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Social Factors and Productivity Growth in Canada and the United States
Carolyn Mac Leod and Jianmin Tang∗
Micro-Economic Policy Analysis Branch
Industry Canada
December 2002
Abstract
This paper tests the hypothesis that social factors may be a determinant of the difference in
labour productivity growth rates between Canada and the United States. Three social factors
(health, crime and inequality), together with other factors such as, human capital and physical
capital intensity, are examined. We find that capital intensity and human capital were the most
significant factors behind labour productivity growth, and that property crime had a significant
and negative effect on Canada’s productivity growth rate. In addition, we find that social factors
such as, health (indicated by life expectancy and potential years of life lost) and inequality
(indicated by various Gini coefficients on income), were insignificant in explaining labour
productivity growth in the two countries. Furthermore, no evidence is found that those social
factors were responsible for the differential labour productivity growth rates between Canada and
the United States, although there is evidence that they were significant in explaining differences
in labour productivity growth across some OECD countries.
∗
The views expressed in this paper are of the authors and do not necessarily reflect those of Industry Canada or the
Government of Canada.
I.
Introduction
Canada has been trailing the United States (U.S.) in productivity performance since the
early 1980’s. The labour productivity gap, defined as the difference in real gross domestic
product (GDP) per hour worked, between the two countries widened from about 12 percent in
1980 to 16 percent in 2000 (Figure 1). Given that labour productivity is the key to improvements
in the standard of living, commonly measured as real GDP per capita, the widening gaps have
caused great concern among policy makers and researchers1. This cross-national difference in
productivity has been blamed on many factors, some of which include, poor innovation
performance, the widening investment gap, less competition, a relatively lower skilled worker
force2, poor workplace management and managerial practices, and heavier tax burdens. In
addition to these factors, many have put the blame on Canada’s social policies.
In some very obvious respects, Canada and the U.S. are quite different countries. Canada,
for example, tends to be more socialist than the U.S. and, hence, differs from it in various,
fundamental ways. Canada provides universal access to health care while the U.S. does not.
Some common health indicators suggest that the health of Canadians is above that of Americans.
For instance, life expectancy is longer in Canada than in the U.S. (Figure 2). Similarly, potential
years of life lost (PYLL), a summary measure of preventable premature deaths occurring in the
younger years, are lower in Canada than in the U.S. Canada is also recognized to be safer
compared to the U.S. Its national violent crime rate is considerably lower than that of the U.S.,
although its property crime rate was slightly above that in the U.S. in the 1990s (Figure 3).3 In
1
Productivity is not everything but, in the long run, it is almost everything. A country’s ability to improve its
standard of living over time depends almost entirely on its ability to raise its output per worker (Krugman, 1994).
2
Canada has a worker force with more people having post-secondary education, but with fewer people having
university education. This is pervasive across all manufacturing industries (Rao, Tang and Wang (2002)).
3
These results are consistent with the findings by Statistics Canada (2001).
2
addition, Canada subsidizes all levels of education, and income in Canada is more equally
distributed than in the U.S.4 Canada’s income inequality, as measured by the Theil statistic, was
below that of the U.S. most of the time between 1962 and 1997 (Figure 4).5
“Social policy might well be one factor which could have potential growth effects...
...recent research has put forward the hypothesis that social factors may also be a major
determinant of productivity growth” Harris (2001). It could be that Canada possesses a higher
level of social capital than does the U.S., which may explain the differences in productivity
growth between the two countries. The objective of this paper is to test the hypothesis that social
factors may be a determinant of the differences in labour productivity growth rates.
It should be noted at outset that this paper examines possible effects of social factors on
productivity growth, not the other way around. There is a general consensus that productivity
improvements tend to have a positive effect on social capital. Productivity improvements
increase a country’s economic base. The increased economic base enables the country to
increase its social capital or to raise the quality of life of its citizens by investing more in
education, health, crime reduction or environment. This relationship is interesting, but is not the
focus of this paper.
To some extent, the inquiry about the relationship between social factors and economic
growth or productivity is an empirical question because their theoretical relationship is found to
be mixed (Arjona, Ladaique and Pearson, 2001). In order to shed light on the relationship
between productivity and social factors, the focus of this paper will, therefore, be empirical. For
4
Income inequality, to some extent, is due to inequality in education because individual earnings are closely linked
to educational attainment (Castelló et al. (2002)).
5
The Theil statistic reflects industrial earnings inequality. By splitting up a population into different income groups,
the Theil statistic measures the degree of income dispersion. The higher the level of the Theil statistic, the greater
the inequality is. The Theil statistic will be discussed in section III.
3
the theoretical side, please see Temple (2000), Argona, Ladaique and Pearson (2001), and the
models listed in Appendix A.
Social capital, in terms of trust, social norms and institutions, has long been known to
affect economic outcomes, but empirical analysis of its impact on productivity is relatively scant,
especially for Canada, as indicated in review papers by Harris (2001) and Osberg (2001). Due to
the relative novelty of the conceptualization of the term, social capital and, the negligible
research linking it to productivity, we are left balancing ourselves on experimental ground.
There, very well, may not be a sole definition of social capital. By basing our measure of
social capital on non-material aspects of quality of life, in particular, by selecting health,
criminality (safety) and inequality, we believe that social capital is reasonably captured in terms
of its relation with productivity, although surely, not all angles will be covered. This measure of
social capital will be discussed further at the beginning of the literature review. Health is a factor
that may affect productivity since healthier workers tend to have more energy and to be more
concentrated. In addition, a healthier labour force has fewer sick leaves, which reduces
replacement costs such as transaction and learning costs. Criminality may also be a factor
influencing productivity since a less safe environment increases business production costs in
terms of higher insurance costs, more resources for safety, and higher costs for attracting skilled
labour. Finally, inequality may affect productivity mainly due to the well-known trade-off
theory between equity and efficiency. To be motivated, those workers who are harder working
and more productive should be rewarded more than others. However, too much dispersion will
cause labour unrest and depress low-end workers, which tends to have a negative impact on
productivity.
4
These three social factors, together with other factors such as, human capital and physical
capital intensity, are thus used to explain the differences in the labour productivity growth
between Canada and the U.S. over the period of 1968-97. We find that capital intensity and
human capital were the most significant factors behind labour productivity growth, and that
property crime had a negative effect in both countries, but only significant for Canada. In
addition, we find that social factors such as health (indicated by life expectancy and PYLL) and
inequality (indicated by various Gini coefficients on income)6 were insignificant in explaining
the labour productivity growth in the two countries. Furthermore, we find no evidence that those
social factors were responsible for the differential labour productivity growth between Canada
and the U.S., although there is evidence that those factors were significant in explaining
differences in labour productivity across some OECD countries over the period of 1970-90.
In the following section, a brief review of the literature on social capital and its impact on
economic growth will be provided. In section III, a regression model, which links social factors
and labour productivity, is presented. The measurement issues associated with social factors and
other variables are discussed in section IV, with the rationale on how social factors (health, crime
and inequality) affect productivity. The estimation results for Canada and the Unites States are
discussed in section V. Concluding remarks are given in the final section, section VI.
