Local multipliers of public employment: Long-run
evidence from the late development of the Spanish
public sector
Jordi Jofre-Monseny 1 , Jos´e I. Silva2 , and Javier V´azquez-Grenno1
1
Universitat de Barcelona and Institut d’Economia de Barcelona
2
University of Kent and Universitat de Girona
Preliminary Draft: May 22, 2014
Abstract
In this paper we estimate long-run local multiplier effects of public administration
employment. Specifically, we regress 1980-2001 private job changes in Spanish cities
on contemporaneous changes in public administration jobs. The Spanish public sector
developed with the advent of democracy after Franco’s death in 1975 and, thus, the
period we study is characterized by a very large increase in public administration employment. Given path dependency in the location of public administration activities,
our instrumental variables strategy uses the 1970 employment distribution to predict
the location of public administration jobs in the 1980-2001 period. The results indicate that public employment crowds-in non-tradable jobs and crowds-out tradable jobs,
changing the local industry mix.
JEL Classifications: J45, H70, R12.
Keywords: public employment, local multipliers, industry mix.
1
Introduction
Public employment constitutes a significant fraction of employment in many countries around
the world. In 2008, the employer of 15 percent of all US workers was the public sector. In
Europe, Germany and Spain show a similar proportion, while public sector employment in
France represents as much as 27 percent of total employment1 .
Public employment is not evenly distributed across cities, with the public administration
being typically clustered in a few (capital) cities. Brasilia and Ottawa, in Brazil and Canada
respectively, are two very prominent examples of “government cities”. From the viewpoint
of a city, what are the consequences of hosting public administration services on a large
scale? And more specifically, what are the indirect employment effects of a local public
sector expansion?
Public employment will increase the demand for services such as housing, restaurants,
bars, lawyers or hair-dressers, expanding private employment. This crowding-in effect is the
focus of traditional Input-Output analysis. However, local wages and prices might respond to
the positive labor demand shock. This price adjustment will tend to crowd-out private employment. Analyzing the equilibrium responses of private employment to public employment
shocks is especially important for regional policy as public employment has been used to
overcome spatial inequalities in different countries including Italy (Alesina et al., 2001), UK
(Faggio and Overman, 2014) and Spain (Marqu´es-Sevillano and Rossell´o-Villallonga, 2004).
Using city-level data from Spain, and using the empirical framework developed by Moretti
(2010), we estimate the multiplier effects of public administration employment. Specifically,
we regress 1980-2001 city-level changes in private jobs (in the tradable or the non-tradable
sector) on contemporaneous changes in public administrative jobs. Besides trying to control
for changes in private employment determinants, we also resort to an instrumental variables
strategy that exploits path-dependency in the location of public administration jobs. The
results indicate that adding public administrative jobs in a city increases employment in
1
International Labour Organization statistics - France’s figure corresponds to 2006.
2
the non-tradable sector and decreases employment in the tradable sector. Hence, hosting a
large public administration changes a city’s industry mix favoring non-tradable over tradable
activities. The estimates also imply that, at the aggregate level, public sector jobs crowd-in
private jobs.
Our paper is closely related to Faggio and Overman (2014) that estimates multiplier
effects of public sector job relocations away from London. Their results, based on 20042008 employment changes at the British Local Authority level, indicate that overall private
employment does not change with public employment although the industry mix is changed
in favor of the non-tradable sector. We complement their study in several ways. First,
we focus on employment changes in the very long-run, allowing for possible sluggish price
adjustments. Second, the period we study is characterized by a very large increase in the
Spanish public sector that followed the advent of democracy after Franco’s death in 1975.
Specifically, between 1980 and 2001, the employment in the public administration increased
from 523,434 to 1,258,512. This late development of the Spanish public sector enables us to
use the geographical distribution of the pre-democratic and immature public administration
in 1970 (with only 296,207 employees) to predict city-level changes in public administration
jobs in the 1980-2001 period. Finally, we formalize a stylized variant of the Roback (1982)
spatial equilibrium model that can rationalize our empirical results and helps to clarify the
channels through which public administration employment affects private employment in the
tradable and non-tradable sectors so differently.
Our paper also relates to an empirical (macroeconomics) literature studying the labor
market effects of public employment at the national level. Edin and Holmlund (1997), using
data for 22 OECD countries from the 1960s to 1990s, find that public sector employment
reduces unemployment in the short-run (by 0.3 percent) and has no significant effect in the
long-run. When focusing on the Swedish case, these authors conclude that the public sector
growth over the 1960s and 1970s contributed to the low Swedish unemployment rate during
those years. Using a longer OECD panel, Algan et al. (2002) find different conclusions, namely
3
that 100 new public jobs crowd-out 150 private sector jobs and increase unemployment by
30 workers. Our estimates are not directly comparable to these results since labor demand
and supply elasticities at the local and national level are likely to differ.
