Productivity and National Standards of Living

Productivity and National Standards of Living

Order Code RL34073
Productivity and National Standards of Living
July 5, 2007
Brian W. Cashell
Specialist in Quantitative Economics
Government and Finance Division

Productivity and National Standards of Living
Summary
Among the goals of economic policy is a rising standard of living, and it is
generally understood that the means to that end is rising productivity. Productivity
relates the quantity of goods and services produced, and the income generated as a
result of that production, to the amount of labor (e.g., hours worked or number of
workers) required to produce it. The most commonly used measure of the living
standard of a nation, is simply the ratio of that income to the total population, without
regard to how the income is actually distributed. If a relatively small share of a
nation’s population works, there will be a large difference between the level of
productivity and that measure of the national standard of living.
Productivity varies over time, and it varies across countries as well. The link
between productivity and living standards is not a direct one, therefore countries with
a high level of productivity may not necessarily have the highest standard of living.
Gross domestic product (GDP) per capita can rise in the absence of an increase in
productivity if (1) employees increase the number of hours they work (hours per
employee); (2) the share of the labor force that is employed rises (i.e., the
unemployment rate drops); or (3) the share of the population that is in the labor force
rises (presuming that the share of any new jobseekers who get jobs is at least as large
as the share of those already in the labor force who have jobs).
A large labor contribution can offset low productivity to raise a nation’s
standard of living. Korea, for example has the second-lowest GDP per hour, but
because its workers work more hours than in any other country shown here, its per
capita GDP is not as close to the bottom of the ranking. The United States has the
second highest per capita GDP after Norway. The United States is also second to
Norway in terms of productivity. France and Germany have relatively high levels of
productivity, but because they both have relatively low employment (and high
unemployment rates), and in France’s case a relatively small share of the population
in the labor force, they fall to the middle with respect to per capita GDP.
There is little question that rising productivity is the single most important factor
behind rising living standards, but the proportion of a nation’s population that is
working is also important. The larger that proportion is, the more goods and services
there are to go around. The share of the population that is working is only partly
subject to the influence of policymakers. The size of the labor force is largely a
function of demographic factors, but the share of that labor force that is employed
can vary with short-term economic conditions, as well as policies that affect the cost
of labor.
This report will not be updated.

Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
From Productivity to the Standard of Living . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
International Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
List of Figures
Figure 1. Per Capita GDP and Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
List of Tables
Table 1. Growth Rates for Selected Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Table 2. Living Standards and Productivity, 2005 . . . . . . . . . . . . . . . . . . . . . . . . . 8
Table 3. Labor Contribution to Living Standards, 2005 . . . . . . . . . . . . . . . . . . . 10

Productivity and National Standards
of Living
Introduction
Among the goals of economic policy is a rising standard of living, and it is
generally understood that the means to that end is rising productivity. Productivity
relates the quantity of goods and services produced, and the income generated as a
result of that production, to the amount of labor (e.g., hours worked or number of
workers) required to produce it. The most commonly used measure of the living
standard of a nation, is simply the ratio of that income to the total population, without
regard to how the income is actually distributed. If a relatively small share of a
nation’s population works, there will be a large difference between the level of
productivity and that measure of the national standard of living.
Productivity varies over time, and it also varies across countries. The link
between productivity and living standards is not a direct one, therefore countries with
a high level of productivity may not necessarily have the highest standard of living.
This report examines the link between productivity and living standards. It does not
address the distribution of either economic well-being or the gains from productivity,
but simply looks at averages.
From Productivity to the Standard of Living
The standard measure of the production of goods and services for a nation is
gross domestic product (GDP). GDP measures the total value of goods and services
produced within a nation’s borders. Productivity is a measure of how much work is
required to produce it. The most basic unit of labor is the hour, thus productivity can
be measured as GDP divided by the total number of hours worked. Productivity may
also be measured as the average contribution of each employee to total production,
or simply GDP divided by employment.
The broadest measure of the living standard of a nation is GDP divided by the
total population. Per capita GDP says nothing about how those national resources
are distributed.1 But the proceeds from productivity gains are shared and do not
accrue solely to workers and the owners of capital. Much of the sharing is simply
done within individual households, but may also come about through policies that
redistribute income, or by public sector investments that benefit everyone. Per capita
1 It is also limited in that, for the most part, it only counts those items for which there is a
value defined in a market, and does not take into account some activities that have economic
value, leisure time for example.