II.
Literature Review: Social Capital and Economic Growth
By dividing wealth of high-income countries into three components: natural capital,
produced assets and human resources (which include both human and social capital), Serageldin
and Crootaert (2000) show that, in 1997, produced assets only accounted for 17-23% of total
wealth. Human resources made up 60-74%. In light of this fact, he questions why the discussion
6
The rationale for using those indicators for health, crime and inequality is provided in section IV.
5
on economic growth has fixated for so long on the contribution from physical capital. In the
economic domain it has been customary to view growth in wealth as a response to increases in
tangible inputs like, physical capital and labour, mostly because these are easier to measure.
Surprisingly, when human capital was first claimed to be contributing to growth, people thought
this revolutionary. Today it is believed strongly, though, that education and knowledge are also
major contributors to economic growth.
What would the critics of human capital have said, then, of the role of an even less
tangible phenomenon like, social capital? Could it possible that yet other forms of capital could
explain productivity over and above those mentioned thus far? As a point of illustration, it
would be difficult to explain the dismal economic growth performance of East Germany over the
nineties with the tools provided to us at this point. East Germany had an educated work force
and large quantities of capital. As a matter of fact, growth in East Germany at the beginning of
the nineties was expected to be guaranteed. The resulting meager economic growth can be quite
“embarrassing”7 in retrospect to economists who had such optimistic expectations.
What is social capital?
The notion of social capital is relatively new. Some of the first theorists on these issues,
Bourdieu (1984), Coleman (1988), and Putnam (1993a,b) defined it to be the norms, networks
and trust that facilitate “co-ordination” and “collective action” between people. At the time, they
were concerned with the disconnectedness of individuals in modern societies indicated, for
example, by the lack of voter turnout and civic engagement. Putnam believed that contacts with
other people through the community or through membership in various organizations increased
communication and trust, which, in turn, facilitated economic exchange. At the extreme, a
7
Osberg (2001)
6
society, where people had complete trust in each other, could do away with having to negotiate
via contracts and intermediaries like banks, lawyers and courts. In the same vein, Fukuyama
(1995) found that, in societies where virtual strangers could trust each other enough to form large
organizations and engage in complex business relationships, social capital was strong. It had to
be that the norms and customs of these societies were solid if people, not binded by family or
friendship, could trust each other to such a degree.
Economists are slowly appealing to these ideas in the search for a better understanding of
how economies differ and grow. According to Serageldin, in addition to the earlier sociological
definitions of social capital, as those mentioned thus far, economic interpretations are also
beginning to emerge, further enhancing the concept. At the micro level, social capital refers to
facilitating the functioning of markets, while at the macro level, it refers to the role that
institutions, legal frameworks and governments play in the organization of production. North
(1990) and Olson (1982) both claim that institutions form a part of social capital. They believe
that institutions, public policy and other modes of social capital grease the mechanisms that
inter-connect other types of capital, thus enhancing the returns from productive activity.
Reduced crime and/or superior law enforcement, for example, will reduce the risk to invest.
Like a shift in the production function, Serageldin and Crootaert (2000) believe that social
capital, similar to technology, can boost the returns to investment.
In light of these definitions, how can social capital be clearly defined in order to enable
empirical research on the matter? Very few have attempted to quantify social capital. In effect,
many even dispute it can be measured as capital at all (Arrow (2000) and Solow (2000)). It was
Solow (2000), though, in comment to Fukuyama’s (1995) work, who stated that social capital
had to be measured in some way, even if inexactly, if it were ever to become more than just a
7
mere notion. Serageldin and Crootaert (2000) believe that an initial step in measuring social
capital is to look at human development and physical quality of life indices. Examples of
indexes of this type include the Index of Social Health (Miringoff, 1996) and the Index of
Economic Well-Being (Osberg and Sharpe, 1998). These indexes include variables like, health
insurance coverage, homicide rates and inequality gaps. They all attempt to measure the health
of the societal environment and can, incidentally, be influenced by the institutions described in
the macro level definition of social capital.
Social capital and economic growth
To date, there has been very little empirical work done linking economic growth and
productivity to social capital. A few of the most cited works have been by Putnam (1993a),
Helliwell (1996a,b) and, Knack and Keefer (1997). Even though these studies have focused
purely on the sociological interpretation of social capital, they will be briefly reviewed here.
Putnam looked at the difference between the functioning of government in the North and
in the South of Italy. He found that government in the South was corrupt, large and inefficient,
while it was the opposite in the North. Interestingly, he could find no link between this
difference in government and, party politics, affluence or political stability. He claims that the
reason for well-functioning governments of the North is due to the high level of trust and civic
engagement of the citizens there. They tend to read the newspapers more. There is higher voter
turnout and more involvement in social clubs. Apparently, the direction of influence runs from
social capital to improved governance and not vice versa. The higher levels of trust found in the
North actually affect the quality of government there. Part of the reason why trust seems to be
such a powerful determining force is grounded in its historical presence. Civic involvement in
8
the North of Italy can actually be traced back to the eleventh millennium. Rice and Feldman
(1995) also claimed similar historical influences on trust levels by looking at where European
immigrants settled in the U.S. For example, it turns out that residents of the state of Minnesota,
who have high trust levels, also have ancestry that can be traced to Norway, for the most part,
where trust levels have been among the highest in the world. Acemoglu (2001) intriguingly
links well-functioning public and private institutions to colonization patterns.
Knack and Keefer test whether Putnam was right in surmising that trust and associational
activity influence growth. By looking at the changes in responses between 1980 and 1992 for 29
countries from the World Values Survey, they find that there is a strong effect from trust on
average per capita income. They did not find that associational activity had any effect on
economic growth, though. They measured trust and civic engagement through the responses on
the survey that asked questions like, “Generally speaking, would you say that most people can be
trusted, or that you can’t be too careful in dealing with people?” and whether they belonged to
some of the following organizations, religious organizations, trade unions, political parties,
human rights and conservation.
Other studies similar to those done by Knack and Keefer have tried to look more
qualitatively at the differences between trust levels in different countries. Helliwell (1996a,b)
attempts to discern whether quality of institutions has an effect on economic growth and finds
that the direction of causation runs the other way. Economic growth has a positive effect on the
quality of institutions. He does not find that either more efficient or more democratic institutions
have an effect on growth.
Inglehart (1994) came up with his own international survey of 35 countries to ask
questions relating to Putnam’s idea of civic engagement. They do find that the correlation
9
between trust and associational memberships is high (r=.65). Apparently, the U.S. saw large
declines in social capital between 1960 and 1990 but Inglehart (1994) found that it was still
higher than most countries. Canada’s social capital was about the same as that in the U.S.,
although associational membership was slightly higher in the United States.
Aside from Knack and Keefer (1997), Rupasingha (2000) was, actually, one of the only
few studies to look at the effects of various measures of social capital on economic growth. As
measures of social capital in the U.S., he uses associational activity, a crime index, the voter
participation rate and public support for charitable organizations. He finds significant effects on
growth for all of them except for charitable giving.
