Can Changes in Age Structure have an impact on the Inflation Rate?The Case of the United States of America

Can Changes in Age Structure have an
impact on the Inflation Rate?
The Case of the United States of America
Department of Economics
University of Essex
WORD COUNT: 7951
By: William Turnning Fang Wang
Supervisor: Dr. Neslihan

Final Year Project EC381-6-FY
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Acknowledgement
I wish to express my special thanks to my supervisors Dr Neslihan for her support in my
dissertation and extend my utmost gratitude to Professor Gianluigi Vernasca for his
continuous care and guidance as a supervisor and as a mentor throughout my final year
project and for the entire duration of my undergraduate studies. I would also like to thank
my friends and family for supporting me throughout university and during times of
COVID-19.

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Abstract
Demographics in different parts of the world are facing an ageing population and a diminishing growth
in population, particularly high-income countries. This paper estimates the relationship between the
growth of age composition and the inflation rate while including other macroeconomic variables as
explanatory variables to ensure the model has a good fit to the true model. This paper estimates the
case in the United States of America from 1960 to 2016, studying the relationship between the inflation
rate and the growth rate of the proportion in different age cohorts. The results show a consistent and
significant relationship between the growth in the proportion of different age cohort and inflation rate,
in which the increase in the proportion of net savers (age between 30 – 64 years old) and retirees
(age between 65 and above) in the economy encourages higher inflation rate. This can be explained
by the Life Cycle Hypothesis combined with other economic theories. In any case, the results suggest
that demographic has an association with the inflation rate in which the projection of age composition
in the future can be used as a tool to better forecast the inflation rate. This could open the possibility
for monetary authorities to better implement monetary policy to sustain their mandate.

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Contents
▪ Abstract 2
▪ Introduction 4
▪ Literature Review 6
▪ Theory 8
▪ Database 10
▪ The Regression Model 12
▪ Analysis and Results 13
▪ Forecast 15
▪ Potential Limitations 16
▪ Conclusion 17
▪ Reference 18
▪ Appendices 20

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Introduction
As demographics in different parts of the world have been changing over the last few decades, many
countries are facing an ageing population and a declining fertility rate, especially in most countries in
the euro area (Nickel et al. 2017). A cadre of economists has tried to study the impact of natural
changes in demographics on macroeconomic variables. In recent years, there has been demonstrated
that demographic changes could have an impact on many macroeconomic variables such as, but not
limited to, gross domestic product, interest rate, investment, current account, and inflation rate. This
paper will try to continue the research on demographic changes and the inflation rate. The importance
of understanding the variation in the inflation rate is due to the essential role that it plays in economic
policies. This research is particularly interesting because it could potentially help explain the partial
variation in the inflation rate through demographic changes. Hence, if there is a reliable projection of
demographics change in the future, this data could help forecast the long-run trend of the inflation
rate. However, demographic effects on macroeconomic variables have not been explored extensively
in the literature. The main objective of this paper is to shed some light on the empirical relationship
between demographic changes and the inflation rate in order to develop a better understanding behind
that possible relationship in the hope of providing support to monetary authorities.
In 2020, we are witnessing a historical pandemic with the COVID-19 virus in which a shock would hit
the demographics around the world. Evidence has shown that older people are more susceptible to
death through this virus, consequently, decreasing more the share of older people in the population
relative to the younger cohort. Hence, the pandemic would change the population projection in
different age cohorts. This would then affect countries' long-run economic behaviour on an aggregate
level, causing a permanent shock. This research paper tries to examine the impact changes in age
composition could potentially have on different macroeconomic variables, specifically on the inflation
rate. This suggests that by understanding the demographic impact on the inflation rate, an article,
therefore, adapts to this shock and takes into consideration the impact of demographics affecting the
inflation rate, thus setting a more appropriate monetary policy to sustain a 2% level of inflation rate in
an economy. Many macroeconomic models utilize different macroeconomic variables to explain the
variation in the inflation rate, however, many macroeconomic variables are endogenous, and this
would create bias within the model. The paper assumes that demographic changes are exogenous and
that macroeconomic variables are unable to influence the variation in the demographic changes. Hence,
by using demographic variables, it can allow a degree of unbiases in the model. Lindh and Malmberg
(1999) state that all macroeconomic variables are theoretically expected to correlate with the age
structure, the difference is that although there is a feedback from these variables to demography, this
feedback changes the overall age structure rather slowly and mainly affects fertility and migration in

