age Pension -- Department of Labor and Social Security BWA-OAP
Botswana 2006/2007 1,864,831 6,040 Public Officers Pension Fund BWA-POPF
Brazil 2008 191,971,506 8,540 National Social Security Institute BRA-NSSI
Burkina Faso 2004 13,290,189 380 National Social Security Fund BFA-CNSS
Canada 2007 32,976,000 43,180 Old Age Security Program CAN-OAS
Canada 2007 32,976,000 43,180 Canada Pension Plan CAN-CPP
Canada (Alberta) 2008 3,500,000 82,490 Alberta Public Service Pension Plan CAN-(AB)PSPP
Canada 2007 32,976,000 43,180 Public Service Pension Plan CAN-PSPP
Canada 2007 32,976,000 43,180 Canadian Forces Pension Plan CAN-CFPP
Croatia 2007 4,436,000 13,200 Pension Insurance Institute HRV-PII
Cyprus 2007 853,814 25,120 Social Insurance Services CYP-SIS
Czech Republic 2007 10,334,160 16,860 Social Security Administration CZE-CSSA
Denmark 2007 5,461,438 56,890 ATP (Danish Labor Market Supplementary Pension) DNK-ATP
Denmark 2007 5,461,438 56,890 Special Pension Savings (SP) Scheme DNK-SP
Dominica 2005 72,000 4,160 Social Security Board DMA-SSB
Egypt, Arab Rep. 2004 75,718,360 1,040 Public and Private Entreprises Employees Pension Fund EGY-PPPF
Egypt, Arab Rep. 2004 75,718,360 1,040 Government Employee Pension Fund EGY-GEPF
Estonia 2007 1,341,672 15,940 Social Insurance Board EST-SIB
Fiji 2006 833,330 3,720 National Profident Fund FJI-NPF
Finland 2008 5,313,399 50,780 Social Insurance Institution of Finland (KELA) FIN-SIIF
France (New Caledonia) 2007 242,400 36,390 Social Welfare Fund of New Caledonia (CAFAT) FRA-(NCL)CAFAT
Georgia 2008 4,307,011 2,970 Social Service Agency GEO-SSA
Ghana 2006 22,393,338 570 Social Security and National Insurance Trust GHA-SSNIT
Grenada 2006 102,823 5,490 National Insurance Board GRD-NIB
Guyana 2002 758,834 950 National Insurance Scheme GUY-NIS
Hungary 2007 10,055,780 13,800 Central Administration of National Pension Insurance HUN-CANPI
India 2006-2007 1,109,811,147 860 Employees' Provident Fund Organization IND-EPFO
Indonesia 2007 224,669,595 1,920 Employees’ Social Security (JAMSOSTEK) IDN-ESS
Ireland 2007 4,356,931 59,610 Department of Social and Family Affairs IRL-DSFA
Jordan 2006 5,542,000 2,680 Social Security Corporation JOR-SSC
Kenya 2006/2007 36,771,613 610 National Social Security Fund KEN-NSSF
Kenya 2006 36,771,613 610 Civil Service Pension Scheme KEN-CSPS
Kenya 2007 37,754,701 720 Local Authorities Pension Trust KEN-LAPT
Korea, Rep. 2006 48,297,000 19,710 National Pension Service KOR-NPS
Kosovo 2007 1,785,000 2,620 Kosovo Pension Administration KOS-KPA
Kosovo 2007 1,785,000 2,620 Kosovo Pension Savings Trust KOS-KPST
Kyrgyz Republic 2006 5,192,100 550 Kyrgyz Republic Social Fund KGZ-KRSF
Latvia 2007 2,276,100 12,640 State Social Insurance Fund (VSAA) LVA-SSIF
Lithuania 2007 3,375,618 11,580 State Social Insurance Fund Board (SODRA) LTU-SSIFB
Macedonia, FYR 2007 2,039,838 3,880 Pendion and Disability Insurance Fund MFD-PDIF
Malaysia 2007 26,555,654 7,000 Employees Provident Fund (KWSP) MYS-EPF
Malaysia 2006 26,094,742 6,000 Social Security Organisation (PERKESO) MYS-SSO
Maldives 2010 315,900 4,690 Maldives Pension Administration Office MDV-MPAO
Mali 2007 12,408,824 580 National Social Insurance Institute (INPS) MLI-NSII
Marshal islands 2005 55,792 2,480 Marshall Islands Social Security Administration MHL-SSA
Mauritius 2005 1,243,253 5,050 Mauritius National Pensions Fund MUS-MNPS
Micronesia, Fed. Sts. 2007 110,123 2,300 Federated States of Micronesia Social Security Administration FSM-SSA
Moldova 2008 3,633,369 1,670 National Office of Social Insurance (CANS) MDA-NOSI
59
List of Public Pension Programs and Abbreviations Used (Continued)
Country Year Population GDP pc, US$ Institution / Program Abbreviation
Morocco 2007 31,224,136 2,410 National Social Security Fund (CNSS) MAR-NSSF
Morocco 2007 31,224,136 2,410 Moroccan Pension Fund (CMR) MAR-MPF
Namibia 2001 1,861,828 1,910 Social Pension NAM-SP
Namibia 2007 2,088,671 4,230 Government Institutions Pension Fund NAM-GIPF
Netherlands 2007 16,381,696 47,510 Social Insurance Bank NLD-SIB
New Zealand 2007 4,228,300 31,850 Superannuation NZL-Super
New Zealand (Cook Islands) 2009 21,300 14,080 National Superannuation Fund NZL-(CI) NSF
Norway 2008 4,768,212 94,570 Labour and Welfare Administration (NAV) NOR-NAV
Pakistan 2006-2007 159,144,934 800 Employees' Old-Age Benefits Institution PAK-EOBI
Philippines 2007 88,718,185 1,620 Social Security System PHL-SSS
Philippines 2007 88,718,185 1,620 Government Service Insurance System PHL-GSIS
Poland 2007 38,120,560 11,160 Social Insurance Institute POL-ZUS
Poland 2007 38,120,560 11,160 Agricultural Social Security Fund POL-KRUS
Portugal 2007 10,608,335 21,040 Social Security Institute PRT-SSI
Romania 2007 21,546,873 7,860 National Pension and Social Insurance Fund ROM-NPSIF
Rwanda 2007 9,454,534 360 Rwanda Social Security Board RWA-RSSB
Samoa 2006 179,004 2,470 Samoa National Provident Fund WSM-SNPF
Senegal 2001 10,164,729 480 Social Insurance Institute for Old-Age Pensions (IPRES) SEN-SII
Sierra Leone 2006 5,270,799 270 National Social Security and Insurance Trust SLE-NASSIT
Singapore 2007 4,588,600 38,520 Central Provident Fund SGP-CPF
