# A walkthrough of the segregation package

The `segregation` package includes functionality to calculate entropy-based segregation measures, namely the Mutual Information Index (M) and the Theil Index (H), which is a normalized version of the M index. The package also includes several methods for decomposing the index into between/within components and into local segregation scores, as well as methods to decompose differences in segregation indices. All of these methods have arguments to obtain standard errors and confidence intervals through bootstrapping.

Detailed information on these indices can be found in the references at the end of this vignette.

## The basic mathematics

The idea of a segregation index is to summarize a contingency table to a single number. For instance, we may have a table with \(U\) units, say schools or occupations, and \(G\) groups, say gender or racial groups. For each combination of unit and group we have a count, \(t_{ug}\). Arranged in a \(U\times G\) matrix \(\mathbf{T}\), this is what the structure of the data looks like:

| \(g=1\) | … | \(g=G\)
--- | --- | --- | ---
\(u=1\) | \(t_{11}\) | … | \(t_{1G}\)
\(u=2\) | \(t_{21}\) | … | \(t_{2G}\)
\(u=3\) | \(t_{31}\) | … | \(t_{3G}\)
… | … | … | …
\(u=U\) | \(t_{U1}\) | … | \(t_{UG}\)

From this matrix, we can define \(t=\sum_{u=1}^U\sum_{g=1}^G t_{ug}\), the total population size. The joint probability of being in unit \(u\) and racial group \(g\) is \(p_{ug}=t_{ug}/t\). Also define \(p_{u \cdot}=\sum_{g=1}^{G}t_{ug}/t\) and \(p_{\cdot g}=\sum_{u=1}^{U}t_{ug}/t\) as the marginal probabilities of units and groups, respectively. The Mutual Information Index is then defined as

\[ M(\mathbf{T})=\sum_{u=1}^U\sum_{g=1}^Gp_{ug}\log\frac{p_{ug}}{p_{u \cdot}p_{\cdot g}}. \] The Theil Index is closely related to the M index, as it is just a normalized version of the Mutual Information Index:

\[ H(\mathbf{T})=\frac{M(\mathbf{T})}{E(\mathbf{T})}, \]

where \(E(\mathbf{T})\) denotes the entropy of the group marginal distribution of \(\mathbf{T}\), i.e. \(E(\mathbf{T})=-\sum_{g=1}^{G}p_{\cdot g}\log p_{\cdot g}\). Dividing through the group entropy has the effect of constraining H to be between 0 and 1.

## Data format

For the examples, we will use a dataset built into the `segregation` package, `schools00`. This dataset contains data on 2,045 schools across 429 school districts in three U.S. states. For each school, the dataset records the number of Asian, Black, Hispanic, White, and Native American students. The `segregation` package requires data in long form (because most segregation data comes in this form), not in the form of contingency tables. Hence, each row of the `schools00` dataset is a unique combination of a given school and a racial group, and the column `n` records the number of students for this combination:

    library("segregation")
    head(schools00[, c("school", "race", "n")])
    #>    school   race     n
    #>    <fctr> <fctr> <num>
    #> 1:   A1_1  asian     2
    #> 2:   A1_1  black    14
    #> 3:   A1_1   hisp    30
    #> 4:   A1_1  white   351
    #> 5:   A1_2  black     9
    #> 6:   A1_2   hisp   101

Note that in the first school, `A1_1`, there are no Native American students. Hence, that row is missing.

If you have data in the form of contingency tables, you can use `matrix_to_long()` to convert them to the long format required for the package. As an example:

    (m <- matrix(c(10, 20, 30, 30, 20, 10), nrow = 3))
    #>      [,1] [,2]
    #> [1,]   10   30
    #> [2,]   20   20
    #> [3,]   30   10
    colnames(m) <- c("Black", "White")
    matrix_to_long(m, group = "race", unit = "school")
    #>    school   race     n
    #>    <char> <char> <num>
    #> 1:      1  Black    10
    #> 2:      2  Black    20
    #> 3:      3  Black    30
    #> 4:      1  White    30
    #> 5:      2  White    20
    #> 6:      3  White    10

The `group` and `unit` arguments are optional.

