Productivity and National Standards of Living

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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.

Introduc tion ..................................................................................................................................... 1
From Productivity to the Standard of Living...................................................................................1
International Comparisons...............................................................................................................6
Conclusion ....................................................................................................................................... 9
Figure 1. Per Capita GDP and Productivity.....................................................................................4
Table 1. Growth Rates for Selected Measures.................................................................................5
Table 2. Living Standards and Productivity, 2005...........................................................................7
Table 3. Labor Contribution to Living Standards, 2005..................................................................8
Author Contact Information............................................................................................................9

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.

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. ¹
Per capita GDP says nothing about how those national resources are distributed. 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 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 ²
relative to the overall population.

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GDP GDP hours employees l abor force=× × ×
popul ation hours employees l abor for ce popula t ion
The equation shows what factors contribute to per capita GDP. First, per capita GDP depends, in ³
part, on the level of productivity (GDP divided by hours). 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.
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

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one hired just before. In other words, among those seeking work the ones who are relatively more ⁴
productive tend to be hired first. This is referred to as diminishing marginal product of labor. 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 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.

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Figure 1. Per Capita GDP and Productivity
^Source:^Depart^{ment of Comme}^{rce, 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 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 Ch}^{ange (%)}
¹⁹⁴^{7 to 197}³¹⁹⁷^{3 to 199}⁵¹⁹⁹^{5 to 200}⁶
^{Per capita real GDP}^2.45^1.77^2.15
^{Real GDP per hou}^r^2.30^1.33^2.07
^{Hours per}^em^ployee^0.09^-0.27^-0.18
^{Employment /}^{labor force}^-0.04^-0.03^0.09
^{Labor force /}^pop^ulation^0.09^0.74^0.16
^Sources:^Depart^{ment of Comm}^{erce, Bureau}^{of Economic Analysi}^{s; Departmen}^{t of Labor, Bu}^reau^{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.
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%, ⁵
suggesting that they expect the per capita GDP growth rate to be about 1.4%.

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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.
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 ⁶
Development (OECD) has published comparable figures. 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.

