Estimating the E�ect of Urban Environment on

Swedish Firm Productivity

Ziada Ghebremikael Tareke∗

September 30, 2011

∗Master Thesis in Econometrics. Course code: EC 9901. Department of Economics,

Stockholm University, SE 106 91, Stockholm. Supervisor: Martin Flodén.

1

1 Introduction

A country's economic growth is determined by its level of productivity, mak-

ing the study of factors driving productivity growth an area of great interest

to Economics. There are many di�erent factors that drive the productiv-

ity forward - Agglomeration Economies are often considered as one of them.

The theory of Agglomeration Economies highlights the importance of urban

environment on productivity. The theory suggests that if an environment

consists of a high concentration of �rms from di�erent industries - it con-

tributes to larger and more diverse cities, which the Agglomeration refers to

as Urbanization Economies.

The purpose of this thesis is to study the extent to which the e�ects of urban-

ization are present in the Swedish economy and to investigate if the e�ects are

associated with the levels of human capital. Furthermore, this study aims to

investigate how the e�ects of urbanization change over time.Previous studies

have found support for the existence of Urbanization Economies, implying

that productivity increases with an increase of the size of the city. It is ac-

knowledged that larger and more diverse cities allow individuals as well as

�rms to exchange and acquire knowledge to a higher extent. The presence of

knowledge diversity combined with close proximity simpli�es the possibility

for individuals as well as �rms to interact - leading to a spread of new ideas

and thereby an increase in productivity. If larger cities enhance the produc-

tivity levels of Swedish �rms and thereby increase the growth levels in the

economy as a whole, it would be crucial for Swedish policy makers to sup-

port economic activity in larger cities, for example taking into consideration

factors such as housing shortages becomes important.1 The lack of housing

today has prevented university students from settling in Stockholm, forcing

1Stenkula and Zenou (2011) and Edward Gleaser makes similar suggestions.

1

them to study elsewhere (Dagens Nyheter 23 Aug. 2011, Svenska Dagbladet

30 Aug. 2011). Hence, it suggests a need to improvement the housing supply

in order to encourage economic activity.

On the other hand, new and advanced Information Technology (IT) is chang-

ing the logic of businesses and the structure of the market (SNS Economic

policy group report 2001). Information Technology is thus changing society's

way of exchanging knowledge. Online university courses internet forums and

blogs are all important examples of factors changing ways of spreading knowl-

edge. The factors previously mentioned all make the proximity aspect less

important in communicating. Banking errands done online are also an exam-

ple of how the importance of being located in larger cities may be changing.

Does this imply a change in the importance of Urbanization Economies? Is

the e�ect of larger cities on productivity growth diminishing over time? It

should be noted that the authors of the SNS report also point out that it

might take a long time before we can see the IT sector's e�ects on produc-

tivity. The meaning of "a long time" is, however, not de�ned. This study

focuses on the time frame between 2001 and 2008 to be relatively long and

investigates if the urbanization has diminished as an e�ect of a blooming IT

sector making it less important to locate in concentrated agglomerations.

This study uses data from by Statistics Sweden to estimate the e�ect of

agglomeration on productivity levels. The data covers the time period be-

tween1997 and 2008. It includes information of �rm level output, interme-

diate inputs, human capital, ownership structures and city sizes. A Cobb-

Douglas production function will be estimated using three di�erent methods;

pooled OLS, Fixed E�ects and Random E�ects. These methods will be used

in order to validate the structure of the data and control for possible un-

2

observed e�ects. The results from the study indicate that larger cities are

important for �rm productivity. In addition, it also con�rms that one of

the underlying generators of Agglomeration Economics is the level of human

capital among workers. Thus, implying that when workers spread ideas and

learn from each other their productivity levels increases. Moreover, the re-

sults indicate some support for the idea of a diminishing e�ect of urbanization

on productivity. This can perhaps be due to advances in the IT sector. The

signi�cance of these results, however, are slightly weaker when estimating

the �xed e�ects model.

