The Failure of Theory to Predict the Way Public SectorOrgani.docx

The Failure of Theory to Predict the Way Public Sector
Organisation Responds to its Organisational
Environment and the Need for a Mosaic-View
of Organisational Theory
Bryane Michael & Maja Popov
Published online: 25 November 2014
# Springer Science+Business Media New York 2014
Abstract What does theory predict about the way government
size and structure
adapts to changes in government’s organisational environment
(particularly to
uncertainty and complexity)? In this paper, we review the
theory and evidence
from the literature about the way government size adjusts to
such changes –
particularly to changes in macroeconomic fundamentals like
gross national prod-
uct (GDP). We find that the traditional theories from the
organisational theory
literature—like the contingency-based view, resource-based
view and the rational
choice view – fail to provide global explanations for much of
the variation we
see in the world around us. Instead, theorists need to adopt a
“mosaic view” of
organisational theory – accepting that different theories may
explain the way
public sector size and structure responds to the uncertainty and
variability in its

(macroeconomic) organisational environment. We also provide
several empirical
hypotheses to test such a mosaic-view.
Keywords Contingency theory. Public sector organisational
theory. Resource-based
view. Size of government . Government structure . “Mosaic
view”
JELCodes . F4 . D7 . E6 . H1 . H4
Public Organiz Rev (2016) 16:55–75
DOI 10.1007/s11115-014-0296-5
The views expressed in this paper remain the views of the
authors alone and do not reflect the views of the
organisations for which the authors work or are affiliated with.
The affiliations shown as of time of writing.
B. Michael (*)
Columbia University (SIPA), 420 W 118th St #1, New York,
NY 10027, USA
e-mail: [email protected]
M. Popov
General Secretariat of the Government of Serbia, 11 Nemanjina
St., Belgrade, Serbia
Introduction
Despite over 40 years of theorizing about public sector
organisation, we still know very
little about how government responds to changes in its
organisational environment. A

variety of theories predict how government size and structure
should respond to the
national macroeconomic environment it regulates (as well as
buys and sells labour, capital
and goods in). Contingency theorists argue – though are now in
relative disrepute – that
government departments and agencies grow, shrink, divide
and/or merge in response to
changes in the macroeconomic environment (Gupta et al. 1994).
Resource-based theorists
– and their newer off-spring who write about “competencies” –
argue that these govern-
ment departments morph, depending on the resources
(budgetary, staffing, know-how and
so forth) they already have available – or can obtain through
bureaucratic and/or political
means (Bryson et al. 2007). Rational-choice theorists, and select
scholars in public
administration, argue that government organisational structure
does (or should)
foresee upcoming challenges and respond to them before they
occur (Robertson
et al. 1993 and especially Vietor 2007). Finally, in opposition to
these classical
theories, a new school of interpretative and post-modern
scholars argue that
government organisational structure reflects cognitive
understandings, culture,
politics and symbols which no empirical study can correctly
capture – or even
try to (Frumkin and Galaskiewicz 2004). Yet, despite these 40-
plus years of
studying public sector organisational theory, most primers about
organisational
theory in the public sector contain almost no actual empirical
studies proving or

disproving the theories they present (Christensen et al. 2007).
In this paper, we argue for a “mosaic view” of organisational
theory – one which
accepts that different organisational theories may apply to
different governments’size and
structure at differing times. By comparing various theories
(often from outside the
organisational theory literature), we find that no one theory of
sufficiently explains the way all governments respond to their
organisational environ-
ments over all time periods. Our paper is organised as follows.
The first section provides a
basic overview of government sizes and the complexity and
variability of the
macroeconomies in which they exist. The section also provides
an overview of the proxies
we use to assess the extent to which public sector organisational
structure adapts to
changes in its organisational environment. The second section
looks at the arguments
and supporting evidence for the contingency-based view of
organisational adaptation. We
find that any contingency-based view of such change proves
more convincing for some
countries than for others. The third section assesses the extent
to which – if governments
do adapt their organisational structure to their macroeconomic
environment - they do so
“rationally.” Namely, do they accurately anticipate future
macroeconomic changes and
change government organisational structure (using whatever
resource available) in antic-
ipation of such change? Or do they observe such changes and
adapt reactively – if at all?

The fourth section assesses the extent to which governments
adapt their structure based on
the resources (tax revenue) at their disposal, rather than due to
changes in the macroeco-
nomic environment. Again, we find that the resource-based view
of organisational change
probably fits some countries better than others. The fifth section
argues for a “mosaic”
view of public sector organisational theory. In such a view,
different theories might apply
to different countries—at different times. We also propose
several hypotheses which in-
depth empirical study could help shed light on. The final section
concludes.
56 B. Michael, M. Popov
We would like to point out several caveats and limitations to
our discussion before
we begin. First, we discuss the extremely broad concept of
“organisational adaption”
using the very narrow definition of government expenditure (as
a proxy for
organisational size). We adopt this tactic to keep our paper to
the journal’s word limits.
Interested readers can see a much fuller description and
discussion of the
various dimensions of public sector organisation in the working
paper version
of this paper.1 Second, we do not attempt to deconstruct the
vast literature on
the topic – from papers which construct the concept of
“organisation” to
extensive reviews of organisational theories. 2 Third, we do not

attempt to
review the literature on public sector organisation.3 Our study
sits uncomfort-
ably at the nexus of macroeconomics and organisational theory
– as we attempt
to assess how well organisational theory helps us understand
public sector
organisation across-borders. As such, we draw from studies
outside of
organisational theory—often interpreting them through an
organisational theorist
lens.4
What Does Government Size and its Organisational
Environment Look Like
Across Countries?
The sizes of governments around the world vary between about
10 % of GDP to over
50 % of GDP. A cursory glance at Fig. 1 shows few similarities
between countries
which allow for generalisations about government sizes.
Lesotho, the Maldives,
Greece, Hungary and France have some of the largest
governments—in terms of the
amount of national resources managed and spent by the
government (spending about
twice the world average).5 Academics since Meltzer and
Richard (1981) have tried to
explain such differences. Economists like Alesina and Wacziarg
(1998) and Rodrik
(1996) have sought explanations—from trade openness to
differences in politics. In
truth, academics have given up on such explanations rather than
settled on a definitive
one.6

1 Michael and Popov (2011) provide an extended (200-page)
working paper version of this paper under the
title The Size and Structure of Government available online.
The working paper contains an extended
discussion of size and structure as well as the model and the
empirical results we call for in this paper.
2 Much organisational theorising over the recent 10–20 years
has exactly argued against attempts like ours to
develop simple definitions and tests of theory. They have
sought to incorporate symbolic and interpretive
understandings of organisation - while accepting multiple
perspectives (Hatch, 1989).
3 The lack of detailed and rigorous studies in public sector
organisational theory may explain why no such
literature review exists to date. Except for Rainey (2009), we
know of no broad and wide-reaching literature
review of organisational theories as applied in the public sector
context.
4 Roy (2009) provides one of the most obvious examples of a
study looking at the issue of changes in
government size to changes in macroeconomic variables from an
economist’s perspective. Readers interested
in following this literature can see his excellent overview. Yet,
readers steeped in organisational theory (or
macroeconomics) will find our import of economic studies into
the organisational theory realm rather
unsettling.
5 We assume, like most authors writing about the size of
government, that government expenditure as a
percent of GDP serves as the most relevant indicator of such
size. Other measures used in the literature include
employment by the government (at various levels), levels of
government consumption, government revenue
(earned through tax and non-tax methods). These other
measures of government size correlate highly with

government expenditure.
6 The citations we provide are dated because authors stopped
writing about the topic. Durevall and Henrekson
(2011) describe the search for such theorising in their title as a
“futile quest.”
A Mosaic-Theory Approach to Public Sector Organisational
Theory 57
Differences in the type and variability of economic shocks in
these government’s
organisational environment (as we define it in this paper) may
in-part explain differ-
ences in government sizes across countries.7 Figure 2 shows the
variability of GDP
over the period 2000–2008 for selected high-income, medium-
income and low-income
countries. Low-income countries’ GDP varied more throughout
the period than GDPs
in the other income groups. The most volatile economies in the
high-income countries
had variances similar to the most volatile economies in the
medium-income countries
group. The least volatile economies in all three income groups
exhibited very similar
levels of (non)volatility during the period – suggesting that
income-level itself makes a
poor predictor of the volatility (and thus uncertainty) of a
national economic environ-
ment. Authors like Suarez and Rogelio (2005) provide a
taxonomy for such change.
Authors likeNaranjo-Gil, David (2009) may take into account
actors’ perceived views
of such variability. Yet, volatility still remains the gold

standard for measuring change
in any industry or economy.
A more detailed analysis of asymmetric macroeconomic shocks
reveals much about
the uncertainty of various governments’ organisational
environments. Figure 3 shows
the magnitude and timing of asymmetric shocks (shocks which
affect one sector of the
economy rather than the entire economy) for high-income,
middle-income and low-
income countries. The figure specifically shows changes in
output in the industrial
sector (as a percent of GDP) relative to changes in the service
sector and/or the
agricultural sector. The index we show in the figure rises as
more resources are drawn
into the industrial sector—and falls as more resources are pulled
into the service or
agricultural sectors.
All three income groups have roughly the same magnitude of
changes in sectoral
production—albeit at different times. High-income countries
tended to have larger
volatility (measured by changes in the change) in industrial
output than countries in
the other income classification groups. Medium-income
countries tended to have more
7 The canonical definition of an organisational environment
from the organizational theory literature defines
such an environment as the “forces outside the boundaries [of
the organization] that can impact upon it [the
organization]” (Hatch 2006). In this paper, we focus on the
macroeconomic environment and leave out the

