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.

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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).

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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.

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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.

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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.

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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 7Osberg (2001)

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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

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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

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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

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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.

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also begin with a Cobb-Douglas production function with the efficiency-enhancing parameter

being a function of human capital and social factors.

(1) Y A K Lt t t t= −α α1 ,

where A f H St t t= ( , ) .

Here, output in time t is equal toYt . 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) P A kt t tk= ( )α ,

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.

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(3) γS )ln(ln)ln()ln( 0 tttkt HkP +++= βαα ,

where [ ])ln()ln()ln()ln( 321 tttt SSS=S 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) γS )ln(ln)ln()ln( tttkt HkP ∆+∆+∆=∆ βα ,

where )/ln()ln( 1−=∆ ttt XXX is the log difference for any variable X at time t, and

[ ])ln()ln()ln()ln( 321 tttt SSS ∆∆∆=∆ S 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.

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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.

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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

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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

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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.

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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.

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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 growth-

impairing policies like, heightened taxes on production, or may even lead to civil unrest.

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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, investment-

induced 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.

20

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.

21

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.

22

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).

23

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.

24

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.

25

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Miringoff, M.L et al., “Monitoring the Nation’s Social Performance: The Index of Social Health”, in E. Zigler, F. Kagan, S. Lynn and N.W. Hall (eds.). Children, Families and Government – Preparing for the Twenty-First Century (Cambridge University Press). Olson, Mancur (1982) “The Rise and Decline of Nations: Economic Growth, Stagflation, and Social Rigidities”, New Haven: Yale University Press. Osberg, Lars (2001) “Economic and Social Aspects of Productivity: Linkages and Policy Implications”, Economics Department, Dalhousie University, March. Osberg, Lars and Andrew Sharpe (1998) “An Index of Economic Well-Being for Canada”, Human Resources Development Canada, December. North, Douglass C. (1990) “Institutions, Institutional Change and Economic Performance”, Cambridge (UK) and New York: Cambridge University Press. Okun, A (1975) Equality and Efficiency: The Big Tradeoff (Washington D.C.: Brookings Institution). Putnam, Robert D. (1993a) “The Prosperous Community”, Prospect, Vol. 4, Issue 13. Putnam, Robert D. (1993b) Making Democracy Work (Princeton University Press, Princeton). Rao, Someshwar, Jianmin Tang and Weimin Wang (2002) “The Importance of Skills for Innovation and Productivity”, International Productivity Monitor, Number 4, Spring. Rice , Tom W. and Jan L. Feldman (1995) “Civic Culture and Democracy from Europe to America”, Mimeo University of Vermont. Rubio, M. (1997) “Perverse Social Capital: Some Evidence from Columbia”, Journal of Economic Issues, 31, pp. 805-16. Rupasingha, Anil, et al. (2000) “Social Capital and Economic Growth: A Country-Level Analysis”, Journal of Agricultural and Applied Economics, 32, 3, pp. 565-572. Schmid, A.A. and L.J. Robison (1995) “Application of Social Capital Theory”, Journal of Agricultural and Applied Economics, 27 (July), pp. 59-66. Serageldin, Ismail and Christiaan Grootaert (2000) “Social Capital, the State, and Development Outcomes”, Social Capital: A Multifaceted Perspective, eds. Partha Dasgupta and Ismail Serageldin, The World Bank, pp. 40-58. Sobel, Joel, (2002) “Can We Trust Social Capital?”, Journal of Economic Literature; Vol. XL; March; pp. 139-154.

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Solow, Robert M. (2000) “Notes on Social Capital and Economic Performance”, Social Capital: A Multifaceted Perspective, eds. Partha Dasgupta and Ismail Serageldin, The World Bank, pp. 3-6. South, Scott J. and Kyle D. Crowder (1997) “Residential Mobility between Cities and Suburbs: Race, Suburbanization, and Back-to-the-City Moves”, Demography, November v. 34, iss. 4, pp. 525-38. Statistics Canada (2001) “Crime Comparisons between Canada and the United States”, Catalogue No. 85-002-XPE, Vol. 21, no. 11. Temple, Jonathan, (2000) “Growth Effects of Education and Social Capital in the OECD Countries”, OECD Working Papers, Vol. VIII, No. 263. Tompa, Emile (2002) “Health Status and Productivity”, Institute for Work and Health, McMaster University, presented at the CSLS conference in 2002 World Bank (1995) Monitoring Environmental Progress. World Bank (1997) Expanding the Measure of Wealth-Indicators of Environmentally Sustainable Development.

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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,

Y K H A Li t i t i t i t i t, , , , ,( )= − −α β α β1

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:

∆ Ωy y s h ni t i t i t i tk

i t i t m i tm

m

n

i t, , , , , , ,= + + + + + + +− − − − −=∑α η β β β β β µ1 1 2 1 3 1 4 1 1

5

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 , αi and ηt rate of population growth, country

dummies and period dummies respectively and µi t, , an error term. Ω i tm, −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= ( , *)

30

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 five-

year 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.

titititi

titititi

PPPIationFemaleEducionMaleEducatIncomeInequalityGrowth

,1,51,4

1,31,21,1,

µηαβββββ

+++++++=

−−

−−−

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.