III. Model
Similar to Mirignoff (1986), Osberg and Sharpe (1998) and, Serageldin and Crootaert
(2000), we focus our measure of social capital on non-material aspects of quality of life. In
particular, we consider three main aspects of quality of life: health, criminality (safety) and
inequality. By selecting those three social factors, we believe that social capital is reasonably
captured in terms of their impacts on productivity, although surely, not all angles will be
covered.
Most empirical work8 examining economic growth is based on the model developed by
Solow (1956) and Swan (1956) and, on that from Mankiw, Romer and Weil (1992) in an
augmented version that includes human capital. The basic premise behind these models is to
measure output as the weighted sum of its constituent parts. Output is usually measured as some
function of physical capital, labour and an efficiency-enhancing parameter. Most often
production is modeled using a Cobb-Douglas production function. Following the literature, we
8
See Appendix B for a listing of growth models used in studying social and related factors.
10
also begin with a Cobb-Douglas production function with the efficiency-enhancing parameter
being a function of human capital and social factors.
(1)
Yt = At Ktα L1t −α ,
where At = f ( Ht , St ) .
Here, output in time t is equal to Yt . The efficiency-enhancing parameter is At . Kt is the capital
stock. Lt is labour input and α is a parameter. It can be seen that the efficiency-enhancing
parameter is also a function of human capital, Ht and social factors, St .
Note that the impacts of these social factors on labour productivity are modeled here
through their influences on production efficiency, although they may have impacts on the inputs
of production such as capital formation and human capital. The estimated effects of those social
factors in this paper are the direct effects and, the indirect effects, through physical and human
capital, are not captured.9 The rationale for these factors affecting production efficiency will be
discussed later in this section.
By dividing the production function by labour, a function of labour productivity is
obtained. It can be expressed as,
(2)
Pt = At ( k t )α k ,
where P is labour productivity, defined as value added per unit of labour input and k is capital
intensity, defined as capital per unit of labour.
By taking the natural log of both sides and expressing A as a linear function of H and S,
equation (2) can be formulated as
9
We ignore the indirect effects in this paper since they are difficult to model. For instance, to model the effects of
those social factors on capital formation, we would also need to identify other factors related to capital formation
such as taxation, regulation, skill availability, and the macro-economic environment for investment. Some of those
variables are often difficult to measure and, hence, most often times excluded.
11
(3)
ln( Pt ) = α 0 + α k ln(k t ) + β ln H t + ln(S t ) γ ,
where ln(S t ) = [ln(S1t ) ln( S 2t ) ln(S 3t )] is the vector of social factors: health, crime, and
inequality.10
The framework is similar to the ones in Arjona, et al. (2001). It also resembles the model
developed by Barro (1991, 1996, 1999). In Barro’s model, the growth of per capita output is a
function of the current level of per capita output and a long-run level of per capita output11. This
long-run level can be determined by various “choice and environmental variables”. These
variables can be those determined by the private sector, like the savings and fertility rates and, by
the public sector, like tax rates, maintenance of law and property rights and, the degree of
political freedom, just to name a few. Most of his regression equations run the dependent
variable, per capita growth of output, on the independent variables, education and social
variables like, life expectancy, the rule-of-law, democracy, inequality and government
consumption.
The first difference form of equation (3) is
(4)
∆ ln( Pt ) = α k ∆ ln(k t ) + β ∆ ln H t + ∆ ln(S t ) γ ,
where ∆ ln( X t ) = ln( X t / X t −1 ) is the log difference for any variable X at time t, and
∆ ln(S t ) = [∆ ln( S1t ) ∆ ln(S 2t ) ∆ ln(S 3t )] is the vector of log differences for social factors: health,
crime, and inequality.
There are two advantages in using the differenced equation as our regression model.
First, it eliminates country-specific effects or time-invariant factors, minimizing the possibility of
10
Essentially, human capital and those social factors are modeled to explain multi-factor productivity that is the
residual of labour productivity net of the contribution from capital intensity.
11
See Appendix B for a more detailed listing of Barro’s methodology.
12
misspecification due to missing such variables. Second, the non-stationary variables in our
analysis, such as labor productivity, become stationary.12
IV. Measurement Issues
In this paper, labour productivity for the total economy is defined as real GDP per hour
worked. Gross domestic product for Canada is from Statistics Canada, and, for the U.S., from
the Bureau of Economic Analysis (see Appendix A for the data sources). Total hours worked for
Canada is from Statistics Canada and, for the U.S., from the Bureau of Labour Statistics.
Capital intensity is defined as capital input divided by total hours worked. Capital input
for Canada is the aggregate capital service index from Statistics Canada.13 Since this index only
begins in 1981, it is extended back to 1961 using the capital input index from the joint project
between Harvard University and Industry Canada (Jorgenson and Lee, 2001). The numbers for
the U.S. were constructed in the same manner, with the capital services index obtained from the
Bureau of Labour Statistics.
The human capital variable is computed as the proportion of the hours, worked by those
with post-secondary education, in total hours worked. The education attainment data for Canada
are from Statistics Canada and, for the U.S., from Professor Dale Jorgenson at Harvard
University, and his research.
To estimate the link between productivity and social factors, it is important to make sure
that productivity and social factor measures are contemporaneous. In other words, the measures
for social factors must, somehow, be associated with production at a given period of time. For
12
Labour productivity variables in this paper are tested for stationarity, using the augmented Dickey-Fuller unit root
test. It is found that they are non-stationary in levels, but become stationary after the first difference.
13
The measure of capital input takes into account the changing composition of different capital assets: machinery
and equipment, structures, land and inventories.
13
this reason, we chose to use output-based measures for the three social factors. Output-based
measures assess the actual state of health and crime, for example, rather than measuring the
number of resources that went in to create a given state. Health expenditures, for instance,
would be an input-based measure. It does not measure the state of health, itself. There are three
main reasons why input-based measures, such as social expenditures, are not favoured here.
First, a high level of social spending during a certain time span does not necessarily mean a
higher level of social capital. For instance, a higher level of spending on crime may be a
response to an increase in crime. Similarly, a country may spend more on health because the
population in the country is less healthy.
Second, it is virtually impossible to identify the quantity inputs that are associated with
each of those social factors. For instance, a certain proportion of health care expenditure may be
devoted to care for the elderly instead of the working population. In terms of productivity, it is
the working-age population that matters. In addition, outside of public expenditure, private
expenditures, such as foundations for medical research and food banks, are also important inputs
for health care, but data on private expenditures for each of these social factors are very difficult
to come by. Finally, even if we were able to pin down the sources, inputs for improving health
or reducing crime could have a long-lasting and delayed outcome. For instance, the health of the
working population not only depends on current health expenditures, but also depends highly on
the quality of health received since birth, which can be linked to the life-long health expenditures
for each of the age cohorts. It is virtually impossible to sift through this expenditure, input-based
type of data. Because of these reasons, in this paper we use output-based measures for health,
crime and inequality, which will be discussed in the remainder of the section. Arjona et al
(2001) and others have attempted to quantify these variables by using government expenditure as
14
substitutes. For example, the status of health could be proxied by the amount of government
spending on its health-care system. Unfortunately, as discussed earlier, the financial resources
expended on health is, very often, neither a good indicator of the quality of health care provided
nor a good indicator of the subsequent health status. In the remaining of this section, we discuss
the measurement issues associated with each of the three social factors.