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GDP is probably the best single statistical measure of national living standards, and
is especially useful for comparisons as it is available for a large number of countries.
To show how per capita GDP and productivity are related, per capita GDP can
be expressed as the product of ratios reflecting the relationship between the overall
population and the amount of work done. Per capita GDP obviously depends on the
level of GDP and the population, but the relationship can be decomposed in a way
that may shed some further light. The following equation shows the decomposition
of GDP. The equation shows that per capita GDP can be expressed as the product
of four ratios: GDP divided by labor hours, which is labor productivity; labor hours
per employee; the share of the labor force that is employed, and the size of labor
force relative to the overall population.2
GDP
GDP
hours
employees
labor force
=
×
×
×
population
hours
employees
labor force
population
The equation shows what factors contribute to per capita GDP. First, per capita
GDP depends, in part, on the level of productivity (GDP divided by hours).3 An
increase in productivity, other things being equal, will raise the average standard of
living. That much is widely understood. As the equation shows, however, per capita
GDP may change for reasons entirely unrelated to productivity.
From a purely arithmetical standpoint, it would appear that neither hours, the
number of employees, nor the size of the labor force could have any effect on per
capita GDP. Suppose, for example, the number of hours worked increased, while the
other variables remained unchanged. In that case, the hours-to-employees ratio
would rise, but it would be offset by a decline in the GDP-to-hours ratio, leaving per
capita GDP unchanged.
But while the equation allows for an accounting of the factors that separate
productivity from per capita GDP, it does not make clear that the variables
themselves are interdependent. For example, it is unlikely that an increase in hours
worked would have no effect on GDP. Only if the increase in hours worked yielded
no additional production of goods and services, would the increase in hours not raise
per capita GDP.
Similarly, an increase in the number of employees would raise the employment-
to-labor force ratio while causing the hours-to-employees ratio to fall. It is unlikely,
however, that employment would rise without a corresponding increase in the
number of hours worked.
2 GDP is a measure or the dollar value of production in a given year, and so hours in this
case refers to the number of hours worked in the course of a year.
3 For more on productivity, see CRS Report RL32456, Productivity: Will the Faster Growth
Rate Continue?, by Brian W. Cashell.

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Finally, an increase in the labor force by itself, other things being equal, would
have no direct effect on per capita GDP because it would lead to both an increase in
the labor force-to-population ratio and a decline the ratio of employment to the labor
force. But that would only hold true if all of any increase in the labor force remained
unemployed.

GDP per capita can rise in the absence of an increase in productivity if (1)
employees increase the number of hours they work (hours per employee); (2) the
share of the labor force that is employed rises (i.e., the unemployment rate drops); or
(3) the share of the population that is in the labor force rises (presuming that the share
of any new jobseekers who get jobs is at least as large as the share of those already
in the labor force who have jobs).
This breakdown also illustrates a weakness in using GDP per capita as a
measure of living standards. Leisure has a value, as does unpaid work done in the
home, but those values are not included in the measure of GDP. Thus, an increase
in GDP brought about by an increase in work may overstate the true increase in
economic well-being, since it requires the sacrifice of leisure.
What is the relationship between GDP and the labor that produces it? Economic
theory provides some basis for answering that question. Labor is generally assumed
to have the characteristic that, in the short run, each additional employee contributes
a smaller amount of production than did the one hired just before. In other words,
among those seeking work the ones who are relatively more productive tend to be
hired first. This is referred to as diminishing marginal product of labor.4 If that is
true, then with reference to the equation above, increases in employment may
generate proportional increases in hours, but they might not lead to proportional
increases in GDP. In other words, each new worker tends to bring down the average
productivity of labor.
That is not to say that increases in employment over time will tend to reduce
average labor productivity. Rather, it says that, other things being equal, a higher
ratio of employment to the labor force is likely to mean a lower ratio of GDP to
hours, or a lower average level of labor productivity. Over longer periods of time,
growth in the stock of physical capital, a more educated labor force, and
technological progress will all contribute to gains in productivity. But, comparing
two otherwise identical economies at a given point in time, the one with a larger
share of its labor force employed will likely have lower average productivity.
It is less clear whether there is a similar relationship between hours worked and
the level of productivity. Individual worker productivity is generally presumed to be
determined by education, training, experience, and the quantity of capital available.
Further, most workers contribute roughly the same number of hours so that, at least
in the short run, an increase in hours is unlikely to happen without an increase in
employment. Unless the productivity of individuals is significantly affected by the
4 This could also happen even if all workers were equally productive. With a fixed stock of
capital, each additional hire would tend to reduce the amount of capital available to each
worker.

