Impartiality and Responsiveness: Conflicting principles in

democratic governance?

Marcia Grimes and Peter Esaiasson

marcia.grimes@pol.gu.se, peter.esaiasson@pol.gu.se

Department of Political Science

University of Gothenburg

Abstract: This paper examines the inherent tension between two principles considered cornerstones of

democratic legitimacy: impartiality in the exercise of public power, and responsiveness to citizens‟

demands, a tension that may intensify with the increasing use of participatory democracy on the output side

of the policy process. We explore this tension both theoretically and empirically in the context of Swedish

urban planning. Laws and regulations related to the construction of new facilities in Sweden require

decision makers to provide opportunities for citizen input at several stages in the planning process. While

these innovations may in some respects enhance the democratic legitimacy of what can at times be quite

contentious decisions, they also aggravate the risk that the negative externalities of projects will be

unevenly distributed in society to the advantage of those most able to defend their own interests. The

analyses consider whether the participatory elements of Swedish planning legislation have resulted in an

uneven distribution of contentious facilities in two urban areas. Building on a unique dataset covering

approximately 120 sited facilities in 300 census tracts in the Sweden‟s second and third largest

metropolitan areas (Göteborg and Malmö), we explore whether factors generally linked to capacity to

mobilize resistance (i.e. income, education, ethnic composition, or political engagement) correlate with the

number of unwanted facilities in the census tract. The results suggest that decision makers in these two

urban areas have tended to favor responsiveness over impartiality.

Paper to be presented at the panel Innovations of democratic governance: for the better

or for the worse? ECPR General Conference, University of Iceland, 24-27th

August 2011.

DRAFT! All comments welcome. Please do not quote without authors‟ permission.

Impartiality in the implementation of authoritative decisions is a key value for democratic

governance and the legitimacy of the political system in its entirety (Gilley 2006). From a

normative perspective, impartiality requires that decisions made in exercising public

power build only on the factors stipulated in the law, and represents the application of a

basic democratic principle in the implementation phase of government decision-making

(Rothstein and Teorell 2008). From a global perspective, one of the prime threats to its

realization is corruption and companion phenomena such as clientelism, graft, pork-barrel

politics, nepotism and political favoritism, i.e. various forms of using of public power for

individual economic or political gain. Another source of deviation from the ideal is

conscious or unconscious discrimination of citizens based on social categories. Because

of well-known and pervasive classification models and even antagonisms toward citizens

due to class, gender, ethnicity, religious affiliation, sexual identity and other socially

meaningful attributes, citizens may receive differential access to public entitlements and

resources (Schram et al 2009; Schweitzer & Stephenson 2007). Empirical research

confirms that perceptions about impartiality in the exercise of public power are

consequential for citizens‟ overall confidence in the state (Gilley 2006; Seligson 2002).

Although the benefits of impartiality are evident and well-documented, a rapidly

expanding literature on the quality of government informs us that it is difficult to achieve

in real-world situations.

While corruption and discrimination have received considerable attention in the literature

on quality of government, there is little discussion about challenges to impartial treatment

which arise as a result of conflicts with other, equally normatively appealing, principles.

This paper concerns one such potentially conflicting ideal, namely government

responsiveness towards citizen demands and grievances. Responsiveness is widely

regarded a core value in democratic governance; in an oft cited passage Robert Dahl

notes that government responsiveness towards expressions of public will is a major

reason to be supportive of representative democracies (Dahl 1989:95; see also Bingham

Powell 2004). A recurrent finding from research on citizen participation is, however, that

individuals with resources such as human or economic capital are much more likely than

others to express their political views publically and to articulate these views effectively

(e.g. Papadopoulos & Warin 2007; Verba, Schlozman & Brady 1995). Drawing from

this, leading scholars in the field warn that citizens speak with unequal voices (Verba

1999).

In a basic theoretical model of the state, these two principles do not come into conflict

since responsiveness serves as a legitimating criterion on the input side of the political

process, while impartiality instead is a key to legitimacy in the implementation stage of

the political process. The distinction between the input and output sides of the political

process may, however, be blurred in empirical reality. By implication, government

authorities may face the difficult choice of either acting responsively by adapting to

publicly expressed wishes and demands from those of many resources, or impartially

even if this requires a degree of deafness to the expressed wishes and demands of

citizens. This paper explores theoretically and empirically the potential conflict between

the two principles impartiality and responsiveness. In order to do so, we draw on research

from fields that seldom converse with one another, including research on quality of

government, distributive and environmental justice, civil society and participatory

democracy. The aim is to highlight that impartiality as a principle is not only at odds with

various forms of abuse of public power but potentially also with other ideals such as

government responsiveness and participatory democracy, as well as citizen activism and

civil society.

In countries with comparatively low levels of corruption and outright discrimination,

responsiveness may constitute one of the strongest deterrents of impartiality in the

exercise of public power. The expansion of participatory democracy in the

implementation stage of the policy process has arguably intensified the actual conflicting

pressure on decision makers to honor both the principles of responsiveness and

impartiality. In recent decades, institutional and legal reforms in Sweden and many other

countries have greatly expanded citizens' opportunities to participate in and exert

influence in the implementation stage of the policy process. To the extent that citizens

actually have a greater possibility to exert influence in policy implementation, these

reforms may have considerably enhanced the effect of unequal voices described by Verba

(1999).

