1 CHAPTER ONE
INTRODUCTION
1.0 Introduction
Relatively, among the Organisation for Economic Cooperation and Development (OECD) Canada, has been experiencing persistent economic growth rates over the last decade. For instance, as at the end of the fiscal year 2012, Gross Domestic Product (GDP) for the United Kingdom (UK) was 2.435 trillion USD, Canada on the other hand recorded 15.68 trillion USD for the same year; representing 2.94 percent of the world economy (World Bank Group, cited in Trading Economics 2013). The impressive growth rate has equally led to an uneven process of economic growth and development among Canada‟s ten provinces and three territories. Thus, it is important to investigate and examine the space-time dimension for analysing Canada‟s inequality. Between the years (1997 to 2007) the period with the fastest-growing incomes in Canada, according to Yalnizyan (2010), the richest 1 percent of Canadians took almost a third of all incomes gains.
This study intends to show that Canada‟s regional inequality has been sensitive to the spatial-temporal hierarchy of multi-mechanism, and brings to the fore, the relative influence of globalisation _ especially effects of the North American Free Trade Area (NAFTA), marketization, regionalism and decentralisation.
Regional inequality is important and hence, relevant issue of government policies (Saint-Paul and Verdier 1996; Benabou 2000/2002; Wei 2002). Krugman (1999), argues that in recent times, the geographical and spatial aspect of the processes of development has become mainstream as differences in economic development are associated with location.
Henceforth, the spatial scale is relevant in regional inequality study and analysis (Wei 2002;
Wei and Fan 2000; Wei and Ye 2009). A limited number of scholars have investigated and analysed the spatial patterns of Canada‟s economic development. For instance, Bourne and Simmons (1979) inquired about Canadian settlement trends in the years: 1971 to 1976;
Simmons and Speck (1986) studied the spatial patterns of social change; Di Biase and Bauder (2004) studied immigration with a link to spatial settlement pattern and labour force characteristics; Balakrishnan and Jurdi (2013) investigated the spatial residential patterns of Aboriginals and their socio-economic integration in selected Canadian cities; Laura and
2 Raymond (2000) explicitly focused their study on the spatial relationships among communities on the Canadian and United States of America (USA) border relative to local economic development; Myles and Hou (2003) studied neighbourhood attainment and residential segregation among visible minorities in Canada; Kitchen (2006) examined the spatial link between crime and socio-economic status in Ottawa and Saskatoon; MacDonald (2007), focused on the spatial distribution of precarious employment by social group and sectors in Canada. However, most of these studies have failed to bring to the fore the spatial- temporal hierarchy of regional inequality and more importantly, the factors underlying regional inequality is unclear within the processes of economic growth and development in Canada, particularly in the face of the intricacies of globalisation.
1.2 Problem Statement
The Royal Commission on Canada's Economic Prospects (the Gordon Commission) of 1957 explicitly recognized the existence of regional disparity in Canada. It gradually became evident that differences existed not only in fiscal capacities but also in opportunities for growth. The Gordon Commission defined the regional problem as a difference or gap between a province's level of income, or unemployment, or other such key variable, and that of another province or relative to the national average. These economic differences between provinces became known as disparity gaps. It also became evident at this time as it is today that, regardless of the national level of prosperity, the gaps did not disappear. They might narrow or widen in line with changes in the national rate of economic growth but the relative differences between the rich and poorer provinces seemed to persist over time. If regional disparities were to be eliminated, it could no longer be considered sufficient for less developed areas to grow at the same rate as the more prosperous regions of the country; they would have to grow at much faster rates. The Gordon Commission (1957) argues that this view of the problem was to have a profound effect on subsequent thinking about regional policies.
Three specific problems are associated with the issues of the regional development.
First, to designate provinces as the areas of discussion is generally believed to have been inappropriate because economic regions do not necessarily conform to political boundaries.
Over the years, federal regional development policies have attempted to define the optimum areas in Canada in which to implement policy. No universally acceptable definition has yet been accepted, however, though some sub-regions (such as Cape Breton Island) have readily
3 been identified. For the most part, Provinces remain the basic geographic unit because most data are available at that level of aggregation and because of the importance of the provincial governments in the overall processes of economic growth and development (ibid.).
The second major problem with this early work was the notion of a disparity gap. The disparity gap, an easily identifiable phenomenon, has on many occasions been mistaken for the regional development problem itself. Defining the problem in terms of a disparity gap in provincial employment levels for instance, has led to a formulation of policies that concentrate on employment; that is, policies that mask the symptoms but do not eliminate the problem. In fact, the existence of a gap in unemployment rates, or in productivity, or in income levels, may be explained by a host of factors acting alone or together on the local economy. Each factor responsible for a disparity gap will often require a different policy solution (ibid.).
It is important to understand the causes of regional disparities in order to prescribe the correct policy approach. While these causes are numerous, they may be classified in five broad categories. First, there are illusory differences, for example, those variables as per capita income, which do not reflect actual differences between regions. In such instances, policy actions are not required at all. Second, there are differences in the natural endowments of local economies, such as the existence of a desirable geographical feature. Again, this may not require any specific policy response. A third cause of regional inequality may be certain federal or provincial actions, which, once identified, can simply be eliminated. Regional disparity may result from "market imperfections," such as monopolies or unionization.
Depending on policy objectives and the precise nature of the imperfection a number of policy options could be considered. A final cause could be structural differences such as "factor endowments" and consumer preferences. Such differences are usually best overcome by allowing market forces to work; this implies labour mobility. Alternatively, political considerations may suggest a policy that involves the subsidization of production. It is suggested that regional disparities in Canada may be caused by a mixture of these factors and this will make it more difficult to identify the correct policy response (ibid.). With the Canadian federation as the focus, this research seeks to investigate, identify, determine and critically analyse the source of regional inequality which may be due to spatial-temporal hierarchy; and hence, will differ from one part of the country to the next and that this will
4 reduce the effectiveness of any policy that seeks a universal and/or one fit all solution through one national policy and programmes.