Slovak Republic 2006 5,391,409 12,810 Social Insurance Agency SVK-SIA
Solomon Islands 2008 510,672 1,290 National Provident Fund (NPF) SLB-NPF
South Africa 2007/2008 48,257,282 5,930 South African Social Security Agency ZAF-SSA
South Africa 2007/2008 48,257,282 5,930 Government Employees Pension Fund ZAF-GEPF
Spain 2007 44,878,945 32,100 National Institute of Social Security ESP-NISS
Sri Lanka 2005 19,668,000 1,240 Employees’ Provident Fund LKA-EPF
Sri Lanka 2001 18,797,000 840 Farmers Pension Scheme LKA-FPS
St. Kitts and Nevis 2007 48,790 10,520 Social Security Board KNA-SSB
St. Lucia 2000 155,996 4,540 National Insurance Corporation LCA-NIC
St. Vincent and the Grenadines 2004 108,531 3,880 National Insurance Services VCT-NIS
Swaziland 2007 1,151,399 2,560 Swaziland National Provident Fund SWZ-SNPF
Swaziland 2008 1,167,834 2,430 PSPF - Public Service Pension Fund SWZ-PSPF
Sweden 2007 9,148,092 50,560 Swedish Social Insurance Agency (SSIA+NPFs):1st Pillar OA Pensions SWE-SSIA/NPFs (OA Pen)
Sweden 2007 9,148,092 50,560 Premium Pension Authority (incl PP+Funds): 2d Pillar SWE-PPA
Syrian Arab Republic 2007 20,082,697 2,020 General Organization for Social Insurance SYR-GOSI
Tanzania 2007/2008 42,267,667 490 Government Employee Pension Fund TZA-GEPF
Tanzania 2007 41,276,209 410 Parastatal Pension Fund TZA-PPF
Tanzania (Zanzibar) 2004/2005 1,040,659 300 Zanzibar Social Security Fund TZA-(ZZB)ZSSF
Thailand 2007 66,979,359 3,690 Government Pension Fund THA-GPF
Thailand 2005 65,945,675 2,670 Social Security Office THA-SSO
Tonga 2009 103,967 2,990 Retirement Fund Board TON-RFB
Trinidad and Tobago 2007 1,328,216 15,740 National Insurance Board TTO-NIB
Turkey 2007 73,003,736 8,860 Social Security Institution TUR-SSI
Uganda 2009 32,368,000 490 National Social Security Fund UGA-NSSF
Ukraine 2007 46,509,350 3,070 Pension Fund of Ukraine UKR-PFU
United Kingdom (Great Britain) 2007/08 60,980,304 45,900 UK Pension Service UK-(GBR)PS
United Kingdom (Anguilla) 2006 13,600 11,900 Social Security Board UK-(ANG)SSB
United Kingdom (Falkland Islan 2007 3,000 69,330 Falkland Islands Pension Scheme UK-(FIS)FIPS
United Kingdom (Jersey) 2006 90,000 58,890 Social Security Department UK-(JER)SSD
United Kingdom (Northern Irela 2007/2008 1,720,000 43,390 Northern Ireland Social Security Agency UK-(NI)NISSA
United States 2007 301,580,000 46,460 SSA Supplementary Security Income USA-SSA/SSI
United States 2007 301,580,000 46,460 SSA Old Age Survivor Disability Insurance USA-SSA/OASDI
United States (Alaska) 2007 676,987 65,730 Alaska Permanent Fund Dividend (PFD) Division USA-(AK)APFDD
United States 2008 304,375,000 47,210 Thrift Savings Plan USA-TSP
Uruguay 2007 3,323,906 7,210 Social Insurance Bank (BPS) URY-SIB
Vanuatu 2006 222,200 2,020 Vanuatu National Provident Fund VUT-VNPF
Vietnam 2008 86,210,781 1,050 Vietnam Social Security VNM-VSS
Yemen 2006 21,637,666 880 General Corporation for Social Security YEM-GCSS
Yemen 2006 21,637,666 880 General Agency for Pensions and Social Security YEM-GAPSS
Zambia 2007 12,313,942 940 Workers Compensation Fund ZMB-WCF
60
Annex 2: Key Institutional and Operational Indicators
Ag
en
cy
DC
Sc
he
me
Pri
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e
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es
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AFG-PD No No No No No No 1 125 320,000 54,370 435 22,213 .. 166 0.002% $3.06 \ 1.02% 0.75% .. ..
ARM-SSSS No Yes Yes No No Yes 70 904 586,486 536,183 1,242 249,681 .. 8,013 0.09% $7.14 $111 0.24% 3.21% .. ..
AUS-ComSuper No No Yes Yes Yes No n.a. 610 271,929 193,867 763 4,873,345 1,399,915 212,202 0.02% $455.57 $521 1.12% 4.35% 15.16% $17,572,156
AZE-SSPF No Yes Yes No No Yes 81 2,616 1,700,200 1,312,900 1,152 1,113,197 .. 37,131 0.11% $12.32 $149 0.32% 3.34% .. ..
BHS-NIB No Yes Yes Yes Yes Yes 27 488 146,752 31,894 366 139,499 155,500 29,830 0.41% $166.98 $358 0.77% 21.38% 19.18% $1,492,000
BHR-GOSI No Yes Yes Yes Yes Yes 4 385 374,466 14,580 1,011 130,104 289,754 17,330 0.09% $44.54 $85 0.18% 13.32% 5.98% $3,930,751
BHR-PFC No No Yes Yes Yes Yes n.a. n.a. 54,000 10,020 n.a. 252,356 278,167 8,596 0.05% $134.27 $293 0.63% 3.41% 3.09% $4,000,654
BLZ-SSB No Yes Yes Yes Yes Yes 13 250 71,719 14,148 343 15,646 25,164 6,850 0.61% $79.78 $970 2.09% 43.78% 27.22% $150,000
BTN-NPPF Yes No Yes Yes Yes Yes n.a. n.a. 38,210 2,855 n.a. 3,657 17,461 868 0.07% $21.14 $555 1.19% 23.74% 4.97% $157,490
BIH-(F)FPDI No Yes Yes Yes No No n.a. n.a. 209,699 288,613 n.a. 443,410 445,690 21,120 0.25% $42.38 $891 1.92% 4.76% 4.74% ..
BIH-(RS)FPDI No Yes Yes Yes No No n.a. n.a. 132,320 173,692 n.a. 171,732 147,017 8,310 0.10% $27.15 $571 1.23% 4.84% 5.65% ..
BWA-OAP No Yes No No No No n.a. n.a. 774,417 77,200 n.a. 22,706 .. 1,022 0.02% $13.24 $177 0.38% 4.50% .. ..
BWA-POPF Yes No Yes Yes Yes No n.a. 35 83,329 3,719 2,487 192,714 148,363 32,125 0.29% $369.05 $2,839 6.11% 16.67% 21.65% $4,112,767
BRA-NSSI No Yes Yes Yes Yes Yes n.a. 39,559 53,741,233 24,950,929 1,989 118,335,666 n.a. 5,086,237 0.31% $64.63 $352 0.76% 4.30% n.a. ..
BFA-CNSS No Yes Yes Yes No No n.a. 800 73,362 297,061 463 15,435 43,661 27,798 0.54% $75.04 $9,175 19.75% 180.09% 63.67% ..
