FINANCIAL SITUATIONS IN MUNICIPALITIES:

IS THE FLUCTUATION OF POPULATION A

MODERATING FACTOR?

Montserrat Capdevila Vilaseca

Programa Universitat Empresa 21

May 2011

Supervised by: Diego Prior & Alan Reeves

INDEX PAGE

1. Introduction 3 2. The study 4 3. The cities in the study 5 4. Analysis of the selected cities 9 4.1 Other factors 9 4.2 Methodology and statistical sources 10 4.3 Results 13 5. City Councils Annual Report 15 6. Financial capacity analysis 15 6.1 Indicators 15 6.2 Methodology and statistical sources 19 6.3 Results 24 7. Solutions for the future 26 7.1 Suggestions 26 7.2 Simulation 27 8. Conclusion 31 9. Sources 33 Appendix I 35 Appendix II 36 Appendix III 37 Appendix IV 38

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1. Introduction

The objective of this paper is to analyse fluctuation of population as a factor which may cause varying financial conditions in municipalities.

The idea of developing this study of municipalities came to me after hearing in all the media about the difficulties that City Councils are going through. During recent months it has come into light that some City Councils have several problems due to the real estate crisis. Some of them have based their ordinary expenses on incomes from property expansion. This has been a risky strategy because of the end of the real estate boom. Nowadays, they see how their incomes have gone down and their economic situation is becoming unsustainable. For instance, Roda de Barà in Tarragona is a municipality which based its growth on second residences and whose income from the building sector decreased about 76% from 2006 to 2010 (30 minutes, February 2010).

The media have reflected this situation in some headlines such as 30% of City Councils will go bankrupt this year from July 2010 or City Councils on the verge of collapse from May 2010 (see the original documents in Appendix I). Municipalities provide a great variety of services such as sports centres, retirement homes or school assistance in the form of grants for books or lunches. If a municipality has economical problems, the consequences will affect the inhabitants as towns will be obliged to reduce services. So, the financial situation of municipalities is of great importance to citizens.

The focus of this study is to see if City Councils are in different financial situations depending on their fluctuation of population. The initial idea is that towns with a significant fluctuation in population will probably have more problems keeping their budget balanced. In this study we will discover it.

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2. The study

This paper is going to determine if fluctuation of population is a factor that affects a City Councils’ financial condition.

Fluctuation of population will be measured by studying three types of municipalities: towns with stable population, those with tourism and those with urbanizations.

The first group are towns that maintain approximately the same number of residents all year round. There is a stable population with no significant seasonal fluctuation.

The second group are municipalities that absorb a large number of citizens during one season in relation to the registered population. Typically, they are beach resort towns or mountain municipalities with tourism in summer or winter respectively. The impact that tourism causes in the city affects their municipal budgets.

The last group are municipalities with numerous residential developments. This means that their population is dispersed around the municipality. Sometimes inhabitants are using it as a dormitory town. So, the population varies from day to night. These citizens are contributing to the economic growth of other neighbouring municipalities in the sense that they often work, go to school or spend their leisure time outside their town of residence.

If we think in terms of cost, it is obvious that cities with tourism have to offer more services during their high seasonal period because they are receiving visitors. For instance, they have to provide more security in tourist zones, maintain larger city parks, provide refuse collection or increase transportation services due to the population increase.

On the other hand, cities with numerous urbanizations where population is spread out sometimes over large distances also have extra expenses. For example, the cost of refuse collection or security services is higher because the distance involved is greater.

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Finally, stable municipalities whose population is concentred in a nucleus, have a regular level of costs with fewer disadvantages.

This study is going to examine not only the costs but also the incomes that municipalities have.

In all these three study groups, seasonal population variation affects the towns in different ways. Our aim is to determine if one of these three groups is likely to suffer financially more than others due to the seasonal fluctuation factor.

The structure of the present study is as follows: first of all, we have chosen some municipalities of each group; then we have used descriptive statistics to avoid physical factors that could affect the analysis, for instance, municipalities with geographical differences or different economical situations such as the unemployment rate; after that we have used indicators of financial condition to reach a conclusion; and finally, we have provided suggestions to improve their current financial situation.

3. The cities in the study

The seasonal variation of population will be measured using three groups of cities: touristic, stable and those with urbanizations. In order to get a sampling of these groups, we have chosen eleven cities for each group, using certain variables to situate the cities in these groups.

The variables used are those of Idescat1. In order to determine the seasonality of the population we have used the Seasonal Populations AFTE (Annual Full Time Equivalent).

The seasonal population AFTE is the difference between arrivals of non resident population minus departures of resident population. The total AFTE population is the seasonal population plus the resident population in that territory.

Idescat has provided the following information: population censuses, non resident population present, resident population not present, seasonal

1 Idescat: Institut d’Estadística de Catalunya. Official statistics website of related to population, economy and society (www.idescat.cat). 5 population and total population. All this data is from 2003, the most recent seasonal population information registered by Idescat.

Considering the total AFTE population and the population censuses, we have established a ratio between them to determine if a municipality has a seasonal population or not.

All touristic municipalities chosen for the study have a ratio higher than one (see chart 1), in other words, the total AFTE population is bigger than the population census. On the other hand, the stable municipalities group have a ratio of approximately one (see below chart 2). So, their total AFTE population is approximately equal to the population census.

Resident Population Stational Total Population population non resident population population TOURISM censuses non E/A present AFTE (D) AFTE (E ) (A) present AFTE (B) B-C A+D AFTE (C ) Lloret de mar 25457 28972 1657 27315 52772 2,073 L'Escala 6997 8712 472 8240 15237 2,178 Palamós 15968 5733 1392 4341 20309 1,272 22625 6846 2963 3883 26508 1,172 Torredembarra 12113 4331 1358 2973 15086 1,245 Salou 16952 27110 1788 25322 42274 2,494 Cambrils 23555 13397 2589 10808 34363 1,459 13431 2541 1830 711 14142 1,053 Castell-Platja d'Aro 7905 13854 707 13147 21052 2,663 Roses 14719 15180 1040 14140 28859 1,961 Vielha e Mijaran 4547 2060 384 1676 6223 1,369 Chart 1: AFTE population in touristic municipalities from the study (data from 2003).