The remainder of this paper is organized as follows. In Section 2 we develop a stylized
theoretical model that clarifies the mechanisms driving our empirical results. Section 3
presents and describes the data and variables used in the analysis and considers the historical
circumstances around the late expansion of the Spanish public sector . Section 4 describes
and justifies the econometric specifications and methods used in this study. Then, section 5
presents and discusses the results of the paper while Section 6 concludes.
2
A model of local multipliers
In this section we develop a stylized variant of the Roback (1982) model to illustrate the
mechanisms by which a shock in local public employment can affect the city’s private employment2 . We consider an economy comprised by many small cities (local labor markets).
In each city, there are two competitive private sectors (tradable and non-tradable). The
tradable (manufacturing) sector produces YT to be sold in the international market at unit
price (PT = 1). The output of the non-tradable sector (YN T ) is consumed locally and, thus,
its price (PN T ) is endogenously determined at the local level. The non-tradable good can
be interpreted as housing. The two sectors employ homogeneous labor (NT and NN T ) and
a sector-specific fixed factor of production (Z T and Z N T ) that can be interpreted as land in
the presence of land use regulations. Markets are assumed to be perfectly competitive and
capital returns accrue to absentee land owners.
Output in the tradable and non-tradable sectors is produced under the following constant
returns to scale production functions (YT
= NTα ZT1−α ) and (YN T = NNη T ZN1−η
T ) where
α, η ∈ (0, 1). Labor is paid its marginal product, yielding the following labor demand
2
See Glaeser (2008) for a detailed exposition of Roback (1982) and variants of it.
4
functions for the tradable and non-tradable sectors:
NT =
NN T =
1
α−1
w ZT
α
1
α−1
(1)
1 w
η−1
ZNT
η PN T
1
η−1
(2)
In addition, we consider a public sector that employs local workers at the city wage level
to provide a national public good (administration) that is not funded locally. We consider
that public sector labor demand is exogenously given (NG ).
Each (homogeneous) worker consumes tradable and non-tradable goods, supplies one unit
of labor, and is perfectly mobile across cities. Hence, a worker’s utility depends on the wage
and the price of the non-tradable good in the city. Since workers are perfectly mobile across
industries and cities, (nominal) wages in a city will be the same in the three sectors (tradable,
non-tradable and public) and utility levels will be equalized across cities. Specifically, the
economy-wide utility level is (V¯ = V (1, PN T , w)). If the utility function is Cobb-Douglas
the indirect utility function is:
1−ρ
V = w(1 − ρ)
ρ
PN T
ρ
= V¯
(3)
where 0 < ρ < 1 reflects the preference for the non-tradable good. Equation 3 indicates
that wages need not be the same in all cities. However, low wages must be compensated by a
low price of the non-tradable good if there are to be workers in a city. Finally, in equilibrium,
local demand must equal supply in the non-tradable sector. The local expenditure in the
non-tradable good is ρ w (NT + NN T + NG ) whereas the wage bill in the sector (w NN T )
amounts to η PN T YN T . Hence, market clearing in the non-tradable sector implies:
5
NN T
= ηρ
NT + NN T + NG
(4)
The non-tradable sector must concentrate a fixed share of total employment that depends
positively on the consumer preferences for the non-tradable good (ρ) and on the labor income
share in the non-tradable sector (η), implying that a positive shock in public employment
(dNG ) has to equal ( η1ρ − 1) dNN T − dNT .
Inserting 1, 2 and 3 in 4 and totally differentiating 4 yields dw/dNG , the change in the
city wage given an increase in public employment
dw
1
> 0
= ∂NN T ∂PN T
1
∂NN T
∂NT
dNG
−1
+
| ¯
−
ηρ
∂w
∂PN T ∂w dV =0
{z }
|∂w
| {z } |
{z
}
+
In turns out that
∂NN T
∂w
+
+
∂NN T
∂PN T
∂PN T
|
∂w dV¯ =0
(5)
-
is positive, indicating that labor demand in
the non-tradable sector is increasing with wages. The reason for this counterintuitive result
is that, for mobile workers to be indifferent across cities, the price of the non-tradable good
must increase faster than wages (reducing “real” wages in terms of non-tradable goods) and
this increases labor demand in the sector. Specifically, for workers’ utility to remain constant
(dV¯ = 0), equation 3 implies that if the city wage increases by 1 percent, the non-tradable
output price must increase by
1
ρ
tradable sector will increase by
percent, and, as a consequence, the employment in the nonρ−1
1
(η−1)
ρ
percent. Hence, it turns out that
dw
dNG
> 0 which
implies that:
dPN T
dNN T
> 0; and
> 0.
dNG
dNG
6
(6)
In the tradable sector,
dw
dNG
> 0 implies that
dNT
dNG
< 0 as labor demand is downward
sloping and, at the city level, the demand for the non-tradable good is completely elastic an
given by PT = 1.