CAN-OAS No Yes No No No No 587 n.a. 18,356,909 4,447,602 n.a. 31,228,029 .. 106,135 0.01% $23.86 $26 0.06% 0.34% .. ..
CAN-CPP No Yes Yes No Yes No 587 n.a. 12,280,000 4,758,774 n.a. 24,509,841 .. 407,784 0.03% $23.93 $26 0.06% 1.66% .. $108,556,085
CAN-(AB)PSPP No No Yes No Yes No n.a. 57 48,075 19,290 1,189 202,898 .. 25,134 0.01% $373.10 $210 0.45% 12.39% .. $4,470,310
CAN-PSPP No No Yes No Yes No 3 700 294,979 231,913 753 4,266,831 .. 176,892 0.01% $335.73 $361 0.78% 4.15% .. $26,314,144
CAN-CFPP No No Yes No Yes No n.a. 139 87,532 108,798 1,412 2,105,020 .. 40,778 0.00% $207.70 $223 0.48% 1.94% .. $7,330,794
HRV-PII No Yes Yes No No No 112 3,399 1,579,463 1,577,301 929 5,917,113 .. 112,370 0.19% $35.60 $125 0.27% 1.90% .. ..
CYP-SIS No Yes Yes Yes No Yes n.a. n.a. 421,352 132,265 n.a. 650,626 619,328 6,097 0.03% $11.01 $20 0.04% 0.94% 0.98% $4,605,660
CZE-CSSA No Yes Yes Yes No Yes 92 8,578 4,880,187 3,347,121 959 15,647,591 17,553,596 265,670 0.15% $32.29 $89 0.19% 1.70% 1.51% ..
DNK-ATP No Yes Yes No Yes No 1 226 3,116,000 716,000 16,956 1,408,049 .. 132,998 0.04% $34.71 $28 0.06% 9.45% .. $71,458,740
DNK-SP Yes Yes Yes No Yes No 1 110 2,547,500 158,450 24,600 307,879 .. 38,209 0.01% $14.12 $12 0.02% 12.41% .. $10,121,791
DMA-SSB No Yes Yes Yes Yes Yes 2 45 17,169 4,489 481 10,344 10,242 1,567 0.52% $72.37 $808 1.74% 15.15% 15.30% $100,664
EGY-PPPF No Yes Yes Yes No Yes 453 23,000 13,910,000 5,184,350 830 1,613,882 952,190 41,477 0.05% $2.17 $97 0.21% 2.57% 4.36% $15,213,743
EGY-GEPF No No Yes Yes No Yes 62 8,000 4,792,000 1,999,000 849 1,258,828 1,517,049 21,465 0.03% $3.16 $141 0.30% 1.71% 1.41% $22,043,040
EST-SIB No Yes Yes No No No 4 609 650,000 675,770 2,177 1,623,212 .. 11,974 0.06% $9.03 $26 0.06% 0.74% .. ..
FJI-NPF Yes Yes Yes Yes Yes No 6 242 331,050 85,577 1,722 178,850 154,605 11,259 0.36% $27.03 $338 0.73% 6.30% 7.28% $1,848,371
FIN-SIIF No Yes Yes No Yes Yes 287 5,864 2,670,000 2,730,600 921 16,282,279 .. 441,206 0.16% $81.70 $75 0.16% 2.71% .. $1,325,668
FRA-(NCL)CAFAT No Yes Yes Yes Yes Yes n.a. 413 97,563 122,441 533 865,633 736,338 36,466 0.41% $165.75 $212 0.46% 4.21% 4.95% $266,571
GEO-SSA No Yes No No No No 77 2,206 2,274,709 1,362,227 618 555,743 .. 18,955 0.15% $13.91 $218 0.47% 3.41% .. ..
GHA-SSNIT No Yes Yes Yes Yes Yes 73 n.a. 854,761 73,311 n.a. 87,152 312,937 54,167 0.43% $58.36 $4,757 10.24% 62.15% 17.31% $1,289,747
GRD-NIB No Yes Yes Yes Yes Yes 3 75 36,715 3,889 541 9,111 16,519 2,289 0.41% $56.37 $477 1.03% 25.12% 13.86% $193,704
GUY-NIS No Yes Yes Yes Yes Yes n.a. 545 130,533 43,352 319 23,472 29,079 4,143 0.57% $23.82 $1,165 2.51% 17.65% 14.25% $111,940
HUN-CANPI No Yes Yes No No No 9 3,872 4,356,500 3,680,304 2,076 15,128,590 .. 158,309 0.11% $19.70 $66 0.14% 1.05% .. ..
IND-EPFO Yes Yes Yes Yes Yes No 242 19,510 44,404,000 5,100,230 2,537 2,671,993 6,127,836 211,634 0.02% $4.28 $231 0.50% 7.92% 3.45% $56,547,543
IDN-ESS Yes Yes Yes Yes Yes Yes 128 2,997 7,941,017 802,504 2,917 442,886 949,153 182,171 0.04% $20.83 $504 1.09% 41.13% 19.19% $6,715,130
IRL-DSFA No Yes Yes No No Yes 132 4,840 3,002,276 1,500,504 930 20,469,579 .. 770,649 0.30% $171.15 $133 0.29% 3.76% .. ..
JOR-SSC No Yes Yes Yes Yes No 19 1,276 689,176 213,548 707 313,962 509,931 27,373 0.18% $30.32 $526 1.13% 8.72% 5.37% $5,218,618
KEN-NSSF Yes Yes Yes Yes Yes No 39 1,800 900,000 50,000 528 33,566 75,529 37,528 0.17% $39.50 $3,009 6.48% 111.80% 49.69% $1,109,557
KEN-CSPS No No No No No No n.a. 198 393,000 158,700 802 238,541 .. 2,692 0.01% $16.96 $1,292 2.78% 1.13% .. ..
KEN-LAPT No No Yes Yes Yes No n.a. n.a. 22,862 4,720 n.a. 10,562 20,828 2,460 0.01% $89.17 $5,754 12.38% 23.29% 11.81% $93,179
KOR-NPS No Yes Yes Yes Yes No 97 4,833 17,740,000 1,830,600 4,049 4,566,447 21,106,200 484,538 0.05% $24.76 $58 0.13% 10.61% 2.30% $190,827,199
KOS-KPA No Yes No No No No 33 158 550,000 151,077 956 100,659 .. 2,806 0.06% $18.57 $329 0.71% 2.79% .. ..
KOS-KPST Yes Yes Yes No Yes No 1 24 238,000 1,592 9,983 2,506 .. 2,748 0.06% $11.47 $203 0.44% 109.67% .. $380,822
KGZ-KRSF No Yes Yes Yes No No 54 1,900 1,029,300 524,000 818 141,766 192,210 6,376 0.22% $4.10 $347 0.75% 4.50% 3.32% $62,262
LVA-SSIF No Yes Yes No No Yes 43 n.a. 1,202,400 1,066,609 n.a. 3,628,299 .. 31,044 0.11% $13.68 $50 0.11% 0.86% .. ..