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Resident Population Stational Total Population population non resident population population STABLE censuses non E/A present AFTE (D) AFTE (E ) (A) present AFTE (B) B-C A+D AFTE (C ) Navàs 5629 300 683 -383 5246 0,932 5361 273 841 -568 4793 0,894 Tona 6486 506 934 -428 6058 0,934 Falset 2595 250 269 -19 2576 0,993 Agramunt 5071 307 442 -135 4936 0,973 Amposta 17759 1402 1623 -221 17538 0,988 Sant Sadurní d' 10708 826 1103 -277 10431 0,974 Tàrrega 13616 1077 1336 -259 13357 0,981 Ulldecona 5997 253 532 -279 5718 0,953 Anglès 5049 333 619 -286 4763 0,943 Móra d'Ebre 4847 408 490 -82 4765 0,983 Chart 2: AFTE population in stable municipalities from the study (data from 2003).

The other variable that determined the placement of municipalities into the third group, with urbanizations, is the mobility of the population for work or study reasons.

Mobility of the population has been taken from by Idescat. The most recent data they registered comes from 2001. The information provided is: displacements inside, displacements outside, displacements from outside, total movements generated and total movements attracted.

If the displacements outside are higher than displacements from outside, it means that the population of this municipality usually works or studies outside the city. We have used this variable to choose the cities from the study with urbanizations. Additionally, we checked each City Council Web Site that they have a high number of urbanizations (see below chart 4). It is fitting to point out the fact that all municipalities chosen for the study have a seasonal ratio of near one; in other words, the total AFTE population is higher than that of the population census (see chart 3).

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Resident Population Stational Total Population population non resident population population WITH URBANIZATIONS censuses non E/A present AFTE (D) AFTE (E ) (A) present AFTE (B) B-C A+D AFTE (C ) 6639 1130 1335 -205 6434 0,969 10869 1514 1398 116 10985 1,011 6209 312 864 -552 5657 0,911 Llagostera 6293 889 700 189 6482 1,030 Vidreres 5559 913 676 237 5796 1,043 13803 1342 1936 -594 13209 0,957 Corbera de 10903 1013 1738 -725 10178 0,934 11110 1058 1644 -586 10524 0,947 Santa Eulàlia de 5175 583 967 -384 4791 0,926 Ronçana L'Ametlla del Vallès 6757 575 1087 -512 6245 0,924 24741 2017 3883 -1866 22875 0,925 Chart 3: AFTE population in municipalities with urbanizations from the study (data from 2003).

Chart 4: Mobility of population in municipalities with urbanizations of the study (data from 2001).

Chart 5 shows the cities of the study.

TOURISM STABLE WITH URBANIZATIONS Lloret de mar Navàs Viladecavalls L'Escala Roda de Ter Piera Palamós Tona Masquefa Sitges Falset Llagostera Torredembarra Agramunt Vidreres Salou Amposta Caldes de Montbui Cambrils Sant Sadurní d'Anoia Arenys de Mar Tàrrega Vallirana Castell-Platja d'Aro Ulldecona Santa Eulàlia de Ronçana Roses Anglès L'Ametlla del Vallès Vielha e Mijaran Móra d'Ebre Sant Pere de Ribes Chart 5: Cities of the study.

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Once the three groups of municipalities have been selected, we proceed to study them.

4. Analysis of the selected cities

In order to study the financial condition of each City Council we first have analysed the cities selected. The idea is to reject those cities that are very different from the others due to external factors. For instance, we cannot compare a city with 30,000 inhabitants to another with 300 inhabitants. These variables would affect our final aim.

We will identify these factors and use statistical sources to eliminate those cities from the study.

4.1. Other factors

Some external physical factors can influence the municipal treasury such as:

- Inhabitants

Municipalities with different populations need to offer different services to the town. The more inhabitants the municipality has, the greater the number of services it has to offer. Choosing cities with a similar number of inhabitants, we will be able to benchmark City Councils with more reliability.

- Altitude

This is a geographical factor that influences municipalities indirectly. Different altitudes cause varying climates and temperatures. It is not the same to spend the winter on the coast as it is in the mountains and this could affect factors such as the level of heating consumption or construction materials of pubic buildings.

- Population density

A City Council with a low population density will have higher expenses than others. For example, the profitability of public services such as garbage collection, street lighting or public transportation is different depending on the municipality’s population density.

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- Unemployment

The level of unemployment is another factor that differentiates two otherwise similar municipalities. City Councils have to respond to unemployed citizens who have problems surviving.

- Outsourcing public services

Some City Councils have decentralized their departments or the services they offer. Outsourcing services causes, for instance, less staff costs; in other words, different budget impacts.

- Economical sectors

It is not the same if a municipality belongs to, for example, the primary sector or to the tertiary one. Their priorities and necessities are different and the City Council will concentrate their financial resources to very different uses.

4.2. Methodology and statistical sources

Now is the time to put all these factors into numbers and analyse them.

The data for the inhabitants, altitude, population density, unemployment and economical sectors have been supplied by Idescat.

It is worth noting that the number of inhabitants for each city is from 2009. The unemployment data comes from 2001 as the most recent information gathered for each municipality. Idescat provides the most recent information about unemployment in absolute terms. In order to compare unemployment between cities we have used the percentage of unemployment which is the number of people actively seeking employment divided by the economically active population. This data comes from 2001. The data for the economical sectors also comes from 2001 which are the most recent figures. The information provided by Idescat has been the percentage for each sector (agriculture, industry, construction and services) which each municipality is devoted to. We have used the service sector percentage to compare the cities.