The following parts of this paper are structured as follows: section 2 will

discuss possible factors generating Urbanization Economies and what pre-

vious studies have found. The data used for estimation will be discussed

in section 3. The empirical framework is explained in section 4 followed by

section 5 where the results are given. In section 6, the concluding remarks

will be presented.

3

2 The Theory of Urbanization Economies

Urbanization is a concept within Agglomeration Economics, which refers to

the bene�ts gained due to the spatial concentration of �rms across di�erent

industries or from the size of the city. The theory focuses on exploring the

choices of individuals and �rms from di�erent industries, to locating close to

each other. It is these choices that can result in the development of larger and

more diverse cities. When �rm productivity bene�ts from the surrounding

environment - external economies arise.2There are four underlying reasons

that generate Urbanization Economies and leading to increasing labor pro-

ductivity, O'Sullivan (2009).

They are summarized as followed. The �rst generator is that �rms often share

the same intermediate inputs. An example is banking services, which is gen-

erally used by all �rms, regardless of industry. Furthermore, �rms located

near each other will experience the same city features, and thus experience

the same public infrastructure. Highways, ports, harbors and universities are

all factors that can be bene�cial to a wide range of �rms and individuals.

The e�ect of this can be lower prices due to shared intermediate inputs which

can have a positive e�ect on productivity.

The second generator is based on the assumption that; a key incentive for

�rms to cluster is the competition for labor and the possibility of varying

wages when the �rm faces good and bad times. When a �rm's demand

varies, labor pooling makes it possible to relocate workers from unsuccessful

�rms to successful ones. Hence the coordination makes it possible to in-

crease total productivity. Firms facing more idiosyncratic shocks are more

spatially concentrated facilitating the relocation of workers, Overman and

Puga (2009).

2See O' Sullivan (2009) for the 5 axioms of urban economics.

4

The third generator steems from notion that the Labor market theories as-

sume that workers and �rms are perfectly matched. This is, however, not

always the case. Large cities can, improve the matching of workers and

�rms which contribute to increase the productivity. If the workforce in a

city increases, the amount of skilled labor also increases, thus, reducing the

mismatching between workers and �rms.

The fourth generator assumes that increasing education levels for workers

also enhances productivity. Workers learning from eachother are then able

to share more knowledge, combined with proximity - these are crucial parts

of knowledge spillover. Individuals are able to easily interact and thus able

to create new ideas. This e�ect is also said to be more important when there

is a high concentration of skilled labor within a city, O'Sullivan (2009) and

Glaeser and Resseger (2010). There are two hypotheses that explain this

relationship; that density causes more skilled workers to interact more eaily

and that these places allows for quicker spread of ideas and whereby compe-

tition pushes up the productivity.3

2.1 Previous studies

There are a wide range of studies within Agglomeration Economics, which

use di�erent approaches to estimate the e�ect that large cities potentially

have. Some of these papers will be summarized in this section.

Rosenthal and Strange (2003), have put together a consistent and detailed

analysis of previous studies within Agglomeration Economics. In their text,

3Glaeser and Resseger (2010) explains how these two hypothesis predicts di�erent out-

comes on income

5

they also discuss an ideal model that captures the agglomerations e�ect on

productivity in the best way. They point out two things; the e�ect that

�rms have on each other depends �rst on their size of activity and second,

the distance between them. A spatial, industrial or temporal increase in

distance would diminish the agglomeration e�ect on each �rm. This would

imply that their ideal model can account for the bene�t of interaction with

other �rms as a function of geographical, industrial and temporal distance by

considering that each �rm is unique. This is, however not easy to estimate

in reality as it would require researchers to somehow capture the diminishing

e�ect of agglomeration as �rms move further away from each other. They

instead mention that studies regularly relate �rms to de�ned regions. This

does, however, often not consider the interaction with �rms in neighboring

area.

Ciccone and Hall (1996), investigates the di�erences in average labor pro-

ductivity by studying the relationship between productivity and spatial den-

sity. They use U.S county level data with information on labor inputs and

state level data with information on output for the year 1988. They aggre-

gate county level data to state level data and estimate two di�erent models.

