Aging workers and pension systems, and their effect on a nation’s productivity and inequality

Michael Molinski and Xinyue Wei

 

 October 20, 2016

Published by Investing Across Borders LLC, in conjunction with Université Paris 1 – Panthéon-Sorbonne

Advising Professor Maria del Carmen Camacho

 

 

 

 

 

Abstract: There are three major themes that this paper will attempt to show. First, it will show how the effects of the age of a workforce can have an impact on a nation’s productivity and growth. Then, by analyzing the effect of life expectancy on productivity, and forecasts of the age of countries, it will show how the age of a workforce is changing and how some countries are aging faster than some others and will continue to do so over the next 35 years. Lastly, it will show how a nation’s pension system can have an effect on workers and their productivity, and demonstrate how the design of a pension system can differ by country.

 

Introduction

Bringing those three seemingly different themes together can be a bit of a challenge, but was necessary if our goal is to provide an outlook on aging, productivity and pensions for future economists and policymakers. In doing so, we also will attempt to show how the economies and demographics of Europe and North America are different, and ways in which they are similar.

This brings up questions like: has the United States inflated its labor force to the extent it can’t be inflated anymore? In their quest for productivity, has the U.S. sapped all the productivity out of their workforce by expanding it to include older workers, women, and migrants as well? And how has that affected the workers themselves? Should policymakers in Europe and elsewhere in the world view this as a signal as something they should avoid? Or is the European model better suited for future aging?

The first section introduces background and literary review, followed by a section showing how the effects of the age of a workforce can have an impact on a nation’s productivity and growth. Then, we analyze the effect of life expectancy on productivity, and forecast the age of countries and their progression. Lastly, it will show how a nation’s pension system can have an effect on workers and their productivity, and demonstrate how the design of a pension system can differ by country. We conclude with a summary of research and additional policy recommendations.

 

1.0 Background and Literary Review

Previous research papers have concluded that younger workforces have more labor productivity than older workforces. In the past, some previous research papers (Bloom, Canning and Sevilla; 2001) have identified work experience and life expectancy as a proxy for age and health. Our concern is not the health of the population but their age, and how that affects productivity. However, while not included in our first regression, we have included life expectancy as part of our second regression, and the age of a population also forms part of our forecasts for the sake of comparison.

In Equilibrium Unemployment, in the section under the heading “The Chronic Weakness of Job Creation in Europe,” the author (Pissarides, 2000) finds that job creation has been weaker in Europe over 30 years, while in the United States job creation has been significantly stronger. This expansion in the United States, the authors say, has been “the result of a rise in the rates of participation and a more sustained growth in the size of the working-age population.” No where do they attribute that job creation to an improvement in technology, which has been a theme for many economists. Aside from that, the labor force participation rate in the United States has slowed since that book was written in 2000. Since the height of the Great Recession in 2009, the U.S. participation rate has fallen to 62.8 percent, a 36-year low, according to the OECD.

 Separately, in a 2013 paper for the Brooking Institution called The Impact of Population Aging and Delayed Retirement on Workforce Productivity, Gary Burtless concluded that there is no impact at all on workforce productivity, at least in the United States. Nonetheless, he bases his conclusion on the fact that “workers between 60 and 74 are more productive than average workers who are younger. Compared with workers between 25 and 59, the pay premium for older workers is currently between 10 percent and 20 percent of the average wage earned by the younger workers.” This conclusion is highly questionable. Pay premium of older workers is hardly a gauge for productivity. Among other things, the paper does not, for example, take into account the overall productivity of an aging workforce and the size of that older workforce vis-a-vis the core workforce.