LTU-SSIFB No Yes Yes Yes No Yes 50 3,970 1,467,000 1,319,807 702 3,093,047 3,712,766 89,471 0.23% $32.11 $129 0.28% 2.89% 2.41% ..
MFD-PDIF No Yes Yes Yes No No 31 733 419,347 280,249 954 590,020 489,924 10,993 0.14% $15.71 $188 0.40% 1.86% 2.24% ..
MYS-EPF Yes Yes Yes Yes Yes Yes 62 5,176 5,400,000 1,075,742 1,251 6,172,165 8,414,667 153,686 0.08% $23.73 $158 0.34% 2.49% 1.83% $92,591,586
MYS-SSO No Yes Yes Yes Yes Yes 45 1,142 5,454,799 259,081 5,003 264,430 432,408 35,943 0.02% $6.29 $49 0.10% 13.59% 8.31% $3,856,127
MDV-MPAO Yes Yes Yes Yes Yes No 1 27 35,000 14,500 1,833 24,438 14,250 569 0.04% $11.49 $114 0.25% 2.33% 3.99% $15,625
MLI-NSII No Yes Yes Yes Yes Yes n.a. 1,717 193,185 43,809 138 53,878 109,193 16,172 0.23% $68.24 $5,466 11.77% 30.02% 14.81% $63,772
MHL-SSA No Yes Yes Yes Yes No 2 26 10,486 3,240 528 10,674 17,621 928 0.67% $67.63 $1,267 2.73% 8.70% 5.27% $56,623
MUS-MNPS No Yes Yes Yes Yes No n.a. n.a. 300,000 253,600 n.a. 190,295 46,345 3,763 0.06% $6.80 $63 0.13% 1.98% 8.12% $1,293,589
FSM-SSA No Yes Yes Yes Yes No 5 34 21,590 6,363 822 13,664 12,784 1,232 0.49% $44.06 $890 1.92% 9.01% 9.63% $47,322
MDA-NOSI No Yes Yes Yes No Yes n.a. 1,316 900,000 1,029,500 1,466 589,240 507,686 14,485 0.24% $7.51 $209 0.45% 2.46% 2.85% ..
61
Key Institutional and Operational Indicators (Continued)
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MAR-NSSF No Yes Yes Yes Yes Yes 71 4,750 1,970,000 818,745 587 880,091 1,352,728 79,999 0.11% $28.69 $553 1.19% 9.09% 5.91% $2,481,588
MAR-MPF No No Yes No Yes No 12 419 855,837 528,368 3,304 1,705,011 .. 21,007 0.03% $15.18 $293 0.63% 1.23% .. ..
NAM-SP No Yes No No No No n.a. n.a. 620,338 107,822 n.a. 33,931 .. 3,049 0.09% $28.28 $688 1.48% 8.99% .. ..
NAM-GIPF No No Yes Yes Yes No n.a. 143 72,370 38,439 775 130,950 152,214 31,949 0.36% $288.32 $3,167 6.82% 24.40% 20.99% $4,984,554
NLD-SIB No Yes No No No No 13 3,247 8,000,000 4,720,092 1,454 40,786,300 .. 281,653 0.04% $59.67 $58 0.13% 0.69% .. ..
NZL-Super No Yes No No No No 20 560 2,280,000 891,575 1,592 6,514,560 .. 29,322 0.02% $32.89 $48 0.10% 0.45% .. ..
NZL-(CI) NSF Yes Yes Yes Yes Yes No 1 n.a. 5,631 254 n.a. 406 4,488 577 0.19% $98.10 $324 0.70% 142.30% 12.87% $19,685
NOR-NAV No Yes Yes No No Yes 293 n.a. 2,500,000 2,121,423 n.a. 44,166,667 .. 1,648,936 0.37% $356.80 $175 0.38% 3.73% .. ..
PAK-EOBI No Yes Yes Yes Yes No 63 938 1,820,000 302,677 2,263 57,170 80,482 9,902 0.01% $4.66 $271 0.58% 17.32% 12.30% $1,824,236
PHL-SSS No Yes Yes Yes Yes Yes 178 6,074 7,863,340 1,729,399 1,579 1,315,229 1,339,788 128,391 0.09% $13.38 $384 0.83% 9.76% 9.58% $5,368,274
PHL-GSIS No No Yes Yes Yes Yes 58 3,100 1,355,558 233,778 513 699,981 884,213 137,946 0.10% $86.79 $2,489 5.36% 19.71% 15.60% $8,886,117
POL-ZUS No Yes Yes Yes No Yes 327 47,588 14,074,500 8,396,800 472 42,610,298 49,017,251 1,133,460 0.27% $50.44 $210 0.45% 2.66% 2.31% $1,253,816
POL-KRUS No Yes Yes Yes No Yes 273 6,390 1,610,000 1,586,800 500 4,949,616 429,740 173,875 0.04% $54.39 $226 0.49% 3.51% 40.46% ..
PRT-SSI No Yes Yes Yes No Yes n.a. n.a. 4,480,804 4,572,059 n.a. 22,365,400 #VALUE! 766,454 0.34% $84.66 $187 0.40% 3.43% n.a. ..
ROM-NPSIF No Yes Yes No No No 43 4,737 5,479,432 4,677,128 2,144 9,383,599 .. 161,583 0.10% $15.91 $94 0.20% 1.72% .. ..
RWA-RSSB No Yes Yes Yes Yes No 30 227 216,304 29,121 1,081 6,726 28,951 7,932 0.23% $32.32 $4,171 8.98% 117.93% 27.40% $206,608
WSM-SNPF Yes Yes Yes Yes Yes Yes n.a. 165 22,526 4,224 162 8,371 11,388 1,666 0.38% $62.28 $1,171 2.52% 19.90% 14.63% $113,450
SEN-SII No Yes Yes Yes Yes No 9 227 170,929 92,175 1,159 28,262 38,089 5,457 0.11% $20.74 $2,007 4.32% 19.31% 14.33% $68,209
SLE-NASSIT No Yes Yes Yes Yes No 6 227 126,749 19,559 645 4,488 16,759 4,874 0.34% $33.32 $5,733 12.34% 108.60% 29.08% $51,176
SGP-CPF Yes Yes Yes Yes Yes Yes n.a. 1,200 1,545,000 962,317 2,089 7,671,600 11,735,056 77,670 0.04% $30.98 $37 0.08% 1.01% 0.66% $93,708,995
SVK-SIA No Yes Yes Yes No Yes 39 5,839 2,000,000 1,419,470 586 4,383,401 3,939,048 102,449 0.15% $29.96 $109 0.23% 2.34% 2.60% ..
SLB-NPF Yes Yes Yes Yes Yes Yes n.a. 104 135,960 3,708 1,343 5,369 13,410 3,473 0.53% $24.87 $896 1.93% 64.70% 25.90% $109,409
ZAF-SSA No Yes No No No No n.a. 7,528 18,036,174 12,386,396 1,645 8,817,002 .. 630,303 0.22% $50.89 $399 0.86% 7.15% .. ..