The empirical analyses focus on an area in which participatory opportunities are arguably

the most extensive of any area of Swedish public administration and governance, namely

decisions related to land use issues. Unlike much research on impartiality and good

government, the empirical analyses presented here focus on authoritative decisions

related the distribution of negative externalities rather than access to benefits and the

provision of public goods and services. More precisely, we examine the geographical

distribution of facilities such as homeless shelters, housing for substance abusers,

criminal justice facilities and psychiatric wards. While certainly not as risky and

objectionable as a radioactive waste repository, these all may potentially have negative

externalities for the immediate vicinity and its residents (Schwitzer and Stephenson 2007,

327). The empirical analyses examine two urban areas in Sweden, a country generally

considered to be among the best performers in the world in terms of impartiality and low

corruption in the exercise of public power (Dahlström, Lapuente & Teorell 2010;

Transparency International 2010; Erlingsson, Bergh, and Sjölin 2008). The analyses

therefore not only provide an examination of how decision making authorities have

navigated the potential conflict between responsiveness and impartiality in two urban

settings, but also somewhat of a critical test of participatory democracy. If participatory

democracy has a detrimental effect on impartiality in public administration in Sweden, it

is reasonable to expect the same effect elsewhere.

In the next section, we explore more in depth the potential tension between

responsiveness and impartiality and how it might play out in the Swedish context. We

also consider other mechanisms that may over the long term distort the ideal of

impartiality in policy outcomes. This discussion helps to specify more theoretically

informed and therefore robust empirical models of patterns observed in the data. The

empirical analyses reveal that responsiveness may in fact have the upper hand at the

expense of impartiality in the urban areas examined, which adds credence to an often

raised note of caution for participatory democracy theory. The analyses also yield some

unexpected findings, however, and these are discussed in the concluding section.

Impartiality versus responsiveness?

Impartiality has recently gained footing as an important, if not the most central,

legitimizing principle on the output side of the political system. The principle of

impartiality stipulates that “[w]hen implementing laws and policies, government officials

shall not take into consideration anything about the citizen/case that is not beforehand

stipulated in the policy or the law” (Rothstein & Teorell 2008, 170). Neither the personal,

social and political preferences of the decision maker nor the personal, social and

political attributes of the citizen should, in other words, influence decisions made in the

enforcement of laws and implementation of policies.

The most obvious and egregious violations of the principle of impartiality are corruption,

the use of public power for the personal or political gain of the office holder, and

discrimination. Studies of corruption provide compelling support for the contention that a

lack of impartiality in the output side of the state apparatus can decrease predictability in

the market and therefore impede economic growth (Fredriksson, List & Millimet 2003;

Lambsdorff 2003), undermine efforts to address environmental degradation, shorten the

life expectancy of a population (Rothstein & Holmberg 2011), and erode citizens‟

confidence in the state (Gilley 2006; Seligson 2002).

Almost contemporaneous to the rise of research and policy debates on impartiality and

the deleterious consequences of corruption, another strand of normative theory has

promoted participatory democracy as a means to strengthen civic mindedness and

democracy more generally (Barber 1984, Mansbridge 1980, Pateman 1970; Vigoda

2002). Some authors have also pointed to participatory institutions as a means of

mitigating corruption, and thereby increasing impartiality in governance (Grimes 2008;

Lee 2007). Empirical research has produced a rather mixed bag of findings with respect

to the consequentialist justifications of participatory decision making (Theiss-Morse &

Hibbing 2005). Others authors argue instead that, in certain types of decisions, citizens

have the right to participate prima facie, as they are directly affected and exposed to the

negative externalities that certain decisions entail. Christian Hunold and Iris Marion

Young (1998) argue, for example, that citizen have the right to participate and exert

influence in decisions that imply the imposition of risks to health and safety. In addition

to being members of the demos of the larger national political association, Hunold and

Young (1998) argue that those potentially affected by the siting of a hazardous facility

belong to the demos of that particular decision, and should therefore axiomatically have

direct say in those issues (for similar reasoning see Bradbury, Branch and Focht 1999;

Rabe 1994; Fischer 1993; Renn, Webler and Kastenholz 1996; Renn, Webler and

Wiedemann 1995).

Regardless of the normative and empirical merits of participatory democracy, it has de

facto become increasingly common in many contexts, and in particular on the output side

of the political system. Recent decades have seen a gradual institutionalization of

participatory decision making with laws requiring the creation of citizen advisory

councils, public surveys, public hearings and public consultation meetings (Bishop &

Davis 2002; King, Feltey & O‟Neill Susel 1998; Papadopulos & Warin 2007). This trend

has culminated in models know as co-governance (Ackerman 2003) or empowered

participatory governance (Fung 2004; Fung & Wright 2003), in which the foremost role

of public administrators is that of a collaborator in governance, rather than implementer

of policy.

