Effects of Administrative Intensity on Organization Performance in an Emergency
Context: Evidence from Hurricane Rita
Sangyub Ryu
Public Management and Policy Analysis Program
Graduate School of International Relations
The International University of Japan
777 Kokusai-cho, Minami Uonuma-shi
Niigata, 949-7277, Japan
Email: sangyubr@iuj.ac.jp
Robert K. Christensen
Department of Public Administration and Policy
School of Public and International Affairs
The University of Georgia
Baldwin Hall 406, 355 South Jackson Street
Athens Georgia, 30605
Email: rc@uga.edu
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Abstract
Classical organization theorists gave much attention to bureaucratic structures for
efficiency maximization. Ironically, next generations of organization theorists criticized
inefficiency of bureaucracy. This study focusing on administrative intensity as a feature
of bureaucracy investigates the possible positive and negative effects of administrative
intensity on organizational performance. Furthermore, this study examines Thompson’s
and Mintzberg’s idea that non-operating cores buffer operating cores from environmental
threats. To do so, this study conducts a natural experiment focusing on Texas school
district’s administrative intensity in the context of Hurricane Rita. Finding shows that
administrative intensity has curvilinear association with organizational performance,
which explains the controversial argument about the effects of administrative intensity.
Finding also shows that the negative impacts of a hurricane on the performance of school
districts are mitigated as school districts hold high administrative intensity. This study
expects to contribute to better understanding of bureaucracy and administrative intensity.
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Introduction
Classical organization theorists gave much attention to efficiency maximization by
searching for certainty (Thompson, 2009). In order to ensure organizational certainty,
they focused on bureaucratic structure (Weber, 1946), scientific management (Taylor,
1916), or administrative management techniques (Gulick, 1937). Although next
generations of organization scholars criticized the limitation of a traditional approach and
moved attention to human behavior in organizations, the importance of bureaucratic
structure or a traditional approach to organizations is still valid (Meier, 2010). Moreover,
if the amount of efforts made for maintaining an organization rather than achieving
organizational objectives are understood as bureaucratization, every organization has
some degrees of bureaucratization (Blau and Scott, 1962). That is, regardless of the
context of an environment, whether it is stable or turbulent, all organizations become
bureaucratization for their survival. Thus, bureaucratization more or less helps
organizations in surviving.
This study narrows down the discussion of bureaucratization to administrative
intensity. Bureaucratization may be measured in various forms including the span of
control, organizational size, or the number of personnel (Chapin, 1951; Frisbie, 1975).
This measure, however, does not differentiate the operating core from non-operating core.
According to Mintzberg (1979), an organization is composed of five parts, which can be
divided in to operating cores which produce an organization’s core functions and nonoperating cores including strategic apex, middle line, technostructure, and support staff
which support for operating cores to function by sealing off the environmental shocks. As
for an organization, increasing either operating cores, non-operating cores, or both means
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bureaucratization, but which cores increase may mean different. In other words, an
increase of non-operating cores as compared to operating cores may mean intensive
administration, or administrative intensity, for an organization (Dalton, Todor,
Spendolini, Fielding, and Porter, 1980; Boyne and Meier, 2013).
This study investigates effects of administrative intensity in an emergency context
where environmental threats are evident. Some scholars support positive impacts of
administrative intensity in that it supports operating cores (Holland, 1963, Hildabrand
and Liu, 1965; Delehanty, 1968; Pondy 1969) while others criticize its negative effects
on organizational performance due to its inefficiency (Bohte, 2001; Chubb and Moe,
1990;Helman, 1951, 1956; Bidwell and Kasarda, 1975). This controversial argument may
result from the curvilinear effects of administrative intensity on organizational
performance. This study attempts to address this issue. Furthermore, this study
investigates a possible moderating role of administrative intensity in the negative
relationship between environmental threats and organizational performance. O’Toole and
Meier (1999, 2003) argued that organizational stability seems rigidity, but it can play a
buffering role to protect organizational core missions. If so, administrative intensity may
be able to protect organizational core preformation from a massive disaster. To
investigate a possible moderating role, this study takes a natural experiment in the context
of Texas school district and Hurricane Rita in 2005.
This study consists of the following parts. First, Weber’s bureaucracy and its
characteristics are introduced. Then, the positive and negative effects of administrative
intensity will be addressed in order followed by the moderating role of administrative
intensity. This study employs O’Toole and Meier’s (1999) contingent model; thus, their
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original model and a modified model for this study will be introduced. Then data and
sample of Texas school district and Hurricane Rita will be explained. Finally, conclusion
and discussion as well as findings will be addressed.