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The outsourcing of public services is the ratio between the public staff expenses from the budget divided by the total expenses from the budget. This data has been provided by each City Council Annual Report (see below point, number 5).

Chart 6 shows the factors data used by the analysis.

INHAB TOURISM ALT DENS UNEMP OUTS SERV 2009 Lloret de mar 39.363 5 808 21% 22,53% 78,5 L'Escala 10.140 14 622 13% 23,52% 67,5 Palamós 18.161 12 1297 12% 39,83% 61,1 Sitges 27.668 10 632 11% 31,58% 7,1 Torredembarra 15.272 15 1755 10% 33,04% 65,6 Salou 26.649 2 1765 22% 29,08% 75,2 Cambrils 31.720 24 901 10% 25,28% 66,4 Arenys de Mar 14.627 10 2151 10% 36,48% 65,1 Castell-Platja d'Aro 10.376 5 476 11% 28,86% 71,6 Roses 20.197 5 440 12% 24,74% 65,7 Vielha e Mijaran 5.710 974 27 4% 24,81% 73,4

INHAB STABLE ALT DENS UNEMP OUTS SERV 2009 Navàs 6.243 370 77 9% 26,41% 42,1 Roda de Ter 6.015 443 2734 10% 24,22% 45,9 Tona 7.955 596 482 6% 29,87% 51,7 Falset 2.864 364 91 8% 18,13% 51,5 Agramunt 5.608 337 70 6% 27,57% 42,2 Amposta 21.240 8 154 8% 28,03% 52,6 Sant Sadurní d'Anoia 12.237 162 644 7% 37,95% 43,8 Tàrrega 16.539 373 187 7% 25,58% 55,4 Ulldecona 7.236 133 57 6% 21,70% 40,3 Anglès 5.569 181 342 7% 20,79% 43,8 Móra d'Ebre 5.695 38 126 6% 23,45% 59,8

WITH INHAB ALT DENS UNEMP OUTS SERV URBANIZATIONS 2009 Viladecavalls 7.322 274 364 7% 39,84% 56,2 Piera 14.324 324 250 11% 23,01% 48,2 Masquefa 8.168 257 478 10% 30,18% 48,2 Llagostera 7.764 160 102 7% 25,62% 51,9 Vidreres 7.430 93 155 9% 25,20% 55,2 Caldes de Montbui 16.885 203 451 7% 35,32% 50,5 Corbera de Llobregat 13.843 342 752 9% 25,91% 63,2 Vallirana 14.066 177 589 7% 25,81% 58,6 Santa Eulàlia de Ronçana 6.802 242 479 6% 29,52% 56,6 L'Ametlla del Vallès 7.949 281 560 7% 20,44% 62,9 Sant Pere de Ribes 28.353 44 695 10% 22,44% 60,7 Chart 6: Other factors data.

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As for our methodology, we have used descriptive statistics to eliminate cities from the study. These will help us to benchmark the financial conditions of each group of City Councils to obtain a representative result.

First of all, we have used Gretl, a statistical package, to obtain the most significant descriptive parameters. Secondly, we have used the boxplots for each variable to see graphically how the information is distributed. This tool has also been provided by Gretl. Then, we have used the interquartile range (IQR) to determine the elimination of municipalities from the study.

The most significant parameters, summary statistics, from Gretl are: mean, median, minimum, maximum and standard deviation. The results are in Appendix II.

We have used the median because it is the point in the middle of the list of numbers studied. This parameter avoids the extremes which is exactly what we are aiming to do.

The interquartile range is shown in the boxplot. This graph uses the following descriptive measures: median, the first quartile, the third quartile, the maximum value and the minimum value. The data from the box contains the middle 50% of all the observations. The median is represented by a stripe and the arithmetic mean is represented by a cross. The first quartile is under the box representing 25% of the sample. The third quartile is above the box representing the last 25% of the sample.

Appendix III shows the boxplots of each factor from the Gretl statistical package.

A boxplot may also indicate which observations, if any, might cause a municipality to be eliminated. The space between the different parts of the box indicates the degree of dispersion.

We have applied the interquartile range to each factor. We have arranged the data of each variable from highest to small. For instance, we have ordered the cities from the highest to lowest number of inhabitants. Then, we have found out

12 the median and the first and the third quartile. After that, we have cut out the outlying 25% at the top and the bottom.

Due to the fact that we have six factors to apply the interquartile range, we cannot definitely cut the sample with the first variable. So, we have scored with 1 the cities rejected in each variable and we have used 0 for the cities in the box.

Finally, we have summed up all the scores from each variable and we have ordered them. Then, we have cut 50% of the sample with higher scores to reject those cites. This methodology allows us to reject half of the initial sample which is the same percentage as the idea of the interquartile range.

4.3. Results

Chart 7 shows the result of applying the interquartile range of each factor. As we said, cities rejected from each group are marked with one and those in the middle of the sample are marked with zero. The last column is the sum of all scores gathered. We have abbreviated the names of the factors for convenience.

