extfieldsofstudy: Geography Medicine

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Mapping Environmental Inequalities Relevant for Health for Informing Urban Planning Interventions—A Case Study in the City of Dortmund, Germany

Spatial differences in urban environmental conditions contribute to health inequalities within cities. The purpose of the paper is to map environmental inequalities relevant for health in the City of Dortmund, Germany, in order to identify needs for planning interventions. We develop suitable indicators for mapping socioeconomically-driven environmental inequalities at the neighborhood level based on published scientific evidence and inputs from local stakeholders. Relationships between socioeconomic and environmental indicators at the level of 170 neighborhoods were analyzed continuously with Spearman rank correlation coefficients and categorically applying chi-squared tests. Reclassified socioeconomic and environmental indicators were then mapped at the neighborhood level in order to determine multiple environmental burdens and hotspots of environmental inequalities related to health. Results show that the majority of environmental indicators correlate significantly, leading to multiple environmental burdens in specific neighborhoods. Some of these neighborhoods also have significantly larger proportions of inhabitants of a lower socioeconomic position indicating hotspots of environmental inequalities. Suitable planning interventions mainly comprise transport planning and green space management. In the conclusions, we discuss how the analysis can be used to improve state of the art planning instruments, such as clean air action planning or noise reduction planning towards the consideration of the vulnerability of the population.

Introduction
The World Health Organization (WHO) and UN Habitat (United Nations Human Settlements Programme) acknowledge in their 2010 report The Hidden Cities that "Where in a city you live and how the city is governed can determine whether or not one benefits from city living" [1]. Such spatial differences in urban areas resulting from environmental conditions in which people grow, live, work and age contribute to health inequalities within cities [2].
In the literature, two explanations for spatial health inequalities are discussed. First, at the level of aggregated data, health differences between populations of different areas can be attributed to to monitor social determinants of health and to develop suitable interventions. Fairburn and Smith [30] developed an indicator-based approach including health inequalities from an integrated perspective of environmental justice and quality of life for the region of South Yorkshire. The need for information on health disparities for small geographic areas is stressed by Rothenberg et al. [31], who developed an urban health index for census tracts for the City of Atlanta, U.S., based on indicators for seven health determinants. Corburn and Cohen [1] discuss how the development of such indicators can act as an instrument for urban health governance, as it helps to identify relevant health policy issues, to generate standards for health equity issues and to improve public accountability and transparency. In addition to their function of assessing and monitoring health inequalities, such urban health equity indicators may also be used to support local stakeholders in identifying planning interventions addressing health inequalities [32].
This paper aims to identify socioeconomically-driven environmental inequalities relevant for health in order to determine the available options for planning interventions from a city-wide perspective by means of neighborhood indicators. To illustrate the applicability of the approach, we calculated all neighborhood indicators for the city of Dortmund, Germany, as the case study area, representing typical medium-to large-sized cities facing significant differences in living and environmental quality, as well as socio-structural composition. Building upon the Spatial Urban Health Equity Indicators (SUHEI) framework [33], we first developed suitable indicators, reflecting problematic environmental health-related conditions in Dortmund. We then applied statistical correlation analysis in order to determine associations between socioeconomic indicators and environmental burdens and resources at the neighborhood level in order to identify health-related inequalities between 170 neighborhoods of Dortmund. The results could assist with the targeting of appropriate planning-related interventions. Our hypothesis is that in Dortmund, people living in neighborhoods with a low socioeconomic position are disproportionally more exposed to negative environmental conditions affecting their health than people living in neighborhoods with a higher socioeconomic position.

Materials and Methods
The Spatial Urban Health Equity Indicators (SUHEI) framework [33] allows us to map exposure to environmental factors affecting health determined by various drivers and pressures. The purpose of the model is to map areas showing a disproportionate exposure of certain socioeconomic groups to environmental burdens in order to identify appropriate planning interventions. Building upon the ideas of Morris et al. [34], who added social context variables to the Driving force-Pressure-State-Exposure-Effect-Action (DPSEEA) framework [28] characterizing the population, the SUHEI framework combines elements of environmental health-related cause-effect indicator frameworks [35] with common (environmental) health equity models [14].
The SUHEI framework distinguishes driver, state and exposure indicators of determinants of health, captured on multiple spatial scales ( Figure 1). Drivers, appearing on various scales from national to sub-local, represent factors that motivate and push the environmental or social processes involved, such as increasing traffic density, public spending or urban development. State indicators, reflecting the current status, map concrete environmental stressors and resources (burdens and benefits), as well as relevant social context variables, both at the city and neighborhood level. Finally, exposure indicators relate the environmental state to social context indicators in order to spatially target health inequalities, e.g., neighborhoods where a high level of noise-related impacts matches with a disproportionately higher share of unemployed or deprived inhabitants. By combining multiple environmental burdens and benefits, cumulative environmental impacts may also be related to social context factors [36]. Exposure indicators, mapped at the neighborhood level, are intended to guide planners in identifying hotspots where specific action needs to be taken, while state and driver indicators help to define what kind of measures are to be taken. The purpose of the indicator model is to examine geographic patterns and identify hotspots of environmental socioeconomically-driven health inequalities. Therefore, state, social context and exposure indicators included in the model are measured in a spatially-explicit manner on a neighborhood scale. In this context, neighborhoods are understood as determining the availability of and access to health-relevant resources in a geographically-defined area [3]. The mapping of indicators at the neighborhood level is indispensable for two reasons. Firstly, neighborhoods are a typical size for health-related urban planning interventions. Secondly, taking into account that environmental justice issues are very sensitive to scale [37], neighborhoods are sufficiently small in size and homogeneous in terms of their socio-spatial structure to allow us to derive evidence of disproportionate impacts.
The city of Dortmund is divided into 170 neighborhoods ranging from 0.1 km 2 to 6.2 km 2 in size. These neighborhoods can be distinguished into urban and suburban neighborhoods, based on a number of socio-demographic, economic, mobility and housing variables. A cluster analysis carried out by the city administration [38] distinguishes 3 different types of urban clusters containing in total 80 neighborhoods (two clusters of around 150 inhabitants/ha population density, one cluster showing a population density of 350 inhabitants/ha) and 2 types of suburban clusters with a total of 90 neighborhoods both having a population density of about 80-90 inhabitants per ha.

Case Study Area
The city of Dortmund (280 km 2 ) is located in the western part of Germany in the former coal mining and steel-producing, highly urbanized region of the Ruhr. The city is home to nearly 600,000 inhabitants [39]. As a result of the economic boost in the middle of the 20th century, Dortmund attracted a large number of migrant workers. Since the 1980s, Dortmund has been through and continues to go through a long-lasting economic transformation due to the closure of coal mines and steel production companies, resulting in a high unemployment rate (12.5%, in Germany 6.7% (2014)) [39,40]. This ongoing transformation process towards a business, trade and service-oriented local economy has resulted in a highly fragmented city revealing large socio-economic disparities.
The city is an archetypical example of many medium-to large-sized cities in Germany facing significant differences in living and environmental quality, as well as the socio-structural composition of its population after going through a long-lasting phase of socio-economic change. The outcomes of this process are urban land use patterns exhibiting a close proximity of residential, industrial and commercial structures, which may eventually bring benefits of increasing vitality and livability of the neighborhood, but are on the other hand typically associated with negative The purpose of the indicator model is to examine geographic patterns and identify hotspots of environmental socioeconomically-driven health inequalities. Therefore, state, social context and exposure indicators included in the model are measured in a spatially-explicit manner on a neighborhood scale. In this context, neighborhoods are understood as determining the availability of and access to health-relevant resources in a geographically-defined area [3]. The mapping of indicators at the neighborhood level is indispensable for two reasons. Firstly, neighborhoods are a typical size for health-related urban planning interventions. Secondly, taking into account that environmental justice issues are very sensitive to scale [37], neighborhoods are sufficiently small in size and homogeneous in terms of their socio-spatial structure to allow us to derive evidence of disproportionate impacts.
The city of Dortmund is divided into 170 neighborhoods ranging from 0.1 km 2 to 6.2 km 2 in size. These neighborhoods can be distinguished into urban and suburban neighborhoods, based on a number of socio-demographic, economic, mobility and housing variables. A cluster analysis carried out by the city administration [38] distinguishes 3 different types of urban clusters containing in total 80 neighborhoods (two clusters of around 150 inhabitants/ha population density, one cluster showing a population density of 350 inhabitants/ha) and 2 types of suburban clusters with a total of 90 neighborhoods both having a population density of about 80-90 inhabitants per ha.