Bureaucracy and Its Characteristics
The classical organization theories put rationality central to all organizations (Gortner,
Nichols, and Ball, 2007). This approach to organizations searches for rationality by
pursuing a closed system of an organization or, at least, making outside forces being
predictable (Thompson, 2003). It assumes that goals and master plans are known, tasks
are repetitive, resources or inputs are always available while outputs disappear without
influencing organizational processes (Thompson, 2003).
Among classical theories, Weber’s (1946) bureaucracy helps understanding
organizations in modern democratic society. Weber believed that organizations can
enhance rationality, and ultimately maximize efficiency by taking bureaucratic structures.
The bureaucracy has a few distinctive characteristics. First, it bases on legal authority
from which the command-and-obedience mechanism is voluntarily derived and operated
between superiors and their subordinates (Fry and Raadschelders, 2008). Second, the
bureaucracy follows a hierarchical command of chain, impersonalized rule-based job
descriptions and office management, official task operation through documentation, and
specialization of individual functions (Weber, 1946). Third, the characteristics of
bureaucracy lead to the specific personnel system (Weber, 1946). For instance, officials
are appointed, not elected, based on professional qualification, and their promotion
depends solely on merit (Fry and Raadschelders, 2008). According to Fry and
Raadschelders (2008), Weber believed that bureaucracy is a more rational and efficient
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organizational form than any other known alternatives because of “its precision, speed,
consistency, availability of records, continuity, potential for secrecy, unity, rigorous
coordination, and minimization of interpersonal friction, personnel costs, and material
costs" (36).
Administrative Intensity and Organizational Performance
Since classical organization theories emphasizing rationalization and efficiency were
proposed, different approaches such as neoclassical organization theories, the
organizational behavior perspectives, or “modern” structural organization theories have
been studied to better understand organizations (Shafritz, Ott, and Jang, 2005). However,
if bureaucratization is defined as the amount of efforts dedicated to maintain the
organization rather than to accomplish its goals directly, all formal organizations have
some sort of bureaucratic forms (Blau and Scott, 1962). Thus, whatever
approaches/perspectives it may be taken to understand organizations, bureaucracy needs
to be taken into account together. In this sense, bureaucratization, or administrative
intensity of an organization deserves attention.
Although bureaucracy is essential to understand organizations, it should be
admitted as well that some use bureaucracy as a pejorative slogan (Olsen, 2005). In this
sense, President Reagan’s view on bureaucracy as a problem is not surprising. However,
an organization’s bureaucratization must not always be understood as negative.
Bureaucratization is a natural change of an organization to maintain itself, to adapt to its
changing environment, and to survive. As its environment becomes more uncertain, a
bureaucracy continues to formalize its functions to seal off its environmental uncertainty.
At that same time, it increases more specialized divisions to adapt to its environment.
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Furthermore, a bureaucracy depends on its external environment to successfully obtain
necessary resources for survival (Eisenstadt, 1959). As a result, an organization’s
bureaucratization is an inevitable process of interaction with its environment to maintain
its structure and to survive.
Administrative intensity is a similar concept with bureaucratization in a sense that
both capture a complex organizational structure. However, administrative intensity may
be distinct from bureaucratization in a way that administrative intensity differentiates
operating cores from non-operating cores. Previous researcher measured
bureaucratization by the total size or total number of functions of an organization (Chapin,
1951; Frisbie, 1975). Administrative intensity, on the other hand, is measured by a ratio
of the number of administrative personnel to the number of production workers (Dalton,
Todor, Spendolini, Fielding, and Porter, 1980). According to Dalton et al. (1980)
administrative personnel include managers, professionals, and clerical workers while
production workers include craftsman, operatives, and laborers. This typology is similar
to Mitntzberg’s (1979) five parts of organization. According to Mintzberg, organizations
consist of strategic apex, middle line, operating cores, technostructure, and support staffs.
Here, operating cores refer to those personnel/divisions in the organization that carry out
the organization’s basic work associated with producing its outputs and outcomes
(Mintberg, 1979). All other parts except operating cores, hereafter called as nonoperating cores, deliver different functions in the organization, but the ultimate purpose
of non-operating cores is to assist operating cores to produce the organization’s core
functions. Thus, increasing administrative intensity provides operating core with a better
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working environment, which ultimately leads to better organizational performance.
Therefore, it can be hypothesized as following:
Hypothesis 1: Administrative intensity increases organizational performance.