3
Data and variables
3.1
Data
We primarily use employment data at the municipality level from Censuses carried out in
1970, 1980 and 2001. These data contain information on counts of employees by municipality
and main economic activity (3-digit level) of the establishment in which the employee works.
We construct city-wide employment levels using the 2008 urban area definitions built by the
Ministry of Housing3 . We work with a total of 83 cities (urban areas) that in 2001 concentrated 67 percent of the population.4 The median city (Ourense) had 126,410 inhabitants
in 2001. The size of the two largest cities - Madrid (5,135,225) and Barcelona (4,391,196)exceeds that of Soria (35,151) and Teruel (33,158) -the smallest two- by a factor of one
hundred.
In terms of outcome variables, we mostly consider (changes in) the employment in the
tradable sector, NT , that we assimilate to manufacturing industries, and in the non-tradable
sector (NN T ). Our main explanatory variable is the employment in the public administration sector (NG ). We focus on this specific component of public employment for two reasons.
First, in contrast to other activities in which the public sector intervenes such as the health
and education sectors, all public administration workers are public employees. Second, as will
shown below, the geography of public administration employment does differ very markedly
from the geography of population. To avoid a mechanical correlation between public administration and non-tradable employment, we leave out from the non-tradable sector those
3
4
The same definitions are used in De la Roca and Puga (2013).
We do not consider Ceuta and Melilla, the two Spanish enclaves in North-Africa.
7
activities where the public sector plays a major role as employer or regulator such as in the
R&D or the Radio and Television industries. We also exclude from our analysis the agricultural, farming, mining and energy industries. In the Appendix B we provide the details of
the industry classifications (and bridges) used thorough the paper.
3.2
Public (administration) employment growth in Spain
In Spain the development of the public sector took place surprisingly late. Figure 1 shows
the evolution of tax revenue to GDP for Spain, France, Germany and the US between 1965
and 2006. In 1965, the fraction of output devoted to tax payments was 14.7 percent in Spain,
a low figure compared to 24.7, 31.6 and 34.2, the corresponding figures for the US, Germany
and France, respectively. By contrast, in 2006 (before the start of the financial crisis) tax
revenue to GDP in Spain was 36.9, a fraction larger than that in the US (26.8) and Germany
(35.7), although still smaller than that of France (44.4). This late development of the public
sector in Spain coincides in time with the advent of democracy after Franco’s death in 1975.
In fact, the growth of the public sector is most intense between 1975 and 1990, a period in
which tax revenue to GDP increased from 18.4 to 32.5 percent.
This process of growth in the tax revenue to GDP ratio was accompanied by a parallel
increase in public employment. According to Census data, the number of public administration workers grew by 140 percent (from 523,434 to 1,258,512) between 1980 and 2001,
while the population only grew by 8 percent during this period. The numbers of workers in
the health and education sectors (where most workers are public employees) experienced a
similar growth rate in this period (128 percent), going from 845,984 to 1,930,578. Hence, our
period of study (1980-2001) is characterized by a very large increase in public employment.
This is an attractive feature of the present study.
8
50
Figure 1: Public sector growth in Spain
Franco’s death
Tax revenue / GDP (%)
30
40
France
Germany
Spain
10
20
US
1965
1975
1985
1995
2005
Source: OECD Statistics.
3.3
The geography of public (administration) employment growth
Public employment is not evenly distributed across Spanish cities, with administrative personnel showing substantial spatial concentration. The size of the public administration sector
in a city is determined, to a large extent, by its political status. In Spain, there are provincial and regional capital cities. Provinces (and the associated capitals) were established in
1833 by Javier de Burgos and constituted the main territorial division of the country until
the advent of democracy. Although provinces were not suppressed, 17 regions (Comunidades
Aut´onomas) were built as aggregations of one or several provinces in 1981. Twenty years later,
Spain was a decentralized country where its Comunidades Aut´onomas spending amounted
to roughly 46 percent of total government spending5 . A similar picture is obtained if one
looks at the distribution of public employees across layers of governments. In 2001, regional
5
Excluding social security spending. See Carri´on-i Silvestre et al. (2008) for a detailed explanation of the
decentralization process.
9
governments employed 45 percent of public employees whereas the central government and
local governments employed the remaining 34 and 21 percent6 .
