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Changing welfare context and income segregation in Amsterdam and its metropolitan area, 2004-2011

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Changing welfare context and income segregation in Amsterdam and its metropolitan area, 2004-2011



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Datum05.12.2018
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Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis

Table 3 Segregation Index for income quintiles and deciles, at regional and at municipal level; disposable household income per neighbourhood, 2004-2011










Region










Municipality










2004

2008

2011




2004

2008

2011

Quintile 1 vs all others

0.249

0.245

0.221




0.151

0.149

0.133

Quintile 2 vs all others

0.131

0.139

0.135




0.125

0.135

0.132

Quintile 3 vs all others

0.097

0.104

0.101




0.076

0.084

0.085

Quintile 4 vs all others

0.137

0.134

0.126




0.083

0.078

0.071

Quintile 5 vs all others

0.260

0.255

0.240




0.269

0.272

0.255

























Decile 1 vs all others

0.235

0.237

0.207




0.131

0.129

0.110

Decile 2 vs all others

0.234

0.227

0.214




0.171

0.177

0.172

Decile 3 vs all others

0.142

0.145

0.140




0.124

0.133

0.131

Decile 4 vs all others

0.111

0.120

0.116




0.107

0.119

0.115

Decile 5 vs all others

0.095

0.099

0.103




0.085

0.091

0.096

Decile 6 vs all others

0.094

0.100

0.093




0.063

0.071

0.069

Decile 7 vs all others

0.114

0.112

0.102




0.070

0.063

0.061

Decile 8 vs all others

0.138

0.135

0.128




0.092

0.089

0.080

Decile 9 vs all others

0.170

0.161

0.153




0.141

0.133

0.128

Decile 10 vs all others

0.301

0.298

0.284




0.329

0.329

0.309
Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis

Table 4 Dissimilarity Index for selected income quintiles, at regional level; disposable household income per neighbourhood, 2004-2011



D-Quintiles

2004

2008

2011

1-2

0.151

0.151

0.137

1-3

0.229

0.231

0.205

1-4

0.294

0.290

0.263

1-5

0.379

0.365

0.333

 







 

2-1

0.151

0.151

0.137

2-3

0.095

0.097

0.088

2-4

0.172

0.174

0.164

2-5

0.307

0.308

0.293

 







 

5-1

0.379

0.365

0.333

5-2

0.307

0.308

0.293

5-3

0.244

0.248

0.241

5-4

0.181

0.185

0.176

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis

Table 5 Share of minimum income households in Amsterdam and in concentration neighbourhoods






Share of minimum income households

in Amsterdam



Share of minimum income households

in concentrations (2 st dev)



Share of minimum income households

In strong concentrations (3 st dev)



(Avg) share of minimum income households in concentrations (2 st dev)

(Avg) share of minimum income households in strong concentrations (3 st dev)



















2004

17.65%

24.3%

5.5%

39.0%

48.2%

2012

16.63%

29.0%

19.6%

39.3%

44.8%



















2012*

16.63%

25.8%

15.0%

40.2%

46.3%

2012*, definitions 2004

Source: Department of Research and Statistics, Municipality of Amsterdam; authors processing and analysis



Table 6 Descriptives of the variables used in the regression model presented in Table 7







%

Average

Standard deviation

Change (difference 2012-2004) in minimum-income households (pp)




-0.24

9.1
















Share social housing in 2004







17.08

21.84

Share owner occupancy 2004







6.65

12.27

Change in share of social housing in percentages points

Increase (>=1pp)

5.2










No change (ref. cat.)

69.5










Decrease (>=-5 and <-1)

5.9










Decrease (>=-20 and <-5)

9.3










Decrease (>=-35 and <-20)

5.8










Decrease (<-35)

4.3







Change in share of owner occupancy in percentages points

Increase (>=45)

2.1










Increase (>=30 and <45)

5.8










Increase (>=15 and <30)

14.4










Increase (>=1 and <15)

21.8










No change (ref. cat)

40.4










Decrease (>=-15 and <-1)

13.5










Decrease (<-15)

2.0







Change in real estate tax value of all dwellings

Increase above 1 st. dev

15.2










Increase between mean and 1 st.dev. (ref.)