Administrative Pathology, or the Dark Side of Administrative Intensity
This study hypothesizes a positive relationship between administrative intensity and
organizational performance. However, more is not always better. Holding more
administrative intensity means bigger bureaucracy, and its big size is regarded as a
principal problem of bureaucracy (Goodsell, 2004). Some scholars argued that
bureaucracy, by nature, tends to expand itself. For instance, Parkinson (1957) argued that
superiors create more positions for their subordinates in order to countercheck one
another and to eliminate possible subordinates’ threats to the superiors. Niskanen (1971)
viewed bureaucrats as self-interested agents. According to him, bureaucrats attempt to
maximize their budgets not for organizational outcomes but for keeping and increasing
their organizational power. Downs (1967) made a similar argument. He contended that
bureaucracy has no restraints on expansion, and it increases its size in order to exercise
more power. After all, the growth in size of bureaucracy results in rigidities and fails to
readily adjust to changing environments (Merton, 1968). For these reasons, some scholars
“bash” bureaucracy. They argue that bureaucratization results in negative outcomes such
as 1) “poor performance,” 2) “excessive power,” and “oppression of the individual”
(Goodsell, 2004. 11).
Scholars of public choice also argue that bureaucracy do not operate efficiently
(Goodsell, 2004). According to Goodsell (2004), bureaucracy depends on a single budget
source, which automatically supplies fund to bureaucrats regardless of their needs. This
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mechanism makes bureaucrats budget maximizers, not profit maximizers, which leads to
unlimited budget expansion, inefficient budget allocation, and, in the end, budget deficit
(Goodsell, 2004). This is more problematic if the size of non-operating cores increases
more than operating cores since they do not directly produce organizational outcomes. As
a result, increasing administrative intensity can distort the budget supply and demand
chain, and cause inefficient, poor performance.
Another bureaucracy bashing results from redtape. More administrative intensity
means more formalization and administrative divisions. In a sense that bureaucracy
increases more specialized division and make more rules to seal off environmental
uncertainty (Thompson, 1964), administrative intensity does not necessarily result in
redtape. However, if the size of non-operating core is increased due to reasons that
Parkinson (1957), Niskanen (1971), or Downs (1967) argued, high administrative
intensity can produce unnecessary, burdensome rules and formalization, which can cause
negative organizational outcomes. Based on the literature above and the literate deriving
the 1st hypothesis above, this study hypothesizes a possible curvilinear relationship
between administrative intensity and organizational performance.
Hypothesis 2: Administrative intensity curvilinearly influences organizational
performance where high administrative intensity negatively influences
organizational performance.
Rediscovery of Administrative Intensity, or Administrative Intensity and
Organizational Uncertainty
From the literature, this study hypothesizes the curvilinear relationship between
administrative intensity and organizational performance. This time, this study changes its
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scope to environmental threats. Earlier, Weber (1946) pointed that bureaucracy aims at
buffering organizational core functions from environmental shocks or at least attempts to
make environments predictable. In regard to bureaucracy which may operate in a closed
system, some argue that administrative intensity may not fit in an open system where
organizational environment is uncertain and instable (Burns and stalker, 1961; Pan and
Tsai, 2012). However, others contend differently that organizations tend to have a high
level of administrative intensity when organizations operate in an uncertain environment
in order to adapt to the changing environment. (Jones, 1977). Moreover, Thompson
(1964) suggested creating a greater number of units that are functionally differentiated in
order to cope with uncertain environments. This change, according to Jones (1977), leads
to the growth of administrative intensity. In other words, administrative intensity is
inevitable to protect an organization’s core functions from its environment.
Mintzberg (1979) posited a similar argument. He suggested five parts of an
organization. He contended that an organization consists of five parts: strategic apex,
middle line, operating core, technostructure, and support staff. Here operating core is
similar to Thompson’s technical core. It produces an organization’s core goods and
services. Strategic apex, according to Mintzberg, is a top manager of the hierarchy while
middle line is middle line managers who link the strategic apex and technical core
through the command chain. Technostructure is analysts that standardize the work of the
organization and assist the organization to adapt to its environment (Mintzberg, 1979).
Finally, the support staff deals with direct support for the operating cores (Mintzberg,
1979). Every last component of the structure is critical to protect the operating core from
its environmental threats and to promote the operating core to accomplish its tasks. Here,
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enhancing non-operating core indicates increased administrative intensity, and it can be
concluded that administrative intensity can enhance organizational performance by
protecting an organization’s operating core when an organization is exposed to
environmental treats.
A similar argument is made by O’Toole and Meier (1999). They argued that
organizational outcomes are vulnerable to negative environmental shocks, but its
negative association can be mitigated by organizational stability. O’Toole and Meier
(2003) introduced five types of organizational stability: structural stability, personnel
stability, procedural stability, production stability, and mission stability. Among them,
structural stability is “multidimensional and includes such elements as size,
formalization, differentiation, and span of control” (O’Toole and Meier, 2003. 45). That
is, when an organization faces negative environmental shocks, organizations can protect
its core mission by enhancing structural stability. Their argument suggests, along with
Thompson and Mintzberg’s emphasis on organizational buffering through structure,
administrative intensity can enhance organizational performance by buffering
environmental shocks. Thus, this study hypothesizes as following:
Hypothesis 3: Environmental threats negatively influence organizational
performance, but administrative intensity can mitigate such a negative influence.