Hypotheses
H1: There is a significant positive relationship between regional in-equality and low income, distance (spatial), time (temporal) and, access to federal employment and Canada‟s capital city __ Ottawa.
H0: There is no significant relationship between regional in-equality and low income, distance (spatial), time (temporal) and, access to federal employment and Canada‟s capital city __ Ottawa.
1.3 Research Questions
Based on the general issues raised in the research problem, the study sought to answer the following key questions posed in order to achieve its stated goal and objectives:
I. What is the form of regional inequality?
II. What is the spatial dimension of federal employment relative to regionalism;
III. What is the spatial dimension of regional income inequality;
IV. What are the prescribed policies intended to minimise employment, income inequality and regional disparities?
1.4 Broad Goal and Objectives of the Study
The broad goal of this study is to employ certain spatial and non-spatial data analysis techniques (spatial statistical techniques involving spatial regression, auto-correlation and correlation, modelling – interpolation and analysis) to investigate and map the shifts in patterns of regional inequality at different geographic scales in Canada since 2009 to 2013 __
Table 1 and Figure 1. Thus, explore the spatial-temporal hierarchy of the mechanisms and, to investigate, identify and examine noted influence of underlining factors. Therefore, due to the geographical location and socio-economic diversity of Canada, this study maintains that regional inequality in Canada is sensitive to spatial scale, and that multi-mechanisms of regional inequality have a spatial-temporal hierarchical structure, which influences the patterns of regional inequality. Henceforth, this study is conducted under the framework of multi-scale, multilevel, and multi-mechanisms as explained below.
5 Region Province/Territory Capital City
Atlantic/eastern region
Newfoundland and Labrador Prince Edward Island
Nova Scotia New Brunswik
St. John‟s Charlottetown Halifax Fredericton
Central Canada Quebec Ontario
Quebec City Toronto
Prairie Provinces Manitoba Saskatchewan Alberta
Winnipeg Regina Edmonton
West Coast British Columbia Victoria
North Nunavut
Northwest Territories Yukon Territory
Iqaluit Yellowknife Whitehorse Table 1: Five/three Regional Model of Canada
Source: Government of Government, (2013).
Figure 1: Five/three Regional Model of Canada Source: Government of Government, (2013).
Based on the broad goal and the problem statement as elucidated above the specific research objectives of this study and focus are:
I. Multi-scale
There are ten provinces and three territories in Canada. These provinces are traditionally grouped into either five or three spatial regions: western, eastern and central __
table 1 and figure 1 respectively. The central part of Canada (Quebec and Ontario) is the
6 industrial and manufacturing heartland of the country. Grouped together, they produce more than three-quarters of all Canadian manufactured goods (Government of Canada 2013).
Apart from mining, fishing and wine industry, the western region (British Columbia) has Canada‟s largest and busiest port handling billions of dollars‟ wealth of goods traded around the world. Historically, apart from being sparsely populated the eastern region lacking natural resources endowment continues to lag the rest of Canada in economic growth and hence, suffers low income levels. This study therefore, intends by use of multi-level regression technique to model and analyse the form of regionalism and thus, to investigate, identify and examine the patterns of regional inequality at three different geographic scales namely: (i) inter-province (selected provinces and/or territories‟), (ii) between the regions;
and (iii) between the provinces of each region (selected municipalities).
II. Multi-mechanism:
Canada‟s economic growth can be described by the triple transitions of regionalism (decentralisation), marketization, and globalisation which have introduced a new set of institutional and market forces (Wei 1999; Cochrane and Perrella 2010). These transitions have effects on geographical concentration. Regional decentralization from the Federal to Provincial and Municipal (local) governments reflects the institutional change, not only triggering inter-regional competition for business, equally important pushing local governments to implement successful development policies (Montinola, Qian and Weingast 1995). Moreover, marketization and the processes of globalisation have created the conditions of comparative advantage and agglomeration economies. The economic reforms within the last few years has stimulated foreign investment and exports however, the preferential policies are unevenly practices in some selected areas, especially within central Canada. Thus, globalisation has enhanced the comparative advantage due to geographical concentration relative to federal employment growth spread within the regions.
III. Multi-level:
Noted three economic belts in Canada has unique geographical, historical, economic and cultural characteristics; administrative divisions and policy-making have spatial hierarchical structure. Canada‟s economic policies have been conveyed through multi-levels of government including federal, provincial/territorial and municipal. The current literature has not effectively identified spatial hierarchy of both economic growth and the underpinning characteristics of regional in-equality. This research explains the process of economic
7 growth at all three levels. Due to economic reform policies, and the relevance of regional in- equality, the spatio-temporal (income –time relationship) is selected at this level for analysis.
Regions are the second level and individual provinces are identified as the third level due to their uniqueness within the Canadian federation _figure 2.
Figure 2: Multi-level Framework of Canada‟s Regional Development Source: Authors Compilation, (2015).
1.5 Data Collection
To allow the researcher achieve the purposes in terms of the goal, objectives and significance of the study, this inquiry will rely mainly on quantitative methods in the analysis of result and therefore, the research will be conducted as an evaluative research. Sources of data to be used for this inquiry will mainly be secondary datasets that include: Constant GDP per capita (GDPPC), per capita foreign direct investment (FDIPC), the share of education (EDC), immigration and population growth rate (POPGR), and GIS shapefile (boundary coordinates/map) of Canada and provinces. The fundamental geographic background database and boundary files are obtained from the Geological Survey of Canada, a department within the Ministry of Natural Resources Canada and the Environmental Systems Research Institute (ESRI) Arcgis online. These social and economic datasets will be obtained primarily from the Government of Canada (Statistics Canada). The commonly used constant GDP per capita of Federal government public sector full employment (military personal as proxy for regional economic activity are chosen as the indicator of overall level (dependent viable) of economic development (Fan and Sun 2008).
8 1.6 Methods
This study explores the inter-regional, inter-provincial, and intra-regional (municipality) in-equality of Canada with geo-statistical (non- spatial) techniques commonly employed in measuring regional in-equality. These include the Gini-coefficient of income measurement.