36.5










Decrease between mean and 1 st. dev below mean

40.3










Decrease more than 1 st. dev. below mean

8.0







Source: Department of Research and Statistics, Municipality of Amsterdam; authors processing and analysis

Table 7 OLS regression analysis of the percentage point (pp) change in minimum income households in six digit postal code areas with at least 20 households and at least 20 dwellings in 2004 and 2012, without large additions or subtractions in housing stock (less than 20%), in Amsterdam, between 2004 and 2012.a



 

 

B

Beta

Sign.

Constant

 

2.074

 

0.00

Share of social housing in % in 2004

 

0.038

0.092

0.00

Share owner occupancy in % in 2004

 

-0.016

-0.022

0.17

Change in share of social housing in perc. points

Increase (>=1 pp)

-0.711

-0.018

0.14

 

No change (ref. cat.)

 

 

 

 

Decrease (>=-5 pp and <-1 pp)

-0.896

-0.029

0.03

 

Decrease (>=-20 and <-5)

-2.555

-0.066

0.00

 

Decrease (>=-35 and <-20)

-4.745

-0.106

0.00

 

Decrease (<-35)

-0.145

-0.004

0.77

Change in share of owner occupancy in perc. points

Increase (>=45)

-6.921

-0.109

0.00

 

Increase (>=30 and <45)

-6.528

-0.168

0.00

 

Increase (>=15 and <30)

-5.333

-0.207

0.00

 

Increase (>=1 and <15)

-3.290

-0.150

0.00

 

No change (ref. cat)

 

 

 

 

Decrease (>=-15 and <-1)

-2.314

-0.087

0.00

 

Decrease (<-15)

-1.977

-0.031

0.02

Change in real estate tax value of all dwellings

Increase above 1 st. dev

-1.161

-0.046

0.04

 

Increase between mean and 1 st.dev. above mean (ref.)

 

 

 

 

Decrease between mean and 1 st. dev. below mean

0.221

0.012

0.38

 

Decrease more than 1 st. dev. below mean

0.886

0.027

0.87

 










 

N= 6264, Adj. R Square=0.128

 

 

 

 

a The average population of the analysed neighbourhoods is 59.3 persons and 33.7 households in 2004. There are 17772 6-digit postal codes. This means that the model covers 35.2% of all postal codes. Most were excluded because of size restrictions in both years and changes in housing stock, which is mostly due to renewal in the post-war periphery and new construction in IJburg and Osdorp. 310 cases were excluded because of data missing.

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 1 Amsterdam Metropolitan Region

A Canal Belt

B Eastern Docklands

C Watergraafsmeer

D South Axis

Figure 2 Dissimilarity Index for selected income quintiles (Q) for natives and migrants, at regional and municipal level; disposable household income per neighbourhood; 2004-2011



Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 3 Location quotients Low Incomes 2011

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 4 Location quotients Middle Incomes 2011

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 5 Location quotients High Incomes 2011

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 6 Location quotients Migrants with Low Incomes 2011

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



Figure 7 Location quotients Natives with High Incomes 2011

Source: Statistics Netherlands; Social Statistical Database (SSD); authors processing and analysis



1 The region is including the municipality of Amsterdam.

2 The figures are minimum estimates, since these only refer to registered poverty, which appears to be underestimated; according to Statistics Netherlands, hidden poverty has increased during the research period.

3 In OLS regression models it is assumed that the outcomes that have been measured are independent from each other. In spatial analysis, however, the relative outcomes for the spatial units are often related to the distances between the units; there might be spatial auto-correlation. However, the distance between spatial units is constant over time and in our situation identical for 2004 and 2012. If we include the differences between these constant distances (which are all zero) as an independent variable that might impact on the difference between the shares of households on minimum income per postcode, the spatial auto-correlation problem does not apply.

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