INHAB 2009 ALT DENS UNEMPL OUTS SERV TOTAL Lloret de mar 1 1 1 1 1 1 6 Palamós 1 1 1 1 1 0 5 Sitges 1 1 0 1 1 1 5 Salou 1 1 1 1 0 1 5 Vielha e Mijaran 1 1 1 1 0 1 5 Agramunt 1 1 1 1 0 1 5 Roses 1 1 0 1 0 1 4 Navàs 1 1 1 0 0 1 4 Roda de Ter 1 1 1 0 0 1 4 Falset 1 1 1 0 1 0 4 Ulldecona 0 0 1 1 1 1 4 Móra d'Ebre 1 0 1 1 1 0 4 Cambrils 1 0 1 0 0 1 3 Castell-Platja d'Aro 0 1 0 1 0 1 3 Amposta 1 1 1 0 0 0 3 Piera 0 0 0 1 1 1 3 Arenys de Mar 0 1 1 0 1 0 3 Sant Sadurní d'Anoia 0 0 0 1 1 1 3 Anglès 1 0 0 0 1 1 3 L'Escala 0 0 0 1 0 1 2

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Torredembarra 0 0 1 0 1 0 2 Tona 0 1 0 1 0 0 2 Masquefa 0 0 0 0 1 1 2 Corbera de Llobregat 0 1 1 0 0 0 2 Santa Eulàlia de Ronçana 1 0 0 1 0 0 2 L'Ametlla del Vallès 0 0 0 1 1 0 2 Sant Pere de Ribes 1 0 0 0 1 0 2 Tàrrega 0 1 0 0 0 0 1 Viladecavalls 0 0 0 0 1 0 1 Llagostera 0 0 1 0 0 0 1 Caldes de Montbui 0 0 0 0 1 0 1 Vidreres 0 0 0 0 0 0 0 Vallirana 0 0 0 0 0 0 0 Chart 7: results after applying the interquartile range.

The cities in dark blue have been rejected. The cities in light blue are the most homogeneous sample which we have decided to use in the study.

In order to chose which cites marked with three points will be rejected, we have used the random function from excel to be impartial. 50% of the 33 cities is 16.5, so we have rejected 16 and we will use the other 17 to analyse their financial condition.

Carrying out this procedure we have got a homogeneous sample of cities. Now, we have a sample where the only factor that differs among these cities is the stability of population structure.

Chart 8 shows the cities that finally will be included in the analysis of the present paper. TOURISM WITH URBANIZATIONS Viladecavalls L'Escala Masquefa Torredembarra Llagostera Arenys de Mar Vidreres Caldes de Montbui STABLE Corbera de Llobregat Tona Vallirana Sant Sadurní Santa Eulàlia de d'Anoia Ronçana Tàrrega L'Ametlla del Vallès Anglès Sant Pere de Ribes

Chart 8: Selected cities.

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5. City Councils Annual Reports

In order to analyse the financial condition of the City Councils, we first have to get their Annual Reports. This data has been supplied by The Public Audit Office of Catalonia, The Sindicatura de Comptes de Catalunya2, which is the institution in charge of auditing the accounting and financial management of the Catalan public sector. Every year they publish the Annual Report on the General Accounts of local corporations among other data.

In most cases not all Annual Reports are at public disposal because they are not found on the database web page. So, we have used the average of the last five years available as a representative sample of their Annual Report. The average is from 2005 to 2009. In some cases, the data available comes from previous years. In these cases, we have used the last Annual Report available.

The data we have taken from the Annual Reports for our study are the budget and the balance sheet.

Appendix IV shows the Annual Reports available, used for the present study.

6. Financial capacity analysis

Now it is time to benchmark the City Councils we have selected. Once we have done the analysis, we will be able to relate the question of fluctuating population with the City Council financial situation.

6.1. Indicators

The financial capacity analysis has been based on the following ratios:

- Index of solvency (Muñiz and Zafra, 2009): current assets divided by current liabilities. This ratio shows the capacity to face up to short term liabilities. If the percentage is higher than one, the City Council can face up to their short term obligations. Whereas if it is smaller than one, it means that the municipality cannot carry out their short term liabilities.

2 The Public Audit Office for Catalonia, la Sindicatura de Comptes de Catalunya, is the Catalan external audit institution charged to audit the public sector (www.sindicatura.cat). 15

- Index of cash assets: cash and other liquid assets divided by current liabilities. This index refers to ability of the City Council to pay its most immediate payment obligations. If the ratio is higher than one, there is the guarantee of facing up to its most recent debts. - Index of guarantee: total assets divided by total liabilities. This percentage shows the distance to bankruptcy. This ratio indicates the distance to bankruptcy, that is to say, how many assets are possessed in relation to amounts owed. - Index of stability: non current assets divided by non current liabilities plus net assets. If this ratio is less than one, it indicates that the working capital is positive. In the case that the ratio is higher than one the working capital is negative; that is to say, current liabilities exceed to current assets. - Index of indebtedness: total liabilities divided by net assets. Index of short-term indebtedness: current liabilities divided by net assets. Index of long-term indebtedness: non current liabilities divided by net assets. These ratios show which is the relation between the debt compared with the equity. - Index of the weight of the financial load (Muñiz and Zafra, 2009): current operations incomes divided by financial liabilities. This information comes from the budget. This ratio indicates the level of the financial load. The level of the financial load is lower if the result of this ratio is as high as possible. - Index of financial independence (Muñiz and Zafra, 2009): incomes without subsidies and transfers divided by total expenses from the budget. The higher this ratio is easier it is it for the City Council to face up to their expenses without receiving help from other administrations. - Index of non-financial budget result (Muñiz and Zafra, 2009): incomes divided by expenses from the budget without financial assets and liabilities.

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This ratio indicates the capacity of having a positive budget result disregarding financing. If this ratio is higher than one, the City Council can face up to their expenses without appealing to banks for financing. - Index of current revenues received (Muñiz and Zafra, 2009): the received incomes divided by total incomes from the budget. This ratio indicates how much income the City Council has actually received compared to its initial forecasts. If the result is close to one, the expected incomes are approximately the same as the received incomes. - Index of net saving: income minus expenses divided by the income from the budget. This ratio compares income with expenses. If the result is zero, income is equal to expenses. If the result is positive, income is higher than expenses and if the result is negative, expenses exceed income. - Debt per inhabitant: number of inhabitants divided by the City Council debt. The number of inhabitants is supplied by Idescat and the local debt comes from The Spanish Economy and Finance Ministry, Ministerio de Economía y Hacenda, database. All this data is from 2009.