One of the most popular measures of income in-equality, the Gini-coefficient is deduced from the Lorenz curve. The Lorenz curve presents the percentage of total income earned by cumulative percentage of the population under study (De Maio 2007). A similar technique and measure of income in-equality is the Coefficient of Variation (CV). It is calculated by dividing the standard deviation of the income distribution of a population by its mean. Thus, the CV is the percentage variation in mean; standard deviation being considered as the total variation in the mean. For instance, should we wish to compare the variability of two or more series, we can use the CV (Ahmed 1995). Henceforth, more equal spread of income distributions tends to have smaller standard deviations and as such, the CV will be smaller in more equal societies (De Maio 2007). On the other hand, the Gini coefficient is premised on the Lorenz curve, representing the cumulative distribution function of a probability distribution (Pigou 1912; Dalton 1920; Theil 1967; Sen 1973; Cowell 1977; Cowell 1980a;
Yitzhaki 1983; Eichhorn 1988; Ahmend 1995; Fan and Sun 2008; Shorrocks 2006 cited in Li and Wei 2010). Henceforth, this study uses this measure in order to compare the results in an attempt to minimize potential miss-interpretation and thus, provide a credible explanation.
Multi-level regression modelling will be applied to investigate, examine and analyse the relationship between spatial extent and regional inequality in Canada. Traditional single –level regression technique treats the units of analysis as independent observations, and thus, fails to recognise hierarchical structures. The results is that standard errors of regression coefficients are underestimated hence, leading to an overstatement of statistical significance.
On the other hand, multi-level regression overcomes this problem and thus, recognises the existence of such hierarchical datasets that allows and enables residual components at each level of analysis. Spatial application of multi-level modelling therefore attempts to separate the effects of personal characteristics and emphases contextual characteristics on behaviour (Duncan and Jones 2000; Fortheringham, Brunsdon and Charlton 2002; Goldstein 1987).
Multi-level regression analysis as employed by Rasbash, Browne, Healy, Cameron and Charlton (2005) is therefore applied to fit these three models in this study:
Yijt = βo + β1Xijt + Ut +
r
jt +e
ijt9 Where Yijt is the dependent variable in region j at year t; Xijt the independent variables in region j at year t;
e
ijt is the standard error of i in region j at year t. This study intends to run single-level (Municipal), two-level (Federal and Provincial/Territorial) and three –level (Time: Federal and Provincial/Territorial) regression models to personal effect, contextual effect and time effect respectively. Five time periods: 2009, 2010, 2011, 2012 and 2013 are included. The dependent variable is Federal government public sector full time employment (military personal) as a proxy for regional economic activity. Henceforth, this study chooses the following five independent variables:1. The Foreign Direct Investment Per Capita (FDIPC) reflects the effect of globalisation.
The more globalised a region is, the more FDI the region has obtained.
2. The share of the Federal government‟s investment in a province‟s fixed asset (FGIPFA) is thus, an indicator of marketization. A higher level of Federal government equalisation programme in the form of direct intervention for instance the transfer of payments reflects lower level of marketization.
3. The Education Level (EDU) in terms of the number of institutions of higher education per 10,000 persons, which represents labour quality, and is also an indicator of marketization.
4. The Immigration and Population Growth Rate (POPGR) is a control variable.
5. The Per Capita Fixed Asset Investment (FAIPC) is equally an identified control variable as it is significant factor of economic growth in Canada.
Furthermore, the study applies spatial analysis techniques in Geographical Information Science and Systems (GIScience). Regional in-equality indexes such as Gini coefficient can only reveal overall in-equality. Although location quotients are useful in depicting the changing status of regions, both types of indexes have limited utility in revealing spatial agglomeration and the character of inter-regional relations (Kiser 1992;
Cmu 2014). However, recent development in GIScience have enabled and thus provided effective tools to analyse spatial regression, association, auto-correlation or correlation, agglomeration and clustering, which can shed more light on regional in-equality in Canada.
Goodchild (2001) defines spatial analysis as a set of techniques whose results are dependent on the locations of the objects being analyzed. Moreover, Anselin‟s (1989) defines spatial analysis as a formal quantitative study of a phenomenon that has spatial contexts in order to interpret the meaning of what is “near” and what is “distant” in a particular context. This definition is predicated on Tobler‟s (1970) definition of spatial
10 analysis: “everything is related to everything else, but near things are more related than distant things” (cited in Tobler 2004). Furthermore, according to Manitoba (1999) spatial analysis is the process of extracting or generating new geographical information by applying analytical techniques to the data that have geographical contexts. It may be used to model the interactions of the geographical phenomena and to predict future events. Fortin and Dale (2005) argue that identifying spatial patterns and their relationships is just the first step for answering a bigger question.
The mean centre as applied in spatial analysis is to identify the geographic center and/or the center of concentration for a set of features (in-equality); it is a point constructed from the average x and y values for the input feature centroid (Esri 2012).
Global Moran‟s Index (Moran‟s I) has been used to detect spatial auto-correlation, and to analyse spatial relationships among regions (Upton and Fingleton 1985; Aneslin 1988/1995/1996). As an indication of spatial concentration, global Moran‟s I can be used to indicate spatial convergence or divergence: an increasing global Moran‟s I means that the rich group (region) continues to accumulate wealth while the poor regions become poorer, and the absolute gap between them is enlarging. A decreasing global Moran‟s I indicate that the clusters are disappearing and a more even distribution occurs. To calculate Moran‟s I, the most important step is to determine a spatial neighbour weight matrix. In this study, a weight matrix will be based on each province‟s spatial contiguity in ArcGIS 10.3.1. Structured like the Pearson‟ product-moment statistic: measure of covariance:
n
i i n
i n
j
j i
ij n
i n
j
ij y y
y y y y w w
I n
)2
(
) )(
(
Source: Yu (2013).
w
ij
is the weight, w
ij
=1 if locations i and j are adjacent and zero otherwise (w
ii
=0, a region is not adjacent to itself).
y
i
and (Y Y ) are the variable in the ith location and the mean of the variable, respectively
n is the total number of observations
I is used to test hypotheses concerning similarity
11 Another non-spatial technique to be applied in this study is that of Ripley‟s K- measure. The statistical technique of Ripley‟s K-Function allows for an analysis of completely mapped spatial point process; data on the locations of a phenomena. More importantly, it enables both bivariate and/or multivariate generalisations applied in the explanation of relationships between two or more point patterns; comparing the clustering of the incidence of regional in-equality between different time periods (Lotwick and Silverman;
cited in Kim 2010).
