--- title: "Introduction to inequantiles: Quantile-Based Inequality Measures for Survey Data" author: "Silvia Scarpa" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to inequantiles: Quantile-Based Inequality Measures for Survey Data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: ../inst/REFERENCES.bib link-citations: yes --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5 ) ``` # Overview The **inequantiles** package provides tools for estimating quantiles and quantile-based economic inequality indicators from survey data, with full support for complex sampling designs. The package offers comprehensive methods for: - **Quantile ratio index (QRI)**: Estimation on superpopulations, complex survey data,and grouped data - **Traditional quantile-based indicators**: Quintile share ratio (QSR), Palma ratio, and percentile ratios (e.g., P90/P10), with flexible quantile estimator selection - **Weighted quantile estimation**: Estimation of quantiles choosing among multiple interpolation rules (types 4-9 plus HD) on complex sampling data - **Linearization techniques**: Estimation of influence function for QRI, QSR, Gini coefficient and quantiles - **Grouped data support**: Estimation of quantiles, QRI and Gini coefficient from frequency tables and grouped data when microdata are unavailable (e.g., fiscal data) - **Sampling variance estimation** for the cited indicators (and custom functions) via rescaled bootstrap method All functions are demonstrated using synthetic survey data included in the package. ## Installation ```{r, eval = FALSE} # Install from GitHub devtools::install_github("silviascarpa/inequantiles") ``` ```{r} library(inequantiles) ``` ## Import synthetic survey data The dataset **synthouse** was synthetically generated to mimic typical survey dataset like Italian EU-SILC. It contains basic information at the individual and household level, including geographical information, sampling weights and equivalised disposable income. ```{r} data(synthouse) head(synthouse) ``` # Theoretical Background Unlike moment-based inequality indicators (e.g., the Gini coefficient), which are highly sensitive to large values in the long tails, indicators which are based solely on quantiles are remarkably resistant to anomalous observations and high distribution skewness. The core of the **inequantiles** package is the quantile ratio index (QRI), an indicator that provides a robust measure of inequality, even in small samples, as it considers the entire distribution and is solely based on quantiles. ## The Quantile Ratio Index (QRI) The QRI was introduced by @prendergast2018simple as a simple, effective inequality measure of economic inequality. The QRI - Considers the entire distribution - Depends solely on quantiles - Is robust to extreme values - Does not require *a priori* choice of specific percentiles - Is nonparametric For a continuous non-negative random variable $Y$ with cumulative distribution function $F(y)$ and quantile function $Q(p) = F^{-1}(p)$, $0 \leq p \leq 1$, define the ratio between symmetric quantiles as: $$R(p) = \frac{Q(p/2)}{Q(1-p/2)}, \quad 0 \leq p \leq 1.$$ $R(p)$ is a non-decreasing function between 0 and 1, which takes value 1 for any $0 \leq p \leq 1$ when income (or another economic variable with positive support) is equally distributed among the individuals. The QRI is then defined as: $$\text{QRI} = 1 - \int_0^1 R(p) \, dp = 1 - \int_0^1 \frac{Q(p/2)}{Q(1-p/2)} \, dp.