An overall evaluation of the theoretical and empirical promise of these models is well

beyond the scope of this article. The issue that is pertinent and central to the analysis at

hand is the implication this increase in the use of citizen participation in public

administration may have for impartiality in policy implementation. While little research

exists on whether participatory governance reproduces or even intensifies societal

inequalities in policy outcomes, the circumstantial evidence offers sufficient cause for

concern. Generations of researchers on political participation have documented the

considerable influence of economic, social and human resources in individuals‟

tendencies to participate in elections as well as other political actions. As Sidney Verba

and colleagues have advise, “…as long as citizens differ in their opinions and interests,

the level playing field of democracy requires that we take seriously the fact that citizens

differ in their capacity and desire to take part politically” (Schlozman, Verba, and Brady

1999, 429; Verba, Schlozman and Brady 1995). Their findings on the significance of

economic and political resources for political participation in the U.S. context have also

been confirmed in other contexts as well, including in comparatively egalitarian societies

such as the Netherlands (Bekkers 2005) and Sweden (Bäck, Teorell & Westholm 2006,

63; Öhrvall 2006, 72-73). If citizens with more education, higher incomes and better

connections with decision makers have an advantage when it comes to expressing their

voices in political and policy discussions, it is necessary to question whether an increase

in participation in governance might not reproduce or even exacerbate existing

inequalities in society.

One objection to the contention that more citizen participation and authoritative

responsiveness may constitute a threat to impartiality may be that the two actually do not

cover the same policy sectors. After all, impartiality is arguably most essential in law

enforcement and the justice system, and in areas of public administration such as

procurement, inspection (safety, building, environmental, etc), collection of revenues

(taxes, fees etc), and allocation of public goods and services (welfare entitlements,

business and building permits, etc) and these areas are generally off limits to citizen

involvement. As the discussion below suggests, however, citizen involvement is

extensive and can have implications for impartiality in at least one category of policy

decisions, namely decisions related to the siting of local unwanted land uses (LULUs).

Impartiality and responsiveness in the siting of LULUs

Locally unwanted land uses include any facility with negative externalities, whether in

terms of risks posed to health and safety, or disturbances and stigmatization that may in

turn affect property values. In Sweden, nowhere is participation more institutionalized

and widespread than in decisions that entail changes in the local physical environment,

which includes the construction of all new facilities. Both the Environmental Code and

Planning and Building Code have in recent decades been changed to require authorities to

arrange public consultation meetings in decisions that entail substantial changes in the

local environment. These institutional arrangements arguably have introduced an

expectation on decision-making officials to become more responsive to citizens‟ views,

knowledge and sentiments with regard to the local environment. If the effect of unequal

voices mentioned above exists in such consultative meetings between citizens and the

state, it seems plausible that impartiality may be compromised.

Is it, however, reasonable and desirable to strive for impartiality in siting LULUs? And

more importantly, how can we conceptualize impartiality in such a policy area? Facilities

providing welfare services benefit all sectors of the population more or less to the same

extent and there are no technical considerations that must take precedent and which make

only a few select locations suitable for such facilities. We argue that in the specific type

of facilities included in this study – those related to social welfare services – it is

reasonable to expect that impartiality in decision making will lead to a fairly even

distribution of facilities within a single jurisdiction (the municipality, in this case). More

to the point, however, the distribution should not correlate systematically with the

demographics of the local population. Such a scaled down definition of impartiality

would, for example, be indefensible with respect to facilities such as airports or wind

power turbines that require specific technical conditions to even be considered as possible

host sites.1 Facilities related to social welfare services (e.g. homeless shelters, substance

abuse centers, psychiatric wards) are moreover less subject to competing criteria of

equitable distribution than other types of facilities. Some authors have argued, for

example, that decisions related to the siting of facilities ought to build on the principle of

priority, which means that a facility should be sited in a community with a greater need

for, or expected utility from, a facility. As an example, a community that produces more

garbage is a stronger candidate for a waste facility than a community that produces less

garbage (Kunreuther, Slovic and MacGregor 1996). We argue that the facilities included

in this study cannot be said to benefit any community or group in society more than any

other. Absent considerations related to technical conditions and the principle of priority,

we therefore maintain that impartial decision making in the siting of facilities included

this study should lead to a distribution not systematically related to the demographics and

political attributes of residents in a local area.

1 Wind power turbines require sufficiently windy conditions, nuclear waste repositories require bedrock

with no contact with groundwater, waste and waste water management facilities are best placed in less

densely populated areas, etc.