Source: University of Texas at San Antonio, (2009).
1.7 Scope and Organisation of the Study
The study center‟s on spatial and non-spatial data analysis techniques (spatial statistical techniques, modelling – interpolation and analysis) to investigate and map the shifts in patterns of regional in-equality at different geographic scales in Canada between 2009 and 2013. The study is presented in five interrelated chapters. The first chapter dwells on the introduction and background of the study, followed by the statement of the problem, research goal and objectives, research questions and organisation of the study. The next chapter two presents the review of relevant literature on the subject matter under consideration. The research design and methods of data collection and collation are spelt out in chapter three. Chapter four presents analysis, discussions and interpretation of the results of the study. Finally, chapter five covers summary of the major findings, conclusion and recommendations.
12 CHAPTER TWO
LITERATURE REVIEW AND THEORETICAL FRAMEWORK
2.0 Introduction
The review of existing relevant data, information and literature is the most fundamental and important aspect of any research exercise (Crow and Semmens 2013). As an undertaking not only inform but more importantly, enlightens the researcher on his/her chosen field of inquiry. The related literature therefore, incorporates the final section of the studies that compares and contrast the results and analysis of categories or themes that emerge from such an inquiry (Creswell 2012). In the nutshell, it allows and enables the researcher to adequately analyse his/her dataset to arrive at an appropriate conclusion and hence, generalisations. Thus, it starts and ends with the research being an integral part of the research process (Crow and Semmens 2007).
There is vast literature on the spatial distribution of income inequality and its effects on the processes of economic growth and development in both the Developed and Developing nations. Without delving into all of the literature on the subject matter under review, this study will focus on few sources and authors on income in-equality and economic development. As such relevant information will be sought from secondary sources of data such as books, journals, reports, and publications. The exercise involved in this process puts into perspective the relationship between spatial income in-equality, economic growth and development. Henceforth, the broad goal is to interrogate, analyse and understand the extent of spatial distribution of employment and income in-equality relative to the processes of economic growth and development. The chapter begins with the theoretical framework and concept of economic growth and development, spatial distribution of employment and income in-equality and its effects on the processes of regionalism.
2.1 Conceptualising the Processes of Development
The process of development is a multi-dimensional process involving the re- organisation and re-orientation of an entire economic and social system. Thus, in the modern analysis of development, the processes of development is not only an economic phenomenon but includes improvements in incomes and output involving radical changes in institutional,
13 social and administrative structures in addition to attitudes, customs and beliefs (Todaro and Smith 2012).
For this research, the process of economic development will restrict itself to and as such is said to be local, region and/or country which is mostly associated with increasing income, consumption, savings and investment levels. There are many dimensions of economic development than income growth; for if income distribution is highly skewed, growth may thus not be accompanied by much progress towards the goals that are usually associated with economic development. Moreover, the orthodox view as espoused by some economists is that the bigger picture of the processes of economic development emanates from economic growth (Centre for Financial and Management Studies 2014).
2.1.1 The Theory of Economic Growth and Development
The literature on the theory of economic growth and development has concentrated on: a) the linear-stages-of-growth model, b) structural changes, c) international-dependence model, and d) the neo-classical free-market approach. Development economists in the 1950s and 1960s analysed the processes of economic development as linear through which all countries must go through. Historically, Rostow‟s (1960) theory of economic development viewed the process as involving five stages starting with the traditional society, the pre- conditions for take-off, take-off, the drive to maturity and the age of high mass consumption.
Inherent within this stages are inner logic and continuity into self-sustaining growth. The approach by which increased investment could lead to growth is viewed within the Harrod- Domar growth model. In its simplest form, the model is based on total new investment needed to propel the economic to higher levels depends on proportion of savings made in an economy. Thus, Net saving (S) is some proportion, s, of national income (Y) such that we have the simple equation: S = sY (Harrod 1939; Domar 1946).
As a point of departure, the structural-change theorists‟ emphasis the path through which an underdeveloped economy could transform their economy from an overreliance on traditional subsistence agriculture to modern, urbanised and industrial society. The best- known advocate of this school of thought is the structural transformation of a subsistence economy as formulated by Arthur Lewis in his two-sector model (1954). The model stipulates a rural subsistence sector dominated by zero marginal labour productivity resulting in the existence of surplus labour. Investment and technological advancement could lead to growth in the modern industrial sector including modern agriculture that permits movement
14 of the noted surplus labour to shift from rural subsistence sector into the modern sector due to higher wages (1954). Thus, the absorbed surplus labour not only promotes industrialisation but stimulates sustained economic growth and development. The international-dependence model posits how domestically, underdeveloped economies in addition to being riddled with institutional, political, and economic rigidities, are caught up in a dependence and dominance relationship on the one hand, and on the other hand, with the developed world (Todaro and Smith 2012).
Todaro and Smith (ibid.) maintains that the most accurate method of measuring development is the Human Development Index that allows literacy rates and life expectancy as they affect productivity and therefore, could lead to economic growth. Starting from the 1980s, the neo-classical counter-revolution centred their arguments and thus, advocated for supply-side macroeconomic policies, rational expectations and the privatization of public corporations. They argued that freer markets enable and promotes an expanded free trade, export expansion that allows the elimination of government regulations and price distortions in factor and product markets. Such policy implementation allows for economic efficiency and economic growth.