Figure 2 plots the presence of administrative personnel in all cities, distinguishing regional
and provincial capitals, and non-capital cities. With two exceptions (Santiago de Compostela
and M´erida7 ), the cities hosting regional governments are also provincial capitals. Two noncapital cities (El Ejido and Elda-Petrer) have the lowest presence of public administration
employees in 2001 with about 1.7 employees per 100 inhabitants. At the other end, Soria
and Teruel (two provincial capitals) have more than 7 public administration employees per
100 inhabitants. More generally, this figure corroborates that being a capital comes along
with public employees, and the difference is especially large for small cities.
Holding population size constant, the presence of public administrative personnel is similar in provincial and regional capitals. This suggests that the process of regional decentralization that took place in Spain between 1981 and 2001 was not accompanied by a significant
relocation of administrative personnel from provincial to regional capitals. On the contrary,
pre-democratic provincial capitals retained their share of public adminstration employment.
On the one hand, provincial institutions (Diputaciones being the more prominent one) persisted into the new democratic regime. On the other hand, provincial capitals managed to
pull regional public administration jobs.
To analyze in a more systematic fashion if there is inertia in the location of public administrative personnel, we study what determines 1980-2001 changes in public administration
jobs. Since population is likely to be the most important determinant, we start by considering that, at a given point in time, the number of public administrative personnel in a city is
explained by a drift and its population, that is, NG = α + β P OP + , with α and β possibly
being year-specific. Subtracting the 1980 from the 2001 level yields:
6
Registro Central de Personal, Ministerio de Hacienda y de Administraciones P´
ublicas.
These two cities are historically important. While M´erida was the capital of the roman Lusitania
province, Santiago is the destination of a major Catholic pilgrimage route. Moreover, these are the third
cities in two bicephalic regions: Galicia (La Coru˜
na and Vigo) and Extremadura (C´aceres and Badajoz).
7
10
8
Figure 2: Public administration employees in 2001 per 100 inhabitants
Soria
6
Teruel
Madrid
Santiago
2
4
Mérida
Barcelona
0
Elda−Petrer
El Ejido
11
13
Population in 2001 (logged)
Provinvial capital
Regional capital
15
Non−capital
Source: Census and own elaboration.
dNG,80−01 = (α01 − α80 ) + β01 (P OP01 − P OP80 ) + (β01 − β80 )P OP80 + (01 − 80 )
(7)
where dNG,80−01 is the 1980-2001 increase in the city public adminstration employment.
Given the aggregate increase in the Spanish pubic sector in the 1980-2001 period (i.e.
β01 > β80 ), equation 7 implies that public administration employment growth will be larger
the larger the baseline city size is (P OP80 ), and the larger the population growth in the period (P OP01 − P OP80 ). To assess if public administration jobs increased more in cities where
the presence of administrative personnel was historically high, conditional on the population
level in 1980 and the 1980-2001 population increase, Figure 3 shows the partial correlation
between 1980-2001 changes in public administration jobs (NG,01 − NG,80 ) against public ad-
11
ministration employment in 1970(NG,70 ). Specifically, to partial out the correlation induced
by the common influence of P OP01 and P OP01 − P OP80 , the plotted variables are the residuals of an equation against these two variables. The data shows a positive relationship with
a Partial R-Squared of 12.5 percent, indicating that, indeed, cities with more public administrative personnel in 1970 attracted more public administrative jobs in the 1980-2001 period.
This finding guides our instrumental variable strategy that we explain in detail below.
8000
4000
0
−4000
−8000
1980−01 change in public admin. jobs (resid.)
Figure 3: Public admin. job increase (1980-01) versus 1970 public
admin. jobs
−8000
Partial R−squared = 0.125
−4000
0
4000
8000
Public admin. jobs in 1970 (resid.)
Note: Both variables are the residuals of a regression on population in 1980 and the 1980-2001
population change.
4
Econometric specification
We estimate the effects of 1980-2001 changes in public administration employment on contemporaneous changes in tradable and non-tradable (private) employment. The baseline
specification is:
12
dNP,80−01 = δ + γ dNG,80−01 + η x + ξ80−01
(8)
where P ⊂ (NT , NN T ). One possible problem with equation 8 is that shocks in private employment could be correlated with public adminstration employment changes. For instance,
if governments use public employment as a redistributive tool to support lagging regions
(Alesina et al. (2001)), public employment could be negatively correlated with employment
changes in the private sector. It could also be the case that thriving cities have more tax
revenues to hire administrative personnel and, as a consequence, positive shocks in private
employment are positively correlated with public administration employment shocks. We
deal with this potential problem in two ways. First, we will control for observed determinants of private employment growth (x) inasmuch as possible. Second, we will also resort to
an instrumental variables strategy based on the observation that there is path-dependency
in the location of public adminstration jobs as seen in section 3.3. Following Moretti (2010),
we define our (shift-share) instrument (dN G,80−01 ) as:
X
N
P Gi,70
NGi,01
i NGi,70 i
dN Gi,80−01 =
where i indexes cities and thus,
P
i
!