It is worth mentioning that we haven’t used the index of guarantee, stability and indebtedness. This is because the public sector has a peculiarity in their equity. They enter public property in the books as a negative amount in the net assets of the balance sheet. So, if a City Council has negative net assets, it could be because they are bankrupt or because they have numerous public properties. The ratios mentioned before can lead to a confusion in the results.

The following step is to calculate the ratios we will use to evaluate the financial condition of each City Council. Chart 9 shows the results.

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Chart 9: Ratios of financial condition.

The cities in orange are from the touristic group, the cities in green are from the stable group and in blue are from the group with urbanizations.

It is not easy to draw a conclusion from this table of results and to determine how fluctuation of population affects the financial condition of the City Council. For instance, L’Ametlla del Vallès has the best index of solvency with 2.385. In other words, they have current assets of more than twice their current liabilities. However, in the same group, with urbanizations, we find the municipality with the worse ratio. Caldes de Montbui has 0.898; this is to say that their current liabilities are greater than their current assets. Another example is the index of savings; Llagostera has the most favourable result with 0.208 whereas Sant Pere de Ribes has the worst ratio, -0.244. In the case of Llagostera, its income it’s greater than its expenses from the budget. Sant Pere de Ribes is completely different from Llagostera despite belonging to the same group. In regard the debt per inhabitant, we can see that L’Ametlla del Vallès, from the group with urbanizations, is in the best position with a debt per inhabitant 229.84. And Torredembarra, from the touristic group, is on the opposite end of the spectrum, with a debt per inhabitant of 1632.661. So, can we say that the touristic group is in worse financial condition than cities with urbanizations? No, we cannot. So, how can we determine if really there are significant differences among the three groups?

In order to benchmark these results we have used statistical sources to help us to reach a conclusion.

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6.2. Methodology and statistical sources

Our objective is to find out if there is a group that stands out among the other groups. We want to know, whether for each indicator used to evaluate financial condition, there is a group with a tendency to have worse results than the others or not. How can we reach a reliable conclusion? Which statistical sources can we apply?

Statistics we’ll help us to interpret the results table. First of all, we have used an Anova Test to find out if there are differences among the median of each group. Then, we have applied another test to compare the means.

The Anova test will help us to determine whether each indicator used for the analysis has differences among the three groups or not. The methodology is explained below.

The Anova test hypotheses are:

Ho: there are no differences among the three groups

H1: there are differences among the three groups

If the null hypothesis is true, it means that the fluctuation of the population doesn’t affect the financial condition of cities.

The test assumptions are as follows:

1. Independence 2. Homogeneity of variances 3. Normality of residuals

Yi ~ N (µi, δ)

푆푆퐴 퐾−1 퐹 = 푆푆퐸 ~ 퐹(푘 − 1, 푛 − 푘) 푁−퐾 where SST = SSA + SSE

푘 푛 2 2 푇∗∗ 푆푆푇 = 푌푖푗 − ((푛1 + 푛2 + ⋯ + 푛푛) 푖=1 푗 =1

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푛 푇 2 푇 2 푆푆퐴 = 푖=1 푖∗ − ∗∗ 푛푘 (푛1 + 푛2 + ⋯ + 푛푛 )

where Yi are the observations; K is three, the groups we analyse: touristic cities, those with a stable population and those with urbanizations; N is seventeen, total observations; 푇∗∗ is the sum of all observations; 푇푖∗ is the sum of all observations from the same group.

We have used the following significance level: α = 0,05

We have applied this test statistic to each indicator used.

The other test used is one which compares the means with k independent samples. This test will help us to see if the means are the same. It will show if there are significant differences among the three means of each group for each indicator. The methodology is explained below.

The test assumptions are as follows:

1. Independence 2. Homogeneity of variances 3. Normality of residuals

The hypotheses are:

H0: there are no differences among all means µ1 = µ2 = µ3 = ... = µn

H1: Not all means are equal

푀푆퐵퐸푇푊퐸퐸푁 퐹푠 = ~ 퐹 (푘−1,푛−푘 ) 푀푆푊퐼푇퐻퐼푁

푛 푆푆퐵푇푊퐸퐸푁 푆푆퐵 푖=1 푛푖 (푌푖 − 푌)^2 푀푆퐵퐸푇푊퐸퐸푁 = = = 푑푓퐵퐸푇푊퐸퐸푁 푘 − 1 푘 − 1

푛 푛 푆푆푊퐼푇퐻퐼푁 푆푆푊 푖=0 푗 =0 푌푖푗 − 푌푖 ^2 푀푆푊퐼푇퐻퐼푁 = = = 푑푓푊퐼푇퐻퐼푁 푛 − 푘 푛 − 푘

where SST = SSW + SSB, Total Sum of Squares = Sum of Squares Within + Sum of Squares Between; K is three, the groups we analyse: cities with tourism, those with urbanizations and

20 those with stable population; n is the number of observations, seventeen; 푌푖푗 is an observation;

푌 푖 is the mean of the k group; 푌 is the mean of all observations.

We have used the following significance level: α = 0,05

We have applied this test statistic to each indicator used.

The results of the Anova test are in chart 10.

TEST

STATISTIC F (k-1, N-k) RESULT

Index of solvency 1,3849 3,74 Do not reject H0

Index of cash assets 1,1502 3,74 Do not reject H0

Index of the weight of the financial load 0,1860 3,74 Do not reject H0

Index of financial independence 2,6104 3,74 Do not reject H0

Index of non-financial budget result 0,7627 3,74 Do not reject H0

Index of current revenues received 0,2579 3,74 Do not reject H0

Index of net savings 1,3613 3,74 Do not reject H0

Debt per inhabitant 1,8357 3,74 Do not reject H0 Chart 10: Anova tests results.

The results of the media comparison test are in chart 11.