The Model
This study employs a contingent model proposed by O’Toole and Meier (1999). The
model predicts organizational performance as a functions of its previous performance,
environmental shocks, organizational stability, and internal and external management as
following:
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𝑋
𝑀
𝑂𝑡 = 𝛽1 (𝑆 + 𝑀1 ) × 𝑂𝑡−1 + 𝛽2 ( 𝑆𝑡) × (𝑀3 ) + 𝑒𝑡
4
------(1)
where, 𝑂𝑡 and 𝑂𝑡−1 are organizational performance at time t and time t-1 respectively;
S denotes organizational stability;
𝑀1 is a management effort to manage the organization;
𝑋𝑡 is environmental shocks at time t;
𝑀3 and 𝑀4 are management efforts to exploit environmental resources and to buffer
environmental threats respectively; and
𝑒𝑡 is some random shocks.
𝛽1 and 𝛽2 are estimable parameters.
Since this study focuses on administrative intensity, its curvilinear impacts, and
its moderating impact on environmental threats, this study modifies its original form into
the following way:
𝑂𝑡 = 𝛽1 𝑂𝑡−1 + 𝛽2 𝑆𝑎 + 𝛽3 𝑆𝑎2 + 𝛽4 𝑋ℎ + 𝛽5 𝑆𝑎 × 𝑋ℎ + 𝛽6 𝑋𝑡 + 𝑒𝑡
------(2)
where, 𝑂𝑡 and 𝑂𝑡−1 are organizational performance at time t and time t-1 respectively;
𝑆𝑎 is administrative intensity;
𝑋ℎ is environmental threats (hurricane impacts);
𝑋𝑡 is all other environmental shocks at time t; and
𝑒𝑡 is some random shocks.
𝛽1 through 𝛽6 are estimable parameters.
In this model, hurricane impacts are included to operationalize environmental threats.
More explanation will follow in the proceeding section.
Unit of Analysis, Sample and Data
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A unit of analysis in this study is a school district in Texas. Some criticizes studying
public administration using school districts as a sample since school districts do not
capture general public administration (Luton, 2007). In a sense that teachers as streetlevel bureaucrats are highly professionalized as compared to other field of public
administration offices, it may be plausible that school districts are limited to represent the
overall public administration. However, according to Meier and O’Toole (2009), more
than 1 percent of public servants are working in this field. Considering no other fields of
public administration hold more than this amount of workforces, it is necessary to
understand school districts as a field of public administration (Meier and O’Toole, 2009).
However, this study admits that due to different characteristics of school districts with
other public administration offices, findings and implications may be carefully applied to
other field of public administration.
This study conducts a natural experiment in the context of Hurricane Rita. In
2005, right after Hurricane Katrina, Hurricane Rita hit the east side of Texas. When Rita
made landfall, it was recorded as a Category 3 hurricane, which, based on the SaffirSimpson Hurricane Scale, can cause the destruction of poorly constructed and managed
buildings.1 According to Meier, O’Toole and Hicklin (2010), seven fatalities and $10
billion in property damage resulted from Rita. For school districts, a total of 243 school
district canceled classes for about 6 days, and in an extreme case, school districts closed
for more than 5 weeks (Meier, O’Toole, and Hicklin, 2010). As an evident environmental
threat, this study employs Hurricane Rita’s impact operationalized by its severity of wind
forces. In order to do so, this study samples only those school districts hit by Hurricane
1
The Saffir-Simpson Hurricane Wind Scale. National Hurricane Center. Accessed on May 13. 2013 at
http://www.nhc.noaa.gov/sshws.shtml.
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Rita. Since the path of the hurricane was random, this type of sample provides a natural
experiment. Some may argue that some school districts were more or less vulnerable to
the hurricane. However, the hurricane’s path was not correlated with school districts’
vulnerability; thus, such factors can be ignorable.
To set a sample, this study utilizes geographic information about Hurricane Rita
obtained from the website “HurricaneMapping.com.”2 The information discloses three
levels of hurricane wind forces which passed Texas school districts: wind at 1) above 74
mph of wind speed; 2) between 58 mph and 74 mph of wind speed; and 3) between 39
mph and 58 mph of wind speed. Using an ArcGIS software, school districts fallen in this
area were chosen as a sample for analysis. Table 1 shows numbers of school districts
under each wind force.