Questions regarding equity and distributive fairness, and thereby indirectly of impartiality

in decision making, are not new in the study of the siting of societal infrastructure. Two

studies examine whether the strength of civil society and political activism in an area

affect the siting of unwanted facilities. Both of these studies – one focusing on the

placement of emergency housing in post-Katrina New Orleans, and the other examining

siting of nuclear power plants, dams and airports in Japan – found that the strength of

local political activism and community organizing deterred the siting of LULUs (Aldrich

2008; Aldrich & Crook 2008). The findings in both studies were robust even under

control for technical criteria, political factors (in the Japan study), income, race (in the

New Orleans study) and a host of other socioeconomic factors. Aldrich (2008, 163)

summarizes his findings as well as the theorized mechanism that explains the findings as

follows: “Localities with strong civil society, both in terms of its quantity and quality,

represent the biggest challenge to long-term siting plans for controversial facilities. Such

localities are more expensive targets for state authorities and developers in terms of both

time (contacting, negotiating with or coercing the individuals) and money (if

redistribution is used).” Aldrich‟s conclusions highlight that the tradeoff between

responsiveness and impartiality may not be cost neutral, i.e. that responsiveness to

citizens‟ demands may in many instances represent a less expensive option. Moreover,

the research indicates that responsiveness may not be a matter of reacting to citizens‟

demands, but rather anticipatory. A well-orchestrated protest can imply costs and delays

in a decision making process, and such considerations may also influence officials‟

decisions.

Evidence of what has come to be known as environmental inequities (also called

environmental racism and environmental injustice) are sufficiently well-documented in

the United States that senators John Lewis and Al Gore proposed the Environmental

Justice Act into the U.S. Congress in 1992 (Ringquist & Clark 1999, 80).2 Empirical

research on environmental justice has documented a correlation between exposure to

facilities that present risks to health and safety and demographic characteristics such as

income and racial composition (Mohai & Saha 2006; Schweitzer & Stephenson 2007,

2 The bill, along with seven other pieces of legislation introduced in Congress in the early 1990s, failed to

be enacted (Ringquist & Clark 1999, 80).

321). Considerable disagreement and ambiguity remain, however, regarding the

mechanism that accounts for inequitable exposure to environmental hazards. While some

claim the existence of a racial state in the U.S. that institutionalizes systemic

discrimination (Goldberg 2002; Kurtz 2009), others advance more diffuse mechanisms

such as societal racism or classism that permeate the decisions of government and

corporate actors, and a third school instead points to a process of self-selection that

attracts lower income populations to areas with lower housing costs (Schweitzer &

Stephenson 2007, 323). A final approach depicts a more nuanced and complex picture

and see patterns in the distribution of risks as the result of sociospatial processes

including “…regional urban-industrial growth, rural underdevelopment, the structure of

industrial production (small facilities vs large ones), proximity to transportation corridors,

and racism” (Cutter and Solecki 1996:395).

This research illustrates that societal inequalities can express themselves in physical real

in society but also indicates that responsiveness may not be the only mechanism at work

in the machinery that links social and economic disparities to differ degrees of exposure

to health and environmental hazards. Though not discussed in the environmental justice

literature, we also take a page from research on corruption and recognize that self-

interested behavior among decision makers may also affect the distribution of unwanted

facilities. Before turning to a discussion of the data and methods, a more detailed

consideration of these mechanisms is warranted.

Discrimination based on socially defined classifications constitutes an obvious and

egregious departure from the principle of impartiality. While discrimination is by no

means absent in Sweden, empirical studies of racism in land use planning in Sweden are

lacking entirely. One historical study does provide some indication of discriminatory

practices in the infrastructure issues, however. In a detailed examination of the debates

waged in the municipal council in the cities of Norrköping and Linköping around the turn

of the century, Hallström (2005) finds evidence that city officials delayed extending

municipal water and waste water to poor residential areas outside the city limits in order

to retain a degree of segregation and separation between these areas and the more affluent

city center. Discriminatory outcomes do not necessarily arise from conscious and

intentional discriminatory intentions, however. Research on welfare programs in the light

of social control theory reveals that impartiality may suffer as the result of much more

subtle and seemingly innocuous cognitive processes as well. Soss, Fording and Schram

(2008) demonstrate that welfare programs in the United States tend to treat target groups

very differently from one another depending on the racial composition of the local

population, and that the divergent treatment concurs closely with prevailing stereotypes

about the work ethics and welfare dependence of racial groups. The authors argue that the

mechanism behind the welfare approaches to different target populations is a racial

classification model (RCM) rather than outright racism. Racial classification does not

imply that policy makers and front-line bureaucrats intend to perpetuate oppression or

seek to inflict harm on certain groups or recipients. An experimental study by the same

authors (Schram, Soss, Fording and Houser 2009, 407) suggests that racial classification

is as prevalent among non-white respondents as among white respondents. African

American welfare recipients were much more likely to be subject to punitive measures

than their White or Latina counterparts, independent of the race of the welfare

caseworker.

If racism or perhaps such classification models are at work in the siting of social welfare

facilities in Sweden, we might expect areas with a higher proportion of immigrants to

host a larger number of facilities. Alternately, such a classification may instead assume

the form of a class bias. It may, for example, be the case that some urban districts are

tacitly regarded as having a higher aesthetic or cultural worth, and are therefore less

likely to be selected to host contentious social welfare facilities.