− NGi,80
(9)
N , is the national employment in the corresponding
sector and year. Hence, the instrument first computes the public adminstration employment
in 2001 that would be observed if each city would retain its 1970 national share in national
public administration employment. Then, it predicts the 1980-2001 change by subtracting
the observed public adminstration employment level in 1980. The instrument is arguably
exogenous to the extent that is independent of private employment shocks that might have
occurred in the 1980-2001 period. Figure 3 suggested that this instrument is relevant. We
will return to this question below.
13
5
Results
Before moving to the regression analysis, we start this section by plotting, in Figure 4 the
1980-01 changes in employment in the tradable (first panel) and non-tradable (second panel)
sector versus contemporaneous changes in public adminstration jobs in all cities. Note that
the two graphs differ in terms of the average increase in jobs in the vertical axis. Although
positive, the average job increase in the tradable sector is small (1,396), with some cities
like Bilbao or Oviedo-Gij´on loosing a large number of jobs in manufacturing industries . In
contrast, all cities experience positive increases in non-tradable sector jobs (36,206) which
reflects the ongoing tertiarization process of the Spanish economy. Turning to our research
question, while there is a slightly negative association between changes in tradable and public
administration jobs, the corresponding correlation between the non-tradable and the public
administration sectors is clearly positive.
The first two columns in Table 1 show the regression results of equation 8. Specifically,
the table reports the coefficients measuring the impact of public administration job changes
(dNGi,80−01 ) on tradable (column 1) and non-tradable (column 2) job changes over the same
period of time. All these specifications include a a set of control variables, x, that include (1)
the 1970-1980 change in population, (2) the 1970-1980 employment change in the own sector
(tradable or non-tradable), (3) the 1980 population level, (4) the 1980 own sector employment
level and, (5) a prediction of the 1980-2001 own sector employment change defined as:
dN P i,80−01 =
X
k
X
N
P ki,80
Nki,01
i Nki,80 i
!
−
X
Nki,80
(10)
k
where k indexes narrowly defined industries in the relevant sector (tradable or nontradable). The predicted employment change in 9 captures the component of the 1980-2001
local employment shock (in the tradable or non-tradable sector) explained by city’s industry
mix in 1980 interacted with 1980-2001 fate of industries at the national level. That is, Bil14
Oviedo−Gijón
Bilbao
0
5000
10000
change in public administration jobs
15000
0
5000
10000
change in public administration jobs
15000
0
change in non−tradable jobs
20000 40000 60000 80000 100000
−50000
change in tradable jobs
−30000
−10000
10000
Figure 4: Changes in tradable and non-tradable jobs (1980-01) versus
contemporaneous changes in admin. jobs
Note: Cities with more than 1,000,000 excluded for the sake of presentation (Madrid,
Barcelona, Sevilla & Valencia).
15
bao’s loss of tradable jobs between 1980-2001 is partly explained because one of the industries
in which Bilbao was specialized in 1980 (the iron and steel industry) lost 38 percent of the
jobs at the national level during this period.
Table 1: The multiplier effects of public administration jobs
Tradable
(dNT,80−01 )
Public admin (dNG,80−01 )
Public admin pred (dN G,80−01 )
Public admin (dNG,80−01 )
Partial R-Squared
F stat
Kleibergen-Paap rk LM stat
Non-tradable
(dNN T,80−01 )
OLS
-0.599**
1.701***
(0.288)
(0.474)
Reduced-form estimates
-0.232***
0.942***
(0.090)
(0.222)
2SLS
-1.956**
3.616**
(0.868)
(1.580)
0.09
0.26
4.15 (0.04)
8.01 (0.00)
4.79 (0.03)
7.29 (0.01)
Notes: 1) Coefficients are the effects of public administration job changes (19802001) on contemporaneous changes in tradable (col. 1) and non-tradable (col.
2- jobs and in population (col. 3). 2) In the first two columns, the regressions include, as controls, the 1970-1980 change in population, the 1970-1980
employment change in the own sector (tradable or non-tradable), the 1980 population level, the 1980 own sector employment level, and, the prediction of the
1980-2001 own sector employment change as defined in the text. 3) Robust
standard errors in parenthesis. 4) ***, **, * denote statistical significance at
the 1, 5 and 10 percent.