[Table 1 about here]
Because Hurricane Rita hit Texas in 2005, Texas school district data in 2005 were
used for all independent and control variables. To make sure a time causal relationship, a
dependent variable was drawn for Texas school district data in 2006. All school district
data were drawn from Texas Education Agency website.3
Variables
Organizational performance
As an organizational performance measure, this study focuses on students’ pass rates of
Texas Assessment of Knowledge and Skills (TAKS). As a state-wide, annual
standardized test, the TAKS measures students’ achievement of their basic academic
skills, and students from grades 3 to 11 should pass the TAKS in order to move upper
This website is run by Sea Island Software, and the author requested and obtained Rita’s GIS data through
email.
3
Data are available at http://ritter.tea.state.tx.us/perfreport/aeis/. Accessed on May 13, 2013.
2
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grade (Meier and O’Toole, 2003). Given that school districts and their local community
care about the TAKS results, superintendents treat the TAKS pass rates as one of the core
organization performance indicators, and focus their management on its results (Meier
and O’Toole, 2003). This study is interested in districts’ performance after Hurricane
Rita, which hit in September 2005. Thus, the TAKS pass rates in 2006 will be used as a
dependent variable. In addition, the O’Toole-Meier model proposes that organizations
have inertial characteristics; thus, to conform the model, its lagged variable is controlled.
Administrative intensity
There is no one rule to measure administrative intensity. Jones (1977) measured
administrative intensity as “the ratio of nonworkflow personnel [supportive or
administrative personnel who are not directly involved in the process of production or
transformation] to the sum of nonworkflow personnel and workflow personnel [those
who are involved in the process of the actual production or transformation]” (19). Dalton
et al. (1980) measured administrative intensity as the number of administrative personnel
(mangers, professionals, and clerical workers) divided by the number of production
workers (craftsman, operatives, and laborers) while Boyne and Meier (2013) focused on
the number of employees in administrative positions out of total employees. Pan and Tsai
(2012) focused on monetary aspects of private firms, and measured administrative
intensity as “the ratio of administrative expense to net sales” (278). This study focuses on
Mintzber’s (1979) division of operating cores and non-operating cores, and measures
administrative intensity as the ratio of non-operating cores to operating cores. Nonoperating cores are personnel who do not directly produce organizational services, and
they include, in school district context, professional support, campus administration, and
15
central administration. Operating cores are teachers and educational aides who directly
assist teachers’ works. The mean of this variable is 16.4 with standard deviation of 4.1.
Hurricane Rita’s wind forces
To measure environmental threats, this study employs the wind forces of Hurricane Rita.
As mention above, geographic information of Hurricane Rita’s wind forces is used. This
measure consists of three dichotomous variables: a low wind force variable is coded as 1
if districts are under the Hurricane Rita’s wind force between 39 and 58 mph; otherwise,
coded as 0. A middle wind force variable is coded as 1 if districts are under the Hurricane
Rita’s wind force between 58 and 74 mph; otherwise, coded as 0. A high wind force is
coded as 1 if districts are under the Hurricane Rita’s wind force more than 74 mph;
otherwise coded as 0. A low wind force variable is used as a baseline for the analysis.
Control variables
In addition to administrative intensity and a hurricane’s wind forces, this study controls
for task difficulties, teaching capacity, and organizational resources. As for task
difficulties, this study controls for the percentage of Hispanic, African-American, and
financially disadvantaged students respectively. According to Burtless (1996), school
districts with a homogeneous student body are more likely to perform better (cited in
Meier, O’Toole, Boyne, and Walker, 2006). Moreover, families of financially
disadvantaged students who are eligible for free or reduced-price school lunch tend to
have a less supportive environment for their study and may suffer from complex and
various learning problems (Jencks and Phillips, 1998 cited in Meier, O’Toole, Boyne, and
Walker, 2006). Also, class size measured by the ratio of the number of students to teacher
is controlled as task difficulties. As for teaching capacity, this study controls for teachers
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experience and their salary. Lastly, as organizational resources, this study controls for
operating expenditure per student, total adopted tax rate, and teacher’s turnover.
To conduct analyses, this study employs OLS regression, and finds that all GaussMarkov assumptions are met except multicollinearity, which results from a squared term
and an interaction term. The descriptive statistics and correlation coefficient are reported
in Table 2.