The processes of economic growth and development is measured in terms of Gross Domestic Product (GDP) which is calculated annually based on data and information on incomes, expenditure and investment for each sector of a country‟s economy (Coakley, Reed and Taylor 2009). By dividing GDP at current market prices by the population results in GDP per capita. According to the United Nations Department of Economics and Social Affairs (1993) a variation of the indicator could be the growth in real GDP per capita, which in actual fact is obtained as percentage change in real GDP divided by the population.
Associated measure of economic well-being is the most popular tools to measure income inequality and distribution is that of the Lorenz curve and the Gini Coefficient.
The Gini Coefficient or the Gini ratio is a mathematical number between zero and one that measures the degree of inequality in the distribution of income. A Gini ratio of zero (0.0
= minimum in-equality) indicating that each member of the population or sample has the same income. A theoretical coefficient of one (1.0 = maximum in-equality) thus, indicates that one member and/or person has all the income. The Gini ratio is based on the Lorenz curve that indicates the proportion of the total income of a population (y axis) that is cumulatively earned by the denominator and/or
x
% of the population under study or review.15 Solt (2009) combined datasets from available surveys to infer comparable series of Gini ratio for both net and market inequality for a number of countries. He defined re- distribution as the difference between the market and net in-equality series. Since the British Government published the Chorley Report in 1987 many in the Organisation of Economic Corporation and Development (OECD) have explored the role of Geographical Information Science and Systems (GIScience) as a tool for the integration of spatial data systems in operational and public policy settings. Within the report, GIScience was seen as most probably providing policy makers and analysts with major new tool for the effective and efficient handling of spatially referenced dataset‟s and their respective attribute information (Worrall and Bond 1997). Thus, the study takes a cue from the 1980s and 1990s when GIS and related spatial data handling and imaging systems become central elements in the restructuring of economic activity, the modernisation of the state, and the administration of social life by public and private organisations (Pickles 1989).
Henceforth, the technology and its applications have metamorphosed beyond a simple mapping tool to a methodology that is used for urban planning, environmental monitoring, analysis and understanding complex spatial problem. Most importantly, it is increasingly being used for human and/or natural resource policy conceptualisation, formulation, implementation, results based monitoring and evaluation for achieving tangible and sustainable development mile-stones.
Ezcurra, Iraizoz, Pascual and Rapun (2008) studied the regional spatial disparity of agriculture within the European Union during the period 1980 to 2001, by means of the information provided by various methodological instruments which enabled them to overcome the drawbacks of conventional convergence analysis. The results obtained reveal that the regional distribution of productivity in the agricultural sector is characterized by the presence of positive spatial dependence. This fact implies that the European regions in close spatial proximity register similar levels of the variable under study, which highlights the relevance of geographical location in this context. The empirical evidence presented also shows that regional disparities have remained almost constant during the time interval considered. However, the increase in density around the European average explains the observed reduction in the degree of bipolarization, while intra-distribution mobility is relatively limited.
16 Finally, the analysis carried out allows them to assess the role of variables such as country of origin, investment per worker in the agricultural sector, regional per capita income or the size of the agric-food industry, in explaining the dynamics of the distribution under analysis.
Outside the European Union, Link, Yanjie, Jinan and Glauben (2012) investigated the temporal and spatial nature of the marginal revenue of land, Total Factor Productivity (TFP) change and its three components: Technical Change (TC), Technical Efficiency (TEC) and Scale Efficiency Change (SEC) as seen in Chinese agricultural production from 1995 to 1999. Employing country-level data, the study made use of both stochastic frontier and mapping analyses methods. The results showed that growth in the marginal revenue of land was diverse across various regions, where most gain occurred in eastern coastal zone, while loss was in the northwest and north China respectively.
Various poverty alleviation programs have helped reduce poverty in Thailand, however, the poverty gap still remains, specifically in rural areas in the north and northeast of the country. The major barrier to poverty alleviation policies and strategies is the weakness of identifying where the poor are, thereby targeting poverty interventions. In view of this, Thongdara, Samarakoon, Shrestha and Ranamukhaarachchi (2011) investigated the potential of descriptive statistics, GIScience, and spatial auto-correlation in recognizing poverty association of a site selected in the northeast Thailand, including identifying factors that influence rural poverty, and investigating underlying factors and spatial associations of poverty at the rural household level. Their results showed that 70% of the households sampled in the study area were poor, and nearly half of their income generated was from farming. Factors influencing farm income were examined by regression statistics and it was found that farm income is related to area cultivated, rice yield, livestock and learning experience of farmers. It was demonstrated that GIScience is a useful tool to identify environmental factors that influence poverty and spatial auto-correlation is an effective method in revealing similarities and dissimilarities of poverty in household units. Use of these two technologies to identify factors underlying rural poverty was analysed and presented.
In a study of agriculture in the United States of America (USA) Trauger (2009) examines public policy agency articulated by farmers and activists in the sustainable agriculture community in Pennsylvania to exploit ruptures in the conventional food system
17 and develop new forms of food provisioning in local economies. The actor-network theory offers roads out of structure/agency dialectics and proposes new possibilities for understanding structure as a network, and agency as the outcome of networking. The research shows agency to be an outcome of collectivities, but is also contingent on leadership, partially distributed throughout the network and not necessarily emancipatory for all enrolled actors.
Although interrelated, the connection and distinction between processes of economic growth and development could be observed from the fact that the former concretes on quantitative changes in an economy. On the other hand, economic development is said to dwell on both the qualitative and quantitative changes within an economy. Economic growth focuses on the optimum utilisation and moreover, development of under-utilised resources of the developed world. Henceforth, it emphasis the real increases in the output of goods and services. Economic development on the other hand, focuses on the utilisation and development of unused resources in a developing economy. As part of its deliberations, it concretes not only on changes in income, saving and investment but equally important is its foci on the socio-economic structure in terms of the institutional and, technological changes within an economy under study (Diffen 2014).
2.1.2 Spatial Income Distribution, Employment Growth and Regionalism
Studies and theories of regional in-equality mostly emphasis three problematic areas.