Health
Health is a vital component to production. In the model of the demand for health by
Grossman (2000), health can be viewed as a capital good, which is essential to the production
process. As a matter of fact, if not taken care of, health can depreciate, as well. Some of the
ways in which health can affect production is that ailing workers, both physically and mentally,
can reduce productivity because of reduced energy and concentration. In addition, a healthier
labour force has fewer sick leaves, which reduces replacement costs such as transaction and
learning costs. Although not modeled in this paper, a healthy labour force also has indirect
impacts on productivity through their influences on production inputs. Individuals who expect to
live longer will be more inclined to devote more resources on improving their education, which
therefore feeds into higher productivity. Savings could also increase if agents believe, by living
longer, that they will need to increase retirement earnings. Increased savings adds to the
accumulation of physical capital. According to Tompa (2002), the few cross-country studies that
include health in growth equations have all found positive, significant influences.
While measures of health, like life expectancy, infant mortality and PYLL, vary less for
developed countries than for less developed countries, it is often the case that these are virtually
the only indicators of health available. Tompa claims that indicators of health more pertinent to
15
developed countries are those like, morbidity, vitality and mental health. Unfortunately, this
type of data has only begun to be collected in the past decade or two and would, thus, not be
appropriate for this study.
Life expectancy and PYLL will be used in the paper since they are broader measures of
the average health status of a country than other measures such as infant mortality. They are not
perfect, but are second-best indicators for health of the working population. Barro (1996) also
uses life expectancy as a proxy for health. Potential years of life lost is a summary measure,
which takes into account premature deaths occurring in the younger years that could have been
prevented. Some of the causes of deaths included are, circulatory, respiratory and digestive
diseases and, deaths due to suicide, drugs, motor vehicle accidents and homicides.14 Infant
mortality in developed countries such as, Canada and the U.S., tends to be more an indicator of
the quality of health care rather than the condition of health of an average citizen. It is
anticipated that both life expectancy and PYLL will have the most impact on labour productivity.
Data on both life expectancy and PYLL are from the OECD Health Database 2001.
Crime
Crime is expected to negatively affect productivity. Rupasingha et al. (2000) find a
significant and negative link between the crime rate in the United States and economic growth15.
They claim that a high rate of crime is an indication for low social capital. Similarly, Schmid
and Robison (1999) claim that crime indicators like, police surveillance and legal service, can
even be proxies for trust.
14
Ideally, deaths due to homicide should be include in crime, but we have no information to separate it out. As we
will see, however, that the inclusion will not cause an overlap problem with the crime variable since the crime
variable considers only property crimes and exclude violent ones.
15
See Appendix B for a more detailed review of their model.
16
Rubio (1997) also claims the same link between crime rates and social capital but in the
context of Columbia. While it may be easier to conceive of distinct effects on productivity in
places that are rife with crime, its consequences are also felt in places a bit less stricken with
crime. Criminality may be a factor influencing productivity since a less safe environment
increases business production costs in terms of higher insurance expenditures, more resources for
safety, and higher costs for attracting skilled labour. South and Crowder (1997) argued that
much of the reason for urban sprawl in the U.S. in the 1970’s and 1980’s was due to rising crime
rates. Even in the early nineties, Foot and Gomez (2001) state, “… social unrest and property
crime that hit most major American (and one or two Canadian cities)… had an appreciable
economic cost. Of course it also led to a counter-reaction, major federal public investment in
inner cities producing what some have called an ‘urban renaissance’ (something which has not
occurred in Canada).” Productive resources fled urban locations during these decades in the
search for safer, less impoverished milieus.
For this paper, property crime was chosen as opposed to other types of crime like, murder
and assault, since property crime is more widespread and representative of the types of crime that
would tend to affect productivity. Besides, crime associated with homicide has been captured to
some extent by the health indicator of PYLL. Property crime includes acts of breaking and
entering, and theft.16 The property crime data for Canada was taken from Statistics Canada
Table, 252-0001. The property crime data for the U.S. is from the Department of Justice.
Inequality
There continues to be a large debate about the effects of income inequality on economic
growth. Various theories have been expounded throughout the last few decades as to the links
16
Auto theft included, but fraud is excluded due to data being unavailable.
17
between inequality and growth. Kuznets (1955) claimed that as poorer countries began to
develop, inequality would rise. Those in the more traditional economic fields would begin to
lose out to those few in emerging sectors of the economy, thus increasing inequality. As
development continued to proceed, inequality would eventually begin to lesson, making the
whole process of development, from an older type of economy to a newer economy, u-shaped.
There have been many objections and exceptions to this theory, though. Asia, for example,
developed quite rapidly and yet had relatively little inequality as compared to countries in Latin
America, which have high levels of inequality but little growth.
Another theory of inequality and growth originates from Okun (1975) who claimed that
there was a trade-off between equity and efficiency. Governments of countries, like Canada, that
spent relatively more on social programs in the aim to distribute wealth equitably, also was
damaging its country’s full potential to grow. By interfering with competitive markets,
governments rendered the economy less efficient. Public finance literature has found that
minimum wage policies, for example, can have high efficiency costs. Arjona et al. (2001) do not
find a definitive answer to the ongoing question of whether inequality aids or obstructs growth.
In fact, their findings tend towards inequality facilitating growth. They do acknowledge, though,
that active social policies, like those that increase human capital development, are good for
growth. Forbes (2000), on the other hand, finds that income inequality is negatively related to
economic growth in the short and medium term.
Finally, political economy hypotheses about the association between inequality and
growth claim that high degrees of inequality can encourage agents to take politically motivated
decisions that could harm growth. Large proportions of poor may be inclined to vote for growthimpairing policies like, heightened taxes on production, or may even lead to civil unrest.
18
In addition to these main explanations, many others have been put forward. Arjona et al
(2001) states that if sizeable proportions of individuals possess little of the economic wealth,
they will have less ability to invest in education, potentially becoming a drag on growth. As a
matter of fact, in the past decade or so, researchers have tended to focus much of the attention on
these market imperfection explanations.
Foot and Gomez (2001) claim that even age structure can have substantial effects on
inequality and economic growth. In societies like the Philippines in the 1960’s, for instance,
where the proportion of young was quite high, borrowing requirements put excess pressure on
the supply of lendable funds, thus pushing up interest rates. Higher interest rates hampered
investment and growth. Some have stated that inequality serves as a good incentive system for
individuals to take risks in high-yielding ventures and that since the rich save more, investmentinduced growth will more likely ensue. Osberg (2001) maintains that, beginning in the nineties,
new growth economics provided for a venue through which economies could both become more
equal and increase their growth rates. In spite of this gamut of hypotheses, surprisingly, a
conclusion has yet to be reached.