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number of hours they work, an increase in hours might be expected to result in a
roughly proportionate increase in GDP.
The share of the population that is in the labor force is not likely to affect
productivity directly. But, other things being equal, it is an important factor in the
level of per capita GDP. Given two otherwise equivalent economies, the one with
a higher percentage of its population in the labor force will also have a higher level
of GDP, unless the additional members of the labor force are unemployed (a higher
unemployment rate) or they contribute nothing to the production of goods and
services. That would mean a higher per capita GDP as well.
Figure 1 shows how per capita real GDP and real GDP per hour in the United
States have grown since 1948. The two series are shown as indexes set equal to 100
in 1948. From 1948 through the mid-1970s, the two measures grew at about the
same rate. Since the mid-1970s, they have diverged. Given the equation presented
above, the reason for that divergence must be an increase in either the number of
hours worked per employee, a decline in the unemployment rate, or an increase in the
proportion of the population in the labor force.
Figure 1. Per Capita GDP and Productivity
Source: Department of Commerce, Bureau of Economic Analysis.
Hours worked per employee cannot account for the divergence because they
have been declining steadily since 1948. Between 1973 and 2006, the index of
average weekly hours published by BLS fell by 7.5%. Neither can the share of the
labor force employed be the reason for the divergence. The civilian unemployment
rate in 1973 was 4.9% compared with 4.6% in 2006, hardly enough of a change to
have made a difference. That leaves the share of the population in the labor force to
examine.
The Bureau of Labor Statistics (BLS) bases its measure of the labor force on the
non-institutional population aged 16 and older. From that population, the BLS

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measure of the labor force is the sum of those who are employed and those who are
actively looking for work. Given that, the ratio of the labor force to the population
was little changed between 1948 and 1975. In 1948, it was 59% and, in 1975, it was
61%. By 2006, however, it had risen to 66%. Two developments account for that
increase. First was a substantial increase in the labor force participation rate of
women. For men, there was a slight decline, from 78% to 73%, between 1975 and
2006. But for women, the share of the population in the labor force rose from 46%
to 60%.
The second factor that caused the labor force to grow more rapidly than the
population was the aging of the baby-boom generation. The baby-boom generation
is generally considered to include those who were born between 1946 and 1964. This
cohort began entering the labor force in 1962 and was all in by 1980.
Most economists agree that, since World War II, the U.S. economy has
experienced several shifts in the long-run rate of productivity growth. The particular
reasons for all of those shifts are not entirely clear, but the data show that beginning
in about 1973, productivity growth slowed significantly from its rate of growth up to
that point. Then in 1995, it appears to have accelerated. Table 1 presents growth
rates over those intervals for productivity, as well as for the other factors that enter
the calculation of per capita GDP.
Table 1. Growth Rates for Selected Measures
Annual Rate of Change (%)
1947 to 1973
1973 to 1995
1995 to 2006
Per capita real GDP
2.45
1.77
2.15
Real GDP per hour
2.30
1.33
2.07
Hours per employee
0.09
-0.27
-0.18
Employment / labor force
-0.04
-0.03
0.09
Labor force / population
0.09
0.74
0.16
Sources: Department of Commerce, Bureau of Economic Analysis; Department of Labor, Bureau of
Labor Statistics.
These figures illustrate some interesting facts. First, it is clear that growth in per
capita GDP fell after 1973. In part, that was due to the decline in productivity
growth. The growth rate of real GDP per hour worked (productivity) fell by almost
a full percentage point. Moreover, growth in the number of hours worked per
employee also fell by almost 0.4 percentage point after 1973. But the declines in
productivity and hours worked were not completely reflected in per capita real GDP.
The growth rate of per capita real GDP only fell by about 0.7 percentage point. The
reason was a big jump in the share of the total population in the labor force. Growth
in that ratio jumped nearly 0.7 percentage point, offsetting some of the decline in
growth or hours and productivity.