other elements such as legal environment, societal, and other
environmental factors in order to limit the scope
of our analysis.
0%
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Biggest 5 Smallest 5 interesting middle countries
Note: The data in the figure show government expenditure as a
percent of GDP for 14 "interesting" countries (and a world
average) out of a set of 124 countries for which the IMF provide
data. In cases where 2009 data were unavailable, we used
data for the latest year available since 2004.
Source: World Development Indicators (2010).
Fig. 1 Little explains differences in the size of government
around the world

steady growth rates in industrial output (with far fewer swings
in the value of industrial
production). Low income countries tended – in general – to
show much less inter-
sectoral macroeconomic volatility than the simple measure of
GDP volatility we used
in Fig. 3 above shows. For many low income countries, the size
of GDP throughout the
period varied much more than the composition of that GDP
between the industrial,
service and agricultural sectors. In all cases, the variance or
change in the broader
macroeconomic environment makes the government’s
organisational environment
more uncertain – as both government and businesspersons have
greater difficulty
deciding to which sector of the economy they should allocate
resources.8
Does Government Adjust its Size Contingent on Macroeconomic
Change?
How does government size respond to changes in the
uncertainty and complexity of the
government’s organisational environment? In the previous
section, we showed the
varying degrees to which the organisational environment of
various governments
around the world changed during the 2000s. Variance in GDP
represents a simple
proxy for the uncertainty and complexity of governments’

(and we will discuss more refined measured later in the paper).
More volatility in GDP
makes planning more difficult—thus increasing overall
uncertainty (Sepulveda-
Umanzor 2004). More volatility also likely corresponds with
more complex economies
– because more complex economies have a greater need to
reallocate resources across
economic sectors, respond quickly and effectively to changes in
tastes and technologies
– and so forth.
Changes in government size positively correlate with the
uncertainty and complexity
of government’s organisational environment—as measured by
the variance of GDP.
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high-income economies medium-income economies low-income
economies
The data in the graph show the volatility of GDP between 1999
and 2009 for all countries for which the IMF provide data. We
show
examples of countries with the highest and lowest GDP
volatility in each income group. We measure such volatility as
the standard
deviation of the log of GDP over the period divided by the
average log value of GDP during the period. We used the
natural log of GDP
(rather than GDP itself) because using log values removes the
effect of relvative size (as bigger economies will have a larger
volatility of
GDP simply because of their size). Log values - by their nature
- describe changes in magnitudes. Thus, by looking at the

standard
deviation of the log of GDP, we are focusing on changes in the
magnitudes of GDP over the period rather than levels
themselves.
Source: World Development Indicators database (2010).
Fig. 2 Different countries have very different economic
environments
8 Indeed, underlying productivity, trade and other factors may
affect government while simultaneously
affecting government agencies and departments. For example,
an IT innovation (like Facebook) may show
itself as increased volatility in the IT sector. However, the
innovation itself may reduce spending on
information collection for certain types of social services
(Caliendo et al. 2013).
Theory 59
Figure 4 shows the relationship between the uncertainty and
complexity of govern-
ment’s organisational environment (as measured by average
variances in GDP) and
changes in the size of government (as measured by average
changes in total govern-
ment expenditure).9 For low-income, medium-income and high-
income economies,
more output volatility corresponds roughly with more volatility
in government expen-
diture during the 2000s. Such a correlation increases in strength
for richer economies.
Low-income economies exhibit a very weak pattern in the data

while high-income
economies show a relatively strong correlation between output
volatility and the
variance of government expenditure.10 In other words,
government size (expenditure)
is contingent on its organisational environment.11
Authors like Kikulis et al. (1995) have observed changes in
organisational size and
structure in response to sector-specific changes.12 Yet, we do
not observe a relationship
between changes in government size and the magnitude of
asymmetric/sector-specific
shocks. The data show—as shown in Fig. 4 – a relationship
between the average size of
9 Government spending will affect the macroeconomy – just
like changes in the macroeconomy may affect the
size and structure of government. Fortunately, for our purposes
of presenting the relationship between these
factors, we do not need to discuss causality. Afonso and Furceri
(2010) provide an excellent overview and data
about such causality.
10 Carmignani et al. (2009) point to several of the causal
factors for governments changing their size and
structure in response to changes in the macroeconomy. Some of
these factors include politicians attempts to
smooth out economic shocks, political pressures which force
politicians and civil servants to respond to
dislocations and so forth.
11 See Meznar and Johnson (2005) for some recent theorising
about contingency theory in a government
context. See Alford (2002) for recent tests of contingency
theory in the public sector context.
12 In Michael and Popov (2011), we provide a detailed
discussion of the way government structure – as well as

size – changes in response to changes in its organisational
environment. We omit this discussion here to keep
the paper readably short.
-20%
-10%
0%
10%
20%
30%
40%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
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High Income Low Income Middle Income
The figure shows the changes in the relative proportion of GDP
in the industrial sector as opposed to in the service sector or the
agricultural sector. We use these changes as a proxy which
might show the effects of sector-specific, asymmetric shocks
(and
thus measure the overall uncertainty of the macroeconomic
environment). We constructed our proxy as follows. We
subtracted the
proportion of the service sector in overall GDP from the
proportion of industry in overall GDP (giving the absolute
change in the
importance of the service and industrial sectors). We divided
these differences by the proportion of GDP in the agricultural
sector
(thus expressiing all "shocks" relative to the size of the
agricultural sector). We calculated the rates of change of these
ratios for
each year (removing any rates of change over 300% or -300%
which might have popped up due to the country having a
relatively
small agricultural sector). We found the arithematic average of
these growth rates between 1999 and 2009 and calculated a
weighted average of these growth rates for each of the three
groups of countries (high-income, medium-income and low-
income).
We used each country's share of 2004 GDP in current US
dollars (as a proportion of the total GDP for that county's
group) as the
weight applied in our weighted average calculation.

low-income countries'
changes in inter-sectoral
output
high-income countries'
changes in inter-sectoral
output
medium-income countries' changes in inter-sectoral output
Fig. 3 Different profiles of uncertainty in governments’
shocks to a macroeconomy (which presumably results in greater
policymaker uncer-
tainty in choosing correctly sizes and targeted policies) and the
size of that country’s
government. Figure 5 shows the relationship between the
magnitude of asymmetric,
sector-specific shocks—as measured by changes in industrial
output relative to service-
sector and agricultural sector output – and changes government
size (as measured by
expenditure). In the simple portrayal shown in Fig. 5, for
economies of all income-
levels, larger industrial sector shocks (relative to other sectors)
do not correlate with
changes in government size–as shown the circular clouds of
dots in the figure.13

Moreover, the data are contradictory—with some authors
finding that government
shrink with macroeconomic change. Figure 6 shows a simple
correlation between
government size and output volatility run by Fatas and Mihov
(2001). Countries with
more output volatility correlate (rather strongly) with smaller
government sizes. Such a
conclusion appears robust to the number of countries included –
as a larger sample (as
shown in the figure) also shows that smaller governments
operate in highly uncertain
macroeconomic environments. Governments thus appear to
shrink – rather than expand
– when confronted with a high variable organisational
environment (if we use domestic
macroeconomic volatility instead of trade openness as our
measure of environmental
uncertainty and complexity). Such evidence runs counter to
results from authors like
Andres et al. (2008) who argue that government size helps to
stabilise economic
change. As such, no general conclusions can be made across
countries – we must take
a “mosaic view” when applying contingency theory to
governments world-wide.
Government probably grows or shrinks depending on the source
and magnitude of
uncertainty in the macroeconomic environment. Governments
shrink (as shown in
Fig. 6) when environmental uncertainty stems from the
macroeconomy itself—sug-
gesting that government uses size as a “shock absorber.”14
Even for changes emanating
from the domestic economy, government size probably does not

grow or shrink
13 The correlation coefficients for each pair of data are all
below 0.40 and not significantly different than zero.
For high-income countries, the correlation coefficient equals
0.34, the coefficient for medium-income
countries equals 0.16 and for low-income countries equals 0.22.
14 A series of papers look at the extent to which government
acts as a shock absorber (insulating the
macroeconomy against adverse shocks) through counter-cyclical
spending and employment practices
(Furceri 2010).
0%
10%
20%
30%
40%
50%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
ave. change in GDP
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Lower-Income
High-Income
The figure shows the relation between average rates of nominal
GDP growth and average growth in government expenditure (in
USD terms) from
1999 to 2009. Our averages represent simple arithematic means
over the period. We use the World Bank's classification of
countries by
income-per-capita in assigning countries to income-groupings.