Part of the reason for this inconclusiveness, is the paucity of measures of inequality.
Forbes used five-year data on the Gini coefficient constructed by Deininger and Squire (1996)
for 40 developed and less developed countries. In short, the Gini coefficient measures inequality
by measuring the cumulative wealth held by the cumulative population. If, for example, 80% of
a nation’s population possessed only 30% of total wealth and a mere 20% of the population held
70% of total wealth, then the Gini coefficient would be high. However, the Deininger and
Squire income Gini coefficient could not be used for the present analysis due to missing data
points.
19
For Canada, we use four different inequality indicators: three Gini coefficients on income
(income before transfers, total money income, and income after tax) and the Theil index. For the
three Gini coefficients, see Osberg and Sharpe (1998). For the U.S., we use three different
inequality indexes for the U.S.: the Theil index and two income Gini coefficients for household
and family. The income Gini coefficients for the U.S. are from the U.S. Census Bureau.
Among those indicators, only the Theil statistic is comparable between Canada and the U.S. The
Theil statistics measures industrial earnings inequality and is somewhat similar in interpretation
to the Gini coefficient. By splitting up a population into different income groups, the Theil
statistic measures the degree of income dispersion. If only one individual in ten owns all the
income, the Theil statistic should be high. Therefore, the higher the level of the Theil statistic,
the greater the inequality. The University of Texas Inequality Project has produced annual Theil
statistics for 162 countries for the years 1963 to 1997. They have compiled this data mostly with
information from the United Nations Industrial Development Organization.
V. Empirical Results
To see the impacts of those social factors on labour productivity growth, we estimated
equation (4) independently for both Canada and the U.S. Due to the possibility of endogeneity
of the social variables, instrumental variables estimation, which is two-staged least squares
estimation (for details, see Davidson and Mackinnon (1993)), is used.17 For Canada, we tried
two different indicators for health (life expectancy and PYLL) and four different types of
inequality indicators (three Gini coefficients on income and the Theil index). In addition to those
17
The instrumental variables are the one-year lags of the following variables: life expectancy, PYLL, infant
mortality, the number of doctors per capita, property crime, murder, and those inequality indicators. The variables
that are not regressors are infant mortality, the number of doctors per capita, and murder. The data sources for these
newly introduced variables are the OECD Health Database for the first two and, Statistics Canada and the U.S.
Federal Bureau of Investigation for the murder variable.
20
variables, we also added capacity utilization and time variables. The introduction of the capacity
utilization variable is used to capture the impact of business cycles on labour productivity
growth. And, the time variable represents the trend or potential exogenous productivity growth.
The estimation for Canada covers the period of 1971-97, and has 25 observations because
of the one-year lag in calculating single-year first difference and one-year lag of the first
differences for some variables used as instrumental variables. The estimation results for Canada
are reported in Table 1. Capital intensity is found to be positive in all eight regressions, and
highly significant in seven of them. For all regressions, human capital was positive and
significant. As for the social factors, we find that only property crime was consistently negative
and significant, and that other social factors were not significant, although they had the expected
signs. In addition, we found that capacity utilization was, generally, positive and significant, and
the time variable was negative, but marginally significant.
For the U.S, we tested two health indicators (life expectancy and PYLL) and three
different kinds of income inequality measures (the two income Gini coefficients and the Theil
index). The estimation covers the period of 1968-97, and has 29 observations. As in Canada,
human capital is found to be positive and significant, and capital intensity is found to be positive,
but less significant (Table 2). In addition, capacity utilization is found to be positive and
significant. But, all other variables were generally insignificant.
Thus, the regression results for both Canada and the U.S. indicate that the most
significant factors influencing labour productivity growth are capital intensity and human capital.
Property crime is found to be negative and significant only for Canada, and all other social
factors are found to be insignificant.
21
To determine the role played by social factors in the differences in labour productivity
growth between Canada and the U.S., we regress the difference in labour productivity growth
between Canada and the U.S. on the differences in growth rates of those social factors and other
variables. We use the same equation as we did for Canada the U.S. independently, except
without the time variable. The time variable is dropped since it is common to both Canada and
the U.S. In addition, the slope coefficients are assumed to be the same for both countries, thus
implying the same average production function for both countries. In terms of estimating the
effects of social factors and other variables on the differences in labour productivity growth in
the two countries, it is crucial that the analysis be based on the same average production
function. Without an average production function, the effects of those independent variables
could not be discerned from the effects due to differences in production technology.18
The estimation covers the years 1968-1997, and has 29 observations. We run the
regression with health indicated by life expectancy or PYLL. Only the Theil index is used for
inequality since it is the only one comparable between Canada and the U.S., as discussed earlier.
The instrumental variable estimation results are reported in Table 3. It is found that capital
intensity and human capital were the significant factors in explaining the differences in labour
productivity growth between Canada and the U.S. and, that other factors, including social
factors, were not found to be significant.
To see if those results hold for cross-section samples, we extend the analysis for Canada
and the U.S. to OECD countries (for details, see Appendix C). Again, we find that capital
intensity and human capital are among the most significant factors explaining the differences in
labour productivity growth across OECD countries. In addition, we find that life expectancy,
PYLL, and the unemployment rate (a proxy for crime) were also generally important factors
18
For a more detailed discussion, see Bernard and Jones (1996) and, Lee and Tang (2001).
22
contributing to the differences in labour productivity growth. The different results from the
OECD regressions on social factors relative to those from the estimation for Canada and the U.S.
may be explained by the fact that the cross-section sample based on OECD countries may
contain more variation than the time-series sample for either Canada or the U.S. These
differences would warrant further study.19
VI. Conclusion
As previously mentioned, this study is most certainly not meant to be an exhaustive
examination of the links between social capital and productivity. Instead, it is intended to pry
open the broad door obstructing our understanding of how social capital and social policy affects
productivity growth. Sobel (2002) finds it odd that such a great number of authors took time to
define social capital at the outset of their works, but this is most likely indicative of our delicate
grasp of the topic. Social capital is yet a relatively new notion to many areas of social science,
let alone being easily amenable to empirical economics. Even though social capital may be a
fairly novel concept to quantifiable research, it is no grounds for doing nothing at all. By
following the advice provided by Serageldin and Crootaert (2000) and, others, the measure of
social capital for this paper was based on non-material aspects of quality of life.
We find that capital intensity and human capital were the significant factors driving
labour productivity growth and those two factors were also important in explaining the labour
productivity growth gap between Canada and the U.S. Except for property crime for Canada,
we find no evidence that those social factors were significant factors contributing considerably to
19
Note, also, that the difference in dealing with the endogeneity problems associated with social factors may be
another source of the difference in the two sets of regressions. In the regression for Canada and the U.S., the
endogeneity problems are dealt with by using instrumental variables estimation. Unfortunately, we don’t know how
to deal with the endogeneity problems for the panel estimation. It is possible that countries with high productivity
growth were able to afford more to improve their social capital than those with low productivity growth.