Case Study Area
The city of Dortmund (280 km 2 ) is located in the western part of Germany in the former coal mining and steel-producing, highly urbanized region of the Ruhr. The city is home to nearly 600,000 inhabitants [39]. As a result of the economic boost in the middle of the 20th century, Dortmund attracted a large number of migrant workers. Since the 1980s, Dortmund has been through and continues to go through a long-lasting economic transformation due to the closure of coal mines and steel production companies, resulting in a high unemployment rate (12.5%, in Germany 6.7% (2014)) [39,40]. This ongoing transformation process towards a business, trade and service-oriented local economy has resulted in a highly fragmented city revealing large socio-economic disparities.
The city is an archetypical example of many medium-to large-sized cities in Germany facing significant differences in living and environmental quality, as well as the socio-structural composition of its population after going through a long-lasting phase of socio-economic change. The outcomes of this process are urban land use patterns exhibiting a close proximity of residential, industrial and commercial structures, which may eventually bring benefits of increasing vitality and livability of the neighborhood, but are on the other hand typically associated with negative environmental impacts [41]. Dortmund is characterized by strong social and ethnic segregation [42]. The city's divide into rather disadvantaged neighborhoods in the north and better off neighborhoods in the south is typical for the entire Ruhr region. Finally, Dortmund reveals significant differences in health outcomes. The average age at death in the districts of Dortmund in 2011 ranges from 66.3 years in the Nordstadt district to 76.3 years in the Hombruch district [43].

Dataset and Methods
In order to obtain an accurate comparison between neighborhoods, all indicators used in this study are mapped as proportions. Environmental state indicators are measured as impacted area as a percentage of the total neighborhood area, while social context indicators are calculated as the number of people showing certain attributes as a percentage of the total population in the neighborhood (Supplementary Materials, Table S1). Taking the entire area of the neighborhood into account acknowledges the fact that the inhabitants also make use of the non-built-up areas, such as parks, roads, public spaces, e.g., for walking, recreation and working, and they are also exposed to environmental burdens in these areas. The focus on neighborhoods as spatial units of the analysis accords with the availability of socio-economic data acknowledging that this is not necessarily equal to the area of environmental impacts. In order to indicate the need for intervention, environmental indicators are calculated as the proportion of areas where the level of detrimental environmental factors exceeds defined threshold values. The study makes use of the datasets as given in Tables 1  and 2 to calculate the selected indicators. The GIS and census data used are provided, unless otherwise noted, by the City of Dortmund. All census data of socio-economic variables are measured at the neighborhood level. All indicators are calculated using ArcGIS 10.3 (Redlands, CA, USA).  Spearman rank correlation coefficients between socioeconomic neighborhood variables were calculated in order to identify a representative indicator describing neighborhood socioeconomic position. Relationships between neighborhood socioeconomic position and environmental variables were analyzed on a continuous scale with Spearman rank correlation coefficients. Finally, quartiles of neighborhood socioeconomic variables and environmental variables were generated (Supplementary Materials, Table S2). Relationships between categorical socioeconomic and environmental variables were analyzed with chi-squared tests. All statistical analyses were performed using SAS statistical software package Version 9.4 (SAS Institute, Cary, NC, USA). In order to map cumulative environmental burdens, categorical environmental variables categorized in quartiles from 1 (low) to 4 (very high) were added. In doing so, all indicators were weighted equally. The indicator green areas indicating an environmental benefit were added in reverse order.

Indicator Development
The selection of indicators for mapping socioeconomically-driven environmental health inequalities in Dortmund was problem-driven and context-specific following a deductive indicator development approach [47]. The selection was guided by recent environmental health studies, theories and conceptual models, as well as the input of stakeholders from the case study area. Selection criteria were the indicators' relevance for environmental health inequalities, their recognition as a health problem by local stakeholders and the possibility of their being influenced by urban planning.
The WHO [48] names physical activity, social impacts, air pollution, noise exposure and unintentional injuries as the main determinants of health in urban settings. From these, the authors identify physical activity, air pollution and noise impacts as the main aspects that may be influenced by urban planning. Studies analyzing socioeconomically-driven environmental health inequalities in Germany based on aggregated data often use similar datasets and indicators, partly because they offer an accurate reflection of the particular problems of many German cities, partly because the datasets are often available for municipalities. Lakes et al. [49] used transport-induced noise data for measuring environmental burdens and a vegetation index as an indicator for environmental benefits in Berlin. As social context factors, the same authors used a highly aggregated index, including factors, such as unemployment, social welfare recipients, child poverty and inhabitants under the age of 18 with an immigrant background [50]. Raddatz and Mennis [17] identify higher proportions of foreigners, as well as poor people as the main factors determining environmentally-unjust situations in cities with respect to the location of toxic release facilities. Riedel et al. [51] used two factors, the unemployment rate and mean income, to determine spatial health inequalities in the Ruhr area.
In order to understand stakeholder preferences and priorities, workshops with local stakeholders were conducted in 2014, in which the main environmental problems, as well as typical social context variables indicating inequalities in the city of Dortmund were identified. Due to the industrial development described above, the built environment, particularly in the more deprived neighborhoods, is characterized by historically-developed land use patterns exhibiting a close proximity of residential areas with other land uses, such as industrial and commercial [52]. Such mixed land use structures, though they may eventually have benefits in terms of increasing the vitality and livability of the neighborhood, are typically associated with negative environmental impacts [41]. Accordingly, noise impacts and limited air quality were identified as the main environmental stressors, while access to good quality green areas for the purposes of recreation, etc., was limited in some parts of the city. With respect to social context variables, the city has, due to its economic history, a significantly higher share of population having a background of migration, compared to the German average. In Germany, social status is still very much associated with citizenship and ethnic background [53,54]. Consequently, indicators, such as the proportion of the population with a background of migration and the proportion of families being welfare recipients, are suitable indicators of the social context of inequalities. The following indicators are therefore used in this study (Table 3). The noise indicator is calculated as the percentage of the total area showing a noise impact of more than 55 db(A) resulting from any of the 5 noise sources included in this study (airport, tram, train, cars, industry). Various studies assume a significant health impact from noise exposure of more than 55 db(A) [55] (pp. [23][24][25]. The actual noise impacts might be even higher in some areas where various noise impacts overlap, producing multiple noise impacts. Air quality in Dortmund is measured using the two most relevant substances, nitrogen dioxide (NO 2 ) and particulate matter (PM 10 ). As for noise emissions, the indicators are calculated as the percentage of the neighborhood where air pollution is above a certain threshold value. Germany's Federal Immission Control Act of Germany (Bundes-Immissionsschutzgesetz (BImschG)) defines for both factors a threshold value of 40 µg/m 2 [56], while other studies suggest from a health perspective much lower values of, e.g., 20 µg/m 3 for PM 10 , as well as various intermediate values [57]. Due to the fact that modelled air quality parameters in Dortmund for large parts of the entire city range between 21 and 40 µg/m 3 , we have taken a threshold value of 30 µg/m 3 for NO 2 and 25 µg/m 3 for PM 10 into account based on the distribution of values over the city in order to determine intra-urban differences. Calculating the indicator as a neighborhood-wide average means that in specific locations, e.g., close to main roads, values might even be much higher.
The indicator green areas summarizes the proportion of green areas as a percentage of the entire neighborhood. As such, the indicator focuses on the availability of green areas, while aspects of the accessibility and quality of the area are not taken into account. All green areas and parks, as well as forest areas having a size of more than 1 ha, being the minimal size for having relevant functions, are included in the indicator calculation. Assuming an accessibility of green areas within a 400-m Euclidian distance [58], green areas in adjacent neighborhoods are also taken into account for the indicator calculation.
All social context indicators are calculated as a percentage of the total population in the neighborhood. The two indicators of employment and welfare measure complementary issues of interest, namely the unemployed people plus the welfare subsidies paid for the non-working share of the population, namely elderly, children and young people. Hence, the indicator of socioeconomic disadvantage is an aggregate of both indicators, indicating the total number of inhabitants with welfare needs.

Results
In the following section, socioeconomically-driven environmental inequalities relevant for health in the neighborhoods of Dortmund will be analyzed based on the indicators discussed above. In doing so, we first analyze associations between social context indicators and their spatial distribution. We then carry out a similar analysis of the environmental indicators, including an analysis of multiple environmental burdens. Finally, we consider the exposure of neighborhoods with significant proportions of socioeconomically-disadvantaged residents and higher levels of environmental burdens, in order to identify spatially the hotspots of environmental health-related inequalities.