Fig. 4 The size of government probably increases as the
government’s organisational environment becomes
more uncertain and complex
Theory 61
indefinitely as the government’s organisational environment
becomes more or less
uncertain. As Debrun et al. try to show in Fig. 7, governments
will respond to increased
macroeconomic volatility up to a point—consuming up to about
45 % of GDP. After
that point, governments become either unwilling to grow more
(because increased
expansion does not translate into reduction of macroeconomic
uncertainty) or unable to
grow more (because large governments crowd out household
consumption and private
sector investment). We find similar support for Debrun et al.’s
model – plotting the line
of best fit for the data in our own sample as the red line above
the x-axis. In both cases,
the reductions in output volatility – for a marginal increase in
government size – seem
to asymptote out when government expenditure equals about 45
% of GDP.
1.5
2

2.5
3
3.5
4
4.5
5
20 30 40 50 60 70 80
government size
o
u
tp
u
t
v
o
la
ti
li
ty
The figure shows the correlation between government size (as
measured by the average amount of government
spending to GDP between 1960 to 1997) and the standard
deviation of real GDP over the same period. We have added
the line of best fit to Fatas and Mihov's data for illustrative

purposes only. For comparative purposes, we have super-
imposed on their graph data from 1999 to 2009 and a line of
best fit (in gray) with a much larger group of countries from
our own dataset.
Source: Fatas and Mihov (2001) for dark black line and authors
for gray line.
Fig. 6 Does a more nuanced view of the data show governments
shrink in response to more uncertain
organisational environments?
0%
25%
50%
-50% 0% 50% 100% 150%
average magnitude of economic shocks
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High Income Low Income Medium Income
The data in the figure show the average changes in government
expenditure compared with changes in the magnitude of
economic shocks between 1999 and 2009. The index of
economic shocks consists of subtracting the percent of
industrial
GDP from the percent of GDP in the service sector and dividing
the resulting difference by agriculture's share in GDP. The
average magnitude of economic shocks shown in the figure
takes the arithematic average of changes in this index of
economic shocks over the period 1999 to 2009 for each of the
96 countries shown in the figure. The average change in
government expenditure shows the simple arithematic average
change in dollar-valued government expenditure between
the same period.
Fig. 5 No relation between average changes in expenditures and
changes in economic shocks
These data point to three conclusions. First, some groups of
countries’ governments
likely adapt to changes in GDP (and thus its organisational
environment) more readily
than others. Taking even the roughest measure of organisational
adaptation (the
correlation coefficient), lower income countries’ governments’
expenditure change
had a correlation coefficient of 0.51 with changes in GDP.
Upper income governments’
changes in size had a correlation coefficient of 0.27. Second,

some of these countries
may expand their government sizes, while others contract them.
Indeed, middle income
countries governments correlation coefficient of changes in
expenditure (and thus
government size) and GDP came in at around −0.42. Third, such
changes in govern-
ment size may be conditional on factors like the size of the
government already and
other factors. We cannot make any simple generalisation across
countries. We must take
a mosaic view of organisational theory—accepting that the
contingency-based view of
public sector organisational change fits better for some
countries than others.
What Do we Know About the Speed of Government’s
Organisational Adjustment
and Rational Expectations?
Organisational theory has a difficult time explaining why
“organisational fields” seem
to promote similar organisational adaption by some
governments, yet not by others.15
Different types of governments adapt to changes in their
with different speeds—in theory rationally as required.16
Differences in changes in
government expenditure and changes in GDP for the previous
year, the current year and
the following year tell us about the government’s overall
adaptive stance toward
15 In the paper that launched this branch of the literature,
DiMaggio and Powell (1983) noticed that some

organisations tend to change together, while others did not.
Even recently, authors like Wooten and Hoffman
(2008) have argued that academics still could not say with any
certainty why some governments change their
organisational structure and others do not.
16 Meltzer and Richard (1981) represent the first to describe the
rational expectations approach to government
organisation. Since then, authors have extended the framework
to cover game theory and a range of other
tools.
-0.6
-0.3
0
0.3
0.6
0.9
0 0.1 0.2 0.3 0.4 0.5 0.6
government size
(expenditure/GDP)
o
u
tp
u
t
v

o
la
ti
li
ty
(i
n
p
e
rc
e
n
t)
The data in the figure show the relation between the size of
government (as measured by government expenditure over GDP)
and
two measures of output volatility. The lower blue line shows the
line-of-best-fit between volatility of GDP growth rates and
government size in Debrun and his co-authors' model. The upper
red line depicts the relation between GDP volatility (in nominal
US
dollar terms) and government size between 1999 and 2009 in
our own dataset. Both lines roughly show that government size
no
longer correlates with decreases in output volatility after
expenditure-to-GDP ratios of about 0.45.
Source: Debrun et al. (2008) for the lower blue line and authors

for the upper red line.
Fig. 7 Government adaptation to the organisational environment
fails to have an effect after government
spends about half of GDP
Theory 63
changes in the macroeconomy. Across countries, the correlation
coefficient between
government’s expenditure changes and changes in GDP comes
to around 0.40. Yet,
subtracting the difference between changes in government
expenditure and changes in
GDP between 2000 and 2008 gives a total “error” in
government’s response to changes
in output of roughly 36 %.17 Theory generally attributes such
error to adoption failures
– as public officials fail to incorporate the organisational field
logics around them
(Egeberg, Morten 2003).
Depending on your view of the nature of change in government
expenditure,
governments in high-income countries adjusted government
sizes strategically while
governments in medium-income countries adjusted
contemporaneously. As shown in
Fig. 8, between 1999 to 2003 changes in government
expenditure relatively closely
matched changes in output in both sets of countries. Only by
2004 did the “match”
between changes in government spending change significantly

from changes in output.
By 2008, we observe changes in government spending again
returning to a closer
tracking of changes in output. Moreover, high-income
countries’ governments tended
to match changes in expenditure (and thus probably government
size) sooner and more
closely with changes in output than medium-income countries’
governments. Figure 8
shows the annual differences between changes in government
expenditure and changes
in output—treating differences as an “error” (though such
differences could reflect
thoughtful policymaking in the presence of counter-cyclical
organisational adaptation
of organisational buffering against an excessively volatile
organisational environment).
For high-income countries, contemporaneous changes in output
correlate less well with
changes government spending than a similar correlation using
lagged changes in
output. The difference between changes in output and
government expenditure is
almost twice as large if we assume that high-income country
governments respond
contemporaneous rather than strategically (changing
government size before changes in
output occur). 18 For medium-income economies, however, the
two approaches to
government’s organisational adaptation to changes in output
yield roughly the same
error. Using our measure of the “fit” of organisational response
to changes in output,
the figure shows that a model of contemporaneous response fit
very well until about
2004 – whereas a model of strategic response fit less well.

Thus, we have – for the
purposes for labelling this set of countries in one category or
the other – chosen to
portray these countries governments’ organisational response as
contemporaneous
rather than strategic.
17 We assume that policymakers will want to adjust government
expenditure pro-cyclically with changes in
GDP – and by exactly the same percentage amount (in other
words, unity represents the optimal elasticity of
government expenditure with respect to GDP). Much empirical
evidence suggests that policymakers instead
adjust government expenditure counter-cyclically. In this case,
the largest “errors” in the figure would best
explain the government’s adaptive response to changes in its
organisational environment. We use the figure to
discuss the method of determining the government’s
responsiveness to changes in its organisational environ-
ment – namely whether certain kinds of governments adaptive
reactively, contemporaneously or strategically –
rather than use the figure to pass judgments or make definitive
conclusions about fiscal policy in these
countries. We put the word “error” in quotes to emphasize that
we take a positive rather than normative view
of the data in this paper – seeking to describe the data rather
than determine a best or optimal response.
18 As described previously, we use the word “strategic” to
describe changes in government expenditure
occurring before changes in output. The lack of a response, or a
counter-cyclical response may be more
“strategic” (as commonly understood in the public
administration literature). We only use the word to describe
changes in government spending in time and do not attach a
value-judgment nor argue that strategic responses
are necessarily superiour to other types of responses.