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Over time, economists have plotted the drastic reduction of mortality and its benefits to the population of helping people live longer, better and healthier lives. And economists have also noticed a direct relationship between life expectancy and income. Samuel H. Preston’s now famous Preston Curve, reproduced below, has stemmed a debate among economists of the effects of the fall in mortality, including Cutler, Deaton and Lleras-Money (2006) in their paper The Determinants of Mortality and Berthélemy and Thuilliez (2014) in The Economics of Malaria in Africa. Even Preston has questioned the effect of income on life expectancy, writing in 1975: “Factors exogenous to a country's current level of income probably account for 75-90 per cent of the growth in life expectancy for the world as a whole between the 1930s and the 1960s. Income growth per se accounts for only 10-25 per cent.”

There’s no debate that the fall in mortality has its benefits to society, but what about productivity? Does productivity bring life expectancy, or is it the other way around?

Lately there has been a plateau of the rise in life expectancy (see chart below) in the more developed countries, especially in the United States, where the average life expectancy was unchanged at 78.8 years in 2012, 2013 and 2014, according to the U.S. Centers for Disease Control. Is this rise a signal that the human body can only withstand so many years before the skeletal system and brain or other organs give out, or will new advances in science continue to address these issues as well as we prolong life even longer? There have been several attempts at explaining the flattening out of life expectancy in the U.S.; most notably an increase in women’s deaths due to opioid abuse and suicide. But is something broader behind all of these causes? In short, is the plateauing of life expectancy a signal that we as a society can’t heap more stress on people and continue to push workers harder?

Source: Data from Centers for Disease Control; forecasts from CDC and Investing Across Borders LLC. Five year increments.

Source: Data from Centers for Disease Control; forecasts from CDC and Investing Across Borders LLC. Five year increments.

So how can the age of workers over time make a difference on a country’s economy, and how can that change differ from country to country? In a January 2014 report entitled Attitudes about Aging: A Global Perspective, the Pew Research Center concluded that “the demographic future for the U.S. is robust in comparison with other countries.” Perhaps, but we will use that same data as part of our forecasts.

As far as pensions are concerned, the questions are multiple. Should all pension systems follow the Western-style model where pensions are funded and managed by the individuals themselves? Some authors have attempted to address this question, such as Pensions and firm performance (Gustman and Clark, 2013), and more broadly Unemployment and Labor Market Rigidities: Europe versus North America, (Nickell, 1997). Our objective here is not so much to provide economic and policy advice on these issues, but to give a glimpse of some of the major economic issues that could affect pension policy.

 

2.0 Age and productivity

Our first objective of this study is to show how the effects of the age of a workforce can have an impact on a nation’s productivity and growth.  We include an aggregate production function in an attempt to test our theory, and simultaneously show the effects of aging workforces on labor productivity, and to measure their strength. We intend to include the following variables in our study: Labor productivity, age, education, technology and capital.

 2.1 The effects of age on productivity

The study constructs an aggregate production function that expresses a nation's output as a function of its inputs and the efficiency with which it uses these inputs. These inputs are physical capital, labor, and human capital in two dimensions of education and age. The data has also been broken down into three age groups to provide more insight into how these age groups are changing. The study uses OECD data for the age groups: youth (15-24), prime working age (25-54) and older workers (55-64). Due to data issues in finding the average age of workers over time, the study uses panel data from 34 OECD countries observed from 2001 to 2014 to measure the effect of age of the working populations on productivity. Unfortunately, the OECD data doesn’t include some important countries like China and Brazil. However, in spite of the missing data, we have found that we are still able to test the theory with the OECD data.

Our central argument is that the average working age of a population is a crucial aspect of human capital, and therefore a critical ingredient of economic growth. A younger workforce is physically and mentally more energetic and robust. Older workers, while they can contribute more from their longer work experience and seniority, tend to peak in their early to middle 40s, and after that they become less productive as their brain skills deteriorate. Younger workers, on the other hand, tend to have a learning curve and are still focused on education from the age of 15-24.

Our model also considers the efficiency with which the core inputs are used. Also, as previous research is somewhat dated. For example, the Bloom paper cited above used data from 1960-1990. We will use data from 2001-2014, which is fresher and it has the advantage of taking into account the aging populations of both countries as baby boomers approach retirement age.