$$ The QRI ranges from 0 (perfect equality) to 1 (maximum inequality). It measures the area between the equi-distribution line ($R(p) = 1$ for all $p$) and the actual inequality curve. This can be easily visualised by the following plots: ```{r} ### Log-Normal distribution plot_inequality_curve(qfunction = qlnorm, qfun_args = list(meanlog = 9, sdlog = 0.3), col = "tomato", lty = 2, label = "LogN(sigma=0.3)") plot_inequality_curve(qfunction = qlnorm, qfun_args = list(meanlog = 9, sdlog = 1.9), col = "blue", lty = 2, add = TRUE, label = "LogN(sigma=1.9)") plot_inequality_curve(qfunction = qlnorm, qfun_args = list(meanlog = 9, sdlog = 3.9), col = "green", lty = 2, add = TRUE, label = "LogN(sigma=3.9)") ``` For superpopulation models defined by theoretical parametric distributions with known quantile functions, you can compute the exact QRI as: ```{r} # Log-normal distribution superpop_qri(qfunction = qlnorm, meanlog = 9, sdlog = 0.2) superpop_qri(qfunction = qlnorm, meanlog = 9, sdlog = 1.7) superpop_qri(qfunction = qlnorm, meanlog = 9, sdlog = 3.2) # Weibull distribution superpop_qri(qfunction = qweibull, shape = 2, scale = 30000) superpop_qri(qfunction = qweibull, shape = 1.5, scale = 30000) ``` Consider now a finite population $U = \{1, \ldots, N\}$, from which a random sample $s$ of size $n$ is selected, typically collected with a complex sampling design $p(s) = Pr(S = s)$, $\forall s \subseteq U$. Let $y_j$, $j \in s$, be the observed values of the variable of interest, with $y_{(1)}, \ldots, y_{(n)}$ denoting its order statistics. Assume that the sample is drawn according to a certain sampling scheme, with inclusion probability $\pi_j = Pr(j \in s)$. The corresponding sampling weight $w_j$ is obtained by the inversion of the inclusion probability, plus, when required, some adjustments for non-response and calibration. Let $W_j = \sum_{i \in s} w_i \mathbf{1}(i \leq j)$ denote the cumulative sum of weights up to ordered observation $j$. Let $\widehat{Q}(p)$ be the $p$ quantile estimator; how to estimate the quantiles on complex sampling data will be treated in the following sections. For survey data from a finite population, @scarpa2025inference estimate the QRI using a grid of $M$ points on $(0, 1)$ as $$ \widehat{\text{QRI}} = \frac{1}{M} \sum_{m=1}^M \left(1 - \frac{\widehat{Q}(p_m/2)}{\widehat{Q}(1-p_m/2)}\right) $$ where $p_m = (m-0.5)/M$, for $ m = 1, \ldots, M$. By default, $M = 100$. ```{r} qri(y = synthouse$eq_income, weights = synthouse$weight, M = 100) ``` $\widehat{\text{QRI}}$ is strictly sensitive to the choice of the quantile estimator, especially in small samples. ## Quantile estimators The $p$ quantile estimator can be expressed as a weighted average of order statistics, $$ \widehat{Q}(p)=y_{(k-1)}+ \left(y_{(k )} - y_{(k- 1)}\right) \left(\frac{p - \widehat{r}_{k - 1 }}{\widehat{r}_{k} - \widehat{r}_{k - 1}} \right), $$ where $\widehat{r}_{k}$ indicates the estimator of the cdf, namely the plotting position, and the selected order $k$ is such that $W_{k-1} - m_{k-1} < W_n p < W_{k} - m_k$, where $m_k$ is determined by the interpolation method between adjacent data points. Linear interpolation between the points $(\widehat r, y_{(k)})$ gives a quantile estimator for complex sampling data. For $p=0$ and $p=1$, define $\widehat{Q} (0)=y_{(1)}$ and $\widehat{Q}(1)=y_{(n)}$. The `csquantile()` function extends standard quantile estimation through the R function `quantile()` to survey data. It adapts the methods described in @hyndman1996sample to weighted data. The possible interpolation rules are summarised in the table below (see @scarpa2025inference for further details): ```{r, echo=FALSE, message=FALSE, warning=FALSE} library(knitr) library(kableExtra) # Creiamo i dati. # Nota: usiamo il doppio backslash \\ per i simboli LaTeX in R df <- data.frame( Estimator = c("$\\widehat{Q}_4(p)$", "$\\widehat{Q}_5(p)$", "$\\widehat{Q}_6(p)$", "$\\widehat{Q}_7(p)$", "$\\widehat{Q}_8(p)$", "$\\widehat{Q}_9(p)$"), rk = c("$\\frac{W_k}{W_n}$", "$\\frac{W_k-\\frac{1}{2}w_k}{W_n}$", "$\\frac{W_k}{W_n+w_n}$", "$\\frac{W_{k-1}}{W_{n-1}}$", "$\\frac{W_k-\\frac{1}{3}w_k}{W_n+\frac{w_n}{3}}$", "$\\frac{W_k-\\frac{3}{8}w_k}{W_n+\frac{1}{4}w_n}$"), mk = c("0", "$\\frac{w_k}{2}$", "$w_np$", "$w_k - w_np$", "$\\frac{w_k}{3} + \\frac{w_n}{3}p$", "$\\frac{3}{8}w_k + \\frac{w_n}{4}p$"), k = c("$W_{k-1} \\le W_n p \\lt W_k$", "$W_{k-1} - \\frac{w_{k-1}}{2} \\le W_n p \\lt W_{k} - \\frac{w_{k}}{2}$", "$W_{k-1} \\le (W_n + w_n)p \\lt W_{k}$", "$W_{k-2} \\le W_{n-1}p \\lt W_{k-1}$", "$W_{k-1} - \\frac{w_{k-1}}{3} \\le (W_{n} - \\frac{w_n}{3})p \\lt W_{k} - \\frac{w_k}{3}$", "$W_{k-1} - \\frac{3w_{k-1}}{8} \\le (W_{n} + \\frac{w_{n}}{4})p \\lt W_{k} - \\frac{3w_{k}}{8}$") ) # Generiamo la tabella per HTML kbl(df, caption = "Table 1: Quantile estimators incorporating sampling weights.", escape = FALSE, # Fondamentale per far leggere il LaTeX a MathJax col.names = c("Estimator", "$\\widehat{r}_k$", "$\\widehat{m}_k$", "$k$"), align = "clll") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F, position = "center") %>% column_spec(1, bold = T) %>% row_spec(0, bold = T) # Intestazione in grassetto ``` They can be easily computed on survey data, as: ```{r} # Compute weighted quartiles csquantile(y = synthouse$eq_income, weights = synthouse$weight, probs = c(0.25, 0.5, 0.75), type = 4) csquantile(y = synthouse$eq_income, weights = synthouse$weight, probs = c(0.25, 0.5, 0.75), type = 7) # Compare without considering the sampling weights csquantile(synthouse$eq_income, probs = c(0.25, 0.5, 0.75), type = 4) ``` An extension of the Harrell-Davis estimator to survey data is also provided, as $\widehat{Q}_{HD}(p)=\sum_{j \in s} \widehat{\mathcal{W}}_{j}(p) y_{(j)}$, where $$ \begin{aligned} \widehat{\mathcal{W}}_{j}(p) = & \, b_{(W_{j} / W_n)}\{(W_n+ w_n) p, W_n - (W_n+ w_n)p + w_n\} \\ & - b_{(W_{j - 1}/ W_n)}\{(W_n+ w_n) p, W_n - (W_n+ w_n)p + w_n\} \end{aligned} $$ ```{r} # Harrell-Davis weighted median csquantile(y = synthouse$eq_income, weights = synthouse$weight, probs = 0.5, type = "HD") ``` Differences among the quantile estimators are particularly evident in small samples and in the distribution tails, as demonstrated in the following example: ```{r} # Compare different quantile types by NUTS3 types <- c(4, 5, 6, 7, 8, 9, "HD") areas <- unique(synthouse$NUTS3) # Function to compute QRI for all types in one area compare_quantiles <- function(region_code, data = synthouse) { idx <- which(data$NUTS3 == region_code) results <- sapply(types, function(t) { csquantile(y = data$eq_income[idx], weights = data$weight[idx], type = t, probs = 0.95) }) return(results) } # Compute for all areas results_quantiles <- sapply(areas, compare_quantiles) rownames(results_quantiles) <- types colnames(results_quantiles) <- areas ## === Quantile estimators for Each NUTS3 Region ===" print(head(t(results_quantiles), n = 10)) ``` The package supports multiple quantile estimation methods (types 4-9 and Harrell-Davis) into quantile-based inequality indicators estimators. Rule `type = 6 `is recommended for the QRI, as it is been shown by @scarpa2025inference to lead to the least biased estimates. ```{r} # Compute weighted QRI qri(y = synthouse$eq_income, weights = synthouse$weight, type = 6) # Type 6 quantile estimator (default) ``` ## QRI estimator sampling variance For complex surveys, the rescaled bootstrap method [@rao1988resampling; @rao1992some] is recommended for the estimation of the QRI estimator sampling variance, as demonstrated by @scarpa2025inference. This can