While the authors we review establish that government size
responds to the uncer-
tainty and complexity of the macroeconomic environment, they
do not discuss whether
policymakers respond on purpose to external shocks.19 While
economists must assume
at government officials act rationally, organisational theorists
make no such claims. The
policymakers of economics can use economic models and
statistics to predict the
emergence of external shocks – and adapt government sizes to
minimise the negative
economic effects of those shocks. 20 In the words of
organisational theory, these
policymakers can act strategically—by predicting changes in
their organisational
environment.21 At the very least, if policymakers cannot
predict the future, they can
reactively adjust to changes which have already occurred in the
past. Much empirical
work from economics helps us assess whether governments’
organisational responses
(or at least sizes) have reacted strategically or reactively to
changes in these govern-
ments’ external macroeconomic environment.22
The extant data show little evidence that organisational sizes
have reactively adapted
to more uncertain and complex macroeconomic environments.
Figure 9 shows the
relationship between government sizes among OECD member

states and past econom-
ic shocks. Government sizes—at least in the OECD – do not
“adjust” to long-run
changes in the uncertainty and complexity of their
macroeconomic policymaking
19 Authors like Fernandez and Rainey (2006) assume managers
always want to change their government
departments’ organisational structure to respond to challenges
(and they provide management guru-like advice
on doing so).
20 Political economy offers the greatest promise for helping us
understand the factors driving the data we
present in this paper. However, with the exception of Besley
and Burgess (2002), a lack of theorising and
applied empirical work has limited our ability to use theory to
discriminate between types of governments.
21 We draw attention to a very basic misunderstanding about
strategy between economic theory and
organisational theory. In economics, strategy describes the best
response to the action of another sentient
player who can predict (and react to the predictions of) other
players. Strategy, in organizational theory, usually
refers to predicting the future and emerging trends and patterns.
We adopt the “future seeing” definition of
organizational theory as we attempt to test theories in
organizational theory rather than economics.
22 From a theoretical perspective, authors like Fernandez and
Pitts (2007) might argue that delays in
organisational adjustment stem from the need to deal with
internal, as well as external, factors.
-10%
-5%

0%
5%
10%
15%
2000 2001 2002 2003 2004 2005 2006 2007 2008
"e
rr
o
r"
i
n
e
x
p
e
n
d
it
u
re
c
h
a

n
g
e High-income
contemp response
(36% "error")
Medium-income-
strategic response (42% "error")
High-Income
strategic response (19% "error")
Medium-income
contemp response
(43% "error")
The figure shows the error in the adjustment of government
expenditure to changes in GDP. The area under each line
represents the difference
between the change in government expenditure and GDP -- thus
representing a type of "error" in government spending
(assuming governments
adapt pro-cyclically). We looked at three scenarios. We first
substract the current year's change in GDP from the current
year's change in
government expenditure to investigate the extent to which
government expenditure contemporaneously adjusts to changes
in GDP. In the
second set of calculations, we subtracted the current year's
change in GDP from the following year's change in government
expenditure to
investigate the extent to which government expenditure changed
reactively. In the third set of calculations, we subtracted the
previous year's

change in government expenditure from the current year's
change in GDP in order to asses the extent of strategic change
in government
expenditure. We do not show reactive responses as they "fit"
much less closely with changes in GDP.
Fig. 8 High income countries adjust expenditure strategically
while medium-income countries
contemporaneously
Theory 65
environments. 23 Several of the most open OECD countries
have relatively open
economies and yet small government sizes (as defined by the
proportion of government
consumption in overall national consumption). Yet, several of
the most closed econo-
mies also have relatively small government sizes. The timing of
government responses
to changes in its (macroeconomic) organisational environment
plays an important part
in the story.
Yet, the data suggest that some governments time their changes
in organisational
size much better (or at least differently) than others. Molina et
al. (2004)—using time
series analysis – test whether changes in government size follow
or precede changes in
trade openness (and thus possibly the complexity and
uncertainty of the overall

macroeconomic environment). We show in Fig. 10 the
correlation coefficients reported
by Molina and co-authors between government size (as
measured by government
consumption) and the complexity-uncertainty of the
government’s macroeconomic
environment. We note two trends in both figures. First, some
countries’ change in
government size “fits” changes in trade openness better if we
assume that that such
changes occur due to strategic organisational adaptation (where
government size
changes precede changes in trade openness) or reactive
adaptation (where government
size changes follow changes in trade openness). Second, some
countries respond (or at
least change) to more trade openness by expanding government
size while other
countries’ government sizes shrink. Why one government would
grow in response to
increased macroeconomic uncertainty, while another
government contracts, remains
one of the unsolved puzzles of public sector organisational
theory.
These data suggest that when governments adapt organisational
size to environment
factors tells us as much as how they respond to these factors.
Governments such as
Japan and Australia (at least in Molina et al.’s study) seem to
have reactive
organisational adaptation to environment factors whereas
Belgium and the UK seem
to have more strategic organisational responses. Countries such
as Germany and
Iceland seem to react to changes in the macroeconomic

environment by shrinking
23 We put the word adjust in quotes because if OECD
government policymakers chose organisational
buffering as an optimal adaptation to a more complex and
uncertain organisational environment, then the
lack of a relationship in the data shown in the figure reflects the
optimal (or at least equilibrium) organisational
response.
0
0.1
0.2
0.3
0.4
0 0.5 1 1.5 2
Historical Trade Openness
G
o
v
e
rn
m
e
n
t

s
iz
e
The data in the figure show the (lack of a) relation between
historical trade openness (as measured by the sum of imports
and exports as a percent of GDP from 1985 to 1994) and ex-post
average government consumption as a percent of GDP
(from 1995 to 1998).
Source: Molana et al. (2004).
Fig. 9 Among OECD member countries, weak relation between
prior exposure to external shocks and
government size
government size whereas Italy and the Netherlands seem to pro-
actively (or strategi-
cally in our terminology) contract.
Yet, not all countries’ governments seem to respond much to
changes in their
external environments. Figure 11 shows the results of time
series analyses assessing
whether current government expenditure depends on previous
government spending or
on other factors.24 The figure shows—marked in red or yellow
– countries where past
government expenditure very well explains current government
expenditure. For most
24 The authors used a procedure known as Auto-Regressive

Integrated Moving Average (or ARIMA)
techniques. These techniques test the extent to which the value
of a variable depends on previous levels of
that variable. While a discussion of time series analysis extends
beyond the scope of our paper, the reader
should know that this test assesses whether past values of a
variable explain that variable better than other
variables.
Reactive Organisational Responses: Governments Like Japan
and Australia
Expand Government After Trade Openness While Germany and
Canada Contract
-0.5
-0.25
0
0.25
0.5
Ja
pa
n
Au
st
ra
lia
Ire

la
nd
Fr
an
ce
No
rw
ay
Au
st
ria Lu
x
US
A
UK
Sw
itz
Be
lg
iu
m
G
re
ec

e
Sw
ed
en
Po
rtu
ga
l
Ita
ly NZ
Sp
ai
n
De
nm
ar
k
Ne
th
er
la
nd
s
Ca

na
da
Fi
nl
an
d
G
er
m
an
y
Ic
el
an
d
more openness brings delayed
increase in government size
more openness brings delayed
decrease in government size
The bars in the figure show the correlation coefficient between
this year's change in trade openness (as defined as imports plus
exports divided by the value of GDP for that year) and the next
year's level of government consumption (again expressed as a
percent of GDP). For example, changes in Japan's trade
openness in any year "explain" about 40% of increases in the

Japanese
government's overall consumption of GDP in the following
year. Similarly, increases in Ireland's trading openness in any
year
explain about 20% of decreases in the amount of Irish
government consumption during the time period the authors
explore. We
interpret high correlations between this year's change in trade
openness the next year's change in government consumption as
a
reactive organisational response.
Source: Molina et al. (2004).
Strategic Organisational Responses: Governments Like Belgium
and the UK
Increase their Size before Openness Comes while Italy and
Netherlands Contract
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Be
lg
iu
m UK
Ja
pa

n
Fi
nl
an
d
Ic
el
an
d
Sw
itz Lu
x
NZ
No
rw
ay
Sp
ai
n
Sw
ed
en
Fr
an

ce
Ca
na
da
G
er
m
an
y
US
A
Au
st
ra
lia
Po
rtu
ga
l
Au
st
ria

Ire
la
nd
De
nm
ar
k
G
re
ec
e
Ne
th
er
la
nd
s
Ita
ly
governments
increasing
size before more openness happens
governments decreasing
size before more openness happens
The bars in the figure show the correlation coefficient between

this year's change in trade openness (as defined as imports plus
exports divided by the value of GDP for that year) and the
previous year's level of government consumption (again
expressed as
a percent of GDP). For example, changes in Belgium's trade
openness in any year "explain" about 20% of increases in the
Belgian government's overall consumption of GDP in the
following year. Similarly, increases in Italy's trading openness
in any year
explain about 20% of decreases in the amount of Irish
government consumption during the time period the authors
explore. We
interpret high correlations between this year's change in trade
openness the previous year's change in government consumption
as a strategic organisational response.
Source: Molina et al. (2004).
Fig. 10 Reactive and strategic organisational responses to
increased exposure to external shocks
Theory 67
of the countries in Akitoby and co-authors’ study, past
government expenditure ex-
plains rather well current expenditure. The authors also test the
extent to which changes
in government expenditure correlate with changes in output in
the current period and in
the past.25 Countries marked in red show countries where past
changes in output best
explain current changes in government expenditure. For a few
countries—Colombia,
Peru, India, Sri Lanka, Thailand, and Ghana – current changes

in output best explained
changes in current government expenditure. For authors like
Walton (2005), such
differences may reflect the degree of bureaucratisation of these
governments more than
anything else. For authors like Grossman, Herschel (1980), they
likely reflect differ-
ences in where these countries are in the business cycle.
As with our previous case, any rational expectations about the
extent to which
governments adapt to their organisational environment depend
on the country. Some
countries like the Belgium, the UK, the Netherlands and the UK
seem to react
strategically. Yet, whereas Belgium and the UK have expanded
government sizes in
anticipation of output variability, the Netherlands and Italy have
shank them. Other
countries’ governments—like Japan, Australia, Germany and
Iceland – seem to adopt
reactive organisational change strategies to changes in
macroeconomic output. Yet,
while Japan and Australia expand their government sizes,
Germany and Iceland
25 The reader familiar with time series analysis will recognize
this as a co-integration test – using a error-
correction model. The authors tested three independent
variables to find their impact on differenced govern-
ment expenditure – differenced past government expenditure,
differenced output and the lag value for
differenced output. We chose the most important factor for each
country based on the size of the corresponding
coefficient. For example, if the regression coefficient for past
changes in output exceeded the value (either