## Computing the M and H indices

Compute the M and H indices using `mutual_total()`:

    mutual_total(schools00, "race", "school", weight = "n")
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.4255390
    #> 2:      H 0.4188083

Interpreting the M is not easy, because it is not normalized. However, the H can range from 0 to 1, so a value of 0.419 would indicate moderate segregation.

The second argument to `mutual_total()` refers to the groups, while the third argument refers to the units. Switching groups and units does not affect the M index, but does change the H index:

    mutual_total(schools00, "school", "race", weight = "n")
    #>      stat        est
    #>    <char>      <num>
    #> 1:      M 0.42553898
    #> 2:      H 0.05642991

This is because the `segregation` package always divides by the marginal group entropy, and it would here hence divide by the entropy of the school distribution, which we would expect to be much larger (as there are many more schools than racial groups). To check, we can use the `entropy()` function:

    (entropy(schools00, "race", weight = "n"))
    #> [1] 1.016071
    (entropy(schools00, "school", weight = "n"))
    #> [1] 7.541018

Therefore, if the H index is used, it is important to specify the groups and units correctly.

For inference (discussed in more detail below), we can use bootstrapping to obtain standard errors and confidence intervals:

    mutual_total(schools00, "race", "school",
      weight = "n",
      se = TRUE, CI = .95, n_bootstrap = 500
    )
    #> 500 bootstrap iterations on 877739 observations
    #>      stat       est           se                  CI        bias
    #>    <char>     <num>        <num>              <list>       <num>
    #> 1:      M 0.4219279 0.0007830229 0.4204618,0.4234001 0.003611033
    #> 2:      H 0.4152426 0.0007170999 0.4137741,0.4165334 0.003565675

As there a large number of observations, the standard errors are very small.

## Between-Within decomposition

We might wonder whether segregation is different across the three different states. We can compute their segregation indices manually (just showing the M for simplicity):

    split_schools <- split(schools00, schools00$state)
    mutual_total(split_schools$A, "race", "school", weight = "n")[1, ]
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.4085965
    mutual_total(split_schools$B, "race", "school", weight = "n")[1, ]
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.2549959
    mutual_total(split_schools$C, "race", "school", weight = "n")[1, ]
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.3450221

Clearly, state A is more segregated than state C, which in turn shows higher school segregation than B. One of the advantages of entropy-based segregation indices is that these three state-specific indices have a simple relationship to the overall index. This is called the between/within decomposition: Total segregation can be decomposed into a term that measures how much the distribution of racial groups differs between states, and into a term that measures segregation within states. If we have \(S\) states (or “super-units” more generally), where each school belongs to exactly one state, the M index can be decomposed as follows:

\[ M(\mathbf{T})=M(\mathbf{S}) + \sum_{s=1}^S p_s M(\mathbf{T}_s), \] where \(\mathbf{T}\) is the full \(U \times G\) contingency table, \(\mathbf{S}\) is the aggregated contingency table of dimension \(S\times G\), \(p_s\) is the population proportion of state \(s\) (such that \(\sum_{s=1}^S p_s=1\)), and \(\mathbf{T}_s\) is the subset of rows of \(\mathbf{T}\) belonging to state \(s\). Put in simple terms, the M index can be decomposed into a between-state segregation index, plus a weighted average of within-state M indices.

For the H index, we are dividing the above formula by \(E(\mathbf{T})\). This makes the formula a bit more complicated, because the normalization has to be offset in the decomposition:

\[ H(\mathbf{T})=H(\mathbf{S}) + \sum_{s=1}^S \frac{E(\mathbf{T}_s)}{E(\mathbf{T})} p_s H(\mathbf{T}_s), \]

where \(E(\cdot)\) is again the entropy of the marginal group distribution. Note that \(E(\mathbf{T})=E(\mathbf{S})\), because the group marginal distributions are identical.