We assume that we can decompose economic growth into two sources: growth in the level of inputs and growth in TFP. We take our inputs to be physical capital, labor, and human capital.

We model output as a function of inputs and technology using the following aggregate production function:

Capture2.png

 where Y is output or gross domestic product (GDP); A represents TFP; K is physical capital; L is the labor force; and human capital consists of two components: 1. age groups and square of age groups, and 2. Level of education and other unobserved factors. We express the effect of human capital on output as the power of an exponential.

Taking logs of the aggregate production function, we derive an equation for the log of output in country i at time t:

Capture3.png

Since labor productivity measures output produced per unit of labor, in order to link the equation (2) with labor productivity, we divide both sides by the product of hours worked and total number of labor force.

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The problem with estimating equation (3) is that it contains endogeneity. While we consider to measure the contribution of inputs to productivity, the change of productivity might also have a reverse impact on the inputs.

We accomplish this by introducing instrumental variables (IVs). We need to find IVs which are uncorrelated with any random TFP shocks, while correlated with the endogenous inputs. We assume that fertility rates and employment by activities (which affect the structure of a country’s economy) serve as valid IVs. These clearly satisfy the two conditions.

We construct a panel of 34 OECD countries observed from 2001 to 2014.

The dependent variable:

lp=Gross GDP /(Hours worked*Labor Force)

where Gross GDP is measured in US dollars/capita; hours worked is measured in hours/worker and Labor Force is measured in thousand persons.

Average annual hours worked is defined as the total number of hours actually worked per year divided by the average number of people employed per year.

Independent variables:

Capture5.png

This indicator looks at adult education level as defined by the highest level of education completed by the 25-64 year-old population. Upper secondary education typically follows completion of lower secondary schooling. The indicator is measured as a percentage of same age population.

agesh_1, 2, 3=Employment rate(%)*Population (persons) /(Hours worked*Labor Force)

Both employment rate and population are divided by three different age groups, which are 15-24, 25-54 and 55-64. Employed people are defined as those aged 15 and over who report that they have worked in gainful employment for at least one hour in the previous week or who had a job but were absent from work during the reference week while having a formal job attachment. Employment rates are shown for three age groups: people aged 15 to 24 (those just entering the labor market following education); people aged 25 to 54 (those in their prime working lives); people aged 55 to 64 (those passing the peak of their career and approaching retirement). This indicator is seasonally adjusted and it is measured as a percentage in the same age group.

Instrumental variables:

fers= Fertility rates

this indicator is measured in children per woman.

emac_a, i, s=Employment by activity (thousand person)

this indicator is divided into three areas of activities, which are agriculture, industry and service.

All the data above are obtained from the database of OECD.

2.2 Estimation and Results

We begin by estimating equation (3) under the assumption that there is no endogeneity. The result is showed in Table 1:

Capture6.png

The result shows that capital has a significantly positive effect on labor productivity, which is easy to understand: the more investment in capital, the more capital per labor force, which has a direct relation with the growth of labor productivity. Both R&D investment and education level negatively affect labor productivity.

The results also showed that a nation’s productivity tends to grow as the core group of workers (agesh_2), aged 25-54, swells. The core worker labor force had a peak value of 1.76. After the core group, the size of the young labor force (agesh_1), those workers ages 15-24, had the most significant effect on labor productivity of a country, with a peak value of 0.09. An older labor force, age 55-64, at first showed a negative result, but once a regression was run, it eliminated heteroskedasticity issues, and the results turned out to be significant, which is in line with our theory (Table 2 and 3). The biggest surprise to us was that the young group (agesh_1) turned out to add even more significance than the old worker group (agesh_3). The older group was actually slightly negatively correlated with the dependent variable, labor productivity.