One investigates the relationship between density and productivity while the

second model estimates whether productivity increases with a diverse con-

centration of intermediate service producers. The results from both models

indicate that density is related to the level of productivity. The study �nds

that a doubling of employment density increases average labor productivity

by 6 percent. In the text, the importance of accting for economic activity

on the county level to explain the variation at the state level is emphasized.

They thereby account for geographical distance and address the problem

stated by Rosenthal and Strange (2003).

6

In the study by Harris and Ioannides (2000), the authors replicate and im-

prove the work by Ciccone and Hall. They �nd that density is a vital compo-

nent in explaining productivity but, excluding the size of population restricts

their study, making it di�cult for them to estimate the direct e�ect of popu-

lation on productivity. The author instead tests both measures as an optional

solution. In addition, estimating the relationship between state level density

and productivity means that Ciccone and Hall are not capturing the im-

portance of urban environments, since it is neglecting the e�ect on a local

level. There use of metropolitan level panel data allows them to control for

productivity di�erences over time. It enables them to control for unobserved

metropolitan attributes such as local policy, which may di�er across urban

areas. With a better suited model and more detailed data, their �ndings

allow them to support the results found by Ciccone and Hall.

Another study related to this, is that by Glaeser and Gottlieb (2009). They

investigate whether high productivity in urban areas is associated to higher

wages rather then an increase in population. They do this by studying the

housing supply. They base their estimations on a standard spatial equilib-

rium model to study the variations in income, housing prices and population.

These factors are in turn said to be driven by exogenous di�erences in pro-

ductivity, amenities and the construction sector. To conclude, their result

shows a connection between city size and. Their result seems to favor the

combination of higher wages and increased population leading to urban suc-

cess. However, the spatial equilibrium model is valid under the assumption

of constant welfare across space implying a weakness of the result.

Glaeser and Resseger (2010) further explore what leads to the higher pro-

ductivity levels in larger cities. Their aim was to reveal whether higher pro-

ductivity was generated by higher human capital or by natural advantages.

7

Their study supports theories of knowledge spillover, suggesting that the ag-

glomeration e�ect is strongly associated with the level of human capital. By

estimating the importance of proximity to nearest body of water, they are

able to rule out natural advantages are the driving force of the higher pro-

ductivity in larger cities. The impact that historical capital investments can,

however, not be as easily identi�ed due to a lack of good measurements.

The study of Andersson and Lööf (2009), studies the e�ects of agglomeration

on Swedish �rms. They use Swedish �rm level data from 1997 to 2004, pro-

vided by Statistics Sweden, to examine the relationship between regional size

and labor productivity. They analyze if �rms located in larger regions are

more productive, and if there are any di�erences in the productivity levels

when comparing small and large �rms. Furthermore, they investigate the

existence of learning e�ects of agglomeration. They estimate a production

function and as they predict, they �nd that agglomeration e�ects do exist

among Swedish �rms and that �rms located in larger regions are also more

productive. They do, however, not �nd any support that there should be

any di�erences in this e�ect depending on �rm size.

The contribution of this thesis to the literature will be to estimate how the

agglomeration e�ect on productivity changes with time. A diminishing ef-

fect of city size on productivity could illustrate the advances made within

the IT sector since it is changing the structure of carrying out business ac-

tivities and the interaction of individuals. Thus, if this can be seen in the

data, the importance of physical proximity for the productivity would indi-

cate a decline. This research will address the weakness brought about by

possible unobserved omitted �rm variables by using panel data methods for

estimation.

8

3 Data description

This study is based on data from the longitudinal integration database for

health insurance and labour market studies (LISA) from Statistics Sweden.

This database contains information on all individuals who are over 16 years

old and registered in Sweden. The purpose of the database is to monitor

these individuals at di�erent stages in their lives, in particular aspects relat-

ing to the workplace, health and family. Hence, it contains several di�erent

data sources.

The part of the LISA database that is of particular interest for this study

contains detailed information on the �rm-level. This section originates from

in the structural business register. The register contains information on all

active, private and public, �rms in Sweden, with exception of �nancial �rms

which are excluded from the register. The information included in this ex-

tensive register is mainly collected from the �rm's annual reports gathered

from the Swedish tax agency . It should be noted, however, that there are

restrictions with regard to �rms included in the LISA database. One such re-

striction is that the included �rm must employees registered. This indicates

that not all �rms in Sweden are included in the database.4The data covers

the years between 1997 and 2008.

