Do Far-Right Mayors Promote Informal Healthcare Chauvinism?
Ethnic Discrimination in Access to Basic Healthcare in Italy
Krzysztof Krakowski, Dina Rosenberg, Alessio Romarri, and Merlin Schaeffer
2022-10-24
1 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

2 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Two examples of welfare chauvinistic policy in Denmark:
(Jørgensen and Thomsen, 2016)

Direct: "Start Aid": Ca. 50% lower benefits to refugees & recent immigrants.
Indirect: "225 hour rule": Both partners in a union need to have worked 225+ hours in the past 12 months to qualify for benefits.

2 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Danes' public opinion on who should to get vaccinated first Source: Schaeffer and Haderup Larsen (2022)

3 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Quality of written answers
from German welfare office administrators Source: Hemker and Rink (2017)

4 / 22

Do far-right mayors
promote informal healthcare chauvinism?

5 / 22

Do far-right mayors
promote informal healthcare chauvinism?

Potential mechanisms

Informal policy.
Hiring.
Erosion of social norms.

5 / 22

A field experiment

6 / 22

A field experiment

Research assistants call 200
Italian "Azienda Sanitaria Locale"
to ask for a general practitioner

Two students with
West-African accent

Two students with
native Italian accent

6 / 22

Interview guideline

7 / 22

Ethnic discrimination in access to basic healthcare

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

8 / 22

Ethnic discrimination in access to basic healthcare

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

8 / 22

Ethnic discrimination in access to basic healthcare

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects.

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

9 / 22

Ethnic discrimination by far-right mayors

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

10 / 22

Ethnic discrimination by far-right mayors

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

10 / 22

Ethnic discrimination by far-right mayors

Municipalities where far-right candidates ran
versus where they won Source: Romarri (2020)

Note: Means with 90 and 95% CI.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

11 / 22

Far-right mayors A regression discontinuity design

Note: $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

12 / 22

Balanced confounders around the margin of victory

Source: Romarri (2020)

13 / 22

Far-right mayors A regression discontinuity design

Note: OLS with 90 and 95% CI fitted separately on each side of the margin of victory. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

14 / 22

Far-right mayors A regression discontinuity design

Note: OLS with 90 and 95% CI fitted separately on each side of the margin of victory. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

14 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

15 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

RD among callers with West-African accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  421
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  136          285
# Eff. Number of Obs.              26           72
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   2.330        2.330
# BW bias (b)                   4.301        4.301
# rho (h/b)                     0.542        0.542
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.536     0.178    -3.012     0.003    [-0.885 , -0.187]    
# Bias-Corrected    -0.616     0.178    -3.457     0.001    [-0.964 , -0.267]    
#         Robust    -0.616     0.196    -3.146     0.002    [-0.999 , -0.232]    
# =============================================================================

15 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

RD among callers with West-African accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  421
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  136          285
# Eff. Number of Obs.              26           72
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   2.330        2.330
# BW bias (b)                   4.301        4.301
# rho (h/b)                     0.542        0.542
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.536     0.178    -3.012     0.003    [-0.885 , -0.187]    
# Bias-Corrected    -0.616     0.178    -3.457     0.001    [-0.964 , -0.267]    
#         Robust    -0.616     0.196    -3.146     0.002    [-0.999 , -0.232]    
# =============================================================================

$Δ_{RDD} = - 0.616 - (- 0.039) = - 0.577$

15 / 22

Far-right mayors a Diff-in-RD design

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

16 / 22

Far-right mayors a Diff-in-RD design

16 / 22

Nr. of questions asked by administrator (0 - 10)

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 806 telephone calls (resp. 391 & 415) to 252 municipalities.

17 / 22

Nr. of questions asked by administrator (0 - 10)

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent. $n$ = 806 telephone calls (resp. 391 & 415) to 252 municipalities.

17 / 22

Nr. of irrelevant questions asked by administrator (0 - 4)

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 805 telephone calls (resp. 392 & 413) to 252 municipalities.

18 / 22

Nr. of irrelevant questions asked by administrator (0 - 4)

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 805 telephone calls (resp. 392 & 413) to 252 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent. $n$ = 805 telephone calls (resp. 392 & 413) to 252 municipalities.

18 / 22

Conclusion

Immigrant minorities face discrimination in access to basic healthcare.
At least those with a West-African accent.
Far-right mayors promote this type of discrimination.
Next step: 3 Mechanisms.

19 / 22

Conclusion

Immigrant minorities face discrimination in access to basic healthcare.
At least those with a West-African accent.
Far-right mayors promote this type of discrimination.
Next step: 3 Mechanisms.

Source: Romarri (2020)

19 / 22

Implications

For immigrant minorities

More exploratory qualitative and field experimental work on further informal channels of welfare chauvinism.

Excess prevalence of diseases that need preventive healthcare?