23
labour productivity growth in either Canada or the U.S. In addition, we find no evidence that
those social factors were responsible for the labour productivity growth gap. Thus, the limited
evidence here suggests that our higher social capital was not responsible for our relative poorer
labour productivity performance in Canada.
However, these findings should be interpreted with caution since they are based on a
small sample size and the social factors are subject to measurement issues. Future studies based
on larger sample sizes and more accurate indicators of social capital would be desirable to
strengthen these findings.
24
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Appendix A: Listing of Growth Equations with Social Factors
1) Arjona, Roman et al., “Growth, Inequality and Social Protection”, OECD, Labour Market and
Social Policy Occasional Papers No. 51, June 2001.
Their initial model was a Cobb-Douglas production function of the following form,
Yi ,t = Kiα,t Hiβ,t ( Ai ,t Li ,t )1−α − β
where Y, K, H, A and L are output, capital stock, human capital, an efficiency factor and labour
input, respectively. By taking the log of this function they obtain the following estimation
equation:
∆ yi ,t = α i + ηt + β1 yi ,t −1 + β2 sik,t −1 + β3hi ,t −1 + β4 ni ,t −1 +
n
∑β Ω
m= 5
m
m
i ,t − 1
+ µi , t
where small letters are in logarithms and yi ,t −1 is the level of output per working age population
(15-64) in country i and in period t minus one. si ,t −1 denotes propensity to accumulate physical
capital, hi ,t −1 the stock of human capital, ni ,t −1 , α i and ηt rate of population growth, country
dummies and period dummies respectively and µi ,t , an error term. Ω im,t −1 is a set of possible
variables which augment the baseline specification of the model. In their model, these were
different measures of inequality and social protection spending. They use various econometric
techniques like instrumental variables, fixed effects models and pooled mean group methods.
2) Barro, Robert J., “Determinants of Economic Growth: A Cross-Country Empirical Study”,
National Bureau of Economic Research, Working Paper 5698, 1996.
He measures the effects on economic growth and investment from a number of variables that
measure government policies and other elements. His base model is the following:
Dy = f ( y , y*)
29
where Dy is the growth rate of per capita output, y is the current level of per capita output and y*
is the long-run or steady-state level of per capita output. Y* depends on an array of choice and
environmental variables. These variables include private sector choices like, the savings rate,
labour supply and fertility rates, on government choices like tax rates, reducing market
distortions, maintenance of the rule of law and property rights, political freedom and also terms
of trade variables (i.e. openness).
He uses instrumental 3-stage least squares with many of the instruments being preceding fiveyear averages. The following are his variables: The dependent variable is the growth rate of real
per capita GDP and the independent variables are the initial value of real per capita GDP, initial
values of male secondary and higher schooling, the log of life expectancy, log of the fertility
rate, a government consumption ratio, a rule-of-law index, change in the terms of trade, a
democracy index, the inflation rate, and region dummies. He says that life expectancy proxies
for health and could also be picking up the quality of human capital.
3) Forbes, Kristin J., “A Reassessment of the Relationship Between Inequality and Growth”,
The American Economic Review, September 2000, pp. 869-887.
Growthi ,t
= β1Inequalityi ,t −1 + β 2 Incomei ,t −1 + β 3 MaleEducationi ,t −1
+ β 4 FemaleEducationi ,t −1 + β 5 PPPI i ,t −1 + α i + ηt + µi ,t
Growth is defined as average annual GDP growth for country i. Inequality is the Gini coefficient
and PPPI is a measure of market distortions. She says that this equation is based on a model
similar to that used in most empirical work on inequality and growth. It is also based on
Perotti’s (1996) study where he finds a negative relationship between growth and inequality.
30
4) Rupasingha, Anil, et al., “Social Capital and Economic Growth: A Country-Level Analysis”,
Journal of Agricultural and Applied Economics, 32, 3, pp. 565-572, 2000.
Dyt = F (Yt −1 , Ht −1 , SCt −1 , Rt −1 )
where Dy is per-capita income growth, Y is initial per-capita income, H is initial human capital
stock, SC is initial social capital stock and R is set of control variables. As measures of social
capital in the U.S., he uses associational activity, a crime index, the voter participation rate and
public support for charitable organizations. He says that their estimation equation closely
follows that of Goetz and Hu (1996).
Dyt = α 0 + α1Yt −1 + α 2 Ht −1 + φSCt −1 + Γ Rt −1 + µt
5) Knack, Stephen and Philip Keefer, “Does Social Capital have an Economic Payoff? A CrossCountry Investigation”, The Quarterly Journal of Economics, November 1997.
Their dependent variable is average annual growth in per-capita income between 1980-1992 for
29 countries. They use the proportion of eligible students enrolled in secondary and primary
schools in 1960, per capita income at beginning of period, price level of investment goods
relative to the U.S., trust and civic engagement (gotten from a survey of trust levels in those
countries).
6) Helliwell, John F., “Economic Growth and Social Capital in Asia”, NBER working Paper No.
5470, 1996a.
He uses a Cobb-Douglas production function to model social capital’s effect on productivity.
The dependent variable is cumulative log differences between 1962 and 1989 in the aggregate
efficiency measure for seventeen OECD countries. The independent variables are, the 1962 log
gap between each country’s efficiency level and that in the U.S., capturing convergence effects
31
and, measures for social capital and educational values. The social capital variables include
thrift, trust, and obedience. All variables are from World Values Survey.
7) Foot, David and Rafael Gomez, “Age Structure, Income Distribution and Economic Growth”,
Mimeo, presented at the IRPP-CSLS conference in 2001.
g = c + λ3 X 1 + θ ( ID) +
∑ β (n / N ) + ξ
i
i
3
where g is the average rate of economic growth in each country, X are the economic variables
other than income distribution that explain economic growth (they used human capital and initial
level of development), and ID is a measure of income inequality and the remaining variable in
the equation is a demographic variable.
32
Appendix B: Data Sources
Variables
Labour
Productivity
(Total Economy)
Capital Intensity
Human Capital
Health
Crime
Inequality
Variables
Labour
Productivity
Capital Intensity
Human Capital
Heath
Unemployment
rate
Inequality
Canada and the United States
Definition
Data Sources
Real GDP per hour
Canada: Statistics Canada
worked
U.S.: Bureau of Economic Analysis for GDP
and Bureau of Labour Statistics for Hours
Worked
Capital input per hour
Canada: Statistics Canada and Jorgenson and
worked
Lee (2001)
U.S.: U.S. Bureau of Labour Statistics
The proportion of hours
Canada: Statistics Canada
worked by those with
U.S.: Prof. Dale Jorgenson at Harvard
post-secondary education University
Life Expectancy,
OECD Health Database 2001
Potential Years of Life
Lost, Infant Mortality, or
the number of doctors per
thousand of population
Property Crime, Murder
Canada: Statistics Canada
U.S.: Department of Justice and Federal
Bureau of Investigation
Gini coefficients on
Osberg and Sharpe (1998) for Canada
income
U.S. Census Bureau for the U.S.