The two additional mechanisms that may shape the distribution of LULUs are self-

selection among residents and self-interested behavior among decision makers. The

theorized self-interest mechanism predicts that the placement of LULUs will be lower in

areas in which key decision makers themselves reside. This aspect is accounted for in the

empirical model below. In order for the self-selection to factor into any observed

correlations between proximity to LULUs and residents‟ attributes, considerable mobility

must take place within the urban areas examined, and follow a pattern in which citizens

with resources move to areas with fewer or no LULUs. Since mobility in general is

considerably lower in Sweden than in the United States, this factor is excluded from the

analysis presented here.

To sum up, several mechanisms can lead to a distribution of facilities with negative

externalities that falls short of the principle of impartiality. In other words, decision

makers may, in fact, consciously or, following the work of Joe Soss and colleagues,

unconsciously as well, make decisions that reflect a host of considerations not stipulated

in policy and the regulatory framework as relevant to decisions regarding urban planning

and land use. The aim of the analyses is to determine whether there are indications that

responsiveness contributes to less impartiality in the Swedish context.3 Moreover, we

explore whether the mechanisms discussed above – classification models and self-

interested behavior among decision makers – seem to have any bearing on the

distribution of local unwanted facilities in Göteborg and Malmö. The next section

discusses the data used to capture these various factors.

Data and measures

The siting of the LULUs included in this study is primarily the responsibility of

municipal governments. Following appeals from affected individuals, administrative

courts may overturn specific decisions, but the responsibility to find a location for the

facility in question remains with the local government. The precise siting process varies

across type of facility, and to some extent across local communities, but high level

bureaucrats play a key role in the proposing suitable sites, while the final power to decide

rests with politicians. To facilitate generalizability of findings, we study two similar but

independent siting contexts, Göteborg and Malmö, the second and third largest urban

areas in Sweden with 500,000 and 250,000 inhabitants respectively.

3 Sweden as a polity by virtually all measures falls quite close to the end point of the scale from impartial to

particularistic (e.g. Transparency International 2010). Very little country specific research exists on

corruption and clientelism in Sweden, however. One of the few exceptions examines corruption in the form

of bribe-paying at the municipal level through a perception based survey directed at the political and civil

service elite in all Swedish municipalities. The study finds that a full 80 percent of municipal politicians

and civil servants completely disagreed with the statement that it is common practice for municipal

politicians and civil servants to use their position in a way that benefits themselves at the expense of the

municipality (Erlingsson, Bergh, and Sjölin 2008, 14ff). While 80 percent constitutes a large majority, the

remaining 20 percent agreed partially or completely with the statement that such behavior is common

practice.

Our unit of analysis is urban district. Each district holds characteristics including the

number of LULUs located there (our dependent variable). To identify comparable urban

districts we took the official zoning scheme of Malmö as our point of departure. This

zoning scheme identifies 134 “delområden” which are fairly internally homogenous with

regard to type of building infrastructure (apartment buildings; small houses; significant

institutions such as hospitals and schools; industries and work places; recreational areas;

or sparsely populated country-like areas). Corresponding zoning schemes in Göteborg,

which are called “primärområden”, are larger in size and more heterogeneous with

regard to type of building infrastructure. To make for comparable cases we divided each

of 94 primärområden in Göteborg into smaller and more homogenous geographical units

which resemble more closely the pre-defined districts in Malmö. Reflecting Göteborg‟s

larger size, this process generated 189 geographical areas (as compared to the 134 areas

in Malmö).

We defined five types of facilities to represent LULUs of interest to us: substance abuse

treatment centers; homeless shelters; refugee centers; criminal justice facilities; and

mental health facilities. All of these facilities were deemed to generate at least some

negative externalities for individuals living in a particular geographical area. Though the

negative externalities are much less severe than those associated with facilities emitting

pollutants or posing risks of potentially catastrophic accidents, they are nonetheless

contentious. One of few studies that examines such facilities finds that affordable housing

can in fact in some cases have a detrimental effect on property values in the U.S. context

(Nguyen 2005). An op-ed piece by the head of Göteborg‟s municipal executive board in

the main regional daily newspaper in Göteborg further confirms that such facilities can

generate considerable controversy. The piece essentially issues a scolding directed at the

residents of Göteborg for obstructing plans to build homeless shelters, noting that 300

appeals had been filed against five of eight planned housing projects (Johansson 2007).

To identify relevant facilities we started with a list of facilitates granted building permits

in Gothenburg from 1997 to 2007 (see Esaiasson and Johansson 2007). We registered the

geographical location of a total of 87 facilities in Gothenburg. For Malmö we used a

different approach. By searching the official home page of Malmö city, and through

telephone interviews with city administrators, we created a list of all such facilities which

were functioning at the time of the investigation or which had been recently closed down.

We then searched the home page of each facility and undertook further telephone

interviews with administrators to establish whether the facility was sited during the

period 1997 to 2007 and, of course, its precise geographical location. Again reflecting

differences in size, this process generated a total of 30 relevant facilities in Malmö.

Social classification models. In order to determine whether any sort of social

classification models similar to those described by Joe Soss and colleagues (Schram,

Soss, Fording and Houser 2009) bias decision making in these two urban settings, we

include three measures demographic characteristics: mean annual income, education

levels, and percentage of residents born outside of Europe.