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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, ⁷
their per capita GDPs are less than three-fourths of that of the United States.
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 $34,4}⁸³⁷^$70,0⁰⁵⁸^$40.4⁷¹³
^{Austria 34,3}⁹⁸⁸^68,7⁷⁷¹¹^41.5³¹⁰
^Belgium^33,1¹⁰^{11 81,2}⁹⁴^{4 52.9}⁹²
^{Canada 34,0}⁵⁸¹⁰^67,9⁷⁶¹³^39.1³¹⁵
^{Czech Repu}^blic^20,6³⁴²³^44,4⁰⁹²⁴^22.1⁸²⁵
^{Denmark 34,1}⁵⁸⁹^67,0⁰⁵¹⁵^43.2⁰⁸
^{Finland 30,9}⁵⁷¹⁵^67,8⁹⁴¹⁴^39.6¹¹⁴
^France^31,1⁷⁶^{14 76,6}³⁹^{6 49.5}⁷⁵
^Germa^{ny 30,7}⁷⁶¹⁷^65,3⁷⁹¹⁷^45.5⁰⁷
^Greece^29,5⁸⁸^{18 79,1}⁶⁷^{5 38.5}⁶¹⁶
^Hunga^{ry 17,4}⁸⁸²⁵^45,7⁴⁷²³^22.9⁴²³
^{Iceland 36,1}⁴⁹⁴^66,3⁴⁶¹⁶^36.9⁸¹⁸
^{Ireland 39,0}²²³^82,5⁸⁵³^50.4²⁴
^Italy^28,2⁸⁴^{19 73,7}¹⁵^{7 40.9}³¹²
^{Japan 30,8}⁴⁴¹⁶^61,9⁹⁷²⁰^34.9³²⁰
^{Korea 22,0}⁹⁸²²^46,6⁹²²²^19.8⁴²⁶
^{Mexico 10,6}²⁸²⁷^27,3¹⁰²⁷^14.3¹²⁷
^{Netherlands 35,1}¹⁰⁶^69,9⁵¹⁹^51.1⁷³
^{New Zealand}^25,9⁵⁸²¹^51,3³³¹²^28.3⁸²¹
^{Norway 47,1}⁹⁹¹^95,3¹⁷¹^70.0⁹¹
^{Portugal 19,8}⁶²²⁴^41,1⁸⁶²⁵^24.4⁴²²
^{Slovak Republic}^15,9⁸³²⁶^38,8⁵⁵²⁶^22.3⁴²⁴
^{Spain 27,4}⁰⁰²⁰^62,6⁷²¹⁹^35.4³¹⁹
^{Sweden 32,1}¹⁵¹³^68,1⁷³¹²^42.9⁶⁹
^{Switzerland 35,9}⁵¹⁵^63,9³²¹⁸^38.5⁴¹⁷
^{United Kingdom}^32,9⁸⁶¹²^68,8⁷⁵¹⁰^41.1⁹¹¹
^{United States}^41,8²⁷²^87,4⁸³²^48.4⁹⁶
^Source:^Orga^{nisation for Econo}^{mic 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 ⁸
working a high number of hours. 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.
Table 3. Labor Contribution to Living Standards, 2005
^{Average Hours Per}^{Employees as a % o}^f^the^{Labor Fo}^{rce as}^{a % o}^f^the
^Employee^{Labor Fo}^rce^Population
^Country^Hours^Rank^%^Rank^%^Rank
^Australia¹⁷³⁰^{14 94.9}¹³^{51.9 11}
^{Austria 165}⁶¹⁹^94.2¹⁴^53.1⁵
^Belgium¹⁵³⁴^{24 91.6}²²^{44.5 23}
^{Canada 173}⁷¹³^93.2¹⁶^53.7³
^{Czech Repu}^blic²⁰⁰²³^92.0²⁰^50.5¹³
^{Denmark 155}¹²²^95.2⁹^53.6⁴
^Finland¹⁷¹⁴^{15 91.6}²¹^{49.8 17}
^France¹⁵⁴⁶^{23 90.1}²⁵^{45.1 22}
^Germa^ny¹⁴³⁷^{25 90.9}²³^{51.8 12}
^{Greece 205}³²^89.6²⁶^41.7²⁴
^Hunga^{ry 199}⁴⁴^92.7¹⁷^41.2²⁶
^Iceland¹⁷⁹⁴^{9 97.4}¹^{55.9 2}
^Ireland¹⁶³⁸^{20 95.6}⁶^{49.4 18}
^{Italy 180}¹⁸^92.2¹⁹^41.6²⁵
^{Japan 177}⁵¹⁰^95.6⁷^52.1⁹

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^{Average Hours Per}^{Employees as a % o}^f^the^{Labor Fo}^{rce as}^{a % o}^f^the
^Employee^{Labor Fo}^rce^Population
^Country^Hours^Rank^%^Rank^%^Rank
^{Korea 235}⁴¹^96.3⁴^49.2¹⁹
^{Mexico 190}⁹⁵^96.5²^40.3²⁷
^{Netherlands 136}⁷²⁶^95.0¹¹^52.8⁶
^New^Zealand¹⁸⁰⁹^{6 96.3}³^{52.5 7}
^Norway¹³⁶⁰^{27 95.4}⁸^{51.9 10}
^{Portugal 168}⁵¹⁶^92.3¹⁸^52.2⁸
^Slovak^Republic¹⁷³⁹^{12 83.8}²⁷^{49.1 20}
^Spain¹⁷⁶⁹^{11 90.8}²⁴^{48.1 21}
^Sweden¹⁵⁸⁷^{21 94.2}¹⁵^{50.0 16}
^{Switzerland 165}⁹¹⁸^95.7⁵^58.8¹
^United^Kingdom¹⁶⁷²^{17 95.2}¹⁰^{50.3 15}
^{United States}¹⁸⁰⁴⁷^94.9¹²^50.4¹⁴
^Source:^Orga^{nisation for Econo}^{mic Co-operation and Development.}

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.
^B^r^ian^W^.^Cash^ell
^Specialis^{t i}ⁿ^Macroecon^o^mⁱ^c^P^o^licy
^bcas^h^e^l^l^@^c^rs^.l^oc.g^ov^{, 7}^-⁷⁸¹⁶