positive or negative) of the other regression coefficients, we
classified that country having reactive
organisational adaptation.
The figure shows the results of an error correction model for
changes in government expenditure for a sample of 51
countries. For
countries marked in red, past changes in output best explain
(have the largest regression coefficient for) changes in
government
expenditure. For countries marked in yellow, contemporary
differences in output best explain (have the largest regression
coefficients for) changes in government expenditure. The
authors did not provide data for countries marked in white.
Source: Akitoby et al. (2006).
Fig. 11 For some countries, the size of government this year
depends on what that size was last year
contract them. Rational expectations cannot completely explain
the universe of coun-
tries.26
Does Government Size Really Depend on the Resources
Available to it?
The data also shows some validity for the resource-based view
of organisational
structure—that government size responds more to tax and other
resources available
than to changes in government’s organisational environment.27
Figure 12 shows that

such an explanation seems most plausible for medium-income
countries – at least when
looking at contemporaneous changes in government expenditure
and revenue. 28
Between 2000 and 2009, the sum of each year’s differences
between low-income
country governments’ expenditure and revenue resulted in an
“error” (as we have
previously defined such error) of 43 %. 29 Adjustment in high-
income economies’
government expenditure showed an “error” of 47 %. Medium-
income country govern-
ments’ expenditure mismatch between expenditure and revenue
over the period
summed to 36 %. Contradicting Moore and Zarandi’s (2011)
findings, the resource-
based explanation of government organisation clearly provides
some explanatory
power—depending on the particular country and time.
Different countries’ governments adapt their organisational
sizes at different speeds
in response to changes in their organisational environments.
Figure 13 shows the best
fitting (possessing the least amount of “error”) adaptive
orientation for various coun-
tries’ government sizes among strategic, contemporaneous,
reactive and resource-based
models of organisational adaptation to changes in the
macroeconomic environment (as
measured by the change in industrial GDP relative to other
sectors). In general, the
changes in the size of governments like those of the USA, China
and Finland correlated
more closely with changes in the sectoral distribution of output

before such changes in
output occurred. Changes in government sizes for countries like
India, Australia,
Kazakhstan and Argentina tended to correlate with changes in
sector output as such
changes in sectoral output occurred. Changes in government
size for countries like
Russia, Algeria, Germany and the UK tended to correlate with
changes in industrial
26 Nsouli and co-authors (2002) describe why we might observe
differences in government organisational
responses even for similar governments and external
environmental changes. Government leaders, acting
rationally, may wish to time and sequence changes in the size
and structure of government to long-run (inter-
generational) objectives.
27 Interestingly, Gali (1994) finds that additional resources (in
the form of higher tax revenues) may actually
cause increased macroeconomic volatility. As such,
governments may find themselves in a trade-off between
pursuing a resource-based approach to sizing and a contingency-
based one. As the government collects more
taxes (resources), it may need to put those resources to work to
deal with increased uncertainty in the
macroeconomic environment!
28 These data show that universal admonitions to “put the
resource-based view to work” do not apply equally
to various governments (Bryson and co-authors, 2007).
29 Just like with our measure of adaptation to changes in
government’s organisational environment, our
measure of government’s “error” in responding to changes in
resources only looks at the extent to which
changes in government size contemporaneously adjusts to
changes in revenues. Policymakers may wish to
break the link between revenues and expenditure in any year in

order to build up budget surpluses (in
anticipation of future economic shocks), pay down previously
acquired debts, or engage in fiscal policy to
stimulate (or dis-stimulate) the macroeconomy. Given this wide
range of organisational objectives, we only
report the positive aspects of organisational adaptation –
ignoring the normative aspects (dealing with the
desirability and/or optimality) of such changes.
Theory 69
output (relative to other sectors) only after such changes in
industrial output occurred.
Finally, for countries like Canada, Iran, and Sweden, changes in
government sizes
correlated most closely with the revenue these governments had
at their disposal in any
given year. The figure illustrates the “mosaic” we have
described in this article. Some
countries’ governments’ organisational response appears to
conform more closely to
one organisational theory (like contingency theory) while others
to a resource-based
view.
Each theory of government organisation provides a partial
explanation for
these data. Even the most die-hard critics of the contingency
theory of govern-
ment organisation must acknowledge that government size
should respond (at
least in part) to changes in the macroeconomic environment.
Fiscal policy

(namely government expenditure on goods, staff and assets like
office desks)
– by law if not by practice in many countries—smoothes out the
effects of
general and asymmetric macroeconomic shocks (which would be
seen in rela-
tively low correlations between changes in government sizes
and macroeco-
nomic changes in some countries). Critics of the resource-based
theorists can
not argue that governments can not expand beyond their means
in the long-run
(namely their revenue and borrowing power). Critics of
rational-choice theorists
can not argue that government can anticipate many kinds of
shocks – rising
grain or oil prices, demographic changes and so forth. Some of
the “strategic”
organisational adaptation we observe in the data probably does
reflect actual
strategic policymaking. Yet, some of the “reactive”
organisational adaptation we
observe in the data may reflect rational organisational buffering
or anti-cyclical
spending. Relatively little in the literature helps us understand
these relation-
ships. A number of authors ask whether government size
responds more
strongly to external shocks or to internal political economy
pressures – clearly
mimicking the debate in the organisational theory between
contingency theory
and organisational politics (Rodrik, Dani 1996; Alesina and
Wacziarg 1998 and
Kimakova 2009 for a much more recent revision of the debate).

-20%
-10%
0%
10%
20%
2000 2001 2002 2003 2004 2005 2006 2007
Medium-Income countries
The figure shows the difference between changes in government
expenditure and revenue for each of the three
categories of countries shown. The difference between changes
in contemporaneous government expenditure and
revenue (with changes occuring in the same year) is lowest
across the entire period for medium-income countries --
which we interpret as most strongly supporting the resource-
based view of government (that government revenue
determines expenditure rather than other considerations). To
derive these indicators, we took the weighted average
of changes in US dollar valued expenditure and revenue.
Weights for changes in government revenues consisted of
the country's 2004 government revenue (in US dollars) as a
percent of the total revenue (also expressed in US
dollars) for that country's income group. Weights for
government expenditure consisted of that country's 2004
expenditure (expressed in US dollars) as a percent of that
country's income group's total expenditure (again
expressed in US dollars). Source: World Development
Indicators (2010).
High-income countries

Low-income countries
Fig. 12 Resource-based view of government organisation most
valid for middle income countries in the
short-run
The Failure of Public Sector Organisational Theory
Despite decades of theorising, we know relatively little about
when and how to apply
organisational theory in a public sector context. Both Hatch’s
(2006) classic textbook and
Christensen et al. (2007) more recent rehash of organisational
theory in a public sector
context, include an entire chapter on the relationship between
the organisational environ-
ment and organisation structure. These works leave three
questions unanswered. First, how
can we quantify the extent to which organisational change
reacts contingently on quantifi-
able changes in the organisational environment? Most of the
studies from organisational
theory rely on surveys and qualitative data from case studies
(Melchor, Huerta 2008). Few -
if any - provide objective and quantifiable data needed to
resolve the on-going debates we
have reviewed in this article.30 Second, to what extent does
government’s organisational
adaptation to its macroeconomic environment follow the tenants
of rational expectations?
Theorising, model-building and prediction become easier with
rational, rather than sym-

bolic/interpretative, organisational strategising (Hitt et al.
2007). Do public officials actually
adjust government size and structure rationally? Third, to what
extent can we discriminate
between countries which place greater emphasis on changing
government size and structure
in response to resources rather than other factors? Resources not
only determine government
size and structure, they also determine the extent to which
resources help support contingent
responses to changes in the macroeconomic environment
(Romer and Romer 2007).31
30 Reverse causality (or government changing its
macroeconomic environment rather than adapting to it) will
always constrain what organisational theorists can say about
government’s organisational adaptation to its
environment. Yet, even observing objective correlations
between organisational change and macroeconomic
change in government’s organisational environment would
already represent a great theoretical and empirical
leap forward.
31 Indeed, Romer and Romer illustrate how the lack of a
“mosaic view” of organisational theory can lead to
wrong conclusions. They categorically assert that, “the results
provide no support for the hypothesis that tax
cuts restrain government spending; indeed, they suggest that tax
cuts may actually increase spending” (1). We
already showed that such a conclusion would not hold for many
countries at many times.
Fig. 13 Countries form a mosaic – with some fitting a rational
expectations contingency-based view of
organisation – and others a resource-based one