Self-interest among decision makers. The measure is simply the number of key decision

makers residing in each urban district. Decision makers in this case include politicians in

one of the following offices: heads of the municipal council and executive boards, as well

as the director and vice-director of the Local Housing and Building Committee. It also

includes the director and manager of the City Planning Office.

Responsiveness and the effect of unequal voices. The demographic factors mentioned

above, in particular income and education, to some extent capture what might be

responsiveness effects. We follow Aldrich (2008) and Aldrich & Crook (2008), who use

electoral turn out and the presence of community organizing, and include additional

indicators of the protest potential of a local community. Electoral turn out and the

proportion of single-family homes serve as indicators of protest potential, as the former

provides an apt measure of political activism and the latter a proxy measure of local

(home owners) associations.

Controls. Since the urban districts differ by type of building infrastructure, we also

control for the existence of non-residential (primarily industrial areas) in the district.

Analyses and Results

When applied the distribution of facilities with negative externalities, the principle of

impartiality entails that only the factors stipulated in planning and building laws

regulations inform decisions regarding the siting of new facilities. With respect to siting

facilities related to social welfare services, the null hypothesis is therefore that facilities

should lack any systematic relationship to the attributes of the population of the urban

district. Several factors may distort such a distribution and the analyses presented here

explore whether any of these seem to be at work in Sweden, widely regarded as a country

with a comparatively high degree of impartiality in public decision making (Dahlström,

Lapuente & Teorell 2010; Erlingsson, Bergh, and Sjölin 2008). Table 1 and 2 suggest

that the number of facilities varies considerably among the districts in the two cities

examined, with the largest proportion having no facilities and the most densely districts

hosting 9 and 6 facilities in Göteborg and Malmö respectively.

Table 1. Count of facilities in different urban districts in Göteborg and Malmö Göteborg Malmö

Count of facilities in area Number of areas

% Areas with each count

Number of areas

% Areas with each count

0 133 70 104 78

1 28 15 16 12

2 13 7 6 4.5

3 5 3 5 3.7

4 4 2

5 3 1.5 2 1.5

6 3 1.5 1 0.8

9 1 0.5

As table 1 reports, the distribution in the number of facilities per district is not normal but

rather highly skewed and a large majority of districts (70 and 78 percent in Göteborg and

Malmö respectively) have no facilities. To avoid inefficient and biased estimates, we

therefore avoid linear regression models. In order to determine which model best predicts

the probability of having zero, one, or a larger number of facilities, we use the countfit

command in Stata, which compares the overall fit of models from four different

regression techniques suitable for count data: poisson regression, negative binomial

regression, zero inflated poisson and zero-inflated negative binomial regression (Long &

Freese 2006, 409). Figures 1 and 2 in the Appendix show the results of the comparison

and suggest that negative binomial regression provides the best predicted probabilities of

the four models.

Table 2. Type and number of social services and welfare facilities in two urban districts.

Göteborg Malmö

Total Max number in any area

Total Max number in any area

Substance abuse treatment

center

39 4 17 4

Homeless shelter 16 2 13 2

Refugee center 1 1 1 1

Criminal justice facility 5 1 5 1

Mental health facility 26 3 6 1

Total 87 9 30 6

The first set of analyses examines whether there is any evidence of the two most

egregious violations of impartiality, namely self-interest and the ethnic composition of

the district, an indicator of racism or racial classification. The analyses with respect to

one of the cities, Malmö, are to date incomplete as data have not yet been compiled on

some of the variables. The discussion will therefore focus to a larger extent on the results

from Göteborg. This baseline models (Models 1 and 3 in Tables 3) explore whether these

two mechanisms, self-interest and cognitive classification models which may,

consciously or unconsciously, influence decision making. Neither of the two cities evince

any pattern of self-interested behavior on the part of decision makers with respect to

siting social welfare facilities. Only in Göteborg (Model 1) did the estimate even

approach established standards of statistical significance (0.18) but the estimate is

positive rather than negative, suggesting that areas in which key decision makers reside

tend if anything to have a higher likelihood of hosting LULUs than other local districts.

The percentage of residents born outside of Europe varies between one and 42 percent in

Göteborg, yet this variation has no relationship to the distribution of LULUs (data for

Malmö have yet to be compiled).

Table 3. Factors related to number of LULUs by urban district in Göteborg and Malmö (negative binomial regression)