ZAF-GEPF No No Yes Yes Yes No 4 705 1,160,000 403,280 2,217 3,406,495 3,678,878 146,379 0.05% $93.64 $734 1.58% 4.30% 3.98% $102,904,534
ESP-NISS No Yes Yes Yes No Yes n.a. 13,000 19,151,400 9,660,574 2,216 118,060,711 124,256,584 782,302 0.05% $27.15 $39 0.08% 0.66% 0.63% ..
LKA-EPF Yes Yes Yes Yes Yes No n.a. 600 2,100,000 93,841 3,656 169,396 271,795 3,545 0.01% $1.62 $61 0.13% 2.09% 1.30% $4,027,362
LKA-FPS No Yes Yes Yes Yes No n.a. 400 388,800 10,366 998 1,300 n.a. 688 0.00% $1.72 $95 0.21% 52.89% n.a. $26,515
KNA-SSB No Yes Yes Yes Yes Yes 2 104 30,953 4,525 341 10,744 22,222 3,538 0.69% $99.73 $440 0.95% 32.93% 15.92% $337,037
LCA-NIC No Yes Yes Yes Yes Yes n.a. 95 41,004 5,556 491 7,360 17,849 2,259 0.32% $48.52 $497 1.07% 30.70% 12.66% $187,922
VCT-NIS No Yes Yes Yes Yes Yes n.a. 53 33,782 4,373 720 5,533 7,987 1,461 0.35% $38.30 $459 0.99% 26.41% 18.30% $100,000
SWZ-SNPF Yes Yes Yes Yes Yes No n.a. 121 125,000 10,970 1,124 10,007 13,243 4,583 0.16% $33.71 $612 1.32% 45.80% 34.61% $161,405
SWZ-PSPF No No Yes Yes Yes No n.a. n.a. 33,000 21,000 n.a. 36,185 51,134 6,232 0.22% $115.40 $2,206 4.75% 17.22% 12.19% $969,075
SWE-SSIA/NPFs (O No Yes Yes No Yes No n.a. 1,300 7,557,655 2,120,000 7,444 27,468,459 .. 398,321 0.09% $41.16 $38 0.08% 1.45% .. $129,975,129
SWE-PPA Yes Yes Yes No Yes No 2 210 5,838,802 449,000 29,942 67,468 .. 277,717 0.06% $44.17 $41 0.09% 411.63% .. $42,019,480
SYR-GOSI No Yes Yes Yes Yes No 23 1,986 1,730,448 180,142 962 274,413 n.a. 8,519 0.02% $4.46 $103 0.22% 3.10% n.a. $151,396
TZA-GEPF No No Yes Yes Yes Yes n.a. n.a. 28,200 743 n.a. 1,136 8,690 1,031 0.00% $35.63 $3,379 7.27% 90.78% 11.87% $46,736
TZA-PPF No Yes Yes Yes Yes Yes 6 170 84,186 16,813 594 19,954 67,127 9,022 0.05% $89.33 $10,123 21.79% 45.22% 13.44% $325,292
TZA-(ZZB)ZSSF No Yes Yes Yes Yes Yes n.a. 42 40,000 816 972 493 3,968 363 0.12% $8.90 $1,379 2.97% 73.78% 9.16% $18,403
THA-GPF Yes No Yes Yes Yes Yes 1 277 1,177,586 20,158 4,324 220,419 657,545 20,087 0.01% $16.77 $211 0.45% 9.11% 3.05% $10,926,651
THA-SSO No Yes Yes Yes Yes Yes n.a. 5,931 8,467,410 1,388,210 1,662 598,332 2,178,012 48,549 0.03% $4.93 $86 0.18% 8.11% 2.23% $9,659,074
TON-RFB Yes No Yes Yes Yes No n.a. 20 3,726 189 196 1,811 3,436 306 0.10% $78.27 $1,216 2.62% 16.92% 8.92% $35,009
TTO-NIB No Yes Yes Yes Yes Yes 15 614 501,450 120,615 1,013 163,811 237,357 15,393 0.07% $24.74 $73 0.16% 9.40% 6.49% $2,259,786
TUR-SSI No Yes Yes Yes No Yes n.a. 24,779 15,059,964 9,566,647 994 55,481,612 33,809,680 752,150 0.12% $30.54 $160 0.34% 1.36% 2.22% ..
UGA-NSSF Yes Yes Yes Yes Yes No 24 600 230,000 10,000 400 20,225 129,556 22,314 0.14% $92.97 $8,816 18.97% 110.33% 17.22% $640,394
UKR-PFU No Yes Yes Yes No No 760 36,000 15,350,000 13,321,042 796 19,493,069 15,002,234 262,694 0.18% $9.16 $139 0.30% 1.35% 1.75% ..
UK-(GBR)PS No Yes No No No No n.a. 11,890 32,105,000 12,000,000 1,009 146,066,704 .. 1,254,955 0.04% $104.58 $106 0.23% 0.86% .. ..
UK-(ANG)SSB No Yes Yes Yes Yes Yes n.a. 25 7,526 771 332 1,988 7,513 1,644 1.02% $198.17 $774 1.67% 82.69% 21.89% $59,444
UK-(FIS)FIPS Yes Yes Yes Yes Yes No 1 n.a. 600 17 n.a. 1,727 3,380 214 0.10% $346.47 $232 0.50% 12.38% 6.32% $47,085
UK-(JER)SSD No Yes Yes Yes Yes Yes n.a. n.a. 87,458 28,165 n.a. 272,730 271,514 10,142 0.19% $87.72 $69 0.15% 3.72% 3.74% $1,072,703
UK-(NI)NISSA No Yes Yes No No Yes 35 5,336 880,000 986,027 350 7,309,112 .. 377,720 0.51% $202.42 $217 0.47% 5.17% .. ..
USA-SSA/SSI No Yes No No No No 1526 23,000 156,677,557 7,359,525 320 39,927,000 .. 2,700,000 0.02% $366.87 $367 0.79% 6.76% .. ..
USA-SSA/OASDI No Yes Yes No Yes No 1526 44,000 163,177,000 52,464,978 4,901 584,967,000 .. 5,800,000 0.04% $26.90 $27 0.06% 0.99% .. $2,239,438,000
USA-(AK)APFDD No Yes No No No No n.a. n.a. 352,000 595,000 n.a. 984,522 .. 7,066 0.02% $11.87 $8 0.02% 0.72% .. ..