Social Context Indicators
Regarding the social context determinants of health, we see a strong positive correlation between all four selected indicators ( Table 4). The indicator migration is strongly correlated with both the unemployment and the welfare indicator, confirming the observation that neighborhoods with a higher proportion of residents with a background of migration are more likely to have a higher proportion of socioeconomically-disadvantaged residents. The combined indicator of socioeconomic disadvantage, that integrates working age people with the elderly and children, also correlates strongly with the migration indicator, confirming that a proportion of deprived population exists over all generations. As the combined indicator of socioeconomically disadvantaged is the most comprehensive social context indicator, we use this one in the exposure analysis below. The spatial distribution of the social context indicator socioeconomically disadvantaged on the basis of neighborhoods ( Figure 2) shows that the northern part of Dortmund has a much higher share of socioeconomically-disadvantaged neighborhoods than the southern part. Almost all urban neighborhoods in the northern half, i.e., north of the central business district (CBD), fall into categories of a high to a very high proportion of socioeconomically disadvantaged. South of the CBD, only one cluster of neighborhoods in the district of Hörde is very highly disadvantaged. In contrast, the cluster of urban neighborhoods directly south of the CBD shows a medium to low share of socioeconomically disadvantaged in all neighborhoods.
However, the neighborhood having in absolute terms the highest share of socioeconomically disadvantaged is the neighborhood of Clarenberg in the district Hörde south of the inner city with a share of 36.6 percent of all people receiving some kind of welfare aid. The vast majority of other high scoring neighborhoods with values of around 30% are concentrated in the district Innenstadt Nord north of the CBD. The majority of neighborhoods scoring low in terms of socioeconomically disadvantaged are concentrated in the suburban districts of the urban periphery. However, the neighborhood having in absolute terms the highest share of socioeconomically disadvantaged is the neighborhood of Clarenberg in the district Hörde south of the inner city with a share of 36.6 percent of all people receiving some kind of welfare aid. The vast majority of other high scoring neighborhoods with values of around 30% are concentrated in the district Innenstadt Nord north of the CBD. The majority of neighborhoods scoring low in terms of socioeconomically disadvantaged are concentrated in the suburban districts of the urban periphery.

Environmental Indicators
The relationships between the various environmental indicators included in the study show a slightly less homogeneous, but no less significant picture ( Table 5). The noise impact indicator correlates moderately positively with the two air quality indicators of PM10 and NO2, i.e., neighborhoods having a high impact in terms of noise also show significant levels of NO2 and PM10. This is not particularly surprising, as all three state indicators are at least partially affected by the same driver, namely motorized transport. While four of the five sources of noise combined in the indicator are transport related (car, tram, train, airport), 54.3% of all NO2 emissions and 60% of PM10 emissions are related to road traffic [45]. All three indicators correlate slightly less and significantly negatively with the green indicator, indicating that neighborhoods having an impact of air, as well as noise pollution also suffer from having a lower share of green areas available.

Environmental Indicators
The relationships between the various environmental indicators included in the study show a slightly less homogeneous, but no less significant picture ( Table 5). The noise impact indicator correlates moderately positively with the two air quality indicators of PM 10 and NO 2 , i.e., neighborhoods having a high impact in terms of noise also show significant levels of NO 2 and PM 10 . This is not particularly surprising, as all three state indicators are at least partially affected by the same driver, namely motorized transport. While four of the five sources of noise combined in the indicator are transport related (car, tram, train, airport), 54.3% of all NO 2 emissions and 60% of PM 10 emissions are related to road traffic [45]. All three indicators correlate slightly less and significantly negatively with the green indicator, indicating that neighborhoods having an impact of air, as well as noise pollution also suffer from having a lower share of green areas available. The maps shown in Figure 3 exemplify spatial variations concerning the analyzed indicators. Regarding the availability of green areas the most southerly neighborhoods do somewhat better (Figure 3a). In particular, the suburban neighborhoods are much better equipped with green areas, here mainly in the form of urban forests. However, various neighborhoods to the north of the CBD also seem to benefit from a good supply of green areas, e.g., in terms of parks. With respect to NO 2 , the entire central area is very badly affected (Figure 3b), which is very similar to other, comparable German cities, e.g., Berlin [59]. In particular, the neighborhoods around the main highway B1, which cuts through the central areas of Dortmund more or less straight from west to east, are highly impacted. With respect to PM 10 , the situation is similar (Figure 3c), but with a stronger focus in the area to the northwest of the CBD. This area is where the remaining industrial areas in Dortmund are concentrated. Finally, the distribution of noise impacts (Figure 3d) indicates a much more diversified and heterogeneous picture, which is similar to patterns disclosed in other German cities [49]. The industrial neighborhoods west of the CBD stand out as having high noise impacts, as do the neighborhoods along the main highway B1 and A45 in the west (going north to south). Additionally, various urban and suburban neighborhoods reveal high to very high noise impact levels due to local noise sources. (Figure 3a). In particular, the suburban neighborhoods are much better equipped with green areas, here mainly in the form of urban forests. However, various neighborhoods to the north of the CBD also seem to benefit from a good supply of green areas, e.g., in terms of parks. With respect to NO2, the entire central area is very badly affected (Figure 3b), which is very similar to other, comparable German cities, e.g., Berlin [59]. In particular, the neighborhoods around the main highway B1, which cuts through the central areas of Dortmund more or less straight from west to east, are highly impacted. With respect to PM10, the situation is similar (Figure 3c), but with a stronger focus in the area to the northwest of the CBD. This area is where the remaining industrial areas in Dortmund are concentrated. Finally, the distribution of noise impacts (Figure 3d) indicates a much more diversified and heterogeneous picture, which is similar to patterns disclosed in other German cities [49]. The industrial neighborhoods west of the CBD stand out as having high noise impacts, as do the neighborhoods along the main highway B1 and A45 in the west (going north to south). Additionally, various urban and suburban neighborhoods reveal high to very high noise impact levels due to local noise sources.
Areas of multiple burdens are neighborhoods where the majority of environmental factors included in the study score high. Figure 4 shows the combination of the four selected environmental indicators. The factor green, being shown above as an environmental benefit, is used here in reverse order indicating a neighborhood of high availability of green with a score of four. The results show that the neighborhoods north of the CBD have the highest levels of multiple environmental burdens, together with the industrialized area west of the city. The neighborhoods scoring highest northeast of the inner city are all areas highly affected by motorized transport.   Areas of multiple burdens are neighborhoods where the majority of environmental factors included in the study score high. Figure 4 shows the combination of the four selected environmental indicators. The factor green, being shown above as an environmental benefit, is used here in reverse order indicating a neighborhood of high availability of green with a score of four. The results show that the neighborhoods north of the CBD have the highest levels of multiple environmental burdens, together with the industrialized area west of the city. The neighborhoods scoring highest northeast of the inner city are all areas highly affected by motorized transport.

Exposure: Environmental Inequalities Relevant for Health
In order to identify hotspots of socioeconomically-driven environmental health inequalities, we relate the social context indicator of socioeconomically disadvantaged to the various relevant environmental indicators we have measured ( Table 6). The social context indicator correlates moderately negatively with the green indicator, indicating that in areas with a higher share of socioeconomically-disadvantaged residents, less green areas are available. Further, we see a

Exposure: Environmental Inequalities Relevant for Health
In order to identify hotspots of socioeconomically-driven environmental health inequalities, we relate the social context indicator of socioeconomically disadvantaged to the various relevant environmental indicators we have measured ( Table 6). The social context indicator correlates moderately negatively with the green indicator, indicating that in areas with a higher share of socioeconomically-disadvantaged residents, less green areas are available. Further, we see a moderately positive correlation between socioeconomically disadvantaged and the two air quality indicators of PM 10 and NO 2 . All three associations support the hypothesis that the neighborhoods of Dortmund containing higher proportions of socioeconomically-disadvantaged residents suffer from a lower environmental quality compared to the better off areas. Only the noise indicator does not significantly correlate with the social context, indicating that noise is a ubiquitous problem in Dortmund that does not distinguish between the affluent and the disadvantaged. The disproportionate supply of environmental goods and bads between neighborhoods of Dortmund having a different socioeconomic profile becomes obvious when considering the spatial pattern of the distribution. Figure 5 relates neighborhoods categorized into four classes according to their share of disadvantaged inhabitants to classes of environmental quality using the PM 10 indicator as an example. Almost 60% of the neighborhoods having a low PM 10 level also have a low to medium level of deprivation. In total, 47 neighborhoods of 170 score low to medium for both indicators, PM 10 and socioeconomically disadvantaged. On the other hand, almost 65% of the neighborhoods showing a very high level of PM 10 also have a high to very high share of socioeconomically-disadvantaged inhabitants. The difference in proportions is significant (p-value < 0.01). Relationships between neighborhood deprivation and NO 2 and green areas were significant, as well (p-value < 0.01). Neighborhoods with a higher proportion of socioeconomically-disadvantaged residents were more exposed to higher NO 2 levels and had less availability of green space. There was no significant difference in proportions between deprivation and noise. moderately positive correlation between socioeconomically disadvantaged and the two air quality indicators of PM10 and NO2. All three associations support the hypothesis that the neighborhoods of Dortmund containing higher proportions of socioeconomically-disadvantaged residents suffer from a lower environmental quality compared to the better off areas. Only the noise indicator does not significantly correlate with the social context, indicating that noise is a ubiquitous problem in Dortmund that does not distinguish between the affluent and the disadvantaged. The disproportionate supply of environmental goods and bads between neighborhoods of Dortmund having a different socioeconomic profile becomes obvious when considering the spatial pattern of the distribution. Figure 5 relates neighborhoods categorized into four classes according to their share of disadvantaged inhabitants to classes of environmental quality using the PM10 indicator as an example. Almost 60% of the neighborhoods having a low PM10 level also have a low to medium level of deprivation. In total, 47 neighborhoods of 170 score low to medium for both indicators, PM10 and socioeconomically disadvantaged. On the other hand, almost 65% of the neighborhoods showing a very high level of PM10 also have a high to very high share of socioeconomically-disadvantaged inhabitants. The difference in proportions is significant (p-value < 0.01). Relationships between neighborhood deprivation and NO2 and green areas were significant, as well (p-value < 0.01). Neighborhoods with a higher proportion of socioeconomically-disadvantaged residents were more exposed to higher NO2 levels and had less availability of green space. There was no significant difference in proportions between deprivation and noise. The visualization of the exposure in the form of relationships between social context indicators and environment indicators allows the spatial targeting of hotspots of environmental inequalities relevant for health. In Figure 6, the map of multiple environmental burdens is matched with four classes of socioeconomic disadvantage ranging from low to very high (in red dots). Four hotspots of environmental inequalities having the highest proportions of socioeconomically disadvantaged, as well as of cumulative environmental burdens can be identified from this map; the four neighborhoods of the Nordstadt directly north of the CBD, a cluster of neighborhoods west of the inner city, which has already been identified as a rather industrial area, and the neighborhood of Alt-Scharnhorst northeast Socioeconomic disadvantage Figure 5. Proportion of socioeconomically-disadvantaged inhabitants per neighborhood in quartiles vs. the proportion of area per neighborhood impacted by PM 10 above the 25 µg/m 3 annual mean in quartiles.
The visualization of the exposure in the form of relationships between social context indicators and environment indicators allows the spatial targeting of hotspots of environmental inequalities relevant for health. In Figure 6, the map of multiple environmental burdens is matched with four classes of socioeconomic disadvantage ranging from low to very high (in red dots). Four hotspots of environmental inequalities having the highest proportions of socioeconomically disadvantaged, as well as of cumulative environmental burdens can be identified from this map; the four neighborhoods of the Nordstadt directly north of the CBD, a cluster of neighborhoods west of the inner city, which has already been identified as a rather industrial area, and the neighborhood of Alt-Scharnhorst northeast of the inner city. While other areas scoring highest on multiple burdens show a lower level of deprivation, areas with the highest level of deprivation like the northern parts of the Nordstadt or the District of Hörde southeast of the inner city score modestly in terms of the environmental quality.
of the inner city. While other areas scoring highest on multiple burdens show a lower level of deprivation, areas with the highest level of deprivation like the northern parts of the Nordstadt or the District of Hörde southeast of the inner city score modestly in terms of the environmental quality.