Göteborg

Model 1

Model 2

b P>z e^b* % ƻ b P>z e^b* % ƻ

% Immigrants -.004 .81 1.00 0.4 -.03 .42 .97 -2.7

Decision-maker residing in

area

.70 .18 2.02 102.2 .49 .44 1.6 63.7

Mean income (1000 SEK) -.01 .01 .99 -1.4

Education (% post-secondary) .05 .01 1.05 5.2

Electoral participation (%) -.02 .46 .98 -2.3

% Single family homes .003 .66 1.0 .3

Non-residential area -.34 .38 .71 -28.8

Malmö

Pseudo

R2

0.03 N=188 Pseudo

R2

0.03 N=188

Model 3 Model 4

b P>z e^b* %ª b P>z e^b* %ª

Decision-maker residing in

area

-15.7 .99 0 -100 -13.2 .988 .000 -97.7

Mean income (1000 SEK) -.014 .03 .99 -1.4

Education (% post-secondary) .06 .002 1.06 5.9

Electoral participation (%) -.06 .047 .94 -6.1

% Single family homes .02 .129 1.02 1.8

Non-residential area .56 .507 1.75 75.1

Pseudo

R2

0.13 N=104 Pseudo

R2

0.17 N=104

ª percentage change in expected count for unit increase in X

The second set of analyses (models 2 and 4) turn to the question of responsiveness by

including in the model measures of income, education, voter turnout and percent single

family homes. While the results suggest that these distorting mechanisms may indeed be

afoot in both Göteborg and Malmö, the findings do not all neatly line up with theoretical

predictions. The baseline models uncover the same pattern in both cities, namely that

income is negatively associated with the number of facilities, while education is

positively associated with LULU counts. How strong are the income and education

effects? Looking first at Göteborg, the percent change estimate indicates that the

predicted number of LULUs decreases by 1.3 percent with a one thousand SEK increase

in the mean income of the area, while the expected number of LULUs instead increases

by 5.1 percent with a one percentage point increase in the proportion of residents with

post-secondary education. Figures 3 and 4 show the probability of having zero LULUs

given different levels of income and education in Göteborg. The likelihood of the urban

area with the lowest mean annual income having no LULUs is approximately 0.4 while

the area with the highest mean annual income has a very high likelihood (approximately

0.9) of having no LULUs. In Malmö, the positive relationship exhibits a fairly similar

pattern. Figure 5 maps the predicted probability of there being zero LULUs by mean

annual incomes in Malmö districts, and shows that the probability that an area with a

mean income of below 200,000 SEK will have zero facilities is 0.6, while this probability

rapidly approaches 1 as mean incomes rise above 400,000 SEK annually. In other words,

in both of the cities examined, areas with a mean income of 400,000 have, in all

likelihood, no LULUs.

Göteborg Fig 3. Probability of zero LULUs at different levels of mean district income

Fig 4. Probability of zero LULUs at different levels of education (% with post-secondary education)

.4.6

.81

Pro

ba

bility o

f h

avin

g z

ero

fa

cilitie

s

100 200 300 400Mean annual income in 1000 SEK

.5.6

.7.8

.9

Pro

ba

bility o

f h

avin

g z

ero

fa

cilitie

s

10 20 30 40 50Percentage with post-secondary education

Malmö Fig 5. Probability of zero LULUs at different levels of mean district income

Fig 6. Probability of zero LULUs at different levels of voter turnout.

.6.7

.8.9

1

Pro

ba

bility o

f ze

ro f

acilitie

s in

are

a

0 200 400 600 800Mean Annual Income in 1000 SEK

.6.7

.8.9

1

Pre

dic

ted

pro

ba

bility o

f ze

ro f

acilitie

s in

th

e a

rea

40 50 60 70 80 90Electoral Participation

How can we understand the observed effects in the light of the theoretical questions

posed at the outset? What do they tell us about responsiveness versus impartiality?

Careful consideration of the coefficients of the other variables in the model (as well as the

results of supplementary analyses not shown in the table) takes us a few steps closer to an

answer. That higher mean incomes tend to be linked to a higher likelihood of zero

LULUs in the district may namely be a function of several different mechanisms

including protest capacity and an inclination among decision makers to protect the

aesthetic value of some districts more than others. If the income effect exclusively

captured the responsiveness mechanisms, then we would expect the effect to diminish or

disappear entirely with electoral participation included in the model. This does not occur

in either city, however. Similarly, the percent of single-family homes, another indicator

of protest potential, also does not diminish the income effect.

The measure of electoral turnout, which sought to capture evidence of the effect of

unequal voices, has no significant relationship to the number of facilities in the district.

The proportion of single family homes in the area, a measure intended to capture whether

decision makers avoid placing undesirable facilities in certain districts because those

facilities, consciously or unconsciously, seem more incompatible with the social

circumstances in one district that another. This mechanism, akin to the racial

classification model introduced by Schram et al (2009), also does not seem to explain the

income effect, as areas with more single-family homes do not tend to host a larger

number of facilities.

The positive relationship between education levels and number of LULUs appears in both

Göteborg and Malmö, and additional data and analyses will be carried out in the future in

order to better understand these effects. In neither city are the estimates a figment of

multicollinearity, as they remain unchanged even if income measures are omitted. One

possible interpretation finds support in supplementary analyses of the Göteborg data (not

shown in table). The first relates to the fact that education levels in Sweden have

skyrocketed in the span of the past three generations and education measures therefore

also capture an age demographic in the districts. What does age have to do with protest

potential? Numerous field visits to public consultation meetings in land use issues in

Sweden suggest that retirees are those most involved in land use planning (Grimes 2005).

Areas with lower levels of education may therefore also have a larger of retired people,

who are more inclined and equipped to become involved and protest unwanted facilities.