Theory 71
Some of the most recent studies show why leaving these
questions unanswered
poses problems for organisational theorists. In one of the most
relevant studies for our
own research, Andres et al. (2008) uncover several statistically
significant correlations
between UK managers’ perceptions of their agency’s
organisational environment and
the type of organisational strategy their agency managers
pursued.32 As shown in
Fig. 14, these managers thought that highly centralised agencies
followed a reactive as
well as strategic organisational adaptation. Agencies highly
involved in planning
corresponded with reactive organisational adaptation (using our
labels instead of
theirs). Agencies following incrementalist policies tended to
follow reactive and
strategic organisational adaptation to changes ii their
organisational environments.
Agencies with a high amount of environmental uncertainty
tended to adapt their
agencies reactively.
Yet, the questions we posed above show why studies like this
fail to advance the
discipline. First, the study relies on subjective data, which can
neither be independently
confirmed through further studies, nor compared across
departments and/or countries.
Second, we do not know to what extent adaptation comes from
contingent reactions to

organisational environment, to the resources available to these
officials or as the result
of rational planning for the future. Each of these theoretical
perspectives plays a role in
UK government agencies—and thousands of others world-wide.
We need tools to
discriminate between agencies – allowing us to empirically
know which theories play
a stronger or weaker role in explaining organisational
responses.
These studies illustrate the need for a mosaic-view of
organisational theory. As we
have illustrated in this paper, we can characterise such a view
by three major charac-
teristics. First, a mosaic-view of organisational theory accepts
that a contingency-based
view, a rational expectations approach and resource-based
approach hold explanatory
power—to greater or lesser extents in each case. Second, such a
view accepts that
objective, empirical data can help discriminate (differentiate)
between organisations
adapting contingently to its organisational environment versus
those driven by avail-
able resources. Third, a mosaic-view of organisational theory
takes time into account—
accepting that organisational environments may adapt
strategically in some cases and
reactively in others. Such a mosaic-based approach to theory
helps us understand
government organisational change without resorting to
increasingly complex models
like Damanpour (1996).
The mosaic-view of government’s organisational adaptation to

its organisational
environment provides for testable hypotheses which provide
explanatory and predictive
power to these basic views of organisational change. Figure 15
shows the 6 hypotheses
which come from a mosaic view of theories of government
organisational adaptation to
its macroeconomic environment. A mosaic view of the theory
driving (or at least
explaining) government’s organisational change means the size
and structure of gov-
ernment reacts in particular ways to changes in the uncertainty
and complexity of the
32 One study hardly seems like the basis for a valid literature
review. In our defence, we searched all the major
search engines for papers dealing with “organizational
environment” and variations of government, structure,
public sector, strategy, “strategic adaptation” and so forth. The
paucity of papers in this area reflects deeper
epistemological problems around the definition of government
structure and the organisational environment of
the public sector – as well as the lack of data from which to
evaluate various theories. We rather blithely gloss
over all these serious considerations in our study in order to
arrive at some empirically-derived conclusions
(however tenuous they might be).
macroeconomic environment. Statistical methods can measure
and correlate these
reactions. Such empirical work would take organisation theory a
long way from

“proving or disproving” a particular view. As we have shown in
figures in this paper,
we can literally see a mosaic of responses, coloured by groups
of countries and by time.
Conclusions
What does organisational theory teach us about public sector
organisational adaptation
to its organisational environment? Do the main theories covered
in most organisational
theory classes actually apply to governments? In this paper, we
review the literature—
using data to guide our discussion. We find that governments
change their
organisational structure concomitantly with changes in their
organisational environ-
ment (as we define it). Yet, theory provides little basis for
predicting the size,
-0.6
-0.4
-0.2
0
0.2
0.4
Decentralisataion Planning Incrementalism Uncertainty R2
b
e

ta
c
o
e
ff
ic
ie
n
ts
strategic adaptation (in green)
reactive adaptation
(in red)
contemporaneous adaptation
(in yellow)
The data in the figure show the extent to which several
variables correlate with the organisational strategy of UK
government
agencies. For example, the perception that an agency follows a
decentralising strategy correlates with UK's managers'
perceptions
that their agency adapts reactive (and strategically) to its
organisational environment. Agencies whose managers foillow
incrementalist strategy correlate with managers' perceptions that
those agencies engage in reactive and contemporanous
organisational adaptation. We have relabelled the authors'
labels for the dependent variables from prospective to
"strategic," low
cost and differentiated defending as "contemporanous"

adaptation and reacting as "reactive" organisational structure.
Source: Andrews et al. (208).
Fig. 14 Decentralising and incrementally changing agencies
engage in strategic and reactive organisational
adaptation – those in highly uncertain environments adapt
reactively
Hypothesis 1: The size of government depends on the
uncertainty and complexity of
government’s organisational environment
Hypothesis 2: Different governments will have different
preferences for responding to the
uncertainty and complexity of the macroeconomic environment
Hypothesis 3: Different countries will adapt reactively,
strategically or contemporaneously to
changes in the macroeconomic environment.
Hypothesis 4: Government structure changes in response to
changing uncertainty and
complexity of the macroeconomic environment
Hypothesis 5: Differences in the uncertainty and complexity of
various countries’
macroeconomic environments significantly explain changes in
government structures.
Hypothesis 6: Different governments’ structures can respond
strategically, contemporaneously
or reactively to changes in their macroeconomic environments
Fig. 15 Six hypotheses deriving from a mosaic-view of

Theory 73
magnitude and even direction (expansion or contraction) of such
change. Different
governments respond differently, and no over-arching theory
can explain or predict
their organisational responses. We thus argue for a mosaic-view
of organisational
theory—one which allows for contingency-based view,
resource-based view and the
rational expectations view of organisational adaptation to
varying degrees at varying
times. Data – not theory – can point to the cases when each
theory helps us explain
government’s size and structure.
These results imply two research directions, which we take up
in future papers. First,
theorists need a micro-model of organisational change – a
generalisable model testable for
a range of countries (rather than all countries universally).
Second, we need further
empirical analysis, to identify which sets of countries respond
with similar organisational
changes to changes in their organisational environment. By
knowing this, we can decide
which theory—contingency-based view, resource-based view,
rational expectations-based
view or none of them – best fit each particular case. We provide
6 hypotheses which test
the mosaic-based view of organisation theory we present in this
paper. The question lies
not in which theory of organisational theory is right—but

instead which theory is right for
which country, during which time period, and to what extent.
Further empirical work will
help to answer these questions.
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Theory 75
Reproduced with permission of the copyright owner. Further
reproduction prohibited without
permission.
c.11115_2014_Article_296.pdfThe...AbstractIntroductionWhat
Does Government Size and its Organisational Environment
Look Like Across Countries?Does Government Adjust its Size
Contingent on Macroeconomic Change?What Do we Know
About the Speed of Government’s Organisational Adjustment
and Rational Expectations?Does Government Size Really
Depend on the Resources Available to it?The Failure of Public
Sector Organisational TheoryConclusionsReferences
PUBLIC LEADERSHIP
A review of the literature and
framework for future research
Rick Vogel and Doris Masal
Rick Vogel
Chair of Public Management & Public Policy
Zeppelin University
Friedrichshafen

Germany
E-mail: [email protected]
Doris Masal
Chair of Public Management & Public Policy
Zeppelin University
Friedrichshafen
Germany
E-mail: [email protected]
Abstract
This study analyses and reviews the litera-
ture on public leadership with a novel com-
bination of bibliometric methods. We detect
four generic approaches to public leadership
(i.e. a functionalist, a behavioural, a biogra-
phical and a reformist approach) which differ
with regard to their philosophy of science
(i.e. objective vs subjective) and level of ana-
lysis (i.e. micro-level vs multi-level). From
our findings, we derive four directions for
future research which involve shifting the
focus from the aspect of ‘leadership’ to the
element of ‘public’, from simplicity to com-
plexity, from universalism to cultural relati-
vism and from public leadership to public
followership.
Key words
Leadership, publicness, followership, biblio-
metrics, network analysis
© 2014 Taylor & Francis
Public Management Review, 2015
Vol. 17, No. 8, 1165–1189,

http://dx.doi.org/10.1080/14719037.2014.895031
INTRODUCTION
In his seminal review of research on public leadership, Van
Wart (2003) empathically
called for more research in the field of public leadership, where
‘the needs are great
and the research opportunities are manifold’ (2003, 225). A
decade later, it has
become widely accepted that public leadership is a phenomenon
worth studying
because in an increasingly complex and ambiguous world, new
challenges and
pressures are placed on public organizations and their leaders
(Van Wart 2013).
The public sector is rapidly changing, and so is the nature and
significance of public
leadership. In face of these profound changes, scholars of public
administration (PA)
now broadly agree that leadership is crucial to the efficiency
and accountability of
public organizations and thus deserves greater attention (Raffel,
Leisink, and
Middlebrooks 2009b; Teelken, Ferlie, and Dent, 2012).
However, despite, or
perhaps because of, the growing number of publications on this
topic, public leader-
ship remains an elusive concept (e.g. Dull 2009; Fernandez
2005). The fragmented
state of the field makes it difficult to gain an overview of the
various streams of
research and to grasp how they relate to each other.
This study aims to map the literature on public leadership, to