Theil statistics (industrial The University of Texas
earnings Gini
coefficients)
OECD
Definition
Data Sources
Real GDP per employee OECD Economic Outlook
Business capital stock
per employee
Average years of higher
schooling or percentage
of higher schooling
attained or completed
Life expectancy or
potential years of life lost
A proxy for crime
OECD Economic Outlook
Barro and Lee’s education database
OECD Health Database 2001
OECD Economic Outlook
Theil statistics (industrial The University of Texas and Castelló (2002)
earnings Gini
Coefficients or Education
Gini coefficients
33
Appendix C: An Extension to OECD Countries
As an extension of the study for Canada and the U.S., the analysis is also conducted for a
group of 20 OECD countries, as listed in Table C1.20 Before proceeding, a few details must be
kept in mind. Labour productivity is expressed as real GDP per employee, rather than per hour
worked since data for hours worked is not available for all of the OECD countries. Similarly,
capital intensity is defined as business capital stock per employee.21 Labour productivity and
capital intensity data are from OECD Economic Outlook. The measure for human capital is
taken from Barro and Lee’s education database, which is only available for every five years.22
The two measures of human capital that are used in this study are average years of higher
schooling and the percentage of higher schooling attained or completed. Higher schooling here
is equivalent to post-secondary education or above.
The unemployment rate, also from the OECD Economic Outlook, is substituted for crime
because the quality of currently available crime data for many OECD countries is very poor.23
The proxy is based on the observation that crime and unemployment is highly correlated (Table
C2). Data on life expectancy and PYLL are from the OECD health database 2001. Finally,
20
The group is selected mainly based on data availability.
Ideally, capital stock for the total economy should be used here. Unfortunately, no such data is available.
22
Barro and Lee have compiled education data for 142 countries for every five years, between 1960 and 2000. They
claim to have improved on most other measures of education by taking into account various factors. The required
duration in a certain level of schooling fluctuates from country to country. A university degree may involve 3 years
in one country and take four years in another. Even though the latter would actually have more education, by
classifying both countries as having completed university, this is concealed. Barro and Lee have accounted for these
differing durations. They have also adjusted for individuals that have repeated levels.
23
Crime data could be obtained from the United Nations World Surveys on Crime Trends and Criminal Justice
Systems, 1970-1994 database. The crime data, however, could not be used in rigorous statistical testing due to its
poor quality. For many countries, the series can jump down or up more than twenty times in a single year for no
obvious reason. The United Nations merely asks national justice departments to provide them with their respective
crime data and do not check the overall international data base for consistency, in terms of definitions of crime and
methodology. The crime variable was, thus, excluded from OECD regressions.
21
34
while the Theil measure for inequality is used, Castelló’s education Gini coefficients will also be
employed, which is only available for every five years.24
Due to the fact that inequality and education data are only provided every five years, and
to the constraint put upon us by the other variables in the model, the panel estimations for the
OECD countries begin in 1970 and closed off with 1990 for every five years. With twenty
OECD countries and 4 time periods, panel estimations are based on 80 observations.
The regression model for the OECD analysis is the same as equation (5) for Canada and
the U.S. analyses.25 The panel estimation results for the 20 countries, with different
combinations of social factors, are presented in Table C3.26 Regressions (1)-(3) are different
from regressions (4)-(6). The former group uses life expectancy as an indicator for health and
the latter uses PYLL as an indicator. Basically, the results from the two groups are similar to
those from the analysis for Canada and the Unites States alone. Physical and human capital
variables were positive and significant. Life expectancy (PYLL) was positive (negative) and
significant. It seems that PYLL fits the model for the OECD better than it does for the CanadaU.S. regressions. The unemployment rate was negative, and significant when the education Gini
is used as the indicator for inequality. Unlike the Canada-U.S. regressions, both the Theil and
24
Castelló et al. (2002) use education data constructed by Barro and Lee (2000) to construct human capital Gini
coefficients for 108 countries from 1960 to 2000, for every five years. This education Gini coefficient measures the
inequality of years of schooling within a population. The rationale behind utilizing education inequality for income
inequality is that income is closely linked to education attainment.
25
According to the convergence theory in productivity, the model should also include a variable representing the
initial labour productivity level of each country. Basically, the convergence theory predicts that low productivity
countries will catch up with high productivity countries due to forces such as technology spillovers from the latter to
the former. If the theory is correct, the coefficient on the variable should be negative and significant. However, our
estimations consistently show that the variable in this paper is positive and significant. In addition, other variables
except for capital intensity become insignificant under the presence of the variable. We suspect that the variable
captures other effects such as, education and health, since high productivity countries also tend to have higher levels
of education and health than low productivity countries. Because the estimated effect is very difficult to interpret,
we exclude the convergence variable in the final analysis.
26
Unfortunately, pool estimation cannot deal with endogeneity problems by using instrumental variables.
35
the Gini statistics here failed to be significant in all of the estimations, even at 10 percent level of
the significance.
Regressions (7) and (8) are based only on a group of rich countries, based on GDP per
capita (purchasing power parity based), among the selected OECD countries in Table C1. The
countries that have been excluded are Greece, Iceland, Ireland and Spain. The results do not
differ from the previous regression runs. Only inequality increases slightly in significance. The
similar results provide evidence on the robustness of these results to both rich and poor OECD
countries.
36
Table 1: Instrumental Variable Estimates of Impacts of Social Factors on
Labour Productivity Growth in Canada
Capital Intensity
Human Capital
Life Expectancy
CAN-1
0.333
(2.6)*
0.283
(2.8)*
0.941
(1.2)
CAN-2
0.318
(2.3)*
0.304
(2.8)*
0.422
(0.5)
CAN-3
0.342
(2.6)*
0.295
(2.8)*
0.488
(0.5)
CAN-4
0.210
(0.6)
0.345
(6.0)*
0.335
(0.4)
Potential Years of Life lost
Property Crime
Inequality
(Gini on income before transfers)
Inequality
(Gini on income after transfers)
Inequality
(Gini on total money income)
Inequality
(Theil Index)
Capacity Utilization
Time
Durbin-Watson
# Observations
-0.099
(-2.1)*
0.333
(1.5)
-0.124
(-3.1)*
-0.121
(-3.0)*
-0.126
(-1.7)**
CAN-5
0.309
(2.9)*
0.302
(4.5)*
CAN-6
0.299
(2.5)*
0.295
(3.9)*
CAN-7
0.288
(2.1)*
0.284
(3.5)*
CAN-8
0.332
(2.5)*
0.285
(4.5)*
-0.102
(-0.8)
-0.118
(-2.6)*
0.164
(1.3)
-0.172
(-1.1)
-0.137
(-3.4)*
-0.189
(-1.0)
-0.137
(-3.4)*
-0.175
(-1.9)
-0.148
(-2.4)*
-0.005
(-0.0)
0.056
(0.4)
0.014
(0.1)
0.222
(3.1)
-0.0002
(-1.6)
1.97
25
0.093
(0.9)
-0.0002
(-1.3)
1.72
25
0.114
(1.1)
-0.0002
(-1.2)
1.73
25
-0.019
(-0.1)
0.019
(0.3)
0.032
(0.4)
-0.0001
(-0.8)
1.76
25
0.158
(2.3)
-0.0002
(-1.8)
1.76
25
0.122
(1.9)**
-0.0003
(-1.9)**
1.48
25
0.115
(1.7)**
-0.0003
(-1.7)**
1.96
25
-0.016
(-0.4)
0.120
(2.3)*
-0.0003
(-2.1)*
1.48
25
Note: t-statistics are in parenthesis.