Discussion
The results reveal that certain neighborhoods of Dortmund facing significantly higher impacts from environmental burdens also house significantly larger proportions of inhabitants of lower socioeconomic position. Similar results have been obtained from other studies in comparable cities and regions of Germany. Riedel et al. [51] (p. 88), e.g., determine for the Ruhr region, which Dortmund is a part of, "that individuals with social risks associated with a low education or a low income are more frequently affected by chemico-physical risks", in this case air pollutants (PM2.5) and noise exposure. The geographical pattern of environmental inequalities as derived in this study is comparable to findings from Shrestha et al. [60] for the same city, who locate strong inequalities predominantly in the northern part of the urban core of the city.
Measuring environmental inequalities relevant for health in the form of indicators represents a useful and easy to replicate approach, especially as working with indicators is common practice in both public health and urban planning [61]. In particular, developing small-scale indicators at a neighborhood level allows the detection of areas of significant inequalities spatially. Moreover, the development of the indicators is based on data that municipalities often collect regularly or that are relatively easy to obtain. Therefore, the approach presented here is relatively easy to replicate. Finally, defining indicators based on existing environmental standards and threshold values, e.g., Figure 6. Hotspots of socioeconomically-driven environmental health inequalities: multiple environmental burdens and the proportion of socioeconomically-disadvantaged residents per neighborhood.

Discussion
The results reveal that certain neighborhoods of Dortmund facing significantly higher impacts from environmental burdens also house significantly larger proportions of inhabitants of lower socioeconomic position. Similar results have been obtained from other studies in comparable cities and regions of Germany. Riedel et al. [51] (p. 88), e.g., determine for the Ruhr region, which Dortmund is a part of, "that individuals with social risks associated with a low education or a low income are more frequently affected by chemico-physical risks", in this case air pollutants (PM 2.5 ) and noise exposure. The geographical pattern of environmental inequalities as derived in this study is comparable to findings from Shrestha et al. [60] for the same city, who locate strong inequalities predominantly in the northern part of the urban core of the city.
Measuring environmental inequalities relevant for health in the form of indicators represents a useful and easy to replicate approach, especially as working with indicators is common practice in both public health and urban planning [61]. In particular, developing small-scale indicators at a neighborhood level allows the detection of areas of significant inequalities spatially. Moreover, the development of the indicators is based on data that municipalities often collect regularly or that are relatively easy to obtain. Therefore, the approach presented here is relatively easy to replicate. Finally, defining indicators based on existing environmental standards and threshold values, e.g., 55 L den db(A) of noise, allows the application of the model in different political and legislative contexts and offers the possibility of testing different scenarios by varying given standards.
Corburn rightly points to the limitation of indicator-based approaches, saying that "cross sectional measures of single built and social environmental features of urban neighborhoods, tend to ignore the cascading and relational effects of inequalities in urban areas" [61] (p. 23). In a similar manner, it needs to be acknowledged that the indicators used do not necessarily measure all aspects relevant for health. The indicator green areas for instance focusses on the availability of green areas, while the aspects of the accessibility and quality of the area are not taken into account in this analysis. Finally, the study does not include any indicators on the availability of healthcare services and infrastructures, such as hospitals or welfare facilities, which also influence health-related inequalities [62].
Further limitations of the study are that by summarizing several environmental indicators, homogenous impacts on health are assumed per unit, and by using aggregated data for environmental factors, we cannot link levels of exposure to where people live within a neighborhood. Another limitation is the assumption of the spatial homogeneity of the relationships between variables across the study area [63]. Various scholars have recently applied methods like geographically-weighted regression (GWR) to investigate spatial variations in the relationships between predictors, such as environmental or socioeconomic factors, and health outcome variables, such as childhood obesity or physical activity [64,65].
A potential source of uncertainty is the scale of the analysis, particularly the size and assumed homogeneity of the neighborhoods. In order to investigate heterogeneities within our study sample, we performed a correlation analysis separately for urban (n = 80) and suburban neighborhoods (n = 90). Both analyses yielded very similar results for the correlation as the global analysis; hence, we can assume that spatial heterogeneities are negligible. Additionally, the temporal resolution of the dataset is a potential source of uncertainties for the analysis. While most of the data are from the years 2013 to 2014, the air quality indicators are modelled using base data from 200 to 2012. Hence, the air quality indicators bear a slightly higher level of uncertainty.
The analysis of environmental inequalities relevant for health aimed to identify entry points for urban planning interventions targeting increased urban health equity. The results presented in Section 3 revealed spatial patterns of inequalities across the entire city. These helped to identify hotspots of exposure that might lead to negative health impacts, i.e., neighborhoods with significant proportions of inhabitants of lower socioeconomic positon were exposed to disproportionate environmental burdens.
The toolbox of urban planning provides different instruments of planning, i.e., specific, more or less judicially formalized and institutionalized plans and procedures that can be implemented in any city. These instruments may address the existing built up area or the development of new infrastructure. As shown in Section 3, several of the identified environmental burdens we identified resulted from transport and industrial activities; others from a lack of access to green areas. Accordingly, transport planning and green space management are relevant sectoral fields of urban planning. Suitable planning instruments addressing existing built-up areas focus on the physical environment based on environmental planning law. The reduction of noise and air emissions is dealt with in instruments that are based on EU legislation, namely the EU noise directive [66] and the EU clean air directive [67]. Both have been implemented in Germany in the Federal Immission Control Act.
However, neither clean air action planning nor noise reduction planning require consideration of peoples' vulnerability or aspects of equity [68]. Currently, only existing environmental standards and the results of participation procedures are used as the basis for identifying points of intervention. In practice, this means that both planning instruments are likely to be implemented in areas showing higher levels of environmental burdens, particularly where residents do have the capacity to raise their concerns, but not necessarily in areas with higher shares of socioeconomically-disadvantaged residents. The results of the analysis shown above could be used as an additional criterion to target intervention areas and to prioritize suitable actions.
Urban planning also provides health-promoting instruments, such as green space management. Existing green space management could focus on the identified neighborhoods of low green space availability, taking the additional burden of noise into account. The assignment of quiet areas is an element of noise action planning and could be merged with green space management, especially in those areas facing noise burden.
Furthermore, the Federal Building Code of Germany includes the "Social City" program, which pays particular attention to the built environment in deprived neighborhoods. To date, the identification and delineation of target areas has mainly been guided by social indicators. Using the results of the above analysis may help to strengthen health aspects in the selection of target areas [69]. As the program is institutionalized in the Federal Building Code, which demands not only a healthy urban development, but also that land use is socially just, it also provides a good basis for the integration of health and equity aspects.