Capture7.png

As mentioned before, there might exist endogeneity of our independent variables, which will cause the OLS estimators to become inconsistent. Therefore, we introduce the IVs and ran a 2SLS estimation to see if the coefficients and significance of variables would change. The results are showed in Table 3:

Capture8.png

The results above show that almost all variables except capital are insignificant, which is quite undesirable. In order to find out which estimator is more correct, we decided to run a Hausman test. The Hausman test shows if there does exist endogeneity. If the H0 of Hausman test can be rejected, then OLS estimator is inconsistent and we must choose a 2SLS estimator. Otherwise, OLS estimator is more efficient than 2SLS. The results of the Hausman test are as follows:

Capture9.png

We can see that the P-value is 0.2639>0.05, so we fail to reject H0 at the 5% significance level. This favors OLS, since it is evident that OLS is consistent. Therefore, we take the OLS estimator as our final result.

2.3 Summary

The results showed that a nation’s productivity tends to grow as the core group of workers (agesh_2), aged 25-54, swells. The age of young and older workers is significant. However, the effect of the older group, while it is significant, the correlation with labor productivity is negligible. In other words, while a country’s productivity increases when it adds more workers regardless of age, by adding younger and older workers it takes away from the core worker group, which is by far the most productive of the three age groups.

How could this data be used by economists and policymakers? We believe that our research shows a “barbell effect” of the labor force in the United States, with a larger percentage of young and older workers on either end of the workforce, compared to countries like France and most of Europe. This trend can be seen clearly in the following three graphs:

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Source: OECD labor market statistics

Note also that the amount in the young group has widened between 2004 and 2014, widening from to 54% in Q4 2004 to 58.4% in Q4 2014, while the amount in the older group has also widened from 60.2% to 61.4%. The core group shrunk from 79.1% in 2004 to 77% in Q4 2014.

Does this mean that the European model will be better prepared to face an aging population than that of the U.S. 10 or 20 years from now? Frankly, if our theory proves correct, yes it does. But let’s not get ahead of ourselves just yet. We must also test life expectancy.

3.0 Life Expectancy and Aging

The problem in testing life expectancy is that we are testing the life expectancy of the entire country’s population, not just the working population, which is the slice of the population responsible for a nation’s productivity. Credible data on the life expectancy of the working population is not yet available. Nonetheless, as previous studies have shown, life expectancy does have a significant effect on a nation’s productivity.

We simply add life expectancy (LE) to equation (3) and re-run the regression. Again, we are testing against the effect on labor productivity (lp). Labor productivity is the ratio of the output of goods and services to the labor hours devoted to the production of that output. The result is that life expectancy has a significant effect on labor productivity. The results, shown below, indicate that life expectancy is indeed significant, as the low P-values show, and the strong coefficient show that life expectancy is highly correlated with labor productivity (lp). As a nation’s life expectancy grows, productivity goes up. As before, we introduced instrumental variables to test endogeneity of our independent variables and ran a 2SLS estimation, and we also ran a Hausman test. The results confirmed our OLS estimation, showing both a linear and non-linear regression.

However, this time the negative correlation between the older workers group and labor productivity was even greater, though still significant.

 

 

3.1 Forecasting age of a nation

Now that we know that life expectancy has a significant effect on productivity, we can show how a nation ages over time and how aging can effect productivity and compare it to other countries. To do this, we use survey data from January 2014 from the Pew Institute, which is replicated below in chart form. We have chosen to use survey data rather than run a regression simply because, like the issue with average age of a working population, there is no credible data available for the median age of a population. Each country has its own methods and politics that go into figuring out the median age.

Note that the median age of a population, like life expectancy, is across the entire population not just the working population. But unlike life expectancy, median age of a population is likely to be a negative factor in terms of productivity. Common sense says that as the median age of a population increases, it will become less productive, whereas by extending life expectancy will have a positive effect. For example, the peak age of a worker will increase, making the worker more productive, and making the overall productivity of a work force also increase.