To compute the decomposition using the `segregation` package, use:

    # total segregation
    (total <- mutual_total(schools00, "race", "school", weight = "n"))
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.4255390
    #> 2:      H 0.4188083
    # between-state segregation:
    #     how much does the racial distributions differ across states?
    (between <- mutual_total(schools00, "race", "state", weight = "n"))
    #>      stat        est
    #>    <char>      <num>
    #> 1:      M 0.09924370
    #> 2:      H 0.09767398
    # within-state segregation:
    #     how much segregation exist within states?
    (mutual_total(schools00, "race", "school", within = "state", weight = "n"))
    #>      stat       est
    #>    <char>     <num>
    #> 1:      M 0.3262953
    #> 2:      H 0.3211343

Note that \(0.426 = 0.0992 + 0.326\) and \(0.419 = 0.0977 + 0.321\). The results indicate that about 75% of the segregation is within states. In other words, differences in the racial composition of the three different states account for less than 25% of segregation.

By using `mutual_total()` with the `within` argument, we can obtain the overall within component, but we do not obtain the decomposition by state. To do so, we can use `mutual_within()`:

    (within <- mutual_within(schools00, "race", "school",
      within = "state", weight = "n", wide = TRUE
    ))
    #> Key: <state>
    #>     state         M         p         H ent_ratio
    #>    <fctr>     <num>     <num>     <num>     <num>
    #> 1:      A 0.4085965 0.2768819 0.4969216 0.8092501
    #> 2:      B 0.2549959 0.4035425 0.2680884 0.9361190
    #> 3:      C 0.3450221 0.3195756 0.3611257 0.9402955

This is a much simpler way to obtain state-specific segregation scores compared to subsetting manually, as shown in the beginning of this section. In addition to the M and H indices, we also obtain `p`, the population proportion of the state (\(p_s\) above), and `ent_ratio`, which is \(E(\mathbf{T}_s)/E(\mathbf{T})\) from above. Hence, we can recover the total within-component using

    with(within, sum(M * p))
    #> [1] 0.3262953
    with(within, sum(H * p * ent_ratio))
    #> [1] 0.3211343

which is exactly the same as before. The quantity \(p_s M(\mathbf{T}_s)\) is itself of interest, because it shows how much the states contribute to the segregation total, when taking into account their size. By adding the between component, we can calculate the contribution of the four components:

    # merge into a vector
    components <- c(between$est[1], within$M * within$p)
    names(components) <- c("Between", "A", "B", "C")
    signif(100 * components / total$est[1], 3)
    #> Between       A       B       C
    #>    23.3    26.6    24.2    25.9

Each of the four components contributes about a quarter to the total segregation of 0.426. Note that state A is the smallest state (27.7% of the population), but contributes the largest percentage (26.6%) to total segregation. Hence, the decomposition shows that it is important to look at both \(p_s\), the state sizes, as well as \(M(\mathbf{T}_s)\), within-state segregation.

The between-within decomposition can also be applied repeatedly in a hierarchical setting. For instance, in the `schools00` dataset, schools are nested within districts, and districts are nested within states. Therefore, we can ask: How much segregation is due to segregation between states, how much segregation is due to between-district segregation within states, and how much segregation is due to between-school segregation within districts? The package provides a convenience function for this use case:

    mutual_total_nested(schools00, "race", c("state", "district", "school"),
      weight = "n"
    )
    #>     between          within   stat        est
    #>      <char>          <char> <char>      <num>
    #> 1:    state                      M 0.09924370
    #> 2:    state                      H 0.09767398
    #> 3: district           state      M 0.23870880
    #> 4: district           state      H 0.23493319
    #> 5:   school state, district      M 0.08758648
    #> 6:   school state, district      H 0.08620114
    # This is a simpler way of running the following three decompositions manually:
    # mutual_total(schools00, "race", "state", weight = "n")
    # mutual_total(schools00, "race", "district", within = "state", weight = "n")
    # mutual_total(schools00, "race", "school", within = c("state", "district"), weight = "n")

## Local segregation

The M index (but not the H index) allows for another decomposition, into local segregation scores. To define this decomposition, let \(p_{g|u} = t_{ug} / t_{u \cdot}\) be the conditional probability of being in group \(g\), given that one is in unit \(u\). We can then define the local segregation score of unit \(u\) as

\[L_u = \sum_{g=1}^G p_{g|u}\log\frac{p_{g|u}}{p_{\cdot g}}\] The weighted average of the \(L_u\) is again \(M(\mathbf{T})\), i.e. \(M(\mathbf{T}) = \sum_{u=1}^U p_{u\cdot}L_u\).