be easily estimated on data collected through two-stage stratified sampling design as ```{r, eval = FALSE} # Pseudo-code for rescaled bootstrap for the estimation of the sampling variance of the QRI estimator var_qri <- rescaled_bootstrap( data = synthouse, y = "eq_income", strata = "NUTS2", psu = "municipality", weights = "weight", estimator = qri, by_strata = TRUE, B = 100, seed = 456) ``` ## Other Inequality Indicators Most common quantile-based inequality indicators in the literature are the quintile share ratio (QSR), the Palma ratio and the interdecile ratios. All estimators implemented in this package allow the user to choose the quantile estimation method via the `type` argument. ### Quintile Share Ratio (QSR) The QSR compares the income share of the top 20% to the bottom 20%. Its estimator is defined as $$ \widehat{{QSR}} = \frac{\sum_{j \in s}w_j y_j \mathbf{1}\left\{ y_j \geq \widehat{Q}(0.8)\right\} }{\sum_{j \in s} w_j y_j\mathbf{1}\left\{ y_j \leq \widehat{Q}(0.2)\right\} } \ . $$ ```{r} # Compute QSR share_ratio(y = synthouse$eq_income, weights = synthouse$weight, type = 4) ``` ### Palma Ratio The Palma ratio compares the top 10% to the bottom 40% aggregated income: $$ \widehat{{Palma}} = \frac{\sum_{j \in s}w_j y_j \mathbf{1}\left\{ y_j \geq \widehat{Q}(0.9)\right\} }{\sum_{j \in s} w_j y_j\mathbf{1}\left\{ y_j \leq \widehat{Q}(0.4)\right\} } \ . $$ ```{r} # Compute Palma ratio share_ratio(y = synthouse$eq_income, weights = synthouse$weight, type = 7, prob_numerator = 0.90, prob_denominator = 0.40) ``` ### Percentile Ratios Very often National Statistical Offices measure inequality using ratios between percentiles, for example: $$ \widehat{{P}90/{P}10} = \frac{\widehat{Q}(p=0.9)}{\widehat{Q}(p=0.1)} $$ Default the P90/P10 is estimated. ```{r} # P90/P10 ratio ratio_quantiles(y = synthouse$eq_income, weights = synthouse$weight, type = 7) # P75/P25 ratio ratio_quantiles(y = synthouse$eq_income, weights = synthouse$weight, prob_numerator = 0.75, prob_denominator = 0.25, type = 6) ``` ## Comparing Indicators Across Groups Multiple inequality indicators exist in the literature, each capturing different aspects of the economic distribution. Different indicators may provide complementary perspectives on inequality, so researchers should choose the most appropriate measure based on their specific research questions and the distributional features of interest. The `inequantiles()` function computes all quantile-based indicators at once, or any subset of them, among QRI, QSR, Palma ratio and interquantiles ratio. ```{r} # Compare all indicators inequantiles(y = synthouse$eq_income, weights = synthouse$weight, indicators = "all") ``` Standard errors can be obtained via the rescaled bootstrap by setting `se = TRUE`. Note that this requires specifying the survey design variables (`strata`, and optionally `psu` and `N_h`). ```{r se-example, eval=FALSE} # QRI and QSR with their standard errors for one region via rescaled bootstrap nord <- synthouse[synthouse$NUTS1 == "N", ] qri_qsr <- inequantiles( y = nord$eq_income, weights = nord$weight, indicators = c("qri", "qsr"), se = TRUE, data = nord, strata = "NUTS2", psu = "municipality", B = 200, seed = 42 ) ``` # Linearization techniques via influence function The package provides linearization methods using the influence function estimation for some measures. The quantile estimator influence function (IF) is measured in @osier2009variance as $$ {I}(\widehat{Q}(p))_{k} = \frac{p - \mathbb{1}(y_k \leq \widehat{Q}(p)) }{\widehat{F}'(\widehat{Q}(p)) N}, $$ where $\widehat{F}'(y) = \frac{1}{\widehat N}\frac{1}{h \sqrt{2 \pi}} \sum_{j \in s} w_j \operatorname{exp} \left\{-\frac{(y - y_j)^2}{2h^2} \right\}$. Following @deville1999variance and @osier2009variance, the influence function for the ratio of quantiles can be estimated as $$ {I}\left(\frac{\widehat{Q}(p_n)}{\widehat{Q}(p_d)}\right)_{k} = \frac{\left( \frac{p_n - \mathbf{1}(y_k \leq \widehat{Q}(p_n))} {\widehat{f}(\widehat{Q}(p_n)) \, \widehat{N}}\right)\widehat{Q}(p_d) -\left( \frac{p_d - \mathbf{1}(y_k \leq \widehat{Q}(p_d))}{\widehat{f}(\widehat{Q}(p_d)) \, \widehat{N}}\right)\widehat{Q}(p_n)}{ \widehat{Q}(p_d)^2} $$ The QSR estimator IF, as demonstrated by @langel2011quintile, can be computed as $$ \begin{split} I(\widehat{QSR})_{k} &= \frac{y_k-\left\{0.8 \widehat{Q}(0.8)-\left(\widehat{Q}(0.8)-y_k\right) \mathbf{1}\left[y_k \leq \widehat{Q}(0.8)\right]\right\}}{\widehat{Y}_{0.2}} \\ &\quad - \frac{\left(\widehat{Y}-\widehat{Y}_{0.8}\right)\left\{0.2 \widehat{Q}(0.2)-\left(\widehat{Q}(0.2)-y_k\right) \mathbf{1}\left[y_k \leq \widehat{Q}(0.2)\right]\right\}}{\widehat{Y}_{0.2}^2} \end{split} $$ where $\hat{Y}_{\alpha} = \sum_{j \in s} w_j y_j \mathbf{1}[y_k \leq \widehat{Q}(\alpha)]$. This can be extended easily to other quantile-based share ratios, e.g. the Palma index. @scarpa2025inference demonstrated that the QRI IF can be estimated as $$ \begin{split} {I}(\widehat{QRI})_{k} &= - \int_0^1 \frac{\left(\frac{\frac{p}{2} - \mathbb{1}(y_k \leq \widehat{Q}(p/2))}{\widehat{F}'(\widehat{Q}(p/2)) \widehat N}\right) \widehat{Q}(1-p/2) - \left(\frac{(1 - \frac{p}{2}) - \mathbb{1}(y_k \leq \widehat{Q}(1-p/2))}{\widehat{F}'(\widehat{Q}(1-p/2)) \widehat N}\right) \widehat{Q}(p/2)}{ \widehat{Q}(1-p/2)^2}dp \ . \end{split} $$ The Gini coefficient IF can be approximated as (see @langel2013variance) as $$ {I}(\widehat{G})_{k} =\frac{1}{\hat{N} \hat{Y}}\left\{2 W_k\left(y_k-\hat{\bar{Y}}_k\right)+\hat{Y}-\hat{N} y_k-\hat{G}\left(\hat{Y}+y_k \hat{N}\right)\right\} $$ $$ \text{with} \qquad \hat{Y} = \sum_{j \in s}w_j y_j; \quad \hat{\bar{Y}}_j=\frac{\sum_{l \in S} w_l y_l 1\left(W_l \leqslant W_j\right)}{W_k}. $$ ### Comparison of the influence function values ```{r} # Select a subset for clearer visualization n_obs <- 200 set.seed(123) idx <- sample(nrow(synthouse), n_obs) # Extract data y_subset <- synthouse$eq_income[idx] w_subset <- synthouse$weight[idx] # Compute the IF for some indicators if_gini_vals <- if_gini(y_subset, w_subset) if_qri_vals <- if_qri(y_subset, w_subset, type = 6) if_qsr_vals <- if_share_ratio(y_subset, w_subset, type = 4) if_palma_vals <- if_share_ratio(y_subset, w_subset, type = 4, prob_numerator = 0.90, prob_denominator = 0.40) if_q50_vals <- if_quantile(y_subset, w_subset, probs = 0.5, type = 6) if_ratioquantiles_vals <- if_ratio_quantiles(y_subset, w_subset, prob_numerator = 0.90, prob_denominator = 0.10, type = 6) ``` ```{r} # Create the plot library(ggplot2) library(scales) # plot_df <- data.frame( Income = rep(y_subset, 6), IF_Value = c( if_qri_vals, if_qsr_vals, if_gini_vals, if_palma_vals, if_q50_vals, if_ratioquantiles_vals ), Indicator = rep(c("QRI", "QSR", "Gini", "Palma", "Median", "P90/P10"), each = n_obs) ) ggplot(plot_df, aes(x = Income, y = IF_Value, color = Indicator)) + geom_line(linewidth = 0.8, alpha = 0.7) + geom_hline(yintercept = 0, linetype = "dashed", color = "gray40") + facet_wrap(~ Indicator, scales = "free_y", ncol = 2) + scale_x_continuous(labels = scales::comma) + labs( title = "Influence Functions: How Each Observation Affects the Estimate", subtitle = paste("Based on", n_obs, "randomly sampled households"), x = "Equivalized Income", y = "Influence Function Value", ) + theme_minimal(base_size = 11) + theme( legend.position = "none", strip.text = element_text(face = "bold", size = 11), plot.title = element_text(face = "bold", size = 13), plot.subtitle = element_text(color = "gray40"), panel.grid.minor = element_blank() ) ``` We observe that **Gini Coefficient**: - Shows generally increasing unbounded pattern with income - Less sensitive to low incomes **Median**: - Step function that admits only two values - Robust but ignores most of the distribution **P90/P10**: - Step function that admits only three values - Robust but ignores most of the distribution **Palma ratio**: - Sharp discontinuities at $Q(0.9)$ and $Q(0.4)$ - Near-zero influence in the middle of distribution - High sensitivity to observations at boundary quantiles **QRI**: - Balanced influence across the entire distribution - Jump around the median - Upper bound as income increases - More robust than the other inequality indicators **QSR**: - Sharp discontinuities at $Q(0.2)$ and $Q(0.8)$ - Near-zero influence in the middle 60% of distribution - High sensitivity to observations at boundary quantiles # Grouped Data Functions When only frequency tables are available (common with administrative or tax data), the package provides specialized functions. Consider grouped data divided into $L$ classes with known boundaries, observed frequencies $f_1, \ldots, f_L$ and total amounts $Y_1, \ldots, Y_L$. Let: - $L_l$ be the lower bound of the $l$-th class - $U_l$ be the upper bound of the $l$-th class - $h_l = U_l - L_l$ be the $l$-th class width - $N = \sum_{i=1}^{L} f_i$ be the total frequency - $C_{l} = \sum_{i=1}^{l} f_i$ be the cumulative frequency up to the $l$-th class - $c_l = Y_l / \sum_{i=1}^{L} Y_i$ is the share in group $l$ - $s_l = \sum_{k=1}^{l} c_k$ is the cumulative share up to group $l$ - $p_l = f_l / \sum_{i=1}^{L} f_i$ be the population share of group $l$ - $u_l = \sum_{k=1}^{l} p_k$ is the cumulative population share up to group $l$ - $s_0 = u_0 = 0$ by convention For example ```{r} # Example: Income distribution in frequency table format income_freq <- c(120, 180, 150, 80, 40, 20, 10) income_tot <- c(18800, 16300, 44700, 33900, 21500, 22100, 98300) income_lower <- c(0, 15000, 30000, 45000, 60000, 80000, 100000) income_upper <- c(15000, 30000, 45000, 60000, 80000, 100000, 150000) ``` ## Quantile Computation for Grouped Data The quantile class for the ${p}$-th quantile is the first class ${l}$ such that: $$ {l = min\{l: C_l \geq pN \}} $$ The ${p}$-th quantile ${Q(p)}$ is then estimated by linear interpolation within the quantile class: $$ {\widetilde{Q}(p) = L_l + \frac{(pN - C_{l-1})}{f_l} \cdot h_l} $$ ```{r} # Estimate quantiles from grouped data quantile_grouped(freq = income_freq, lower_bounds = income_lower, upper_bounds = income_upper, probs = c(0.25, 0.5, 0.75)) ``` ## QRI for Grouped Data By using the quantile measure for grouped data, the QRI is approximated as: $$ {{QRI}} \approx \frac{1}{M}\sum_{m=1}^{M}\left(1 - \frac{\widetilde{Q}(p_m/2)}{\widetilde{Q}(1 - p_m/2)}\right) $$ ```{r} # Compute QRI from grouped data qri_grouped(freq = income_freq, lower_bounds = income_lower, upper_bounds = income_upper, M = 100) ``` ## Gini coefficient from Grouped Data The Gini coefficient is approximated by linear interpolation of cumulative shares, as: $$ {G \approx 1 - \sum_{l=1}^{L} (s_l + s_{l-1})(u_l - u_{l-1})} $$ ```{r} # Estimate quantiles from grouped data gini_grouped(Y = income_tot, freq = income_freq) ``` # Getting Help If you encounter issues or have questions: - Open an issue on [GitHub](https://github.com/silviascarpa/inequantiles/issues) - Check the function documentation: `?qri`, `?csquantile`, etc. - Contact the maintainer: silvia.scarpa@unimore.it # References # Session Info ```{r} sessionInfo() ```