identify emerging
approaches to research within that literature and to provide
directions for future
research. Our emphasis is on organizational leadership in a
distinct PA perspective,
rather than on other forms of public leadership, such as
political, community or
military leadership (Raffel, Leisink, and Middlebrooks 2009a;
Van Wart 2003, 2013;
Van Wart and Dicke 2008). The contribution of this study is
threefold: first, we extract
a bibliometric map that will provide orientation with regard to
the past history, present
state and future development of research on public leadership
and will help scholars
navigate this fragmented field. This helps encouraging dialogue
across multiple per-
spectives and preserving theoretical, conceptual and
methodological balance. Second,
we advance bibliometric applications by combining two
established methods – namely
co-citation analysis and bibliographic coupling – in a new way
that allows for the
integration of two distinct sets of results, obtained by applying
each method separately,
in a single map. Because bibliographic data reflect the
references that authors cite in
scholarly publications, bibliometric maps can be said to
represent the self-portrait of a
scientific community that its members have unconsciously
drawn over time. However,
a quantitative approach to the literature does not substitute for,
but rather comple-
ments, introductions to and commentaries on the subject by
recognized experts in the

field (e.g. Denis, Langley, and Rouleau 2005; Raffel, Leisink,
and Middlebrooks 2009a;
Teelken 2012; Van Slyke and Alexander 2006; Van Wart 2003,
2013).
The remainder of this article is organized as follows: in the next
section, the data and
methods of the bibliometric analysis are introduced. We will
compile relational data
extracted from the bibliographies of nearly 800 journal articles,
process them on two
levels of analysis using network and factor analysis and finally
integrate the results by
1166 Public Management Review
means of correspondence analysis. The third section is devoted
to our results which
reveal four generic approaches to public leadership (i.e. a
functionalist, a behavioural, a
biographical and a reformist approach) that differ with regard to
their philosophy of
science (i.e. objective vs subjective) and level of analysis (i.e.
micro-level vs multi-
level). Within this map, it can be seen that five clusters of
current research are related
to six clusters of foundational works on public leadership. In
the fourth section, we
assess the current state of the field and identify four avenues
deserving further
investigation. The paper concludes with a brief summary.
MAPPING RESEARCH ON PUBLIC LEADERSHIP WITH
BIBLIOMETRICS

Bibliometrics is a subfield of the history and sociology of
science that emerged from the
application of statistical methods to the analysis of formal
communication among
scholars (for a review, see Verbeek et al. 2002). In view of the
‘publish-or-perish’
attitude in contemporary academia, bibliometric mapping
provides a powerful set of
methods for tracking the creation and dissemination of
scientific information on a
particular topic. When used descriptively rather than
evaluatively (Van Leeuwen
2004), bibliometric methods reveal the history and current state
of research and
indicate which trends are likely to emerge in the future.
Data
We extracted bibliographic data for journal articles covered by
the Thomson Reuters
Social Science Citation Index® (SSCI). We focused on peer-
reviewed journals because
they play a key role in communicating scientific knowledge,
acknowledging intellec-
tual property and exercising reputational control in academia.
The period of inves-
tigation covers all available years up to and including 2011. Our
data sampling was
restricted to English-language journals in the subject category
of ‘Public
Administration’. Within this category, we did not differentiate
further by means of
quality indicators (such as the impact factor) because we aimed
to cover a wide range
of journals, including both highly ranked outlets in the core of

the field and less
prestigious journals at the margins. We then proceeded with a
broad query within
this category, selecting articles, reviews and proceedings papers
that included the
search term ‘leadership’ in the title, abstract and/or keywords.
Given that ‘leader-
ship’ is not a technical term, items with low or no relevance to
the research subject of
public leadership tend to be weakly related to more significant
publications in this
field and were thus eliminated at later stages of the analysis.
After extraction from the
database, the bibliographic data were thoroughly cleaned in
order to adjust different
spellings and to eliminate typing errors. Moreover, references to
various editions of
Vogel & Masal: Public leadership 1167
the same book were harmonized. The final data set contained
787 citing documents
with 33,183 references to 25,645 cited documents.
Methods
We processed the bibliographic data in four steps (see Figure
1). The procedure we
followed is based on two generic methods of bibliometric
retrieval, i.e. co-citation
analysis and bibliographic coupling. These two methods deal
with different levels of
analysis and thus differ significantly in their results (for
comparisons, see Jarneving

2005; Vogel and Güttel 2013). Whereas most bibliometric
studies apply either of the
two methods, we chose to combine them in a new way and
integrate the results in a
single map of research on public leadership.
Co-citation analysis and bibliographic coupling
Co-citations and bibliographic couplings are different kinds of
intertextual relationships
that arise when scholars compile the reference lists of their
publications. These
relationships are used as bibliometric measures that are
considered to indicate the
degree of similarity between the co-cited or coupled documents.
On that basis, large
bodies of literature can be grouped into clusters of publications
that tend to be
homogeneous within the clusters, but heterogeneous between
them. Despite their
apparent similarities, the two methods have important
differences. As Figure 2
shows, a co-citation is a relationship between cited documents,
whereas a bibliographic
coupling links citing documents. A co-citation is said to occur
when two publications a
and b are cited by another publication A (Small 1973).
Documents are thus co-cited if
Correspondence analysis
Integrating results
Compiling relational data1
Network analyses
Reducing networks to the cores2

Factor analyses
Clustering the network cores3
4
Co-citation analysis Bibliographic coupling
Figure 1: Analytical procedure
they appear on the same reference list. In contrast, a
bibliographic coupling arises when
two publications A and B cite the same source a (Kessler 1963).
Documents are thus
coupled if their bibliographies include at least one common
reference. Put differently, a
co-citation is a relationship between texts that are included as
references in another
text, whereas a bibliographic coupling is a link between texts
that include the same
reference.
The two methods differ in several respects (Jarneving 2005;
Vogel and Güttel 2013),
but their fundamental difference concerns their time horizons:
co-citation analysis is
biased towards the past of a scientific field because it focuses
on cited documents which
are inevitably older than citing documents (with the exception
of publications that are
cited as ‘forthcoming’). As citations accumulate over time, this
method identifies
classical works that are frequently, and often ceremonially

rather than substantively,
cited together. This makes co-citation analysis particularly
suitable for the detection of
high-impact publications that represent the intellectual history
and origins of a parti-
cular research field. However, this focus creates some
convergence towards the main-
streams of research.
In contrast, bibliographic coupling is biased towards current
research because it traces
more recent publications, independently of the frequency with
which they have been
cited. In that sense, it records the production, rather than the
consumption, of scientific
texts. This method allows for some divergence since it covers
all citing documents,
including those with little chance of receiving attention in the
future because they
depart too much from the mainstream and/or have been
published in peripheral
journals. Thus, combining co-citation analysis and bibliographic
coupling offers the
advantage of considering both convergent and divergent trends
in the field under
investigation.
Network analyses
Despite the differences between co-citation analysis and
bibliographic coupling, the
results obtained with each method have the same relational data
structure. Two
symmetrical matrices emerged from the first step of the
bibliometric analysis: the
co-citation matrix contained all cited documents as row and
column headers and the

Citing
Documents
Cited
Documents
A B
aa b
A
Co-citation
Bibliographic
coupling
Figure 2: Co-citation and bibliographic coupling
Source: Vogel and Güttel (2013).
co-citation counts as values, whereas the coupling matrix
assigned the number of
bibliographic couplings to every pair of citing documents. The
main diagonals of the
matrices were considered as missing values. In the next step, we
processed the raw
matrices by means of network analysis. Since the matrices were
quadratic and
symmetric, we analysed them as undirected one-mode networks
(Wasserman and
Faust 2009). The goal of this step was to separate the cores of

the bibliographic
networks in order to focus the analysis on highly interdependent
publications while
eliminating loosely connected documents on the periphery. For
this purpose, we
employed a categorical core/periphery model proposed by
Borgatti and Everett
(1999). By using this procedure for data reduction, we avoided
the arbitrary setting
of absolute or relative thresholds and instead applied an
algorithm consistently to
both networks. After reduction to the network cores, the co-
citation matrix
consisted of 330 documents, whereas the coupling matrix
contained 121
documents.
Factor analyses
The aim of the third step was to cluster the rows and columns of
the matrices into groups
of similar publications. In keeping with bibliometric standards
set in previous works
(McCain 1990), we first converted the raw frequency counts in
the matrices into
correlations based on Pearson’s coefficient. The clustering
results that are produced by
means of relative measures of document similarity have proved
to be more balanced than
those produced by means of absolute frequencies, because they
are less affected by outliers
of extremely high (or low) citation rates (McCain 1990). After
the conversion, we applied
factor analyses with Varimax rotation and Kaiser normalization
to the correlation
matrices. We excluded documents with no significant loadings
(i.e. loadings <0.4;

