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Applying the TTWA method to German data
Labour Market Areas: current development
and future use
Workshop
Roma, 16.06.2017
...
2
1. Contents
Data:
- German commuting data
- three different regional levels
Our Approach
- Evaluate results of > 100 par...
3
2. Data: Commuting between regions
402 Districts 2 000 municipal regions
(very small [area, inhabi-
tants] regions merge...
4
3. Approach: Overview
 apply “LabourMarketAreas” with a variety of parameters
to the German data on different regional ...
5
3. Approach: additional quality measures
 the commuting ratios on aggregated level
[StatClusterData]$marginal:
((sum(…$...
6
3. Approach: additional quality measures
 an own concept of cohesion: NW-density within LMA
Commuting matrix (within a ...
7
3. Approach: parameter selection
Analyze
some of the
StatCluster
Data-results
and
additional
indicators
8
3. Approach: parameter selection
Analyze some of the
StatClusterData-
results and additional
indicators:
visuals & corre...
9
3. Approach: parameter selection
some of the StatClusterData and additional indicators
Indicators for cohe-
sion/homogen...
10
3. Approach: 3 examples for 2000 municipal r.
Poor self-containment Self-containment Good self-containment
SC_dem.(mean...
11
4. Relevance of basic unit choice
 Do the same parameters produce similar results with
different basic units?
 We com...
12
4. Relevance of basic unit choice: Ex.1
Param: sz15000-20000 sc0.7-0.9
4,500 municipal unions 2,000 municipal regions 4...
13
4. Relevance of basic unit choice: Ex.2
Param: sz10000-50000 sc0.7-0.85
4,500 municipal unions 2,000 municipal regions ...
14
4. Relevance of basic unit choice: Ex.3
Param: sz10000-100000 sc0.75-0.85
4,500 municipal unions 2,000 municipal region...
15
4. Relevance of basic unit choice
 Do the same parameters produce similar results with
different basic units?
 We com...
16
5. Discussion
 What are appropriate measures for self-containment,
homogeneity, and cohesion?
 StatClusterData and ow...
17
Thanks for your attention!
Dr. Per Kropp
Institute for Employment Research
Regional Research Network
Frau-von-Selmnitz-...
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P. Kropp, B. Shwengler, Applying the TTWA method to German data

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Workshop Istat Aula Magna
Labour Market Areas: current developments and future use
Roma, 16 giugno 2017

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P. Kropp, B. Shwengler, Applying the TTWA method to German data