The first panel in the Table 1 reports Ordinary Least Squares (OLS) estimates. These
results indicate that more public administration jobs reduce employment in the tradable
sector. Specifically, the estimates imply that one additional public administration job causes
0.6 job losses in the tradable sector. In contrast, public administration employment expands
employment in the non-tradable sector. Here, one public job creates 1.7 additional jobs.
These results imply that placing public administrative personnel in a city changes the city’s
industry mix by crowding-out employment in the manufacture and expanding it in nontradable sectors. Since the positive effect on the non-tradable sector dominates the negative
effect on the tradable sector, one extra public adminstration job has a positive effect on the
16
total private employment.
The second panel shows the estimates of regressions where the explanatory variable of
interest, i.e. public administration jobs (dN Gi,80−01 ), has been replaced by the instrument
(dN G,80−01 ) isolating the variation explained by the fact that, due to path dependency, public
administration grew more in cities that in 1970 had a large public administration. These
(reduced-form) estimates indicate that the higher the predicted public administration job
increase, the lower the employment increase in the tradable sector and the higher the increase
in the non-tradable sector. Hence, these estimates are qualitatively similar to their OLS
counterparts.
Finally, the third panel shows the 2-Stage Least Squares (2SLS) results. In addition to
being a valid instrument, dN G,80−01 must also be relevant. The last three rows report FirstStage statistics. The partial R-squared values are relatively large and both the F- and the
Kleibergen and Paap (2006) rk LM- statistics are significant at the 5 percent level. However,
the F-test values are lower than 10, and evidence the limited strength of our instrument which
results in relatively imprecise 2SLS estimates (the standard errors increase by a factor of 3
with respect to the OLS estimates). Despite this, the 2SLS (and the reduced-form) estimates
are qualitatively similar to their OLS counterparts.
Besides public administration, the public sector (including its firms) is active in other sectors of the economy, namely, health, education, railway transportation, air transportation,
postal services and telecommunications, public sewage, libraries and museums, and diplomatic representations. Although these activities employ both private and public employees,
the public sector plays a major role in all these (regulated) industries. Therefore, in Table 2
we redo the analysis considering this broader definition of public sector.
The results are qualitatively similar to our baseline estimates that only considered public administration. Nevertheless, the instrument relevance increases significantly when using
this broader definition, increasing the precision of the 2SLS estimates. Here, the OLS and
the 2SLS results are very close, suggesting that, controlling for determinants of private em17
Table 2: Multiplier effects: Broader public sector definition
Tradable
(dNT,80−01 )
Public admin (dNG,80−01 )
Public admin pred (dN G,80−01 )
Public admin (dNG,80−01 )
Partial R-Squared
F stat
Kleibergen-Paap rk LM stat
Non-tradable
(dNN T,80−01 )
OLS
-0.583***
1.429***
(0.147)
(0.185)
Reduced-form estimates
-0.210***
0.408***
(0.057)
(0.139)
2SLS
-0.757***
1.252***
(0.197)
(0.313)
0.415
0.332
29.90 (0.00)
30.66 (0.00)
11.44 (0.00)
10.538 (0.00)
Notes: 1) Coefficients are the effects of public administration job changes (19802001) on contemporaneous changes in tradable (col. 1) and non-tradable (col.
2- jobs and in population (col. 3). 2) In the first two columns, the regressions include, as controls, the 1970-1980 change in population, the 1970-1980
employment change in the own sector (tradable or non-tradable), the 1980 population level, the 1980 own sector employment level, and, the prediction of the
1980-2001 own sector employment change as defined in the text. 3) Robust
standard errors in parenthesis. 4) ***, **, * denote statistical significance at
the 1, 5 and 10 percent.
ployment changes, shocks in private employment are not correlated with public employment
changes.
6
Summary and final remarks
In this paper we have estimated the long-run effects of public administration employment
on private employment, distinguishing between the effects on the tradable and non-tradable
sectors. Specifically, we examine employment changes in Spanish cities between 1980 and
2001, a period of time characterized by a very large increase in public employment in Spain.
Exploiting path dependency in the location of public administration activities, our instrumental variables strategy uses the 1970 employment distribution across cities to predict the
location of public administration jobs in the 1980-2001 period. Our results indicate that
public administration employment has a positive multiplier effect for the non-tradable sector
18
(restaurants, bars, hair-dressers, etc.) and a negative effect for the tradable sector.
A stylized variant of the Roback (1982) spatial equilibrium model can rationalize these results. An additional public worker increases the demand for non-tradable goods (e.g. restaurants, bars or hair-dressers). However, public employment also shifts the labor demand curve
outwards, increasing wages which tends to reduce employment. For workers to be indifferent
across cities, the price of non-tradable goods must increase faster than wages since workers
only spend a fraction of their income in non-tradable goods. As a result, employment in the
non-tradable sector increases with public employment. For the non-tradable sector, more
public workers do not significantly raise demand and, therefore, employment is reduced as
wage are higher.