McCain 1990) from further analysis. This reduced the samples
to 282 cited documents
and 107 citing documents. Table 1 provides a summary of the
factor extraction. The
cluster labels emerged from our interpretations of the eleven
extracted factors. For this
purpose, we reviewed the assigned documents thoroughly,
searched for substantial
commonalities within the clusters and chose a self-explanatory
label for each cluster.1
Correspondence analysis
Up to that point, a separate bibliometric analysis was run in a
similar manner for the
co-citation and coupling matrix. In the final step, we integrated
the intermediate results
using correspondence analysis (CA). Like a multidimensional
scaling approach, CA is an
exploratory multivariate technique for visualizing categorical
data in a low-dimensional
space (Greenacre 1984). To our knowledge, this study is the
first to use CA for
integrating the results of co-citation analysis and bibliographic
coupling in a single
bibliometric map. This approach makes it possible to determine
the degree of
correspondence between the forefront of research (i.e. the
coupling clusters) and the
foundations on which this research is built (i.e. the co-citation
clusters). To integrate
those intermediate results, we first created a two-way frequency

cross-tabulation with
Table 1: Factor extraction
Factor Label
Number of
documents
Eigen-
value
Variance explained
Most characteristic references
(in brackets: factor loadings)(%) (Cum. %)
Co-citation analysis
1.1 New public
management
74 98.720 29.915 29.915 1. Moe 1994 (.904); 2. Barzelay and
Armajani 1992 (.891); 3. de Leon and
Denhardt 2000 (.889)
1.2 Leadership theory 79 61.745 18.711 48.626 1. Kotter 1990
(.969); 2. Blake and
Mouton 1964 (.968); 3. House and
Mitchell 1974 (.967)
1.3 Organization
publicness
45 33.548 10.166 58.792 1. Bozeman 1987 (.877); 2. Wolf 1994
(.869); 3. Rainey, Leisink, and
Middlebrooks, 1995 (.869)

1.4 Organization
theory
27 28.594 8.665 67.456 1. Ouchi 1981 (.835); 2. Trice and
Beyer
1993 (.774); 3. Selznick 1949 (.772)
1.5 Public service
innovation
27 25.896 7.847 75.304 1. Borins 1998 (.907); 2. Light 1998
(.894); 3. Mohr 1969 (.893)
1.6 New public
administration
21 18.756 5.684 80.988 1. Marini 1971 (.794); 2. Luke 1998
(.785); 3. Ospina and Dodge 2005
(.783)
Bibliographic coupling
2.1 Public leadership
outcomes
21 32.403 26.779 26.779 1. Andrews and Boyne 2010 (.946); 2.
Andrews et al. 2011 (.944); 3. Cho
and Ringquist 2011 (.933)
2.2 Transformational
public leadership
31 29.109 24.057 50.836 1. VanWart and Kapucu 2011 (.878);
2.
Jaskyte and Dressier 2005 (.869); 3.
Paarlberg and Lavigna 2010 (.866)

2.3 Public leadership
ethics
26 16.561 13.687 64.523 1. Boyne and Dahya 2002 (.855); 2.
Lee,
Moon, and Hahm 2010 (.852); 3.
Lambright and Quinn 2011 (.836)
2.4 Collaborative
public leadership
15 15.183 12.548 77.071 1. Hartmann and Khademian 2010
(.940); 2. Kronenberg and Khademian
2009 (.932); 3. Au 1996 (.929)
2.5 Public reform
leadership
14 11.267 9.311 86.382 1. Hansen 2011 (.755); 2. Awortwi 2010
(.731); 3. Moon and de Leon 2001
(.726)
the aggregated number of references to the cited documents in
each co-citation cluster
that were contained in the citing documents in each coupling
cluster. This contingency
table was subsequently converted into a two-dimensional scatter
plot.
FOUR APPROACHES TO PUBLIC LEADERSHIP

The map presented in Figure 3 shows the results of the
bibliometric analysis at the most
aggregated level. The scatter plot displays the extracted clusters
of literature, arranged
in a two-dimensional space with four quadrants. The black
clusters have emerged from
the bibliographic coupling analysis, thus indicating recent
trends in research on public
leadership, whereas the white clusters have resulted from the
co-citation analysis, thus
reflecting various traditions on which current research builds.
The spatial distances
between the clusters were calculated on the basis of references
that connect biblio-
graphic coupling clusters to co-citation clusters. The closer a
black cluster lies to a
white one, the greater the number of publications contained in
the former that cite
publications contained in the latter. In order to arrive at the best
fitting labels for the
elements of our framework, we pursued an inductive approach:
we thoroughly
reviewed the clustered publications, gave the clusters proper
names, arranged them
2.2 Transformational
Public Leadership
2.3 Public
Leadership Ethics
2.4 Collaborative
Public Leadership
2.5 Public Reform
Leadership

1.1 New Public
Management
1.2 Leadership
Theory
1.4 Organization
Theory
1.3 Organizational
Publicness
1.5 Public Service Innovation
1.6 New Public
Administration
2.1 Public Leadership
Outcomes
Multi-Level
S
u
b
je
c
tiv
eO
b
je

c
ti
v
e
Micro-Level
Functionalist
Approach
Behavioural
Approach
Biographical
Approach
Reformist
Approach
I
II
IV
III
Figure 3: Map of research on public leadership
Note: ● = bibliographic coupling cluster; ○ = co-citation
cluster; cluster size weighted by mass.

in the two-dimensional space and continued with an
interpretation of the emerging
dimensions and boxes. Our interpretations will become more
evident from the brief
descriptions of the approaches below.
The horizontal dimension represents public leadership research
from the perspective
of the philosophy of science, with ‘objective’ and ‘subjective’
as the two ends of the
continuum. This dimension is broadly consistent with the
philosophical dimension in
Burrell and Morgan’s (1979) analysis of paradigms in
organizational sociology.
According to Burrell and Morgan (1979), all scientific inquiries
into the social world
are guided by basic assumptions about it and about appropriate
ways of investigating it.
From the perspective of an objective philosophy of science, the
social world constitutes a
reality of its own that exists independently of individual
cognition and has a tangible
structure. Researchers can acquire knowledge about this reality
and investigate the
structural elements and causal relationships it comprises; their
primary interests are the
regularities and laws governing the observed reality. In terms of
methodology, objecti-
vists consider large-scale studies and quantitative methods most
appropriate for detect-
ing general rules and patterns.
On the contrary, the subjective philosophy of science assumes
that the social world does not
exist outside the human mind, but is rather a product of
subjective experience.

Consequently, knowledge about the social world can only be
acquired by those who
experience reality directly from within cultural systems of
meaning. Researchers who follow
this approach thus avoid imposing a frame of reference from
outside, and instead try to adopt
the perspective of those who create and interpret reality. With
regard to methodology,
subjectivists favour small-scale studies and qualitative methods
because they are mainly
concerned with the particularities of individual cases. They thus
aim to gain in-depth
knowledge on historical contexts, institutional conditions and
personal backgrounds, which
is best acquired by means of interpretive methods applied in
case study designs.
The vertical dimension reflects the level of analysis in research
on public leadership.
The continuum from the ‘micro-level’ to the ‘multi-level’ can
be best illustrated with
Coleman’s widespread heuristic for the analysis of social
phenomena (1990), which has
become popularly known as ‘bathtub’. According to Coleman
(1990), two general
modes of analysis of social phenomena predominate in
contemporary research. The first
mode is concerned with explaining and understanding individual
behaviour; this repre-
sents the micro-level of analysis at the bottom of the ‘tub’,
where individuals are of
primary interest. This mode of analysis involves observations
not on the level of the
system as a whole but on the level of the individual as part of
the system. The micro-
level focus does not preclude that individual behaviour may be

explained by factors that
go beyond personal characteristics and derive from the broader
social context.
The second mode of social research is concerned with
explaining and understanding
the social system as a whole (rather than the behaviour of
individuals) at the macro-
level of analysis – the top of the ‘bathtub’. The notion of
‘system’ is fairly broad and
includes any form of social organization, ranging from small
groups to entire societies.
However, changes on the system-level result from the actions of
individuals who are
part of these systems. Thus, social phenomena may be
investigated at a lower level than
that of the system, at which the system’s components can be
studied, and subsequently
the aggregated outcomes of these elements may be examined at
the systemic level. This
transition from the micro- to the macro-level rarely occurs
directly through the mere
summation of behavioural effects but is more likely to be
emergent and nondetermi-
nistic. We refer to research that considers both the micro- and
macro-level of analysis,
whether implicitly or explicitly, as ‘multi-level’.
The combination of the two dimensions yields four approaches
to public leadership.
It should be noted here, however, that the philosophical and the

analytical dimensions
of studies on public leadership are independent of each other, as
a certain philosophical
stance does not predetermine the level of analysis on which
research is carried out.
Furthermore, the distinctiveness of the four quadrants has been
exaggerated for
analytical purposes. The basic pairings outlined above (i.e.
objective vs subjective,
micro-level vs multi-level) are extreme positions at the opposite
ends of the two
dimensions. As already mentioned, these extreme positions do
not represent a dichot-
omy but mark the ends of a continuum characterized by
considerable variation. Below,
we will outline the four perspectives and summarize their basic
positions and key topics
drawing on the literature assigned to the respective clusters.
The functionalist approach to public leadership
Currently, the functionalist approach is most clearly adopted by
publications in a cluster
that we have labelled Public Leadership Outcomes. In these
studies, various performance
measures on which public leadership may have an impact serve
as dependent variables;
these include service performance (Andrews and Boyne 2010;
Andrews et al. 2011),
customer satisfaction (Andrews and Boyne 2010), work-unit
performance (Cho and Lee
2011; Cho and Ringquist 2011), perceived network
effectiveness (McGuire and Silvia
2009), internal efficiency (Ritz 2009) and property-tax
collection (Avellaneda 2009).
Several authors in this cluster place particular emphasis on

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