TEST

STATISTIC F (k-1, N-k) RESULT

Index of solvency 0,9033 3,74 Do not reject H0

Index of cash assets 1,0441 3,74 Do not reject H0

Index of the weight of the financial load 0,1677 3,74 Do not reject H0

Index of financial independence 0,6184 3,74 Do not reject H0

Index of non-financial budget result 0,1888 3,74 Do not reject H0

Index of current revenues received 0,0190 3,74 Do not reject H0

Index of net savings 1,5839 3,74 Do not reject H0

Debt per inhabitant 1,3262 3,74 Do not reject H0 Chart 11: Media comparision test results.

The result in both tests shows that there we fail to reject H0 in each indicator at the 5% level. That is to say, there are no differences among the three groups for each indicator. It is logical that both tests give the same result. Surprisingly, contrary to what we expected at the onset of the study, fluctuation of population doesn’t seem to affect the financial condition of the City Councils.

But, have we used the best test to determine this?

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We have used two parametric tests to reach our conclusion based on the following assumptions: independence, homogeneity of the variances and the normality of the residuals. If we think of these assumptions, we can come to the conclusion that we really don’t know how the residuals are distributed. The parametric tests need assumptions regarding the nature or the form of the populations involved. The non parametric tests do not need these assumptions; they are tests of free distribution.

The following list indicates the basic difference among the parametric and the non parametric tests:

PARAMETRIC TEST NON PARAMETRIC TESTS

Assumed distribution: Normal Any

Assumed variance: Homogeneous Any

Data set relationship: Independent Any

Usual centre measure: Mean Median

So, we can use a non parametric test which does not impose any of the assumptions that parametric tests do.

What are the advantages that a non parametric test provides?

 The non parametric methods can be applied to a large variety of situations because they do not have the rigid requirements of parametric methods. Especially, non parametric methods do not need to assume the normality of the distribution.  Non parametric methods usually involve more simple computations than parametric methods and they are, therefore, easier to deal with and apply.  A non parametric test can be applied to non numerical data.  It can be used in small samples.

As a result of the above, it is more appropriate to use a non parametric method for our analysis. We don’t know how the data is distributed; moreover, our sample is small. Thus, we have used a non parametric test called the Kruskall- Wallis test.

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This test is going to determine whether there are or not differences among the three groups of the study for each indicator used. So, we will discover whether fluctuation of population really affects the financial condition of the City Councils or not.

The Kruskall-Wallis hypotheses are:

H0: there are no differences among all means

µ1 = µ2 = µ3 = … = µn

H1: there are differences among all means

If the null hypothesis is true, it means that the fluctuation of the population doesn’t affect the financial condition of cities. Otherwise, fluctuation of population would be a factor to influence the municipal budgets.

The methodology to construct the test statistic is as follows: first of all, it is necessary to construct a table with all the observations in each group of cities as seen below. The following step is to put all the observations in order, assigning a number to each observation. Number one is for the small observation and seventeen for the higher observation, since we have seventeen cities in total. Then, we replace each observation with the number we have assigned. Once we have finished this step, we can calculate the test statistic. We have to repeat the same process for each indicator.

TOURISM STABLES WITH URBANIZATIONS

nT1 nS1 nU1 … ... …

nTn nSn nUn

TT TS TU Tall

MT MS MU Mall T means the sum of the observations for each group. And M is the average.

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푆푆푏푞 (푅) 퐻 = ~ 흌² ( ) 푛 푛 + 1 휶, 풌−ퟏ 12

2 2 2 2 푇퐴 푇퐵 푇퐶 푇푎푙푙 푆푆푏푞 푅 = + + + … − 푛퐴 푛퐵 푛퐶 푁 where N is the total observations; T is the sum of all observations from a group .

We have used the following significance level: α = 0,05

Chart 12 shows the results.

TEST STATISTIC 휒² (α, k-1 ) RESULT

Index of solvency 56,4386 0,1026 Reject H0

Index of cash assets 54,3209 0,1026 Reject H0

Index of the weight of the financial load 52,4961 0,1026 Reject H0

Index of financial independence 56,2150 0,1026 Reject H0

Index of non-financial budget result 54,8170 0,1026 Reject H0

Index of current revenues received 52,3882 0,1026 Reject H0

Index of net savings 55,4941 0,1026 Reject H0

Debt per inhabitant 54,7020 0,1026 Reject H0 Chart 12: Kruskall-Wallis test results.

The results show that for each indicator there is the same conclusion: we reject the null hypothesis at the 5% significance level. In other words, there are differences in the indicators results depending on the group that a City Council belongs to.

6.3. Results

So far, we have found out that the fluctuation of population is a factor that causes a City Council to have a different financial situation. In order to discover which group is more likely to have a worse financial condition than the others, we have used the median of each indicator. Chart 13 emphasizes in orange the group with a worse median than the others.

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WITH TOURISM STABLE URBANIZATIONS MEDIAN MEDIAN MEDIAN Index of solvency 2,2071 1,3429 1,7181 Index of cash assets 0,9354 0,4237 0,9665 Index of the weight of the financial load 12,6351 16,7678 11,1793 Index of financial independence 0,9050 0,7269 0,7457 Index of non-financial budget result 1,0835 0,9690 0,9879 Index of current revenues received 0,8468 0,8672 0,8614 Index of net savings 0,1377 0,0056 0,0180

Debt per inhabitant 1.074,2604 906,1899 633,3188 Chart 13: Median of index results.

Contrary what was expected, we have reached the conclusion that the group with worse financial condition is that of cities with stable population because more than half of the ratios indicate it.

Despite the fact that tourism municipalities have extra expenses to cover the additional population they receive in a specific period, they have bigger scope to increase their revenues. For instance, they have a great number of services such as bars, leisure activities or commercial shops that generate extra income due to the tourism. Moreover, they always have to be in mind the additional expenses they have forecasted due to their touristic sector’s needs. It is essential to carry out a good management of municipal finances to bring life to the touristic sector.