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the short run. But using demography will more likely satisfy the exogeneity assumptions than the
average macroeconomic model.
According to the Global Monitoring Report (2015) by the World Bank, the global population growth
is slowing down. Some countries with high concentrated poverty still maintain a young and growing
population, however high- and middle-income countries are facing an ageing population. Global
growth is projected to grow at a slower rate along with the decrease in population growth.
Furthermore, the world's population is growing much slower and ageing very fast, in which the
working-age share of the population has peaked in 2012 and is now declining. According to the United
Nations (2019), the global fertility rate fell from 3.2 births per woman in 1990 to 2.5 in 2019 and is
expected to decline even further to 2.2 in 2050. In high-income countries, the fertility rate has declined
from 1.82 births per woman in 1990 to 1.67 in 2020. Similarly, middle-income countries, the fertility
rate has declined from 3.04 births per woman in 1990 to 2.35 in 2020. Such a trend cannot be neglected,
and if demographics do play an important role in explaining the variation in the inflation rate, it could
potentially help policymakers in better forecasting inflation rate trends and conduct more diligent
monetary policy.
According to Milton Friedman (Friedman, 1963), “Inflation is always and everywhere a monetary
phenomenon” which inherently implies a relationship between inflation rate and money supply growth.
However, money supply growth has not seen a consistent level of growth in the inflation rate in recent
history. Central Banks around the world are wondering why the persistent growth in money supply
or the decrease in the interest rate in the economy is not pushing the inflation rate higher or be at 2%
(frequent monetary policy mandate around the world). Since the cause in the variation in inflation rate
is ambiguous and challenging to keep it under control in the long run. This paper tries to explore an
alternative perspective on the issue of inflation rate in which the paper strongly believes that
demographic composition could play a role in affecting the variation in the inflation rate. In many
economic theories defining inflation rate, age structure composition (demographics) are rarely
included as a potential variable that could impact the macroeconomic variables and the reason could
be that economists assume demographics to be consistent over time. In the life cycle hypothesis by
Modigliani (1966), the model assumes stationary population growth to model the saving and
consumption behaviour in the long run. However, empirical data has shown that the growth in
population is not stationary. In this paper, instead of using population growth, we model the growth
rate of the proportion of different age cohorts to see if the share of the different age cohort, that
behaves differently economically, will lead to changes in the saving and behaviour on an aggregate level.
The world population is ageing and with this imbalance in the age structure in our population, many
economic theories that try to predict the outcome of certain implementation could be omitting an
important factor of how population structure is able to influence changes in the economic theory. This

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paper will try to investigate the possible impact of the change in the proportion of age structure that
could potentially affect the inflation rate.
Literature Review
In terms of the empirical evidence on the relationship between demographic changes and
macroeconomic variables, the research by Yoon et al. (2018) represents an interesting analysis. They
use an empirical model to analyze the macroeconomic effects of changes in demographic variables.
They focus on how changes in demographics could impact macroeconomic variables such as economic
growth, inflation rate, savings and investment, and fiscal balances. With a strong emphasis on the
impact on the inflation rate, their paper states that population ageing could affect the inflation rate
through demand and supply channels. On the demand side, an increase in elderly share could
potentially impact aggregate consumption. On the supply side, ageing could affect the inflation rate in
both directions. A decrease in labour supply could increase the demand for labour relative to its supply,
hence wages will increase and lead to an increase in the inflation rate. On the contrary, due to a lack
of labour caused by ageing, more elderly or female populations might participate in the labour force
which could potentially lower the wages because these cohorts tend to work in a "Low wage area",
thus putting downward pressure on the inflation rate. They found that population growth in the OECD
countries has a positive association with inflation rate implying that population growth may have a
positive association with aggregate demand causing the rise in the inflation rate, a result known as the
demand-pull inflation rate. This is the result when the aggregate supply adjusts slower than aggregate
demand in the face of a demographic shock. Another finding in the paper is that share of the elderly
has a significant negative association with the inflation rate. These findings are consistent with the one
in Nickel et al. (2017). In that paper, they use a cointegrated VAR model and finds a positive long-run
relationship between the Euro area core inflation (HICP excluding energy and food) and the growth
rate of the dependency ratio (working-age population divided by total population).
In addition to this, they also find that the relationship between dependency ratio and inflation rate still
holds after controlling for the effect of short-term interest rate in affecting the inflation rate. However,
the effect of demographics on the variability on the inflation rate diminishes after considering the
impact of short-term interest rates on the inflation rate. The results show that there is a significant
association between changes in demographic variables and the inflation rate, but the association is
weaker than the association between inflation rate and short-term interest rates. Lindh (2000),
proposes reasons as to how demographic shifts can affect the inflation rate. (1) Age structure change
can affect saving behaviour in the economy and an ageing population could induce upward pressure
on the inflation rate, thus suggesting a positive association between ageing population and inflation
rate. (2) Changes in saving rate would shift the IS curve thus affecting both aggregate demand and