19
References
Alesina, A., Danninger, S., and Rostagno, M. (2001). “Redistribution Through Public Employment: The Case of Italy.” IMF Staff Papers, 48 (3), 2–44.
Algan, Y., Cahuc, P., and Zylberberg, A. (2002). “Public employment and labour market
performance.” Economic Policy, 17 (34), 7–66.
Carri´on-i Silvestre, J. L., Espasa, M., and Mora, T. (2008). “Fiscal Decentralization and
Economic Growth in Spain.” Public Finance Review, 36 (2), 194–218.
De la Roca, J., and Puga, D. (2013). “Learning By Working In Big Cities.” Working Papers
2013-1301, CEMFI.
Edin, P.-A., and Holmlund, B. (1997). “Sectoral structural change and the state of the labour
market in sweden.” In H. Siebert (Ed.), Structural Change and Labour market Flexibility,
89–121, Mohr Siebeck.
Faggio, G., and Overman, H. (2014). “The effect of public sector employment on local labour
markets.” Journal of Urban Economics, 79 - Spatial Dimensions of Labor Markets, 91–107.
Glaeser, E. L. (2008). Cities, Agglomeration and Spatial Equilibrium. Oxford University Press.
Kleibergen, F., and Paap, R. (2006). “Generalized reduced rank tests using the singular value
decomposition.” Journal of Econometrics, 133 (1), 97–126.
Marqu´es-Sevillano, J. M., and Rossell´o-Villallonga, J. (2004). “Public employment and regional redistribution in spain.” Hacienda P´
ublica Espa˜
nola, 170 (3), 59–80.
Moretti, E. (2010). “Local multipliers.” American Economic Review, 100, 373–377.
Roback, J. (1982). “Wages, rents, and the quality of life.” Journal of Political Economy,
90 (6), 1257–1278.
20
A
Appendix: The Model
A.1
Production in the tradable sector
The production function for firms in the tradable sector is Cobb-Douglas with constant
returns to scale:
1−α
YT = NTα Z T
(A-1)
where α (0, 1), Z T represents land used in the tradable sector which we assume is fixed.
NT is the labor in tradable sector. We assume that firms are competitive and maximize
profits over NT .
1−α
max{NTα Z T
NT
− wNT }
(A-2)
The first order condition (FOC) for labor in the tradable sector is:
1−α
αNTα−1 Z T
− w = 0
then, labor demand in the tradable sector is given by:
w =
αZ T
NT
or
21
1−α
(A-3)
NT =
A.2
1
α−1
w ZT
α
1
α−1
(A-4)
Production in the non-tradable sector
The production function for firms in non-tradable sector also is Cobb-Douglas with constant
returns to scale:
1−η
YN T = NNη T Z N T ,
(A-5)
where η (0, 1), Z N T represents land used in the non-tradable sector which we assume is
fixed. NN T is labor in tradable sector.
We assume that non-tradable firms are competitive and maximize profits over NT , yielding
the following FOC for labor:
1−η
ηPN T NNη−1
T ZNT − w = 0
(A-6)
then, labor demand in the non-tradable sector is:
w = ηPN T
ZNT
NN T
1−η
(A-7)
or
η−1
NN T =
w ZNT
PN T η
22
1
! η−1
(A-8)
A.3
Consumers
Each homogeneous consumer supplies one unit of labor, is perfectly mobile across cities and
consumes non-tradable and tradable goods:
U = CNρ T CT1−ρ
(A-9)
with ρ (0, 1). Given A-9, the indirect utility function is given by A-10. Perfect mobility
implies that local prices (wages and the price of non-tradable goods) must adjust to equalize
utility across cities. V¯ is the economy-wide utility level consumers can achieve:
1−ρ
V = w(1 − ρ)
ρ
ρ
PN T
= V¯
(A-10)
V¯
(A-11)
then,
ρ−1
w = (1 − ρ)
ρ
−ρ
PN T
or
PN T = ρ
A.4
w (1 − ρ)1−ρ
V¯
ρ1
(A-12)
Market clearing in the non-tradable sector
Utility maximization implies that a fraction ρ of total labor income is spent on non-tradable
goods,
23
ρ w (NT + NN T + NG ) = PN T YN T
(A-13)
We can express the FOC for labor as:
w
NN T = PN T YN T
η
(A-14)
doing some algebra and combining the equations A-13 and A-14 yields:
NN T
= ηρ
NT + NN T + NG
A.5
(A-15)
Comparative statics
Plugging A-4, A-8 and A-12 into(A-15) defines:
φ(w, NG ) = (1 − η ρ) NN T (w, PN T (w)) − η ρ (NT (w) + NG ) = 0
(A-16)
Taking the total derivative in equation A-16 yields:
dφ = (1 − η ρ)
∂NN T
∂NN T ∂PN T
+
∂w
∂PN T ∂w
dw − η ρ dNG − η ρ
which can be rearranged as:
24
∂NT
dw = 0 (A-17)
∂w
dw
= dNG
1
1
ηρ
− 1 ∂N∂wN T +
∂NN T ∂PN T
|
∂PN T
∂w dV¯ =0
−
∂NT
∂w
In order to know the sign of the expression A-18 we need to know the sign of
the sign of
∂NN T
∂w
+
∂NN T ∂PN T
|
.