Conclusions
The aim of this paper was to map socioeconomically-driven environmental inequalities relevant for health at the neighborhood level in order to identify options for concrete planning interventions. The analysis of the City of Dortmund revealed that various hotspots of environmental inequalities exist within the city. All selected socioeconomic indicators correlated quite strongly and helped to identify specific neighborhoods that house a significantly higher proportion of residents of a lower socio-economic position. We further found significant associations between the selected environmental indicators resulting in cumulative environmental burdens in various neighborhoods. Finally, we mapped levels of exposure by combining environmental state and social context indicators. The neighborhoods showing significant levels of multiple environmental burdens and at the same time housing a large proportion of socioeconomically-disadvantaged residents clearly demonstrate health-related environmental inequalities.
The toolbox of urban planning includes various planning instruments that help to address issues as identified above. In particular, instruments such as clean air action planning and noise reduction planning address environmental burdens as mapped here. However, in current planning practice, these instruments do not distinguish between populations of different socioeconomic positions, failing to recognize that those of lower socioeconomic position are more vulnerable and less able to cope with environmental burdens. Accordingly, the case for considering the vulnerability of the population in planning practice [62] might be strengthened based on the analysis carried out in this paper.
Further research is needed to advance the study of health-related environmental inequalities into studying environmental justice issues, because this requires an additional analysis of the severity, consequences or morality of the inequalities [18]. This entails the evaluation of the magnitude of the uneven distribution of environmental burdens and amenities [15]. Another challenging research issue is the assessment of cumulative environmental impacts. Going beyond the pure overlaying or adding up of multiple burdens indicating a spatial and temporal concurrence necessitates, among other things, the assessment of combined and synergetic effects of multiple environmental stressors [70]. Finally, it would be interesting to study the impacts of proposed interventions in terms of reduced inequalities in the long run. Although intra-urban socioeconomic patterns are affected by various and even contradicting trends and developments from the sub-city to the national and European level, the indicator framework as developed in this study might as well be used for a long-term panel analysis for this purpose.


The Dynamics of Oil and Stock Prices Comovements

The dynamic relationships between crude oil prices, oil sector stock price indices and stock market price indices is examined and evidence of cointegration between these variables is found. A vector error-correction (VEC) model reveals that the stock prices of companies in the integrated oil and gas sector have a long-run negative relationship with oil prices while those in the oil and gas exploration and production sector have a long-run positive relationship with oil prices. Both indices have a long-run relationship with midand small-capitalization stock prices. However, following shocks to oil sector indices, oil prices tends to restore the long-term equilibrium. These findings should be useful to investors in their attempts at appropriately structure their overall portfolios.

Introduction
This paper examines the short-term and long-term relationships between the price movements of crude oil, a commodity of major economic importance, and stock market prices. An understanding of interactions between commodity and equity markets is useful in portfolio diversification strategies for investors. The issue is of particular relevance in a period of great instability in stock and bond markets caused by the recent global recession and by fears of a possible future inflationary environment.
There is some research in the literature on the connections between oil spot prices and oil future prices. (Note 1) Less work has been done on the relationship between oil prices and stock market returns. It is often assumed that this relationship is negative: increases in oil prices raise costs in many industries, depressing stock prices by lowering expectations about future earnings and dividends. Alternatively, a growing economy can increase demand on oil and tend to raise stock prices. (Note 2) Both notions suggest that oil price movements should be associated with stock market returns. However, published results to date have produced conflicting assessments on the nature of this relationship and on the direction of causality. In this paper, we extend prior research by examining not only the relationships between oil prices and broad stock market prices but also their interrelationships with oil sector stock prices. Rising oil prices has increased investors' interest in trading oil-related stocks. However, this requires an understanding of the relationships between oil prices, oil sector stock prices and broad stock market prices. Findings of differences in behavior between these indices will be useful to investors in their attempts at appropriately structure their overall portfolios.
Research on oil-stock price relationships has typically been carried out using regression analysis, which provides short-term analysis of linkages. Generally, this approach concludes that oil price movements do affect broad stock market indices. However, little attention has been devoted to the analysis of their dynamic long-term interactions. Our research employs the cointegration technique, a methodology that is specifically designed for the study of the long-term interrelationships. Exploration of the long-term linkages is particularly important for investors in that it will give additional insight into the long-term benefits of portfolio diversification, which may differ importantly from what is indicated by short-term regression analysis

Literature Review
As mentioned previously, surprisingly few empirical studies have been conducted on the relationships between oil prices and developed as well as emerging stock market prices. Using regression analysis, Al-Mudhaf and Goodwin (1993) and Huang, Masulis, and Stoll (1996) find no relationship between oil prices and broad-based U.S. stock returns (as represented by the S&P 500) but provide some evidence that oil futures prices have a direct impact on oil sector stock prices. In contrast, Sadorsky (1999) and Papapetrou (2001) report significant relationships between oil prices and broad-based stock returns. More recently, Hammoudeh and Eleisa (2004) and Hammoudeh, Dibooglu, and Eleisa (2004) find no relationships between oil prices and S&P oil industry stock indices for developed stock markets using linear cointegration analysis but conclude that oil sector stock investments may be profitable in periods of high volatility in oil prices. Similarly, Maghyereh (2004) finds very weak evidence that there is a relationship between crude oil prices and stock market returns in emerging markets. However, using nonlinear cointegration analysis, Maghyereh and Al-Kandari (2007) conclude that oil prices affect stock price indices in the Gulf Cooperation Council (GCC) countries. Given that the empirical evidence on the impact of oil prices on stock market prices has been mixed, the potential and need for additional research into the oil/financial markets relationship is apparent.
In this paper, we extend the understanding of the dynamic relationships between oil prices and stock market returns by using three types of variables identified in the literature: crude oil prices, oil sector stock price indices and stock market price indices representing large, mid and small capitalization companies. A multivariate cointegration analysis is performed to investigate their short-term and long-term behavior as the relationships of oil-linked company stock prices to oil prices and to broad stock market prices is also not clear. The outcomes should increase our understanding of the long-term dynamic relationships between changes in oil prices and changes in stock market prices. A finding of significant long-term linkages between these different prices (commodity and equity) will be useful to investors interested in more appropriately structuring their portfolios. Additionally, it should provide information on cross-market behavior beneficial to investors in their diversification strategies.
The rest of the paper is organized as follows. The data are described in Section 3. Section 4 presents briefly the methodology and reports the empirical results. Section 5 contains the concluding remarks.

Data and Descriptive Statistics
Crude oil (spot and futures) prices, oil sector stock prices and stock market prices indices are obtained through FactSet Research Systems Inc., a leading provider of broad scope of data and analytics headquartered in Norwalk, Connecticut. We use daily and weekly (Wednesday-to-Wednesday) data over the period January 3, 1996 through July 21, 2010 for a total of 3800 daily and 760 weekly observations. FactSet's WTI crude oil spot is used. The S&P 500 Integrated Oil & Gas (INTEG) and the S&P 500 Oil & Gas Exploration & Production (EXPL) price indices represent oil sector stock prices. This is because the oil industry consists of companies dealing with the exploration, production, refinement and distribution of oil and gas. Companies in the Oil & Gas Exploration and Production index are only engaged in the exploration and production of oil and gas which includes searching for crude oil, drilling and operating wells referred to as the "upstream" or E&P sector of the industry. Companies in the Integrated Oil & Gas index are also involved with at least one other significant activity in the refining, marketing and transportation of oil, gas and/or refined products referred to as the "downstream" or R&M sector of the industry. These large firms have global operations (Chevron Corp. and Exxon Mobile), are fully integrated (operate in both upstream and downstream sectors of the industry) or may concentrate in the E&P or R&M sector. The S&P 500 (LCAP) index is used to proxy for large company stocks. Since many oil and gas exploration and production companies are not large capitalization firms, the S&P mid-cap 400 (MCAP) and the S&P small-cap 600 (SCAP) indexes are also included in the analysis. All price or level series are converted to natural logarithmic form. The returns series are then constructed as the first differences of the logged price or level series. Summary statistics for the weekly returns series are shown in Table 1. Over the 1996-2010 sample period, the oil price series has the highest mean return and the highest standard deviation while the large-cap price series has the lowest mean return and the lowest standard deviation. All of the return series have nonsymmetric distributions. Negative skewness of all the return series implies a thicker lower tail, that is, the return indices are skewed to the left. The kurtosis statistics indicate that all the returns series are more peaked than a normal distribution. For a normal distribution kurtosis is equal to 3.0. The Jarque-Bera statistic confirms that none of the series are normally distributed. Table 1 also reports the average correlation statistics between the weekly returns of each pair of indices for the sample period. As expected, oil returns (OILS) are more correlated with oil sector stock returns (EXPL and INTEG) and have low correlation with broad stock returns (LCAP, SCAP, MCAP). In fact, oil (OILS) and large capitalization stocks (LCAP) return indices have the lowest correlation coefficient. The S&P 500 Integrated Oil & Gas (INTEG) return index has higher correlation coefficients with all stock market return indices, (especially with the LCAP and the SCAP indices). Similarly, the S&P 500 Oil & Gas Exploration & Production (EXPL) return index is more correlated with all the market stock return indices than with the oil return index (especially with the MCAP index).