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inflation rate. (3) Change in saving rate will change the expected return on domestic deposits, thus in
the long-run price level will have to adjust such that the real money holdings remain unchanged such
that price level will have to adjust such that the real money supply goes back to its initial level. (4) The
higher working-age population is associated with higher productivity in the economy, thus as the
working-age population starts declining so will the aggregate demand. Hence, demand-pull inflation,
inflation levels will be positively associated with working-age population growth. (5) As the working-
age population decreases, labour supply will decrease, and this may lead to an increase in wages such
that the working-age population is positively associated with the inflation rate. (6) If net savers increase
then the tax revenue will decrease thus increasing the budget deficit assuming government spending
is unchanged. This might lead to inflationary pressure in the economy. Furthermore, Lindh (1998),
explores the impact of demographic age structure on investing and saving decisions. The reason is that
for such decisions, the inflation rate and expected inflation rate do matter. This paper uses age
structure to explain the variation in inflation rate because age structure is believed to correlate with
many of the macroeconomic variables that try to explain the variation in inflation rate such as, but not
limited to; GDP, money supply, interest rate, unemployment rate. Thus, by using age structure, the
regression is more likely to satisfy the exogeneity assumptions than any average macro model. They
used data from the OECD countries and tries to explain the variation of inflation rate with 5 different
age group; young adults (age 15-29), mature adults (age 30-49), middle-aged adults (age 50-64), young
retirees (age 65-74) and old people (age 75). The results show that the OECD countries' inflation rate
is highly correlated with age group variation. Later, Lindh (2000), study the possibility of forecasting
inflation rate through demographic age structure. Since it is possible to forecast demographic changes
in the upcoming years with reasonable precision, then this would imply a more reliable data to forecast
economic behaviour in the future. The results show that there is a significant association between age
structure variation in the OECD countries and the inflation rate. In which it is projected that that age
structure induced inflation rate pressure will be very low not omitting the possibility of deflationary
pressure. Nevertheless, this methodology is criticized by Goldman Sachs (2018), because they believe
that saving and investment decisions are based on one’s expectation of life expectancy. Thus, if one
expects his life to live longer, then his consumption behaviour would adjust such that he smooths his
lifetime consumption. The results from Goldman Sachs report (2018) indicates that for the next
decades, due to the change in demographics in the US, there will be upward pressure on the inflation
rate.
Correspondingly, Gajewski (2015) presents a theoretical and empirical work that focuses on 34 OECD
countries in which he tries to find an association between ageing and inflation rate. The result shows
a negative association between ageing and the inflation rate. Likewise, Fedotenkov (2016) conducts an
empirical paper to explain the association between ageing and inflation rate. The paper concluded that
population ageing in terms of a decrease in fertility rate and an increase in longevity associates with a

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decline in prices. Such occurrence is due to agents expecting to live longer thus are in need to create
higher savings thus will produce more goods to the market, but spending does not increase hence
deflation. Fujita and Fujiwara (2016) also studied the association between ageing and macroeconomic
variables such as inflation rate and real interest rate. They developed a New Keynesian search/
matching model to examine the effect of a change in demographics. The results show that 40 percent
of the decrease in the real interest rate in the last 30 years in Japan. However, there are also other
research papers related to monetary policy is ineffective in an era of ageing population. Imam (2013),
shows that in five developed economies (US, Canada, Japan, UK, Germany), monetary policy has been
ineffective due to population ageing. This argument suggests that not only could ageing population
affect the economy but also there would not be a way to stabilize the economy unless the government
uses fiscal policy. Contrary to the previously mentioned research, Juselius and Takas (2015) have found
that there is a negative correlation between the share of the working-age population and inflation rate,
in which demography explains around a third of the variation in the inflation rate. While a larger share
of young and old people is correlated with a higher inflation rate. This result is consistent with Basso,
H. et al. (2016) findings in their paper on Demographic Structure and Macroeconomic.
Theory
The population affecting the macroeconomy has been studied extensively in the past. Thomas Robert
Malthus was the first economist to join a demographic study with macroeconomics where he wrote
a book named "An Essay on the Principle of Population" in 1798. His theory is still widely studied today
while many believe his theory to be outdated. The Malthusian Trap coined to his theory was a
phenomenon in which excessive population growth would lead to a shortage of supply per capita and
lead to a lower standard of living per capita. This would then lead to a decline in population, however,
after the industrial revolution population started increasing exponentially where the population
doubled from 1 billion to 2 billion in just 130 years from 1800 to 1930. But before the industrial
revolution, it took the world 1800 years to reach 1 billion in population. The population then doubled
to 4 billion in 44 years (1974) which grew faster than exponential. Such growth and structure in a
population should be taken into consideration while generating economic models. In this paper, we
focus on different theories that might explain how the age composition of the population, particularly
the proportion in different age groups, might affect the inflation rate.
This paper tries to determine the potential effect the growth of different age cohorts might have on
the inflation rate while considering the effect other macroeconomic variables have on the inflation
rate. The main theory this paper tries to explain is how the growth of share in different age cohorts
might influence the inflation rate. This paper assumes that the 5 different age cohort behaves slightly
differently in their decisions to savings and consumption behaviour and the agents within an age cohort