These includes the question whether regional equality increases or decreases over time, the causes of in-equality, and the development strategy for reducing regional in-equality (Lipshitz 1992). Thus, there has been debate between the convergence and divergence schools of thought with neo-classical theory and Inverted-U models widely representing the convergence school of thought. To a large extent, the neoclassical growth theory emphasis equilibrium conditions and therefore, the importance of the market in allocating resources.
Henceforth, it considers regional in-equality as a transitory phenomenon and an inevitable stage for the final equilibrium.
In addition, the Inverted-U theory argues that regional in-equality increases during the early stages of development and decreases as the economy matures (Hirschman 1958;
Williamson 1965; Friedmann 1966; Alonso 1980). Hirschman and Perroux (cited in Wei and Ye 2009) called for government intervention to promote the development of growth poles;
that is development from above or top-down processes of development. However, in the
18 1990s scholars such as Barro and Sala-I-Martin (1991/1992) brought forth new perspectives on convergence and regional inequality. The β-convergence argues that poorer regions grow more rapidly than wealthier regions; the absolute difference to a degree may not decline over a period of time. This neoclassical model and approach with its emphasis on convergence has been criticized (Venables 2005; Silva 2007). In view of this, Krugman (1991/1999) espoused a new geography with emphasis on locational variables that integrates traditional location theories and economic geography into this approach. These theories however, de- emphasise such important factors as institutional effects, spatial scale, spatial hierarchy, and time dimension (We and Ye 2009).
Proposing new analytical frameworks, Wei (2002); Wei and Ye (2009); and Yang and liang (1994) applied these theories to their investigation and analysis of regional in-equality in China. Firstly, Wei (2002) and Wei (1999) proposed the notion of multi-scale and multi- mechanism frameworks by which they argued that for instance, China‟s processes of economic reform could better be understood as a triple process of decentralisation, marketization and globalisation. According to them, the issue of regional in-equality in China is very sensitive to geographical scale and is thus, influenced by multiple mechanisms.
Others have conducted their research by investigating the effects of fiscal decentralisation on regional in-equality (Wei 1996; Kanbur and Zhang 2005; Tsui and Wang 2008). On the other hand, scholars such as Kanbur and Zhang (2005) and Fu (2007) turned to emphasis the effects of foreign investment on regional in-equality. Lu and Wang (2002); Ho and Li (2008) studied government policy bias and their effects on regional equality. With Fan and Sun (2008) arguing that for instance, the central government of China policies since the 1990s of developing the interior parts of China has reduced regional inequality. Whilst Ying (2003) studied labour mobility and its relative effects on regional inequality, Sun and Wang (2005);
Lu and Wei (2007); Segal (2008) investigated the processes of the globalisation of science and technology on economic development and regional inequality. Thirdly, a more vigorous methodological inquiry and contributions have emerged.
Using applications in Geographical Information Systems (GIS) such as the techniques of visualisation, spatial regression, and Geographically Weighted Regression (GWR), Ying (2003), Yu and Wei (2003/2008); Ye and Wei (2005); Wei and Ye (2009) were able to demonstrate that regional in-equality in China is particularly sensitive to geographical clustering and agglomeration. Furthermore, Yu (2006) and Yu and Wei (2008) presented
19 spatial-temporal analysis based on spatial and panel data that comprehensively represented the dynamics of China‟s regional growth and economic development.
Recently, others have equally examined the impacts of regional differences on the processes of sustainable development in England, United Kingdom (UK) with GIScience spatial analysis (Hube, Owen and Cinderby 2007); and in the Massachusetts, United States of America (USA) with GWR (Ogneva-Himmelberger, Pearsall and Rakshit 2009). They concluded their studies by pointing out that due to the strong interactive relationship between socio-economic in-equality and environmental protection, proposed relevant policy intervention should be developed by emphasising both socio-economic and geographical (location and environmental) conditions (Li and Wei 2010).
A 2011 study by the International Monetary Fund (IMF) found the following. When growth is looked at over the long term, the trade-off between efficiency and equality may not exist. In fact, equality appears to be an important ingredient in promoting and sustaining growth. The difference between countries that can sustain rapid growth for many years or even decades and the many others that see growth spurts fade quickly may be the level of inequality. Countries may find that improving equality may also improve efficiency, understood as more sustainable long-run growth (Berg and Ostry 2011).
Based on the above integrated literature review, three interrelated areas deserve further in-depth investigations and study. The scale and nature of regional in-equality should be further studied. Thus, although there has been a number of research on the causes and mechanisms of the rising in-equality in Canada, not much is known about the relative relevance of these contributing variables in view of the spatial-temporal hierarchical nature of the country. This study is therefore intended to fill the noted gap with an attempt at bringing to the fore the spatial-temporal and contextual nature of regional inequality in Canada.
20 CHAPTER THREE
RESEARCH DESIGN, METHODOLOGY AND DATA REQUIREMENTS 3.0 Introduction
The chapter discusses the processes and procedure of data gathering and data analysis in this study. Henceforth, the chapter provides details of the methods and design employed to achieve the broad goal and specific objectives of the study __ see chapter one. The last few decades have seen increasing attempts to foster collaborative approaches to spatial planning and optimal decision-making at different levels of abstraction and policy making. Geo-spatial analysis as a sub field of Geo-informatics applies statistical techniques to datasets which have geographical or geo-spatial underpinnings. Its applications include military and intelligence use, disaster and emergency management, public health, economic, regional and urban planning, forestry and climate science. The inter-relationship between human as a species and the environment is a complex process that requires knowledge, skills and information to make decisions. The application of GIScience enables us to build such model of the environment that makes it much simpler and easier to understand and hence, make informed decisions (Aronoff 1995).
Thus, to achieve the goal and objectives of this study, a mixed method (qualitative _ case study and quantitative _ Lorenz curve and Gini Coefficient and/ratio, geo-spatial techniques involving spatial regression, association, auto-correlation or correlation, agglomeration and clustering approach is employed. They are intended to supplement each other as both types of methods bear elements of rationality and non-rational influences (Pavlovskaya 2006).