The sample used for this study consists of 324485 observations but many of

the included �rms do not exist in every time period, resulting in unbalanced

data. It further includes observations on private �rms, within the service and

manufacturing industry.5The Swedish standard industry classi�cation (SNI)

is used to identify industries. During the studied time period, the de�nition

of di�erent industries has been changed twice. In order to SNI 2002 has been

4About 400 000 �rms in LISA year 2008 while 900 000 in the business sample 2008.5Governmental �rms have been excluded from the study

9

translated into SNI 92 for the latter years. In addition, it is also assumed

that the years between 2007 and 2008 are classi�ed according to SNI 2002.

This resolves into 42 industries with 2 digit codes.6 Firms with less than 10

employees have not been included for two reasons. The �rst being due to the

limited information available for these �rms and the second for the interest of

investigating the labor productivity.To obtain information on the population

size for the 290 municipalities, the LISA database has been matched with

population statistics.7

The level of �rm productivity is de�ned by the value added. The measure-

ment for capital is a book value measure derived from �rms annual reports.

The Value added and capital measures are de�ated by the CPI value, so they

can be expressed in �xed prices. Human capital levels are captured by the

share of employees with a bachelor degree or higher. This variable will ac-

cording to Andersson and Lööf (2009) indicate the R D levels within �rms.

A �rm is de�ned as foreign owned if more than half of the voting rights are

held by one or more foreign owners. Due to the lack of available data the

extent of foreign ownership for the years of 1997 to 2000 is based on whether

they were foreign owned in year 2001. The number of employees within a

�rm will de�ne the �rms size. This will increase the validity of the results.

Important to note, is that this study do not include data on the �rms import

and export activities. This implies that a possible important determinant of

productivity levels is missing.8Below follows a table of descriptive statistics

with the main variables included in the estimations.

6Firms with unde�ned industries are not included in this study.7Firms without information on municipality belonging are excluded from the data.8Andersson, Lööf and Johansson (2008)

10

In the table,lnLaborProd, lnCapital, HumanCapital and lnPopulation are

divided by number of employees within �rm.

4 Empirical framework

This paper uses a standard Cobb-Douglas production function in order to

estimate the e�ect of agglomeration on productivity. The Cobb-Douglas

function has the property that the technological progressbecomese identical

regardless of how it is augmented, Carlin and Soskice (2006). Here, it is

considered to be Hicks - Neutral, a�ecting labor and capital proportionally

and growing at a constant rate.9 The production function looks as follows:

9The same speci�cation is suggested by Ciccone and Hall (1996) and Henderson (1986)

11

(1) (Y )it = AitKαitH

βitL

γit

The left hand side in equation 1 illustrates the output de�ned as value added,

which is determined by the technological progress level A, capital input vari-

able K, labor input variable L and H captures the human capital in the labor

force. The subscript i varies over �rms while t varies over the di�erent

time periods. The parameters α and γ measure how the amount of output

responds to changes in the input levels. To get an expression for the labor

productivity, the equation above is divided by the amount of labor input

resulting in following equation:

(2) (y)it = AitKαitH

βitL

γ−1it

multiplying withLα

Lαand

Lβ

Lβleads to:

(3) (y)it = Aitkαith

βitL

γ−1+α+βit

The equation above express k and h as per worker capital and human capital.