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behaves relatively similar in their savings and consumption behaviour. The first assumption is derived
from the life-cycle hypothesis from Franco Modigliani wherein his research found that in the earlier
part of a person's life they tend to accumulate a stock of wealth which will then be used to support
their consumption in the latter part of their life. The second assumption is based upon the rational
that the same age cohort behave relatively similarly in their consumption and saving behaviour. Thus,
an increase in the share of a cohort that prefers to save this should affect the populations saving and
consumption behaviour. This could then be implemented in different economic theories to determine
how it would then be impacted by the inflation rate.
Saving and consumption behaviour within the borders of a country is crucial in determining the results
of many macroeconomic variables. However, at different stages in life, agents in an economy do not
behave consistently. This is because there are different goals and demands throughout life. According
to the life cycle hypothesis; at a young age people tend to be borrowers and as they mature, they start
accumulating savings and they start dissaving when they get old. The reason for such consumption and
saving behaviour is for agents in the economy to achieve their preferred distribution of consumption
over their lifetime subject to a lifetime budget constraint. Given that at different ages we behave
differently, the dynamic structure of the population should affect the saving and consumption
behaviour in the economy, whether the economy is over/under-saving or over/ under consuming.
According to Modigliani (1966), if population growth results in income growth per employed which
results in productivity growth. The growth in population and growth in income will grow at a
consistent rate. Besides, a result of population growth is that the ratio of younger households in their
accumulation phase to older households in their dissaving phase will give rise to a positive net flow of
savings. However, in 1966 the population is expected to work for 40 years and retire for 10 years but
in 2016 the expected years of work are roughly 44 years and people are expected to retire for more
than 16 years because of advancement in medical care. Thus, the dissaving phase for older people may
be prolonged. Furthermore, data from the United Nation, in 1960 the proportion of the population
less than 15 years old is 30.7% and in 2016 is around 19.03%. Suggesting a decline in fertility rate. At
the same time, the working-class population (15 – 64 years old) is 23.7% of the population in 1960 and
34.51% of the population in 2016. An increase of roughly 10 % in the productivity population. This
suggests an increase in the proportion of the saving phase in the economy of roughly 10% and due to
people expected to live longer, the dissaving phase has would not be dissaving as fast as they should
have. Consequently, downward pressure on the saving rate. Modigliani referred to a balance in the
transfers where an increase in the ratio of younger households in their accumulation phase to older
households in their dissaving phase will give rise to a positive net flow of savings. Empirical data is
consistent with the idea of Modigliani in the sense that in 2016, we are experiencing a positive net
flow of saving. Furthermore, given that fertility rate is falling globally from 3.2 births per woman in
1990 to 2.5 births in 2019, it would be clear that we will see a decline in the younger households in

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their accumulation phase to older households in their dissaving phase which would lead to a negative
flow of savings. This would lead to upward pressure in saving rate but could also be crowded out from
the fact that old people will be dissaving slower due to longevity or bequeath to younger cohorts for
them to save. Thus, it is important to note that population dynamics throughout history have changed
and economic models should take into consideration these changes. Similarly, the United States of
America births per woman has declined from 3.65 in 1960 to 1.77 in 2017. This would subsequently
lead to the same demographic shift as the global population and lead to a negative flow of savings in
the future.
Alternatively, if changes in the age structure composition have a potential impact on affecting aggregate
consumption then this would lead to a greater increase in aggregate demand, multiplier effect. In the
context of the IS-LM model, the IS curve will shift. Assume initially, the economy is at full employment,
then a positive shift in the IS curve will lead to an increase in the interest rate. The price level will then
have to increase, assuming money supply is fixed, such that the real money balance decreases. This
would result in a negative shift in the LM curve so that the economy would go back to full employment.
Furthermore, in the aggregate demand and aggregate supply model; an increase in proportion of the
age cohort that consumes more relative to other age cohorts would lead to an increase in the
consumption by the economy. This would lead to an increase in aggregate demand in which aggregate
supply would follow to reach the long-run aggregate supply, suggesting an increase in the price level.
An example of demand-pull inflation. According to Lindh and Malmberg (1999) when the population
share of middle-aged adults is large, productivity would increase, and this would lead to an increase in
the growth rate of GDP per worker. Through the Balassa-Samuelson effect, it is evident that the
productivity growth relative to a foreign country will lead to a differential in income which will lead to
higher prices of goods.
Database
To investigate the possible association between age structure and inflation rate, we collects historical
data of age structure, inflation rate, interest rate, real GDP and money supply from a variety of sources
from 1960-2016. Annual population data in the United States is collected from the United Nations
Population database as an estimation. The data set is produced by the Department of Economic and
Social Affairs. Population data is then segmented into different age groups; young adults (YA; 15-29
years old), mature adults (MA; 30 – 49 years old), middle-aged adults (MAG; 50-64 years old), young
retirees (YR; 65-74 years old) and old people (OP; +75 years old). Each group is then divided by the
total population in that year to get the proportion of each age group relative to the total population.
This paper has omitted the age cohort from 0 to 14 due to multi-collinearity, potential similar
consumption and saving behaviour traits as young adults, and potential endogeneity to the inflation