Methodology and Empirical Results
We apply the cointegration methodology of Johansen (1988) and Johansen and Juselius (1990), henceforth referred to as JJ, to test for the presence of long-run equilibrium relationships between the time-series variables in our system (details of the cointegration methodology are discussed in the appendix). Cointegration tests allow us to determine whether a set of endogenous variables (e.g., oil price, oil-sector stock prices and stock market prices) share a common long-run stochastic trend (have a long-term relationship), while allowing for the possibility of short-run divergences. A finding of cointegration indicates interdependence of the endogenous variables, which may be the result of economic linkages between the markets or arbitrage activity between investors.

Unit Root Tests
Two or more nonstationary time series are cointegrated if a linear combination of the variables is stationary. If there is such a stationary linear combination, the nonstationary series are said to be cointegrated. This stationary linear combination is referred to as a cointegrating equation, representing a long-run equilibrium relationship between the variables. If there is just one long-term relationship between the variables, they share a single route of convergence The first step in the analysis is to examine each series for the presence of unit roots, to determine if the index series are nonstationary. Nonstationarity is a precondition for cointegration; additionally, all the series must be integrated of the same order. For this, the Augmented Dickey-Fuller (ADF) test and Phillips and Perron (PP) test are applied to the levels and first differences of each series; the null hypothesis is that a series is nonstationary, so rejection of the unit root hypothesis supports stationarity. The results, using the Schwartz Information Criterion (SIC), are presented in Table 2. (Note 3) For all the level series under study the null hypothesis of a unit root is not rejected. However, when the tests are applied to the first differences of the series, the null is rejected indicating that they are stationary. Consequently all the level series are integrated of order one, that is, I(1). ** Significant at the 5 percent level.

Cointegration Tests
Once the nonstationarity requirements of the level series are met, the JJ procedure can be applied to determine whether the time series are cointegrated. This test determines the rank r of the coefficient matrix of a vector autoregression (VAR) model of the series, with the rank indicating whether there is cointegration, as well as the number of cointegrating relationships. Two likelihood ratio tests are used, the trace test and the maximum eigenvalue test, to determine the number of cointegrating vectors. The trace test null hypothesis is that there is a maximum of r cointegrating vectors against the alternative that the number is equal to n, the number of series in the model. That is, the null hypothesis, that r = 0, is tested against the alternative hypothesis, r  1. If this null is rejected, the null of r ≤ 1 is tested against the alternative that r  2. If this null is rejected, the null becomes r ≤ 2, tested against r  3, and so on. The maximum eigenvalue test has the identical null hypothesis, while the alternative is r+1 cointegrating vectors.
For comparison with previous studies, we first perform bivariate (2x2) cointegration tests involving an oil sector price index (INTEG or EXPL) with the oil spot price series (OILS) and an oil sector price index (INTEG or EXPL) with each stock market price index (LCAP, MCAP or SCAP. We find no evidence of cointegration of the oil sector price indices on a bilateral basis with either the oil price series or with each stock market price index. Only when we proceed to a multivariate (6x6) OIL grouping model consisting of the two oil sector price series (INTEG and EXPL), the oil spot price index (OILS), and the three stock market price indices (LCAP, MCAP, and SCAP) do we find evidence of cointegration. (Note 4) The test results for the weekly series are reported in Table 3. The model assumes an unrestricted intercept in the cointegrating equation and no deterministic trend in the data. A log of one is used, based on the Schwartz Information Criterion (SIC). The r denotes the maximum number of cointegrating vectors.
** Significant at the 5 percent level.
The results show that the weekly series in our OIL grouping model are cointegrated according to the trace test, although not according to the maximum eigenvalue test. The trace test suggests that there is one significant cointegrating vector in the model. This implies that there are five common stochastic trends, indicating a degree of market integration. Therefore, we conclude that there exists a stationary, long-run relationship between oil sector stock prices (INTEG and EXPL), oil prices (OILS), and stock market prices (LCAP, MCAP, and SCAP).

Tests for the Dynamics of the OIL Grouping Model
Since the evidence indicates cointegration for the OIL model grouping, the use of an unrestricted vector autocorrelation (VAR) model in first differences would be misspecified, as information on the long-term equilibrium relationships is lost. Therefore, the next step is to estimate a Vector Error-Correction (VEC) model. We present the cointegrating vectors, for the weekly data, normalized to the Integrated Oil and Gas sector index (INTEG) in Panel A of Table 4   ** Significant at the 5 percent level.
The significance and size of the coefficients on the cointegrating equation in Table 4 capture the response of each series in the VEC model to departures from the long-run equilibrium. The results indicate that both oil sector price indices are significantly, but very differently, affected in the long-run by oil prices and broad stock market prices. More specifically, the stock prices of integrated companies have a significant and negative long-term relationship to oil prices. This result is not surprising as these larger, more established and diversified firms may experience declining profit margins when oil prices increase if their activities are more on the downstream (refining and marketing) than the upstream (exploration and production) sector of the oil and gas industry. In addition, the stock prices of companies in the integrated oil and gas sector have a more significant relationship to stock prices. However, the direction of the long-run relationship is not clear as the stock prices of integrated firms are positively (negatively) related to the prices of medium (small) capitalization stocks. Thus, a one-percentage point change in the mid-cap price index could be associated with a 3.27 percent change (in the same direction) in the integrated oil and gas price index while a one-percentage point change in the oil price index could be associated with a smaller change, 1.31 percent (in the opposite direction), in the integrated oil and gas price index.
When we normalize the cointegrating vector on the oil and gas exploration and production index, there is, as expected, a positive long-run relationship with oil prices. The stock prices of E&P companies are also more significantly affected by changes in broad stock prices: they have a negative (positive) long-run relationship with the medium (small) capitalization index. In absolute terms, the ordering of the coefficients for the oil and gas exploration and production index is the same than that of the integrated oil and gas index (although with contrasting signs). Since the opposite sign on the mid-and small-cap indices is consistent but somewhat puzzling, we also examined the results of several different forms of our model, as well as using a variety of lag lengths. The sign results were consistent across the different models for both the daily and the weekly series.
The coefficients on the error-correction terms in the VEC models, shown in Table 5, represent the speed of adjustment to disturbances to long-run disequilibrium. For both oil sector models with weekly data, it is the oil price variable which adjusts to restore the long-run equilibrium. The other variables do not adjust significantly. For the integrated oil and gas index model the response is inverse, while for the oil and gas exploration and production index model the response is direct. The stock market indices do not have significant coefficients of adjustment for either oil sector index. This implies that the relationship of the broad stock market prices to the stock prices of companies in the oil and gas industry is of a long-run rather than a short-run nature.  ** Significant at the 5 percent level.
Variance decompositions of the forecast errors are also used to further examine the nature of the short-term relationships in the OIL grouping model. Variance decompositions measure the proportion of the forecast error variance of each series that is explained by its own innovations and by innovations from the other series in the grouping. There is a lesser degree of interdependence (or a greater degree of independence) when a series is shocked and a greater (lesser) proportion of its forecast error variance can be attributed to its own innovation rather than innovations from the other series. We report results at horizons of 1, 13, 26, 39, and 52 weeks for the oil sector price indices in Table 6. (Note 5) Both oil sector indices explain a very large proportion of the forecast error variance by their own innovations, as opposed to innovations from the other series. However, both oil sector indices are affected by innovations in the mid-cap index and to a smaller degree, by innovations in the oil price index. In addition, the integrated oil and gas index is affected by innovations in the oil and gas exploration and production index while the oil and gas exploration and production index is impacted by innovations in the small-cap index. The large-cap index has comparatively little effect on either of the oil sector indices. Overall, the variance decomposition results are consistent with the results of the VEC model. Note: In percentage terms -rows may not sum to 100 due to rounding error.