To obtain the local segregation scores \(L_u\), along with the marginal weights \(p_{u\cdot}\), use `mutual_local()`:

    mutual_local(schools00, "race", "school", weight = "n", wide = TRUE)
    #> Key: <school>
    #>       school        ls            p
    #>       <fctr>     <num>        <num>
    #>    1:   A1_1 0.1826710 0.0004522985
    #>    2:   A1_2 0.1825592 0.0004978701
    #>    3:   A1_3 0.2756157 0.0006642066
    #>    4:   A1_4 0.1368034 0.0005685061
    #>    5:   A2_1 0.3585546 0.0004260948
    #>   ---
    #> 2041: C165_1 0.3174930 0.0004568556
    #> 2042: C165_2 0.3835477 0.0005297702
    #> 2043: C165_3 0.2972550 0.0005650883
    #> 2044: C166_1 0.3072281 0.0011586588
    #> 2045: C167_1 0.3166498 0.0005354667

Local segregation scores are based on much less data than the full M index, so it often makes sense to obtain confidence intervals. The following code plots the length of the 95% confidence interval in relation to the size of each school:

    localse <- mutual_local(schools00, "race", "school",
      weight = "n",
      se = TRUE, wide = TRUE, n_bootstrap = 500
    )
    #> 500 bootstrap iterations on 877739 observations
    localse$lengthCI <- sapply(localse$ls_CI, base::diff)
    with(localse, plot(x = p, y = lengthCI, pch = 16, cex = 0.3))

Although the relationship is not deterministic, larger schools have shorter confidence intervals.

Because the M is symmetric, local segregation scores can also be obtained for groups. The equivalent definition for the local segregation score of group \(g\) is then

\[L_g = \sum_{u=1}^U p_{u|g}\log\frac{p_{u|g}}{p_{u \cdot}},\]

and, as expected, \(M(\mathbf{T}) = \sum_{g=1}^G p_{\cdot g}L_g\).

To obtain these scores, switch the group and unit arguments in `mutual_local`:

    (localg <- mutual_local(schools00, "school", "race", weight = "n", wide = TRUE))
    #> Key: <race>
    #>      race        ls           p
    #>    <fctr>     <num>       <num>
    #> 1:  asian 0.6287673 0.022553401
    #> 2:  black 0.8805413 0.190149919
    #> 3:   hisp 0.7766327 0.151696575
    #> 4:  white 0.1836393 0.628092178
    #> 5: native 1.4342644 0.007507927

These results show that the racial groups experience very different levels of segregation: White students are less segregated than Asian, Black, Hispanic, and, especially, Native American students.

## Inference

The four main functions of the packages, `mutual_total()`, `mutual_within()`, `mutual_local()`, and `mutual_difference()` all support inference through bootstrapping. Inference for segregation indices is tricky, and the standard error estimates and confidence intervals should not be trusted too much when there is little data, and especially when the segregation index is very close to either 0 or maximum segregation.