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rate. The annual inflation rate in the United States of America is collected from the Federal Reserve
Economic Data using the Consumer Price Index for all Urban Consumers: All items in U.S. City
Average (code: CPIAUCSL), which is collected from the U.S. Bureau of Labour Statistics. The
Consumer Price Index (CPI) measures the average monthly price change of goods and services paid
by urban consumers, CPIAUCSL roughly accounts for 88 percent of the total population in the United
States of America. The inflation rate used in the model is the year over the year growth rate of
consumer price index from December at time t relative to December at time t-1. Furthermore, to
avoid the possibility of bias, other economic variables are collected to improve the fit of the model.
Economic variables that could potentially explain the variation of inflation rate are interest rate, real
GDP per capita, and money supply. The annual interest rate in the United States of America is collected
from the Federal Reserve Economic Data using the Effective Federal Funds Rate (code: FEDFUNDS).
According to the Federal Reserve Economic Data, the federal funds rate is the interest rate at which
depository institutions trade federal funds with each other overnight. The effective fund rate is
published monthly on the Board of Governors of the Federal Reserve Systems where market
participants react to the actual and expected movements of this interest rate. The annual interest rate
used in the model is the arithmetic average monthly interest rate throughout the year. Interest rate is
often associated with the variation in inflation rate due to interest rate being able to nudge saving and
investment behaviour and thus affecting the inflation rate. The annual real GDP in the United States
of America is also collected from the Federal Reserve Economic Data using Real Gross Domestic
Product (code: GDPCA), which is collected from the Bureau of Economic Analysis (code: A191RX).
The real GDP is the total market value of final goods and services produced within a country in a given
period of time adjusted for the inflation rate. The output of a country could potentially influence the
inflation rate given by the theory of the AD-AS model in which if aggregate demand for goods and
service increases faster than aggregate supply, the inflation rate could, therefore, be a consequence of
an increase in GDP. The paper then divides the annual real GDP by the corresponding population in
that year to get Real GDP per capita. The annual Money Supply is collected from the Federal Reserve
Economic Data using M2 for United States (Code: MYAGM2USM052S), which its main source is from
the International Monetary Fund. According to the Federal Reserve Economic Data, M2 includes all
currency and money in checking accounts plus saving deposits (including money market deposit
accounts), small-denomination time deposits (time deposits in amounts of less than $100,000), less
IRA and Keogh balances at other depository corporations and balances in retail money market mutual
funds, less IRA and Keogh balances at money market mutual funds. Data used for money supply is the
growth rate of money supply year over year in the United States of America, in which end of the year
money supply is used to find out the growth annual growth rate of the money supply. From the quantity
theory of money, we infer that the inflation rate has a direct relation with money supply, in the long
run, thus including money supply in the model would improve the fit given that there is a relationship

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between inflation rate and money supply. This will also allow other variables coefficient to be a better
representation of the true model.
The Regression Model
This paper adopts a similar model to that of Lindh, T. and Malmberg, B. (2000) and included other
relevant explanatory variables that may explain the variation in the inflation rate. The model adopts a
time series multiple linear regression that uses ordinary least squares to find the corresponding
coefficients for each explanatory variable. The specification of this model can help determine the
association not only from other macroeconomic variables and the inflation rate but also the association
between the changes in different age cohorts in explaining the variation in the inflation rate.
The model will be the following:
𝜋 𝑡 = 𝛽0 + 𝛽1 △ 𝑌𝐴𝑡 + 𝛽2 △ 𝑀𝐴𝑡+ 𝛽3 △ 𝑀𝐴𝐺𝑡 + 𝛽4 △ 𝑌𝑅𝑡 + 𝛽5 △ 𝑂𝑃𝑡+ 𝛽6 𝑅𝑆𝑅𝑡+ 𝛽7log⁡(𝐺𝐷𝑃𝐶) 𝑡+ 𝛽8 △ 𝑀2𝑡 + 𝛽9 𝑡+𝑢𝑡
Where t denotes years; t= 1,2,3….57
The inflation rates (which is in percent) is denoted by the symbol π is regressed on the following 5
annual growth rates of the proportion of each age cohort of the population: YA (15-29 years old), MA
(30 – 49 years old), MAG (50-64 years old), YR (65-74 years old) and OP (+75 years old). According
to Lindh and Malmberg (2000), the segment of the age group is based on the group's consumption and
saving behaviour pattern. This means that people between the age of 15-29 have similar consumption
and saving behaviour while different from those who are aged between 30 – 49. Young adults tend to
have higher consumption than income because they tend to allocate their time towards education
rather than working. Also, if young adults who decide to work instead of pursuing education tend to
have lower pay. This means that people aged between 15 -29 tend to have relatively higher
consumption and have low or negative savings. Mature adults tend to have accumulated savings and
keeping their consumption relatively fixed. In addition, they tend to have family obligations such as
providing for an offspring. In contrast, middle-aged adults tend to have passed their family obligations
and are financially better and will start increasing their consumption and decreasing the amount of
savings. Young retirees will start receiving pension claims and consume through their pension or
savings. Empirical studies have shown that young retirees and old people have not shown behaviour
of rapid dissaving which could be a consequence of bequest. De Nardi et al. (2004) have suggested
that old people tend to continue saving due to potential expensive medical care during old age.
The real interest rate is denoted as RSR is included in the model because of the negative association
interest rate has with the inflation rate. Although interest rates are likely to be simultaneously
determined with the inflation rate, where monetary policy tends to accommodate either economic
output or the inflation rate, it is included to improve on the fitness and prevent the possibility of bias
from omitting relevant variables. The log (GDPC) denotes the logarithm of GDP per Capita which is