USA-TSP Yes No Yes No Yes Yes 1 380 2,600,000 353,000 7,771 11,660,000 .. 104,300 0.001% $35.32 $35 0.07% 0.89% .. $260,000,000
URY-SIB No Yes Yes Yes Yes Yes n.a. 4,338 1,004,886 1,276,927 526 2,421,305 1,578,122 157,960 0.66% $69.23 $446 0.96% 6.52% 10.01% $623,698
VUT-VNPF Yes Yes Yes Yes Yes No 2 39 27,922 710 734 1,802 8,438 1,417 0.32% $49.48 $1,138 2.45% 78.61% 16.79% $68,691
VNM-VSS No Yes Yes Yes Yes Yes 714 16,500 8,800,000 3,000,000 715 n.a. n.a. 57,300 0.06% $4.86 $215 0.46% n.a. n.a. $4,232,545
YEM-GCSS No Yes Yes Yes Yes No 8 395 98,900 4,163 261 3,664 27,217 2,573 0.01% $24.96 $1,318 2.84% 70.22% 9.45% $183,908
YEM-GAPSS No No Yes No Yes Yes 22 917 473,500 71,022 594 76,636 .. 6,014 0.03% $11.04 $583 1.26% 7.85% .. $1,119,903
ZMB-WCF No Yes Yes Yes Yes No 21 277 168,000 39,367 749 6,903 24,970 4,743 0.04% $22.87 $1,130 2.43% 68.71% 18.99% $32,000
62
Annex 3: Benchmarking Performance of Public Pension Programs
Total Admin Expenditures, US$
Actual Projected Actual Projected Staffing Expenditures Staffing Expenditures
AFG-PD 125 202 166,000 747,000 0.62 0.22 B B
ARM-SSSS 904 1,358 8,013,000 19,000,000 0.67 0.42 B B
AUS-ComSuper 610 505 82,824,000 50,100,000 1.21 1.65 A B
AZE-SSPF 2,616 2,783 37,131,000 44,800,000 0.94 0.83 A A
BHS-NIB 488 263 29,830,000 16,900,000 1.85 1.77 B B
BHR-GOSI 385 160 12,011,000 11,100,000 2.40 1.08 C A
BHR-PFC n.a. 132 8,596,000 8,438,000 n.a. 1.02 n.a. A
BLZ-SSB 250 202 6,850,000 4,421,000 1.24 1.55 A B
BTN-NPPF n.a. 94 868,000 1,303,000 n.a. 0.67 n.a. B
BIH-(F)FPDI n.a. 1,005 21,120,000 9,871,000 n.a. 2.14 n.a. C
BIH-(RS)FPDI n.a. 684 8,310,000 6,769,000 n.a. 1.23 n.a. A
BWA-OAP n.a. 185 1,022,000 3,131,000 n.a. 0.33 n.a. B
BWA-POPF 35 28 5,808,000 1,136,000 1.25 5.11 A C
BRA-NSSI 39,559 81,442 5,086,000,000 2,630,000,000 0.49 1.93 B B
BFA-CNSS 800 1,298 27,798,000 4,262,000 0.62 6.52 B C
CAN-OAS n.a. 3,627 106,100,000 277,000,000 n.a. 0.38 n.a. B
CAN-CPP n.a. 3,866 301,600,000 483,000,000 n.a. 0.62 n.a. B
CAN-(AB)PSPP 57 49 7,084,000 10,000,000 1.15 0.71 A B
CAN-PSPP 700 294 82,860,000 38,600,000 2.38 2.15 C C
CAN-CFPP 139 169 15,548,000 22,400,000 0.82 0.69 A B
HRV-PII 3,399 1,654 112,400,000 61,400,000 2.05 1.83 C B
CYP-SIS n.a. 681 6,097,000 28,600,000 n.a. 0.21 n.a. B
CZE-CSSA 8,578 10,285 265,700,000 320,000,000 0.83 0.83 A A
DNK-ATP 226 695 22,228,000 106,000,000 0.33 0.21 B B
DNK-SP 110 214 15,063,000 33,400,000 0.52 0.45 B B
DMA-SSB 45 106 1,567,000 2,475,000 0.43 0.63 B B
EGY-PPPF 23,000 22,421 41,477,000 122,000,000 1.03 0.34 A B
EGY-GEPF 8,000 9,321 21,465,000 51,800,000 0.86 0.41 A B
EST-SIB 609 784 11,974,000 33,200,000 0.78 0.36 A B
FJI-NPF 242 384 8,084,000 8,696,000 0.63 0.93 B A
FIN-SIIF 5,864 3,788 441,200,000 493,000,000 1.55 0.89 B A
FRA-(NCL)CAFAT 413 613 36,466,000 53,200,000 0.67 0.69 B B
GEO-SSA 2,206 1,774 18,955,000 26,100,000 1.24 0.73 A B
GHA-SSNIT n.a. 741 54,167,000 4,879,000 n.a. 11.10 n.a. C
GRD-NIB 75 95 2,289,000 2,637,000 0.79 0.87 A A
GUY-NIS 545 486 4,143,000 4,430,000 1.12 0.94 A A
HUN-CANPI 3,872 3,535 158,300,000 133,000,000 1.10 1.19 A A
IND-EPFO 19,510 13,506 200,800,000 116,000,000 1.44 1.73 B B
IDN-ESS 2,997 3,907 91,238,000 52,900,000 0.77 1.72 A B
IRL-DSFA 4,840 2,173 770,600,000 192,000,000 2.23 4.01 C C
JOR-SSC 1,276 778 27,373,000 14,200,000 1.64 1.93 B B
KEN-NSSF 1,800 338 37,528,000 2,505,000 5.32 14.98 C C
KEN-CSPS 198 390 2,692,000 2,215,000 0.51 1.22 B A
KEN-LAPT n.a. 82 2,366,000 689,000 n.a. 3.43 n.a. C
KOR-NPS 4,833 3,481 415,000,000 213,000,000 1.39 1.95 B B
KOS-KPA 158 310 2,806,000 4,363,000 0.51 0.64 B B
KOS-KPST 24 21 1,248,000 506,000 1.15 2.47 A C
KGZ-KRSF 1,900 1,937 6,376,000 7,938,000 0.98 0.80 A A
LVA-SSIF n.a. 1,988 31,044,000 67,300,000 n.a. 0.46 n.a. B
LTU-SSIFB 3,970 4,701 89,471,000 118,000,000 0.84 0.76 A A
MFD-PDIF 733 912 10,993,000 12,700,000 0.80 0.87 A A
MYS-EPF 5,176 4,215 128,800,000 127,000,000 1.23 1.01 A A
MYS-SSO 1,142 1,358 35,123,000 38,000,000 0.84 0.92 A A
MDV-MPAO 27 119 569,000 3,175,000 0.23 0.18 B B