“*” denotes significance at the 5% level.
“**” denotes significance at the 10% level.
37
Table 2: Instrumental Variable Estimates of Impacts of Social Factors on
Labour Productivity Growth in the United States
Capital Intensity
Human Capital
Life Expectancy
US-1
US-2
US-3
US-4
US-5
US-6
0.472
(2.0)*
0.174
(2.8)*
0.478
(0.4)
0.448
(1.7)**
0.193
(2.9)*
0.999
(0.7)
0.368
(1.7)**
0.234
(2.0)*
0.024
(0.0)
0.330
(1.11)
0.160
(3.4)*
0.375
(1.3)
0.178
(3.4)*
0.404
(1.6)
0.307
(1.6)
-0.135
(-0.6)
-0.021
(-0.3)
-0.069
(-0.6)
-0.092
(-0.4)
-0.046
(-0.7)
0.184
(0.6)
-0.058
(-0.8)
Potential Years of Life lost
Property Crime
Inequality
(Gini for household)
Inequality
(Gini for family)
Inequality
(Theil Index)
Capacity Utilization
Time
Durbin-Watson
# Observations
-0.098
(-1.6)
-0.117
(-0.9)
-0.121
(-1.8)**
-0.062
(-1.1)
-0.268
(-1.8)**
0.222
(4.0)*
-.00004
(-0.3)
2.01
29
0.208
(3.7)*
-0.00004
(-0.3)
1.86
29
-0.173
(-1.4)
0.038
(0.7)
0.176
(2.2)*
-0.00005
(-0.3)
2.02
29
0.204
(2.7)*
0.00002
(0.2)
2.05
29
0.201
(2.6)*
0.00003
(0.3)
1.99
29
0.073
(0.9)
0.173
(1.6)
-0.00006
(-0.4)
2.11
29
Note: t-statistics are in parenthesis.
“*” denotes significance at the 5% level.
“**” denotes significance at the 10% level.
38
Table 3: Instrumental Variable Estimates of the Impacts of Social Factors on
the Difference in Labour Productivity Growth Rates Between Canada and the U.S.
Variables
Capital Intensity
Human Capital
Life Expectancy
CAN-US-1
CAN-US-2
0.305
(2.2)*
0.202
(4.0)*
0.428
(0.5)
0.448
(2.5)*
0.190
(2.4)*
Potential Years of Life lost
Property Crime
0.022
(0.3)
0.016
(0.4)
0.062
(0.5)
1.76
29
Inequality
(Theil Index)
Capacity Utilization
Durbin-Watson
# Observations
0.242
(1.3)
0.112
(1.2)
0.009
(0.2)
0.155
(0.9)
2.12
29
Note: t-statistics are in parenthesis.
“*” denotes significance at the 5% level.
“**” denotes significance at the 10% level.
Table C1: List of OECD countries included in Panel Estimations
Canada
U.S.
Australia
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Netherlands
New Zealand
Norway
Spain
Sweden
Switzerland
39
Table C2 Correlation Coefficients between Crime and Unemployment Rate
in Selected OECD Countries
Country
Canada
U.S.
Denmark
Finland
France
Ireland
Italy
Correlation between
Theft and Unemployment Rate
0.83
0.67
0.61
0.60
0.45
0.79
0.64
Correlation between
Robbery and Unemployment Rate
0.74
0.24
0.75
0.35
0.95
0.82
0.93
Table C3 Social Factors and Labour Productivity Growth: OECD Countries
Rich a
All Selected Countries
Capital Intensity
Average Yeas of
Higher Schooling
Percentage of
Higher Schooling
Life Expectancy
Potential Year of
Life lost
Unemployment
Rate
Theil
(income inequality)
Education Gini
(educa. inequality)
Adjusted R
# Observations
(1) b
0.397
(8.01)*
0.037
(2.14)*
2.279
(4.21)*
(2)
0.404
(8.39)*
0.036
(2.34)*
2.222
(4.08)*
(3)
0.409
(8.58)*
(5)
0.403
(8.12)*
0.038
(2.31)*
0.028
(1.76)**
2.254
(4.06)*
-0.014
-0.016
-0.015
(-1.06)
0.004
(0.32)
(-2.70)*
(-2.49)*
0.002
(0.08)
0.17
80
-0.008
(-0.25)
0.16
80
0.12
76
(4) b
0.400
(8.06)*
0.039
(2.17)*
(6)
0.410
(8.35)*
(7)
0.411
(5.62)*
0.033
(2.27)*
(8)
0.440
(5.29)*
0.036
(2.10)*
0.029
(1.77)**
2.112
(3.41)*
-0.286
(-3.50)*
-0.017
-0.291
(-3.50)*
-0.020
-0.293
(-3.45)*
-0.019
-0.015
-0.230
(-2.16)*
-0.018
(-1.40)
0.004
(0.30)
(-3.51)*
(-3.27)*
(-2.22)*
(-2.93)*
0.013
(0.46)
0.17
80
0.002
(0.06)
0.15
80
0.023
(0.90)
0.19
68
0.030
(1.06)
0.14
68
0.11
76
Note: t-statistics are in parenthesis.
“*” denotes significance at the 5% level.
“**” denotes significance at the 10% level.
a
Greece, Ireland and Spain are excluded.
b
Switzerland is excluded due to missing data on Theil.
40
Figure 1
Canada's Relative Labour Productivity* Compared to the U.S.
1.00
0.95
0.90
0.85
0.80
0.75
1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000
*Real GDP per hour worked in 1993 dollars, PPP based.
Source: Statistics Canada, U.S. Bureau of Labor Statistics, and U.S. Bureau of
Economic Analysis
Figure 2
Canada's Life Expectancy and Potential Years of Life Lost (PYLL)*
Relative to the U.S.
1.20
1.10
Life
Expectancy
1.00
0.90
0.80
Potential Years
of Life Lost
0.70
0.60
1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000
* Yeas of loss per 100,000 population aged 0-64
Source: OECD Health Database 2001
41
Figure 3
Canada's Property Crime and Murder Rates* Relative to the U.S.
1.20
1.00
Property
Crime
0.80
0.60
0.40
Murder
0.20
0.00
1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998
*Property crime (minus fraud) and murder per 1000 persons
Source: Statistics Canada, U.S. Department of Justice and Federal Bureau of Investigation
Figure 4
Canada's Inequality Relative* to the U.S.
1.20
1.00
0.80
0.60
0.40
1962
1967
1972
1977
1982
1987
1992
1997
*Industrial earnings inequality.
Source: University of Texas Inequality Project.
42