Controlling for the proportion of residents between the ages of 65 and 85 in the Göteborg

data supports this hypothesis, as the proportion of retirees in the area is negatively

associated with the number of facilities, and in fact does render the education measure

insignificant.

In contrast to the Göteborg case, the data suggest that the unequal voices mechanism may

to a larger extent be at play in the Malmö case (Model 4). The measure of electoral

turnout has a statistically significant and quite strong impact on the predicted number of

LULUs in a district. For every increase of one percentage points in voter participation,

the predicted probability of having an additional facility decreases by 6.1 percent. Figure

6 shows the relationship between voter turnout and the likelihood of having zero

facilities, and clearly shows that districts with high turnout are very likely to have no

LULUs whereas there is only a 60 percent likelihood of there being no LULUs in districts

with low turnout (around 50 percent). As a further indication of the strength of this effect,

we can compare it to Aldrich & Crook‟s results from New Orleans. The coefficient for

voter turnout on numbers of emergency housing units in that study (also building on

negative binomial regression) was -0.05, compared to the -0.06 estimate in the Malmö

data.

Finally, while evidence of a responsiveness effect is less marked and more circumstantial

in the Göteborg case, it may also be the case that decision makers in Göteborg may

simply have opted for a different strategy to avoid potentially costly protests. Models 2

and 4 include a dummy variable that captures whether the district contains large tract of

land zoned for industrial uses (non-residential area). The estimates for both cities are not

significant. In Göteborg, however, 12 of the 87 facilities (i.e. 14 percent), were placed in

areas zoned as industrial rather than residential. While placing facilities such as homeless

shelters, psychiatric wards and substance abuse centers in industrial areas may avert

conflict, it may not be optimal from the standpoint of the longer term aim of some of

these social services, which may include strengthening a person‟s employability,

independence and overall social and psychological well-being.

Discussion and conclusions

The analyses of LULUs in Göteborg and Malmö to some extent support the prior

assessments of governance in Sweden produced in country comparative studies. The

analyses produced no evidence of egregious discrimination of the use of public power for

private gain. Neither the proportion of immigrants in the district, nor whether key

decision makers resided in the area had any relationship to the number of contentious

facilities in a district. However, the data from both of the urban centers evince some

patterns that reveal that decision making may not be entirely free from mechanisms that

distort impartiality.

Indications that decision makers in these two urban areas may choose to give in to

citizens‟ demands rather than attempting to assure impartiality include the negative effect

of voter turnout in Malmö, and the negative income estimate in both cities. Even the

positive education estimate may suggest that responsiveness wins out over impartiality,

though additional investigation is needed on this point.

The effect of political activism in Malmö was, moreover, quite strong and on par with the

effect observed in the siting of emergency housing in post-Katrina New Orleans.

Interestingly enough, however, the effect of political activism does not appear to be the

mechanism that explains the link between income and low numbers of LULUs. The

income estimate remained under control for electoral participation. The effect of income

on low numbers of LULUs does not, in other words, seem to represent either actual or

anticipatory responsiveness to preferences of citizens known to have a greater capacity to

advance their own position and interests. The income effect instead may, in other words,

a different mechanism such as an unconscious (or conscious) classification model that

shapes the thinking of decision makers.

The theoretical discussion and empirical findings point to ambiguity in the

responsiveness concept, however. Aldrich (2008) attributed the tendency of decision

makers in Japan and New Orleans to avoid communities with comparatively strong

mobilization capacities to a desire to increase the efficiency of the decision-making

process. Aldrich undeniably has a point. Extensive protests and appeals add to the costs

of a decision-making process, and decision makers have good reason to limit the cost of

each individual facility siting process. It may be this tradeoff between impartiality on the

one hand and responsiveness/efficiency on the other that accounts for the fact that

authorities in Göteborg have in some cases opted to site facilities in non-residential

(industrial) areas. While responsiveness/efficiency have won out in Malmö, authorities in

Göteborg have identified a different strategy which preserves some degree of impartiality

without risking costly processes. Theoretically and empirically, the paper illustrates that

the factors that may distort impartiality encompass both legitimate and less legitimate

driving forces, and the distinction is not always unambiguous.

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Appendix Figure A1. Comparison of difference between observed counts and predicted probabilities from four models on (Göteborg data).

-.1

-.0

5

0

.05

.1

Observ

ed-P

redic

ted

0 1 2 3 4 5 6 7 8 9Count

PRM NBRM

ZIP ZINB

Note: positive deviations show underpredictions.

Note: PRM-Poisson regression; NBRM-Negative binomial regression; ZIP-Zero inflated poisson; ZINB-Zero inflated negative binomial regression.

Figure A2. Comparison of difference between observed counts and predicted probabilities from four models (Malmö data).

-.1

-.05

0

.05

.1

Observ

ed-P

redic

ted

0 1 2 3 4 5 6 7 8 9Count

PRM NBRM

ZIP ZINB

Note: positive deviations show underpredictions.

Note: PRM-Poisson regression; NBRM-Negative binomial regression; ZIP-Zero inflated poisson; ZINB-Zero inflated negative binomial regression.