EUROPEAN COMMISSIONEurostat

Directorate E: Sectoral and regional statisticsUnit E-4: Regional statistics and Geographical Information

LABOUR MARKET AREAS

Final report on Labour Market Areas in Hungary

Hungary

Agreement number: 08141.2015.001-2015.500

Prepared by

András Kezán, HCSO (project leader)

Dániel Szilágyi, HCSO

József Gerse, HCSO

Balázs Jankó, HCSO

János Pénzes PhD, University of Debrecen

Ernő Molnár PhD, University of Debrecen

Gábor Pálóczi, University of Debrecen

EUROSTAT/Contract No.: Agreement number: 08141.2015.001-2015.500

Doc. No.: 1.0

Issue/Rev.: 1.0

Date: 2017-06-30

Organization

The project was carried out in the cooperation of HCSO and researchers from the University of Debrecen. The HCSO had not experience in this field in the past, involvement of external specialist was needed. (At the end this cooperation was useful for both sides.)

Molnár Ernő Phd – University of DebrecenPénzes János Phd – University of DebrecenPálóczi Gábor – University of DebrecenFábián Zsófia Phd – HCSOGerse József – HCSOJankó Balázs – HCSOSzilágyi Dániel - HCSO

The process

At first an R course was organized for the colleagues involved in the project, in order to be able to test and make changes in the script if it is needed. This step was very useful througout the whole implementation process, my colleagues can made the changes are needed to the personalization of the script or the outcomes.

In cooperation with the researchers a research plan was elaborated. In this plan we tried to define the aim of the research, the main principles that we wan to follow through the research. We are also defined data we had to used.

In cooperation with our colleagues from University of Debrecen we compiled the existing methods and tried to compared them with each other and the EURO method. We tried to evaluated them, finding the advantages and disadvantages of these different methods.

In the next step we cleaned the commuting data from unwanted noises. Basically we used census data and the basic building blocks were LAU2 units.

After we tried to test the R script on the raw commuting matrix. At first many random parameter sets tested in order to get information of the outliers, and the probable range of proper parameters.

A new algorythm has written in order to get systematic and robust sample to evaluate the outcomes. This algorythm changed one of the parameters in each run with a certain amount. That enabled us to have the results of more than 1000 results.

Common principles did not exist at that time, so we have to use those principles which was elaborated by ESTAT to choose the most proper method in the evaluation of the results. At first we were checking the indicators, and trying to pairing them with the principles, that they are measuring. After we tested each indicator how they can be used for our purposes. We found that none of these indicator is suitable for the evaluation solely. We also try to use multivariate statistical models to find the best method, but we found that none of them is suitable to choose the optimal set of parameters by the usage of them.

After this we tried to find the optimal sets of parameters on an experimental basis. Fort hat purpose the stability maps were used. At first the most stable LMAs was selected out, and after the most unstable LMAs were picked up. The validity and their inner cohesion was analysed and evaluated. The most valid LMAs were chosen a looked up in which parameter sets they occured. Finally we considered the parameter set as the optimal one in which each valid LMA is occured.

Finally the exclaves were filtered and linked to those LMAs that they have the strongest links.

In order to fulfil task disseminating this new geography we had done many scientific publications in the implementation period. We had one written publication in scientific magazine “Területi statisztika”. Our colleagues held many oral presentations in different scientific forums in order to raise the attention to this project and it outcomes. We also prepared the publication “Labour Market Areas in Hungary” in Hungarian language, but this is not issued that thanks to the questionable elements of our research. (The HCSO is only publishing validated outcomes that are mature enough (accepted by many scientific stakeholders) to be used generally.) Until we reach this stability and maturity in the methodology only other scientific issues are welcomed.

The HCSO has been in close connection with other NSIs that are involved to the project. The HCSO was represented by three different colleagues in Rome at the LMA training and at the workshop also. Meetings organized by ESTAT was also participated by the Hungarian project leader.

About the outcomes

84 LMA was created by the selected set of parameters and the later revisions, and that’s less than half of the number of microregions. Consequently the optimal delineation results slightly larger districts compared to microregions, but all of these labour market districts are smaller than the area of the counties, except for Budapest (which is in fact not a county administratively, but it is on the same level with them in NUTS hierarchy). The average population of a district was 118 000. The standard deviation was 337 000 person when Budapest LMA was included in the calculation, and 85 000 person when not. The population of the LMA with the least inhabitant was under 18 000, while the one with the most was above 3 000 000.