[Table 2 about here]
Results
Table 3 shows OLS regression results. Model 1 of Table 3 tests curvilinear effects of
administrative intensity on organizational performance. First, both administrative
intensity and its squared term is statistically significant at 0.01 significance level. A
positive coefficient for administrative intensity and a negative coefficient for squared
term of administrative intensity indicate that administrative intensity has an inverted Ushaped relationship with organizational performance. That is, up to a certain point, the
growth of administrative intensity positively influences organizational performance, but
after the point, more increase in administrative intensity decreases organizational
performance. This results support the first and second hypothesis. Figure 1 presents a
graphical outcome of this particular model. As shown, the turning point is 18.201. That
is, when the ratio of non-operating core to operating core increases up to about 18
percent, administrative intensity positively influences organizational performance.
However, more than 18 percent of administrative intensity negatively influences
organizational performance. The mean of administrative intensity in this sample is
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16.397. Thus, more than half of school districts enjoy positive impacts of their
administrative intensity.
[Figure 1 about here]
Model 2 in Table 3 adds hurricane’s wind forces to Model 1. It turns out that
severe wind force (at more than 75 mph) negatively influences organizational
performance as compared to mild wind forces (at between 39 and 57 mph) while
moderate wind force (at between 58 and 74 mph) does not statistically influence
organizational performance. That is, as compared to school districts hit by hurricane wind
force at between 39 and 57 mph, school districts hit by hurricane wind force at more than
75 mph shows 5.337 percent lower in the TAKS pass rates. Given these control variables,
Model 2 shows consistent curvilinear relationship between administrative intensity and
organizational performance as found in Model 1.
Model 3 in Table 3 tests moderating effects of administrative intensity in the
negative relationship between hurricane wind forces and organizational performance.
First, the result shows that wind force at between 58 and 74 mph does not statistically
influence organizational performance. Its interaction term is not statistically significant as
well. However, severe wind force at more than 75 mph negatively influences
organizational performance. When the value of administrative intensity is hypothetically
zero, school districts hit by hurricane wind force at more than 75 mph has lower TAKS
pass rates than school districts hit by wind force at between 39 and 57 mph by 27.735
percent. This negative influence is moderated by administrative intensity since the result
shows positive and statistically significant impacts of its interaction term. In other words,
as school districts increase their administrative intensity, the negative impact of severe
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hurricane wind forces on performance decreases. Moderating effects are not found from
those school districts hit by hurricane wind forces at between 58 and 74 mph. This may
imply that administrative intensity plays a significant buffering role only when
organizational threats are evident and massive. Hypothesis 3 is supported.
Model 4 of Table 3 integrates curvilinear and moderating effects of administrative
intensity on organizational performance. Although the statistical power for administrative
intensity becomes lower, both curvilinear relationship and moderating role of
administrative intensity are consistently found as they are in Model 1 through Model 3.
Graphical outcomes are shown in Figure 2. Regardless of the severity of a hurricane,
there are curvilinear relationship between administrative intensity and organizational
performance. When the severity is taken into account, impacts of administrative intensity
on organizational performance result in a complicated, but interesting story; when the
size of administrative intensity is low, its impacts on organizational performance is also
low when the severity is high. However, previous findings suggest that low
administrative intensity is not helpful for improving organizational performance. When
the level of administrative intensity increases, its effects on organizational performance
become higher when the severity of a hurricane is high. The turning points for two graphs
are also different. When threats of a hurricane do not exist, the turning point is 14.650
while when treats of a hurricane is severe, the turning point is 23.664. This implies that,
when an organization faces an environmental threat, the turning point becomes higher;
that is, more administrative intensity than usual leads to an increase of organizational
performance.
[Figure 2 about here]
19
As for control variables, lagged TAKS pass rate variable is found statistically,
positively significant across models. This finding supports that organizations have inertial
characteristics as proposed by O’Toole and Meier (1999). Percentage of African
American students and financially disadvantaged students are found to negatively
influence organizational performance. Even if it is out of this study’s scope, as far as this
study finds students’ ethnicity and their family incomes matter for task difficulties and
their academic achievement, future research needs to focus on this matter. Lastly, total
adopted tax rate is found to be negatively associated with organizational performance.
School districts have their own tax power; they set their own tax rates and collect taxes.
The finding shows that school districts with high tax rates have lower performance. This
needs careful interpretation. Mostly, school districts with financially disadvantaged areas
tend to collect more taxes to supplement their budget deficit. Thus, school districts with
higher tax rates are poor school districts, and they may hold academically poor
environments. However, this is just one possible scenario, and more investigation needs
to follow for precise understanding.