MLI-NSII 1,717 523 16,172,000 3,503,000 3.28 4.62 C C
MHL-SSA 26 57 827,000 1,046,000 0.45 0.79 B A
MUS-MNPS n.a. 816 2,068,000 22,000,000 n.a. 0.09 n.a. B
FSM-SSA 34 82 969,000 1,421,000 0.41 0.68 B B
MDA-NOSI 1,316 4,910 14,485,000 37,000,000 0.27 0.39 B B
Country-Agency Staffing Benchmark Coefficient Performance category
63
Benchmarking Performance of Public Pension Programs (Continued)
Total Admin Expenditures, US$
Actual Projected Actual Projected Staffing Expenditures Staffing Expenditures
MAR-NSSF 4,750 3,855 79,999,000 60,100,000 1.23 1.33 A B
MAR-MPF 419 824 15,933,000 17,700,000 0.51 0.90 B A
NAM-SP n.a. 253 3,049,000 2,942,000 n.a. 1.04 n.a. A
NAM-GIPF 143 220 9,977,000 5,457,000 0.65 1.83 B B
NLD-SIB 3,247 3,788 281,700,000 306,000,000 0.86 0.92 A A
NZL-Super 560 901 29,322,000 58,400,000 0.62 0.50 B B
NZL-(CI) NSF n.a. 16 438,000 860,000 n.a. 0.51 n.a. B
NOR-NAV 14,523 2,777 1,649,000,000 325,000,000 5.23 5.07 C C
PAK-EOBI 938 1,193 9,902,000 10,200,000 0.79 0.97 A A
PHL-SSS 6,074 7,729 119,700,000 93,000,000 0.79 1.29 A B
PHL-GSIS 3,100 1,494 121,000,000 18,500,000 2.08 6.54 C C
POL-ZUS 47,588 26,069 1,133,000,000 618,000,000 1.83 1.83 B B
POL-KRUS 6,390 5,547 173,900,000 135,000,000 1.15 1.29 A B
PRT-SSI n.a. 13,361 766,500,000 475,000,000 n.a. 1.61 n.a. B
ROM-NPSIF 4,737 4,768 161,600,000 126,000,000 0.99 1.28 A B
RWA-RSSB 227 255 6,891,000 1,372,000 0.89 5.02 A C
WSM-SNPF 165 110 1,666,000 1,859,000 1.50 0.90 B A
SEN-SII 227 530 5,457,000 3,358,000 0.43 1.63 B B
SLE-NASSIT 227 207 4,874,000 936,000 1.09 5.21 A C
SGP-CPF 1,200 3,058 77,670,000 267,000,000 0.39 0.29 B B
SVK-SIA 5,839 4,941 102,400,000 132,000,000 1.18 0.78 A A
SLB-NPF 104 112 3,473,000 1,268,000 0.93 2.74 A C
ZAF-SSA 7,528 12,778 630,300,000 278,000,000 0.59 2.27 B C
ZAF-GEPF 705 1,146 39,035,000 34,000,000 0.62 1.15 B A
ESP-NISS 13,000 26,014 782,300,000 1,190,000,000 0.50 0.66 B B
LKA-EPF 600 473 3,545,000 5,411,000 1.27 0.66 B B
LKA-FPS 400 123 688,000 1,131,000 3.26 0.61 C B
KNA-SSB 104 94 3,512,000 3,913,000 1.11 0.90 A A
LCA-NIC 95 117 2,259,000 2,882,000 0.81 0.78 A A
VCT-NIS 53 105 1,461,000 2,360,000 0.50 0.62 B B
SWZ-SNPF 121 109 4,139,000 2,014,000 1.11 2.06 A C
SWZ-PSPF n.a. 162 6,232,000 2,862,000 n.a. 2.18 n.a. C
SWE-SSIA/NPFs (OA 1,300 1,799 71,486,000 252,000,000 0.72 0.28 B B
SWE-PPA 210 483 35,513,000 69,200,000 0.44 0.51 B B
SYR-GOSI 1,986 711 8,519,000 10,900,000 2.79 0.78 C A
TZA-GEPF n.a. 61 1,031,000 385,000 n.a. 2.68 n.a. C
TZA-PPF 170 300 9,022,000 1,639,000 0.57 5.50 B C
TZA-(ZZB)ZSSF 42 68 363,000 314,000 0.62 1.16 B A
THA-GPF 277 250 15,086,000 5,351,000 1.11 2.82 A C
THA-SSO 5,931 5,967 48,188,000 98,400,000 0.99 0.49 A B
TON-RFB 20 17 262,000 366,000 1.15 0.72 A B
TTO-NIB 614 677 15,393,000 34,900,000 0.91 0.44 A B
TUR-SSI 24,779 30,560 752,200,000 626,000,000 0.81 1.20 A A
UGA-NSSF 600 129 22,314,000 851,000 4.64 26.22 C C
UKR-PFU 36,000 29,198 262,700,000 330,000,000 1.23 0.80 A A
UK-(GBR)PS 11,890 9,436 1,255,000,000 734,000,000 1.26 1.71 B B
UK-(ANG)SSB 25 41 1,547,000 1,860,000 0.61 0.83 B A
UK-(FIS)FIPS n.a. 6 214,000 949,000 n.a. 0.23 n.a. B
UK-(JER)SSD n.a. 213 7,426,000 25,400,000 n.a. 0.29 n.a. B
UK-(NI)NISSA 5,336 1,579 377,700,000 115,000,000 3.38 3.28 C C
USA-SSA/SSI 23,000 5,815 2,700,000,000 460,000,000 3.96 5.87 C C
USA-SSA/OASDI 44,000 44,162 5,100,000,000 5,520,000,000 1.00 0.92 A A
USA-(AK)APFDD n.a. 585 7,066,000 59,900,000 n.a. 0.12 n.a. B
USA-TSP 380 675 94,300,000 86,600,000 0.56 1.09 B A
URY-SIB 4,338 4,865 158,000,000 149,000,000 0.89 1.06 A A
VUT-VNPF 39 30 1,417,000 484,000 1.31 2.93 B C
VNM-VSS 16,500 13,438 57,300,000 122,000,000 1.23 0.47 A B
YEM-GCSS 395 75 2,573,000 713,000 5.30 3.61 C C
YEM-GAPSS 917 684 6,014,000 5,907,000 1.34 1.02 B A
ZMB-WCF 277 273 4,583,000 2,653,000 1.02 1.73 A B
Country-Agency Staffing Benchmark Coefficient Performance category
64
Annex 4: Benchmarking Costs Performance
(Administrative Costs Net of Asset Management Expenditures over Beneficiaries, Nominal US$)
Source: Author’s al ulatio s.