Conclusion and Recommendation
This paper investigates the dynamic relationships between WTI crude oil (spot or futures) prices, two oil sector stock price indices and three stock market price indices representing large, mid and small capitalization stocks, referred to as the OIL grouping model, over the 1996-2010 time period. Multivariate cointegration tests are performed to investigate long-term comovements between the series, while allowing for the possibility of short-run divergences. Variance decompositions are also employed to describe the long-and short-run dynamics of the series.
One cointegrating relationship between the series in the model is found, indicating long-run interdependence between them (and reduced benefits from diversification). A vector error-correction (VEC) model reveals that the stock prices of companies in the integrated oil and gas sector have a long-run negative relationship with oil prices but a more significant positive relationship to the stock prices of companies in the S&P mid-cap index while those in the oil and gas exploration and production sector have a long-run positive relationship with oil prices but a more significant positive relationship to the stock price of companies in the S&P small-cap index. However, following shocks to both oil sector indices, it is the oil price series which tends to restore the long-term equilibrium in the OIL grouping model. Variance decompositions indicate that both oil sector indices are affected by innovations in the mid-cap index and to a smaller degree, by innovations in the oil price index. These findings should be useful to investors in their attempts at appropriately structure their overall portfolios. More specifically, a decrease (increase) in oil prices may prompt investors to become interested in investing in the stock of companies in the refining and marketing or downstream (exploration and production or upstream) sector of the oil and gas industry, especially if they expect broad U.S. stock market prices to increase in the long run. z t = x t -ay t (1) is generally also I(1). However, if there is an (a) such that z t is integrated of order zero, I(0), the linear combination of x t and y t is stationary and the two variables are said to be cointegrated (Engle and Granger, 1987). If two variables are cointegrated, there is an underlying long-run relationship between them. In the short run the series may drift apart, but if they are cointegrated, they will move toward long-run equilibrium through the error-correction mechanism.
The first step in the analysis is to test each series for the presence of unit roots. This can be done by means of the Augmented Dickey Fuller (ADF) test, an extension of the Dickey and Fuller (1981) method. The ADF test uses a regression of the first differences of the series against the series lagged once, and lagged difference terms, with optional constant and time trend terms: y t = a 0 + a 1 t + y t-1 + b i y t-i+1 + e t In the equation  is the first difference operator, a 0 is an intercept, a 1 t is a linear time trend, e t is an error term, and i is the number of lagged first-differenced terms such that e t is white noise. The test for a unit root has the null hypothesis that  = 0. If the coefficient is significantly different from zero, the hypothesis that y t contains a unit root is rejected. If the test on the level series fails to reject, the ADF procedure is then applied to the first-differences of the series. Rejection leads to the conclusion that the series is integrated of order one, I(1).
A limitation of the Dickey-Fuller test is that it assumes that the errors are statistically independent and have a constant variance. In 1988, Phillips and Perron (PP) generalized the ADF test: T is the number of observations and the disturbance term  t is such that E( t ) = 0, but there is no requirement that the disturbance term is serially uncorrelated or homogeneous. The equation is estimated by ordinary least squares and the t-statistic of the b 1 coefficient is corrected for serial correlation in  t using the Newey-West (1987) procedure for adjusting the standard errors.
The Johansen (1988) approach to testing for cointegration relies on the relationship between the rank of a matrix and its characteristic roots, or eigenvalues. Let X t be a vector of n time series variables, each of which is integrated of order (1), and assume that X t can be modeled by a vector autoregression (VAR): X t = A 1 X t-1 + ... + A p X t-p +  t Rewrite the VAR as x t = x t-1 + x t-i +  t where  = A i -I,  i = -A i . If the coefficient matrix  has reduced rank r < k, there exist k x r matrices  and  each with rank r such that  = ´ and 'x t is stationary. The number of cointegrating relations is given by r, and each column of  is a cointegrating vector. Three cases are possible. First, if  is of full rank, all elements of X are stationary, and none of the series has a unit root. Second, if the rank of  = 0, there are no combinations which are stationary and there are no cointegrating vectors. Third, if the rank of  is r such that 0 < r < k, then the X variables are cointegrated and there exist r cointegrating vectors. Equation (5) can be modified to allow for an intercept and a linear trend.
The number of distinct cointegrating vectors can be obtained by determining the significance of the characteristic roots of . To identify the number of characteristic roots that are not different from unity we use two statistics, the trace test and the maximum eigenvalue test:  trace (r) = -Tln(1 - i ) and  max (r,r+1) = -Tln(1 - r+1 ) where  i = the estimated values of the characteristic roots (eigenvalues) obtained from the estimated  matrix, r is the number of cointegrating vectors, and T = the number of usable observations. The trace test evaluates the null hypothesis that the number of distinct cointegrating vectors is less than or equal to r against a general alternative. The maximum eigenvalue test examines the number of cointegrating vectors versus that number plus one. If the variables in X t are not cointegrated, the rank of  is zero and all the characteristic roots are zero. Since ln(1) = 0, each of the expressions ln(1 - i ) will equal zero in that case. Critical values for the test are provided by Johansen and Juselius (1990) and by Osterwald-Lenum (1992).


Determinants of Adoption of Recommended Cassava Production Practices on Yield and Income among Farmers in Bwari and Kuje Area Council, Abuja, Nigeria

The study was aimed at examining and determining the effects of adoption of the recommended cassava production practices (RCPPs) on the yield and income benefit among farmers; as well as the constraints faced by farmers in adoption of the recommended practices. The study used a multi-stage sampling procedure to select 120 registered cassava farmers. Frequencies, percentages, means, and z – test were employed for data analyses. Result of the Z-test revealed that the mean yield of cassava before and after adoption were 3,832 t/ha and 6,387 t/ha respectively; with a differential of 67%. The mean income of farmers before and after the adoption Original Research Article Adamu et al.; AJAEES, 9(4): 1-7, 2016; Article no.AJAEES.22088 2 was N464, 642.00 and N714, 833. 00 respectively; and the differential mean was 54%. The identified major constraints for low and non-adoption of some of the recommended cassava production practices were limited scale and uneven distribution of farmland, insufficient funds and complex nature of technologies to farmers. It was recommended that technology developers should develop technologies that are simple, cost effective and easily adoptable by farmers. On the other hand, promoters of technology adoption should intensify efforts targeted at improving service delivery and the promotion of the adoption of recommended cassava production practices by the farmers especially those technologies that recorded low levels of adoption. But more than this, it is recommended that the applicable technologies should be appropriate, adaptive, adoptive and sustainable.

INTRODUCTION
Nigeria is one of the world's largest producers of cassava; with about 37 million tons of cassava cultivated on 2.5 hectares of land; and with a national average yield of 14.8 metric tons per hectare [1]. However, a total output of 38.17 million metric tons was reportedly harvested from 4.118 million hectares of land and an average yield of 9.271 tons per hectares [2]. Nigeria's production account for 19% of the world output and 34% of Africa's output [3].
Priority extensive research on cassava by the International Institute of Tropical Agriculture (IITA) and National Root Crops Research Institute (NRCRI) over the years in Nigeria has led to the development of different varieties that are resistance to major disease. These varieties give over 50% higher yields compared to local varieties [4]. The persistent problem of food shortage in Nigeria has led to increasing agricultural productivity, which involves the use of improved high yielding crop varieties. These have been popularised among farmers through extension services of the ADPs. The multiplication, distribution and adoption of TMS varieties by farmers have led to tremendous yield increases over the years. For instance, presently Nigerian cassava production is reported to be by far the largest in the world. Indeed, the expansion of cassava production had been relatively steady since 1980 where production stood at about 12 million tonnes; and by between 1988 to 1992 productions increased to 15 and 26 million tonnes respectively; owing to the release of improved varieties. In fact, the Food and Agriculture Organization (FAO) of the United Nations in Rome estimated that Nigeria produced approximately 34 million tonnes of Cassava in 2002 for Nigeria [5,6]; and 39 million tons in 2003 The Nigerian economy is basically agrarian, with majority of the people living in squalor and very poor standard of living. Most of the farmers are subsistence small holders, producing on 1.2 hectares of land under a traditional system characterized by low technology and production efficiency. Besides, the farmers are also faced with problems of natural resource inputs, especially land, water, labour and managerial skills. Without doubt, poverty among farm families goes beyond material deprivation to insecurity, vulnerability, and exposure to risks, shocks and stress. These poor conditions of the rural communities have continued to deteriorate since independence due to severe neglect emanating from poor and inconsistent policies formulation and implementation by successive governments in Nigeria [8].
While the introduction of technologies to a social system is designed to achieve certain outcomes, whether this is achieved or not would depend on changes that are noticed among the target groups (FN Adun, University of Ibadan, Nigeria. Unpublished). Studies by [9], revealed that improved farm equipment enables farmers to increase land area under cultivation and achieve higher incomes. Similarly, [10] reported that the adoption of improved farming equipment for crop production resulted in an increase in total yields, expanding the agricultural land area under production, increasing land use intensities and changing the nature of crop enterprises. [11][12][13] noted that income is likely to increase, especially if production expands as a result of increases in yield per hectare and the adoption of production practices which sustain soil fertility over time.
A number of constraints to the adoption of Recommended Cassava Production Practices (RCPPs) have been identified by many researchers. For instance, [14], reported that untimely weed control and wrong application of herbicides affect growth and development of cassava tubers. [15] revealed that 95 percent of the respondents indicated high labour cost as a major constraint to cassava production. [16], found that a large potential exist for the adoption of improved cassava varieties if farmers can obtain more cassava stems suitable for planting. While, [17], opined that the factors limiting improved cassava production are uneven supply of suitable planting materials and other farm inputs such as fertilizer, herbicides, pesticides and processing facilities.
The problem limiting the production of cassava in the Federal Capital Territory (FCT), Nigeria, is the over dependence on traditional methods of production by farmers. Traditional method in this study refers to the farmers' old ways of cultivating cassava such as: Close or wide spacing, shallow planting depth, and the continuous use of local varieties such as; "Nwanye" and "Dan Warri" with low yield potentials. The RCPPs is package is aimed at the improvement on cassava yield. (O Osuagwu, University of Nigeria, Nsukka, Nigeria. Unpublished), stated that cassava yield can be increased by adoption of recommended practices or by expanding the land area under cassava cultivation. The process of increasing cassava yield through modernization is depended on the extent to which farmers become aware of the existence of such recommended practices, develop interest, evaluate, try them and become convinced of their relevance. It is only then that it can be expected that the farmers would adopt completely all the components of the recommended production practices.
Several studies [GAA Okoosi, Obafemi Awolowo University, Ile-Ife, Nigeria. Unpublished, 15,8] revealed that farmers in the study area have access to different improved cassava production practices and cassava hybrid cultivars thus: TMS 30572, TMS 4 (2) 1425, TMS 99/2132, TMS 98/0581, NR 8032, MS6. The Federal Government of Nigeria through the Cassava Multiplication Programme (CMP), the Root and Tuber Expansion Programme (R-TEP) and the Abuja Agricultural Development Project (ADP) have all made a lot of commitment in promoting the adoption of these recommended cassava production practices. However, in spite of all these efforts, farmers in the study area still practice the traditional cassava production methods [18]. Consequently, the production of cassava in the study area in terms of its yield is relatively low [19]. Therefore, the objective of this study is to evaluate the determinants militating against the adoption of recommended cassava production practices on yield and income among farmers in Bwari and Kuje area council Abuja, Nigeria.