31

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.

Dy F Y H SC Rt t t t t= − − − −( , , , )1 1 1 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).

Dy Y H SC Rt t t t t t= + + + + +− − − −α α α φ µ0 1 1 2 1 1 1Γ

5) Knack, Stephen and Philip Keefer, “Does Social Capital have an Economic Payoff? A Cross-Country 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

32

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 X ID n Ni i= + + + +∑λ θ β ξ3 1 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.

33

Appendix B: Data Sources

Canada and the United States Variables Definition Data Sources Labour Productivity (Total Economy)

Real GDP per hour worked

Canada: Statistics Canada U.S.: Bureau of Economic Analysis for GDP and Bureau of Labour Statistics for Hours Worked

Capital Intensity Capital input per hour worked

Canada: Statistics Canada and Jorgenson and Lee (2001) U.S.: U.S. Bureau of Labour Statistics

Human Capital The proportion of hours worked by those with post-secondary education

Canada: Statistics Canada U.S.: Prof. Dale Jorgenson at Harvard University

Health Life Expectancy, Potential Years of Life Lost, Infant Mortality, or the number of doctors per thousand of population

OECD Health Database 2001

Crime Property Crime, Murder Canada: Statistics Canada U.S.: Department of Justice and Federal Bureau of Investigation

Inequality Gini coefficients on income Theil statistics (industrial earnings Gini coefficients)

Osberg and Sharpe (1998) for Canada U.S. Census Bureau for the U.S. The University of Texas

OECD Variables Definition Data Sources Labour Productivity

Real GDP per employee OECD Economic Outlook

Capital Intensity Business capital stock per employee

OECD Economic Outlook

Human Capital Average years of higher schooling or percentage of higher schooling attained or completed

Barro and Lee’s education database

Heath Life expectancy or potential years of life lost

OECD Health Database 2001

Unemployment rate

A proxy for crime OECD Economic Outlook

Inequality Theil statistics (industrial earnings Gini Coefficients or Education Gini coefficients

The University of Texas and Castelló (2002)

34

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. 21 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.

35

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 Canada-

U.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.

36

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.

37

Table 1: Instrumental Variable Estimates of Impacts of Social Factors on

Labour Productivity Growth in Canada

CAN-1 CAN-2 CAN-3 CAN-4 CAN-5 CAN-6 CAN-7 CAN-8 Capital Intensity 0.333 0.318 0.342 0.210 0.309 0.299 0.288 0.332 (2.6)* (2.3)* (2.6)* (0.6) (2.9)* (2.5)* (2.1)* (2.5)* Human Capital 0.283 0.304 0.295 0.345 0.302 0.295 0.284 0.285 (2.8)* (2.8)* (2.8)* (6.0)* (4.5)* (3.9)* (3.5)* (4.5)* Life Expectancy 0.941 0.422 0.488 0.335 (1.2) (0.5) (0.5) (0.4) Potential Years of Life lost -0.102 -0.172 -0.189 -0.175 (-0.8) (-1.1) (-1.0) (-1.9) Property Crime -0.099 -0.124 -0.121 -0.126 -0.118 -0.137 -0.137 -0.148 (-2.1)* (-3.1)* (-3.0)* (-1.7)** (-2.6)* (-3.4)* (-3.4)* (-2.4)* Inequality 0.333 0.164 (Gini on income before transfers) (1.5) (1.3) Inequality -0.005 0.056 (Gini on income after transfers) (-0.0) (0.4) Inequality 0.014 -0.019 (Gini on total money income) (0.1) (-0.1) Inequality 0.019 -0.016 (Theil Index) (0.3) (-0.4) Capacity Utilization 0.222 0.093 0.114 0.032 0.158 0.122 0.115 0.120 (3.1) (0.9) (1.1) (0.4) (2.3) (1.9)** (1.7)** (2.3)* Time -0.0002 -0.0002 -0.0002 -0.0001 -0.0002 -0.0003 -0.0003 -0.0003 (-1.6) (-1.3) (-1.2) (-0.8) (-1.8) (-1.9)** (-1.7)** (-2.1)* Durbin-Watson 1.97 1.72 1.73 1.76 1.76 1.48 1.96 1.48 # Observations 25 25 25 25 25 25 25 25

Note: t-statistics are in parenthesis. “*” denotes significance at the 5% level. “**” denotes significance at the 10% level.