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Based on the Pew’s research, the U.S. population is projected to get relatively younger by virtue of being among those countries that are aging the slowest. The projected increase in the median age in the U.S. is from 37 in 2010 to 41 in 2050. Britain, France, Russia and Nigeria are on similar paths. The other countries in Europe are likely to age faster than the United States. Spain leads the way, with its median age increasing from 40 in 2010 to 50 in 2050. Generally speaking, European populations are older than the U.S., and that gap should stretch further between 2010 and 2050. Sharp increases are expected in the median age in Latin American countries. Countries in Asia and the Middle East are also turning gray rapidly. Japan currently has the oldest population in the world, and the median age there is projected to increase from 45 in 2010 to 53 in 2050. China’s population, with a median age of 46 in 2050, is expected to be older than those in Russia, France and Britain by that date.

The rapid increases in median age are a reflection of the rising proportions of seniors (65 and older) in the populations of all countries. In the U.S., the share of seniors is expected to increase from 13.1% in 2010 to 21.4% in 2050. However, all of the European countries have a larger share of seniors, and that gap is expected to continue or widen until 2050. But take a look at Latin America. Mexico, for example, is expected to triple its population of seniors, from 6.0% to 20.2%, and in Brazil, from 6.9% to 22.5%. The share almost doubles in Argentina, rising to 19.4% in 2050.

Needless to say, a huge part of the world is getting older. The U.S. may be graying at a slower pace than many, but it is still graying. Those countries that can prepare their economies for this aging will be better prepared for this shift. Some countries are moving faster toward an aging working population than some other countries, due in part to government policies that have either sought to reform pension systems or, in the case of China, control the birthrate.

 

4.0 Pensions and their effect on an economy

Europe’s reluctance to change its pension system may in the end be to its advantage, especially as Europe encourages its workers to retire earlier. This may come as a surprise as many policymakers in France, Germany and particularly in England have been pressured to implement a Western-style pension system much like the defined contribution system in the United States, which relies on individuals to save for their own retirement, as opposed to firms or the government. As a result, workers in the United States are being encouraged to keep working well into their retirement in order to pay for their own retirement income. The graph below shows the net pension wealth of both public and private pension systems of OECD countries. Note the relatively low U.S. net pension wealth versus France and most of Europe. Even countries like Brazil and India have a larger pension system

Source: OECD, pensions at a Glance

Asia, on the other hand, is equally unprepared to pay the pensions of its workers, but the policies in Asia are due to government policies on controlling its population’s birthrates. China, for the most part, relies on the families themselves to take care of their elders. The reduced birthrate in China has indeed had a large effect on creating an older workforce, although due to the lack of data we couldn’t show that.

China has a pension system that is in dire need of reform, while other countries in the region—most notably Japan and Korea—are well of their way in terms of pension reform.

According to our own analysis of OECD data, France had a pension system that accounted for between 11.8% in 2001 and 13.8% in 2011 of its GDP, while the U.S. paid 5.7% in 2001 and 6.7% in 2011 of GDP. Clearly, there are political issues that need to be considered as well in both France and the United States, but U.S. workers are already struggling to pay for their own retirement.

Riding on the coattails of abuses by several public and private pension plans, the Employee Retirement Income Security Act, ERISA, was sold to the American public in 1974 as a way of protecting people’s pensions by insuring that pension plans would be made secure and backed by the federal government.

It worked, but not as the American public thought it would. It did protect pensions, but as soon as the ink was dry on ERISA corporate America began eliminating pensions from its balance sheets. From 1980 through 2015, the proportion of private wage and salary workers participating in defined benefit pension plans fell from 38 percent to 15 percent, according to the U.S. Bureau of Labor Statistics.