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included because if on aggregate income is increasing per capita, there would potentially be demand-
pull inflation. △M2 denotes the growth of money supply which is used to explain the growth rate of
prices because of the quantity theory of money. Furthermore, a time trend (t=1,2, 3,…57) is included
in the model as an independent variable to solve a spurious problem, controlling for exogenous
increments in the independent and dependent variable. All macroeconomic variables have been tested
for non-stationarity through an autoregressive model. Through Stata each variable is tested using an
Augmented Dickey-Fuller test (ADF-GLS test) and have all failed to reject the null hypothesis.
Analysis and Results
The objective of the model is to determine whether the growth of the proportion of different age
cohort has a potential effect on determining the variation in inflation rate and if there is a significant
association to what degree do age structure affect inflation rate. The model is regressed in the program
Stata and will use the time series Ordinary Least Squares (OLS) Model. The model contains all the
aforementioned variables from the US from the period 1960 to 2016.
Table 1: Regression for the year 1960 to 2016: (1) OLS regression (2) OLS regression with Newey West Standard
Errors (3) OLS regression without demographic variables (4) OLS regression without demographic variables with Newey
West Standard Errors

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Preliminary results show that the growth rate in the proportion of young adults (yachg) shows a 10%
significance level in explaining the variation in inflation rate while the growth rate in the proportion of
old people (opchg) show a 5% significance level in explaining the variation in the inflation rate.
Surprisingly, the growth rate in the proportion of mature adults, middle-aged adults, and young retirees
shows a 1% significance level in explaining the variation in the inflation rate. All growth in each age
cohort shows a positive association towards the inflation rate, however, the magnitude in the
association is largest in the working-class cohort (age between 30-64 years old). A 1% increase in the
proportion of young adults in the population associates with a 1.328% increase in the inflation rate. A
1% increase in the proportion of mature adults in the population associates with a 4.288% increase in
the inflation rate. A 1% increase in the proportion of middle-aged adults in the population associates
with a 3.278% increase in the inflation rate. A 1% increase in the proportion of young retirees in the
population associates with a 1.698% increase in the inflation rate. A 1% increase in the proportion of
old people in the population associates with a 1.646% increase in the inflation rate. Real interest rate
and inflation rate show a negative association at a 5% significance level, which is consistent with the
theory. The logarithm of gross domestic product per capital shows insignificance in explaining the
variation in the inflation rate. While the growth rate of money supply shows a 1% significance level in
explaining the variation in the inflation rate. However, the growth rate of money supply shows a
negative association with the inflation rate which is inconsistent with the theory. After using Newey
West Standard Errors, we find the significance of the growth rate in the proportion of mature adults
and middle-aged adults to remain 1% significant while the growth rate in the proportion of young
retiree to change from 1% significant to 5% significant and the growth rate in the proportion of young
adults and old people to be significant at 10%. At the same time, the significant level of the association
between real interest rate and inflation rate to change from 5% to 10%.
To see whether demographic variables are essential in explaining the variation in the inflation rate, the
additional model is tested without demographic variables. Results can show the significance of other
macroeconomic variables in explaining the variation in the inflation rate. After running regressions
without demographic variables, model (3) and (4), the logarithm of gross domestic product per capita
becomes significant at 1% in explaining the variation of the inflation rate. This could be because the
change in demographic potentially explains the variation in the logarithm of gross domestic product
per capita making model (3) and (4) suffer from endogeneity problem. Not to mention, the growth
rate of the money supply becomes insignificant in explaining the variation in the inflation rate which is
not consistent with the quantity theory of money. However, since the regression model is tackling
relations of variables today, it only measures the short-run effect, and inflation could take time to
catch up to the growth of the money supply. An alternative explanation for this phenomenon is the
Some Unpleasant monetarist Arithmetic, Sargent and Wallace (1981), in which a reduction in money