To interpret the coe�cients in percentage values, equation 3 will be expressed

in logarithmic values:

(4) ln(y)it = lnAit + αlnkit + βlnhit + (γ − 1 + α+ β)lnLit

12

The technological progress indicates e�ects on Y which are not due to changes

in input. As in Andersson and Lööf (2009), it is assumed here that the

agglomeration e�ect enters the model through A as seen below:

(5) lnAit = b1lnpopulationst1 + b2foreignit2 + λt3 + λj4 + εit

Hence, A is de�ned by the size of the city, the ownership structure, where

time and industry dummies are also included. The above equation is merged

with equation 2 as follows:

(6) ln(y)it = b1lnpopulationst1 + b2foreignit2 + λt3 + λj4 + αlnkit + βlnhit+

+(γ − 1 + α+ β)lnLit + εit

The main variable of interest throughout this study is lnPopulation. Its coef-

�cient will capture the e�ect that city size has on the productivity. The use of

panel data for the estimation of the model enables more informative data to

be acquired through increasing variability, less collinearly between variables,

more degrees of freedom and higher e�ciency, Gujarati (2003). These ad-

vantages, according to Wooldridge (2009), contribute to more accurate test

statistics being obtained. Three di�erent methods will be used for estimating

the stated model; the �rst one is the Pooled OLS. In order for the pooled

OLS to estimate consistent parameters, the idiosyncratic error εit cannot be

correlated with the unobserved e�ect ai. These components together form

the composite error term.

13

(7) ln(y)it = b1lnpopulationst1 + b2foreignit2 + λt3 + λj4 + αlnkit + βlnhit+

+(γ − 1 + α+ β)lnLit + ai + εit

In this equation, ai contains all unobserved factors that a�ect the produc-

tivity. The above equation shows that the unobserved e�ect cannot be cor-

related with any of the explanatory variables. This is, in order to ensure

that the estimates are unbiased and consistent. It is possible that these

unobserved e�ects consist of �rm characteristics that may be signi�cant de-

terminants of �rm productivity. These could be factors such as di�erences

in �rm culters; strong leadarship and di�erent wage setting strategies. If not

controlled for, these would cause heterogeneity biases and thereby violate the

OLS assumptions. This is where Fixed e�ects model can be used as a substi-

tute and will thus be applied in this thesis, Rosenthal and Strange (2003). In

contrast to the Pooled OLS, the Fixed e�ects method allows the unobserved

e�ects to be correlated with the explanatory variables. By observing vari-

ables over time, this method makes it possible to study their changes and

eliminate the unobserved �rm-speci�c e�ects that are constant over time.

Hence, estimation of the city size coe�cient can be made by holding the

unobserved e�ects constant and treating them as unknown intercepts.

The main di�erence between a �xed e�ect and random e�ect model depends

on the assumptions made about the unobserved e�ects. The random e�ects

model is applied when one believes that the unobserved e�ect is uncorrelated

with the explanatory variables. In this case, elimination of the unobserved

e�ect would lead to ine�cient estimators. However, �xed e�ects regression is

considered to be a more applicable tool according to Wooldridge (2009). In

order to ensure the structure of the unobserved e�ects, both random e�ects

14

and �xed e�ects will be applied and a Hausman test will be performed to

determine the signi�cance of the coe�cients.

Although, both the Fixed and Random e�ects approaches are appreciated

methods for Panel data analysis, some argues, Anderson and Loof (2009),

and Soderbom and Sato (2011), that these methods are not always su�-

cient if data su�ers from autocorrelation, heteroskedasticity and endogenity

problems. An alternative approach would be to estimate General Method

of Moments (GMM), which allows for heteroskedasticity and serial correla-

tion over time. It is, however, also mentioned that the GMM estimator can

generate invalid estimates. Henderson (2003) evaluates which models that

are best suited to estimate the e�ect of agglomeration . He �nds that �xed

e�ect regression with metropolitan time-�xed e�ect and �rm �xed e�ect is

preferred when controlling for endogenity and therefore this paper will not

treat the GMM estimator.

Since it is likely that the error term is correlated within a �rm across time,

robust clustered standard errors are applied. An example of this is where

the development of the �rm in one year is likely to be correlated with the

development in the previous year. Since the study does not have any infor-

mation of �rms' import and export activity, omitted variable bias might be

captured in the estimates.