3.1 Research Design and Methodology
The study adopts a mixed methods approach, utilizing quantitative and qualitative techniques. Thus, the study will make use of a mixed method (aspects of quantitative and qualitative) with specific reference to data gathering, analysis and interpretation (Denzin and Lincoln 1994). Moreover, the study is premised as a case study within the philosophical tool box of qualitative research approach and/or ontology. Such an approach allows for qualitative techniques for instance, a case study and content examination and analysis based on meaning, representation, and explanation of the systematic detection of underlying concepts and patterns in texts used as evidence (Miles and Huberman; Strauss, cited in
21 Pavlovskaya 2005). “Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trust-worthiness with hypothetical examples drawn from the area of end-of-life care” (Hsieh and Shannon 2005, p1277).
On the one hand, quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques (Given 2008). The purpose of this quantitative research was to use statistical tools in analysing identified and/or relevant data-sets. Moreover, the quantitative approach assisted the researcher to determine and compare association by using descriptive statistics such as percentage analysis, and measure of central tendency (averages).
As a result, the quantitative research approach also helped in offering fundamental connection between the literature review and statistical expression of quantitative relationships. This approach was hence considered because it provides much deeper understanding in addressing the research goal, problem and associated questions.
Furthermore, the basis for choosing the quantitative approach was to allow the researcher to tests and validate already constructed theories about how and why this study is needed (Creswell, 2009).
The study used both approaches because a single approach has weaknesses (Creswell, 2002). In case, only quantitative methods were used, study would have been limited by close- ended form inquiry and questions that required for instance, five-point Likert scale type responses and if only qualitative methods were used, results could not be analysed statistically and therefore, cannot be generalized to provide reliable research results (Creswell, 2002).
The quantitative aspect is grounded within a post-structuralist philosophy that enables the use of statistical, analytical, and modelling methods that incorporates the sensitivity of local context, spatial dimensions, complexity, dynamism, and openness of sociological
22 processes (Fortheringham; Poon, cited in Pavloyskaya 2005, p2005). Hence, within this approach, the study uses the Gini coefficient/ratio and Lorenz curve, Statistical Package for Social Science (SPSS version 23), XTools Pro,1 GIScience techniques and analysis2 (ArcGIS version 10.3.1) and Arcgis online involving thematic classification and query of feature attributes to examine spatial patterns and relationships between the spatial-temporal distribution of employment, income inequality as well as regionalism in Canada. This is the most commonly used measure of inequality.
The coefficient varies between 0, which reflects complete equality and 1, which indicates complete inequality (one person has all the income or consumption, all others have none). Graphically, the Gini coefficient can be easily represented by the area between the Lorenz curve as in figure 3 and the line of perfect equality (45%) divided by the area under this line. In the figure 3 below, the Lorenz curve maps the cumulative income share on the vertical axis against the distribution of the population on the horizontal axis (World Bank 2012).
Figure 3: The Lorenz Curve
Source: (World Bank 2012).
1 XTools Pro: is a comprehensive set of useful vector spatial analysis, shape conversion, and table management tools for ArcGIS that greatly enhances ArcGIS functionality and allows ArcGIS users to get to a new level of efficiency and performance.
2 GIScience techniques and analysis: spatial regression; geographically weighted regression (GWR);
association, auto-correlation or correlation; agglomeration and clustering.
23 From the above figure _2, the Gini coefficient can be calculated as G = A/(A + B).
Mathematically, the Gini coefficient is defined and thus, formulated as:
Where F(y) is the cumulative probability distribution of income
µ
is its mean and y * is its upper limit (Dorfmann 1979).The mixed methods approach ensures that the weaknesses in quantitative approach to some extent are covered by the qualitative approach when they are used together for a study which requires exploration, description and causal explanation (Creswell 1995; Moon and Moon 2004). As stated in the objectives of the study, there is the need to explore, investigate and analyse public policies that will maintain an adequate standard of living for Federal employees and thus, minimise regional income disparities. Also, the researcher will investigate and analyse the extent of spatial distribution of income in-equality relevant for the sustenance of the Canadian Federation.
There has been several philosophical and methodological arguments about the use of mixed methods of inquiry (Moon and Moon 2004; Jones 2004; Creswell et al 2004; Yu 2004). The main argument against the use of mixed methods points to the issues of it not being wholly positivist or wholly interpretive in nature (Moon and Moon 2004). Similarly, Scandura and Williams (2000), and Mingers (2001) have stated that the mixed method is rarely used in research reports and journal articles. However, Creswell (2004) suggests that the use of the approach has to do with the superiority of methodological divide debates between most researchers who would like to hold allegiance to either quantitative methods on one hand, or those who would like to be referred to as qualitative practitioners on the other hand.
A number of proponents for mixed methods (Jones 2004; Lyncy 1991; Yu 2004) have equally argued that triangulation between quantitative and qualitative methods produces better result and outcomes. Yu (2004) thus, builds on the arguments of Philips and Burbules (2000) that „pro-observation‟ does not always lead to realism stance and that, quantitative research methodology is not always objective. He is in support of the use of variety of methods and the use of triangulation when necessary. Moon and Moon (2004) brings forth, successful examples of mixed methods and in studies such as Phenomenography (an approach grounded in the interpretivist paradigm, where personal interviews are blended with
24 large scale quantitative data for analysis) employed by Ference Marton and Noel Entwistle as cited in Jones (2004). The application of mixed methods to this study is believed to add up to the strength of the approach and moreover, help to thoroughly, and appropriately deal with the problem being investigated.
