Financing Development:Private Capital Mobilization and Institutional Investors
This report discusses key issues around the mobilization of private capital for development.Investment requirements are huge, especially for infrastructure, climate and other SDGrelated investments. External finance for developing countries stagnated in the years beforethe pandemic, followed by a major setback in 2020/2021. The focus is in particular oninstitutional investors, whose exposure to less-developed countries is still very low, even more so in unlisted assets and projects. There is a potential for progress as asset owners seek new diversification opportunities in growth markets. The main burden is on governments to create favourable business conditions for investable long-term assets.Policy makers, development finance institutions and investors should utilize the full spectrum of investment vehicles – commercial, impact and blended finance.
JEL classification: F21, F3, G15, G18, G2, H54, H57, J32, L9, M14, O16, O18, 019, Q01, Q5
Keywords: development finance, private capital, institutional investors, emerging markets,blended finance, infrastructure investment, asset owners, impact investment, SDG investing, multilateral development banks, development finance institutions.
Pension Systems in the Developing World: Current Challenges and Future Directions
Today, people’s greatest financial concern is no longer paying their short-term bills or credit-card debt. According to the new study by Zurich Insurance Group and the University of Oxford (2019), ‘retirement security is the top financial worry’ for workers in 14 out of 16 countries. Likewise, recent surveys on old-age income suggest that nearly half of the respondents from different parts of the world do not feel secure about having a comfortable retirement (AARP Foundation, 2018; Credit Suisse, 2020).
While a lack of retirement savings has turned out to be a global phenomenon, most studies cover the design of pension systems in developed countries, which face relatively few challenges compared to developing ones. Moreover, from a handful of papers on developing regions, there is a tendency to discuss pension-related issues in the context of specific countries or topics. To this end, this study aims to provide an overall and detailed picture of the public and private pension systems in the developing world, including the present challenges and future directions.
The first part of the paper presents an overview of public pensions in developing countries. It illustrates the impact of ageing on sustainability and the adequacy of pay-as-you-go plans, along with some suggestions for the future of state pensions. In the second part, the paper focuses on private pension systems in the developing world and discusses the reasons for low pension savings with respect to the issues of coverage, contribution, and investment performance. This section also concludes by proposing certain recommendations for private pensions in the light of financial as well as behavioural and technological developments.
Nudges and Networks: How to use behavioural economics to improve the life cycle savings-consumption balance.
We show how nudges can be used both to encourage people to save enough to provide a decent standard of living in retirement and to draw down their accumulated pension fund to maximize retirement spending, without the risk of running out of money. Networks can help too, particularly employer-based networks.
JEL code: D91
Modelling Seasonal Mortality with Individual Data
Stephen J. Richards, Stefan J. Ramonat, Gregory T. Vesper and Torsten Kleinow.
Most studies of seasonal variation in mortality rely on aggregated death counts at population level. In this paper we use individual data to present a series of models for diﬀerent aspects of seasonal variation. The models are ﬁtted to a variety of international pensioner data sets and suggest a high degree of commonality across countries with diﬀerent climates and diﬀerent health systems. The power of individual life-history survival modelling allows the detection of seasonal patterns in even modest-sized portfolios. We measure the tendency for seasonal ﬂuctuationstoincreasewithage, and we again ﬁnd strong similarities between geographically distinct populations. We further ﬁnd that seasonal eﬀects are generally uncorrelated with gender, but that low-income pensioners can suﬀer greater seasonal swings than high-income ones. Finally, we propose a single-parameter measure for the extent to which winter mortality is a spike and summer mortality is a shallower trough, and show results for a variety of data sets.
seasonal mortality, excess winter mortality, survival model.
The Impact of Covid-19 on Future Higher-Age Mortality
Andrew J.G. Cairns, David Blake, Amy R. Kessler & Marsha Kessler
Covid-19 has predominantly affected mortality at high ages. It kills by inflaming and clogging the air sacs in the lungs, depriving the body of oxygen ‒ inducing hypoxia ‒ which closes down essential organs, in particular the heart, kidneys and liver, and causes blood clots (which can lead to stroke or pulmonary embolism) and neurological malfunction.
Evidence from different countries points to the fact that people who die from Covid-19 are often, but not always, much less healthy than the average for their age group. This is true for England & Wales – the two countries we focus on in this study. The implication is that the years of life lost through early death are less than the average for each age group, with how much less being a source of considerable debate. We argue that many of those who die from coronavirus would have died anyway in the relatively near future due to their existing frailties or co-morbidities. We demonstrate how to capture this link to poorer-than-average health using a model in which individual deaths are ‘accelerated’ ahead of schedule due to Covid-19. The model structure and its parameterization build on the observation that Covid-19 mortality by age is approximately proportional to all-cause mortality. This, in combination with current predictions of total deaths, results in the important conclusion that, everything else being equal, the impact of Covid-19 on the mortality rates of the surviving population will be very modest. Specifically, the degree of anti-selection is likely to be very small, since the life expectancy of survivors does not increase by a significant amount over pre-pandemic levels.