[Table 3 about here]
Conclusion and Discussion
Bureaucracy has been equivalent to inefficiency and criticized by politicians, scholars,
and the public. However, the beginning and development of study on bureaucracy
pursued maximization of organizational efficiency, and all organizations have some
degree of bureaucratization to manage their organizations. In the context of public
schools, some argue that bureaucrats are the cause of bureaucratization (Chubb and Moe,
1990). According to Chubb and Moe (1990), bureaucrats are self-interested, and they
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maximize their interests by bureaucratization. Agreeing with bad effects of
bureaucratization, Chubb and Moe point not bureaucrats themselves but public education
systems in which politicians, interest groups and bureaucrats work together as a cause of
bureaucratization. Whatever the causes might be, bureaucratization is regarded as a major
determinant of poor performance of organizations.
Opponents make a different argument. They argue that bureaucratization is an
outcome to respond to organizational problems; in other words, when organizations have
problems, they take actions to resolve problems, which causes bureaucratization (Meier,
Polinard, and Wrinkle, 2000; Smith and Meier, 1994). Thus, bureaucratization is
inevitable and essential to run organizations.
These two opposite arguments are relevant to this study since this study focuses
on administrative intensity as a part of bureaucratization. This study finds that both
arguments are both right and wrong depending on situations that organizations face. The
current study suggests that high administrative intensity which is derived by adding more
non-operating cores in an organization as compared to operating cores reduces burdens,
which, otherwise, should be taken care of by operating cores in addition to an
organization’s core functions. However, too high administrative intensity can cause
inefficient relocation of resources. An inverted U-shaped association between
administrative intensity and organizational performance can be found in most
organizations, but the right level of administrative intensity can be different across
organizations. Therefore, finding a right level of administrative intensity for each
organization is essential.
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The importance of administrative intensity measured by the ratio of non-operating
core to operating core is more evident in an emergency context. Thompson (1964) and
Mintzberg (1979) point that operating core (or technical core) provides core functions of
the organization, and non-operating core protects the operating core from the
environmental turbulent. Therefore, the value of administrative intensity becomes more
noticeable as the extent of environmental threats is increasing. In the context of massive
natural disaster, organizations with higher administrative intensity are likely to
protect/improve their core functions and performance. Findings still show an inverted Ushaped relationship between administrative intensity and organizational performance in
the context of natural disaster, it is also found that the turning point of the inverted Ushaped relationship is much higher when organizations face massive environmental
threats. Thus, this study admits that excessive level of administrative intensity may cause
organizational inefficiency and results in poor organizational performance, but up to a
right level, administrative intensity help organizations enhancing their performance both
in a normal situation and in an emergency situation.
This study has some limitations and further studies need to follow in order to
resolve the limitations. This study is conducted in the context of public school systems.
Considering that more than half of state and local budget is spent in the education field
(Kettl and Fesler, 2007), studying public schools is essential to understand public
organizations. However, public schools are distinct from other public organizations in
that the street bureaucrats in the public schools (teachers) are highly professionalized as
compared to others (Meier and O’Toole and, 2009). Thus, any findings of this study need
22
to be carefully applied to other organizations. Of course, future research needs to follow
in different contexts.
Nonetheless, this study provides a unique opportunity to understand effects of
administrative intensity. This study is one of the only a few public management studies
taking a natural experiment. Hurricane Rita hit the east side of Texas, and those districts
randomly hit by the hurricane are chosen as a sample of this study. Thus, this sample
provides a good opportunity for scholars to investigate effects of administrative
intensity.4
4
Authors very much regret that many residents lost their family and friends as well as their properties due
to Hurricane Rita.