  1. 1. Applying the TTWA method to German data Labour Market Areas: current development and future use Workshop Roma, 16.06.2017 Per Kropp* Barbara Schwengler Institute for Employment Research *Regional Research Network, Halle
  2. 2. 2 1. Contents Data: - German commuting data - three different regional levels Our Approach - Evaluate results of > 100 parameter sets < “StatClusterData” + additional quality measures - Illustrate balance between self-containment & cohesion/homogeneity - Illustrate effects of different aggregation levels of basic units Discussion
  3. 3. 3 2. Data: Commuting between regions 402 Districts 2 000 municipal regions (very small [area, inhabi- tants] regions merged) about 4 500 municipal unions (“Gemeindeverbände”) Admin. Data Own aggregation Admin.data
  4. 4. 4 3. Approach: Overview  apply “LabourMarketAreas” with a variety of parameters to the German data on different regional levels  evaluate results: “StatClusterData” and further statistics:  commuting ratios on aggregated level,  home-work-ratio: standard deviation of absolute values,  a “golden corridor”-measure,  an own concept of cohesion: NW-density within LMA  maps and various plots for the visual evaluation.  R-script that combines the LabourMarketAreas-package facilities with our approach can be provided.  compare quality measures  not finished: further network measures for cohesion
  5. 5. 5 3. Approach: additional quality measures  the commuting ratios on aggregated level [StatClusterData]$marginal: ((sum(…$EMP_live)-sum(…$EMP_live_work)) / sum(…$EMP_live)) all COMMUTER / all EMPOYEES  a “golden corridor” measure: % of all LMA within golden_corridor_ percent  the HWR - standard deviation of absolute values: Home_Work_Ratio_abs_sd
  6. 6. 6 3. Approach: additional quality measures  an own concept of cohesion: NW-density within LMA Commuting matrix (within a LMA) Standardized matrix Data Null-model from - to A B C D E Sum_liv e from – to A B C D E Sum_live from - to A B C D E Sum_live A 1000 100 50 0 0 1150 A 0.278 0.028 0.014 0.000 0.000 0.319 A 0.112 0.097 0.071 0.030 0.009 0.319 B 200 900 80 10 0 1190 B 0.056 0.250 0.022 0.003 0.000 0.331 B 0.116 0.100 0.073 0.031 0.010 0.331 C 50 70 600 20 0 740 C 0.014 0.019 0.167 0.006 0.000 0.206 C 0.072 0.062 0.046 0.019 0.006 0.206 D 10 15 50 300 5 380 D 0.003 0.004 0.014 0.083 0.001 0.106 D 0.037 0.032 0.023 0.010 0.003 0.106 E 5 5 20 10 100 140 E 0.001 0.001 0.006 0.003 0.028 0.039 E 0.014 0.012 0.009 0.004 0.001 0.039 Sum_work 1265 1090 800 340 105 3600 Sum_work 0.351 0.303 0.222 0.094 0.029 1.000 Sum_work 0.351 0.303 0.222 0.094 0.029 1.000 Commuter only from – to A B C D E Sum_live from - to A B C D E Sum_live A 0.028 0.014 0.000 0.000 0.042 A 0.097 0.071 0.030 0.009 0.207 B 0.056 0.022 0.003 0.000 0.081 B 0.116 0.073 0.031 0.010 0.230 C 0.014 0.019 0.006 0.000 0.039 C 0.072 0.062 0.019 0.006 0.160 D 0.003 0.004 0.014 0.001 0.022 D 0.037 0.032 0.023 0.003 0.096 E 0.001 0.001 0.006 0.003 0.011 E 0.014 0.012 0.009 0.004 0.038 Sum_work 0.074 0.053 0.056 0.011 0.001 0.194 Sum_work 0.239 0.203 0.177 0.084 0.028 0.731 Within LMA commuter ratio (unstandardized density) Within commuter ratio in a random distribution LMA_density = Within LMA commuter ratio / random distribution = 0.194 / 0.731 = 0.266 (= InternalCohesionFlows)
  7. 7. 7 3. Approach: parameter selection Analyze some of the StatCluster Data-results and additional indicators
  8. 8. 8 3. Approach: parameter selection Analyze some of the StatClusterData- results and additional indicators: visuals & correlations
  9. 9. 9 3. Approach: parameter selection some of the StatClusterData and additional indicators Indicators for cohe- sion/homogeneity?? Indicators for self- containment What to do??? * Scale reversed (high values are favourable) * * * * * *
  10. 10. 10 3. Approach: 3 examples for 2000 municipal r. Poor self-containment Self-containment Good self-containment SC_dem.(mean): 0.810 0.821 0.862_ Q_modularity: 0.805 0.810 0.836_ Good cohesion “balanced” with cohesion Poor cohesion EMP_work(Std.): 313,411 330,620 497,982_ LMA_density(mean): 0.080 0.075 0.063_ NbClusters: 147 130 70_ Param: sz15000-20000 sc0.7-0.9 sz10000-50000 sc0.7-0.85 sz10000-100000 sc0.75-0.85_ What is well balanced?
  11. 11. 11 4. Relevance of basic unit choice  Do the same parameters produce similar results with different basic units?  We compare results of three parameter sets (the previous example) for:  4 500 municipal unions  2 000 municipal regions  402 districts
  12. 12. 12 4. Relevance of basic unit choice: Ex.1 Param: sz15000-20000 sc0.7-0.9 4,500 municipal unions 2,000 municipal regions 402 districts SC_dem.(mean): 0.816 0.810 0.819 Q_modularity: 0.808 0.805 0.806 EMP_work(Std.): 339,925 313,411 326,376 LMA_density(mean): 0.579 0.488 0.356 NbClusters: 138 147 135
  13. 13. 13 4. Relevance of basic unit choice: Ex.2 Param: sz10000-50000 sc0.7-0.85 4,500 municipal unions 2,000 municipal regions 402 districts SC_dem.(mean): 0.827 0.821 0.821 Q_modularity: 0.813 0.810 0.805 EMP_work(Std.): 357,437 330,620 316,884 LMA_density(mean): 0.590 0.495 0.355 NbClusters: 116 130 131
  14. 14. 14 4. Relevance of basic unit choice: Ex.3 Param: sz10000-100000 sc0.75-0.85 4,500 municipal unions 2,000 municipal regions 402 districts SC_dem.(mean): 0.876 0.862 0.854 Q_modularity: 0.843 0.836 0.832 EMP_work(Std.): 614,362 497,982 441,957 LMA_density(mean): 0.642 0.511 0.337 NbClusters: 48 70 85
  15. 15. 15 4. Relevance of basic unit choice  Do the same parameters produce similar results with different basic units?  We compare results of three parameter sets (the previous example) for:  4 500 municipal unions  2 000 municipal regions  402 districts Results differ (e.g. NbClusters) Better modularity and density values for more disaggregated basic units Self-containment and EMP_work(st.dev.) depend strongly on NbClusters
  16. 16. 16 5. Discussion  What are appropriate measures for self-containment, homogeneity, and cohesion?  StatClusterData and own measures show that homogeneity (std.EMP_work[live], NbCluster) and self-containment (Mean.SC_demand[supply], mean.lma_commuter_percent, Q_modularity, total_commuter_percent*) are contradictory aims.  We have no established measure for cohesion (mean/iqr.InternalCohesionFlows*?, mean.LMA_density_com*?) * Own computation  How to balance self-containment and cohesion in order to find a good LMA delineation for a certain country?  Aggregation level of basic units matters.
  17. 17. 17 Thanks for your attention! Dr. Per Kropp Institute for Employment Research Regional Research Network Frau-von-Selmnitz-Str. 6, D-06110 Halle Germany Phone: +49/345-1332-236 (secretary: -255; fax: -555) mail: per.kropp@iab.de www.iab.de/iab-sachsen-anhalt-thüringen Dr. Per Kropp Institute for Employment Research Regional Research Network mail: per.kropp@iab.de Barbara Schwengler Institute for Employment Research Department D1 - Establishments and Employment mail: barbara.schwengler@iab.de

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