15
supply today, implies that individuals expect money supply to increase in the future. This would then
lead to agents in the economy to expect inflation to go up in the future and as a result, the inflation
rate will go up today. This theory is consistent with the empirical finding in which the growth of money
supply and the inflation rate are negatively associated. Moreover, through omitting demographic
variables, we see that the model suffers from a lower adjusted R2 from 75.4% to 32.4%. At the same
time, the F statistics have decreased from 20.06 (model 1) to 7.707 (model 3), which shows that
demographic variables are jointly significant in explaining the variation in the inflation rate.
Forecast
Demographic projection in the upcoming decade has been estimated by the United Nations Database,
in which the estimate is quite accurate to a degree of precision. This suggests that by utilizing the
model we can implement the growth in different proportions of age cohort and forecast how inflation
rate in the future might be affected by age structure keeping all else equal.
Table 2: United Nations Population forecast: The growth rate of proportion in 5 different age cohorts with respect to
the base year 2020.
Year opchg yrchg magchg machg yachg
2021 3.17% 1.93% -1.17% 0.34% -0.70%
2022 6.57% 3.70% -2.42% 0.80% -1.43%
2023 10.18% 5.33% -3.71% 1.34% -2.18%
2024 13.90% 6.81% -5.00% 1.88% -2.96%
2025 17.74% 8.16% -6.25% 2.37% -3.77%
2030 37.99% 10.33% -11.60% 3.63% -7.24%
2040 74.83% -1.63% -9.28% 2.17% -12.81%
2050 83.18% -0.27% -2.98% -2.94% -12.76%
2060 90.37% 12.60% -5.13% -5.37% -14.68%
2070 114.85% 10.32% -8.70% -5.88% -17.23%
2080 131.88% 9.15% -9.65% -7.72% -17.77%
2090 141.30% 7.30% -8.90% -8.98% -18.36%
2100 147.29% 10.18% -10.46% -9.26% -19.60%
Table 2 shows the growth rate of different proportions of age structure in the upcoming years. As
you may have noticed the proportion of old people in the economy is forecasted to increase
tremendously in the upcoming future and given the declining fertility rate, younger cohort share is
forecasted to decline. Given that model, mature adult, and middle-aged adults tend to be the cohort
that affects inflation rate the most we will see deflationary results due to change in the proportion in
age structure. Putting 2060 into perspective; old people share will increase by 90.37% which will

16
increase inflation rate by 1.487492%, young retiree share will increase by 12.6% which will increase
inflation rate by 0.214%, middle-aged adults share will decrease by 5.13% which will decrease inflation
rate by 0.168%, mature adults share will decrease by 5.37% which will decrease inflation rate by 0.23%
and young adults share will decrease by 14.68% which will decrease inflation rate by 0.195%. This
suggests that the change in the composition of age structure in the economy will lead to an increase
in a 1.1% increase in the inflation rate in 2060, keeping all else equal. In 2100; old people share will
increase by 147.29% which will increase inflation rate by 2.42%, young retiree share will increase by
10.18% which will increase inflation rate by 0.173%, middle-aged adults share will decrease by 10.46%
which will decrease inflation rate by 0.343%, mature adults share will decrease by 9.26% which will
decrease inflation rate by 0.398% and young adults share will decrease by 19.6% which will decrease
inflation rate by 0.26%. This suggests that the change in the composition of age structure in the
economy will lead to an increase in the 1.592% increase in the inflation rate in 2060, keeping all else
equal. Controversially, the demographic shock in 2020 through the COVID-19 virus would essentially
reduce the population of the United States of America and the age cohort most affected could be of
old age and young retirees. This essentially would slow down the decrease in the growth rate in the
proportion of middle-aged adults and mature adults. As a result, given the model, it would lead to
inflationary pressure to the United States of America in the upcoming years after the pandemic.
Although the preliminary results and the projection of age structure in the future suggest that the
change in demographic structure could lead to inflationary pressure in the future, the pandemic would
speed up the inflationary pressure.
Potential Limitations
This paper is formed on the hypothesis that demographics could potentially be used as an essential
exogenous variable that could potentially explain the variation in the inflation rate. There are many
limitations to the study; data from the United States of America between the years 1960 to 2016
which only includes 57 observations. Although the adjusted R2 is 75.4%, which is high, due to the
limitation of the data, it is challenging to confidently conclude the findings to be a true representation
of the real world. If observations could be collected on a quarterly or monthly basis, the model could
be more precise in finding the association between demographics and the inflation rate.
Furthermore, there are also other research papers investigating the influence that politics has on the
inflation rate, which is relevant to the relationship between demographics and inflation rate. For
example, Doepke and Schneider (2006), Bullard et al. (2012) and Katagiri et al. (2014) argue that given
that ageing population leads to a population with a higher density of old people in the population and
that old people prefer less inflation relative to young people. For voting reasons, politicians will tend
to favour a lower inflation rate to such that their voting power increases. This suggests another