Benefits and dangers of digitized administration?

20 / 22

Implications

For immigrant minorities

More exploratory qualitative and field experimental work on further informal channels of welfare chauvinism.

Excess prevalence of diseases that need preventive healthcare?

Benefits and dangers of digitized administration?

For everyone

"No one is safe until everyone is safe"
(Farrar and Ahuja, 2021)
- Pre-reg hypothesis: Far-right mayors increased excess death rate during first wave Italian COVID-19 pandemic.

20 / 22

Thank you for your attention!

20 / 22

References

Calonico, S., M. D. Cattaneo, and R. Titiunik (2014). "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs: Robust Nonparametric Confidence Intervals". In: Econometrica, pp. 2295-2326.

Farrar, J. and A. Ahuja (2021). Spike: The Virus vs. The People - the Inside Story. Profile Books.

Hemker, J. and A. Rink (2017). "Multiple Dimensions of Bureaucratic Discrimination: Evidence from German Welfare Offices". In: American Journal of Political Science, pp. 786-803.

Jørgensen, M. B. and T. L. Thomsen (2016). "Deservingness in the Danish context: Welfare chauvinism in times of crisis". In: Critical Social Policy, pp. 330-351.

Kitschelt, H. and A. J. McGann (1997). The radical right in Western Europe: A comparative analysis. Michigan: University of Michigan Press.

Romarri, A. (2020). Do Far-Right Mayors Increase the Probability of Hate Crimes? Evidence From Italy. SSRN Scholarly Paper ID 3506811. Rochester, NY: Social Science Research Network.

Schaeffer, M. and M. Haderup Larsen (2022). "Who Should Get Vaccinated First? Limits of Solidarity during the First Week of the Danish Vaccination Programme". In: European Sociological Review, p. jcac025.

20 / 22

Appendix20 / 22

Binary success "Yes" & "Probably yes": 1; else: 0

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

20 / 22

Binary success "Yes", "Probably yes", or "Don't know": 1; else: 0

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

20 / 22

Perfectly balanced data

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 54 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 492 telephone calls (resp. 246 & 246) to 123 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 492 telephone calls (resp. 246 & 246) to 123 municipalities.

21 / 22

Balanced data

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 686 telephone calls (resp. 343 & 343) to 220 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 686 telephone calls (resp. 343 & 343) to 220 municipalities.

22 / 22

Single rule versus in coalition

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.

22 / 22

La Lega

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals (capped at -2) based on 2,000 bootstrap samples stratified by Italian/West-African accent.

22 / 22

RDD 1st election round

22 / 22

RDD 2nd election round

22 / 22

Do Far-Right Mayors Promote Informal Healthcare Chauvinism?
Ethnic Discrimination in Access to Basic Healthcare in Italy
Krzysztof Krakowski, Dina Rosenberg, Alessio Romarri, and Merlin Schaeffer
2022-10-24
1 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

2 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Two examples of welfare chauvinistic policy in Denmark:
(Jørgensen and Thomsen, 2016)

Direct: "Start Aid": Ca. 50% lower benefits to refugees & recent immigrants.
Indirect: "225 hour rule": Both partners in a union need to have worked 225+ hours in the past 12 months to qualify for benefits.

2 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Danes' public opinion on who should to get vaccinated first Source: Schaeffer and Haderup Larsen (2022)

3 / 22

The far-right and welfare chauvinism

A system of social protection for those who belong to the ethnically defined community

Kitschelt and McGann (1997)

Quality of written answers
from German welfare office administrators Source: Hemker and Rink (2017)

4 / 22

Do far-right mayors
promote informal healthcare chauvinism?

5 / 22

Do far-right mayors
promote informal healthcare chauvinism?

Potential mechanisms

Informal policy.
Hiring.
Erosion of social norms.

5 / 22

A field experiment

6 / 22

A field experiment

Research assistants call 200
Italian "Azienda Sanitaria Locale"
to ask for a general practitioner

Two students with
West-African accent

Two students with
native Italian accent

6 / 22

Interview guideline

7 / 22

Ethnic discrimination in access to basic healthcare

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

8 / 22

Ethnic discrimination in access to basic healthcare

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

8 / 22

Ethnic discrimination in access to basic healthcare

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects.