5 Regression results

This section presents the results, which is conducted by using the pooled

OLS, �xed e�ects and random e�ects methods. The measure of the city size

will be base the population of the 290 municipalities. The signi�cance of the

results will be determined by the coe�cients p-value. The result from the

15

Hausman-test will determine the structure of possible unobserved �rm spe-

ci�c e�ects, indicating the best suited estimation method. Throughout all

regressions the coe�cients of the random e�ect have been shown to be incon-

sistent, and the null hypothesis of the test is therefore rejected. Although the

�xed e�ect is preferred, the results from the random e�ect will be partially

illustrated in this section. The standard errors are clustered and robust as

an attempt to correct for heteroscdastisity and serial autocorrelation. All

regressions are estimated including time and industry dummies. The results

are valid under the condition that all �rms are located at the same place

within a municipality. It is also assumed that larger cities consist of more

diverse �rms.

Table 2 provides descriptive statistics on human capital and productivity

levels for all the 290 municipalities, divided into 7 di�erent groups. The

groups consist of: the greater Stockholm area, Malmö area and Göteborg

area. Municipalities with universities are divided into two groups of larger

and smaller population. The remaining two groups separate municipalities

into areas with a population of more than 25 000 or less than 25 000. This

16

classi�cation is based on a de�nition from the year 2010 and thereby unable

municipalities to vary across groups. Yet, it provides the study with an al-

ternative measure of the city size. The table illustrates the mean value of

labor productivity and human capital, which indicates that the values might

increase with the city size. This alternative measure of municipality groups

will be used for estimation in table 7.

Table 3 illustrates the e�ect of agglomeration on productivity. Results

are based on 292 055 observations with 51 836 �rm unique groups. The main

variable of interest is lnpopulation . Column 1 presents the results from the

pooled OLS, showing that all coe�cients are statistically signi�cant. The

results suggests that an increase in city size by 1 percent will lead to an

increase in productivity by 1.1 percent in average, given that everything else

is held constant. Columns 2-3 are estimated with �xed e�ects and random

e�ects methods to take account for any unobserved �rm speci�c e�ect. It can

be seen that the result for the estimated coe�cient of lnpopulation varies

17

in these columns. The result from the �xed e�ects model shows a weaker

and insigni�cant e�ect of city size. Although an interpretation of this result

is not possible, it suggest a similar connection between city size and level of

productivity as shown in the pooled OLS results.

The results in table 4 are obtained by estimating separate regressions for

high and low skilled workers. A city with high skilled workers is de�ned by a

workforce where the share of human capital is higher than 25.025 percent. A

workforce is de�ned as low skilled if the share of human capital is less than

17.65 percent. These cut o� points are provided by Glaeser and Resseger

(2010), de�ning the 100 most well educated areas and less educated areas.

It is assumed here, that these cut o� points also should apply for Swedish

data. The results in column (1) - (3) are estimated for the groups of low

skilled and column (4) - (6) for the high skilled workers. By looking over

the results in these tables, one can see that there is a clear di�erence in the

coe�cients estimated for these two groups. The result in column (1) suggests

a slightly weaker but signi�cant result for the e�ect of city size on the level

of productivity. Comparing this results with the corresponding regression for

the high skilled, an increase in city size by 1 percent will in general lead to

an increase in productivity by 4 percent, given that everything else is held

18

constant. This indicates that the level of human capital is strongly generates

the e�ect that agglomeration has on productivity levels. An explanation for

this could be as the knowledge spillover theory suggests. When the workforce

contains of more educated people and are located near each other, the spread

of knowledge increase and their by leading to higher productivity. However,

the result estimated with the �xed e�ects method is not signi�cant. These

results do however not rule out that other factors may generate agglomera-

tion economies leading to higher productivity levels.

To investigate if the e�ect of urbanization has diminished with time, sepa-

rate regression of the production function can be estimated for each year.

19

The coe�cient of lnpopulation for each year will indicate the development

throughout the time period. In table 5, result from the coe�cient of inter-

est is provided, summarizing the development of the agglomeration e�ect.

The coe�cient has dropped from 0.010 in 1997 to 0.006 in 2008. This could

suggest that the e�ect of agglomeration is changing due to for example im-

provement in the IT sector, or this change is re�ecting something else. This

mentioned change can also be controlled for by estimating regression with

panel data. To control for the change, the city size variable is interacted with

year dummies. The results of each coe�cient will then show the importance

of city size given a certain year compared with the reference category. Table

5 shows the results from interacting year dummies with city size coe�cient.