Economic growth that generates inhabitants from municipalities with urbanizations usually goes to neighbouring cities due to the fact that citizens usually use these municipalities as a dormitory town. However, they collect a great amount of taxes from property.

As we can see in the table above cities with stable population are the group with more problems in their financial situation. Other municipalities always have to be in mind the additional expenses they have forecasted due to their characteristic needs. On the contrary, municipalities with stable population don’t have any pressure to be worried about the budget.

It should be mentioned that we have to be in mind that the extrapolation of the results comes from a small sample.

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7. Solutions for the future

In this step, we want to show what the solutions for the municipality’s financial situation are. The reality is that a large number of municipalities have a huge debt at the same time that incomes are decreasing due to the crisis. This situation cannot continue for long.

7.1. Suggestions

This financial situation can be alleviated by different measures which we expose below:

1. The Central Government can plan a larger amount of capital for the local entities. This way they can offer the same services of the same quality as to present. In other words, the Government can increase state transfers. But, up to now the Central Government has been in no condition to increase a public expense. The pressure not only from the European Union but also from the market makes this solution unviable. So, City Councils cannot expect a bail-out from the Central Government. 2. The solution has to come from within. So, City Councils can carry out a merger of some neighboring municipalities. This can be a solution to reduce expenses and to take advantage of economies of scale. 3. Another possibility is the tightening of belts. City Councils should cut the expenses they can’t pay. It is not a good option to continue maintaining services without obtaining enough income. Moreover, they should inform inhabitants of how much costs of services are per capita. This transparency would help citizens to understand the effort City Councils are making to remedy the situation. In addition, municipalities should check the current taxes for nurseries, retirement homes, etc. Not all services can be free for everyone. They should bring taxes up to date. Another significant expense for municipalities is salaries. In order to have the required staff when necessary, the staff should become more flexible. This would delete unnecessary expenses from salaries.

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Reducing services is an unpopular measure that can be carried out if there is no other solution. City Councils have an unsustainable model where they spend more money than they take in. Municipal incomes have to be increased or municipal expenses have to be cut.

7.2. Simulation

Now, we will analyze in depth the viability of reducing services.

In order to cut services we have to refer to the current law to determine its viability. The following step is going to explain the obligatory competences that City Councils have under the current laws.

The law that regulates local competencies is Law 7/85, from 2nd of April, Lley Reguladora de las Base de Régimen Local. The articles 25 to 26 are those which specify their competences.

The municipalities have the right to promote any kind of activities and provide the public services they consider appropriate to satisfy citizens’ necessities. Municipalities can offer the following services (article 25):

- Security - Traffic organization - Civil protection, prevention and fire extinctions - Urban development organizations and management, parks and gardens, street paving, rural roads conservation... - Artistic and historical heritage - Environmental preservation - Provisions, slaughterhouse, fairs, market-place and users protection - Protection of public health - Participation in the primary health attention management - Cemeteries and funeral services - Social services - Water supply and public lighting, cleaning services, reminders, sewage system and residual water treatment - Public transportation

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- Cultural activities and installations, tourism - Participation in education planning, school centres, management...

Article 26 regulates the obligatory services that municipalities have to offer depending on the number of inhabitants that they have (see chart 14). Municipalities with Municipalities with Municipalities with All municipalities more than 5.000 more than 20.000 more than 50.000 inhab.(additionally) inhab. (additionally) inhab.(additionally) - Public lighting - Public parks - Civil protection - Urban transport - Cemetery - Public library - Social services - Environmental - Waste Collecte - Market - Prevention and protection - Cleaning services - Waste treatment fire extinction - Water supplying - Public sports - Sewage system centres - Guarantee the central town access - Paving roads - Food and drink control Chart 14: Obligatory expenses table.

According to a study carried out by Vilalta (2005), chart 15 shows what average expenses of City Councils from Catalonia were for obligatory services and non obligatory ones in 2005.

Expenses from Catalonia municipalities (2005)

31%

Obligatory expenses Non Obligatory expenses

69%

Chart 15: Distribution of obligatory and non obligatory expenses (Vilalta, 2005).

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Chart 16 shows the functional expenses of City Councils which go to non obligatory services. The information comes from 2005 for Catalan municipalities.

Funtional clasification of the non obligatory expenses from Catalan municipalities (2005) Culture

Security and civil protection

2% Education 4% 10% 23% Social promotion 4% Other comunitary and social 4% services Security and social protection 9% Transference to public 21% administrations 11% Health services

12% Town planning and apartments

Others

Chart 16: Functional expenses (Vilalta, 2005).

This functional classification is very useful to know exactly what the non obligatory expenses are used for.

Taking advantage of the ratios calculated in the Annual Reports of the City Councils, we now recalculate them simulating the budget cuts suggested to express the change numerically.

We simulate an extreme situation where local entities have reduced their expenses to the maximum allowed by current law. The best way to know which part of expenses goes to non obligatory services is to have the functional expense classification for each municipality. However, this information is not available to the citizens; so, in order to recalculate the ratios we reduce the 30.6% (see chart 15) of the current expenses as in Vilalta’s analysis. We have used the same percentage for all municipalities and for the items from the budget destined to non obligatory services.

We have reduced the following items from the budget by 30.6%: personnel, social and current goods, investment, current transference and capital transference. The only items we do not modify are: financial expenses, financing assets and financing liabilities. Once we have cut the percentage of

29 non obligatory expenses from the budget, we have recalculated the ratios of cities with stable population group to compare them with the current indicators (see chart 17 below).

Chart 17: Comparison of ratios: before and after simulation.

The only ratios that change from the initial ones are the indicators that use budget data: index of financial independence, index of non-financial budget result and index of net savings.