To estimate standard errors and confidence intervals, use `se = TRUE`. The coverage of the confidence interval can be specified in the `CI` argument. The number of bootstrap iterations can be specified as well:

    (se <- mutual_total(schools00, "race", "school",
      weight = "n",
      se = TRUE, CI = .95, n_bootstrap = 500
    ))
    #> 500 bootstrap iterations on 877739 observations
    #>      stat       est           se                  CI        bias
    #>    <char>     <num>        <num>              <list>       <num>
    #> 1:      M 0.4218987 0.0007341604 0.4204607,0.4232986 0.003640254
    #> 2:      H 0.4152139 0.0006910442 0.4138632,0.4165450 0.003594378

The confidence intervals are based on the percentiles from the bootstrap distribution, and hence require a large number of bootstrap iterations for valid interpretation. The estimate `est` that is reported in the results has already been “debiased”, i.e. the bias that has been estimated from the bootstrap distribution (which is reported in `bias`) has been subtracted from the usual maximum-likelihood estimate that we would obtain from `mutual_total` with `se = FALSE`. The confidence interval is centered around the debiased estimate.

On balance, confidence intervals are preferred over the standard error because the bootstrap distribution can be skewed, especially when segregation is very low or very high. For this example, we can see that the standard errors provide almost identical coverage to the confidence intervals, as

    # M
    with(se, c(est[1] - 1.96 * se[1], est[1] + 1.96 * se[1]))
    #> [1] 0.4204598 0.4233377
    # H
    with(se, c(est[2] - 1.96 * se[2], est[2] + 1.96 * se[2]))
    #> [1] 0.4138595 0.4165684

provide effectively the same coverage as the confidence intervals obtained from the percentile bootstrap.

Whenever the bootstrap is used, the bootstrap distributions for each parameter are reported in an attribute `bootstrap` of the returned object. This can be used, for instance, to check whether the bootstrap distribution is skewed. The following code computes local segregation scores for all schools, and then shows a histogram of the bootstrap distribution for school C137_9, which has a very low local segregation score:

    local <- mutual_local(schools00, "race", "school",
      weight = "n",
      se = TRUE, CI = .95, n_bootstrap = 500
    )
    #> 500 bootstrap iterations on 877739 observations
    # pick bootstrap distribution of local segregation scores for school C137_9
    ls_school <- attr(local, "bootstrap")[school == "C137_9" & stat == "ls", boot_est]
    hist(ls_school, main = "Bootstrap distribution for school C137_9")

For this school, the bootstrap distribution is skewed. If precise inference about this specific school is needed, the standard error should not be interpreted, and the confidence interval should only be interpreted when the number of bootstrap iterations is large.

If you are concerned that your contingency table is too small to provide reliable segregation estimates, the package also provides a function `mutual_expected()` that simulates random cell counts under independence from the marginal distributions of your table. For the `schools00` dataset:

    mutual_expected(schools00, "race", "school", weight = "n", n_bootstrap = 500)
    #>         stat         est           se
    #>       <char>       <num>        <num>
    #> 1: M under 0 0.004805241 7.351936e-05
    #> 2: H under 0 0.004729237 7.235652e-05

Here, there is no concern about bias due to a small sample size.

## Decomposing differences in indices

The command `mutual_difference()` can be used to decompose differences in segregation, as described in Elbers (2021). The default, and recommended method, is to use `method = shapley` (or `method = shapley_detailed`). The other methods (`mrc`, `km`) exist mostly for testing purposes, and are not recommended. Details on the procedure and how to interpret the terms of the decomposition are found in Elbers (2021).

    mutual_difference(schools00, schools05, "race", "school", weight = "n")
    #>              stat          est
    #>            <char>        <num>
    #> 1:             M1  0.425538976
    #> 2:             M2  0.413385092
    #> 3:           diff -0.012153884
    #> 4:      additions -0.003412776
    #> 5:       removals -0.011405093
    #> 6: group_marginal  0.017871051
    #> 7:  unit_marginal -0.011712727
    #> 8:     structural -0.003494338

This method also supports inference by setting `se = TRUE`.

## References

Elbers, B. (2021). A Method for Studying Differences in Segregation Across Time and Space. Sociological Methods & Research. https://doi.org/10.1177/0049124121986204

Mora, R., & Ruiz-Castillo, J. (2011). Entropy-based Segregation Indices. Sociological Methodology, 41(1), 159–194. https://doi.org/10.1111/j.1467-9531.2011.01237.x

Theil, H. (1971). Principles of Econometrics. New York: Wiley