The U.S. government later created the 401(k) in 1978, but the 401(k) was never expected to support workers through retirement. It has been embraced by corporations as a way to attract workers by having the workers’ pay for their own retirement, thus eliminating pensions from corporate balance sheets. Also, by eliminating pensions, corporations no longer needed the voice of workers at the bargaining table. The Social Security and Medicare systems in the United States provide only limited resources for seniors, and should not be confused with European-style pensions.

To use Germany as an example of how Western-style pension system reforms have been implemented, over the 2000s Germany passed legislation that was supposedly aimed at correcting supply side weaknesses brought on by a flood of workers after the reunification of Germany. Those reforms included company-level pacts for employment and competitiveness, guarantees of employment security, vocational training, concessions on the extent and flexibility of working hours and on labor mobility, wage concessions, and a reduction of the influence of unions to enable consensus-based decision making in firms. In the end, these reforms have led to more people falling out of unions and have also reduced the role of labor as a voice in pension systems. There has also been a significant debate as to whether or not supply side weakness even warranted the passage of German’s reforms, or whether demand shocks were to blame for Germany’s weakness at the time (Carlin and Soskice, 2009).

 

4.1 What has the effect been on retirees, and on inequality

Currently, about 43% of private workers take part in their 401(k) or other defined contribution plan, according to the U.S. BLS. That isn’t enough to support most of the retirees throughout their retirement. Through the end of 2015, the average 401(k) balance at Fidelity Investments, one of the country’s biggest 401(k) providers, was just $91,300—a paltry amount compared to what retirement specialists like those at Fidelity say you would need to save for retirement.

The drop in U.S. pensions also contributed to the rising gap between the rich and poor, and that inequality led in part to the Great Recession of 2008-2009, said French economist Thomas Piketty at a conference this week in Paris.

What’s more, by pushing older workers into the labor force the U.S. economy has taken jobs away from younger workers who could be more productive. Keep in mind this is not to suggest that older workers can’t be as productive as younger workers. Experience and knowledge go a long way. But when taken across the entire U.S. economy, older workers tend to be less productive on average. Globally, the peak average age for workers in terms of their productivity tends to be around 43.

The result has been a workforce that is incapable of funding or managing their own pensions and have been encouraged to stay in the workforce longer, thus taking jobs away from a younger workforce that is more productive.

Also, there has been a significant reduction in union participation and collective wage bargaining over the past 40 years in the United States, which in part is due to the removal of unions in setting and managing pensions.

One study, Card (2001), finds that the decline in unionization explains between 15% and 20% of the growth in wage inequalities in the United States during the 1980s. Since that study was conducted union participation has been reduced even further, while the inequality gap has widened.

 

Conclusion and Policy Recommendations

The research above is not by any means conclusive, but it does provide interesting perspectives about the age of workers, their productivity, the increasing age of countries, and the ongoing changes in pensions. It shows that Europe, based on the current age of its workforce, is better prepared at having a more productive workforce because it relies less on younger and older workers than the United States. We attribute this in part due to the slow process toward changing the pension reform system in Europe, and the unpreparedness of the United States to provide pensions for its’ workers and to encourage workers to stay in the workforce longer.

Also, in terms of productivity, by reducing pensions and forcing workers to keep working longer, the United States is creating and fueling an older workforce. As these older workers get increasingly older, they will have no pensions and no means of sustaining themselves, and the government is likely to have to step in to fill the gap.

But there are cultural differences as well. Asia, and to some extent Europe, have family systems where those families take care of their elders. Americans don’t do that as much. In part that’s because the divorce is higher in the United States, and also because in America people are more apt to turn to nursing homes for the elderly, rather than family case. What’s more, Europe’s elders are more respected than those in the United States. That’s partly because seniors have pensions, and are more apt to reach out a hand to young workers when they are unemployed. When Spain’s economy was suffering recently and many of young workers were unemployed, the first door that they knocked on was their grandparents.

Perhaps our research can add to the list of concerns when policymakers realize that a wider core workforce is more productive and may prompt them to think twice before cutting pensions.

 

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