Covid-19, all-cause mortality, frailty, co-morbidities, mortality deprivation, accelerated deaths model
JEL code: J11
Social Infrastructure Finance and Institutional Investors. A Global Perspective
Social infrastructure has endured a long period of neglect in most developed and emerging countries, with chronic underinvestment exposed by the coronavirus crisis 2020. Private sector investment in social infrastructure has widely fallen back over the last decade – this in contrast to economic infrastructure. One of the outcomes of the last global (financial) crisis 2007/08 was a slow revival of economic infrastructure policies, and a growing involvement of institutional investors. This is the first, more systematic account of social infrastructure investment from an international perspective, leading to several key conclusions. The public sector will remain the dominant funding and financing source. Nonetheless, much more private capital could flow with greater clarity on social assets and projects, given their very diverse specific characteristics. There are various investment strategies that can realistically be improved and expanded. Sustainability, impact and SDG investing open a new door for asset owners.
Simple Explicit Formula for Near-Optimal Stochastic Lifestyling
Ales Cerny and Igor Melichercik
In life-cycle economics, the Samuelson paradigm (Samuelson, 1969) states that the optimal investment is in constant proportions out of lifetime wealth composed of current savings and the present value of future income. It is well known that in the presence of credit constraints this paradigm no longer applies. Instead, optimal life-cycle investment gives rise to so-called stochastic lifestyling (Cairns et al., 2006), whereby for low levels of accumulated capital it is optimal to invest fully in stocks and then gradually switch to safer assets as the level of savings increases. In stochastic lifestyling not only does the ratio between risky and safe assets change but also the mix of risky assets varies over time. While the existing literature relies on complex numerical algorithms to quantify optimal lifestyling, the present paper provides a simple formula that captures the main essence of the lifestyling effect with remarkable accuracy.
finance, optimal investment, stochastic lifestyling, Samuelson paradigm, power utility.
2010 MSC: 90C20, 90C39, 35K55, 49J20
One size fits all: How many default funds does a pension scheme need?
David Blake, Mel Duffield, Ian Tonks, Alistair Haig, Dean Blower & Laura MacPhee.
In this paper, we analyse the number of default investment funds appropriate for an occupational defined contribution pension scheme. Using a unique dataset of member risk attitudes and characteristics from a survey of a large UK pension scheme, we apply cluster analysis to identify two distinct groups of members in their 40s and 50s. Further analysis indicated that the risk attitudes of the two groups were not significantly different, allowing us to conclude that a single lifestyle default fund is appropriate.
investment choices, cluster analysis, risk attitudes, risk capacity, defined contribution pension schemes.
JEL: G11, G41
Grouping Individual Investment Preferences in Retirement Savings: A Cluster Analysis of a USS Members Risk Attitude Survey
David Blake, Mel Duffield, Ian Tonks, Alistair Haig, Dean Blower & Laura MacPhee
Cluster analysis is used to identify homogeneous groups of members of USS in terms of risk attitudes. There are two distinct clusters of members in their 40s and 50s. One had previously ‘engaged’ with USS by making additional voluntary contributions. It typically had higher pay, longer tenure, less interest in ethical investing, lower risk capacity, a higher percentage of males, and a higher percentage of academics than members of the ‘disengaged’ cluster. Conditioning only on the attitude to risk responses, there are 18 clusters, with similar but not identical membership, depending on which clustering method is used. The differences in risk aversion across the 18 clusters could be explained largely by differences in the percentage of females and the percentage of couples. Risk aversion increases as the percentage of females in the cluster increases, while it reduces as the percentage of couples increases because of greater risk sharing within the household. Characteristics that other studies have found important determinants of risk attitudes, such as age, income and (pension) wealth, do not turn out to be as significant for USS members. Further, despite being on average more highly educated than the general population, USS members are marginally more risk averse than the general population, controlling for salary, although the difference is not significant.
investment choices, cluster analysis, risk attitudes, risk capacity, defined contribution pension schemes
JEL: G11, G41
CBDX: A Workhorse Mortality Model from the Cairns-Blake-Dowd Family
Kevin Dowd, Andrew J.G. Cairns & David Blake
The purpose of this paper is to identify a workhorse mortality model for the adult age range (i.e., excluding the accident hump and younger ages). It applies the “general procedure” (GP) of Hunt and Blake (2014) to identify an age-period model that fits the data well before adding in a cohort effect that captures the residual year-of-birth effects arising in the original age-period model. The resulting model is intended to be suitable for a variety of populations, but economises on the number of period effects in comparison with a full implementation of the GP. We estimate the model using two different iterative Maximum Likelihood (ML) approaches – one Partial ML and the other Full ML – that avoid the need to specify identifiability constraints.
mortality rates, Cairns-Blake-Dowd mortality model, CBDX mortality model
G220, G230, J110