CRS-6
The data also show that, after 1995, productivity growth picked up again
although not quite to the pre-1973 rate. There were also increases in the rates of
growth of both hours worked per employee and the share of the population in the
labor force. The increase in the growth rate of per capita GDP was only about half
of the increase in the growth rate of productivity, however. The reason is that the
growth rate of the share of the population in the labor force fell by nearly 0.6
percentage point. The reason for that drop is that the share of the population in the
labor force stabilized after all the baby boomers were old enough to be counted as
part of the labor force, and were no longer a factor pushing up the ratio.
The biggest reason for the discrepancy between per capita GDP growth and
productivity growth has been changes in the share of the population in the labor
force. Absent those changes, the data suggest that changes in productivity growth
would have been fully reflected in the growth rate of per capita GDP.
But just as the entry of the baby-boom generation into the labor market caused
per capita GDP to grow faster than productivity for a time, as the baby boomers age
and exit the labor market there is likely to be an extended period where per capita
GDP grows more slowly than productivity, as happened in the late 1950s and early
1960s. Actuaries at the Social Security Administration project that labor force
growth will fall below population growth for much of the period between 2010 and
2035. The difference between the two is about 0.3 percentage point. The 2007
Social Security trustee’s intermediate projection also assumes a productivity growth
rate of 1.7%, suggesting that they expect the per capita GDP growth rate to be about
1.4%.5
International Comparisons
Having established the connection between productivity and per capita GDP
makes it possible to assess how the United States compares with other industrialized
countries. Per capita GDP is the single most widely used measure to make
international comparisons of living standards, but comparing GDP across countries
is complicated because it is calculated in different currencies. Exchange rates
resulting from trading in currency markets are not suitable for converting GDPs from
various countries into a common unit of account. Those exchange rates are
influenced by cross-country differences in prices, and they are also subject to the
influence of changes in financial markets.
Exchange rates are greatly affected by international flows of financial capital.
They may rise or fall because of speculation in a currency or because of changes in
interest rates. For example, a rise in interest rates in one country will tend to draw
in foreign capital. Foreign investors, however, must first buy that country’s currency
in order to buy financial assets denominated in that country’s currency. That drives
up the value of that country’s currency without there having been any change in the
price levels of goods and services either in that country or abroad.
5 See the trustee’s 2007 report at [http://www.ssa.gov/OACT/TR/TR07/].

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Because of those complications, economists have devised a way of estimating
an exchange rate that is based only on the differences in the prices of goods and
services across countries. This is referred to as the purchasing power parity currency
conversion rate (PPP). For example, if a liter of soda costs $2.00 in the United States
and 2.30 euros in France, then the PPP conversion ratio for that particular item is
2.3/2, or 1.15. PPP ratios for entire economies are based on weighted averages
reflecting all of the goods and services that add up to GDP. The price an American
would have to pay for that soda would reflect not only the difference in price but also
the relative values of the two currencies. The PPP allows cross-country comparisons
of the prices paid by residents of a country for goods and services in that country.
Estimates of GDP can thus be converted to a common unit of account, or currency,
to allow comparisons of economic well being in different countries.
Table 2 presents data for 27 countries for which the Organisation for Economic
Co-operation and Development (OECD) has published comparable figures.6 The
amounts have all been converted to U.S. dollars using OECD estimates of purchasing
power parity exchange rates. The first column shows per capita GDP. The second
column shows GDP per employee, which is a measure of the productivity of the
working population. The third column shows GDP per hour worked. For each
measure, the country’s ranking is also given.
The United States has the second highest per capita GDP after Norway. Two
measures of productivity are shown. Norway has the highest production per worker,
and the United States is second. With respect to production per hour worked,
however, the United States is sixth, after Norway, Belgium, the Netherlands, Ireland,
and France. The figures show a considerable range across countries. Mexico has a
per capita GDP that is just 22.5% of Norway’s, and a GDP per hour that is 20.5% of
Norway’s.
High levels of productivity do not necessarily make for a correspondingly high
national living standard. Even though France and Germany have a higher GDP per
hour than the United States, their per capita GDPs are less than three-fourths of that
of the United States.7
6 These data are taken from the OECD Factbook 2007.
7 See Margarida Duarta and Diego Restuccia, “The Productivity of Nations,” Federal
Reserve Bank of Richmond Economic Quarterly, Summer 2006, pp. 195- 223.

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Table 2. Living Standards and Productivity, 2005
Per Capita GDP
GDP Per Employee
GDP Per Hour
$U.S.
Rank
$U.S.
Rank
$U.S.
Rank
Australia
$34,483
7
$70,005
8
$40.47
13
Austria
34,398
8
68,777
11
41.53
10
Belgium
33,110
11
81,294
4
52.99
2
Canada
34,058
10
67,976
13
39.13
15
Czech Republic
20,634
23
44,409
24
22.18
25
Denmark
34,158
9
67,005
15
43.20
8
Finland
30,957
15
67,894
14
39.61
14
France
31,176
14
76,639
6
49.57
5
Germany
30,776
17
65,379
17
45.50
7
Greece
29,588
18
79,167
5
38.56
16
Hungary
17,488
25
45,747
23
22.94
23
Iceland
36,149
4
66,346
16
36.98
18
Ireland
39,022
3
82,585
3
50.42
4
Italy
28,284
19
73,715
7
40.93
12
Japan
30,844
16
61,997
20
34.93
20
Korea
22,098
22
46,692
22
19.84
26
Mexico
10,628
27
27,310
27
14.31
27
Netherlands
35,110
6
69,951
9
51.17
3
New Zealand
25,958
21
51,333
12
28.38
21
Norway
47,199
1
95,317
1
70.09
1
Portugal
19,862
24
41,186
25
24.44
22
Slovak Republic
15,983
26
38,855
26
22.34
24
Spain
27,400
20
62,672
19
35.43
19
Sweden
32,115
13
68,173
12
42.96
9
Switzerland
35,951
5
63,932
18
38.54
17
United Kingdom
32,986
12
68,875
10
41.19
11
United States
41,827
2
87,483
2
48.49
6
Source: Organisation for Economic Co-operation and Development.