38

Table 2: Instrumental Variable Estimates of Impacts of Social Factors on Labour Productivity Growth in the United States

US-1 US-2 US-3 US-4 US-5 US-6

Capital Intensity 0.472 0.448 0.368 0.330 0.375 0.404 (2.0)* (1.7)** (1.7)** (1.11) (1.3) (1.6) Human Capital 0.174 0.193 0.234 0.160 0.178 0.307 (2.8)* (2.9)* (2.0)* (3.4)* (3.4)* (1.6) Life Expectancy 0.478 0.999 0.024 (0.4) (0.7) (0.0) Potential Years of Life lost -0.135 -0.092 0.184 (-0.6) (-0.4) (0.6) Property Crime -0.098 -0.121 -0.062 -0.021 -0.046 -0.058 (-1.6) (-1.8)** (-1.1) (-0.3) (-0.7) (-0.8) Inequality -0.117 -0.069 (Gini for household) (-0.9) (-0.6) Inequality -0.268 -0.173 (Gini for family) (-1.8)** (-1.4) Inequality 0.038 0.073 (Theil Index) (0.7) (0.9) Capacity Utilization 0.222 0.208 0.176 0.204 0.201 0.173 (4.0)* (3.7)* (2.2)* (2.7)* (2.6)* (1.6) Time -.00004 -0.00004 -0.00005 0.00002 0.00003 -0.00006 (-0.3) (-0.3) (-0.3) (0.2) (0.3) (-0.4) Durbin-Watson 2.01 1.86 2.02 2.05 1.99 2.11 # Observations 29 29 29 29 29 29

Note: t-statistics are in parenthesis. “*” denotes significance at the 5% level. “**” denotes significance at the 10% level.

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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 CAN-US-1 CAN-US-2 Capital Intensity 0.305 0.448 (2.2)* (2.5)* Human Capital 0.202 0.190 (4.0)* (2.4)* Life Expectancy 0.428 (0.5) Potential Years of Life lost 0.242 (1.3) Property Crime 0.022 0.112 (0.3) (1.2) Inequality 0.016 0.009 (Theil Index) (0.4) (0.2) Capacity Utilization 0.062 0.155 (0.5) (0.9) Durbin-Watson 1.76 2.12 # Observations 29 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

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Table C2 Correlation Coefficients between Crime and Unemployment Rate in Selected OECD Countries

Country Correlation between

Theft and Unemployment Rate Correlation between

Robbery and Unemployment Rate Canada 0.83 0.74 U.S. 0.67 0.24 Denmark 0.61 0.75 Finland 0.60 0.35 France 0.45 0.95 Ireland 0.79 0.82 Italy 0.64 0.93

Table C3 Social Factors and Labour Productivity Growth: OECD Countries

All Selected Countries Rich a (1) b (2) (3) (4) b (5) (6) (7) (8) Capital Intensity 0.397 0.404 0.409 0.400 0.403 0.410 0.411 0.440 (8.01)* (8.39)* (8.58)* (8.06)* (8.12)* (8.35)* (5.62)* (5.29)* Average Yeas of 0.037 0.036 0.039 0.038 0.033 0.036 Higher Schooling (2.14)* (2.34)* (2.17)* (2.31)* (2.27)* (2.10)* Percentage of 0.028 0.029 Higher Schooling (1.76)** (1.77)** Life Expectancy 2.279 2.222 2.254 2.112 (4.21)* (4.08)* (4.06)* (3.41)* Potential Year of -0.286 -0.291 -0.293 -0.230 Life lost (-3.50)* (-3.50)* (-3.45)* (-2.16)* Unemployment Rate

-0.014 -0.016 -0.015 -0.017 -0.020 -0.019 -0.015 -0.018

(-1.06) (-2.70)* (-2.49)* (-1.40) (-3.51)* (-3.27)* (-2.22)* (-2.93)* Theil 0.004 0.004 (income inequality) (0.32) (0.30) Education Gini 0.002 -0.008 0.013 0.002 0.023 0.030 (educa. inequality) (0.08) (-0.25) (0.46) (0.06) (0.90) (1.06) Adjusted R 0.12 0.17 0.16 0.11 0.17 0.15 0.19 0.14 # Observations 76 80 80 76 80 80 68 68

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.

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*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

1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 20000.75

0.80

0.85

0.90

0.95

1.00

Figure 1Canada's Relative Labour Productivity* Compared to the U.S.

* Yeas of loss per 100,000 population aged 0-64Source: OECD Health Database 2001

1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 20000.60

0.70

0.80

0.90

1.00

1.10

1.20

Figure 2Canada's Life Expectancy and Potential Years of Life Lost (PYLL)* Relative to the U.S.

Life Expectancy

Potential Years of Life Lost

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*Property crime (minus fraud) and murder per 1000 personsSource: Statistics Canada, U.S. Department of Justice and Federal Bureau of Investigation

1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 19980.00

0.20

0.40

0.60

0.80

1.00

1.20

Figure 3Canada's Property Crime and Murder Rates* Relative to the U.S.

Property Crime

Murder

*Industrial earnings inequality.Source: University of Texas Inequality Project.

1962 1967 1972 1977 1982 1987 1992 19970.40

0.60

0.80

1.00

1.20

Figure 4Canada's Inequality Relative* to the U.S.