As we can see, all these three indexes improve with the reduction. For instance, the index of financial independence which measures the capacity that a municipality has to face up to their expense without depending on the transference of other public institutions improves considerably. In all cases this ratio is around one. The index of non-financial budget result is higher than one in all cases; in other words, City Councils have the capacity to face up to their expenses with their incomes without the financial support. The index of net savings, which indicates if the budget is positive or not, improves considerably. Before the simulation, this ratio was higher than 0.25 in all cases. After the simulation this ratio was approximately one with two municipalities with a negative budget. This means that with the same collection, a municipality can provide their obligatory services.

So, City Councils can reduce the services they offer according to the law. Obviously, budget cuts would be an unpopular measure that can entail different arguments. However, with the reduction of non obligatory expenses any City Council improves their financial condition and, additionally, solves their budget problems making their budgets become as seen in the simulation.

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8. Conclusions

As this study has proved, the fluctuation of population factor interferes in municipalities’ financial situations. The most affected group of municipalities are those with a stable population during all the year.

Municipalities with tourism or with urbanizations always have to be aware of the additional expenses they have forecasted due to their characteristic needs. On the contrary, stable municipalities are not under this pressure.

Touristic municipalities know perfectly which part of their expenses has to be assigned to the touristic sector’s needs. They know that they have to offer good quality services during the seasonal period and promote a positive image of their town for visitors to take good memories home. This is essential to bring life to the service sector of their city. They are interested in doing this. So, these kinds of municipalities have no choice but to organize their accounts better to face up to their situation. Moreover, as a consequence of their condition, they collect more taxes. For instance, it is obvious that in touristic cities there is more activity such as a great number of bars and restaurants, leisure activities, commercial shops, etc. This activity leads the collection of a larger amount of taxes than in cities without tourism. This extra collection covers their extra expenses.

Municipalities with urbanizations, on the contrary, have a fluctuation of population on a daily basis because typically people use these cities as a dormitory town. So, the economic growth that these inhabitants generate goes to neighboring cities. They have to provide security services, garbage collection, supply water, etc, to each urbanization, which is obligatory. They must balance their budget to offer these services despite the fact that the population is spread out over the municipality. However, they collect a significant income from two kinds of different taxes: building permits and local property taxes.

Summarizing, municipalities with stable population, that seemingly should have less difficulties to have a better balanced budget, are those with the worst financial situation. Organizations try to innovate and to do their best when they are under pressure. This phenomenon leads municipalities with tourist activities

31 and with urbanizations to adopt creative solutions to result their budgetary problems while municipalities with stable population are more relaxed.

In this paper we also have showed the possibility of reducing municipal expenses to the maximum allowed by law to improve their financial situation as seen in the simulation.

It is also important to point out the future work to continue the research on the relationship among municipalities’ financial situations and the factors that influence them. For instance, in all this paper we have assumed the management as an exogenous variable; in other words, we didn’t take into account the managers capabilities. Future researches can lead to analyze this influence.

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9. Sources

Internet sources:

Institut d’Estadística de Catalunya  www.idescat.cat

Ministerio de Economía y Hacienda  www.meh.es

Agencia Tributaria, local tax system  http://www.agenciatributaria.es/wps/portal/Fisterritorial?channel=40887783aa58 d110VgnVCM1000004ef01e0a____&ver=L&site=09f531dc09b20110VgnVCM1 000004ef01e0a____&idioma=es_ES&menu=0&img=0

El país www.elpais.es

El economista  www.eleconomista.es

Papers:

Dollery, B; Case, L and Brynes, J (2006) “Local Government Failure: why does Australian Local Government Expenditure Permanent Financial Austerity?” Australian Journal of Political Science Vol. 41, No. 3, September, pp. 339-353

Kloha, P; Weissert, C.S and Kleine, R (2005a). “Developing and testing a composite model to predict local fiscal distress”, Public Administration Review, 65, 3, 313 – 23.

Kloha, P.; Weissert, C.S and Kleine, R (2005b). “Someone to watch over me. State monitoring of local fiscal conditions”, American Review of Public Administration, 35, 3, Sptember, 313-23

Muñiz, M. A and Zafra, J. (2009) “Financial Condition, cost efficiency and the quality of local public services”, Funcas working paper, number 462.

Vilalta, Maite (2005) “La despesa de carácter discrecional dels ajuntaments i el seu finançament”, Diputació de .

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Books:

Newbold, Paul (2003): “Statistics for business and econometrics”, 5th edition, Prentice Hall.

González Pascual, Julián (2008): “Análisis de la empresa a través de su información económica-financiera: fundamentos teóricos y aplicaciones”, ediciones Pirámide, 2ª edición

Jiménez, S. M., García-Ayusi Coarsí, M. and Sierra Molina, G. J. (2002): “Análisis financiero”, 2ª edición, Ediciones Pirámide.

Siegel, Sidney (1990): “Estadística no paramétrica: aplicada a las ciencias de la conducta”. Editorial Trillas, 3ª edición.

Report:

30 minutes, TV3, the party is over (s’ha acabat la festa), 07/02/2010: http://www.tv3.cat/30minuts/reportatges/1739/Sha-acabat-la-festa

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APPENDIX I: News related to City Councils financial problems.

El Economista, 13.07.2010

El País; 21.03.2010

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APPENDIX II: Summary statistics from Gretl statistical package.

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APPENDIX III: Boxplots.

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APPENDIX IV: Annual Reports used for the study.

2008 2007 2006 2005 2004 TOURISM 2009 L'Escala X X Torredembarra X X Arenys de Mar X

004

2008 2007 2006 2005 2 STABLE 2009 Tona X X X Sant Sadurní d'Anoia X X X Tàrrega X

Anglès X

2008 2007 2006 2005 2004 2003 WITH URBANIZATIONS 2009 Viladecavalls X X Masquefa X Llagostera X X X X Vidreres X X X Caldes de Montbui X Corbera de Llobregat X Vallirana X X X Santa Eulàlia de Ronçana X X L'Ametlla del Vallès X

Sant Pere de Ribes X

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