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

9 / 22

Ethnic discrimination by far-right mayors

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

10 / 22

Ethnic discrimination by far-right mayors

Note: Means with 90 and 95% CI. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

10 / 22

Ethnic discrimination by far-right mayors

Municipalities where far-right candidates ran
versus where they won Source: Romarri (2020)

Note: Means with 90 and 95% CI.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

11 / 22

Far-right mayors A regression discontinuity design

Note: $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

12 / 22

Balanced confounders around the margin of victory

Source: Romarri (2020)

13 / 22

Far-right mayors A regression discontinuity design

Note: OLS with 90 and 95% CI fitted separately on each side of the margin of victory. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

14 / 22

Far-right mayors A regression discontinuity design

Note: OLS with 90 and 95% CI fitted separately on each side of the margin of victory. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

14 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

15 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

RD among callers with West-African accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  421
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  136          285
# Eff. Number of Obs.              26           72
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   2.330        2.330
# BW bias (b)                   4.301        4.301
# rho (h/b)                     0.542        0.542
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.536     0.178    -3.012     0.003    [-0.885 , -0.187]    
# Bias-Corrected    -0.616     0.178    -3.457     0.001    [-0.964 , -0.267]    
#         Robust    -0.616     0.196    -3.146     0.002    [-0.999 , -0.232]    
# =============================================================================

15 / 22

Far-right mayors a Diff-in-RD design

Bias-corrected non-parametric RD with optimized bandwidth (Calonico, Cattaneo, and Titiunik, 2014)

RD among callers with native Italian accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  391
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  116          275
# Eff. Number of Obs.              32           88
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   3.035        3.035
# BW bias (b)                   6.032        6.032
# rho (h/b)                     0.503        0.503
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.026     0.021    -1.257     0.209    [-0.066 , 0.014]     
# Bias-Corrected    -0.039     0.021    -1.892     0.058    [-0.079 , 0.001]     
#         Robust    -0.039     0.023    -1.669     0.095    [-0.085 , 0.007]     
# =============================================================================

RD among callers with West-African accent

# Sharp RD estimates using local polynomial regression.
# 
# Number of Obs.                  421
# BW type                       mserd
# Kernel                   Triangular
# VCE method                       NN
# 
# Number of Obs.                  136          285
# Eff. Number of Obs.              26           72
# Order est. (p)                    1            1
# Order bias  (q)                   2            2
# BW est. (h)                   2.330        2.330
# BW bias (b)                   4.301        4.301
# rho (h/b)                     0.542        0.542
# 
# =============================================================================
#         Method     Coef. Std. Err.         z     P>|z|      [ 95% C.I. ]       
# =============================================================================
#   Conventional    -0.536     0.178    -3.012     0.003    [-0.885 , -0.187]    
# Bias-Corrected    -0.616     0.178    -3.457     0.001    [-0.964 , -0.267]    
#         Robust    -0.616     0.196    -3.146     0.002    [-0.999 , -0.232]    
# =============================================================================

$Δ_{RDD} = - 0.616 - (- 0.039) = - 0.577$

15 / 22

Far-right mayors a Diff-in-RD design

16 / 22

Far-right mayors a Diff-in-RD design

16 / 22

Nr. of questions asked by administrator (0 - 10)

17 / 22

Nr. of questions asked by administrator (0 - 10)

17 / 22

Nr. of irrelevant questions asked by administrator (0 - 4)

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 805 telephone calls (resp. 392 & 413) to 252 municipalities.

18 / 22

Nr. of irrelevant questions asked by administrator (0 - 4)

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 805 telephone calls (resp. 392 & 413) to 252 municipalities.

18 / 22

Conclusion

Immigrant minorities face discrimination in access to basic healthcare.
At least those with a West-African accent.
Far-right mayors promote this type of discrimination.
Next step: 3 Mechanisms.

19 / 22

Conclusion

Immigrant minorities face discrimination in access to basic healthcare.
At least those with a West-African accent.
Far-right mayors promote this type of discrimination.
Next step: 3 Mechanisms.

Source: Romarri (2020)

19 / 22

Implications

For immigrant minorities

More exploratory qualitative and field experimental work on further informal channels of welfare chauvinism.

Excess prevalence of diseases that need preventive healthcare?

Benefits and dangers of digitized administration?

20 / 22

Implications

For immigrant minorities

More exploratory qualitative and field experimental work on further informal channels of welfare chauvinism.

Excess prevalence of diseases that need preventive healthcare?

Benefits and dangers of digitized administration?

For everyone

"No one is safe until everyone is safe"
(Farrar and Ahuja, 2021)
- Pre-reg hypothesis: Far-right mayors increased excess death rate during first wave Italian COVID-19 pandemic.

20 / 22

Thank you for your attention!

20 / 22

References

Farrar, J. and A. Ahuja (2021). Spike: The Virus vs. The People - the Inside Story. Profile Books.

Hemker, J. and A. Rink (2017). "Multiple Dimensions of Bureaucratic Discrimination: Evidence from German Welfare Offices". In: American Journal of Political Science, pp. 786-803.

Jørgensen, M. B. and T. L. Thomsen (2016). "Deservingness in the Danish context: Welfare chauvinism in times of crisis". In: Critical Social Policy, pp. 330-351.

Kitschelt, H. and A. J. McGann (1997). The radical right in Western Europe: A comparative analysis. Michigan: University of Michigan Press.