Figure 1. Labour Market Areas in Hungary

The average number of employed persons in LMAs was 43 500, that value is reduced to 29 000 when Budapest LMA (with 1,26 million person) is excluded from calculation. With the latter version, the standard deviation is 31 000 person in LMAs. The final set of parameters created LMAs with fairly enclosed labour markets, pretty weak links join them. The value of supply side self-containment is slightly lower compared to demand side, but is still above 0.8 almost everywhere (Figure 58, 59). Demand side values are between 0.8 and 0.9.

Figure 2: Supply side self-containment value and the number of employed persons in LMAs, 2011

Figure 3: Demand side self-containment values and the number of employed persons in LMAs, 2011

% 6.2 – 10.010.1 – 12.512.6 – 15.015.1 – 17.517.6 – 24.4

Figure 4: Unemployment rate, 2011

Summary

The specialties of the spatial structure in Hungary had a great influence on how to adapt the method, and highlighted lots of problems, which have to be resolved before applying the method in the whole European Union. The following notes are concluded:

o The intensity of work-related commute has very significant differences in different parts of Hungary,

o The settlement network in different parts of the country has significant differences, which lead to very different relations amongst settlements

o Commute to centers (nodal type of commute) is the most typical in Hungary, which also affects results of the method.

The following notes are concluded about the application of the method:

o Several methods can be applied during testing sets of parameters, but whatever option is chosen, it is worth to implement a systematic, automatized method. Systematic, big samples help to understand effects of parameters, and not appropriate sets of parameters can be quickly abandoned.

o Having studied and tested several indicators on national level, we concluded that solely none of them is capable of guaranteeing optimization process. The only indicator suitable for evaluating the delineation was Q modularity (Newman–Girvan 2004).

o The characteristics of the settlement and commute network in Hungary exaggerates the effect of the Smart measure – onto a very high level of distortion –, which fundamentally favours smaller centers (see Chapter 6.7.2., 7.). Problems, which were impossible to cope while complying with principles, could be fully resolved only by modifying the EURO-method. After reviewing options in the method, and considering all the factors, the Smart measure has been replaced with the Curds measure in the algorithm.

o The quest to find optimal parameters was inevitably accompanied by researchers’ subjectivity, as neither our own research, nor literature offered alternative solutions to manufacture objective results. (see national indicators).

o It was very useful to study strongest commute links of units while searching optimal delineation.

o Studying stability of boundaries was also very useful to evaluate results.o As the algorithm of the EURO method doesn’t cover adjacency relations, exclaves had to be

sorted out after the delineation process. This was based on commute relations primarily, but dilemmas emerged about spatial contiguity (see Chapter 7.2.3.).

o Similarly to the indicators on country level (e.g. Q modularity), multivariate models favour to create relatively few LMAs, with bigger sizes, therefore those units have better indicators. These analytical methods can’t reflect on possible errors in spatial relations (e.g. LMAs where relations are not represented well). Considering these properties, multivariate models should be ignored while choosing the optimal sets of parameters, even though this approach could (have) provide(d) proper base to evaluate statistics and to classify variables.

Several problems left even after these conclusions, which have to be resolved prior applying the method in all countries in the European Union:

o Common principles should be set for testing and selecting parameters to apply the method in every country (filter parameter-sets for testing).

o Common evaluating criteria should be set to select the optimal set of parameters.

o Current general selection and evaluation principles should be finetuned, as the definition of adjacency, spatial contiguity is not clear, which was apparent in the case of Hungary.

o Objective indicators about the whole delineation should be selected to support validation process, and to define how these could be used for evaluation, optimization. Methods to find optimal solutions could be tested like genetic algorithm.

o In association with the latter proposal, clarified methods would be welcomed for common and expected evaluation.

o Further tests and finetuning are needed for the EURO method and for its components, especially for the Smart measure, as the measure often creates LMAs which are not in accordance with real commute relations and results wrong delineations.

In general, the EURO method and similar, not pre-determined spatial delineation methods are deemed suitable to delineate local labour market areas. However, use of the Smart measure in the current method is wrong based on our experiences, as its' results are not in accordance with real phenomena in areas with special spatial and commute characteristics. That’s why we think it’s questionable to integrate this measure into methodological directives. We recommend to study the current method further, and to finetune it prior the recommendation of its use by the European Commission.