23
Table 1. School Districts under Hurricane Influence
Hurricane Wind Force
Number
Percent
39-58 mph
73
38.62
58-74 mph
63
33.33
74+ mph
52
28.04
24
Table 2. Correlation and Descriptive Statistics
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(1) TAKS Pass Rates 2006
1.000
(2) Administrative Intensity
0.066
1.000
(3) Administrative Intensity Squared
0.020
0.984
1.000
(4) Wind Forces at 55-75 mph
0.058
0.257
0.265
1.000
(5) Administrative Intensity x Wind Force (55-75 mph)
0.060
0.407
0.419
0.962
1.000
(6) Wind Forces at 75+ mph
-0.247
-0.159
-0.157
-0.441
-0.425
(7) Administrative Intensity x Wind Force (75+ mph)
-0.184
-0.006
-0.019
-0.423
-0.407
0.957
1.000
(8) % Latino Student
-0.049
0.226
0.218
0.248
0.275
-0.282
-0.244
1.000
(9) % African American Student
-0.462
0.254
0.270
0.015
0.046
-0.071
-0.033
0.162
1.000
(10) % Low-income Students
-0.550
-0.033
-0.006
-0.019
-0.046
0.115
0.122
0.275
0.561
1.000
(11) Class Size
0.260
0.520
0.481
0.239
0.319
-0.152
-0.076
0.449
-0.038
-0.327
1.000
(12) Teachers Salary in Thousand
0.172
0.360
0.332
0.324
0.371
-0.157
-0.133
0.504
0.004
-0.252
0.665
1.000
(13)Teachers Experience
0.037
0.008
-0.018
-0.165
-0.189
0.067
0.110
-0.336
0.094
-0.081
-0.153
0.024
1.000
(14) Operating Expenditure per Pupil (in thousand)
-0.238
-0.358
-0.302
-0.119
-0.160
0.196
0.109
-0.287
0.058
0.275
-0.726
-0.268
0.017
1.000
(15) Total Adopted Tax Rate
-0.034
0.183
0.170
0.173
0.228
-0.035
-0.066
0.390
-0.045
-0.170
0.460
0.472
-0.354
-0.209
1.000
(16)Teacher's Turnover
-0.224
0.010
0.045
-0.012
-0.014
0.013
0.025
0.087
0.314
0.397
-0.202
-0.263
-0.306
0.309
-0.047
1.000
(17) TAKS Pass Rate 2005
0.611
0.019
-0.009
0.028
0.037
-0.166
-0.152
-0.126
-0.430
-0.598
0.248
0.183
0.024
-0.174
0.028
-0.277
189
189
189
189
189
189
189
189
189
189
189
189
189
189
189
189
Observation
(17)
1.000
1.000
189
58.46
6
13.92
7
Mean
64.868
16.397
285.696
0.333
5.963
0.280
4.305
16.583
17.326
51.526
13.323
38.433
12.425
6.651
1.480
16.228
Standard Deviation
13.789
4.114
138.093
0.473
8.790
0.450
7.223
15.508
14.618
15.985
1.995
3.184
1.959
1.030
0.114
7.286
Minimum
27
4.545
20.657
0
0
0
0
0.700
0
2.200
7.549
33.452
5.593
4.895
1.225
0
11
Maximum
96
27.648
764.412
1
27.648
1
23.823
74.800
72.800
95.100
17.796
48.181
17.410
11.935
1.815
49.828
94
25
Table 3. Curvilinear and Moderating Effects of Administrative Intensity on Performance
Model 1
Model 2
Model 3
VARIABLES
Coefficient Coefficient Coefficient
Administrative Intensity
2.985***
Administrative Intensity Squared
(0.032)
-0.299
(1.098)
(1.083)
(0.330)
-0.082**
-0.085***
-0.067**
(0.032)
(0.033)
-7.710
(1.862)
(7.533)
(7.492)
0.303
0.392
(0.432)
(0.431)
-27.735***
-24.477***
(2.014)
Administrative Intensity x Wind Force (75+ mph)
% Low-income Students
Class Size
Teachers Salary in Thousand
Teachers Experience
Operating Expenditure per Pupil (in thousand)
Total Adopted Tax Rate
Teacher's Turnover
0.115
(1.155)
-6.416
-5.337***
Wind Forces at 75+ mph
1.963*
-1.262
Administrative Intensity x Wind Force (55-75 mph)
% African American Student
Coefficient
3.035***
Wind Forces at 55-75 mph
% Latino Student
Model 4
0.043
(7.856)
(7.947)
1.420***
1.208**
(0.479)
(0.485)
0.009
0.016
(0.074)
(0.078)
(0.079)
(0.078)
-0.197***
-0.237***
-0.236***
-0.230***
(0.068)
(0.069)
(0.069)
(0.068)
-0.231***
-0.173**
-0.165**
-0.171**
(0.078)
(0.080)
(0.080)
(0.079)
0.272
0.687
0.484
0.611
(0.873)
(0.876)
(0.872)
(0.866)
0.014
0.069
0.299
0.199
(0.432)
(0.440)
(0.439)
(0.438)
0.062
0.058
-0.015
-0.077
(0.482)
(0.482)
(0.486)
(0.482)
-0.174
0.289
-0.155
0.381
(1.312)
(1.306)
(1.278)
(1.293)
-22.851***
-20.620**
-15.577*
-17.379**
(8.247)
(8.169)
(8.235)
(8.208)
0.137
0.126
0.090
0.097
(0.127)
(0.125)
(0.126)
(0.124)
0.363***
0.333***
0.335***
0.325***
(0.068)
(0.068)
(0.067)
(0.067)
59.363***
48.150**
69.494***
53.956**
(21.517)
(21.624)
(20.303)
(21.509)
189
189
189
189
R-squared
0.508
0.528
0.533
0.545
Adjusted R-squared
0.475
0.490
0.493
0.502
TAKS Pass Rate 2005
Constant
Observations
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
26
Figure 1.
27
Figure 2.
28