Romarri, A. (2020). Do Far-Right Mayors Increase the Probability of Hate Crimes? Evidence From Italy. SSRN Scholarly Paper ID 3506811. Rochester, NY: Social Science Research Network.

20 / 22

Appendix20 / 22

Binary success "Yes" & "Probably yes": 1; else: 0

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

20 / 22

Binary success "Yes", "Probably yes", or "Don't know": 1; else: 0

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.
$n$ = 812 telephone calls (resp. 391 & 421) to 257 municipalities.

20 / 22

Perfectly balanced data

21 / 22

Balanced data

Note: Point estimate with 90 and 95% CI based on OLS with standard errors clustered on the level of 72 regions. Adjusted for gender of the caller and municipality fixed effects. $n$ = 686 telephone calls (resp. 343 & 343) to 220 municipalities.

22 / 22

Single rule versus in coalition

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals based on 2,000 bootstrap samples stratified by Italian/West-African accent.

22 / 22

La Lega

Note: $Δ_{RDD}$ estimates based on rdrobust() with 90 and 95% BCa confidence intervals (capped at -2) based on 2,000 bootstrap samples stratified by Italian/West-African accent.

22 / 22

RDD 1st election round

22 / 22

RDD 2nd election round

22 / 22

↑, ←, Pg Up, k	Go to previous slide
↓, →, Pg Dn, Space, j	Go to next slide
Home	Go to first slide
End	Go to last slide
Number + Return	Go to specific slide
b / m / f	Toggle blackout / mirrored / fullscreen mode
c	Clone slideshow
p	Toggle presenter mode
t	Restart the presentation timer
?, h	Toggle this help
o	Tile View: Overview of Slides

Do Far-Right Mayors Promote Informal Healthcare Chauvinism?

Ethnic Discrimination in Access to Basic Healthcare in Italy

Krzysztof Krakowski, Dina Rosenberg, Alessio Romarri, and Merlin Schaeffer

2022-10-24

The far-right and welfare chauvinism

The far-right and welfare chauvinism

The far-right and welfare chauvinism

The far-right and welfare chauvinism

Do far-right mayors promote informal healthcare chauvinism?

Do far-right mayors promote informal healthcare chauvinism?

A field experiment

A field experiment

Interview guideline

Ethnic discrimination in access to basic healthcare

Ethnic discrimination in access to basic healthcare

Ethnic discrimination in access to basic healthcare

Ethnic discrimination by far-right mayors

Ethnic discrimination by far-right mayors

Ethnic discrimination by far-right mayors

Far-right mayors A regression discontinuity design

Balanced confounders around the margin of victory

Far-right mayors A regression discontinuity design

Far-right mayors A regression discontinuity design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Nr. of questions asked by administrator (0 - 10)

Nr. of questions asked by administrator (0 - 10)

Nr. of irrelevant questions asked by administrator (0 - 4)

Nr. of irrelevant questions asked by administrator (0 - 4)

Conclusion

Conclusion

Implications

Implications

References

Appendix

Binary success "Yes" & "Probably yes": 1; else: 0

Binary success "Yes", "Probably yes", or "Don't know": 1; else: 0

Perfectly balanced data

Balanced data

Single rule versus in coalition

La Lega

RDD 1st election round

RDD 2nd election round

The far-right and welfare chauvinism

Help

Do Far-Right Mayors Promote Informal Healthcare Chauvinism?

Do Far-Right Mayors Promote Informal Healthcare Chauvinism?

Ethnic Discrimination in Access to Basic Healthcare in Italy

Krzysztof Krakowski, Dina Rosenberg, Alessio Romarri, and Merlin Schaeffer

2022-10-24

The far-right and welfare chauvinism

The far-right and welfare chauvinism

The far-right and welfare chauvinism

The far-right and welfare chauvinism

Do far-right mayors promote informal healthcare chauvinism?

Do far-right mayors promote informal healthcare chauvinism?

A field experiment

A field experiment

Interview guideline

Ethnic discrimination in access to basic healthcare

Ethnic discrimination in access to basic healthcare

Ethnic discrimination in access to basic healthcare

Ethnic discrimination by far-right mayors

Ethnic discrimination by far-right mayors

Ethnic discrimination by far-right mayors

Far-right mayors A regression discontinuity design

Balanced confounders around the margin of victory

Far-right mayors A regression discontinuity design

Far-right mayors A regression discontinuity design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Far-right mayors a Diff-in-RD design

Nr. of questions asked by administrator (0 - 10)

Nr. of questions asked by administrator (0 - 10)

Nr. of irrelevant questions asked by administrator (0 - 4)

Do far-right mayors
promote informal healthcare chauvinism?

Do far-right mayors
promote informal healthcare chauvinism?

Do far-right mayors
promote informal healthcare chauvinism?

Do far-right mayors
promote informal healthcare chauvinism?