From the statistically signi�cant coe�cients, the pooled OLS suggest that the

e�ect of city size is diminishing with time. This result could be generated

by advanced IT. This thesis is limited in that way that it could not ensure

that this drop is statistically signi�cant due to advanced IT. The results from

the �xed e�ects regression suggest the opposite relationship, implying that

it might be too early to estimate the e�ect IT might have on agglomeration

economies.

Table 7 shows the robustness of the e�ect of urbanization on productivity.

This is done by computing regressions with change in data sample and in the

model. Four di�erent methods are presented below. In this table, the results

from �xed e�ects regression and pooled OLS will only be demonstrated.

Firms can have more than one working places that might be located in

di�erent municipalities. This attribute is therefore account for in columns

(1) and (2) by performing regressions with �rms with only one working place.

20

The results indicate that same e�ect of city size on productivity as the re-

gressions demonstrated in table 2.

The price of goods and services is likely to vary within a country. Higher

prices in some place could therefore re�ect higher productivity. It is likely

that eating out or staying in a hotel will be more expensive in larger cities.

Hence, this could mislead the relationship between productivity and city size.

Column 1 estimates the e�ect of city size on productivity excluding the ser-

vice industry since this industry is probably more sensitive to di�ering in

prices. SNI code 15 - 36 is de�ned as manufacturing industry. The results

show that an increase in city size leads to higher productivity. The result

has therefore not changed from what is shown in table 1. If it had changed,

the e�ect of city size could have been driven by prices.

Column (5) - (6) is estimated with a rede�ned city size variable. This

measure consists of a municipality classi�cation of 7 groups. Municipalities

with less than 25 000 individuals are here the reference category. For the

21

municipality groups with signi�cant coe�cients, it is possible to read that

�rms within large metropolitan areas are more productive compared with

�rms in less populated areas. Again, studying the results from �xed e�ects

regression in column (6), the dummy variable coe�cients indicates insigni�-

cant results. These results also favor the �xed e�ects estimates. Overall, the

�ndings from table 7 are in line with previous demonstrated results.

6 Concluding remarks

This thesis aimed to answer three questions. Does the city size have any

e�ect on �rm productivity? How has this e�ect changed over time and does

it di�er when controlling for areas with skilled and less skilled labor? The

questions were studied using mainly two di�erent estimation methods, Pooled

OLS and Fixed E�ect Method. Previous literature has stated that agglom-

eration seems to have a positive e�ect on productivity. The literature also

mentioned that an important generator is the level of human capital. This

thesis con�rmed that there is support for agglomeration e�ects in Sweden.

It also showed that it is highly generated by the level of human capital. In

order to capture the advantages of agglomeration - policy implications should

favor possibilities for �rms and individuals to mobilize more easily in, and

to, larger cities, through the expansion of the housing supply market and in-

frastructure. Stockholm is case of where these policies should apply in order

to increase the productivity, which leads to higher growth.

On the other hand, as the internet usage is heavily increased, it is changing

our way to interact, which may have a diminishing impact on the agglomer-

ation e�ect. In an attempt to estimate this e�ect, the Pooled OLS method

22

was used, which provided the study with some support for it. However, the

Fixed E�ect Method suggested the opposite. Yet, the era of the IT boom

has not reached its peak, implying that the full e�ect is still to be revealed.

The results were not as solid for the Fixed E�ect Method as it was for the

Pooled OLS. However, it would be interesting to investigate how the other

three underlying generators of agglomeration, namely intermediate inputs,

labor pooling and matching of workers and �rms are associated statistically

to agglomeration and if there is a prime generator. It would also be interest-

ing to further examine if the IT sector's e�ect will be clearer.

23

7 References

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http://scripts.abe.kth.se/cesis/documents/WP170.pdf

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24

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http://www.econ.brown.edu/faculty/henderson/willandstuart.pdf

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http://ideas.repec.org/p/tuf/tuftec/0016.html

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http://www.scb.se/Pages/List257742.aspx

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8 Appendix

Figure: Productivity and city size

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27