17
perspective as to how ageing population might have a negative association with the inflation rate. In
addition, the exchange rate is also considered a big factor in affecting the inflation rate which this paper
does not take into consideration. The economic intuition is derived from the equation of real exchange
rate, where if assuming two countries have the same purchasing power, an increase in the exchange
rate of a currency relative to the other will lead to a change in price level such that the purchasing
power parity holds. Thus, the exchange rate in simple terms will have a potential effect on the inflation
rate. Yiheyis (2018), demonstrates through the findings in Uganda that there is a long-run association
between a real depreciation in the currency that leads to an increase in the inflation rate. This finding
is also consistent with Nkoro and Uko (2016) in which they find a negative statistical significance
between exchange rate and inflation rate volatility in Nigeria. Lastly, during the period between 1960
– 2016 many economic shocks have affected the inflation rate that is not modeled in this paper. For
example, the end of the gold standard in 1973 where the US dollar is no longer pegged to gold and
the oil crisis which led inflation rate to rise by 8.7% and 12.3% in the consequent year. Reagan tax cut
in 1981 which caused the inflation rate to rise by 12.5%.
Moreover, additional variables could be added to the regression model to explain fully the variation in
the inflation rate. Thus, the model could be suffering from omitted variables making the coefficients of
the explanatory variables’ bias. According to Shilling et al. (2017), there is a significant association
between the inflation rate and market return suggesting that the stock market could create a wealth
effect causing aggregate demand to increase and lead to inflationary pressure in the economy, demand-
pull inflation. Besides, many pieces of literature specify that house prices tend to rise with inflation.
Lusht (1978), investigates the relationship between the inflation rate and the real estate value and
found that investing in real estate or real estate stocks could potentially hedge against inflation risk,
suggesting that there is a possible association between house price and the inflation rate.
Conclusion
In conclusion, there has been a significant number of research papers pointing to an association
between demographic changes and the inflation rate, even though the estimated magnitude of this
effect differs in the different empirical analysis. This research paper shows that there is a significant
association between demographic change and inflation rate. As most developed countries are facing a
lower fertility rate and an increase in longevity, the population has and will continue to age. The
economic consequences of such a natural shift in demographics could help us better understand the
trend of the inflation rate. Thus, by better understanding, the long-run trend of the inflation rate,
especially the portion changes in the demographic structure of an economy, this paper can help central
banks in improving the way they set inflation rate targeting policies over time.

18
Through empirical analysis, the paper has found a positive association between the inflation rate and
all growth rate of proportion in 5 age cohorts. Mature adults and middle-aged adults have shown the
largest magnitude in explaining the variation in the inflation rate. The forecasted inflation rate from
the change in age structure for the year 2060 is expected to be 1.1%, keeping all else constant.
However, through the paper it is evident that a model to determine inflation rate is very challenging
and quantitative model can only explain and forecast inflation rate to some extent in there are many
limitations and improvements that can be made to improve the precision on the expected inflation
rate for the future and allow monetary authorities to conduct policy that accommodates their mandate
accurately.
Reference
Ameriks et al. (2004). “How Do Household Portfolio Shares Vary With Age?” manuscript, Columbia
University
Bobeica, E., et al. (2017). Demographics and inflation (No. 2006). ECB Working Paper.
De Nardi (2004) Why Do the Elderly Save? The Role of Medical Expenses
Fedotenkov, I. (2018). Population ageing and inflation with endogenous money creation. Research in
Economics, 72(3), 392-403.
Friedman, M., (1963). Inflation Causes and Consequences. Asian Publishing House.
Fujita, S., & Fujiwara, I. (2016). Declining trends in the real interest rate and inflation: The role of
aging.
Gajewski, P. (2015). Is ageing deflationary? Some evidence from OECD countries. Applied
Economics Letters, 22(11), 916-919.
Goldmansachs Report (2018). Much Ado About Something? Demographics, Inflation and Asset
Prices. Report retrieved from
https://www.gsam.com/content/dam/gsam/pdfs/common/en/public/articles/2018/demographics-
paper.pdf?sa=n&rd=n
Imam, P., (2014). Shock from Graying: Is the Demographic Shift Weakening Monetary Policy
Effectiveness. International Journal of Finance & Economics, 20(2), pp.138-154.
Juselius M. and Takáts E (2016) The age-structure–inflation puzzle
KSOY, Y., et al. (2016) Demographic Structure and Macroeconomic Trends
Lindh, T. and Malmberg, B. (1998). Age structure and inflation–a Wicksellian interpretation of the
OECD data. Journal of Economic Behavior & Organization, 36(1), 19-37.

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Lindh, T. and Malmberg, B. (2000). Can age structure forecast inflation trends?. Journal of Economics
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Modigliani, Franco (1966). "The Life Cycle Hypothesis of Saving, the Demand for Wealth and the
Supply of Capital". Social Research. 33 (2): 160–217. JSTOR 40969831
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20
Appendices
Table 3: United Nation Population forecast: Proportion of 5 different age cohort with respect to the whole
corresponding year’s population
op yr mag ma ya
2020 6.91% 9.72% 18.97% 25.68% 20.34%
2021 7.13% 9.91% 18.75% 25.77% 20.20%
2022 7.37% 10.08% 18.51% 25.89% 20.05%
2023 7.61% 10.24% 18.27% 26.03% 19.90%
2024 7.87% 10.38% 18.02% 26.17% 19.74%
2025 8.14% 10.51% 17.78% 26.29% 19.58%
2030 9.54% 10.72% 16.77% 26.62% 18.87%
2040 12.08% 9.56% 17.21% 26.24% 17.74%
2050 12.66% 9.69% 18.40% 24.93% 17.75%
2060 13.16% 10.95% 18.00% 24.30% 17.36%
2070 14.85% 10.72% 17.32% 24.17% 16.84%
2080 16.02% 10.61% 17.14% 23.70% 16.73%
2090 16.68% 10.43% 17.28% 23.38% 16.61%
2100 17.09% 10.71% 16.99% 23.31% 16.36%

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