This the country, t is time, ?

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paper analyzes the connection between workforce demographics, which includes
the aging of the workforce, and its effect on aggregate productivity. This
paper passes the lower bound test as the journal is ranked at 21 out of the top
250 economic journals. The author, James Feyrer, takes previous empirical
growth studies that only focus on dependency ratios, which is the ratio of
workers to nonworkers, and uses them as the base for his paper since they have
previously neglected to account for demographic change in the past decade. The
author argues that this paper differs from past papers as it does not focus on
the age structure of the entire population, but instead, the workforce. Past
papers that have focused on the entire population “tend to conflate dependency
ratio effects from the direct effects caused by the characteristics of
workers”. (Feyrer, 2007).

            The dependent variables used in this
paper are the log of output and the results of a decomposition of output into
physical capital, human capital, and productivity. The independent variables
are the proportion of the workforce classified by age group in groups of ten.
For example, W10, here represents those in the workforce between the ages of 10
and 19. The data used for the workforce composition originates from The
International Labour Organization (ILO), which compiles cross-country data on
the number of workers in five-year age groups from ages 10 to 65, and data from
the United Nations (UN) on population by five-year age groups to produce worker
counts. The age categories are later collapsed into ten-year intervals, and are
normalized by population and workforce size. The author uses the intervals
starting from W10 all the way to W60, but has chosen to exclude the 40-year age
group, W40, since this age group tends to have the highest coefficient when
included. There are two different sample sizes used; the first is an 87-country
sample that includes all countries for which data exists, except oil exporting
countries, and the second sample uses 19 countries within The Organization for
Economic Co-operation and Development (OECD). A strength in this paper is the
large sample size that is composed of both rich and poor countries that allows
the author to analyze the age structure differences and levels of productivity
between them. A log specification is used with the following equation, where i is the country, t is time, ? is capital’s share of output which is assumed to be
1/3,  is the capital-output ratio in the steady
state, Ai,t is
productivity, and hi,t is
human capital per worker.

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From the results in Table 1 below, there
is a strong correlation between the demographic structure of the workforce and
output, which is heavily driven by the productivity residual. When there is a
5-percentage point shift from the 30-year age group to the 40-year age group,
there is an increase by over 15% in per-worker output that is associated with
it. (Feyrer, 2007). An important factor in the key results is that simple
reverse causality from productivity to demographics is not driving the results.

            There are a few concerns with the
regressions shown in Table 1 that the author makes note of in the paper; one of
them being that the imputation itself may be invalid and that immigration may
have an effect by moving the age structure in response to productivity shifts. However,
the author is able to address these issues by performing several regressions
with unimputed values. To address the issue with immigration, lagged population
demographics as an instrument can be used which also eliminates reverse
causality. The results of the new regressions are presented in Table 2 below.
This table shows that for large cohorts that are between the ages of 15 and 39
are associated with significantly lower productivity. (Feyrer, 2007). Although the
results for these cohorts appear to be stable and are not a result of
immigration, the results for those aged 50 and above are not as clear. Those in
W50 were seen to have lower productivity compared to the comparison group W40,
as well as smaller coefficients compared to younger cohorts. To check for
robustness, out-of-sample projections for output growth are used from the
period of 1990-1995 along with coefficients that were estimated using the
1960-1990 data.

            Overall, this paper states that
there is in fact a strong correlation between workforce demographics and
productivity and output, however there is a weak conclusion that does not
explicitly state how an aging workforce will have an impact on productivity. Based
on results from Table 1, the evidence suggests that as someone in the 30-year
age group gets older and moves towards the 40-year age group, there will be an
increase in productivity that the workers will be able to provide, but the
paper does not explain the reason behind this and if it is due to exogenous
factors like education or experience. Therefore, it can only be assumed to a
certain extent that there is a positive relation between age and productivity
for this age group. It is mentioned that poor countries have a lower proportion
of age 40 workers and thus have lower productivity compared to richer
countries. More research needs to be done to further examine the effect of an
aging workforce, but the results in this paper can be interpreted that those
who are below the age of 40 are associated with lower levels of productivity,
and that for both rich and poor countries, the 40-year age group tends to be
the prime age group that is associated with the highest levels of productivity. 


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