-
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
Beneficiaries
LI: US$500
Baseline
Collection in-house
Special schemes
Funds management
High estimate
- 20 40 60 80
100 120 140 160 180 200 220 240 260 280 300 320
Beneficiaries
LMI: US$2,500
-
40
80
120
160
200
240
280
320
360
400
440
480
520
560
Beneficiaries
UMI: US$8,000
-
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
Beneficiaries
LHI: US$15,000
-
100
200
300
400
500
600
700
800
900
1,000
1,100
1,200
1,300
Beneficiaries
UHI: US$40,000
Social Protection & Labor Discussion Paper Series Titles 2012-2014
No. Title 1501 Defining, Measuring, and Benchmarking Administrative Expenditures of Mandatory Social Security
Programs by Oleksiy Sluchynsky, February 2015
1425 Old-Age Financial Protection in Malaysia: Challenges and Options by Robert Holzmann, November 2014 1424 Profiling the Unemployed: A Review of OECD Experiences and Implications for Emerging Economies by Artan Loxha and Matteo Morgandi, August 2014 1423 Any Guarantees? An Analysis of China’s Rural Minimum Living Standard Guarantee Program by Jennifer Golan, Terry Sicular and Nithin Umapathi, August 2014 1422 World Bank Support for Social Safety Nets 2007-2013: A Review of Financing, Knowledge Services
and Results by Colin Andrews, Adea Kryeziu and Dahye Seo, June 2014 1421 STEP Skills Measurement Surveys: Innovative Tools for Assessing Skills
by Gaëlle Pierre, Maria Laura Sanchez Puerta, Alexandria Valerio and Tania Rajadel, July 2014
1420 Our Daily Bread: What is the Evidence on Comparing Cash versus Food Transfers? by Ugo Gentilini, July 2014
1419 Rwanda: Social Safety Net Assessment by Alex Kamurase, Emily Wylde, Stephen Hitimana and Anka Kitunzi, July 2012 1418 Niger: Food Security and Safety Nets by Jenny C. Aker, Carlo del Ninno, Paul A. Dorosh, Menno Mulder-Sibanda and Setareh Razmara,
February 2009 1417 Benin: Les Filets Sociaux au Bénin Outil de Réduction de la Pauvreté
par Andrea Borgarello et Damien Mededji, Mai 2011 1416 Madagascar Three Years into the Crisis: An Assessment of Vulnerability and Social Policies and
Prospects for the Future by Philippe Auffret, May 2012 1415 Sudan Social Safety Net Assessment
by Annika Kjellgren, Christina Jones-Pauly, Hadyiat El-Tayeb Alyn, Endashaw Tadesse and Andrea Vermehren, May 2014
1414 Tanzania Poverty, Growth, and Public Transfers: Options for a National Productive Safety Net Program by W. James Smith, September 2011
1413 Zambia: Using Social Safety Nets to Accelerate Poverty Reduction and Share Prosperity by Cornelia Tesliuc, W. James Smith and Musonda Rosemary Sunkutu, March 2013
1412 Mali Social Safety Nets
by Cécile Cherrier, Carlo del Ninno and Setareh Razmara, January 2011 1411 Swaziland: Using Public Transfers to Reduce Extreme Poverty
by Lorraine Blank, Emma Mistiaen and Jeanine Braithwaite, November 2012 1410 Togo: Towards a National Social Protection Policy and Strategy
by Julie van Domelen, June 2012 1409 Lesotho: A Safety Net to End Extreme Poverty
by W. James Smith, Emma Mistiaen, Melis Guven and Morabo Morojele, June 2013 1408 Mozambique Social Protection Assessment: Review of Social Assistance Programs and Social
Protection Expenditures by Jose Silveiro Marques, October 2012
1407 Liberia: A Diagnostic of Social Protection
by Andrea Borgarello, Laura Figazzolo and Emily Weedon, December 2011 1406 Sierra Leone Social Protection Assessment
by José Silvério Marques, John Van Dyck, Suleiman Namara, Rita Costa and Sybil Bailor, June 2013 1405 Botswana Social Protection
by Cornelia Tesliuc, José Silvério Marques, Lillian Mookodi, Jeanine Braithwaite, Siddarth Sharma and Dolly Ntseane, December 2013
1404 Cameroon Social Safety Nets
by Carlo del Ninno and Kaleb Tamiru, June 2012 1403 Burkina Faso Social Safety Nets
by Cécile Cherrier, Carlo del Ninno and Setareh Razmara, January 2011 1402 Social Insurance Reform in Jordan: Awareness and Perceptions of Employment Opportunities for
Women by Stefanie Brodmann, Irene Jillson and Nahla Hassan, June 2014 1401 Social Assistance and Labor Market Programs in Latin America: Methodology and Key Findings from
the Social Protection Database by Paula Cerutti, Anna Fruttero, Margaret Grosh, Silvana Kostenbaum, Maria Laura Oliveri, Claudia
Rodriguez-Alas, Victoria Strokova, June 2014
1308 Youth Employment: A Human Development Agenda for the Next Decade by David Robalino, David Margolis, Friederike Rother, David Newhouse and Mattias Lundberg, June
2013 1307 Eligibility Thresholds for Minimum Living Guarantee Programs: International Practices and
Implications for China by Nithin Umapathi, Dewen Wang and Philip O’Keefe, November 2013
1306 Tailoring Social Protection to Small Island Developing States: Lessons Learned from the Caribbean by Asha Williams, Timothy Cheston, Aline Coudouela and Ludovic Subran, August 2013 1305 Improving Payment Mechanisms in Cash-Based Safety Net Programs by Carlo del Ninno, Kalanidhi Subbarao, Annika Kjellgren and Rodrigo Quintana, August 2013 1304 The Nuts and Bolts of Designing and Implementing Training Programs in Developing Countries
by Maddalena Honorati and Thomas P. McArdle, June 2013 1303 Designing and Implementing Unemployment Benefit Systems in Middle and Low Income Countries:
Key Choices between Insurance and Savings Accounts by David A. Robalino and Michael Weber, May 2013 1302 Entrepreneurship Programs in Developing Countries: A Meta Regression Analysis by Yoonyoung Cho and Maddalena Honorati, April 2013 1301 Skilled Labor Flows: Lessons from the European Union by Martin Kahanec, February 2013 To view Social Protection & Labor Discussion papers published prior to 2013, please visit www.worldbank.org/spl.
F e b r u a r y 2 0 1 5
Abstract
This study provides a framework for comparison and benchmarking of administrative expenditures of public and private social security programs. The paper presents the genesis of the inquiries into the subject, reviewing some of the most relevant literature on administrative expenditures and the costs of mandatory programs produced over the past two decades. The quantitative analysis builds on the extensive body of literature, but our framework evolved considerably from earlier studies. Our dataset includes over 100 observations and a broad set of explanatory variables. We developed and compared a number of standardized cost indices discussing their advantages and limitations. We also discuss major cost components and their shares in total program costs. The analysis explains over 90 percent of variation in administrative expenditures. It confirms some of the hypotheses expressed in the earlier studies and presents new evidence of driving factors for costs. We developed three different specifications for statistical analysis. The first set looks at the impact of design of a program on total costs. The second group of specifications assesses differences in costs of managing pension liabilities between the public and private mandatory pension schemes. Finally, on the basis of the third model we generate benchmarks for staffing levels and for the total administrative expenditures. We compare those to the actual indicators and develop standard performance ratios, providing insights into design variations and performance of the programs. We conclude with a discussion of data limitations and implications of our findings.
Defining, Measuring, and Benchmarking Administrative
Expenditures of Mandatory Social Security Programs
Oleksiy Sluchynsky
D I S C U S S I O N P A P E R NO. 1501
© 2015 International Bank for Reconstruction and Development / The World Bank
About this series...
Social Protection & Labor Discussion Papers are published to communicate the results of The World Bank’s work to the development community with the least possible delay. This paper therefore has not been prepared in accordance with the procedures appropriate for formally edited texts.
The findings, interpretations, and conclusions expressed herein are those of the author(s), and do not necessarily reflect the views of the International Bank for Reconstruction and Development/The World Bank and its affiliated organizations, or those of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.
For more information, please contact the Social Protection Advisory Service, The World Bank, 1818 H Street, N.W., Room G7-803, Washington, DC 20433 USA. Telephone: (202) 458-5267, Fax: (202) 614-0471, E-mail: [email protected] or visit us on-line at www.worldbank.org/spl.