3.2 Research Design and Data Analysis
The population for this study is heterogeneous, comprising units of different characteristics. The study will use secondary data and information of Gross Domestic Product (GDP), population, incomes and employment figures of Federal, Provincial, Territory and Metropolitan areas within Canada. Socio-economic and panel datasets between 2009 – 2013 on GDP, employment and population were obtained from Statistics Canada __
Tables 2 - 6. Respective shapefiles3 (Federal, Provincial/Territory and Municipality) are obtained from GeoGratis,4 the University of British Columbia,5 the open-source DIVA-GIS,6 and the Statistics Canada.7
3 Natural Resources Canada: North American Datum 1983 (NAD83) _ In 1990, Canada officially made a change in its National Topographic System (NTS) by officially adopting NAD83 as its new Geodetic Reference System 1980 (GRS80) and/or the Canadian Spatial Reference System (CSRS): http://www.nrcan.gc.ca/earth-sciences/geography/topographic-information/maps/9791
4 GeoGratis – maintained by Natural Resources Canada: http://geogratis.gc.ca/geogratis/en/search;
5 University of British Columbia: http://gis.ubc.ca/data-sources-2/canada/;
6 DIVA – GIS: http://www.diva-gis.org/gdata
7 Statistics Canada: http://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-i-eng.cfm?year=09
25 Table 2: Gross Domestic Product _ Federal _ Provincial_ Territory: 2009- 2013
2009 2010 2011 2012 2013
Canada1 1,645,974 1,567,007 1,662,757 1,760,011 1,819,967 Newfoundland
and Labrador 31,434 24,972 29,063 33,501 33,817
Prince Edward
Island 4,767 4,947 5,202 5,384 5,547
Nova Scotia 35,467 35,254 37,073 38,147 38,397
New Brunswick 28,422 28,825 30,082 31,291 31,543
Quebec 313,595 315,531 329,670 345,287 357,859
Ontario 604,282 595,433 629,500 654,715 674,485
Manitoba 51,920 50,636 52,896 55,169 58,245
Saskatchewan 67,695 60,326 63,379 73,436 77,929
Alberta 295,126 246,717 270,100 298,049 311,898
British Columbia 203,951 195,966 205,996 215,148 219,994
Yukon 1,995 2,107 2,313 2,376 2,631
Northwest
Territories 5,015 4,020 4,787 4,718 4,675
Nunavut 1,608 1,548 1,936 2,036 2,198
Outside Canada 697 724 761 755 747
Source: Statistics Canada (2014).
Table 3: Employment by Province and Territory: 2009- 2013
2009 2010 2011 2012 2013
Canada 14,374,928 14,459,906 14,686,154 14,955,806 15,159,989 Newfoundland and
Labrador 190,521 194,681 199,456 206,271 209,739
Prince Edward Island 62,444 63,385 63,672 63,116 61,926 Nova Scotia 396,306 401,934 401,391 397,356 396,476 New Brunswick 308,688 314,929 312,337 308,069 303,678 Quebec 3,311,030 3,331,588 3,366,076 3,401,750 3,421,588 Ontario 5,502,379 5,536,785 5,608,566 5,696,223 5,753,118
Manitoba 549,973 550,283 556,370 566,346 578,599
Saskatchewan 432,754 434,772 445,655 461,771 471,668 Alberta 1,711,850 1,724,981 1,793,845 1,890,890 1,956,899 British Columbia 1,853,564 1,849,177 1,879,867 1,903,963 1,946,362
Yukon 19,017
19,371 19,959 20,814 20,793
Northwest Territories 26,296 27,204 27,724 27,954 27,761
Nunavut 10,106 10,816 11,236 11,282 11,300
Source: Statistics Canada (2014).
26 Table 4: Federal Government Employment by Metropolitan Areas: 2009- 2013
2009 2010 2011 2012 2013
Total census metropolitan
areas 289,555 297,229 316,826 315,274 315,455
St. John's (N.L.)
4,941 4,899 5,198 5,094 5,046
Halifax (N.S.)
16,998 16,964 17,376 17,381 17,359
Moncton (N.S.) 2,990 2,950 3,224 3,179 3,163
Saint John
(N.B.) 996 1,111 1,084 1,031 1,024
Saguenay
(Que.) 2,399 2,327 2,767 2,667 2,706
Québec (Que.) 14,256 14,306 15,301 14,777 14,240
Montréal (Que.) 22,636 23,576 23,782 23,722 23,689
Trois-Rivières
(Que.) 417 454 481 446 457
Sherbrooke
(Que.) 1,180 1,197 1,247 1,145 1,178
Ottawa- Gatineau (Ont.-
Que.) 119,177 124,841 132,600 134,095 135,865
Kingston (Ont.) 4,455 4,624 6,821 6,877 6,972
Peterborough
(Ont.) 340 326 326 321 322
Oshawa (Ont.) 298 295 268 241 275
Toronto (Ont.) 21,015 21,367 23,121 22,609 22,346
Hamilton (Ont.) 2,885 2,909 3,207 3,042 3,088
St. Catharines-
Niagara (Ont.) 1,168 1,257 1,192 1,212 1,183
Kitchener- Cambridge-
Waterloo (Ont.) 1,005 1,094 1,123 1,181 1,228
Brantford (Ont.) 287 314 347 202 180
Guelph (Ont.) 593 590 626 556 578
London (Ont.) 2,565 2,316 2,831 2,607 2,543
Windsor (Ont.) 2,074 2,123 2,192 2,059 1,975
Barrie, Ontario 194 214 242 204 204
Greater Sudbury
(Ont.) 2,049 2,083 2,246 2,108 2,194
Thunder Bay
(Ont.) 863 809 974 964 938
Winnipeg (Man.) 11,814 12,042 12,592 12,389 11,973
Regina (Sask.) 3,326 3,263 3,358 3,224 3,103
Saskatoon
(Sask.) 2,484 2,466 2,605 2,584 2,582
Edmonton
(Alta.) 12,989 13,272 14,364 14,107 13,921
Calgary (Alta.) 4,183 4,324 4,631 4,495 4,382
Kelowna (B.C.) 360 369 373 362 837
Abbotsford-
Mission (B.C.) 1,536 1,499 1,605 1,642 1,679
Vancouver
(B.C.) 17,344 17,360 18,541 18,440 17,917
Victoria (B.C.)
9,738 9,688 10,181 10,311 10,308
Source: Statistics Canada (2014).
