Causes of ethnic segregation in a nineteenth century city

The case of Vyborg

Antti Härkönen

UEF

2024-09-26

Introduction

Spatial segregation

  • a classic theme of urban sociology
  • implications both for individuals and society
  • causes of spatial segregation studied using empirical data
  • socio-economic segregation studied as a possible cause of ethnic segregation

Location of Vyborg

Vyborg

  • Vyborg 1 castle founded in the late 13th century
  • town privileges 1403
  • conquered by Russians in the Great Northern War (1700–1721)

Population

  • German and Swedish speaking Lutheran elites
  • Finnish commoners
  • large Russian garrison 1710–1917

Segregation

Causes of segregation

Pre-modern causes:

  1. policies of segregation
  2. guild-based segregation

Modern causes1:

  1. discrimination
  2. prejudice
  3. income differences
  4. different preferences of groups
  5. different housing market information

Discrimination

  • lateral
  • e.g. housing market discrimination

Prejudice

  • horizontal and possibly reciprocal

Income differences

  • socio-economic segregation creates ethnic segregation

Different preferences

  • different groups value different things
  • location of services, e.g. churches

Housing market information

  • knowledge
  • differences in perceived value

Segregation policies

  • explicit policies of segregation
  • attempts to segregate Russian and Finnish commoners into different suburbs

Guild-based differentiation

  • in pre-industrial world, guild members were expected to live near one another
  • most guilds in Vyborg tiny
  • some attempts to created own areas for retired soldiers and cart drivers

Data

Sources used

Sources from the National archives of Finland
Signum Original year Digitization process
Town plan of Vyborg. Vyborg military engineer detachment’s archive of plans for fortifications and buildings, 7, 11. 1878 Georeferenced using ground control points, vectorized manually into shapefile
Vyborg province poll tax registers 1880 Digitized manually into CSV
Financial office of the city of Vyborg, Municipal tax levies and payment registers 1880 Digitized manually into CSV

Poll tax records

poll tax record columns in 1894
column description
plot_number Plot number
taxpayer_men Men paying poll tax
taxpayer_women Women paying poll tax
no_tax_men Men exempt from poll tax
no_tax_women Women exempt from poll tax
in_russia_men Men legally residing in Russia proper
in_russia_women Women legally residing in Russia proper
total_men Total men
total_women Total women
independent Civil servants, entrepreneurs, and financially independent
white_collar White collar workers
worker_industry Workers in industry
worker_other Other workers
servants Servants
other Other employment status
non_resident Resident elsewhere
orthodox Orthodox
other_christian Non-Lutheran and non-Orthodox Christian
other_religion Other religions
draftable 21-year-old males eligible for draft

Estimating the size of Russian population

  • over 90% of Orthodox in Vyborg Russian

Estimating the size of Lutheran population

\[ \begin{equation} P_{Lutheran} = \begin{split} (P_{total\_men}+P_{total\_women}) \\ − (P_{Orthodox}+P_{other\_Christian}+P_{other\_religion}) \end{split} \end{equation} \]

Growth of Vyborg

Map of Vyborg in 1839

Population growth

Population growth in key areas
District 1822 1880
Centre 1192 2506
St. Anna 244 117
Vyborg suburb 642 756
St Petersburg suburb 1512 2685

Spatial analyses

Work flow

Flow chart of workflow

Population surface model

Population surface model

Based on Martin, Tate, and Langford (2000).

\[ P_i=\sum^N_{j=1} P_j w_{ij} \]

\[ w_{ij} = \begin{cases} \left( \frac{k^2 - d^2_{ij}}{k^2 + d^2_{ij}} \right)^\alpha & \text{if} \hspace{1cm} d_{ij < k} \\ 0 & \text{else} \end{cases} \]

Biweight kernel

::: {#cell-kernel profile .cell execution_count=2}

Kernel function

:::

:::

Map Vyborg plot owners in 1768

Map of density of Orthodox population

Map of income distribution in Vyborg

Explaining segregation

Regression model

  • Bayesian multilevel linear regression model with spatial correlation between observations (N=540)
  • predictors are the natural logarithm of average local income and distance to nearest orthodox church
  • predicted variable is the proportion of Russians in a location
  • regression coefficients are different for each area of Vyborg

Partial pooling

  • observations are partially pooled

Regression model (1)

\[ O_i \sim MvNormal(\mu, \textbf{K}) \]

\[ \mu_i = \beta_{0,k[i]} + \beta_{1,k[i]} \textit{ln(W)} + \beta_{2,k[i]} C_i \]

\[ k \in 1,2,3,4 \hspace{1cm} i,j \in 1,2,3, \dots 539 \]

\[ \beta_k \sim MvNormal \left( \theta, \begin{bmatrix} 0.1 & 0 & 0 \\ 0 & 0.1 & 0 \\ 0 & 0 & 0.1 \end{bmatrix}\right) \]

\[ \theta \sim MvNormal \left( \begin{bmatrix} 0 \\ 0 \\ 0 \end{bmatrix}, \begin{bmatrix} 0.1 & 0 & 0 \\ 0 & 0.1 & 0 \\ 0 & 0 & 0.1 \end{bmatrix}\right) \]