METHODOLOGY
The study was conducted in the Federal Capital Territory (FCT), Abuja, Nigeria. Abuja was created and carved out in 1976 from Kaduna, Niger, Kwara and Plateau States. The FCT is centrally located, lying within latitudes 8°25 ′N and 9°20 ′N, and longitudes 6°45'E and 7°39'E. Abuja shares a boundary with Kaduna State to the north and with Kogi State to the south. It is also bounded to the east and west by Nassarawa and Niger States respectively. What is referred to as a "Local Government Area" in states are designated "Area Councils" in Abuja. There are six Area Councils in Abuja namely: Abaji, Bwari, Gwagwalada, Kuje, Kwali and Abuja Municipal Area Councils (AMAC). Abuja covers an area of 8,000 square kilometres with a total estimated population of 1,899,622 in 2012 [20]. The people are predominantly peasant farmers cultivating crops such as yam, cassava, maize, sorghum, rice, groundnut, beans and vegetables. They are engaged in small, medium and large-scale livestock production as well as marketing their produce. The people live mostly in organised settlements, towns and cities [21].
For this study, a multi-stage sampling technique was used to select the study area and sample size. Two Area Councils (Bwari and Kuje) were purposively selected in the first stage. In the second stage, two (Kuduru and Igu) out of the ten districts were randomly selected from Bwari using a table of random number technique. Three districts (Rubuchi, Gwagwalada and Gudunkarya) were also selected from the fifteenth district of Kuje giving a total of five districts. This selection was based on the intensity and concentration of farming activities, particularly cassava production, in the study area. In the third stage, assigned value number of random selection method was employed to select two villages (Gutau and Kuduru) from Kuduru district; three villages (Igu, Panunike and Tokolo) from Igu; two villages (Rubochi and Kujekwa) from Rubochi; two villages (Gwargwada and Gidan Bawa) from Gwargwada and one village from Gudunkarya. In the fourth stage, the list of farm villages and households from ward councilors revealed that a total number of registered farmers in these five districts were one thousand, two hundred (1,200). This formed the sample frame for the study. In the fifth stage, one hundred and twenty (120) of the respondents, about 10% of this population, were randomly taken because the farmers in the study area were homogeneous in their mode of operations.
A well-structured questionnaire was use to collect information from the farmers. The data included information on the socio-economic, institutional and technological characteristics of the respondents. Interview method, informal observation and pictorial information were also gathered; more appropriately, as means of eliciting adequate information on the study area.
The study made use of eight recommended production practices and these are: Recommended planting material; healthy stem cutting with 4 -5 nodes; recommended planting time -April and June, then August or September; recommended method of planting; planting on ridges at an angle; recommended planting depth (5 cm -10 cm); recommended plant spacing (1 m x 1 m) for sole cropping; recommended fertilizer rate (NPK 15:15:15, 4 -8 bags per hectare) and weeding (herbicide application); Pre-emergence herbicide -Alachlor (Lasso) at 300 ml/ CP15 knapsack pump; Selective postemergence herbicide -Round-up at 4-5 liters per hectare; harvesting (cassava lifters).
Descriptive statistics was used to analyze the general constraints faced by farmers on adoption of Recommended Cassava Production Practices (RCPPs) and the Z-statistic was used to examine the effect of adoption of RCPPs on income and yield of the respondents.
The z-test model was used to compare the differences in yield and income of the farmers before and after adoption of recommended practices. This was used to achieve the objective of the study. Where: x 1 = individual observation x ഥ = mean of the distribution n = sample size. Table 1 gives the results of the z-analysis showed the effect of adoption on the output and income of the respondents before and after the adoption of recommended cassava practices -RCPPs. The average yield was estimated at 3,832.4 t/ha before the adoption of recommended cassava practices and 6,387.33 t/ha after the adoption; giving a 67% percent increase. The minimum yield before and after the adoption of recommended cassava practices were 1,800 and 3,000 t/ha respectively. The maximum yield before and after the adoption of recommended practices were 9,330 and 15,550 t/ha respectively. The z-calculated was 21.25 above the z-critical of 1.96 at 1% level of probability. This implies that there was significant difference between the yield of cassava farmers before and after the adoption of recommended cassava production practices. The results revealed that the yield of farmers after adoption of the cassava recommended practices was higher than the yield before adoption. The result further showed that, an average income of N464, 641.67 before the adoption of recommended cassava practices and N714, 833.00 after the adoption of recommended cassava practices were obtained. The minimum income per hectare obtained before and after the adoption of cassava recommended practices were N260, 000.00 and N400, 000.00 respectively. The maximum income per hectare obtained before and after the adoption of recommended practices were N1, 222,000.00 and N1, 880,000.00; giving a 54% percent mean increase. The z-calculated was 5.38 and this was significant at 1% level of probability. These findings revealed that the income of farmers after adoption of the cassava recommended practices was higher than the income before its adoption.

Effect of Adoption of Recommended Cassava Production Practices on Yield and Income
The finding was consistent with the report of [10], who noted that the adoption of improved farm practices resulted in an increase in total production by improving yields and income. The mean percentages for both yield and income were higher and this implied that the adoption of cassava recommended practices had a significant influence on the yield and income of the cassava farmers. Table 2 shows that there was no increase in the amount of money spent on Cookers; after adoption of recommended production practices -RCPPs. However, the result indicated that there was an increased in the amount of money spent on grinding machines, livestock, televisions, radios and telephones. Still, the amount of money spent on motorcycles, cars, sprayers, houses, cushion chairs and generators after adoption of the RCPPs doubled the amount spent on the aforementioned items; implying a general preference for the later. It is worthy to note that the money spent on the purchase of bicycles decreased after the adoption of the RCPPs. This implied that the farmers had extra money with which they could buy motorcycles for their comfort. Definitely, respondents were able to purchase most household property after the adoption of the recommended practices improved their yields and incomes greatly -a clear indication of the change in the respondents' standard of living. This is in conformity with the reported observation of (A Isah, Ahmadu Bello University, Zaria, Nigeria. Unpublished); who highlighted that money generated from the adoption of recommended rice practices was use to purchase more household properties.

Constraints to Adoption of Recommended Cassava Production Practices by Farmers
The major constraints faced by farmers in the adoption of recommended cassava production practices are presented in Table 3. Scarcity of farmlands as indicated by majority (77.50%) of the respondents ranked as the foremost constraint; followed by insufficient extension agents (27.50%); and the least being harmful effects caused by pests and diseases and a poor information network (6.67%).

CONCLUSION AND RECOMMENDA-TIONS
The findings of this study have revealed that cassava farmers are handicapped by a scarcity of farmland suggesting their inability to expand the current level of cassava production. Also, inadequate funds were is a limiting factor on adoption of Recommended Cassava Production Practices (RCPPs) among farmers. However, the study noted that the adoption of RCPPs increased the yield and income of the farmers. This is reflected in the ability of the farmer to meet up with their socio-economic responsibilities; and improvement in their living conditions. But, in spite of the fact that the aadoption of RCPPs had positive and significant effects on yield and income of farmers, and holds great potential, there was low level of adoption of this technology due to the high cost attributable to it.
Hence, it is recommended that appropriate technologies, which should be made available to the farmers, must be affordable and timely availed by the agencies and stakeholders involved. Furthermore, since the major constraint on adoption of recommended cassava production practices was limited scale and uneven distribution of farmlands, it is recommended that appropriate adaptive and adoptive sustainable technologies that would suit the fragmented nature of farmlands should be developed by research institutes and made available for use by the farmers.