∂PN T
∂w dV¯ =0
In order to sign
+
and
(A-19)
+
-
∂NN T
∂w
∂NT
∂w
Note that:
−α
w α−1
∂NT
ZT
=
< 0
∂w
(α − 1)α | α{z }
| {z }
(A-18)
∂NN T ∂PN T
|
∂PN T
∂w dV¯ =0
we can plug PN T (equation A-12) into NN T
(equation A-8) obtaining:
"
NN T = Z N T
1
# β−1
w
(A-20)
1
βδ( wv (1 − δ)1−δ ) δ
Then, we can take the derivative of equation A-20 with respect to w which yields:
β
# 1−β
1
β−1
1 − 1δ
1
ZNT
w
> 0
1
1
w
w
1−δ ) δ
β−1
β
δ)1−δ ) δ
δ(
(1
−
δ)
v
| {z } | βδ( v (1 −{z
{z
}
} | {z } |
"
-
+
+
(A-21)
-
Given the sign of the derivatives of the equations A-19 and A-21, it turns out that:
dw
1
= > 0
∂NN T
∂NT
1
∂NN T ∂PN T
dNG
−1
+
| ¯
−
ηρ
∂w
∂PN T ∂w dV =0
|∂w
{z }
| {z } |
{z
}
+
+
25
-
B
Appendix: Industry classifications
Description
Broad' public sector
Public administration
Education
Primary
Secondary
Tertiary
Health
Railway transportation
Air transportation
Postal services & telecommunication
Public sewage
Libraries, archives & museums
Diplomatic representations
Tradable sector
CNAE 52 (1970)
CNAE-74 (1980)
81
821
.
.
.
822
711
717
731-734
522
.
.
20-39
CNAE-93 (2001)
91
931,932, 933, 934
931 932
933
934
941-945
711-712
741-742, 753
761-762
921
967
990
22, 24, 25-49
75
801-804
801
802
803
851
601
621-623
641-642
900
925
990
15-39
CNAE-74 to CNAE-93 bridge for narrowly defined industries
Description
CNAE 52 (1970)
413
416
415
411-412
414
417-419, 420-421, 423
422
424-428
429
431-437,439,453-455
441,442, 456
451-452
461-467
471-472
473
475
474
114
130
140
251-253, 255
254
481
482
246
241 247
242
243
245, 249
221-223
312-316, 319
321-326, 329
345
330
341-343, 346,347
353
393
399
361
362
363
371-372
381
382
383
389
468
491
492
494
495
CNAE-74 (1980)
151
152
153
154
155
156-158
157
159
160
171-177, 182
181, 183, 191,192
193
201-205
211
212
221
222
231
232
233
241-247
244
251
252
261
262-264
265
266
268
271-273
281-287
291-296
297
300
311-316
333
334
335
341
342
343
351
352
353
354
355
361
362
363
364 365
366, 371, 372
CNAE-93 (2001)
CNAE-74 to CNAE-93 bridge for narrowly defined industries
Non-tradable sector
611, 612, 621, 622, 623, 624, 625, 626,
627, 628, 629, 631, 632, 633, 634, 635,
636, 638, 639, 641, 642, 643, 644, 649,
713, 714, 718, 719, 721, 722, 726, 727,
729, 825, 826, 829, 831, 833, 841, 842,
843, 844, 845, 846, 849
26
50
61-64, 67
65,66
602
754
756
811-814, 819
821-823
831
832
833
861, 869
834
854
855
851-853
856, 859
845
841-842, 846
843
844
922
946
961-963
980
971-973, 979,849
45
50-51-52
55
721-723, 729, 751
631
634
651-652
660
671
672
701
702
703
711
712
713
714
721-726
741
742-743
744
747
852
921
950
632, 745, 746, 748, 924, 493, 930
© Copyright 2024 ExpyDoc