Regression model (2)

\[ \textbf{K}_{ij} = \eta^2 exp(-75 \rho^2 d^2_{ij}) + 0.01 \times I_{540} \]

\[ \eta^2 \sim Normal(1, 0.2) \] \[ \rho^2 \sim Normal(1, 0.2) \]

Multilevel Bayesian regression

Variable Shape Description
O 540 Normalized proportion of Russian Orthodox of the local population
W 540 Smoothed total income in a location in öre
C 540 Distance to nearest Orthodox church in 1799 in kilometres
d 540 x 540 Distance matrix holding pairwise distances between plots
θ 3 Hyperparameter for β
β 4 x 3 Linear regression coefficients for each district
η2 1 Parameter for the covariance function
ρ2 1 Parameter for the covariance function

Plate diagram of Bayesian regression model

Change of segregation

Surface-based segregation index

  • index S works by comparing changes in population density surfaces

S

after O’Sullivan and Wong (2007)

\[ S = 1 - \frac{∯_R min(p_{L}, p_{O}) \hspace{2mm}dR}{∯_R max(p_{L}, p_{O}) \hspace{2mm}dR} \]

where \(p_L\) and \(p_O\) are the normalised population densities of Lutheran and Orthodox populations respectively

Spline model (1)

\[ S_{year} \sim Normal(\mu_{year}, \sigma) \]

\[ \mu_{year} = \alpha + \sum_{k=1}^K w_k B_{k,year} \]

\[ \alpha \sim Normal(0.45, 0.01) \] \[ \sigma \sim HalfNormal(0.05) \]

Spline model (2)

\[ B = \begin{bmatrix} 1 & 0.687 & 0.295 & 0.02 & 0 & 0 & 0 & 0 \\ 0 & 0.299 & 0.601 & 0.612 & 0.367 & 0.276 & 0.007 & 0 \\ 0 & 0.015 & 0.104 & 0.367 & 0.612 & 0.658 & 0.209 & 0 \\ 0 & 0 & 0 & 0 & 0.02 & 0.066 & 0.784 & 1 \end{bmatrix} \]

\[ w_k \sim Normal(0, 0.1) \]

Spline model code

import pymc as pm

with pm.Model() as model:
    a = pm.Normal("α", μ_a, σ_a)
    w = pm.Normal("w", mu=μ_w, sigma=σ_w, shape=B.shape[1])
    μ = pm.Deterministic(
      "μ", a + pm.math.dot(np.asarray(B, order="F"), w.T
    ))
    σ = pm.HalfNormal('σ', σ_σ)
    S = pm.Normal("S", μ, σ, observed=regression_data['200'])
    idata = pm.sample(1000, tune=1000, chains=2)

Plate diagram of Bayesian spline regression model

Results

Regression

  • no evidence for income or preferences as causes of segregation

Change over time

  • spatial segregation decreases 1880–1900 and increases 1900–1917
  • exogamy rate of Russians declines constantly 1880–1917
  • concentrations of Russians changes over time
  • changes of urban space likely decreased segregation after 1860

Variable Mean SD HDI, 95%
θ0 −0.027 0.096 −0.227 0.15
θ1 0.027 0.085 −0.142 0.193
θ2 −0.135 0.096 −0.309 0.067
β0,0 −0.609 0.299 −1.162 −0.013
β0,1 0.104 0.056 −0.009 0.209
β0,2 −1.076 0.314 −1.702 −0.487
β1,0 0.097 0.3 −0.46 0.743
β1,1 0.142 0.14 −0.117 0.433
β1,2 −0.037 0.316 −0.625 0.626
β2,0 0.118 0.299 −0.509 0.677
β2,1 0.119 0.074 −0.024 0.261
β2,2 −0.287 0.312 −0.905 0.306
β3,0 0.016 0.272 −0.54 0.515
β3,1 0 0.069 −0.141 0.135
β3,2 −0.496 0.248 −0.991 −0.024
scaled η² 0.93 0.04 0.852 1.006
ρ² 1.0 0.099 0.812 1.194

Conclusions

Segregation

  • no monocausal explanations work
  • more complex causal system likely at work
  • high quality spatial data allows rejection of overtly simplistic models

References

Dawkins, Casey J. 2004. “Recent Evidence on the Continuing Causes of Black-White Residential Segregation.” Journal of Urban Affairs 26 (3): 379–400.
Härkönen, Antti. 2022. “A Novel Aggregation Strategy for Producing Population Density Surfaces Using Poll Tax Data: The Case of Vyborg in 1880.” International Journal of Geographical Information Science 36 (12): 2427–45. https://doi.org/10.1080/13658816.2022.2094931.
———. 2024. “Explaining the spatial segregation of ethnic groups in an early industrial city: the case of Vyborg.” Digital Scholarship in the Humanities 39 (2): 532–47. https://doi.org/10.1093/llc/fqae017.
Martin, David, Nicholas J. Tate, and Mitchel Langford. 2000. “Refining Population Surface Models: Experiments with Northern Ireland Census Data.” Transactions in GIS 4 (4): 343–60. https://doi.org/https://doi.org/10.1111/1467-9671.00060.
O’Sullivan, David, and David W. S. Wong. 2007. “A Surface-Based Approach to Measuring Spatial Segregation.” Geographical Analysis 39 (2): 147–68. https://doi.org/https://doi.org/10.1111/j.1538-4632.2007.00699.x.