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Table 3 presents data showing how the share of a nation’s population that
contributes labor may influence its measured standard of living. A high per capita
GDP may be the result of high productivity, or it can be the result of a large
proportion of a nation’s population working, or working a high number of hours.8
A large labor contribution can offset low productivity to raise a nation’s standard of
living. Korea, for example has the second-lowest GDP per hour, but because its
workers work more hours than in any other country shown here, its per capita GDP
is not as close to the bottom of the ranking.
Iceland is another example. With respect to productivity it places in the bottom
half of countries shown here, but because it employs a relatively large proportion of
its population it ranks much higher with respect to per capita GDP. France and
Germany, in contrast have relatively high levels of productivity as measured by GDP
per hour, but because they both have relatively low employment (and high
unemployment rates), and in France’s case a relatively small share of the population
in the labor force, they fall to the middle with respect to per capita GDP.
It may be in the case of countries like Germany and France, that there is a small
trade-off between the national average level of productivity, and the proportion of the
labor force that is employed. In those countries where labor costs are relatively high,
perhaps because of relatively greater regulation of labor markets, some who might
otherwise be hired are not because they are not productive enough to cover the costs
of hiring them.
8 See Bart van Ark and Robert H. McGuckin, “International comparisons of labor
productivity and per capita income,” Monthly Labor Review, July 1999, pp. 33-41.

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Table 3. Labor Contribution to Living Standards, 2005
Average Hours
Employees as a %
Labor Force as a %
Per Employee
of the Labor Force
of the Population
Country
Hours
Rank
%
Rank
%
Rank
Australia
1730
14
94.9
13
51.9
11
Austria
1656
19
94.2
14
53.1
5
Belgium
1534
24
91.6
22
44.5
23
Canada
1737
13
93.2
16
53.7
3
Czech Republic
2002
3
92.0
20
50.5
13
Denmark
1551
22
95.2
9
53.6
4
Finland
1714
15
91.6
21
49.8
17
France
1546
23
90.1
25
45.1
22
Germany
1437
25
90.9
23
51.8
12
Greece
2053
2
89.6
26
41.7
24
Hungary
1994
4
92.7
17
41.2
26
Iceland
1794
9
97.4
1
55.9
2
Ireland
1638
20
95.6
6
49.4
18
Italy
1801
8
92.2
19
41.6
25
Japan
1775
10
95.6
7
52.1
9
Korea
2354
1
96.3
4
49.2
19
Mexico
1909
5
96.5
2
40.3
27
Netherlands
1367
26
95.0
11
52.8
6
New Zealand
1809
6
96.3
3
52.5
7
Norway
1360
27
95.4
8
51.9
10
Portugal
1685
16
92.3
18
52.2
8
Slovak Republic
1739
12
83.8
27
49.1
20
Spain
1769
11
90.8
24
48.1
21
Sweden
1587
21
94.2
15
50.0
16
Switzerland
1659
18
95.7
5
58.8
1
United Kingdom
1672
17
95.2
10
50.3
15
United States
1804
7
94.9
12
50.4
14
Source: Organisation for Economic Co-operation and Development.

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Conclusion
Per capita income may be one of the most widely cited measures of national
standards of living, but it is limited in that it says nothing about how income is
distributed. Nonetheless, there is little question that rising productivity is the single
most important factor behind rising living standards, but the proportion of a nation’s
population that is working is also important. The larger that proportion is, the more
goods and services there are to go around. The share of the population that is
working is only partly subject to the influence of policymakers. The size of the labor
force is largely a function of demographic factors, but the share of that labor force
that is employed can vary with short-term economic conditions, as well as policies
that affect the cost of labor.
It may also be the case that one nation’s average level of productivity is lower
than another’s because it employs a larger share of its less skilled and less productive
workers. But even if those workers are relatively less productive, the goods and
services they produce represent an addition to the nation’s living standard. Higher
productivity is not the only way to raise living standards. High levels of labor force
participation and employment are also important.